SPECT

Multimodal computational models for early prediction of peritoneal recurrence in gastric cancer

ABSTRACT Gastric cancer represents a significant disease burden and is a leading cause of cancer-related deaths in the United States and globally. Approximately 80% of gastric cancer patients are diagnosed at an advanced stage, with the peritoneum being the most common site of relapse (peritoneal recurrence) after radical surgery. Nearly 50% of patients with advanced-stage gastric cancer develop peritoneal recurrence post-surgery, resulting in a median survival of only 3–6 months and a markedly reduced quality of life. Early peritoneal recurrence is primarily characterized by micro-metastasis, which traditional imaging techniques struggle to detect due to the small size of metastatic nodules. Predicting the likelihood and timing of peritoneal recurrence is crucial for identifying at- risk patients, enabling timely interventions that could improve survival rates and quality of life. Unfortunately, reliable predictive biomarkers and models for peritoneal recurrence in gastric cancer are lacking in clinical practice, highlighting an urgent need for innovative predictive tools. This proposal aims to develop and validate novel predictive models for early peritoneal recurrence in gastric cancer, leveraging advanced deep learning techniques and multimodal integration of clinical, radiological (CT), and histopathological (hematoxylin and eosin, H&E) data. In Aim 1, we will develop a rational approach for predicting peritoneal recurrence by creating a novel deep learning multimodal method guided by genomics knowledge. Additionally, we will integrate both deep learning-extracted features and traditional hand-crafted radiomics features with clinical data to improve prediction accuracy. Aim 2 focuses on developing a robust prediction model of peritoneal recurrence utilizing a pre-trained foundation model from large-scale H&E image data. Aim 3 will combine CT, H&E, and clinical data to further enhance predictive capabilities, employing an innovative cross-modal collaborative optimization approach for multimodal data integration. All models will be trained and internally validated using a retrospective cohort from Atrium Health Wake Forest Baptist Comprehensive Cancer Center and externally validated in two independent cohorts from additional institutions to ensure robustness across populations and imaging protocols. Additionally, we will compare our models with existing methods, including clinical staging and alternative fusion strategies. If successful, these models will enhance risk stratification and prediction of peritoneal recurrence in gastric cancer patients, significantly improving survival rates and quality of life by identifying those likely to develop peritoneal recurrence post-surgery and facilitating timely intervention. Furthermore, they can help avoid the risk of complications and extra medical costs associated with overtreatment. Since the information is derived from routinely examined CT, H&E and clinical data, they could be seamlessly integrated into current clinical workflows. The AI technology developed through this project has the potential to benefit underserved populations in low- resource settings and reduce healthcare disparities in the U.S.

BKCa Channel Contributions to Cerebellar Regulated TSC-Associated Neuropsychiatric Disorders

Project Summary TSC is associated with neurodevelopmental disability including cognitive disability and autism spectrum disorders (ASD) that make up part of TSC associated neuropsychiatric disorders (TAND). The mechanisms for TAND remain poorly understood but studies have increasingly implicated cerebellar dysfunction in the pathogenesis of cognitive and behavioral deficits in both TSC and other neurodevelopmental disorders. A shared feature is cerebellar Purkinje cell (PC) dysfunction. Changes in intrinsic properties of PCs results in both motor and cognitive/ behavioral changes in disease models and in individuals afflicted by these disorders. Mechanistic underpinnings of these altered properties remain unknown, but a significant emerging body of data implicate ion channel dysfunction as the primary etiology of these deficits. The current proposal seeks to delineate the ion channel contribution to PC dysfunction and to TAND-relevant behaviors. In doing so, these studies will produce significant both short- and long-term impact. Short-term: These proposed studies will provide a mechanistic understanding of the contribution of ion channels to the neuronal dysfunction in the cerebellum that has been demonstrated to be causally linked to abnormal TAND-relevant behaviors. In addition, we will target specific ion channels both genetically and pharmacologically to evaluate the benefits of ion channel restoration on both electrophysiological abnormalities but also the TAND-relevant behaviors observed in the model. Long-term: These studies, thus, provide a framework for subsequent clinically-relevant therapeutic development for TAND. First, these studies will uncover the ability for TAND-relevant behaviors to be improved upon targeting ion channel alterations in TSC. These studies will also define molecular targets on which therapeutic development can be targeted, thereby potentially providing a molecular-informed pipeline for therapeutic development. In addition, these studies will utilize clinically-available, FDA-approved pharmacological agents to target ion channel function and investigate the potential therapeutic benefits for these agents for TAND-relevant behaviors. Thus, these studies will address a core gap in knowledge to achieve a better mechanistic understanding of TAND and to develop therapeutic opportunities to address TAND. These studies will not only reveal previously understudied and novel mechanistic underpinnings for these behaviors but will provide pre-clinical insights into the therapeutic utility of clinically-utilized agents for the treatment of TAND-related behaviors, thus potentially providing both immediate and long-term opportunities for the treatment of TAND. Moreover, although these studies focus on TSC, these mechanisms may prove generalizable beyond TSC and provide a shared basis and therapeutic opportunity for other neuropsychiatric/developmental conditions.

Structural and functional characterization of autoimmune antibodies against NMDAR

Project Summary. The goal of this project is to understand the origins and molecular mechanisms underlying the anti-cancer autoimmune response against the N-methyl-D-aspartate receptor (NMDAR) and its correlation with anti-N-methyl-D-aspartate receptor autoimmune encephalitis (NMDARAE). While anti-cancer immune responses can promote tumor elimination, they may also lead to the production of self-reactive antibodies that trigger autoimmune diseases. NMDARAE is the most common form of immune-mediated encephalitis, which results in prominent neuropsychiatric symptoms, including seizures, psychosis, and memory deficits. NMDARs belong to a family of ligand-gated ion channels expressed exclusively in the central nervous system. They are involved in various aspects of brain development and function, including learning and memory. They respond to the neurotransmitter glutamate and a co-agonist, glycine or D-serine, to mediate excitatory neurotransmission, which plays a central role in synaptic plasticity. NMDARAE is associated with ovarian teratomas, where aberrant NMDAR expression is believed to trigger an autoimmune response. In NMDARAE, anti-NMDAR antibodies, as well as B cells and antibody-secreting cells, cross the blood-brain barrier via unknown mechanisms, resulting in the presence of anti-NMDAR antibodies at high titers within the brain and cerebrospinal fluid (CSF). These antibodies target NMDARs, modulating their function and contributing to disease pathology. Emerging evidence, supported by our preliminary data, suggests that NMDARs are also expressed in triple-negative breast cancer (TNBC), extending the relevance of anti-NMDAR autoimmunity beyond ovarian teratomas. In our TNBC mouse model, which ectopically expresses NMDARs (TNBC-NMDAR), we observed the onset of anti-NMDAR autoimmunity, where the produced antibodies cause both anti-tumor activity and symptoms such as lowered seizure threshold, mirroring key features of NMDARAE. Here, we will establish this TNBC mouse model as we develop molecular methods to characterize it. Aim 1 will focus on establishing and characterizing the TNBC- NMDAR mouse model. We will develop a detection method utilizing the intact tetrameric NMDAR channel proteins and a method to isolate B cells expressing B cell receptors against NMDAR from biological samples by using fluorescently labeled intact NMDAR proteins, followed by single-cell RNA sequencing. Aim 2 will utilize single-particle cryo-electron microscopy (cryo-EM) to investigate the interactions between NMDAR and the cloned antibodies, providing insights into epitope recognition, NMDAR subtype specificity, and conformational changes induced by antibody binding. Aim 3 will assess the impact of the cloned antibodies on NMDAR channel activity using electrophysiology. We will also assess anti-tumor activity and NMDARAE onset by each antibody clone. Together, the proposed research will gain insights into the link between anti-cancer anti-NMDAR autoimmunity and NMDARAE. It will also elucidate which functional properties of the cloned antibodies promote anti-tumor activity while contributing to NMDARAE, thereby informing potential therapeutic strategies.

HIV-1 Matrix and Envelope Protein Interactions

It is important to characterize how HIV-1 proteins fulfill their functions in order to develop new approaches for curtailing the AIDS epidemic. One of the remaining frontiers of HIV-1 research concerns the mechanisms by which the HIV-1 matrix (MA) and envelope (Env) proteins collaborate with each other to ensure the assembly of infectious viruses. The HIV-1 MA protein directs the delivery of precursor Gag (PrGag) proteins to the plasma membranes (PMs) of infected cells, and drives the formation of lipid raft-like, liquid ordered (Lo) membrane domains. This membrane reorganization attracts a number of proteins that favor lipid raft-type microdomains. Such proteins appear to assemble into virus particles as innocent bystanders, and this appears to be how Env proteins that carry cytoplasmic tail deletions (CT) can be incorporated into virions. In contrast, wild type (WT) Env proteins additionally require an interaction with MA proteins to assemble into viruses. This is most easily understood in the context of the lattice that MA proteins construct at the PMs of infected cells. In particular, multiple lines of evidence imply that the CTs of WT Env proteins are trapped by MA lattices in immature, assembling virus particles, and then are released after assembled viruses are processed into their mature forms. Despite a seeming consensus on the MA-Env interaction steps, there are a number of very significant unknowns. Using our recent and preliminary results as a foundation, and taking advantage of the unique expertise of our collaborators, we propose the characterization of WT and mutant MA lattices, and of interactions of MA and Env with each other, and with membrane lipids. Our results will help clarify how MA and Env cooperate; they will illuminate aspects of host cell protein-membrane interactions; and they will foster the development of new approaches to intefere with HIV-1 replication.

Modulating the Action of Cylindrical Proteases to Eliminate Neisseria Gonorrhea and Chlamydia Trachomatis Infections

Project Summary/Abstract Sexually transmitted bacteria diseases caused by Chlamydia trachomatis (Ctr) and Neisseria gonorrhoeae (NG) are the two most common sexually transmitted bacterial diseases. The infections caused by these pathogens may result in infertility, ectopic pregnancy, blindness, and perinatal mortality. Over 1.70 M cases of chlamydia and 0.65 M cases of drug-resistant gonorrhea are reported yearly in the US. Women with gonorrhea are co- infected with chlamydia in 17.6%–57.9% of cases, while women with chlamydia are co-infected with gonorrhea in 2.1%–17.2% of cases. These infections are treated with broad spectrum antibiotics, which can favor the development of resistance on NG/CTr but also in other bacteria, or damage the microbiota, diminishing its protective function and allowing bacteria and viruses to infect the patient. The Caseinolytic protease (ClpP) proteolytic machinery regulates protein turnover and homeostasis and is key in bacterial growth and development The machinery consists of the proteolytic unit (the ClpP) and its chaperone (ClpX), which transports proteins to be degraded, and it is termed the ClpXP. Our theory is that molecules that inhibit the action of the ClpX chaperone can become efficient antibacterial agents against both pathogens. We have found that the dihydrothiazepines can erradicate both pathogens and prevent the action of the ClpXP complex. Our goal is to advance the dihydrothiazepines as selective agents against Ctr and NG infections. To develop these therapeutic agents, we have envisioned four specific aims. Specific Aim 1. Synthesis and Optimization of the Pharmacophore. Our goal is to use computational models to design dihydrothiazepines molecule that will be synthesized, purified, and characterized using chemical techniques. The molecules will be tested against Ctr and NG and their toxicity against human cells evaluated. Also, we will determine their effect in other bacterial, including those from the microbiota. Specific Aim 2. Assessment of Stability and In Vivo Activity. We will study the stability of the most active molecules under various conditions. Then, we will study the pharmacokinetics, biodistribution , and antibacterial activity against Ctr and NG in mice. Specific Aim 3. Target Validation and Effect. We will study the ability of the compounds to inhibit the activity of ClpX using a luciferase assay and to block protein degradation. We will try grow crystal of the protein and the molecule and will study if the molecules prevent the assembly of the ClpXP system. Finally, we will assess the ability of the bacteria to develop resistance to the molecules.

Specificity requirements and functional properties of microbiota-reactive peri-weaning Tregs

PROJECT SUMMARY This application seeks to define the specificity requirements and functional properties of regulatory T cells (Tregs) that maintain tolerance to the microbiota. RORgt+ Tregs generated during an early-life peri-weaning window (from approximately P14 to P28 in mice) are particularly critical for intestinal tolerance. Mice that first encounter their microbiota outside this window still generate Tregs, but these cells are functionally inferior to those induced during the peri-weaning period and fail to maintain tolerance. The features of peri-weaning Tregs that make them so essential for intestinal homeostasis are not well defined. Here we propose to test two non-mutually exclusive hypotheses: 1) that the unique functionality of peri-weaning Tregs requires a distinct functional state; and 2) that reactivity with specific members of the microbiota is required for peri-weaning Tregs to maintain intestinal tolerance to a complex SPF microbiota. We have developed a model of intestinal inflammation based on oral delivery of the non-steroidal anti- inflammatory drug (NSAID) piroxicam that reveals underlying immune dysregulation in mice with defects in peri-weaning Tregs. When we applied this model to gnotobiotic mice colonized with defined microbiota communities we found that one community (OMM12) induced Tregs capable of preventing inflammation while the other community (ASF) did not, despite similar induction of RORgt+ peri-weaning Tregs by both communities. This exciting result suggests a previously unappreciated specificity requirement for induction of peri-weaning Tregs and indicates that differences in the microbes encountered early in life can have lifelong ramifications for immune tolerance. To better understand the basis of this specificity requirement, we developed a pipeline to rapidly screen the reactivity of T cells and applied it to mice colonized with the protective OMM12 community. This analysis revealed that the antigen-specific Treg response is biased toward only a subset of the microbiota. Thus, by tracking and characterizing microbiota-reactive peri-weaning Tregs at unprecedented resolution, we uncovered an unexpected bias in the microbiota-reactivity of Tregs. We are now ideally positioned to examine how the specificities and functional properties of peri-weaning Tregs are linked to their unique role in intestinal tolerance. In Aim 1, we will define the specificity of microbiota- reactive peri-weaning Tregs at homeostasis, using new tools developed through our screening pipeline, and we will determine whether missing the weaning period alters Treg responses to the microbiota. In Aim 2, we will compare the transcriptional programs of peri-weaning and post-weaning Tregs to identify peri-weaning- specific features. We will also build on our analyses from Aim 1 to determine if functional differences are linked to reactivity with specific members of the microbiota. In Aim 3, we will explore why specific members of the microbiota are required for induction of protective peri-weaning Tregs. We will define communities of microbes that do or do not confer protection in our piroxicam model, and we will profile the Tregs in these communities, including microbiota-reactive Tregs with defined specificities, to test the hypothesis that a key aspect of peri- weaning Treg function is specificity for only certain gut microbes.

Linear diribonucleotides regulation of bacterial physiology and infections

RNA degradation was thought to proceed through endonucleolytic fragmentation, followed by exo- ribonuclease trimming which generate short RNA fragments that are turned over into mononucleotides by oligoribonuclease (Orn). In the last funding period, we published data supporting that only specific enzymes (Orn, NrnA, NrnB, and NrnC) cleave diribonucleotides into monoribonucleotides, and that prokaryotic organisms need to encode at least one diribonuclease to fulfill this specific function. These results support a new perspective on RNA degradation in which the short oligoribonucleotides are processed through a sequence of discrete steps involving distinct enzymes. In addition, linear diribonucleotides appear to be biologically active molecules since we reported that mutants lacking these enzymes accumulate diribonucleotides and have altered cell growth, biofilm formation, motility, and sporulation. Here we present additional preliminary data supporting diribonucleotides as active signaling molecules in the cell including: 1. Specific enzymes act trinucleases to generate diribonucleotides, 2. RNase AM of Pseudomonas aeruginosa ∆orn is a cryptic diribonuclease, 3. Two enzymes in central metabolism are diribonucleotide- binding proteins, and 4. P. aeruginosa ∆orn has virulence defects in an animal model of catheter-associated urinary tract infection. Our past publications and preliminary data provide the scientific premise for our hypothesis that cells generate linear dinucleotides from RNA degradation and linearization of cyclic dinucleotides, which can bind target proteins to alter cell physiology and pathogenesis. To test these aims, we will perform the following specific aims: In Aim 1, we will characterize the generation and degradation of diribonucleotides by characterizing how diribonucleases and triribonucleases bind their respective substrates through molecular biology, biochemistry, and computational docking. In Aim 2, we will identify effects of dinucleotides on bacterial metabolism and physiology by characterizing the binding proteins that specifically interact with linear diribonucleotides. Building on our success of identifying cellular diribonucleotide receptors, we will screen for additional proteins from open reading libraries of P. aeruginosa and Bacillus anthracis. We will exploit the strains available to us that lack all diguanylate cyclases to reveal whether the effect of linear diribonucleotides is independent of c-di-GMP signaling. In Aim 3, we will characterize the effect of expression levels of dinucleases and the effect of dinucleotide accumulation on bacterial physiology and pathogenesis. We will develop mass spectrometry methods to detect di- and triribonucleotides. We will employ existing mutants lacking diribonucleases, including P. aeruginosa ∆orn to study the defects in chronic infection in a murine model of catheter-associated urinary tract infection. Results from these studies will advance our understanding of RNA degradation and open a new area of signaling by linear diribonucleotides with the potential to be applied to novel antibacterial strategies.

Improved Surgical Visibility and Navigation during Endoscopic Treatment of Upper Tract Urothelial Carcinoma

Project Summary The importance of localizing and treating all upper tract urothelial cancer (UTUC) tumors during a renal sparing, endoscopic treatment is emphasized by the high risk of cancer progression from inadequate tumor treatment. Insufficient treatment necessitates kidney and ureteral removal (i.e., nephroureterectomy). Nephroureterectomy permanently compromises renal function, and increases morbidity and mortality, while negatively impacting a patient’s quality of life. In contrast, endoscopic treatment (i.e., using a laser to ablate only the tumors) improves long-term outcomes by sparing healthy kidney tissue. However, endoscopic treatment is underutilized compared to nephroureterectomy because it is difficult to accomplish. Successful endoscopic treatment is dependent on the surgeon’s ability to create a mental 3D map of the branched, intrarenal endoscopic anatomy intraoperatively from preoperative 2D imaging, which is extremely difficult. Since mental mapping relies on hand-eye coordination, memory, and spatial reasoning, it is inherently imprecise and its impact on accuracy and tumor treatment is dependent on the surgeon’s experience. To make matters worse, even when tumors are successfully visualized, the surgeon often cannot accurately assess the location of tumor margins or infer pathologic grade due to the limited field of view and depth of field (10mm and 6mm on average, respectively) of current scopes. The scopes only provide visualization of a small part of the surgical field at any instant. These inherent challenges prevent many surgeons from attempting endoscopic tumor treatment since incomplete treatment leads to a devastating, oncologic outcome. Our overall goal is to create an enhanced visualization and navigational system that makes endoscopic UTUC tumor treatment easier and more accurate for all surgeons, enabling wider utilization. Toward this goal, our specific objective in this proposal is to test the hypothesis that our system can make endoscopic UTUC surgery more accurate and efficient. To test this hypothesis, we propose three Specific Aims: Aim 1 involves the development of an automatic, real-time segmentation and grading system of UTUC tumors during endoscopic treatment. Aim 2 integrates a 3D navigational map of collecting system anatomy, which includes tumor and endoscope location, during endoscopic surgery. Aim 3 evaluates the system in patients, with zero risk to the human subjects. The endpoint of this R01 will be a fully validated enhanced visualization and navigational system for endoscopic UTUC surgery, which would provide the necessary experimental data towards a large-scale, multi-center clinical trial and future FDA approval. As our system would require only software integration to current endoscopic surgical cameras, all existing endoscopic surgical systems could in principle immediately benefit from the results of this project. In this way, we believe the success of our project will facilitate improved UTUC treatment and mitigate progression to a higher risk extirpative surgery.

Factors Driving Wear and Implant Failure in Total Shoulder Arthroplasty

Polyethylene (PE) wear and implant-related failure remain leading causes of revision in total shoulder arthroplasty (TSA), a procedure which now surpasses the growth rate of hip and knee arthroplasty. Both anatomic (aTSA) and reverse (rTSA) TSA outcomes are heavily influenced by complex interactions between rotator cuff function, scapular motion, implant design, and patient-specific loading—factors not adequately captured in current preclinical implant testing standards. Emerging evidence suggests that PE wear progression in TSA is highly dependent on shoulder kinematics, joint loading, implant positioning, and individual patient factors. Nonetheless, data on in vivo motion and load profiles remain sparse, and few tools exist to link these profiles to clinically relevant wear patterns or associated periprosthetic inflammatory tissue responses. Accordingly, the primary objective of this project is to develop validated, patient-specific models that predict PE wear in TSA and identify modifiable surgical, design, and rehabilitation targets to improve implant longevity and restore patient mobility. Additionally, we will establish histopathological hallmarks that indicate TSA failure caused by PE wear debris. Our central hypothesis is that specific shoulder kinematics and joint loading drive distinct PE wear patterns in TSA associated with mechanical failure or inflammatory-mediated osteolysis, depending on implant design and positioning. To achieve the overall objective of this work, shoulder motions and muscle excitations across 25 activities of daily living will be collected at pre-op and post-op (>6 months) in both aTSA and rTSA patients, with long-term follow-up of patient-reported outcomes via validated surveys (5 years). Unsupervised machine learning will categorize patients into movement-based phenotypes, which will then inform a multi-scale modeling framework to estimate in vivo shoulder joint loads and implant wear across the varying movement strategies. Predicted wear patterns will be validated using state-of-the-art preclinical wear simulators. Simultaneously, we will quantify how patient, surgical, and implant factors contribute to wear in retrieved TSA components (>400 samples), correlating imaging-based wear patterns with clinical outcomes, patient-reported function, inflammatory tissue responses, and radiographic indications of loosening. For that purpose, we will establish benchmarks of TSA wear rates and introduce a new histopathological approach augmented by infrared spectroscopic imaging. This work is innovative because we are linking patient-specific movement patterns following TSA with multi-scale computational models to predict PE wear, breaking the current approaches of using generic motions and loads in existing testing standards. This work will produce the first integrated, publicly available database of TSA kinematics, joint loading, and PE wear patterns and rates, along with validated computational tools to inform implant design, surgical planning, rehabilitation strategies, and personalized risk assessment. Ultimately, these advances will improve functional outcomes and long-term success for TSA patients and enable better preclinical testing methods and standards.

Behavioral, Implementation & Community Sciences Core

PROJECT SUMMARY: BEHAVIORAL, IMPLEMENTATION, AND COMMUNITY SCIENCES (BICS) CORE Like many US jurisdictions, New York City (NYC) is not on track to achieve 2030 End the Epidemic (EHE) 95- 95-95 goals. By the end of 2023, 95% of people with HIV (PWH) in NYC had been diagnosed with HIV, but only 88% of those were in HIV care, and of those, only 80% were virally suppressed. Further, in 2022, only 40% of individuals estimated to need PrEP were prescribed it. Highly efficacious biomedical HIV treatment and prevention interventions have the potential to end the HIV epidemic, but only if they are accessed and used. Yet, behavioral, social, and structural determinants of real-world adoption as well as population-level impact of HIV prevention, care, and treatment innovations have not been addressed adequately for individuals or communities. Meeting EHE goals will depend on behavioral, implementation, and community sciences research that identifies factors contributing to these outcomes, informs interventions to address them, and ensures that communities affected by HIV are engaged throughout the research process. The Behavioral, Implementation, and Community Sciences (BICS) Core will facilitate such rigorous, innovative research by Columbia University (CU) and Weill Cornell Medicine (WCM) investigators – particularly early career investigators (ECIs) and those new to HIV research – to help achieve EHE 2030 goals. The BICS Core will support the use of relevant theories, methods, and analytic approaches to advance the integration of context-specific behavioral, implementation, and community sciences perspectives across the research continuum – from basic research through scale-up and sustainment of evidence-based interventions. The Core has three Aims: (1) Behavioral science: To support CFAR users in developing, selecting, and integrating behavioral science methodologies across the research continuum; (2) Implementation science: To support CFAR users in designing and conducting implementation studies and related health services research and (3) Community science: To facilitate rigorous community-based participatory research across the research continuum to strengthen and sustain stakeholder engagement that will optimize research translation and impact. Led by Core Co-Directors Robert Remien and Bruce Schackman and Core Associate Directors Delivette Castor, Shashi Kapadia, and Justin Knox, the BICS Core will use multiple approaches to achieve each of these aims, including substantive scientific consultations on proposed or ongoing research; access to resources and tools; and seminars and educational activities that promote integration of these methods into EHE research. The Core, thus, will support CU-WCM CFAR investigators and outside collaborators – including ECIs and investigators new to HIV research – to advance local and national EHE goals.

Administrative Core

CORE A: PROJECT SUMMARY/ABSTRACT Administrative Core The administrative core will be led by Dr. Jordan Pober, the overall PI of this P01 application. Dr. Pober has had past experience as PI of an NHLBI P01 focused on allograft vasculopathy. He also has administrative experience at Yale as the founder and director of two Yale interdepartmental programs: Vascular Biology and Therapeutics and Human and Translational Immunology. The co-leader of the Core is Dr. Marie Robert, a surgical pathologist with extensive expertise in celiac disease (CeD) who has served in the recent past as the head of the scientific advisory board to the Beyond Celiac organization. The principal task of the Core will be to facilitate interactions among Project, Core and Collaborating Site personnel to foster synergies to address the overall aims of the proposal. Specific tasks include (1) organizing an executive committee of all Project, Core and Site Leaders with advisory and review responsibilities; (2) organizing monthly review meetings, each meeting focused on an individual project and site and (sometimes) core activities involving all program personnel and our internal advisors; (3) organizing an external advisory committee of experts to participate in an annual review of the whole program; and (4) managing budgetary and regulatory functions of the program. The innovative aspects of Core A is its prioritization of team science, bringing together the insights and knowledge of clinical-based and laboratory-based investigators.

Dissecting the role for astrocytes in mediating adverse outcomes of maternal immune activation.

Prenatal infections cause maternal immune activation (MIA), a major risk factor for several neurodevelopmental disorders, including schizophrenia, autism spectrum disorders (ASD), and attention deficit hyperactivity disorder (ADHD). Consequently, elucidating the mechanisms by which MIA alters brain function is critical for understanding the pathophysiology of these disorders and developing effective treatments. While the effects of MIA on neurons and microglia have been extensively studied, the impact of MIA on astrocytes, key regulators of brain physiology and homeostasis, remain unknown that significantly impedes our understanding the mechanisms of MIA-induced neurobehavioral abnormalities. To address this major knowledge gap, we conducted pilot studies that suggest that MIA increases impulsivity-like behaviors and amphetamine-induced hyperactivity and enhances extracellular levels of glutamate (GLU) and dopamine (DA) in the dorsal striatum (DS). MIA also increased pro-inflammatory signatures of astrocytes, including up- regulation of the Nuclear Factor kappa B (NF-κB) pathway and increased GFAP immunoreactivity in DS astrocytes. Collectively, these novel findings support our overarching hypothesis that MIA increases astrocyte reactivity, leading to increased gliotransmission (e.g., GLU), which in turn enhances DS DA release and DA- dependent behaviors. To test this hypothesis, we will leverage the expertise of the research team in molecular, physiological and neurobehavioral approaches and conduct the following Specific Aims: In Aim 1, we will identify the MIA-induced cellular and physiological changes characteristic of astrocyte reactivity. In Aim 2, we will determine the circuit mechanisms by which MIA increases DA signaling. In Aim 3, we will identify the molecular mechanisms whereby reactive astrocytes contribute to MIA-induced cellular and behavioral abnormalities. These studies will enhance the current understanding of the effects of MIA on brain functions and generate new insight into potential treatment strategies for MIA-associated neurodevelopmental disorders.

Linking Single-Cell Transcriptomic, Morphological, and Temporal Signatures of Vulnerability in Neurodegeneration

Neurodegeneration involves complex cellular phenotypes and molecular changes that vary widely among the cells of the nervous system. Current methodologies permit either detailed molecular profiling (e.g., single-cell transcriptomics) or functional phenotyping (e.g., live imaging of neuronal activity), but not both in the same cells. Thus, it is difficult to directly link a neuron's functional state or fate with its gene expression profile. To address this limitation, we developed an innovative technology, VISTA-FISH (Video Imaging with Spatial- Temporal Analysis by FISH), that couples prospective live-cell imaging with high-resolution spatial transcriptomic profiling of the same cells. This approach enables in situ comparisons of gene expression in neurons that exhibit divergent behaviors or outcomes. Using VISTA-FISH, we will profile iPS-derived human neurons to link single-cell gene expression, morphology, and temporal phenotypes to study molecular pathways driving resilience as well as susceptibility. After exposing neurons carrying TDP43 and C9orf72 mutations to a stimulus inducing TDP43 aggregation, we will jointly record TDP43 localization and neuron activity using live-cell microscopy, then measure single-cell gene expression of the same cells (Aim 1). We will also combine live-cell measurements of TDP43 half-life with CRISPR screening and single-cell gene expression (Aim 2). These rich datasets will enable us to determine transcriptomic changes associated with differences in protein aggregation, protein synthesis, and protein degradation in individual cells, providing an unprecedented molecular perspective on factors responsible for vulnerability and resilience to neurodegeneration.

Cytoskeletal connectors: Deciphering the fundamental mechanisms of cytoskeletal dynamics and transport

PROJECT SUMMARY The cytoskeleton is a dynamic network of filamentous structures, including microtubules and actin, that regulate essential cellular processes such as cell shape, growth, and signaling. Cytoskeleton also serves as tracks for molecular motors, which transport a variety of cellular cargoes, including organelles, macromolecules, and vesicles. These cargoes are linked to motors by specialized connector proteins. Disruptions in connector proteins are implicated in a range of neurodevelopmental and neurodegenerative diseases, as well as cancers. Despite their importance, these proteins continue to be understudied, primarily due to their perceived role as passive linkers and the technical challenges in working with them. However, recent discoveries suggest that connector proteins may play more active roles, in some cases even have enzymatic functions. This proposal aims to uncover mechanisms of connector protein functions through a detailed investigation of actin-microtubule and motor-cargo interactions. Actin and microtubules are linked by the spectraplakin family of large and evolutionarily conserved proteins, critical for neuronal development and differentiation. Recent discoveries of ATPase domains within these proteins suggest they may haves beyond simply linking cytoskeletal components. One goal of this proposal is to investigate the role of spectraplakin’s ATPase domains via structural, biochemical, and cell biology approaches. Another goal is to explore how dynamic changes in motor-cargo connectors facilitate the transport of diverse cargoes along microtubule tracks. The focus will be on the cytoplasmic dynein-1 (dynein) and the connectors (adaptors) that activate and link dynein to cargo. Dynein is a microtubule minus-end directed motor that plays essential roles in cell division, and transports hundreds of different cellular cargoes. While several motor-cargo connectors have been identified, the regulatory mechanisms enabling cargo transport are not fully understood. We are investigating whether connector proteins work together to activate dynein movement and/or facilitate cargo handoff between different dynein complexes. Using innovative approaches, including time- resolved cryo-EM, complex in-vitro reconstitutions, and live-cell imaging in induced neurons, we are uncovering critical mechanisms that govern cytoskeletal connector proteins, furthering our understanding of how the cytoskeleton regulates essential cellular processes.

Clinical Trial Readiness of MEG Biomarkers in Children Across the Autism Spectrum

PROJECT SUMMARY Biological and phenotypic heterogeneity of autism spectrum disorder (ASD) poses a major challenge for clinically focused research and interventions. Brain electrophysiological phenotyping holds promise for parsing this heterogeneity. Using magnetoencephalography (MEG), findings of diminished and delayed auditory evoked responses (e.g. the ~50ms component, M50 and, specifically, its latency: M50L) have reproducibly been shown in ASD, with correlation to behavior. Additionally, abnormal resting state activity and network functional connectivity has been identified as an electrophysiological hallmark. Such passively-acquired signatures may serve as objective biomarkers in subtyping autistic individuals, including stratifying patients for inclusion in clinical trials according to biology, rather than behavior alone. However, despite their abundant promise, these measures are not yet permeating clinical trial design, nor being utilized in clinical practice, in part because of their lack of standardized implementation and analysis. This proposal seeks to remedy this by using rigorous and standardized, scalable and sharable methods with two leading MEG measures to determine their measurement- reliability as well as their sensitivity to inter-individual differences in clinically-relevant aspects of autism features, general cognitive ability and language and communication. Specifically adopting a 12-week repeated scanning design, mimicking the duration of a typical pharmaceutical trial or behavioral intervention, we will acquire each of these two MEG metrics at baseline and 12-week follow-up to assess interval change. Additionally, we will evaluate test-retest variability with an intermediate measurement point 4-weeks after baseline. As such we will characterize both intra-subject variability (measurement precision) and inter-subject variability which will be correlated with dimension axes of autism features, general cognitive ability and language skills, as well as major co-occurring condition confounds. These studies will recruit a broad range of 240 autistic children, paralleling the CDC’s prevalence data on intellectual ability and encompassing the group considered as having “profound autism”. This is enabled by our adoption of MEG-PLAN, a strategy developed over the last decade in our group and demonstrated to enhance inclusive participation in MEG scanning studies, even in non-verbal participants. Data will be compared to a control group of age-matched typically-developing peers. The two MEG measures will also be assessed for their ability to identify clusters of less heterogeneous neurophysiological phenotype as a novel basis for stratification or subtyping of the heterogeneous autism population. In culmination, this study addresses key “clinical readiness” aspects of utilization of MEG biomarkers for ASD including profound autism, for both stratification (inclusion/trial selection) and monitoring of response to intervention, and will, ultimately, pave the way for the adoption of such biomarkers as adjunctive tests in increasingly-routine clinical practice.

Examining the foundations of reading comprehension: a longitudinal study of brain and behavior starting in infancy

SUMMARY Reading comprehension (RC) is one of the most complex skills that we utilize daily and is crucial for functioning in modern society, but despite its significance for academic achievement, employment prospects, and mental health, many children and adults do not exhibit proficient RC abilities. New theoretical models aiming to explain variability in RC suggest a dynamic interplay and co-development among ‘precursor’ foundational and cognitive- linguistic skills, interacting with environmental and socio-ecological factors across the developmental timeline of learning to read. Behavioral and neuroimaging studies in school-age children have demonstrated critical mechanistic support for these multifactorial RC models by identifying the developmental trajectories of precursor skills and further showing that brain areas, tracts, and networks typically underlying language and cognitive skills are also involved in RC. Nevertheless, the precursor skills that support RC start developing in infancy and the brain correlates underlying these precursors begin to develop in utero, which suggests that typical and atypical RC developmental trajectories could diverge long before school age. As such, examining RC development using a multifactorial, longitudinal approach that includes brain and behavior starting in infancy is critical for developing theoretical frameworks that can inform early preventative and intervention strategies. Here, we propose a comprehensive longitudinal study of RC development in which we examine direct and indirect effects on RC from brain, behavioral, familial risk, and environmental data from infancy to adolescence. To achieve this goal, we will combine two existing longitudinal cohorts, one ranging from infancy to late childhood (n = 174) and the other from preschool to early adolescence (n = 137). By applying state-of-the-art pediatric neuroimaging analyses, multiple indicator growth model structural equation models, and an innovative behavior- brain co-development measurement index to this unique, combined dataset, we will be able to identify brain and behavioral measures in infancy that directly and indirectly support subsequent RC development (Aim1). We will further characterize how longitudinal trajectories of behavioral measures as well as brain structure, function, and white matter organization contribute to RC development and how familial risk and environmental factors shape these trajectories (Aim 2). Finally, we will examine how the co-development of brain and behavior, as measured with an innovative co-development index, relates to subsequent RC (Aim 3). If successful, we will contribute the first multifactorial longitudinal model of RC development comprising direct and indirect effects from brain, behavior, brain-behavior co-development, familial risk, and environmental measures beginning in infancy. Understanding RC development using a multifactorial longitudinal lens will be crucial for building theoretical models and developing experimental designs focused on early preventative and intervention approaches long before the start of formal schooling.

ATPase Chromatin Remodeling Complexes as Modulators of HIV-1 Latency and Therapeutic Targets

Abstract Significance: HIV persists in long-lived CD4⁺ T cell reservoirs despite suppressive ART, as integrated proviruses remain poised for reactivation. Chromatin remodeling is a central barrier to durable silencing, yet most studies have focused on SWI/SNF family members. The roles of non- SWI/SNF remodelers remain poorly defined, limiting our ability to rationally design host-directed “block-and-lock” cure strategies. Our unbiased shRNA screen of all 16 human remodeler ATPases identified EP400, CHD1, and CHD9 as repressors and INO80A, SMARCA5, and CHD2 as activators, establishing chromatin remodeling as a key determinant of HIV latency. Innovation: Our prior studies revealed that the p400 complex regulates HIV transcription through dual mechanisms: directly, by engaging Tat via the DMAP1 subunit to block Tat-TAR RNA interactions and restrict p-TEFb recruitment; and indirectly, by altering host transcriptional programs that control T cell activation states. Building on this mechanistic precedent and methodological platform, we now focus on INO80A, SMARCA5, CHD1, and CHD2, remodelers from distinct ATPase families that govern Tat-independent checkpoints at initiation, pause release, and elongation. Methodologically, we will apply TurboID-ChAP-MS (locus-specific proteomics), BEM-seq (single-nucleosome mapping), and degron-mediated acute depletion with ATPase-dead rescue to interrogate remodeler function with unprecedented resolution. Approach: Aim 1 will define the ATPase requirement and transcriptional checkpoints regulated by INO80A, SMARCA5, CHD1, and CHD2 using degron/CRISPR perturbations, ChIP-seq, nascent RNA profiling, and nucleosome mapping. Aim 2 will characterize remodeler-specific complexes and Tat dependence at the HIV promoter via TurboID proximity labeling integrated with chromatin affinity purification-mass spectrometry. Aim 3 will test combinatorial perturbations in Jurkat and primary CD4⁺ T cell latency models, including ART-suppressed donor cells, to identify synergistic “block-and-lock” strategies that enforce durable proviral silencing. Impact: By defining remodeler-specific mechanisms at discrete transcriptional checkpoints and leveraging their enzymatic, druggable activities, this work will establish chromatin remodeling as a therapeutic axis for durable HIV suppression and functional cure.

Protective efficacy and immunogenicity of a live attenuated Chlamydia strain

PROJECT SUMMARY The main goal of this project is to rigorously evaluate the immunogenicity and protective efficacy of a mutant, live attenuated Chlamydia trachomatis (CT) vaccine strain in an established nonhuman primate (NHP) model that accurately mimics many aspects of human CT infection. This work is highly significant, as CT is the leading cause of bacterial sexually transmitted infection and an important causative agent of morbidity in women. Although the development of an effective CT vaccine is an urgent medical priority, no approved vaccines exist and it is imperative to pursue new candidates. Historical evidence supports the vaccine efficacy of whole Chlamydia organisms in protecting the reproductive tract from reinfection, primarily using C. muridarum infections in a mouse model. Recent advances in Chlamydia genetic engineering now allow for the development of genetically attenuated strains which can be evaluated as live vaccines in preclinical models. We recently characterized a human-tropic CT mutant with a disruption in garD (CT∆garD); this mutant is sensitive to an intracellular, IFNγ activated defense mechanism and we demonstrated that this strain was attenuated in the female NHP genital tract. In a pilot vaccine efficacy study, we further demonstrated that immunization of macaques with CT∆garD was safe and elicited protection against subsequent challenge with wildtype CT. A unique feature of this strain is that it arrests at an intracellular stage and thus presents a broad array of desirable T and B cell antigens that are broadly conserved across circulating CT strains. We will first generate an improved genetically attenuated CT strain that harbors a clean deletion of garD, and we will subsequently genetically and phenotypically validate its attenuation phenotype. We will then conduct an immunogenicity and efficacy study in female macaques to determine the optimal dosing regimen of live attenuated CT for eliciting protective cellular and humoral immune responses, and also protective efficacy, against challenge with a wild type circulating clinical CT strain. These studies will investigate the potential for a live attenuated human tropic vaccine candidate in a macaque preclinical model and pave the way for greater understanding of immune correlates of protection against CT.

Multiplex single-cell chemical genomics to identify small molecule modulators of tumor cell-intrinsic immunogenicity in glioblastoma

PROJECT SUMMARY/ABSTRACT Glioblastoma multiforme is the most common and aggressive primary brain cancer. Despite a multimodal treatment regimen of surgical resection, chemotherapy, radiotherapy, and tumor-treating fields, most patients succumb to the disease within two years of diagnosis. Cancer immunotherapy strategies have emerged as a powerful tool for treating aggressive solid tumors such as melanoma and non-small cell lung cancer. However, current strategies have led to low response rates in glioblastoma, resulting from its low immunogenicity. The proposed research program aims to identify small molecules capable of increasing the immunogenicity of glioblastoma cells, focusing on altering gene expression programs associated with recognition by the immune system and the ability of cytotoxic immune cells to target glioblastoma for destruction. We will use highly multiplex chemical transcriptomic profiling to determine the molecular consequence of exposing glioblastoma neurosphere models to 3,792 small molecules, targeting the majority of cellular activities and clinically relevant drug targets as well as a collection of previously identified immunomodulators. We will then determine how each exposure alters the expression of gene programs associated with tumor cell immunogenicity and response to therapy, including the expression of genes associated with the recognition by the immune system and those associated with immune checkpoints, as well as programs more broadly correlated with resistance to anti-cancer therapies. Chemical hits that meet specific criteria will be subjected to a medicinal chemistry review to further classify compounds by their suitability for treating malignancies in the brain. We will then screen chemical hits to determine their ability to modulate immune-mediated tumor cell killing using tumor- immune cell co-culture. Lastly, we will leverage gene editing and flow cytometry to validate hits based on on- target molecular effects and further refine the mechanism of action by inspecting the ability of drugs to modulate immunogenic programs at the protein level. Our chemical genomics screens aim to provide crucial information regarding the link between pathway activity and immunomodulation in GBM, a critical step to guide future efforts in GBM immunotherapy. More broadly, our study will establish single-cell chemical genomics as a scalable platform for phenotype-based screening for preclinical prioritization of chemical modulators of complex transcriptional phenotypes and provide a framework for hit prioritization, establishment of pipeline robustness and hit validation in the context of single- cell chemical genomics screens.

Dosing and Deployment Trial: A Home-based Optokinetic Treatment for Ipsilesional Gaze Deviation

Stroke can have devastating consequences including ipsilesional gaze deviation (IGD), which directly impacts mobility and falls. IGD, a hallmark sign of spatial neglect (SN), is a major predictor of poor recovery and can persist after inpatient rehabilitation with targeted treatments. Our preliminary data show that more than half of stroke survivors who have SN at the time of admission to inpatient rehabilitation still have SN at time of discharge, even after treatment. Therefore, because of the challenges of the traditional rehabilitation paradigm we need to bring treatments into the home setting. We plan to examine the feasibility and deployment of Eyemove, an optokinetic stimulation treatment, which induces brain neural plasticity and improves spatial exploration, in turn reducing SN symptoms, including IGD. We hypothesize that by treating IGD, improvements in mobility and fall risk scores will occur, as participants can now interact with the space that was previously “neglected”. Here, we propose to test the following aims with 50 community-dwelling individuals with SN, by identifying the practical dosage associated with mobility improvement: Aim 1 will determine feasibility and acceptability of home deployment of Eyemove. We will collect qualitative information from stroke survivors and their care partners, to determine their pre-treatment and post-treatment perspectives of this home treatment. Aim 2 will determine whether Eyemove in the home is associated with improved mobility-related outcomes (including risk of falls) and to evaluate sufficient dosing. We will randomize participants into either 3 or 5 sessions of a 40-minute treatment given over a week-long intervention period. The primary outcome will be the Mobility Assessment Course and secondary outcomes will be the Stroke Assessment of Fall Risk and the Life Space Assessment. For Aim 1, we expect to learn practical suggestions for home implementation and obtain reports of post-experience enthusiasm and acceptability for specific aspects of the intervention. Our hypotheses for Aim 2 are: 1a-- After controlling for pre-treatment score changes (T2-T1), the intervention (T3) will lead to improved mobility/ fall risk compared to baseline (T1), regardless of treatment group; 1b-- The amount of mobility/ fall risk improvement (T3-T1) in the 3- session and 5-session groups will be different. The expected findings will provide critical insight into the use of Eyemove for spatial neglect remediation. Results from this research will be used to develop a subsequent R01 proposal that uses pragmatic, randomized clinical trial methods to determine the efficacy of Eyemove, in order to provide an effective, accessible treatment to remediate SN at home and improve individuals’ ability to move without spatial bias or risk of falls.

Post-diagnosis changes in body composition and renal cell cancer survival

ABSTRACT Significance. Clear cell renal cell carcinoma (ccRCC) is the most common form of kidney cancer and most lethal subtype, and there is great interest in the identification of potentially modifiable prognostic factors. Although weight status seems to be relevant, the relationship between body mass index (BMI) at diagnosis and survival among ccRCC patients indicates that mortality is lowest among those classified as overweight or obese at the time of diagnosis by BMI. This has resulted in confusion in clinical guidance for weight management among ccRCC patients. Recent work involving body composition features (adipose and muscle tissue) has provided some insight, but we do not understand how weight or body composition changes after diagnosis relate to survival, nor how these changes relate to pathological and molecular tumor features— information which is needed to resolve this controversy. Rigorous analytical approaches are further required to accurately address these questions. Innovation. Our study is highly innovative in that 1) we will be the first to leverage a large-scale cohort of ccRCC patients with multiple assessments of weight and body composition from diagnosis onward; 2) we will examine tumor characteristics, including molecular features, as potential drivers of these changes; and 3) we will use a rigorous joint modeling approach to simultaneously model the post-diagnosis trajectories of weight and body composition and their relationships with cancer outcomes in the most statistically sound manner. Our findings will inform clinical management of, and identify modifiable body composition features to improve survival for the growing number of ccRCC patients. Approach. We will use available data from the RESOLVE cohort, an NCI-funded retrospective cohort of 1,239 Stage I-III clear-cell renal cell carcinoma (ccRCC) patients diagnosed between 2000-2020 at Memorial Sloan Kettering Cancer Center. These data include clinical and patient-level factors collected from the medical record, including repeated height and weight assessments, body composition measures from existing computed tomography scans, pathological and molecular tumor characteristics, and overall survival (OS) and disease-free survival (DFS). We will use a joint modeling approach to simultaneously model changes in post-diagnosis body weight (Aim 1) and OS and DFS, as well as post-diagnosis changes in muscle and adipose tissue features (Aim 2) and OS and DFS. Models will include molecular tumor characteristics as predictors of these longitudinal trajectories. Impact. These results will provide crucial insight into the relationship between body composition changes and outcomes among ccRCC patients, and potentially identify tumor-related characteristics driving these associations. These results will resolve apparent paradoxes around the relationship between obesity and ccRCC mortality and identify potential targets for nutrition and physical activity interventions on body composition.

Chromatin-Based Mechanisms Linking Transcriptional Dysregulation to Genome Instability in Neurodevelopmental Disorders.

PROJECT SUMMARY/ABSTRACT Neurons depend on a finely tuned interplay between chromatin regulation and genome maintenance, yet they are acutely vulnerable to DNA damage generated during activity-dependent transcription of long, synaptic genes. Disruption of this balance is increasingly recognized as a driver of neurodevelopmental disorders (NDDs) such as autism spectrum disorder (ASD), intellectual disability, and epilepsy. High-confidence genetic studies converge on regulators of histone H3 lysine 4 (H3K4) methylation, such as the writers ASHIL and Klv1T2C and the eraser KDNISB, as recurrently mutated loci in NTIDs. The overarching goal of this study is to investigate how dysregulated H3K4 methylation compromises genome integrity in human neurons, thereby contributing to the pathogenesis of NDDs. The central, hypothesis is that coordinated II3K4 methylation safeguards neuronal genomes by maintaining an open chromatin architecture that permits the efficient detection and repair of transcription-coupled DNA lesions. The rationale/Or this study is to define the epigenetic control of DNA repair, which will illuminate a shared pathogenic hub across multiple ~I)D-linked genes. During the mentoredK99 phase, I will define how ASHIL, KMT2C, and KDM5B regulate chromatin structure and DNA repair at baseline and during transcriptional stress. Aim-1: I will use isogenic iPSC-derived cortical neurons with patient-relevant mutations or CRrSPRi knockdowns of these regulators, applying an integrated multi-omic pipeline: CUT&Tag and Micro-C to map H3K4 methylation and 3D chromatin topology. Aim-2: I will use Paired-Damage-seq, and CUT&RUN to chart oxidative lesions, repair synthesis, and recruitment of key repair factors; and RNA-seq to relate damage hotspots to altered gene expression. Aims l and 2 will be performed under the guidance of Dr. Lizarraga and Dr. Morrow, experts in the field of neurodevelopmental biology. My advisory team brings unique and complementary skills, enhancing my knowledge in 3D chromatin structure, transcription-coupled repair, gene editing, and multi-omics analysis. I will utilize these skills in the R00 phase (Aim 3), expanding the framework to include additional H3K4 regulators (e.g., LSD1, KMT2A) and broader neural lineages, thereby developing a comprehensive model. This study is innovative in its integration of single-cell D.NA damage mapping with chromatin topology and transcriptional profiling, enabling a direct and mechanistic connection between disrupted H3K4 methylation and genome instability. By uncovering how H3.K4 methylation prevents transcription-coupled genome instability in the developing brain, this research will address a critical gap in our understanding of NDD mechanisms. This award will enable me to launch an independent research program dedicated to determining mechanisms of chromatin-based processes that maintain genome stability in the developing human brain.

Magnetic resonance true temperature imaging with high spatial and temporal resolution

ABSTRACT The knowledge of temperature and temperature distribution within the brain can be critical to understanding the healthy and diseased brain, its response to acute injury, and in monitoring critically important thermal interventions. There are several temperature sensitive properties such relaxation rates and the proton resonance frequency shift (PRFS) that can be measured with magnetic resonance imaging (MRI) methods but these methods can only measure temperature change. The PRFS method, which provides the most accurate measurement of temperature change can only measure true tissue temperature if the starting true temperature distribution is known. Fortunately, MR spectroscopy (MRS) methods have been developed that show great promise in the measurement of true temperature. These methods rely on the detection of a temperature independent spectral peak of protons bound to carbon atoms in high concentration metabolites, such as N- acetylaspartate (NAA), creatine (Cr) and choline (Cho) which can be used as a reference for the temperature dependent spectral peak of water protons. Both single voxel spectroscopy (SVS) methods and MRS imaging (MRSI) methods have been described but are slow because of the long readout time needed to achieve adequate spectral resolution and the need to perform multiple averages due to the low signal being measured. Echo-planar spectroscopic imaging (EPSI) speeds up MRSI by interleaving an oscillating imaging gradient to spatially encode one of the imaging dimensions simultaneously with spectral readout. Unfortunately, SVS, MRSI, and even EPSI are unsuitable for clinical applications because of the low spatial resolution (voxel size 1 cm3) and temporal resolution (multiple minutes). The goal of this project is to develop an MRI technique that can measure true temperature in the whole brain at spatial and temporal resolutions that enable clinical utility for acutely assessing and longitudinally monitoring healthy and diseased brain tissue, and real time monitoring of thermal interventional therapies. This innovative true temperature measurement technique combines EPSI, for low resolution background field measurements, with PRFS for high spatial and temporal resolution water proton measurements. While conventional EPSI methods interleave volumetric acquisitions with and without water suppression, we propose an innovative modification to take advantage of the very strong water signal to obtain a very high resolution, dynamic method for true temperature measurements. The MRI pulse sequence will be refined, validated (Aim 1), applied to healthy subjects and post-surgery patients at risk for infections (Aim 2), and applied to essential tremor (ET) patients during the required delay between repeated focused ultrasound sonications (Aim 3). Successful completion of the aims of this study will result in a clinically practical method to obtain true temperature measurements in the brain with a spatial and temporal resolution sufficiently high to meet the needs of monitoring focal thermal therapy treatments as well as to provide true temperature measurements over the entire brain for assessment of the state of the brain with disease, infection, and injury.

Understanding antiretroviral phosphorylation and dephosphorylation using mass spectrometry imaging-based enzyme histochemistry

PROJECT SUMMARY Our overall goal is to understand the mechanistic differences in the activation and deactivation of two widely used first-line antiretroviral drugs: tenofovir (TFV) and emtricitabine (FTC) in colonic tissues. HIV is a global health problem and roughly 1.3 million people became newly infected with HIV globally in 2022. Pre-exposure prophylaxis (PrEP) is an HIV prevention strategy where HIV-negative individuals use antiretrovirals to reduce the risk of HIV infection. Specifically, oral fixed-dose combinations of two antiretrovirals, namely, TFV (TFV; prescribed as TFV disoproxil fumarate or TFV alafenamide prodrugs) and FTC are FDA-approved for HIV PrEP. The pharmacologically active forms of TFV and FTC are TFV-diphosphate (TFV-DP) and FTC-triphosphate (FTC-TP), respectively, and these phosphorylated metabolites are found in cells. Unfortunately, high variability in the responses of TFV and FTC can lead to poor clinical outcomes, including therapeutic failure. However, the molecular mechanisms responsible for the observed variability in TFV and FTC responses are poorly understood. Although the observed variability in TFV and FTC drug responses is likely to be multifactorial, alterations in drug activation and deactivation can contribute to the observed variability in drug responses. Phosphorylation of TFV is known and recent studies suggest that nucleotidases may involve in the dephosphorylation of TFV metabolites. Although the kinases that phosphorylate FTC in peripheral blood mononuclear cells are known, the kinases that are responsible for the phosphorylation of FTC in putative sites of HIV infection such as colonic tissues are yet to be determined. Notably, unprotected receptive anal intercourse has a 20-fold higher risk of HIV transmission than vaginal intercourse. Thus, understanding the biotransformation of TFV and FTC in colonic tissue is important since it is a susceptible tissue to HIV infection. Recently, we have reported the enzymatic activities of nucleotidases toward the pharmacologically active metabolites of TFV and FTC in vitro. However, the mechanistic details of the biotransformation of the above drugs in HIV susceptible tissues such as colonic tissues are yet to be elucidated. Gaining a mechanistic understanding of the biotransformation of TFV and FTC in putative sites of HIV infection is important to improve their therapeutic efficacy. As such, in this application, we propose an innovative mass spectrometry imaging-based interdisciplinary approach to understand the biotransformation of TFV and FTC in the colon. Aim 1 will establish the role of nucleotide kinases and nucleotidases in regulating TFV and FTC metabolites in colonic cells mechanistically. Aim 2 will characterize the region- and cell-type-specific expression patterns, as well as enzymatic activities of nucleotide kinases and nucleotidases in situ. The proposed project will provide novel understandings of TFV and FTC activating and deactivating mechanisms that can be leveraged to optimize the therapeutic efficacy of the above drugs.

Targeting subtype specification as a driver of PDAC health disparities

PROJECT SUMMARY Pancreatic ductal adenocarcinoma (PDAC) is a deadly disease that is refractory to current treatment strategies due in part to adaptive mechanisms of chemoresistance. Racial health disparities also confound the treatment and care of these patients. Blacks (people with African genetic ancestry) have significantly higher incidence rates of PDAC and decreased survival times compared to Caucasians (White genetic ancestry) even after socioeconomic status and tumor stages are controlled. Therefore, it is possible different racial groups exhibit unique molecular characteristics in PDAC tumors that contribute to these health disparities. The unique molecular characteristics that distinguish PDAC tumors between racial groups exhibiting disparities have the potential to identify new therapeutic targets. In a previous study, we identified 4 distinct subtypes of PDAC (Metabolic, Progenitor-like, Proliferative, and Inflammatory) that can be distinguished using multivariate analysis of quantitative proteomic data. While these PDAC subtypes are predictive of therapeutic response, this has not yet been analyzed in disparity factor balanced studies. We have examined the proteomes of primary PDAC tumors using quantitative mass spectrometry and identified unique protein signatures for Blacks and Whites. PDAC tumors from Black patients display features consistent with the Inflammatory subtype of PDAC, which is characterized by an inflamed microenvironment expressing complement proteins that can promote resistance to chemotherapy. Therefore, it is possible that race influences subtype and Blacks could preferentially develop the more aggressive and treatment refractory Inflammatory subtype. Strategies are needed to modulate subtype to improve response to chemotherapy. Toward this goal, our proteomic analysis identified polycomb repressor complex 1 (PRC1) protein RNF2 as being upregulated in PDACs from Blacks compared to Whites. We have also discovered that RNF2 regulates mRNA expression of the PDAC subtype specification factor GATA6 and inhibiting RNF2 promotes a molecular shift toward the more chemosensitive Classical subtype of PDAC. Therapeutic targeting can be achieved with Tazemetostat that inhibits the upstream PRC2 to prevent RNF2 binding the GATA6 promoter leading to its increased expression. Additionally, the Inflammatory subtype characterized by innate immune complement protein activation could be targeted with another FDA approved drug, Avacopan, which has not previously been studied in PDAC. Therefore, the Specific Aims of this proposal are designed to: 1) Evaluate the extent to which Tazemetostat treatment impacts chemotherapy-induced subtype plasticity in patient derived organoids; and 2) To determine the extent to which strategies targeting pathways associated with PDAC disparities affect progression and subtype characteristics in vivo. The successful completion of these aims has the potential to be moved quickly into phase I clinical trials since both Tazemetostat and Avacopan are FDA approved drugs. Furthermore, if successful, this project has the potential to mitigate health disparities in PDAC and broadly improve patient outcomes by implementing new precision interventions. The mouse models we propose faithfully recapitulate pancreatic cancer's clinical syndrome, histopathology and molecular properties, including the often-unique features of the stromal and immune responses that constitute the complex desmoplasia of this disease, which cannot be addressed using in vitro model systems

Overcoming Treatment Resistance by Targeting Polyploid Breast Cancer Cells with AI assisted Single-Cell Analysis

Therapy resistance remains a formidable challenge in breast cancer treatment, with emerging evidence identifying polyploid giant cancer cells (PGCCs) as key drivers. These cells, arising through whole-genome doubling (WGD) events, exhibit enhanced resistance to therapies, contributing to disease relapse. PGCCs are characterized by enlarged cell and nuclear sizes, increased DNA content, and greater resilience compared to non-PGCCs. Their prevalence escalates with disease progression and therapeutic stress, underscoring their critical role in treatment resistance. As such, we hypothesize that inhibiting polyploid cancer cells can effectively reduce therapeutic resistance. Despite this, effective strategies targeting PGCCs are limited, hindered by the lack of high-throughput methods to assess PGCC viability and abundance. Traditional screening assays lack the sensitivity to detect the elimination of small populations of PGCCs, while current detection methods, such as visual inspection and flow cytometry, are not suited for high-throughput compound screening. Our preliminary work has established a high-throughput single-cell morphological analysis pipeline capable of quantifying PGCCs, and we successfully screened 2,726 compounds for their efficacy on PGCCs. Based on the preliminary success, we aim to further improve its robustness and accuracy under diverse staining and imaging conditions, ensuring consistent performance across multiple labs for widespread use in PGCC/WGD studies, with deep learning to accelerate the discovery of therapeutic strategies targeting PGCCs. In addition to empirical screening, our scRNA-Seq analysis of PGCCs has revealed altered gene expression, particularly in genes associated with FOXM1, a transcription factor critical in cell cycle regulation and linked to poor outcomes in various cancers. PGCCs also show altered ferroptosis regulators and elevated reactive oxygen species (ROS), indicating susceptibility to ferroptosis. Here, we propose two independent and complementary aims. Aim 1: We will develop and validate a robust deep learning–based single-cell morphological analysis pipeline for accurate PGCC/non-PGCC discrimination across variable staining, imaging, and lab settings. The model will be benchmarked on independent datasets from external labs and released as open-source, version-controlled software with full documentation to support reproducibility and broad adoption in PGCC/WGD research. Aim 2: Leveraging our screen of 2,726 FDA-approved compounds and mechanistic studies of FOXM1 and ferroptosis, we will prioritize and validate therapies that eradicate PGCCs and reduce treatment resistance. Using patient- derived cells, 3D spheroids, and syngeneic/xenograft models, we will rigorously assess top candidates as monotherapy and in combination with standard-of-care agents. Successful completion of this project will accelerate PGCC/WGD research, advance therapeutic strategies to overcome breast cancer resistance, and especially deliver benefits to patients with high PGCC burden. Given the prevalence of WGD across solid tumors and its induction by standard therapies, our approach holds broad clinical relevance and translational impact.

Multi-modal Micro Electrode Fluidic Array (MEFA) Shells for Brain Organoids

Abstract Brain organoids (BOs) derived from human stem cells bridge the gap between monolayer cell culture studies and animal models, which have well-documented limitations. Monolayer cell culture models fail to accurately replicate the 3D interconnectivity in the brain; animal models, while helpful, are limited due to interspecies differences, with most research focusing on rather phenotypical rather than mechanistic aspects. Concurrent with the advancement of BO models is the urgent need to develop 3D micro instrumentation supporting these organoids to investigate brain development and disease in their accurate physiological environment. Conventional microelectrode arrays (MEAs) used for neuronal cell culture studies are planar, which limits recording access to a small fraction of cells on the bottom side of the organoid. Also, conventional microfluidics is inherently planar, and while recent advances in 3D MEAs and 3D microfluidics have enabled electrical and chemical interrogation in 3D, combining both features with tunability and precision to allow independent and simultaneous control is challenging. Recently, we reported new 3D micro instrumentation in the form of 3D shell MEAs and demonstrated its applicability for electrical recording from BOs. They feature lithographically patterned and chip-integrated electrodes and self-folding polymer shells that can be triggered to wrap around BOs to measure electrical activity from the entire organoid surface. The 3D MEA shell system is modeled on and resembles a miniaturized electroencephalography (EEG) cap; the process used to make them is size-scalable, chip-integrated, and mass- producible. In the research, we aim to develop and validate 3D Micro Electrode Fluidic Array (MEFA) shells with multi-modal electrical recording and biochemical control capabilities, offering high spatiotemporal resolution, tunability, and scalability. Since 3D spatiotemporal patterns of neurochemicals play a critical role in molecular and cellular events of neural development and disease, we propose to apply and validate the MEFA shells in two studies that mimic neurodevelopment and monitor the spatiotemporal effects in neurological disorders and their treatments in vitro. We anticipate that the proposed 3D MEFAs would revolutionize brain sciences by permitting real-time, in-situ studies of electrical and chemical stimulation and interrogation of BOs in a high- throughput manner. The proposed 3D scalable, reproducible, and tunable 3D micro instrumentation for BOs has broad relevance to understanding brain development in utero and the development of anatomically accurate drug and toxicity screening platforms for brain sciences and neurological disorders.

Uncovering genetic determinants of carbapenem resistance in Klebsiella pneumoniae

Carbapenem-resistant Klebsiella pneumoniae represents an urgent global health threat due to its increasing prevalence and high mortality rates, necessitating a comprehensive understanding of its resistance mechanisms. While key resistance mechanisms and their genetic determinants are known, such as beta- lactamases and porin mutations, the cause of resistance in many strains remains elusive. Moreover, other strains that carry known genetic carbapenem-resistance factors have been found to still be susceptible to carbapenems for unclear reasons. Further, strains can carry genetic elements which, while not conferring resistance directly, can promote resistance indirectly by accelerating its acquisition, such as through mutations in DNA repair systems or mobile genetic elements. To address these knowledge gaps, we propose a genome-wide association study (GWAS), with the aim of maximizing the discovery of gene variants associated with meropenem resistance, with experimental validation of candidates to identify true causal variants. We will overcome limitations of prior studies in the following ways: 1) We have compiled an expanded data set of publicly available K. pneumoniae genomes from strains isolated across a wide distribution of countries, with in hand access to >100 isolates upon which experimental validation studies will be performed. 2) We will perform comprehensive capture of genetic variants by employing a reference-free GWAS, utilizing unitigs, stretches of DNA sequence that represent the entire spectrum of genetic variation. 3) We will enhance statistical power to detect genetic variants with even subtle effects on resistance by using a quantitative, continuous minimum inhibitory concentration (MIC) phenotype to meropenem rather than a binary designation of resistant or susceptible. 4) We will reduce the number of false positives arising from correlation, or linkage disequilibrium (LD), with known carbapenemase and other known resistance factors by performing a conditional GWAS, where known factors are included as covariates. 5) We will further mitigate confounding effects due to population structure and LD, which cause non-random relationships between variants, by utilizing a pangenome-wide regression with an elastic net penalty. 6) Crucially, we will functionally validate our findings, which will include genetic variants associated with increased resistance, whether through direct or indirect mechanisms, as well as those that may restore susceptibility in strains already possessing known resistance factors. We will bridge the gap between GWAS findings and functional validation by leveraging our high-throughput experimental capabilities. This integrated approach promises to uncover novel mechanisms of carbapenem resistance, its acquisition, and susceptibility in K. pneumoniae, with the potential to inform the development of future diagnostics or therapeutic strategies.

Identifying host-interacting proteins of Sneathia vaginalis

PROJECT SUMMARY AND ABSTRACT Sneathia vaginalis is a member of the human normal flora of the vagina. It is also a human pathogen that is associated with preterm birth and amniotic fluid infections as well as the most common bacterial species associated with HPV infection and cervical cancer. The identification of S. vaginalis as a human pathogen is recent and little is known about how this bacterium interacts with the host in either its commensal or pathogenic lifestyles. With the exception of a single toxin, no additional virulence factors have been identified. Our preliminary data demonstrates that S. vaginalis grows to high cell density in rich media; however, it fails to grow planktonically in other media unless host cells are present indicating that S. vaginalis relies on host cells for nutrients. This is consistent with its reduced genome. In addition, bacterial proliferation requires close proximity with the host cells and previous studies demonstrate that S. vaginalis can bind a variety of host cells. The proteins that mediate contact are unknown. We hypothesize that proteins on the surface of S. vaginalis are critical for host cell adhesion. We will use two Aims to examine this. Aim 1 will use mass spectrometry to identify S. vaginalis surface proteins. Aim 2 generates deletion strains of potential adhesins identified in Aim 1 as well as predicted host-interacting proteins that have already been identified bioinformatically based on those in other bacteria. The mutant bacteria are then tested in host-cell adhesion assays. Together, these aims will identify for the first time the proteins found on the surface of S. vaginalis while identifying proteins that interact with host cells that would be expected to contribute to either its commensal or pathogenic lifestyles or both. Moreover, these studies would be used to inform clinical lab practice as surface-expressed proteins could be used to identify identifying markers of S. vaginalis detection.

Developing a novel technology for studying T cell differentiation in vivo

Summary CRISPR-based genetic screens have revolutionized our understanding of gene functions and molecular mechanisms across various biological processes. In the field of T cell biology, CRISPR screens have played a pivotal role in identifying genes that impact critical aspects, such as T cell development, differentiation, and function. However, traditional screens have struggled to distinguish genes with diverse mechanisms of action, necessitating further investigations. To address this challenge, researchers have harnessed the power of CRISPR screens combined with single-cell sequencing (scCRISPR-seq), enabling the simultaneous assessment of genetic perturbations and high-dimensional phenotypes at the single-cell level. While scCRISPR- seq has predominantly been performed in vitro using immortalized cell lines, its physiological relevance is limited due to oversimplified biological context and disparities compared to primary cells. This limitation highlights the urgent need for large-scale in vivo scCRISPR-seq with primary T cells. However, various challenges have discouraged its widespread adoption. The use of viral vectors for sgRNA delivery compromises physiological relevance, as the in vitro activation conditions fail to faithfully represent the intricate T cell priming process in vivo. Moreover, viral vector components and continuous Cas9 expression can trigger immunogenicity and cytotoxicity, leading to cell depletion and hindering long-term studies. Additionally, current scCRISPR-seq methods face technical limitations, including low editing efficiency and inadequate perturbation identity recovery rates, which impede efficient large-scale in vivo applications. Fortunately, recent advances in ribonucleoprotein complex (RNP) transfection have addressed many of these challenges. This cutting-edge technology enables efficient gene editing in primary T cells without the need for in vitro activation or permanent Cas9 expression. Leveraging the high editing efficiency of RNP transfection, the investigator’s team aims to develop a novel strategy for in vivo T cell CRISPR screens. This innovative approach involves arrayed RNP transfection and co- transfer of T cells that recognize the relevant antigens. Instead of traditional genetic barcodes, the strategy utilizes congenic markers (CD45.1/45.2 and CD90.1/CD90.2) from donor TCR transgenic T cells as "external barcodes." These markers facilitate the recovery of gene perturbation identity at the single-cell level through the application of CITE-seq. Importantly, this RNP-based strategy seamlessly integrates with existing single-cell sequencing protocols, enabling the comprehensive assessment of transcripts, epitopes, and chromatin accessibility simultaneously. To demonstrate the efficacy of this strategy, the team plans to develop two benchmarking approaches: RNP-CET-seq to investigate the role of TCR regulators in T cell exhaustion and RNP-CATE-seq to map the gene regulatory atlas of exhausted CD8 T cells. In summary, the proposed RNP- based scCRISPR-seq strategy overcomes the limitations of current approaches, enabling large-scale, multi- module in vivo genetic screens within a physiologically relevant context across various disease models.

Intrinsic and extrinsic mechanisms underlying trigeminal nerve deficits in familial dysautonomia

PROJECT SUMMARY Rare diseases impose a significant burden on the US healthcare system, accounting for nearly half of all expenditures for their treatment. This statistic alone supports the need to invest in research to develop therapeutic interventions for rare diseases since the economic benefit outweighs the continued expense of financial resources. Familial dysautonomia (FD) is a rare, hereditary disease that arises from a splice site mutation in Elongator acetyltransferase complex subunit 1 (ELP1) and impacts the nervous system. To date, FD patients continue to face life-threatening complications involving basic involuntary functions like swallowing and somatosensation because there is no cure for this ultimately fatal neuropathy. FD patients exhibit symptoms due to defects in their somatosensory trigeminal nerves, whose cell bodies reside in the trigeminal ganglion (TG) and are derived from neural crest and placode cells. Recent studies from our lab using an FD mouse model (Elp1 deleted from neural crest cells) revealed TG axon outgrowth and target tissue innervation deficits, recapitulating phenotypes observed in FD patients. However, the mechanisms by which Elp1 mediates normal TG development, and how this goes awry in FD, remain largely elusive. To gain insight into Elp1 function, we performed mass spectrometry to evaluate the TG proteome of normal and FD mouse embryos. Our results uncovered statistically significant increases in extracellular matrix (ECM) and ECM binding proteins, pointing to altered TG biomechanical properties and, more broadly, changes in mechanotransduction, the process by which cells translate extrinsic cues into intrinsic signaling pathways that modulate gene expression. Importantly, proper axon outgrowth relies upon mechanotransduction as growth cones on axons sense and respond to their environment. In the head, this environment consists of ECM and cranial mesenchyme cells, but the impact of Elp1 loss from the latter is not known, including the potential for altered tissue biomechanics that could influence TG axon outgrowth. We hypothesize that loss of Elp1 induces changes in the biomechanical properties of both the TG/nerves and ECM/cranial mesenchyme, modifying mechanotransduction and leading to TG defects in FD, which we will interrogate in the following Specific Aims: 1) define the biomechanical properties of the TG/nerves and ECM/cranial mesenchyme and 2) determine the role of cranial mesenchyme Elp1 in mediating proper TG axon outgrowth. Our innovative research proposal takes a systems-level, multidisciplinary approach involving embryology, biomechanics, and high-resolution microscopy, with the goal of integrating molecular, cellular, and tissue data. These results will significantly advance our knowledge of the molecular mechanisms underscoring TG development and, collectively, inform treatment strategies for birth defects or disorders like FD with TG dysfunction, as well as nerve repair and/or regeneration after injury or disease.

Primary cilia protein IFT88 governs smooth muscle phenotype and vascular remodeling

Project Summary/Abstract Cardiovascular disease remains the leading cause of death in the United States, accounting for nearly 1 million deaths in 2022. Vascular diseases such as atherosclerosis, aneurysm, and coronary artery disease are regulated largely by smooth muscle cells (SMCs) residing in the blood vessel wall. The central dogma of vascular SMC biology is that differentiated cells can de-differentiate and give rise to a spectrum of alternative phenotypes promoting invasion, proliferation, fibrosis, and inflammation, but the mechanisms regulating SMC phenotypic transitions are poorly understood. Intraflagellar transport 88 (IFT88) is an essential protein for the formation of primary cilia, centriole-associated plasma membrane organelles that project into the extracellular milieu and regulate cell cycle reentry and responses to stimuli like growth factors and mechanical strain. Non- ciliary functions of IFT88 also include progression of the cell cycle checkpoint and polarized motility, both of which are functionally critical for SMC-mediated vascular remodeling. Little is known about the functional role of the primary cilia in SMCs and the role of the essential cilia protein IFT88 in regulating SMC phenotype. To address this gap in knowledge, my postdoctoral studies focus on the role of IFT88 in the context of intimal hyperplasia (K99). During the independent phase (R00), I will apply these findings to arteriovenous fistula (AVF) maturation, a surgical intervention often required for dialysis individuals with polycystic kidney disease (PKD), an IFT88 loss-of-function disease. I will test my central hypothesis that cilia are key regulators of SMC phenotype in three Specific Aims: 1) determine the role of IFT88-dependent SMC primary cilia in mechanotransduction of extracellular matrix (ECM) stiffness (K99), 2) determine the role of IFT88 in pathological intimal hyperplasia (K99), and 3) test whether SMC IFT88 expression is required for adaptive remodeling of grafted veins following AVF placement (R00). Overall, we propose that IFT88+ ciliated SMC represent an unidentified subclass of the SMC phenotype spectrum that is primarily responsible for vascular remodeling and is an attractive potential target for treatment of vascular diseases. Building on strong existing collaborations, we have formed a research and mentoring team with expertise in SMC pathophysiology, primary cilia biology, mechanobiology, AVF surgery, and PKD to complete the proposed aims. The additional training in cell-ECM interactions (Aim 1, K99), in vivo murine ligation injury and in vivo cilia imaging (Aim 2, K99), and AVF surgery and PKD pathology (Aim 3, R00) will be indispensable for preparing the PI, Dr. O’Brien, for his career as an independent investigator. Completion of the proposed aims will also contribute directly to an understanding of the function of IFT88-dependent primary cilia in SMCs and may likely identify novel therapeutic targets for treatment of vascular diseases.

The multiciliation cycle: a variant cell cycle coordinating centriole biogenesis and ciliogenesis

Project summary/Abstract Differentiating multiciliated cells line the mammalian airway and are critical for protecting the lungs from inhaled pathogens and particulates. Multiciliated cells have a distinct architecture from other cell types, having hundreds of centrioles, each of which matures into a basal body and nucleates a motile cilium. Defects in multiciliation cause a form of Primary Ciliary Dyskinesia (PCD), a lung disease. Most cells generate two centrioles and one cilium per cell cycle. We found that differentiating multiciliated cells redeploy cell cycle regulators into a novel cell cycle variant, which we refer to as the multiciliation cycle, to break these rules, generate hundreds of centrioles and cilia, and coordinate their differentiation. The multiciliation cycle redeploys many mitotic cell cycle regulators, including cyclin-dependent kinases (CDKs) and their cognate cyclins. For example, Cyclin D1-CDK4/6, regulators of mitotic G1 to S progression, is required for multiciliated cell fate initiation and entry into the multiciliation cycle. While we have focused on lung multiciliated cells, others have found that cell cycle regulators similarly participate in multiciliation of ependymal cells of the brain. Some cells, such as mammalian trophoblast giant cells, employ cell cycle variants like the endocycle to bypass mitosis. We propose that the multiciliation cycle is another cell cycle variant that augments some aspects of the canonical cell cycle, such as centriole synthesis, and blocks others, such as DNA replication. During the multiciliation cycle, E2F7, a transcriptional regulator of canonical S to G2 progression, is expressed at high levels. During multiciliated cell differentiation, E2F7 directly dampens expression of genes encoding DNA replication machinery and terminates the S phase-like gene expression program. Loss of E2F7 causes a reacquisition of DNA synthesis in multiciliated cells and dysregulation of multiciliation cycle progression, disrupting centriole maturation and ciliogenesis. We propose that multiciliated cell differentiation is coordinated by an alternative cell cycle that organizes, instead of cell proliferation, the steps of cell differentiation. In this project, we investigate how the multiciliation cycle redeploys the mitotic cell cycle regulatory framework to generate many centrioles without undergoing DNA synthesis or cytokinesis. More specifically, we seek to uncover how CDKs and cyclins are regulated to control the amount and timing of basal body synthesis, how Retinoblastoma (RB) protein controls the transcriptional program of multiciliation, and how E2Fs advance the multiciliation cycle. This work will test the hypothesis that multiciliation is organized by a variant cell cycle that uncouples centriole synthesis from DNA replication and mitosis. We propose that his variant cell cycle orchestrates progression through sequential phases required to construct the multiciliated cells that protect the lungs.

2026 Thiol-Based Redox Regulation and Signaling Gordon Research Conference and Gordon Research Seminar

PROJECT SUMMARY This proposal requests support for the 10th meeting of the biennial Gordon Research Conference (GRC) and associated Gordon Research Seminar (GRS) on Thiol-Based Redox Regulation and Signaling to be held at the Rey Don Jaime Grand Hotel, Castelldefels, Spain on July 11-12 (GRS) and July 12-17 (GRC), 2026. Regulation of protein function through the post-translational modification of specific cysteine residues (thiol oxidation) plays an important role in cellular adaptation to local and global changes to endogenous and environmental oxidants. A key challenge for the redox-signaling field is to understand how thiol-based signaling mechanisms are integrated into cellular redox homeostasis and how these events facilitate communication between molecules, organelles, cells, and tissues to initiate and coordinate a specialized biological outcome. Significant emphasis for the 2026 meeting will be placed on an exploration of a wider range of cysteine thiol chemistry placed within a cellular context of other, often competing, oxidative or acyl modifications, some of which derive from environmental exposures, and contribute to cancer, aging and the progression of disease. In addition, we will discuss new insights into how cellular redox status impacts metabolic disease and new mathematical and analytical approaches to understand how redox gradients or “waves” impact the spatial and temporal aspects of signaling. A long-term objective is to use this new information to develop diagnostics and therapeutics for a wide range of redox-associated diseases that impact public health. This meeting provides a unique forum for extensive and immersive interaction among chemists, biologists, structural biologists and redox tool-builders, interested in a range of animal and cellular model systems, with clinical researchers and physicians focused on disease processes. While the thematic area of the conference is intentionally broad, its relevance to specialized NIH institutes is highly significant. Not only is redox toxicity proposed as a primary driver of chemically-induced pathology in humans, notably in aging and age-associated diseases, protection from these pathologies by “supersulfides” holds considerable promise. In keeping with the GRC tradition, the 2026 meeting will highlight presentations that emphasize unpublished work, creating a distinctive intellectual experience that enhances the excitement of the meeting. Investigators new to the meeting, junior investigators and graduate and post-graduate trainees will be welcomed. The associated GRS will provide a more intimate forum where graduate and postdoctoral trainees present their research to their peers, while receiving constructive comments from a few senior investigators who serve as mentors. We intend that the GRS/GRC meetings will attract and increase retention of junior scientists in the field of redox biology. We anticipate that the GRC will enhance the education of researchers at all career levels, generate new ideas and collaborations aimed at understanding thiol-based redox regulation and dysfunction, and enable future progress in the prevention, detection, and treatment of a wide-range of human diseases associated with perturbations in redox homeostasis.

Development of a multi-modal mouse model of cluster headache

PROJECT SUMMARY / ABSTRACT Cluster headache (CH), which affects about 1 in 1,000 people, is a severe and debilitating primary headache disorder characterized by repeated attacks occurring in clusters over weeks or months. CH has clearly defined features: severe pain (worse than childbirth), facial autonomic changes (such as a watery eye), restlessness, and a striking circadian pattern of attacks (at the same time each day like clockwork in approximately 70.5% of patients). CH also has a well-defined pathophysiology of 3 systems: the trigeminovascular pain system, the autonomic nervous system, and the hypothalamic system (in particular the posterior hypothalamus, the first brain area activated during an attack). Despite the well-known features and systems involved in CH, no disease- specific treatments are available: all CH treatments are repurposed medications from other diseases. This lack of CH-specific treatments is due in large part to the lack of a viable animal model that faithfully recapitulates the aforementioned CH features. To develop a specific animal model for CH, we previously studied a trigeminovascular headache model (repeated nitroglycerin injections), and discovered a circadian pattern of pain responses that reflects the clockwork-like pattern of attacks in CH patients. Furthermore, our analysis also identified a recently discovered CH modifier gene Mertk (MER proto-oncogene, tyrosine receptor kinase) to be highly rhythmically expressed in the trigeminal ganglion. Deletion of Mertk (Mertk-KO) altered the normal circadian rhythm of pain sensitivity by increasing pain sensitivity over 24 hours. Finally, activation of the posterior hypothalamus (via c-Fos staining) was observed after NTG administration in wild-type mice. Based on these exciting preliminary findings, we hypothesize that a combination of trigeminovascular (nitroglycerin), genetic (Mertk-KO), and hypothalamic (direct optogenetic activation of the posterior hypothalamus) manipulations will generate the first multi-modal animal model of CH. In Aim 1 (the R61 phase), we will determine the contributions of each aspect of our combined model, alone or in combination (a 4x2 grid of NTG or control, Mertk KO mouse or wild-type control, and optogenetic injection or control). Our milestone for progression to the R33 phase will be significant differences in at least two pain behaviors in our model compared to controls. In Aims 2 and 3 (the R33 phase), we will validate our model through face validity (lacrimation and restlessness), construct validity (CGRP, PACAP, and VIP in the trigeminal ganglion and hypothalamus), and predictive validity (ability of first-line and new treatments to ameliorate the pain behaviors of our model). This project is highly significant and innovative, addressing a profound need for a specific and comprehensive animal model for this devastating yet understudied disease. With the unique combination of complementary expertise in CH (laboratory and clinical), circadian biology, pharmacology, optogenetics and pain, we are ideally suited to generate this combined CH model with the goal of providing insights into CH pathophysiology and developing novel therapeutics.

Consciousness at the edge of chaos

Over the last 20 years, neuroimaging and electrophysiology techniques have become central to understanding the mechanisms that accompany loss and recovery of consciousness. Much of this research is performed in the context of healthy individuals with neurotypical brain dynamics. Yet, a true understanding of how consciousness emerges from the joint action of neurons has to account for how severely pathological brains, often showing phenotypes typical of unconsciousness, can nonetheless generate a subjective viewpoint. In this presentation, I will start from the context of Disorders of Consciousness and will discuss recent work aimed at finding generalizable signatures of consciousness that are reliable across a spectrum of brain electrophysiological phenotypes focusing in particular on the notion of edge-of-chaos criticality.

Continuity and segmentation - two ends of a spectrum or independent processes?

Developmental and evolutionary perspectives on thalamic function

Brain organization and function is a complex topic. We are good at establishing correlates of perception and behavior across forebrain circuits, as well as manipulating activity in these circuits to affect behavior. However, we still lack good models for the large-scale organization and function of the forebrain. What are the contributions of the cortex, basal ganglia, and thalamus to behavior? In addressing these questions, we often ascribe function to each area as if it were an independent processing unit. However, we know from the anatomy that the cortex, basal ganglia, and thalamus, are massively interconnected in a large network. One way to generate insight into these questions is to consider the evolution and development of forebrain systems. In this talk, I will discuss the developmental and evolutionary (comparative anatomy) data on the thalamus, and how it fits within forebrain networks. I will address questions including, when did the thalamus appear in evolution, how is the thalamus organized across the vertebrate lineage, and how can the change in the organization of forebrain networks affect behavioral repertoires.

Recent views on pre-registration

A discussion on some recent perspectives on pre-registration, which has become a growing trend in the past few years. This is not just limited to neuroimaging, and it applies to most scientific fields. We will start with this overview editorial by Simmons et al. (2021): https://faculty.wharton.upenn.edu/wp-content/uploads/2016/11/34-Simmons-Nelson-Simonsohn-2021a.pdf, and also talk about a more critical perspective by Pham & Oh (2021): https://www.researchgate.net/profile/Michel-Pham/publication/349545600_Preregistration_Is_Neither_Sufficient_nor_Necessary_for_Good_Science/links/60fb311e2bf3553b29096aa7/Preregistration-Is-Neither-Sufficient-nor-Necessary-for-Good-Science.pdf. I would like us to discuss the pros and cons of pre-registration, and if we have time, I may do a demonstration of how to perform a pre-registration through the Open Science Framework.

Cognitive maps as expectations learned across episodes – a model of the two dentate gyrus blades

How can the hippocampal system transition from episodic one-shot learning to a multi-shot learning regime and what is the utility of the resultant neural representations? This talk will explore the role of the dentate gyrus (DG) anatomy in this context. The canonical DG model suggests it performs pattern separation. More recent experimental results challenge this standard model, suggesting DG function is more complex and also supports the precise binding of objects and events to space and the integration of information across episodes. Very recent studies attribute pattern separation and pattern integration to anatomically distinct parts of the DG (the suprapyramidal blade vs the infrapyramidal blade). We propose a computational model that investigates this distinction. In the model the two processing streams (potentially localized in separate blades) contribute to the storage of distinct episodic memories, and the integration of information across episodes, respectively. The latter forms generalized expectations across episodes, eventually forming a cognitive map. We train the model with two data sets, MNIST and plausible entorhinal cortex inputs. The comparison between the two streams allows for the calculation of a prediction error, which can drive the storage of poorly predicted memories and the forgetting of well-predicted memories. We suggest that differential processing across the DG aids in the iterative construction of spatial cognitive maps to serve the generation of location-dependent expectations, while at the same time preserving episodic memory traces of idiosyncratic events.

What it’s like is all there is: The value of Consciousness

Over the past thirty years or so, cognitive neuroscience has made spectacular progress understanding the biological mechanisms of consciousness. Consciousness science, as this field is now sometimes called, was not only inexistent thirty years ago, but its very name seemed like an oxymoron: how can there be a science of consciousness? And yet, despite this scepticism, we are now equipped with a rich set of sophisticated behavioural paradigms, with an impressive array of techniques making it possible to see the brain in action, and with an ever-growing collection of theories and speculations about the putative biological mechanisms through which information processing becomes conscious. This is all good and fine, even promising, but we also seem to have thrown the baby out with the bathwater, or at least to have forgotten it in the crib: consciousness is not just mechanisms, it’s what it feels like. In other words, while we know thousands of informative studies about access-consciousness, we have little in the way of phenomenal consciousness. But that — what it feels like — is truly what “consciousness” is about. Understanding why it feels like something to be me and nothing (panpsychists notwithstanding) for a stone to be a stone is what the field has always been after. However, while it is relatively easy to study access-consciousness through the contrastive approach applied to reports, it is much less clear how to study phenomenology, its structure and its function. Here, I first overview work on what consciousness does (the "how"). Next, I ask what difference feeling things makes and what function phenomenology might play. I argue that subjective experience has intrinsic value and plays a functional role in everything that we do.

Pharmacological exploitation of neurotrophins and their receptors to develop novel therapeutic approaches against neurodegenerative diseases and brain trauma

Neurotrophins (NGF, BDNF, NT-3) are endogenous growth factors that exert neuroprotective effects by preventing neuronal death and promoting neurogenesis. They act by binding to their respective high-affinity, pro-survival receptors TrkA, TrkB or TrkC, as well as to p75NTR death receptor. While these molecules have been shown to significantly slow or prevent neurodegeneration, their reduced bioavailability and inability to penetrate the blood-brain-barrier limit their use as potential therapeutics. To bypass these limitations, our research team has developed and patented small-sized, lipophilic compounds which selectively resemble neurotrophins’ effects, presenting preferable pharmacological properties and promoting neuroprotection and repair against neurodegeneration. In addition, the combination of these molecules with 3D cultured human neuronal cells, and their targeted delivery in the brain ventricles through soft robotic systems, could offer novel therapeutic approaches against neurodegenerative diseases and brain trauma.

Vision for perception versus vision for action: dissociable contributions of visual sensory drives from primary visual cortex and superior colliculus neurons to orienting behaviors

The primary visual cortex (V1) directly projects to the superior colliculus (SC) and is believed to provide sensory drive for eye movements. Consistent with this, a majority of saccade-related SC neurons also exhibit short-latency, stimulus-driven visual responses, which are additionally feature-tuned. However, direct neurophysiological comparisons of the visual response properties of the two anatomically-connected brain areas are surprisingly lacking, especially with respect to active looking behaviors. I will describe a series of experiments characterizing visual response properties in primate V1 and SC neurons, exploring feature dimensions like visual field location, spatial frequency, orientation, contrast, and luminance polarity. The results suggest a substantial, qualitative reformatting of SC visual responses when compared to V1. For example, SC visual response latencies are actively delayed, independent of individual neuron tuning preferences, as a function of increasing spatial frequency, and this phenomenon is directly correlated with saccadic reaction times. Such “coarse-to-fine” rank ordering of SC visual response latencies as a function of spatial frequency is much weaker in V1, suggesting a dissociation of V1 responses from saccade timing. Consistent with this, when we next explored trial-by-trial correlations of individual neurons’ visual response strengths and visual response latencies with saccadic reaction times, we found that most SC neurons exhibited, on a trial-by-trial basis, stronger and earlier visual responses for faster saccadic reaction times. Moreover, these correlations were substantially higher for visual-motor neurons in the intermediate and deep layers than for more superficial visual-only neurons. No such correlations existed systematically in V1. Thus, visual responses in SC and V1 serve fundamentally different roles in active vision: V1 jumpstarts sensing and image analysis, but SC jumpstarts moving. I will finish by demonstrating, using V1 reversible inactivation, that, despite reformatting of signals from V1 to the brainstem, V1 is still a necessary gateway for visually-driven oculomotor responses to occur, even for the most reflexive of eye movement phenomena. This is a fundamental difference from rodent studies demonstrating clear V1-independent processing in afferent visual pathways bypassing the geniculostriate one, and it demonstrates the importance of multi-species comparisons in the study of oculomotor control.

Neurobiological Pathways to Tau-dependent Pathology: Perspectives from flies to humans

SWEBAGS conference 2024: The involvement of the striatum in autism spectrum disorder

The Brain Prize winners' webinar

This webinar brings together three leaders in theoretical and computational neuroscience—Larry Abbott, Haim Sompolinsky, and Terry Sejnowski—to discuss how neural circuits generate fundamental aspects of the mind. Abbott illustrates mechanisms in electric fish that differentiate self-generated electric signals from external sensory cues, showing how predictive plasticity and two-stage signal cancellation mediate a sense of self. Sompolinsky explores attractor networks, revealing how discrete and continuous attractors can stabilize activity patterns, enable working memory, and incorporate chaotic dynamics underlying spontaneous behaviors. He further highlights the concept of object manifolds in high-level sensory representations and raises open questions on integrating connectomics with theoretical frameworks. Sejnowski bridges these motifs with modern artificial intelligence, demonstrating how large-scale neural networks capture language structures through distributed representations that parallel biological coding. Together, their presentations emphasize the synergy between empirical data, computational modeling, and connectomics in explaining the neural basis of cognition—offering insights into perception, memory, language, and the emergence of mind-like processes.

LLMs and Human Language Processing

This webinar convened researchers at the intersection of Artificial Intelligence and Neuroscience to investigate how large language models (LLMs) can serve as valuable “model organisms” for understanding human language processing. Presenters showcased evidence that brain recordings (fMRI, MEG, ECoG) acquired while participants read or listened to unconstrained speech can be predicted by representations extracted from state-of-the-art text- and speech-based LLMs. In particular, text-based LLMs tend to align better with higher-level language regions, capturing more semantic aspects, while speech-based LLMs excel at explaining early auditory cortical responses. However, purely low-level features can drive part of these alignments, complicating interpretations. New methods, including perturbation analyses, highlight which linguistic variables matter for each cortical area and time scale. Further, “brain tuning” of LLMs—fine-tuning on measured neural signals—can improve semantic representations and downstream language tasks. Despite open questions about interpretability and exact neural mechanisms, these results demonstrate that LLMs provide a promising framework for probing the computations underlying human language comprehension and production at multiple spatiotemporal scales.

Decision and Behavior

This webinar addressed computational perspectives on how animals and humans make decisions, spanning normative, descriptive, and mechanistic models. Sam Gershman (Harvard) presented a capacity-limited reinforcement learning framework in which policies are compressed under an information bottleneck constraint. This approach predicts pervasive perseveration, stimulus‐independent “default” actions, and trade-offs between complexity and reward. Such policy compression reconciles observed action stochasticity and response time patterns with an optimal balance between learning capacity and performance. Jonathan Pillow (Princeton) discussed flexible descriptive models for tracking time-varying policies in animals. He introduced dynamic Generalized Linear Models (Sidetrack) and hidden Markov models (GLM-HMMs) that capture day-to-day and trial-to-trial fluctuations in choice behavior, including abrupt switches between “engaged” and “disengaged” states. These models provide new insights into how animals’ strategies evolve under learning. Finally, Kenji Doya (OIST) highlighted the importance of unifying reinforcement learning with Bayesian inference, exploring how cortical-basal ganglia networks might implement model-based and model-free strategies. He also described Japan’s Brain/MINDS 2.0 and Digital Brain initiatives, aiming to integrate multimodal data and computational principles into cohesive “digital brains.”

How do we sleep?

There is no consensus on if sleep is for the brain, body or both. But the difference in how we feel following disrupted sleep or having a good night of continuous sleep is striking. Understanding how and why we sleep will likely give insights into many aspects of health. In this talk I will outline our recent work on how the prefrontal cortex can signal to the hypothalamus to regulate sleep preparatory behaviours and sleep itself, and how other brain regions, including the ventral tegmental area, respond to psychosocial stress to induce beneficial sleep. I will also outline our work on examining the function of the glymphatic system, and whether clearance of molecules from the brain is enhanced during sleep or wakefulness.

Feedback-induced dispositional changes in risk preferences

Contrary to the original normative decision-making standpoint, empirical studies have repeatedly reported that risk preferences are affected by the disclosure of choice outcomes (feedback). Although no consensus has yet emerged regarding the properties and mechanisms of this effect, a widespread and intuitive hypothesis is that repeated feedback affects risk preferences by means of a learning effect, which alters the representation of subjective probabilities. Here, we ran a series of seven experiments (N= 538), tailored to decipher the effects of feedback on risk preferences. Our results indicate that the presence of feedback consistently increases risk-taking, even when the risky option is economically less advantageous. Crucially, risk-taking increases just after the instructions, before participants experience any feedback. These results challenge the learning account, and advocate for a dispositional effect, induced by the mere anticipation of feedback information. Epistemic curiosity and regret avoidance may drive this effect in partial and complete feedback conditions, respectively.

Use case determines the validity of neural systems comparisons

Deep learning provides new data-driven tools to relate neural activity to perception and cognition, aiding scientists in developing theories of neural computation that increasingly resemble biological systems both at the level of behavior and of neural activity. But what in a deep neural network should correspond to what in a biological system? This question is addressed implicitly in the use of comparison measures that relate specific neural or behavioral dimensions via a particular functional form. However, distinct comparison methodologies can give conflicting results in recovering even a known ground-truth model in an idealized setting, leaving open the question of what to conclude from the outcome of a systems comparison using any given methodology. Here, we develop a framework to make explicit and quantitative the effect of both hypothesis-driven aspects—such as details of the architecture of a deep neural network—as well as methodological choices in a systems comparison setting. We demonstrate via the learning dynamics of deep neural networks that, while the role of the comparison methodology is often de-emphasized relative to hypothesis-driven aspects, this choice can impact and even invert the conclusions to be drawn from a comparison between neural systems. We provide evidence that the right way to adjudicate a comparison depends on the use case—the scientific hypothesis under investigation—which could range from identifying single-neuron or circuit-level correspondences to capturing generalizability to new stimulus properties

On finding what you’re (not) looking for: prospects and challenges for AI-driven discovery

Recent high-profile scientific achievements by machine learning (ML) and especially deep learning (DL) systems have reinvigorated interest in ML for automated scientific discovery (eg, Wang et al. 2023). Much of this work is motivated by the thought that DL methods might facilitate the efficient discovery of phenomena, hypotheses, or even models or theories more efficiently than traditional, theory-driven approaches to discovery. This talk considers some of the more specific obstacles to automated, DL-driven discovery in frontier science, focusing on gravitational-wave astrophysics (GWA) as a representative case study. In the first part of the talk, we argue that despite these efforts, prospects for DL-driven discovery in GWA remain uncertain. In the second part, we advocate a shift in focus towards the ways DL can be used to augment or enhance existing discovery methods, and the epistemic virtues and vices associated with these uses. We argue that the primary epistemic virtue of many such uses is to decrease opportunity costs associated with investigating puzzling or anomalous signals, and that the right framework for evaluating these uses comes from philosophical work on pursuitworthiness.

Beyond Homogeneity: Characterizing Brain Disorder Heterogeneity through EEG and Normative Modeling

Electroencephalography (EEG) has been thoroughly studied for decades in psychiatry research. Yet its integration into clinical practice as a diagnostic/prognostic tool remains unachieved. We hypothesize that a key reason is the underlying patient's heterogeneity, overlooked in psychiatric EEG research relying on a case-control approach. We combine HD-EEG with normative modeling to quantify this heterogeneity using two well-established and extensively investigated EEG characteristics -spectral power and functional connectivity- across a cohort of 1674 patients with attention-deficit/hyperactivity disorder, autism spectrum disorder, learning disorder, or anxiety, and 560 matched controls. Normative models showed that deviations from population norms among patients were highly heterogeneous and frequency-dependent. Deviation spatial overlap across patients did not exceed 40% and 24% for spectral and connectivity, respectively. Considering individual deviations in patients has significantly enhanced comparative analysis, and the identification of patient-specific markers has demonstrated a correlation with clinical assessments, representing a crucial step towards attaining precision psychiatry through EEG.

Characterizing the causal role of large-scale network interactions in supporting complex cognition

Neuroimaging has greatly extended our capacity to study the workings of the human brain. Despite the wealth of knowledge this tool has generated however, there are still critical gaps in our understanding. While tremendous progress has been made in mapping areas of the brain that are specialized for particular stimuli, or cognitive processes, we still know very little about how large-scale interactions between different cortical networks facilitate the integration of information and the execution of complex tasks. Yet even the simplest behavioral tasks are complex, requiring integration over multiple cognitive domains. Our knowledge falls short not only in understanding how this integration takes place, but also in what drives the profound variation in behavior that can be observed on almost every task, even within the typically developing (TD) population. The search for the neural underpinnings of individual differences is important not only philosophically, but also in the service of precision medicine. We approach these questions using a three-pronged approach. First, we create a battery of behavioral tasks from which we can calculate objective measures for different aspects of the behaviors of interest, with sufficient variance across the TD population. Second, using these individual differences in behavior, we identify the neural variance which explains the behavioral variance at the network level. Finally, using covert neurofeedback, we perturb the networks hypothesized to correspond to each of these components, thus directly testing their casual contribution. I will discuss our overall approach, as well as a few of the new directions we are currently pursuing.

Combined electrophysiological and optical recording of multi-scale neural circuit dynamics

This webinar will showcase new approaches for electrophysiological recordings using our silicon neural probes and surface arrays combined with diverse optical methods such as wide-field or 2-photon imaging, fiber photometry, and optogenetic perturbations in awake, behaving mice. Multi-modal recording of single units and local field potentials across cortex, hippocampus and thalamus alongside calcium activity via GCaMP6F in cortical neurons in triple-transgenic animals or in hippocampal astrocytes via viral transduction are brought to bear to reveal hitherto inaccessible and under-appreciated aspects of coordinated dynamics in the brain.

There’s more to timing than time: P-centers, beat bins and groove in musical microrhythm

How does the dynamic shape of a sound affect its perceived microtiming? In the TIME project, we studied basic aspects of musical microrhythm, exploring both stimulus features and the participants’ enculturated expertise via perception experiments, observational studies of how musicians produce particular microrhythms, and ethnographic studies of musicians’ descriptions of microrhythm. Collectively, we show that altering the microstructure of a sound (“what” the sound is) changes its perceived temporal location (“when” it occurs). Specifically, there are systematic effects of core acoustic factors (duration, attack) on perceived timing. Microrhythmic features in longer and more complex sounds can also give rise to different perceptions of the same sound. Our results shed light on conflicting results regarding the effect of microtiming on the “grooviness” of a rhythm.

Roles of inhibition in stabilizing and shaping the response of cortical networks

Inhibition has long been thought to stabilize the activity of cortical networks at low rates, and to shape significantly their response to sensory inputs. In this talk, I will describe three recent collaborative projects that shed light on these issues. (1) I will show how optogenetic excitation of inhibition neurons is consistent with cortex being inhibition stabilized even in the absence of sensory inputs, and how this data can constrain the coupling strengths of E-I cortical network models. (2) Recent analysis of the effects of optogenetic excitation of pyramidal cells in V1 of mice and monkeys shows that in some cases this optogenetic input reshuffles the firing rates of neurons of the network, leaving the distribution of rates unaffected. I will show how this surprising effect can be reproduced in sufficiently strongly coupled E-I networks. (3) Another puzzle has been to understand the respective roles of different inhibitory subtypes in network stabilization. Recent data reveal a novel, state dependent, paradoxical effect of weakening AMPAR mediated synaptic currents onto SST cells. Mathematical analysis of a network model with multiple inhibitory cell types shows that this effect tells us in which conditions SST cells are required for network stabilization.

Stability of visual processing in passive and active vision

The visual system faces a dual challenge. On the one hand, features of the natural visual environment should be stably processed - irrespective of ongoing wiring changes, representational drift, and behavior. On the other hand, eye, head, and body motion require a robust integration of pose and gaze shifts in visual computations for a stable perception of the world. We address these dimensions of stable visual processing by studying the circuit mechanism of long-term representational stability, focusing on the role of plasticity, network structure, experience, and behavioral state while recording large-scale neuronal activity with miniature two-photon microscopy.

Time perception in film viewing as a function of film editing

Filmmakers and editors have empirically developed techniques to ensure the spatiotemporal continuity of a film's narration. In terms of time, editing techniques (e.g., elliptical, overlapping, or cut minimization) allow for the manipulation of the perceived duration of events as they unfold on screen. More specifically, a scene can be edited to be time compressed, expanded, or real-time in terms of its perceived duration. Despite the consistent application of these techniques in filmmaking, their perceptual outcomes have not been experimentally validated. Given that viewing a film is experienced as a precise simulation of the physical world, the use of cinematic material to examine aspects of time perception allows for experimentation with high ecological validity, while filmmakers gain more insight on how empirically developed techniques influence viewers' time percept. Here, we investigated how such time manipulation techniques of an action affect a scene's perceived duration. Specifically, we presented videos depicting different actions (e.g., a woman talking on the phone), edited according to the techniques applied for temporal manipulation and asked participants to make verbal estimations of the presented scenes' perceived durations. Analysis of data revealed that the duration of expanded scenes was significantly overestimated as compared to that of compressed and real-time scenes, as was the duration of real-time scenes as compared to that of compressed scenes. Therefore, our results validate the empirical techniques applied for the modulation of a scene's perceived duration. We also found interactions on time estimates of scene type and editing technique as a function of the characteristics and the action of the scene presented. Thus, these findings add to the discussion that the content and characteristics of a scene, along with the editing technique applied, can also modulate perceived duration. Our findings are discussed by considering current timing frameworks, as well as attentional saliency algorithms measuring the visual saliency of the presented stimuli.

Epileptic micronetworks and their clinical relevance

A core aspect of clinical epileptology revolves around relating epileptic field potentials to underlying neural sources (e.g. an “epileptogenic focus”). Yet still, how neural population activity relates to epileptic field potentials and ultimately clinical phenomenology, remains far from being understood. After a brief overview on this topic, this seminar will focus on unpublished work, with an emphasis on seizure-related focal spreading depression. The presented results will include hippocampal and neocortical chronic in vivo two-photon population imaging and local field potential recordings of epileptic micronetworks in mice, in the context of viral encephalitis or optogenetic stimulation. The findings are corroborated by invasive depth electrode recordings (macroelectrodes and BF microwires) in epilepsy patients during pre-surgical evaluation. The presented work carries general implications for clinical epileptology, and basic epilepsy research.

Learning produces a hippocampal cognitive map in the form of an orthogonalized state machine

Cognitive maps confer animals with flexible intelligence by representing spatial, temporal, and abstract relationships that can be used to shape thought, planning, and behavior. Cognitive maps have been observed in the hippocampus, but their algorithmic form and the processes by which they are learned remain obscure. Here, we employed large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different versions of linear tracks in virtual reality. The results provide a detailed view of the formation of a cognitive map in the hippocampus. Throughout learning, both the animal behavior and hippocampal neural activity progressed through multiple intermediate stages, gradually revealing improved task representation that mirrored improved behavioral efficiency. The learning process led to progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent struture of the task. We show that a Hidden Markov Model (HMM) and a biologically plausible recurrent neural network trained using Hebbian learning can both capture core aspects of the learning dynamics and the orthogonalized representational structure in neural activity. In contrast, we show that gradient-based learning of sequence models such as Long Short-Term Memory networks (LSTMs) and Transformers do not naturally produce such orthogonalized representations. We further demonstrate that mice exhibited adaptive behavior in novel task settings, with neural activity reflecting flexible deployment of the state machine. These findings shed light on the mathematical form of cognitive maps, the learning rules that sculpt them, and the algorithms that promote adaptive behavior in animals. The work thus charts a course toward a deeper understanding of biological intelligence and offers insights toward developing more robust learning algorithms in artificial intelligence.

Dyslexia, Rhythm, Language and the Developing Brain

Recent insights from auditory neuroscience provide a new perspective on how the brain encodes speech. Using these recent insights, I will provide an overview of key factors underpinning individual differences in children’s development of language and phonology, providing a context for exploring atypical reading development (dyslexia). Children with dyslexia are relatively insensitive to acoustic cues related to speech rhythm patterns. This lack of rhythmic sensitivity is related to the atypical neural encoding of rhythm patterns in speech by the brain. I will describe our recent data from infants as well as children, demonstrating developmental continuity in the key neural variables.

Of glia and macrophages, signaling hubs in development and homeostasis

We are interested in the biology of macrophages, which represent the first line of defense against pathogens. In Drosophila, the embryonic hemocytes arise from the mesoderm whereas glial cells arise from multipotent precursors in the neurogenic region. These cell types represent, respectively, the macrophages located outside and within the nervous system (similar to vertebrate microglia). Thus, despite their different origin, hemocytes and glia display common functions. In addition, both cell types express the Glide/Gcm transcription factor, which plays an evolutionarily conserved role as an anti-inflammatory factor. Moreover, embryonic hemocytes play an evolutionarily conserved and fundamental role in development. The ability to migrate and to contact different tissues/organs most likely allow macrophages to function as signaling hubs. The function of macrophages beyond the recognition of the non-self calls for revisiting the biology of these heterogeneous and plastic cells in physiological and pathological conditions across evolution.

Reimagining the neuron as a controller: A novel model for Neuroscience and AI

We build upon and expand the efficient coding and predictive information models of neurons, presenting a novel perspective that neurons not only predict but also actively influence their future inputs through their outputs. We introduce the concept of neurons as feedback controllers of their environments, a role traditionally considered computationally demanding, particularly when the dynamical system characterizing the environment is unknown. By harnessing a novel data-driven control framework, we illustrate the feasibility of biological neurons functioning as effective feedback controllers. This innovative approach enables us to coherently explain various experimental findings that previously seemed unrelated. Our research has profound implications, potentially revolutionizing the modeling of neuronal circuits and paving the way for the creation of alternative, biologically inspired artificial neural networks.

Seizure control by electrical stimulation: parameters and mechanisms

Seizure suppression by deep brain stimulation (DBS) applies high frequency stimulation (HFS) to grey matter to block seizures. In this presentation, I will present the results of a different method that employs low frequency stimulation (LFS) (1 to 10Hz) of white matter tracts to prevent seizures. The approach has been shown to be effective in the hippocampus by stimulating the ventral and dorsal hippocampal commissure in both animal and human studies respectively for mesial temporal lobe seizures. A similar stimulation paradigm has been shown to be effective at controlling focal cortical seizures in rats with corpus callosum stimulation. This stimulation targets the axons of the corpus callosum innervating the focal zone at low frequencies (5 to 10Hz) and has been shown to significantly reduce both seizure and spike frequency. The mechanisms of this suppression paradigm have been elucidated with in-vitro studies and involve the activation of two long-lasting inhibitory potentials GABAB and sAHP. LFS mechanisms are similar in both hippocampus and cortical brain slices. Additionally, the results show that LFS does not block seizures but rather decreases the excitability of the tissue to prevent seizures. Three methods of seizure suppression, LFS applied to fiber tracts, HFS applied to focal zone and stimulation of the anterior nucleus of the thalamus (ANT) were compared directly in the same animal in an in-vivo epilepsy model. The results indicate that LFS generated a significantly higher level of suppression, indicating LFS of white matter tract could be a useful addition as a stimulation paradigm for the treatment of epilepsy.

Visual mechanisms for flexible behavior

Perhaps the most impressive aspect of the way the brain enables us to act on the sensory world is its flexibility. We can make a general inference about many sensory features (rating the ripeness of mangoes or avocados) and map a single stimulus onto many choices (slicing or blending mangoes). These can be thought of as flexibly mapping many (features) to one (inference) and one (feature) to many (choices) sensory inputs to actions. Both theoretical and experimental investigations of this sort of flexible sensorimotor mapping tend to treat sensory areas as relatively static. Models typically instantiate flexibility through changing interactions (or weights) between units that encode sensory features and those that plan actions. Experimental investigations often focus on association areas involved in decision-making that show pronounced modulations by cognitive processes. I will present evidence that the flexible formatting of visual information in visual cortex can support both generalized inference and choice mapping. Our results suggest that visual cortex mediates many forms of cognitive flexibility that have traditionally been ascribed to other areas or mechanisms. Further, we find that a primary difference between visual and putative decision areas is not what information they encode, but how that information is formatted in the responses of neural populations, which is related to difference in the impact of causally manipulating different areas on behavior. This scenario allows for flexibility in the mapping between stimuli and behavior while maintaining stability in the information encoded in each area and in the mappings between groups of neurons.

Using Adversarial Collaboration to Harness Collective Intelligence

There are many mysteries in the universe. One of the most significant, often considered the final frontier in science, is understanding how our subjective experience, or consciousness, emerges from the collective action of neurons in biological systems. While substantial progress has been made over the past decades, a unified and widely accepted explanation of the neural mechanisms underpinning consciousness remains elusive. The field is rife with theories that frequently provide contradictory explanations of the phenomenon. To accelerate progress, we have adopted a new model of science: adversarial collaboration in team science. Our goal is to test theories of consciousness in an adversarial setting. Adversarial collaboration offers a unique way to bolster creativity and rigor in scientific research by merging the expertise of teams with diverse viewpoints. Ideally, we aim to harness collective intelligence, embracing various perspectives, to expedite the uncovering of scientific truths. In this talk, I will highlight the effectiveness (and challenges) of this approach using selected case studies, showcasing its potential to counter biases, challenge traditional viewpoints, and foster innovative thought. Through the joint design of experiments, teams incorporate a competitive aspect, ensuring comprehensive exploration of problems. This method underscores the importance of structured conflict and diversity in propelling scientific advancement and innovation.

Trends in NeuroAI - Meta's MEG-to-image reconstruction

Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri). Title: Brain-optimized inference improves reconstructions of fMRI brain activity Abstract: The release of large datasets and developments in AI have led to dramatic improvements in decoding methods that reconstruct seen images from human brain activity. We evaluate the prospect of further improving recent decoding methods by optimizing for consistency between reconstructions and brain activity during inference. We sample seed reconstructions from a base decoding method, then iteratively refine these reconstructions using a brain-optimized encoding model that maps images to brain activity. At each iteration, we sample a small library of images from an image distribution (a diffusion model) conditioned on a seed reconstruction from the previous iteration. We select those that best approximate the measured brain activity when passed through our encoding model, and use these images for structural guidance during the generation of the small library in the next iteration. We reduce the stochasticity of the image distribution at each iteration, and stop when a criterion on the "width" of the image distribution is met. We show that when this process is applied to recent decoding methods, it outperforms the base decoding method as measured by human raters, a variety of image feature metrics, and alignment to brain activity. These results demonstrate that reconstruction quality can be significantly improved by explicitly aligning decoding distributions to brain activity distributions, even when the seed reconstruction is output from a state-of-the-art decoding algorithm. Interestingly, the rate of refinement varies systematically across visual cortex, with earlier visual areas generally converging more slowly and preferring narrower image distributions, relative to higher-level brain areas. Brain-optimized inference thus offers a succinct and novel method for improving reconstructions and exploring the diversity of representations across visual brain areas. Speaker: Reese Kneeland is a Ph.D. student at the University of Minnesota working in the Naselaris lab. Paper link: https://arxiv.org/abs/2312.07705

Piecing together the puzzle of emotional consciousness

Conscious emotional experiences are very rich in their nature, and can encompass anything ranging from the most intense panic when facing immediate threat, to the overwhelming love felt when meeting your newborn. It is then no surprise that capturing all aspects of emotional consciousness, such as intensity, valence, and bodily responses, into one theory has become the topic of much debate. Key questions in the field concern how we can actually measure emotions and which type of experiments can help us distill the neural correlates of emotional consciousness. In this talk I will give a brief overview of theories of emotional consciousness and where they disagree, after which I will dive into the evidence proposed to support these theories. Along the way I will discuss to what extent studying emotional consciousness is ‘special’ and will suggest several tools and experimental contrasts we have at our disposal to further our understanding on this intriguing topic.

Inducing short to medium neuroplastic effects with Transcranial Ultrasound Stimulation

Sound waves can be used to modify brain activity safely and transiently with unprecedented precision even deep in the brain - unlike traditional brain stimulation methods. In a series of studies in humans and non-human primates, I will show that Transcranial Ultrasound Stimulation (TUS) can have medium- to long-lasting effects. Multiple read-outs allow us to conclude that TUS can perturb neuronal tissues up to 2h after intervention, including changes in local and distributed brain network configurations, behavioural changes, task-related neuronal changes and chemical changes in the sonicated focal volume. Combined with multiple neuroimaging techniques (resting state functional Magnetic Resonance Imaging [rsfMRI], Spectroscopy [MRS] and task-related fMRI changes), this talk will focus on recent human TUS studies.

Neuromodulation of subjective experience

Many psychoactive substances are used with the aim of altering experience, e.g. as analgesics, antidepressants or antipsychotics. These drugs act on specific receptor systems in the brain, including the opioid, serotonergic and dopaminergic systems. In this talk, I will summarise human drug studies targeting opioid receptors and their role for human experience, with focus on the experience of pain, stress, mood, and social connection. Opioids are only indicated for analgesia, due to their potential to cause addiction. When these regulations occurred, other known effects were relegated to side effects. This may be the cause of the prevalent myth that opioids are the most potent painkillers, despite evidence from head-to-head trials, Cochrane reviews and network meta-analyses that opioids are not superior to non-opioid analgesics in the treatment of acute or chronic non-cancer pain. However, due to the variability and diversity of opioid effects across contexts and experiences, some people under some circumstances may indeed benefit from prolonged treatment. I will present data on individual differences in opioid effects due to participant sex and stress induction. Understanding the effects of these commonly used medications on other aspects of the human experience is important to ensure correct use and to prevent unnecessary pain and addiction risk.

Neuroinflammation in Epilepsy: what have we learned from human brain tissue specimens ?

Epileptogenesis is a gradual and dynamic process leading to difficult-to-treat seizures. Several cellular, molecular, and pathophysiologic mechanisms, including the activation of inflammatory processes.  The use of human brain tissue represents a crucial strategy to advance our understanding of the underlying neuropathology and the molecular and cellular basis of epilepsy and related cognitive and behavioral comorbidities,  The mounting evidence obtained during the past decade has emphasized the critical role of inflammation  in the pathophysiological processes implicated in a large spectrum of genetic and acquired forms of  focal epilepsies. Dissecting the cellular and molecular mediators of  the pathological immune responses and their convergent and divergent mechanisms, is a major requisite for delineating their role in the establishment of epileptogenic networks. The role of small regulatory molecules involved in the regulation of  specific pro- and anti-inflammatory pathways  and the crosstalk between neuroinflammation and oxidative stress will be addressed.    The observations supporting the activation of both innate and adaptive immune responses in human focal epilepsy will be discussed and elaborated, highlighting specific inflammatory pathways as potential targets for antiepileptic, disease-modifying therapeutic strategies.

Use of brain imaging data to improve prescriptions of psychotropic drugs - Examples of ketamine in depression and antipsychotics in schizophrenia

The use of molecular imaging, particularly PET and SPECT, has significantly transformed the treatment of schizophrenia with antipsychotic drugs since the late 1980s. It has offered insights into the links between drug target engagement, clinical effects, and side effects. A therapeutic window for receptor occupancy is established for antipsychotics, yet there is a divergence of opinions regarding the importance of blood levels, with many downplaying their significance. As a result, the role of therapeutic drug monitoring (TDM) as a personalized therapy tool is often underrated. Since molecular imaging of antipsychotics has focused almost entirely on D2-like dopamine receptors and their potential to control positive symptoms, negative symptoms and cognitive deficits are hardly or not at all investigated. Alternative methods have been introduced, i.e. to investigate the correlation between approximated receptor occupancies from blood levels and cognitive measures. Within the domain of antidepressants, and specifically regarding ketamine's efficacy in depression treatment, there is limited comprehension of the association between plasma concentrations and target engagement. The measurement of AMPA receptors in the human brain has added a new level of comprehension regarding ketamine's antidepressant effects. To ensure precise prescription of psychotropic drugs, it is vital to have a nuanced understanding of how molecular and clinical effects interact. Clinician scientists are assigned with the task of integrating these indispensable pharmacological insights into practice, thereby ensuring a rational and effective approach to the treatment of mental health disorders, signaling a new era of personalized drug therapy mechanisms that promote neuronal plasticity not only under pathological conditions, but also in the healthy aging brain.

Brain Connectivity Workshop

Founded in 2002, the Brain Connectivity Workshop (BCW) is an annual international meeting for in-depth discussions of all aspects of brain connectivity research. By bringing together experts in computational neuroscience, neuroscience methodology and experimental neuroscience, it aims to improve the understanding of the relationship between anatomical connectivity, brain dynamics and cognitive function. These workshops have a unique format, featuring only short presentations followed by intense discussion. This year’s workshop is co-organised by Wellcome, putting the spotlight on brain connectivity in mental health disorders. We look forward to having you join us for this exciting, thought-provoking and inclusive event.

Self as Processes (BACN Mid-career Prize Lecture 2023)

An understanding of the self helps explain not only human thoughts, feelings, attitudes but also many aspects of everyday behaviour. This talk focuses on a viewpoint - self as processes. This viewpoint emphasizes the dynamics of the self that best connects with the development of the self over time and its realist orientation. We are combining psychological experiments and data mining to comprehend the stability and adaptability of the self across various populations. In this talk, I draw on evidence from experimental psychology, cognitive neuroscience, and machine learning approaches to demonstrate why and how self-association affects cognition and how it is modulated by various social experiences and situational factors

Anticipating behaviour through working memory (BACN Early Career Prize Lecture 2023)

Working memory is about the past but for the future. Adopting such a future-focused perspective shifts the narrative of working memory as a limited-capacity storage system to working memory as an anticipatory buffer that helps us prepare for potential and sequential upcoming behaviour. In my talk, I will present a series of our recent studies that have started to reveal emerging principles of a working memory that looks forward – highlighting, amongst others, how selective attention plays a vital role in prioritising internal contents for behaviour, and the bi-directional links between visual working memory and action. These studies show how studying the dynamics of working memory, selective attention, and action together paves way for an integrated understanding of how mind serves behaviour.

Brain network communication: concepts, models and applications

Understanding communication and information processing in nervous systems is a central goal of neuroscience. Over the past two decades, advances in connectomics and network neuroscience have opened new avenues for investigating polysynaptic communication in complex brain networks. Recent work has brought into question the mainstay assumption that connectome signalling occurs exclusively via shortest paths, resulting in a sprawling constellation of alternative network communication models. This Review surveys the latest developments in models of brain network communication. We begin by drawing a conceptual link between the mathematics of graph theory and biological aspects of neural signalling such as transmission delays and metabolic cost. We organize key network communication models and measures into a taxonomy, aimed at helping researchers navigate the growing number of concepts and methods in the literature. The taxonomy highlights the pros, cons and interpretations of different conceptualizations of connectome signalling. We showcase the utility of network communication models as a flexible, interpretable and tractable framework to study brain function by reviewing prominent applications in basic, cognitive and clinical neurosciences. Finally, we provide recommendations to guide the future development, application and validation of network communication models.

Attending to the ups and downs of Lewy body dementia: An exploration of cognitive fluctuations

Dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD) share similarities in pathology and clinical presentation and come under the umbrella term of Lewy body dementias (LBD). Fluctuating cognition is a key symptom in LBD and manifests as altered levels of alertness and attention, with a marked difference between best and worst performance. Cognition and alertness can change over seconds or minutes to hours and days of obtundation. Cognitive fluctuations can have significant impacts on the quality of life of people with LBD as well as potentially contribute to the exacerbation of other transient symptoms including, for example, hallucinations and psychosis as well as making it difficult to measure cognitive effect size benefits in clinical trials of LBD. However, this significant symptom in LBD is poorly understood. In my presentation I will discuss the phenomenology of cognitive fluctuations, how we can measure it clinically and limitations of these approaches. I will then outline the work of our group and others which has been focussed on unpicking the aetiological basis of cognitive fluctuations in LBD using a variety of imaging approaches (e.g. SPECT, sMRI, fMRI and EEG). I will then briefly explore future research directions.

Freeze or flee ? New insights from rodent models of autism

Individuals afflicted with certain types of autism spectrum disorder often exhibit impaired cognitive function alongside enhanced emotional symptoms and mood lability. However, current understanding of the pathogenesis of autism and intellectual disabilities is based primarily on studies in the hippocampus and cortex, brain areas involved in cognitive function. But, these disorders are also associated with strong emotional symptoms, which are likely to involve changes in the amygdala and other brain areas. In this talk I will highlight these issues by presenting analyses in rat models of ASD/ID lacking Nlgn3 and Frm1 (causing Fragile X Syndrome). In addition to identifying new circuit and cellular alterations underlying divergent patterns of fear expression, these findings also suggest novel therapeutic strategies.

Vision Unveiled: Understanding Face Perception in Children Treated for Congenital Blindness

Despite her still poor visual acuity and minimal visual experience, a 2-3 month old baby will reliably respond to facial expressions, smiling back at her caretaker or older sibling. But what if that same baby had been deprived of her early visual experience? Will she be able to appropriately respond to seemingly mundane interactions, such as a peer’s facial expression, if she begins seeing at the age of 10? My work is part of Project Prakash, a dual humanitarian/scientific mission to identify and treat curably blind children in India and then study how their brain learns to make sense of the visual world when their visual journey begins late in life. In my talk, I will give a brief overview of Project Prakash, and present findings from one of my primary lines of research: plasticity of face perception with late sight onset. Specifically, I will discuss a mixed methods effort to probe and explain the differential windows of plasticity that we find across different aspects of distributed face recognition, from distinguishing a face from a nonface early in the developmental trajectory, to recognizing facial expressions, identifying individuals, and even identifying one’s own caretaker. I will draw connections between our empirical findings and our recent theoretical work hypothesizing that children with late sight onset may suffer persistent face identification difficulties because of the unusual acuity progression they experience relative to typically developing infants. Finally, time permitting, I will point to potential implications of our findings in supporting newly-sighted children as they transition back into society and school, given that their needs and possibilities significantly change upon the introduction of vision into their lives.

Present and Future of the diagnostic work-up multiple sclerosis: the imaging perspective

The development of visual experience

Vision and visual cognition is experience-dependent with likely multiple sensitive periods, but we know very little about statistics of visual experience at the scale of everyday life and how they might change with development. By traditional assumptions, the world at the massive scale of daily life presents pretty much the same visual statistics to all perceivers. I will present an overview our work on ego-centric vision showing that this is not the case. The momentary image received at the eye is spatially selective, dependent on the location, posture and behavior of the perceiver. If a perceiver’s location, possible postures and/or preferences for looking at some kinds of scenes over others are constrained, then their sampling of images from the world and thus the visual statistics at the scale of daily life could be biased. I will present evidence with respect to both low-level and higher level visual statistics about the developmental changes in the visual input over the first 18 months post-birth.

Consciousness in the age of mechanical minds

We are now clearly entering a new age in our relationship with machines. The power of AI natural language processors and image generators has rapidly exceeded the expectations of even those who developed them. Serious questions are now being asked about the extent to which machines could become — or perhaps already are — sentient or conscious. Do AI machines understand the instructions they are given and the answers they provide? In this talk I will consider the prospects for conscious machines, by which I mean machines that have feelings, know about their own existence, and about ours. I will suggest that the recent focus on information processing in models of consciousness, in which the brain is treated as a kind of digital computer, have mislead us about the nature of consciousness and how it is produced in biological systems. Treating the brain as an energy processing system is more likely to yield answers to these fundamental questions and help us understand how and when machines might become minds.

Why spikes?

On a fast timescale, neurons mostly interact by short, stereotypical electrical impulses or spikes. Why? A common answer is that spikes are useful for long-distance communication, to avoid alterations while traveling along axons. But as it turns out, spikes are seen in many places outside neurons: in the heart, in muscles, in plants and even in protists. From these examples, it appears that action potentials mediate some form of coordinated action, a timed event. From this perspective, spikes should not be seen simply as noisy implementations of underlying continuous signals (a sort of analog-to-digital conversion), but rather as events or actions. I will give a number of examples of functional spike-based interactions in living systems.

Auditory input to the basal ganglia; Deep brain stimulation and action-stopping: A cognitive neuroscience perspective on the contributions of fronto-basal ganglia circuits to inhibitory control

On Thursday, May 25th we will host Darcy Diesburg and Mark Richardson. Darcy Diesburg, PhD, is a post-doctoral research fellow at Brown University. She will tell us about “Deep brain stimulation and action-stopping: A cognitive neuroscience perspective on the contributions of fronto-basal ganglia circuits to inhibitory control”. Mark Richardson, MD, PhD, is the Director of Functional Neurosurgery at the Massachusetts General Hospital, Charles Pappas Associate Professor of Neurosciences at Harvard Medical School and Visiting Associate Professor of Brain and Cognitive Sciences at MIT. Beside his scientific presentation on “Auditory input to the basal ganglia”, he will give us a glimpse at the “Person behind the science”. The talks will be followed by a shared discussion. You can register via talks.stimulatingbrains.org to receive the (free) Zoom link!

BEHAVIORAL AND SPECTRAL EEG BIOMARKERS OF EPILEPTOGENESIS AND PHARMACORESISTANCE IN A LONGITUDINAL PILOCARPINE MODEL OF TEMPORAL LOBE EPILEPSY

FENS Forum 2026

Label-free biomolecular spectroscopy with vibrational fiber photometry deep in the mouse brain

FENS Forum 2024

Brain-body interactions in emotions: perspective matters

Normative models of spatio-spectral decorrelation in natural scenes predict experimentally observed ratio of PR types

COSYNE 2022

Regionally distinct striatal circuits support broadly opponent aspects of action suppression and production

COSYNE 2022

Regionally distinct striatal circuits support broadly opponent aspects of action suppression and production

COSYNE 2022

Machine learning of functional network and molecular mechanisms in autism spectrum disorder subtypes

COSYNE 2023

Sparse coding predicts a spectral bias in the development of V1 receptive fields

COSYNE 2022

Thalamic head-direction cells are organized irrespective of their inputs

COSYNE 2022

Thalamic head-direction cells are organized irrespective of their inputs

COSYNE 2022

A White Matter Ephaptic Coupling Model for 1/f Spectral Densities

COSYNE 2022

A White Matter Ephaptic Coupling Model for 1/f Spectral Densities

COSYNE 2022

Eigenvalue spectral properties of sparse random matrices for neural networks

COSYNE 2023

Detecting rhythmic spiking through the power spectra of point process model residuals

COSYNE 2023

Sparse coding predicts a spectral bias in the development of V1 receptive fields

COSYNE 2022

The scale-invariant covariance spectrum of brain-wide activity in larval zebrafish

COSYNE 2023

Spectral learning of Bernoulli latent dynamical system models for decision-making

COSYNE 2023

Thalamic head-direction cells are organized irrespective of their inputs

COSYNE 2023

On the benefits of analog spikes: an information efficiency perspective

COSYNE 2025

Covariance spectrum in nonlinear recurrent neural networks and transition to chaos

COSYNE 2025

Envelope representations substantially enhance the predictive power of spectrotemporal receptive models in the human auditory cortex

COSYNE 2025

Humans can use positive and negative spectrotemporal correlations to detect rising and falling pitch

COSYNE 2025

A prospective code for value in the serotonin system

COSYNE 2025

Prospective and retrospective coding in cortical neurons

COSYNE 2025

Spectral analysis of representational similarity with limited neurons

COSYNE 2025

Aggression and reward processing in Autism Spectrum Disorder (ASD)

Analysis of complex social behaviour during an extended time period in a valproic acid animal model of autism spectrum disorder

Analysis of hippocampal participation in social interactions in a genetic model of autistic spectrum disorder

Autism spectrum disorder-related voltage-gated potassium channel mutation Kv7.3 R2C decreases dopamine neuron excitability and social interaction in mice

Blood RNA sequencing identifies dysregulated gene expression in children with autism spectrum disorder

Normative models of spatio-spectral decorrelation in natural scenes predict experimentally observed ratio of PR types

COSYNE 2022

Cerebellar inflammation supports autism-related behaviors in the CNTNAP2 mouse model of autism spectrum disorders

Correlation patterns in working-memory tasks: fMRI fractal and spectral analysis

Decoding of a broad spectrum of actions from temporal evolution of neural activity in monkey premotor cortex

Developmental Deficits in MGE-derived Interneurons Underlie Circuit Malformation in a CNTNAP2 Knockout Mouse Model of Autism Spectrum Disorder

Dietary low-level glyphosate and genetic predisposition: a double-hit in autism spectrum disorders?

Sex differences in Autism Spectrum Disorder: a proteome analysis

Different types of cortical progenitors contribute to specific aspects of cortical folding

Directional accuracy of point-to-point arm movements in different body orientations with respect to gravity

A new approach to inferring the eigenspectra of high-dimensional neural representations

COSYNE 2022