map

TACTIC: Tuberculosis Active Case Tracking via Interpersonal Connections

PROJECT SUMMARY/ABSTRACT Tuberculosis (TB) remains the leading infectious cause of death worldwide. Interruption of transmission is the most effective strategy to reduce incident infections, yet current approaches often fail to reach individuals for timely testing and treatment. This study addresses that gap by leveraging social networks to identify individuals at highest risk of transmitting TB, specifically, people who use drugs (PWUD). We will evaluate respondent-driven sampling (RDS), a peer7 based community recruitment strategy, to identify TB cases among PWUD and the household contacts (HHCs) of those with TB disease (RDS-TB) in Kampala, Uganda. Conducting this work in a high-prevalence setting such as Kampala where our team has established expertise allows us to overcome recruitment challenges common in settings in the United States while generating findings that are directly translatable. This is particularly relevant given that higher TB prevalence and larger outbreaks in the United States have been associated with the use of methamphetamine, heroin, and crack/cocaine, drugs that we will study. In Aim 1, we will compare the effectiveness and reach of RDS-TB with a traditional clinic-based index case HHC approach for TB case finding. We will screen 2,000 PWUD and their HHCs, estimate the number needed to screen to identify one case of TB disease, and compare the demographic and network characteristics of RDS-TB recruits with clinic-based HHCs. Whole genome sequencing will be used to characterize transmission dynamics. In Aim 2, we will compare the yield of individual and combined TB diagnostic strategies for community-based active case finding. Participants will undergo chest radiography with computer-aided detection, tongue swab testing for TB nucleic acid amplification tests (NAAT), and sputum testing for NAAT and mycobacterial culture. We will identify the minimal combination of tests needed to meet World Health Organization target product profile thresholds for screening. In Aim 3, we will define the conditions under which RDS-based screening can effectively interrupt TB transmission. We will develop an agent-based model informed by social network data from individuals with and without TB, incorporating drug use patterns and demographic characteristics. This project will generate a practical, scalable roadmap for social network–based TB active case finding in high28 risk communities. The approach will be readily adaptable to settings in the United States and will inform strategies to interrupt transmission and advance progress toward TB elimination, in alignment with the NIH Strategic Plan for TB Research.

Mechanisms of antigen-specific T cell activation in MOGAD

PROJECT SUMMARY / ABSTRACT The overarching goal of this application is to train Dr. Carson E. Moseley, MD, PhD, who is a clinical neurologist and a research immunologist, to become an independent investigator studying and treating neuroimmunologic disorders. Myelin oligodendrocyte glycoprotein (MOG) antibody-associated disease (MOGAD) is a recently described, severe, neuroinflammatory syndrome of the central nervous system (CNS) with no approved therapies. Although MOG-specific antibodies helped define the disease, MOG antibodies alone are not clearly pathogenic and our understanding of MOGAD immunopathology is limited. CD4+ T cells are a dominant lymphocyte population in MOGAD lesions, yet the targets of T cell responses to MOG and how T and B cells interact to drive pathogenic immune response in MOGAD are unknown. This proposal uses a complementary approach of human and mouse immunology along with new technologies in T cell repertoire mapping and genome editing to dissect MOG-specific CD4+ T cell responses in MOGAD. Additionally, it will use new models to investigate how B cells promote pathogenic T cell differentiation and select pathogenic T cell receptors. The proposed training plan involves mentored training, seminars, formal learning, and advising to ensure completion of the proposed research and Dr. Moseley’s career development. He will train at UCSF, which is an outstanding institute for research and environment for physician-scientists. He will receive training in human immunology and CRISPR-based gene editing technologies. He will be mentored by Dr. Scott Zamvil, a leader in identifying antigen-specific T cell responses in neuroimmunologic disorders, and co-mentored by Dr. Alexander Marson, an expert in CRISPR gene editing to understand lymphocyte function. This application will provide Dr. Moseley with the long-term skills needed to become an independent investigator leading efforts to study and treat neuroimmunologic disorders.

Th17 plasticity in rheumatoid arthritis

ABSTRACT The objective of this grant application is to explore the plasticity of Th17 in arthritis. Interleukin-17A (IL-17A) producing Th17 are present in the blood and synovium of patients with rheumatoid arthritis (RA). However, targeting of IL17A has been insufficient to control joint inflammation of RA patients. One potential scenario is that in the context of worsening RA joint inflammation, Th17 undergo conversion into pathogenic IL17A- negative cell populations, collectively called exTh17. The conversion of Th17 into exTh17 has been documented in the context of neuroinflammation, colitis, and infection. However, the occurrence of Th17 plasticity in autoimmune arthritis and its potential role in perpetuating synovial inflammation has remained mostly unexplored. We generated a novel fate-mapping mouse model of autoimmune arthritis, which allows to follow the conversion of Th17 into exTh17, and collected preliminary data suggesting that Th17 undergo significant loss of IL17A expression and conversion into exTh17 in the context of synovial inflammation. We also identified exTh17 signatures which might help exTh17 perpetuate joint inflammation despite their loss of IL17A expression. Here our objective is to further elucidate intrinsic (Aim 1) and extrinsic (Aim 2) mechanism of Th17-exTh17 conversion and exTh17-mediated joint inflammation, and explore the potential role of exTh17 in RA interstitial lung disease (ILD, Aim 3) a feared and often untreatable complication of established RA. Our long-term goal is to leverage the knowledge of local immune cell phenotypes and how they change at various stages of disease to enable stage-specific and personalized therapies of RA which minimize non- specific immunosuppression.

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.

Characterization and functional impact of somatic numtogenesis in the human cortex

Project Summary This project focuses on studying nuclear mitochondrial insertions (numts), which are fragments of mitochondrial DNA that get integrated into the nuclear DNA of human cells. While this process, called numtogenesis, occurs naturally and can be passed down to future generations, it has also been observed to occur somatically in our bodies. Historically the function of numts has been difficult to study because they are repetitive and difficult to map with short read sequencing technologies, but there is emerging evidence that they can influence cell function and play a role in diseases, aging, and even complicate genetic studies. Our recent research discovered numts in the human brain’s cortex, and their presence appeared to be linked with earlier death, suggesting they may play a role in aging. However, due to limitations in the data we used, we could not fully explore the extent or impact of these insertions across different tissues or individuals. This project aims to map and study numts in more detail, especially in the human cortex, to further explore this ongoing transfer of DNA from the mitochondria to the nuclear genome and their potential to impact aging and brain function. We will accomplish this by 1) improving sequencing methods to detect numts, 2) comparing their presence across different tissues, and 3) investigating how they affect gene expression and DNA structure. By the end of the project, we aim to provide a model for how such somatic variation may occur and impact cellular function at the tissue level.

Neural circuits for disinhibition in the cerebellum

ABSTRACT Our long-term goal is to understand how the cerebellum adapts and improves movements in response to motor errors. A critical component of this process is signaling from olivary climbing fibers that, by providing strong excitatory drive onto Purkinje cells, induces long-term synaptic plasticity to instantiate corrective adjustments in motor behavior. However, this signaling process is tightly regulated by molecular layer interneurons (MLIs). By strongly inhibiting Purkinje cells, MLIs oppose climbing fiber-driven excitation and gate the induction of corrective plasticity. Thus, for error-driven climbing fiber-induced plasticity and learning to occur effectively, Purkinje cells must undergo disinhibition through the suppression of MLI-mediated input. Notably, MLI ensembles are composed of several subtypes and have a highly structured interconnectivity and are responsive to convergent climbing fiber inputs, suggesting that climbing fiber synchrony- whose functional significance is poorly understood- can selectively engage MLI networks to alter the state of Purkinje cell inhibition. This engagement may balance inhibition and excitation of Purkinje cells during motor errors, creating a circuit mechanism conducive for the acquisition of adaptive learning. The objective of this proposal is to determine how distinct MLI circuits are organized to modulate Purkinje cell excitability through disinhibition in a context-dependent manner, enabling plasticity and learning in response to motor errors. We will employ functional recordings, circuit-targeted activity manipulations, and behavioral analysis to reveal how error-driven instructive signaling emerges from these circuits. In the first aim, we will use in vivo high-density electrophysiology to map functional interactions among MLIs, climbing fibers, and Purkinje cells in the flocculus during the vestibulo-ocular reflex. We will test whether, during motor errors, climbing fibers synchronize their firing to selectively engage disinhibition of Purkinje cells through MLI subtypes in adapting versus non-adapting contexts. In the second aim, we will combine acute slice recordings and molecular anatomy to define direct versus spillover climbing fiber synapses onto MLI subtypes. We will identify synaptic markers and measure climbing-fiber-evoked currents in MLI subtypes, revealing how structural connectivity supports rapid, subtype-specific circuit engagement. In the third aim, we will determine how long-range inputs to the inferior olive, specifically inhibitory projections from the vestibular nuclei, dynamically tune climbing fiber synchrony in vivo and thereby learning through differential engagement of disinhibitory MLI networks. Using functional recording and optogenetic manipulation during the vestibulo- ocular reflex performance, we will establish causal links between climbing fiber synchrony, MLI network state, and adaptive behavior. By fully understanding the logic of instructive signaling, emergent from cerebellar circuit organization and behavioral engagement, we will advance our knowledge of cerebellum-dependent learning processes and provide broader insights into the neural mechanisms of learning and adaptation more generally.

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.

Mechanisms of age-related inflammatory dysregulation in the pathogenesis of periodontal disease

Periodontal disease is a chronic inflammatory condition that affects the supporting tissues of the dentition. Similar to other chronic inflammatory conditions, the prevalence of periodontal disease increases with age. Dysregulation of the host inflammatory response is central to the pathogenesis of periodontal disease and other age-related diseases. Therefore, an improved understanding of the pathologic mechanisms that contribute to age-related inflammatory dysregulation is needed to better manage periodontal disease in older adults. Towards understanding a mechanism of age-related inflammatory dysregulation in periodontal disease, we will investigate the role of triggering receptor expressed on myeloid cells 2 (TREM2). TREM2 is a potent immunoregulator expressed on macrophages. Signaling through TREM2 downregulates inflammation, in part, through inhibition of inflammatory cytokine expression. Dysregulation of TREM2 has been implicated in chronic inflammatory disease and age-related conditions, such as Alzheimer’s disease, liver disease, and osteoarthritis. However, the role of TREM2 in periodontal disease is understudied. Therefore, we propose to study TREM2 in the pathogenesis of periodontal disease and age-related inflammatory dysregulation. Our preliminary work has demonstrated that TREM2 is critical in macrophage immunoregulatory processes in the periodontium and TREM2 dysregulation contributes to periodontal disease in mice. We have shown that Trem2 is expressed in macrophages isolated form the periodontium in mice. We demonstrated that old mice expressed less Trem2 in the periodontium compared to young, which was associated with local inflammatory dysregulation and increased periodontal disease severity. Interestingly, Trem2 depletion in young mice resulted in increased inflammatory dysregulation and periodontal disease severity, similar to what is observed in old mice. From the preliminary data, we hypothesize that TREM2 modulates macrophage activity in the periodontium and age-related dysregulation of TREM2 drives a pathologic inflammatory response in periodontal disease. In Aim 1, we will demonstrate the extent to which TREM2 modulates inflammation and periodontal disease severity using old, young, and Trem2-/- mouse models of periodontal disease. In Aim 2, we will develop tissue-specific, single cell map of the immune cells in the periodontium and understand the effect of age and Trem2 on immune cell phenotypes and subpopulations. Findings from this proposal will elucidate a novel mechanism in age-related inflammatory dysregulation in the pathogenesis of periodontal disease and further advance our understanding of the role of TREM2 within oral tissues. This proposal was designed to generate a novel body of work that will be used to develop the independent research program of an early stage investigator and to support an R01 proposal to be submitted at the completion of this project period.

Spatial Mapping to Detail the Role of Biomolecules in Governing Biofilm Organization and Resiliency to Stress in Pseudomonas aeruginosa Biofilms

PROJECT SUMMARY The bacterium Pseudomonas aeruginosa is a leading cause of hospital acquired infections, exhibiting substantial antibiotic tolerance due to growth in biofilms. Our previous work shows how biofilm fitness is increased by alkyl quinolones (AQs), a class of molecules produced by the Pseudomonas Quinolone Signal (PQS) pathway of Pseudomonas aeruginosa. AQs form aggregates that spatially limit regions of cell death and reduce overall cell death in biofilms. Spatial studies build on ”what” molecules are doing by revealing when, where, and with whom they are found. Others have shown that AQs transiently bind amyloids and our preliminary results find that amyloid localization is shifted in the absence of AQs. However, the spatial relationships of these molecules have not been investigated. Our research combines multiple spatial analytical techniques, such as fluorescence microscopy, polarized light microscopy, confocal Raman microscopy to assemble detailed maps of AQ and amyloid localization during biofilm development. Using transgenic strains we will also determine amyloid distribution as a function of AQ abundance. This work will build on previous findings that AQ concentrations are able to shift locally in response to stress. We hypothesize that this can impact the localization of amyloids and allow biofilms to respond locally to stress, shielding the greater biofilm from damage. We will map biomolecular distribution of entire colony biofilms in response to stress to determine if local responses have the ability to shield more distal regions of the biofilm. The capacity of spatial biomolecular organization to increase bacterial resilience and infection virulence is an understudied area that has the potential to bring to light to novel targets for therapeutics to fight biofilm infections.

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.

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.

Structure-function and mechanistic studies of a specific glycosyltransferase complex in fusion-driven pediatric gliomas

Abstract Glycosylation is a co/post-translational modification involved in cell-matrix interactions, antigen-antibody interactions, tumor invasion, and cell motility. Abnormal glycosylation is a hallmark of cancer, with various glycosylation-related genes linked to glioma prognosis and tumor heterogeneity. Pediatric low-grade gliomas (pLGGs) stand as the most common childhood central nervous system tumor, accounting for 30%-40% of all CNS tumors in children. Despite its relatively low mortality rate, pLGGs are associated with devastating lifelong morbidity. The most common alteration found in 75% of tumors is the KIAA1549:BRAF fusion, causing an aberrant activation of the MAPK/ERK signaling pathway. Current treatments, such as traditional chemotherapies and targeted therapies, have limitations such as resistance, lack of specificity, toxicity and paradoxical activation of the MAPK pathway. This highlights the urgent need for novel therapeutic approaches. Investigations into KIAA1549:BRAF-driven pLGGs identified their dependency on the protein-O-mannosyl transferase (POMT) complex for survival. In contrast, BRAFV600E-mutant cells did not show dependency, suggesting the POMT complex as a vulnerability and promising target in KIAA1549:BRAF-driven pLGGs. Therefore, our goal is to characterize the POMT complex structurally and biochemically and study its roles in KIAA1549:BRAF-driven pLGGs. In this proposal, we aim to 1) determine the high-resolution structures of the complex in its unbound, substrate-bound, and inhibitor-bound forms and 2) elucidate the POMT complex mechanisms in KIAA1549:BRAF-driven pLGGs. We will define the critical functional domains, active sites, interaction interfaces and translational modifications crucial for enzymatic activity using cryo-EM techniques, mutagenesis, and functional studies. To study biological pathways and molecular events modulated by the POMT complex, we will implement global proteomics and transcriptomics analysis in well-characterized disease models. In parallel, we will assess the effect of the POMT complex on the MAPK/ERK signaling pathway. This study will guide the structure-based design of probes and drugs targeting the POMT complex and will unveil glycosylation-mediated oncogenesis in pediatric gliomas. It will aid in the development of new targeted therapies and the identification of new biomarkers for pLGGs harboring the KIAA1549:BRAF fusion. The research will be conducted in the Fischer lab at Dana-Farber Cancer Institute, which provides a collaborative and resource-rich environment. The career development plan includes training in scientific writing, mentoring, and presentation skills, as well as interdisciplinary networking with experts in structural biology and pediatric oncology. The candidate’s career goal is to establish an independent research laboratory focused on developing new therapeutic modalities for pediatric neurooncology. The training provided through this fellowship represents a critical step toward achieving this goal.

Addressing C-F bonds and amyloid-formation in biological systems

The ingestion, pulmonary inhalation, and dermal infiltration of C-F bond-containing compounds, most commonly found in the form of per- and polyfluoroalkyl organic acids, causes oxidative stress, inflammation, DNA damage, and developmental defects in infants and adults. These chemicals accumulate in the brain, disrupt neurological function and compromise cognitive and locomotory behavior. Yet, we lack a high-resolution road-map of the interactions between C-F bonds and biomolecular assemblies driving the trajectory towards neurodegenerative outcomes. This gap constitutes a significant barrier to advancing measures designed to mitigate C-F chemistry-associated neurotoxicity. Emerging experimental and computational data from our laboratory reveals that perfluorooctanoic acid, perfluorodecanoic acid and perfluorosulfonic acid corrupt biomolecular structures through C-F:side-chain interactions in tested soluble, globular proteins found in milk and tissues (matrices where C-F chemistries have been detected). Furthermore, they impaired the physiological function in these proteins through displacement of physiological ligands or by compromising the binding of co-factors. The neuroblastoma-derived SHSY-5Y cell line insulted with the said C-F moieties displayed altered gene expression corresponding to reactive oxygen species (ROS), protein ubiquitination, inflammation along with compromised cytoskeletal integrity. C-F bond ingestion ablated dopaminergic (DA) neurons in the nematode C. elegans and induced locomotory deficits in a manner mimicking paraquat. Based on these findings, we propose to gather data towards our hypothesis that C-F bond exposure perturbs biomolecular, cellular and organismal assemblies to onset neurodegeneration-linked trajectories. In Aim 1, we will determine whether organic fluoroacids alter mRNA levels in differentiated SHSY-5Y cells and in neuroprotective gut bacteria (Lactobacillus rhamnosus, Bifidobacterium lactis and Lactobacillus acidophilus). We will examine whether the neuroblastoma cell line exposed to C-F chemistry displays readouts designed to inform the onset of neurodegeneration-associated trajectories (including α-synuclein aggregation). In Aim 2, we will further address in a preclinical model whether C-F burden induces protein aggregation (α-synuclein, amyloid β, mHTT), interferes with dopaminergic neuronal assembles and induces locomotory deficits. Completion of the proposed work will complement ongoing experimental biophysical, structural (crystallographic, NMR) and computational (docking, molecular dynamics simulations) mapping of the interactions between these anthropogenic “forever” chemicals and amyloid-forming proteins potentially resulting in a soluble-to-toxic transformation. It will prepare the stage for vertebrate testing. The findings from this relatively understudied area likely exposes interventional targets for C-F chemistry associated neurotoxicity, spurs therapeutic efforts and can also guide the development of more biocompatible alternatives.

Personalized Spatial Regulatory Networks to Decode Breast Cancer Microenvironments

PROJECT SUMMARY Triple-negative breast cancer (TNBC) is an aggressive subtype with early recurrence, high metastatic burden, and limited treatment options. While genomic alterations contribute to its progression, epigenetic plasticity and spatial organization within the tumor microenvironment (TME) play critical roles in intra-tumor heterogeneity, immune evasion, and therapy resistance, yet remain poorly understood. To address this, we will develop a cost- effective and scalable methodology that integrates spatial ATAC-seq, spatial in situ transcriptomics (Xenium), and single-nucleus (sn) Epi Multiome sequencing (snRNA-seq + snATAC-seq) from core-needle biopsies, enabling high-resolution mapping of gene regulatory networks within the intact TME. Our preliminary data from six TNBC biopsies demonstrate that spatial in situ transcriptomics and spatial ATAC-seq provide critical insights into tissue architecture but suffer from data sparsity, necessitating the integration of single-nucleus Epi Multiome data to enhance cell-type annotation and impute missing genomic features. In Aim 1, we will establish a multi- modal workflow that maximizes molecular insights from limited biopsy material by optimizing tissue-preserving and multiplexed sequencing approaches. This includes leveraging patient-specific genetic variation to deconvolute nuclei-derived data and linking it to spatial transcriptomic and spatial chromatin accessibility profiles. In Aim 2, we will develop a computational framework to integrate these multi-layered datasets, enabling spatially resolved epigenomic-transcriptomic analysis that identifies key regulatory chromatin elements and transcriptional programs associated with TNBC progression, immune infiltration, and therapy resistance. This project will generate the first comprehensive, patient-specific spatial regulatory atlas of TNBC, providing fundamental insights into how chromatin accessibility and gene expression interact within the TME. Ultimately, this work will pave the way for novel precision oncology strategies, biomarker discovery, and the development of targeted therapies that address TNBC’s spatial and molecular heterogeneity.

Glycoengineering core a(1,3)-fucose motifs to enhance HIV-1 envelope vaccine immunogenicity

Project Summary The HIV-1 envelope glycoprotein (Env) is the sole target of neutralizing antibodies (NAbs). We previously developed a vaccine platform integrating three innovations: (1) the uncleaved prefusion-optimized (UFO) trimer design to stabilize Env; (2) multilayered single-component self-assembling protein nanoparticles (1c-SApNPs) for multivalent trimer display; and (3) enzymatic trimming of oligomannose glycans on CHO cell-produced Env immunogens. Glycan trimming substantially improved Env immunogenicity by enhancing tier 2 NAb elicitation, reducing off-target responses to immunodominant glycan sites, and increasing responder rates. These vaccine candidates are now in phase 1 clinical trials (NCT06541093; NCT06905275). Building on this foundation, we propose a novel strategy to enhance immunogenicity by incorporating core α(1,3)-fucose into HIV-1 Env. Core α(1,3)-fucose, a key allergenic epitope in many plant and insect glycoproteins, is highly immunogenic in humans and other mammals. Our central hypothesis is that the targeted introduction of core α(1,3)-fucose will convert the glycan shield from an immune-evasive barrier into an immunogenic trigger that promotes NAb induction. Glycoengineered cell lines expressing α(1,3)-fucose will enable production of highly immunogenic Env vaccines suitable for preclinical and clinical testing. Importantly, particulate display of these Env trimers on 1c-SApNPs can suppress IgE-mediated allergic pathways by inducing high-affinity protective IgGs, ensuring vaccine safety. Aim 1 will focus on producing core α(1,3)-fucosylated HIV-1 Env immunogens. We will begin by developing a transient insect cell expression system using BTI-TN-5B1-4 (“High Five” or Hi5) cells to produce Env with short paucimannose glycans bearing native α(1,3)-fucose. To further enhance α(1,3)-fucosylation, we will co-express exogenous core α(1,3)-fucosyltransferases in insect and CHO cells. We will validate glycan profiles and characterize the biochemical, biophysical, structural, and antigenic properties of the resulting immunogens. Aim 2 will assess the immunogenicity of these glycoengineered HIV-1 Env immunogens. Using our previously established glycan-trimmed Env immunogens as benchmarks, we will immunize mice, rabbits, and nonhuman primates (NHPs). Mice will be used for early-stage immunogen and adjuvant screening; rabbits to evaluate glycan hole-targeting NAb responses; and key vaccine formulations will advance to NHP studies. We will assess autologous and heterologous tier 2 NAb responses and vaccine responder rates. Aim 3 will elucidate the functional, structural, repertoire, and mechanistic basis of vaccine-induced immunity. We will isolate NAbs via Env-specific single-cell sorting and antibody cloning, map epitopes by electron microscopy (EM) and X-ray crystallography, perform next-generation sequencing (NGS) of B-cell repertoires, and trace NAb lineages. Finally, we will investigate antigen trafficking, retention, presentation, and germinal center (GC) reactions in lymph nodes. Together, these studies will define a new class of glycoengineered HIV-1 vaccines and establish core α(1,3)-fucose as a novel immunomodulatory tool to overcome glycan shield-mediated immune evasion.

Prefrontal-thalamic goal-state coding segregates navigation episodes into spatially consistent parallel hippocampal maps

Neural Representations of Abstract Cognitive Maps in Prefrontal Cortex and Medial Temporal Lobe

OpenNeuro FitLins GLM: An Accessible, Semi-Automated Pipeline for OpenNeuro Task fMRI Analysis

In this talk, I will discuss the OpenNeuro Fitlins GLM package and provide an illustration of the analytic workflow. OpenNeuro FitLins GLM is a semi-automated pipeline that reduces barriers to analyzing task-based fMRI data from OpenNeuro's 600+ task datasets. Created for psychology, psychiatry and cognitive neuroscience researchers without extensive computational expertise, this tool automates what is largely a manual process and compilation of in-house scripts for data retrieval, validation, quality control, statistical modeling and reporting that, in some cases, may require weeks of effort. The workflow abides by open-science practices, enhancing reproducibility and incorporates community feedback for model improvement. The pipeline integrates BIDS-compliant datasets and fMRIPrep preprocessed derivatives, and dynamically creates BIDS Statistical Model specifications (with Fitlins) to perform common mass univariate [GLM] analyses. To enhance and standardize reporting, it generates comprehensive reports which includes design matrices, statistical maps and COBIDAS-aligned reporting that is fully reproducible from the model specifications and derivatives. OpenNeuro Fitlins GLM has been tested on over 30 datasets spanning 50+ unique fMRI tasks (e.g., working memory, social processing, emotion regulation, decision-making, motor paradigms), reducing analysis times from weeks to hours when using high-performance computers, thereby enabling researchers to conduct robust single-study, meta- and mega-analyses of task fMRI data with significantly improved accessibility, standardized reporting and reproducibility.

Non-invasive human neuroimaging studies of motor plasticity have predominantly focused on the cerebral cortex due to low signal-to-noise ration of blood oxygen level-dependent (BOLD) signals in subcortical structures and the small effect sizes typically observed in plasticity paradigms. Precision functional mapping can help overcome these challenges and has revealed significant and reversible functional alterations in the cortico-subcortical motor circuit during arm immobilization

Cognitive maps, navigational strategies, and the human brain

Harnessing Big Data in Neuroscience: From Mapping Brain Connectivity to Predicting Traumatic Brain Injury

Neuroscience is experiencing unprecedented growth in dataset size both within individual brains and across populations. Large-scale, multimodal datasets are transforming our understanding of brain structure and function, creating opportunities to address previously unexplored questions. However, managing this increasing data volume requires new training and technology approaches. Modern data technologies are reshaping neuroscience by enabling researchers to tackle complex questions within a Ph.D. or postdoctoral timeframe. I will discuss cloud-based platforms such as brainlife.io, that provide scalable, reproducible, and accessible computational infrastructure. Modern data technology can democratize neuroscience, accelerate discovery and foster scientific transparency and collaboration. Concrete examples will illustrate how these technologies can be applied to mapping brain connectivity, studying human learning and development, and developing predictive models for traumatic brain injury (TBI). By integrating cloud computing and scalable data-sharing frameworks, neuroscience can become more impactful, inclusive, and data-driven..

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.

Circuit Mechanisms of Remote Memory

Memories of emotionally-salient events are long-lasting, guiding behavior from minutes to years after learning. The prelimbic cortex (PL) is required for fear memory retrieval across time and is densely interconnected with many subcortical and cortical areas involved in recent and remote memory recall, including the temporal association area (TeA). While the behavioral expression of a memory may remain constant over time, the neural activity mediating memory-guided behavior is dynamic. In PL, different neurons underlie recent and remote memory retrieval and remote memory-encoding neurons have preferential functional connectivity with cortical association areas, including TeA. TeA plays a preferential role in remote compared to recent memory retrieval, yet how TeA circuits drive remote memory retrieval remains poorly understood. Here we used a combination of activity-dependent neuronal tagging, viral circuit mapping and miniscope imaging to investigate the role of the PL-TeA circuit in fear memory retrieval across time in mice. We show that PL memory ensembles recruit PL-TeA neurons across time, and that PL-TeA neurons have enhanced encoding of salient cues and behaviors at remote timepoints. This recruitment depends upon ongoing synaptic activity in the learning-activated PL ensemble. Our results reveal a novel circuit encoding remote memory and provide insight into the principles of memory circuit reorganization across time.

Contentopic mapping and object dimensionality - a novel understanding on the organization of object knowledge

Our ability to recognize an object amongst many others is one of the most important features of the human mind. However, object recognition requires tremendous computational effort, as we need to solve a complex and recursive environment with ease and proficiency. This challenging feat is dependent on the implementation of an effective organization of knowledge in the brain. Here I put forth a novel understanding of how object knowledge is organized in the brain, by proposing that the organization of object knowledge follows key object-related dimensions, analogously to how sensory information is organized in the brain. Moreover, I will also put forth that this knowledge is topographically laid out in the cortical surface according to these object-related dimensions that code for different types of representational content – I call this contentopic mapping. I will show a combination of fMRI and behavioral data to support these hypotheses and present a principled way to explore the multidimensionality of object processing.

Rethinking Attention: Dynamic Prioritization

Decades of research on understanding the mechanisms of attentional selection have focused on identifying the units (representations) on which attention operates in order to guide prioritized sensory processing. These attentional units fit neatly to accommodate our understanding of how attention is allocated in a top-down, bottom-up, or historical fashion. In this talk, I will focus on attentional phenomena that are not easily accommodated within current theories of attentional selection – the “attentional platypuses,” as they allude to an observation that within biological taxonomies the platypus does not fit into either mammal or bird categories. Similarly, attentional phenomena that do not fit neatly within current attentional models suggest that current models need to be revised. I list a few instances of the ‘attentional platypuses” and then offer a new approach, the Dynamically Weighted Prioritization, stipulating that multiple factors impinge onto the attentional priority map, each with a corresponding weight. The interaction between factors and their corresponding weights determines the current state of the priority map which subsequently constrains/guides attention allocation. I propose that this new approach should be considered as a supplement to existing models of attention, especially those that emphasize categorical organizations.

Mapping the neural dynamics of dominance and defeat

Social experiences can have lasting changes on behavior and affective state. In particular, repeated wins and losses during fighting can facilitate and suppress future aggressive behavior, leading to persistent high aggression or low aggression states. We use a combination of techniques for multi-region neural recording, perturbation, behavioral analysis, and modeling to understand how nodes in the brain’s subcortical “social decision-making network” encode and transform aggressive motivation into action, and how these circuits change following social experience.

Mapping the Brain‘s Visual Representations Using Deep Learning

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.

Mitochondrial diversity in the mouse and human brain

The basis of the mind, of mental states, and complex behaviors is the flow of energy through microscopic and macroscopic brain structures. Energy flow through brain circuits is powered by thousands of mitochondria populating the inside of every neuron, glial, and other nucleated cell across the brain-body unit. This seminar will cover emerging approaches to study the mind-mitochondria connection and present early attempts to map the distribution and diversity of mitochondria across brain tissue. In rodents, I will present convergent multimodal evidence anchored in enzyme activities, gene expression, and animal behavior that distinct behaviorally-relevant mitochondrial phenotypes exist across large-scale mouse brain networks. Extending these findings to the human brain, I will present a developing systematic biochemical and molecular map of mitochondrial variation across cortical and subcortical brain structures, representing a foundation to understand the origin of complex energy patterns that give rise to the human mind.

How are the epileptogenesis clocks ticking?

The epileptogenesis process is associated with large-scale changes in gene expression, which contribute to the remodelling of brain networks permanently altering excitability. About 80% of the protein coding genes are under the influence of the circadian rhythms. These are 24-hour endogenous rhythms that determine a large number of daily changes in physiology and behavior in our bodies. In the brain, the master clock regulates a large number of pathways that are important during epileptogenesis and established-epilepsy, such as neurotransmission, synaptic homeostasis, inflammation, blood-brain barrier among others. In-depth mapping of the molecular basis of circadian timing in the brain is key for a complete understanding of the cellular and molecular events connecting genes to phenotypes.

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.

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.

Cellular and genetic mechanisms of cerebral cortex folding

One of the most prominent features of the human brain is the fabulous size of the cerebral cortex and its intricate folding, both of which emerge during development. Over the last few years, work from my lab has shown that specific cellular and genetic mechanisms play central roles in cortex folding, particularly linked to neural stem and progenitor cells. Key mechanisms include high rates of neurogenesis, high abundance of basal Radial Glia Cells (bRGCs), and neuron migration, all of which are intertwined during development. We have also shown that primary cortical folds follow highly stereotyped patterns, defined by a spatial-temporal protomap of gene expression within germinal layers of the developing cortex. I will present recent findings from my laboratory revealing novel cellular and genetic mechanisms that regulate cortex expansion and folding. We have uncovered the contribution of epigenetic regulation to the establishment of the cortex folding protomap, modulating the expression levels of key transcription factors that control progenitor cell proliferation and cortex folding. At the single cell level, we have identified an unprecedented diversity of cortical progenitor cell classes in the ferret and human embryonic cortex. These are differentially enriched in gyrus versus sulcus regions and establish parallel cell lineages, not observed in mouse. Our findings show that genetic and epigenetic mechanisms in gyrencephalic species diversify cortical progenitor cell types and implement parallel cell linages, driving the expansion of neurogenesis and patterning cerebral cortex folds.

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

NII Methods (journal club): NeuroQuery, comprehensive meta-analysis of human brain mapping

We will discuss a recent paper by Taylor et al. (2023): https://www.sciencedirect.com/science/article/pii/S1053811923002896. They discuss the merits of highlighting results instead of hiding them; that is, clearly marking which voxels and clusters pass a given significance threshold, but still highlighting sub-threshold results, with opacity proportional to the strength of the effect. They use this to illustrate how there in fact may be more agreement between researchers than previously thought, using the NARPS dataset as an example. By adopting a continuous, "highlighted" approach, it becomes clear that the majority of effects are in the same location and that the effect size is in the same direction, compared to an approach that only permits rejecting or not rejecting the null hypothesis. We will also talk about the implications of this approach for creating figures, detecting artifacts, and aiding reproducibility.

NII Methods (journal club): NeuroQuery, comprehensive meta-analysis of human brain mapping

We will discuss this paper on Neuroquery, a relatively new web-based meta-analysis tool: https://elifesciences.org/articles/53385.pdf. This is different from Neurosynth in that it generates meta-analysis maps using predictive modeling from the string of text provided at the prompt, instead of performing inferential statistics to calculate the overlap of activation from different studies. This allows the user to generate predictive maps for more nuanced cognitive processes - especially for clinical populations which may be underrepresented in the literature compared to controls - and can be useful in generating predictions about where the activity will be for one's own study, and for creating ROIs.

The role of sub-population structure in computations through neural dynamics

Neural computations are currently conceptualised using two separate approaches: sorting neurons into functional sub-populations or examining distributed collective dynamics. Whether and how these two aspects interact to shape computations is currently unclear. Using a novel approach to extract computational mechanisms from recurrent networks trained on neuroscience tasks, we show that the collective dynamics and sub-population structure play fundamentally complementary roles. Although various tasks can be implemented in networks with fully random population structure, we found that flexible input–output mappings instead require a non-random population structure that can be described in terms of multiple sub-populations. Our analyses revealed that such a sub-population organisation enables flexible computations through a mechanism based on gain-controlled modulations that flexibly shape the collective dynamics.

Euclidean coordinates are the wrong prior for primate vision

The mapping from the visual field to V1 can be approximated by a log-polar transform. In this domain, scale is a left-right shift, and rotation is an up-down shift. When fed into a standard shift-invariant convolutional network, this provides scale and rotation invariance. However, translation invariance is lost. In our model, this is compensated for by multiple fixations on an object. Due to the high concentration of cones in the fovea with the dropoff of resolution in the periphery, fully 10 degrees of visual angle take up about half of V1, with the remaining 170 degrees (or so) taking up the other half. This layout provides the basis for the central and peripheral pathways. Simulations with this model closely match human performance in scene classification, and competition between the pathways leads to the peripheral pathway being used for this task. Remarkably, in spite of the property of rotation invariance, this model can explain the inverted face effect. We suggest that the standard method of using image coordinates is the wrong prior for models of primate vision.

Learning through the eyes and ears of a child

Young children have sophisticated representations of their visual and linguistic environment. Where do these representations come from? How much knowledge arises through generic learning mechanisms applied to sensory data, and how much requires more substantive (possibly innate) inductive biases? We examine these questions by training neural networks solely on longitudinal data collected from a single child (Sullivan et al., 2020), consisting of egocentric video and audio streams. Our principal findings are as follows: 1) Based on visual only training, neural networks can acquire high-level visual features that are broadly useful across categorization and segmentation tasks. 2) Based on language only training, networks can acquire meaningful clusters of words and sentence-level syntactic sensitivity. 3) Based on paired visual and language training, networks can acquire word-referent mappings from tens of noisy examples and align their multi-modal conceptual systems. Taken together, our results show how sophisticated visual and linguistic representations can arise through data-driven learning applied to one child’s first-person experience.

Developmentally structured coactivity in the hippocampal trisynaptic loop

The hippocampus is a key player in learning and memory. Research into this brain structure has long emphasized its plasticity and flexibility, though recent reports have come to appreciate its remarkably stable firing patterns. How novel information incorporates itself into networks that maintain their ongoing dynamics remains an open question, largely due to a lack of experimental access points into network stability. Development may provide one such access point. To explore this hypothesis, we birthdated CA1 pyramidal neurons using in-utero electroporation and examined their functional features in freely moving, adult mice. We show that CA1 pyramidal neurons of the same embryonic birthdate exhibit prominent cofiring across different brain states, including behavior in the form of overlapping place fields. Spatial representations remapped across different environments in a manner that preserves the biased correlation patterns between same birthdate neurons. These features of CA1 activity could partially be explained by structured connectivity between pyramidal cells and local interneurons. These observations suggest the existence of developmentally installed circuit motifs that impose powerful constraints on the statistics of hippocampal output.

Causal Symptom Network Mapping Based on Lesions and Brain Stimulation; Converging Evidence about a Depression Circuit Using Causal Sources of Information

It’s our pleasure to announce that we will host Shan Siddiqi and Michael D. Fox on Thursday, March 30th at noon ET / 6PM CET. Shan Siddiqi, MD, is an Assistant Professor of Psychiatry at Harvard Medical School and the director of Psychiatric Neuromodulation Research at the Brigham and Women’s Hospital. Michael D. Fox, MD, PhD, is an Associate Professor of Neurology at Harvard Medical School and the founding director of the Center for Brain Circuit Therapeutics at the Brigham and Women’s Hospital. The talks will be followed by a shared discussion. You can register via talks.stimulatingbrains.org to receive the (free) Zoom link!

Are place cells just memory cells? Probably yes

Neurons in the rodent hippocampus appear to encode the position of the animal in physical space during movement. Individual ``place cells'' fire in restricted sub-regions of an environment, a feature often taken as evidence that the hippocampus encodes a map of space that subserves navigation. But these same neurons exhibit complex responses to many other variables that defy explanation by position alone, and the hippocampus is known to be more broadly critical for memory formation. Here we elaborate and test a theory of hippocampal coding which produces place cells as a general consequence of efficient memory coding. We constructed neural networks that actively exploit the correlations between memories in order to learn compressed representations of experience. Place cells readily emerged in the trained model, due to the correlations in sensory input between experiences at nearby locations. Notably, these properties were highly sensitive to the compressibility of the sensory environment, with place field size and population coding level in dynamic opposition to optimally encode the correlations between experiences. The effects of learning were also strongly biphasic: nearby locations are represented more similarly following training, while locations with intermediate similarity become increasingly decorrelated, both distance-dependent effects that scaled with the compressibility of the input features. Using virtual reality and 2-photon functional calcium imaging in head-fixed mice, we recorded the simultaneous activity of thousands of hippocampal neurons during virtual exploration to test these predictions. Varying the compressibility of sensory information in the environment produced systematic changes in place cell properties that reflected the changing input statistics, consistent with the theory. We similarly identified representational plasticity during learning, which produced a distance-dependent exchange between compression and pattern separation. These results motivate a more domain-general interpretation of hippocampal computation, one that is naturally compatible with earlier theories on the circuit's importance for episodic memory formation. Work done in collaboration with James Priestley, Lorenzo Posani, Marcus Benna, Attila Losonczy.

Autopoiesis and Enaction in the Game of Life

Enaction plays a central role in the broader fabric of so-called 4E (embodied, embedded, extended, enactive) cognition. Although the origin of the enactive approach is widely dated to the 1991 publication of the book "The Embodied Mind" by Varela, Thompson and Rosch, many of the central ideas trace to much earlier work. Over 40 years ago, the Chilean biologists Humberto Maturana and Francisco Varela put forward the notion of autopoiesis as a way to understand living systems and the phenomena that they generate, including cognition. Varela and others subsequently extended this framework to an enactive approach that places biological autonomy at the foundation of situated and embodied behavior and cognition. I will describe an attempt to place Maturana and Varela's original ideas on a firmer foundation by studying them within the context of a toy model universe, John Conway's Game of Life (GoL) cellular automata. This work has both pedagogical and theoretical goals. Simple concrete models provide an excellent vehicle for introducing some of the core concepts of autopoiesis and enaction and explaining how these concepts fit together into a broader whole. In addition, a careful analysis of such toy models can hone our intuitions about these concepts, probe their strengths and weaknesses, and move the entire enterprise in the direction of a more mathematically rigorous theory. In particular, I will identify the primitive processes that can occur in GoL, show how these can be linked together into mutually-supporting networks that underlie persistent bounded entities, map the responses of such entities to environmental perturbations, and investigate the paths of mutual perturbation that these entities and their environments can undergo.

How Children Design by Analogy: The Role of Spatial Thinking

Analogical reasoning is a common reasoning tool for learning and problem-solving. Existing research has extensively studied children’s reasoning when comparing, or choosing from ready-made analogies. Relatively less is known about how children come up with analogies in authentic learning environments. Design education provides a suitable context to investigate how children generate analogies for creative learning purposes. Meanwhile, the frequent use of visual analogies in design provides an additional opportunity to understand the role of spatial reasoning in design-by-analogy. Spatial reasoning is one of the most studied human cognitive factors and is critical to the learning of science, technology, engineering, arts, and mathematics (STEAM). There is growing interest in exploring the interplay between analogical reasoning and spatial reasoning. In this talk, I will share qualitative findings from a case study, where a class of 11-to-12-year-olds in the Netherlands participated in a biomimicry design project. These findings illustrate (1) practical ways to support children’s analogical reasoning in the ideation process and (2) the potential role of spatial reasoning as seen in children mapping form-function relationships in nature analogically and adaptively to those in human designs.

Central place foraging: how insects anchor spatial information

Many insect species maintain a nest around which their foraging behaviour is centered, and can use path integration to maintain an accurate estimate of their distance and direction (a vector) to their nest. Some species, such as bees and ants, can also store the vector information for multiple salient locations in the world, such as food sources, in a common coordinate system. They can also use remembered views of the terrain around salient locations or along travelled routes to guide return. Recent modelling of these abilities shows convergence on a small set of algorithms and assumptions that appear sufficient to account for a wide range of behavioural data, and which can be mapped to specific insect brain circuits. Notably, this does not include any significant topological knowledge: the insect does not need to recover the information (implicit in their vector memory) about the relationships between salient places; nor to maintain any connectedness or ordering information between view memories; nor to form any associations between views and vectors. However, there remains some experimental evidence not fully explained by these algorithms that may point towards the existence of a more complex or integrated mental map in insects.

Retinotopic maps and their relationship to white matter tracts in the human brain

Working memory tasks for functional mapping of the prefrontal cortex in common marmosets

A specialized role for entorhinal attractor dynamics in combining path integration and landmarks during navigation

During navigation, animals estimate their position using path integration and landmarks. In a series of two studies, we used virtual reality and electrophysiology to dissect how these inputs combine to generate the brain’s spatial representations. In the first study (Campbell et al., 2018), we focused on the medial entorhinal cortex (MEC) and its set of navigationally-relevant cell types, including grid cells, border cells, and speed cells. We discovered that attractor dynamics could explain an array of initially puzzling MEC responses to virtual reality manipulations. This theoretical framework successfully predicted both MEC grid cell responses to additional virtual reality manipulations, as well as mouse behavior in a virtual path integration task. In the second study (Campbell*, Attinger* et al., 2021), we asked whether these principles generalize to other navigationally-relevant brain regions. We used Neuropixels probes to record thousands of neurons from MEC, primary visual cortex (V1), and retrosplenial cortex (RSC). In contrast to the prevailing view that “everything is everywhere all at once,” we identified a unique population of MEC neurons, overlapping with grid cells, that became active with striking spatial periodicity while head-fixed mice ran on a treadmill in darkness. These neurons exhibited unique cue-integration properties compared to other MEC, V1, or RSC neurons: they remapped more readily in response to conflicts between path integration and landmarks; they coded position prospectively as opposed to retrospectively; they upweighted path integration relative to landmarks in conditions of low visual contrast; and as a population, they exhibited a lower-dimensional activity structure. Based on these results, our current view is that MEC attractor dynamics play a privileged role in resolving conflicts between path integration and landmarks during navigation. Future work should include carefully designed causal manipulations to rigorously test this idea, and expand the theoretical framework to incorporate notions of uncertainty and optimality.

Verb metaphors are processed as analogies

Metaphor is a pervasive phenomenon in language and cognition. To date, the vast majority of psycholinguistic research on metaphor has focused on noun-noun metaphors of the form An X is a Y (e.g., My job is a jail). Yet there is evidence that verb metaphor (e.g., I sailed through my exams) is more common. Despite this, comparatively little work has examined how verb metaphors are processed. In this talk, I will propose a novel account for verb metaphor comprehension: verb metaphors are understood in the same way that analogies are—as comparisons processed via structure-mapping. I will discuss the predictions that arise from applying the analogical framework to verb metaphor and present a series of experiments showing that verb metaphoric extension is consistent with those predictions.

Orientation selectivity in rodent V1: theory vs experiments

Neurons in the primary visual cortex (V1) of rodents are selective to the orientation of the stimulus, as in other mammals such as cats and monkeys. However, in contrast with those species, their neurons display a very different type of spatial organization. Instead of orientation maps they are organized in a “salt and pepper” pattern, where adjacent neurons have completely different preferred orientations. This structure has motivated both experimental and theoretical research with the objective of determining which aspects of the connectivity patterns and intrinsic neuronal responses can explain the observed behavior. These analysis have to take into account also that the neurons of the thalamus that send their outputs to the cortex have more complex responses in rodents than in higher mammals, displaying, for instance, a significant degree of orientation selectivity. In this talk we present work showing that a random feed-forward connectivity pattern, in which the probability of having a connection between a cortical neuron and a thalamic neuron depends only on the relative distance between them is enough explain several aspects of the complex phenomenology found in these systems. Moreover, this approach allows us to evaluate analytically the statistical structure of the thalamic input on the cortex. We find that V1 neurons are orientation selective but the preferred orientation of the stimulus depends on the spatial frequency of the stimulus. We disentangle the effect of the non circular thalamic receptive fields, finding that they control the selectivity of the time-averaged thalamic input, but not the selectivity of the time locked component. We also compare with experiments that use reverse correlation techniques, showing that ON and OFF components of the aggregate thalamic input are spatially segregated in the cortex.

Multimodal Blending

In this talk, I’ll consider how new ideas emerge from old ones via the process of conceptual blending. I’ll start by considering analogical reasoning in problem solving and the role conceptual blending plays in these problem-solving contexts. Then I’ll consider blending in multi-modal contexts, including timelines, memes (viz. image macros), and, if time allows, zoom meetings. I suggest mappings analogy researchers have traditionally considered superficial are often important for the development of novel abstractions. Likewise, the analogue portion of multimodal blends anchors their generative capacity. Overall, these observations underscore the extent to which meaning is a socially distributed process whose intermediate products are stored in cognitive artifacts such as text and digital images.

Mechanisms of relational structure mapping across analogy tasks

Following the seminal structure mapping theory by Dedre Gentner, the process of mapping the corresponding structures of relations defining two analogs has been understood as a key component of analogy making. However, not without a merit, in recent years some semantic, pragmatic, and perceptual aspects of analogy mapping attracted primary attention of analogy researchers. For almost a decade, our team have been re-focusing on relational structure mapping, investigating its potential mechanisms across various analogy tasks, both abstract (semantically-lean) and more concrete (semantically-rich), using diverse methods (behavioral, correlational, eye-tracking, EEG). I will present the overview of our main findings. They suggest that structure mapping (1) consists of an incremental construction of the ultimate mental representation, (2) which strongly depends on working memory resources and reasoning ability, (3) even if as little as a single trivial relation needs to be represented mentally. The effective mapping (4) is related to the slowest brain rhythm – the delta band (around 2-3 Hz) – suggesting its highly integrative nature. Finally, we have developed a new task – Graph Mapping – which involves pure mapping of two explicit relational structures. This task allows for precise investigation and manipulation of the mapping process in experiments, as well as is one of the best proxies of individual differences in reasoning ability. Structure mapping is as crucial to analogy as Gentner advocated, and perhaps it is crucial to cognition in general.

Analogies between exemplars of schema-governed categories

Dominant theories of analogical thinking postulate that making an analogy consists in discovering that two superficially different situations share isomorphic systems of similar relations. According to this perspective, the comparison between the two situations may eventually lead to the construction of a schema, which retains the structural aspects they share and deletes their specific contents. We have developed a new approach to analogical thinking, whose purpose is to explain a particular type of analogies: those in which the analogs are exemplars of a schema-governed category (e.g., two instances of robbery). As compared to standard analogies, these comparisons are noteworthy in that a well-established schema (the schema-governed category) mediates each one of the subprocesses involved in analogical thinking. We argue that the category assignment approach is able to provide a better account of how the analogical subprocesses of retrieval, mapping, re-representation, evaluation and inference generation are carried out during the processing of this specific kind of analogies. The arguments presented are accompanied by brief descriptions of some of the studies that provided support for this approach.

Modelling metaphor comprehension as a form of analogizing

What do people do when they comprehend language in discourse? According to many psychologists, they build and maintain cognitive representations of utterances in four complementary mental models for discourse that interact with each other: the surface text, the text base, the situation model, and the context model. When people encounter metaphors in these utterances, they need to incorporate them into each of these mental representations for the discourse. Since influential metaphor theories define metaphor as a form of (figurative) analogy, involving cross-domain mapping of a smaller or greater extent, the general expectation has been that metaphor comprehension is also based on analogizing. This expectation, however, has been partly borne out by the data, but not completely. There is no one-to-one relationship between metaphor as (conceptual) structure (analogy) and metaphor as (psychological) process (analogizing). According to Deliberate Metaphor Theory (DMT), only some metaphors are handled by analogy. Instead, most metaphors are presumably handled by lexical disambiguation. This is a hypothesis that brings together most metaphor research in a provocatively new way: it means that most metaphors are not processed metaphorically, which produces a paradox of metaphor. In this talk I will sketch out how this paradox arises and how it can be resolved by a new version of DMT, which I have described in my forthcoming book Slowing metaphor down: Updating Deliberate Metaphor Theory (currently under review). In this theory, the distinction between, but also the relation between, analogy in metaphorical structure versus analogy in metaphorical process is of central importance.

Network inference via process motifs for lagged correlation in linear stochastic processes

A major challenge for causal inference from time-series data is the trade-off between computational feasibility and accuracy. Motivated by process motifs for lagged covariance in an autoregressive model with slow mean-reversion, we propose to infer networks of causal relations via pairwise edge measure (PEMs) that one can easily compute from lagged correlation matrices. Motivated by contributions of process motifs to covariance and lagged variance, we formulate two PEMs that correct for confounding factors and for reverse causation. To demonstrate the performance of our PEMs, we consider network interference from simulations of linear stochastic processes, and we show that our proposed PEMs can infer networks accurately and efficiently. Specifically, for slightly autocorrelated time-series data, our approach achieves accuracies higher than or similar to Granger causality, transfer entropy, and convergent crossmapping -- but with much shorter computation time than possible with any of these methods. Our fast and accurate PEMs are easy-to-implement methods for network inference with a clear theoretical underpinning. They provide promising alternatives to current paradigms for the inference of linear models from time-series data, including Granger causality, vector-autoregression, and sparse inverse covariance estimation.

Mapping learning and decision-making algorithms onto brain circuitry

In the first half of my talk, I will discuss our recent work on the midbrain dopamine system. The hypothesis that midbrain dopamine neurons broadcast an error signal for the prediction of reward is among the great successes of computational neuroscience. However, our recent results contradict a core aspect of this theory: that the neurons uniformly convey a scalar, global signal. I will review this work, as well as our new efforts to update models of the neural basis of reinforcement learning with our data. In the second half of my talk, I will discuss our recent findings of state-dependent decision-making mechanisms in the striatum.

Universal function approximation in balanced spiking networks through convex-concave boundary composition

The spike-threshold nonlinearity is a fundamental, yet enigmatic, component of biological computation — despite its role in many theories, it has evaded definitive characterisation. Indeed, much classic work has attempted to limit the focus on spiking by smoothing over the spike threshold or by approximating spiking dynamics with firing-rate dynamics. Here, we take a novel perspective that captures the full potential of spike-based computation. Based on previous studies of the geometry of efficient spike-coding networks, we consider a population of neurons with low-rank connectivity, allowing us to cast each neuron’s threshold as a boundary in a space of population modes, or latent variables. Each neuron divides this latent space into subthreshold and suprathreshold areas. We then demonstrate how a network of inhibitory (I) neurons forms a convex, attracting boundary in the latent coding space, and a network of excitatory (E) neurons forms a concave, repellant boundary. Finally, we show how the combination of the two yields stable dynamics at the crossing of the E and I boundaries, and can be mapped onto a constrained optimization problem. The resultant EI networks are balanced, inhibition-stabilized, and exhibit asynchronous irregular activity, thereby closely resembling cortical networks of the brain. Moreover, we demonstrate how such networks can be tuned to either suppress or amplify noise, and how the composition of inhibitory convex and excitatory concave boundaries can result in universal function approximation. Our work puts forth a new theory of biologically-plausible computation in balanced spiking networks, and could serve as a novel framework for scalable and interpretable computation with spikes.

The role of population structure in computations through neural dynamics

Neural computations are currently investigated using two separate approaches: sorting neurons into functional subpopulations or examining the low-dimensional dynamics of collective activity. Whether and how these two aspects interact to shape computations is currently unclear. Using a novel approach to extract computational mechanisms from networks trained on neuroscience tasks, here we show that the dimensionality of the dynamics and subpopulation structure play fundamentally com- plementary roles. Although various tasks can be implemented by increasing the dimensionality in networks with fully random population structure, flexible input–output mappings instead require a non-random population structure that can be described in terms of multiple subpopulations. Our analyses revealed that such a subpopulation structure enables flexible computations through a mechanism based on gain-controlled modulations that flexibly shape the collective dynamics. Our results lead to task-specific predictions for the structure of neural selectivity, for inactivation experiments and for the implication of different neurons in multi-tasking.

Associative memory of structured knowledge

A long standing challenge in biological and artificial intelligence is to understand how new knowledge can be constructed from known building blocks in a way that is amenable for computation by neuronal circuits. Here we focus on the task of storage and recall of structured knowledge in long-term memory. Specifically, we ask how recurrent neuronal networks can store and retrieve multiple knowledge structures. We model each structure as a set of binary relations between events and attributes (attributes may represent e.g., temporal order, spatial location, role in semantic structure), and map each structure to a distributed neuronal activity pattern using a vector symbolic architecture (VSA) scheme. We then use associative memory plasticity rules to store the binarized patterns as fixed points in a recurrent network. By a combination of signal-to-noise analysis and numerical simulations, we demonstrate that our model allows for efficient storage of these knowledge structures, such that the memorized structures as well as their individual building blocks (e.g., events and attributes) can be subsequently retrieved from partial retrieving cues. We show that long-term memory of structured knowledge relies on a new principle of computation beyond the memory basins. Finally, we show that our model can be extended to store sequences of memories as single attractors.

Navigating Increasing Levels of Relational Complexity: Perceptual, Analogical, and System Mappings

Relational thinking involves comparing abstract relationships between mental representations that vary in complexity; however, this complexity is rarely made explicit during everyday comparisons. This study explored how people naturally navigate relational complexity and interference using a novel relational match-to-sample (RMTS) task with both minimal and relationally directed instruction to observe changes in performance across three levels of relational complexity: perceptual, analogy, and system mappings. Individual working memory and relational abilities were examined to understand RMTS performance and susceptibility to interfering relational structures. Trials were presented without practice across four blocks and participants received feedback after each attempt to guide learning. Experiment 1 instructed participants to select the target that best matched the sample, while Experiment 2 additionally directed participants’ attention to same and different relations. Participants in Experiment 2 demonstrated improved performance when solving analogical mappings, suggesting that directing attention to relational characteristics affected behavior. Higher performing participants—those above chance performance on the final block of system mappings—solved more analogical RMTS problems and had greater visuospatial working memory, abstraction, verbal analogy, and scene analogy scores compared to lower performers. Lower performers were less dynamic in their performance across blocks and demonstrated negative relationships between analogy and system mapping accuracy, suggesting increased interference between these relational structures. Participant performance on RMTS problems did not change monotonically with relational complexity, suggesting that increases in relational complexity places nonlinear demands on working memory. We argue that competing relational information causes additional interference, especially in individuals with lower executive function abilities.

Learning predictive maps in the brain for spatial navigation

The predictive map hypothesis provides a promising framework to model representations in the hippocampal formation. I will introduce a tractable implementation of a predictive map called the successor representation (SR), before presenting data showing that rats and humans display SR-like navigational choices on a novel open-field maze. Next, I will show how such a predictive map could be implemented using spatial representations found in the hippocampal formation, before finally presenting how such learning might be well approximated by phenomena that exist in the spatial memory system - namely spike-timing dependent plasticity and theta phase precession.

What shapes the transcriptional identity of a neuron?

Within the vertebrate neocortex and other telencephalic structures, molecularly-defined neurons tend to segregate at first order into GABAergic types and glutamatergic types. Two fundamental questions arise: (1) do non-telencephalic neurons similarly segregate by neurotransmitter status, and (2) do GABAergic (or glutamatergic) types sampled in different structures share many molecular features in common, beyond the few genes directly responsible for neurotransmitter synthesis and release? To address these questions, we used single-nucleus RNA sequencing, analyzing over 2.4 million brain cells sampled from 16 locations in a primate (the common marmoset). Unexpectedly, we find the answer to both is “no”. I will discuss implications for generalizing associations between neurotransmitter utilization and other phenotypes, and share ongoing efforts to map the biodistributions of cell types in the primate brain.

Internally Organized Abstract Task Maps in the Mouse Medial Frontal Cortex

New tasks are often similar in structure to old ones. Animals that take advantage of such conserved or “abstract” task structures can master new tasks with minimal training. To understand the neural basis of this abstraction, we developed a novel behavioural paradigm for mice: the “ABCD” task, and recorded from their medial frontal neurons as they learned. Animals learned multiple tasks where they had to visit 4 rewarded locations on a spatial maze in sequence, which defined a sequence of four “task states” (ABCD). Tasks shared the same circular transition structure (… ABCDABCD …) but differed in the spatial arrangement of rewards. As well as improving across tasks, mice inferred that A followed D (i.e. completed the loop) on the very first trial of a new task. This “zero-shot inference” is only possible if animals had learned the abstract structure of the task. Across tasks, individual medial Frontal Cortex (mFC) neurons maintained their tuning to the phase of an animal’s trajectory between rewards but not their tuning to task states, even in the absence of spatial tuning. Intriguingly, groups of mFC neurons formed modules of coherently remapping neurons that maintained their tuning relationships across tasks. Such tuning relationships were expressed as replay/preplay during sleep, consistent with an internal organisation of activity into multiple, task-matched ring attractors. Remarkably, these modules were anchored to spatial locations: neurons were tuned to specific task space “distances” from a particular spatial location. These newly discovered “Spatially Anchored Task clocks” (SATs), suggest a novel algorithm for solving abstraction tasks. Using computational modelling, we show that SATs can perform zero-shot inference on new tasks in the absence of plasticity and guide optimal policy in the absence of continual planning. These findings provide novel insights into the Frontal mechanisms mediating abstraction and flexible behaviour.

Hierarchical transformation of visual event timing representations in the human brain: response dynamics in early visual cortex and timing-tuned responses in association cortices

Quantifying the timing (duration and frequency) of brief visual events is vital to human perception, multisensory integration and action planning. For example, this allows us to follow and interact with the precise timing of speech and sports. Here we investigate how visual event timing is represented and transformed across the brain’s hierarchy: from sensory processing areas, through multisensory integration areas, to frontal action planning areas. We hypothesized that the dynamics of neural responses to sensory events in sensory processing areas allows derivation of event timing representations. This would allow higher-level processes such as multisensory integration and action planning to use sensory timing information, without the need for specialized central pacemakers or processes. Using 7T fMRI and neural model-based analyses, we found responses that monotonically increase in amplitude with visual event duration and frequency, becoming increasingly clear from primary visual cortex to lateral occipital visual field maps. Beginning in area MT/V5, we found a gradual transition from monotonic to tuned responses, with response amplitudes peaking at different event timings in different recording sites. While monotonic response components were limited to the retinotopic location of the visual stimulus, timing-tuned response components were independent of the recording sites' preferred visual field positions. These tuned responses formed a network of topographically organized timing maps in superior parietal, postcentral and frontal areas. From anterior to posterior timing maps, multiple events were increasingly integrated, response selectivity narrowed, and responses focused increasingly on the middle of the presented timing range. These results suggest that responses to event timing are transformed from the human brain’s sensory areas to the association cortices, with the event’s temporal properties being increasingly abstracted from the response dynamics and locations of early sensory processing. The resulting abstracted representation of event timing is then propagated through areas implicated in multisensory integration and action planning.

Learning static and dynamic mappings with local self-supervised plasticity

Animals exhibit remarkable learning capabilities with little direct supervision. Likewise, self-supervised learning is an emergent paradigm in artificial intelligence, closing the performance gap to supervised learning. In the context of biology, self-supervised learning corresponds to a setting where one sense or specific stimulus may serve as a supervisory signal for another. After learning, the latter can be used to predict the former. On the implementation level, it has been demonstrated that such predictive learning can occur at the single neuron level, in compartmentalized neurons that separate and associate information from different streams. We demonstrate the power such self-supervised learning over unsupervised (Hebb-like) learning rules, which depend heavily on stimulus statistics, in two examples: First, in the context of animal navigation where predictive learning can associate internal self-motion information always available to the animal with external visual landmark information, leading to accurate path-integration in the dark. We focus on the well-characterized fly head direction system and show that our setting learns a connectivity strikingly similar to the one reported in experiments. The mature network is a quasi-continuous attractor and reproduces key experiments in which optogenetic stimulation controls the internal representation of heading, and where the network remaps to integrate with different gains. Second, we show that incorporating global gating by reward prediction errors allows the same setting to learn conditioning at the neuronal level with mixed selectivity. At its core, conditioning entails associating a neural activity pattern induced by an unconditioned stimulus (US) with the pattern arising in response to a conditioned stimulus (CS). Solving the generic problem of pattern-to-pattern associations naturally leads to emergent cognitive phenomena like blocking, overshadowing, saliency effects, extinction, interstimulus interval effects etc. Surprisingly, we find that the same network offers a reductionist mechanism for causal inference by resolving the post hoc, ergo propter hoc fallacy.

Bootstrapping the auditory space map via an innate circuit

Bernstein Conference 2024

Learning and using predictive maps for strategic planning

Bernstein Conference 2024

Neuromodulated online cognitive maps for reinforcement learning

Bernstein Conference 2024

A Single-Layer Neuromorphic Encoder Maps EMG Signals into Wrist Kinematics

Bernstein Conference 2024

Topological maps are for robust and wiring-efficient dimensionality reduction

Bernstein Conference 2024

Emergence of an orientation map in the mouse superior colliculus from stage III retinal waves

COSYNE 2022

The geometry of map-like representations under dynamic cognitive control

COSYNE 2022

The geometry of map-like representations under dynamic cognitive control

COSYNE 2022

Goal-directed remapping of enthorhinal cortex neural coding

COSYNE 2022

Goal-directed remapping of enthorhinal cortex neural coding

COSYNE 2022

Hippocampal spatio-temporal cognitive maps adaptively guide reward generalization

COSYNE 2022

Hippocampal spatio-temporal cognitive maps adaptively guide reward generalization

COSYNE 2022

Map Induction: Compositional spatial submap learning for efficient exploration in novel environments

COSYNE 2022

Map Induction: Compositional spatial submap learning for efficient exploration in novel environments

COSYNE 2022

Internally Organized Abstract Task Maps in the Mouse Medial Frontal Cortex

COSYNE 2022

Internally Organized Abstract Task Maps in the Mouse Medial Frontal Cortex

COSYNE 2022

Mice identify subgoal locations through an action-driven mapping process

COSYNE 2022

Mice identify subgoal locations through an action-driven mapping process

COSYNE 2022

Multiscale Hierarchical Modeling Framework For Fully Mapping a Social Interaction

COSYNE 2022

Multiscale Hierarchical Modeling Framework For Fully Mapping a Social Interaction

COSYNE 2022

How neuronal axons get from here to there using gene-expression maps derived from their family trees

COSYNE 2022

How neuronal axons get from here to there using gene-expression maps derived from their family trees

COSYNE 2022

Optogenetic mapping of circuit connectivity in the motor cortex during goal-directed behavior

COSYNE 2022

Optogenetic mapping of circuit connectivity in the motor cortex during goal-directed behavior

COSYNE 2022

Real-time neural network denoising of 3D optogenetic connectivity maps

COSYNE 2022

Real-time neural network denoising of 3D optogenetic connectivity maps

COSYNE 2022

An accessible hippocampal dataset for benchmarking models of cognitive mapping

COSYNE 2023

Dopamine neurons reveal an efficient code for a multidimensional, distributional map of the future

COSYNE 2023

A mechanistic model for the formation of globally consistent maps of space in complex environments

COSYNE 2023

A predictive learning model for cognitive maps that generate replay

COSYNE 2023

A stable sensory map emerges from neurons with unstable tuning properties

COSYNE 2023

An attractive manifold of retinotopic map in a network model explains presaccadic receptive field remapping

COSYNE 2025

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

COSYNE 2025

A computational map of flight control in Drosophila melanogaster

COSYNE 2025

Continuous rotation of allocentric spatial maps in the hippocampus during reorientation

COSYNE 2025

Differential development of L4 and L2/3 V1 maps by eye-opening.

COSYNE 2025

ForageWorld: RL agents in complex foraging arenas develop internal maps for navigation and planning

COSYNE 2025

Geometric model manifold of space, time, and belief in hippocampal cognitive maps

COSYNE 2025

Hidden state inference guides formation of hippocampal cognitive maps during learning

COSYNE 2025

Beyond Cognitive Maps: Gradually Eliminating Spatial Influence in Learned Graph Representations

Bernstein Conference 2024