ECoG

TARGETING VAV1 SCAFFOLDING AND ENZYMATIC FUNCTIONS IN MULTIPLE SCLEROSIS VIA BRAIN-PENETRANT MOLECULAR GLUE DEGRADERS

Abstract Multiple Sclerosis (MS) is a chronic autoimmune disease of the central nervous system (CNS) with significant unmet medical needs, as current therapies offer limited efficacy against neurodegeneration and can have considerable side effects. VAV1, a key signaling protein predominantly expressed in hematopoietic cells, plays a crucial role in T and B lymphocyte activation and is genetically and functionally validated as a therapeutic target in MS. This project proposes an innovative approach to target VAV1 through the development of brain-penetrant molecular glue (MG) degraders. Distinct from Proteolysis Targeting Chimeras (PROTACs) that require a high- affinity ligand for the target protein, molecular glues can mediate degradation by engaging specific protein surface features, such as loops, without the necessity of a dedicated binder. These degraders aim to induce the proteasomal degradation of VAV1, thereby ablating both its enzymatic and scaffolding functions, which are implicated in neuroinflammation. The research strategy involves three primary aims: 1) To optimize lead VAV1 molecular glue degraders for enhanced potency, brain penetration, and favorable pharmacokinetic properties using advanced computational modeling and medicinal chemistry. 2) To evaluate the in vivo efficacy of the optimized VAV1 degraders in preclinical mouse models of MS (Experimental Autoimmune Encephalomyelitis - EAE), assessing their ability to ameliorate disease severity, reduce CNS inflammation and demyelination, and engage VAV1 in the CNS. 3) To investigate the Structure-Activity Relationship (SAR) of a novel non-canonical VAV1 degron motif, aiming to expand the understanding of molecular glue-mediated degradation and enable the rational design of degraders for other challenging therapeutic targets. Successful completion of this project is expected to deliver preclinical candidate VAV1 degraders with the potential for a novel, effective, and safer treatment paradigm for MS. Furthermore, the insights gained into non-canonical degron recognition will significantly advance the field of targeted protein degradation, broadening the scope of "undruggable" targets for therapeutic intervention in various diseases.

The Role of the Intestinal Microbiota in Sepsis Mortality

Project Summary/Abstract Sepsis is a life-threatening condition characterized by a dysregulated host response to infection that can cause multi-organ damage and death. As the leading cause of in-hospital mortality, sepsis mortality rates reach up to 50%, and account for approximately 270,000 deaths and $38 billion annually in health care costs in the United States. Notably, patients with similar medical backgrounds can have vastly different sepsis outcomes— some survive with medical treatment while others die. The reasons for this dichotomy are unknown but is seen across all forms of bacterial bloodstream infections, is not specific to any strain-level differences in the infecting pathogen and cannot be explained by human genetic differences. Human microbiota studies suggest that gut microbial dysbiosis is associated with sepsis mortality and that these alterations influence gut barrier breakdown, leading to gram-negative bacteremia—one of the most common causes of sepsis and mortality. However, there are a lack of studies that investigate the causal role of the intestinal microbiota in sepsis mortality. This K08 proposal will elucidate the role of the intestinal microbiota in sepsis mortality. Utilizing the well- established murine model of sepsis by intraperitoneal injection of lipopolysaccharide (LPS), we combine microbiota taxonomic sequencing and metagenomics, advanced bioinformatic techniques and prediction modeling, with knowledge of mucosal immunity and germ-free mouse systems to characterize the microbiota features and members that correlate with, predict, and cause sepsis mortality. This proposal is organized into two specific aims: (1) identify baseline stool microbial features associated with and predictive of sepsis outcomes and (2) determine how colonization with immunostimulatory microbes heightens sepsis mortality. In this work, I will holistically characterize the host immunologic and microbiota features that are associated with and predictive of mortality and experimentally identify microbes and microbial pathways that cause death in our model. These findings will reveal new microbial and host biomarkers of sepsis mortality and identify novel targets for sepsis prevention and treatment to reduce the overall mortality rate of this deadly disease. My long-term goal is to become an independent physician-scientist who integrates cutting-edge computational methods with experimental biology to identify predictive biomarkers of disease onset and outcomes, investigate how they influence disease processes, and develop novel therapeutic and preventive strategies to improve patient care. This proposal details specific research aims and a structured career development and training plan that will allow me to acquire focused, in-depth and multidisciplinary training under the guidance of an internationally recognized team of experts in clinical infectious diseases, host-microbiota interactions, immunology, immunometabolism, and computational biology. The knowledge generated will address the fundamental role of the microbiota in sepsis outcomes and inform future preventative and therapeutic strategies that will lower the sepsis mortality rate worldwide.

Integrins α4β7 in Leukocyte Rolling in Shear Flow, Firm Adhesion, and Therapy

Abstract. Integrin α4β7 facilitates leukocyte migration to sites of infection and autoimmune disease, making it an important therapeutic target for ulcerative colitis and Crohns disease. However, the currently approved antibody drug vedolizumab targeting α4β7 has limited efficacy. This proposal seeks mechanistic understanding of how α4β7 mediates rolling and firm adhesion of leukocytes during extravasation as well as how therapeutically relevant antibodies modulate α4β7 function to improve drug design. Unlike most integrins, α4β7 mediates rolling adhesion on its ligand MAdCAM. α4β7 can also mediate firm adhesion like α5β1. Integrins typically equilibrate between two low-affinity closed conformations and a high-affinity open conformation. Ligand binding is intimately coordinated with conformational change. During rolling adhesion, receptor-ligand bonds must rapidly form beneath rolling cells as cells are torqued by shear flow onto the substrate. Bonds must also rapidly dissociate at the upstream tethers to the substrate due to hydrodynamic force applied to the cell. To enable their function in rolling adhesion, we hypothesize that α4β7 ligand binding and dissociation and conformational change kinetics are faster than those of other integrins like α5β1 and that α4β7's pathways for conformational change may also differ. We propose that activation of the actin cytoskeleton in the transition from rolling to firm adhesion stabilizes α4β7 in a high-affinity state. Aim 1 will determine high-resolution structures of unliganded α4β7 and its complexes with MAdCAM or medically relevant antibodies using cryo- EM. These structures will reveal how these integrins recognize their ligands, the conformational changes due to ligand binding, and potential structural specializations that enable α4β7 to mediate rolling adhesion. The binding epitopes and conformational specificities of activating antibodies to the β7 subunit will also be defined. The structure of α4β7 bound to vedolizumab will resolve the contention around how it blocks MAdCAM binding. Aim 2 will quantitatively define the mechanisms by which α4β7 mediates both rolling and firm adhesion to improve therapies for inflammatory bowel diseases. Ligand affinity and binding kinetics of α4β7 stabilized in different conformations will be measured as well as single-molecule conformational change rates when bound and unbound to ligand. The effect of mutations that stabilize rolling or firm adhesion will be used to identify parameters important for each adhesion type. The tensile force and bond lifetimes during rolling and firm adhesion will be quantified at the single-molecule level. Together, our studies will enhance our structural, biochemical, and mechanical understanding of α4β7-mediated rolling and firm adhesion and will provide structural and functional information that can be utilized in the development of more effective therapies for inflammatory bowel diseases and multiple myeloma.

Structural and functional characterization of autoimmune antibodies against NMDAR

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

Molecular Mechanism of Immunoglobulin Class Switch Recombination

Antibodies produced by B cells are a critical component of the adaptive immune system in mammals that can respond to and clear a plethora of different pathogens. A key property of B cells is their ability to alter the coding sequence of the immunoglobulin heavy and light chain genes, via VDJ-recombination, somatic hypermutation (SHM) and class switch recombination (CSR). While VDJ-recombination and SHM alter the variable regions of antibodies that directly contact pathogen antigens, CSR changes the constant region of the antibody, which dictates its effector function to optimally respond to the antigen recognized by the antibody. CSR occurs via targeted DNA double strand break (DSB) induction in the switch regions preceding the distinct constant region coding sequences. DSB induction requires active transcription of the switch regions and is initiated by activation-induced cytidine deaminase (AID) induced cytosine deamination (converting cytosine to uracil) within the switch regions. Fusion of the DSBs in the switch regions results in deletion of intervening genomic sequence, completing CSR. Since AID is inherently a mutagenic enzyme that can trigger both point mutations and genomic translocations, its activity has to be tightly controlled, and aberrant AID activity has been directly implicated in the genetic changes that lead to B cell lymphoma formation. Thus, define the molecular mechanism of CSR is critical to understand our adaptive immune system and B cell cancer development, both highly relevant to human health. To study CSR in living B cells, cellular models have been developed to analyze AID function and switch region transcription at the single molecule level. With this new methodology, the critical unanswered question of how AID is specifically recruited to the immunoglobulin heavy chain locus and not other genomic locations will be addressed. In addition, the overall kinetics of CSR will be determined and how transcription controls specific DSB induction in switch regions will be defined. The results of these works will significantly advance our understanding of CSR and provide new insights on how AID contributes to B cell lymphoma formation.

Mechanisms and consequences of cerebrovascular dysfunction in preeclampsia

PROJECT SUMMARY/ABSTRACT Preeclampsia (PE) is a common hypertensive disorder of pregnancy that causes significant maternal and fetal morbidity and mortality worldwide. PE women are at a high risk of stroke, including intracerebral hemorrhage, during the peripartum period, suggesting the sequelae of PE adversely impacts the cerebral circulation to promote hemorrhage. In addition, women with severe early-onset PE are at an 85-fold increased risk of death from intracerebral hemorrhage, importantly suggesting severity of disease promotes greater vulnerability of the cerebral circulation to degradation and rupture. However, the consequences of PE extend far beyond pregnancy and are associated with excessive cardiovascular and cerebrovascular disease risk later in life. Women with previous pregnancy complicated by PE can develop cognitive impairment as early as in their 30’s and 40’s, suggesting PE predisposes the brain to early-onset cognitive impairment. Studies have shown that formerly PE women have changes in gray matter volume and increased white matter lesion burden that occurs as a function of time from pregnancy, suggesting that PE continues to progressively damage the brain long after the affected pregnancy. Thus, our overall goal is to elucidate mechanisms by which women with PE are at risk of intracerebral hemorrhage in pregnancy and cognitive decline later in life. Our preliminary studies found greater vascular degradation, hematoma and cerebral edema in a model of severe PE that was associated with vascular inflammation and microglia activation (neuroinflammation). In addition, we found endothelial dysfunction and diminished neurovascular coupling in PE rats that persisted 5 months postpartum. Impaired neurovascular coupling is well-recognized as an underlying contributor to cognitive decline. These effects in postpartum animals with previous exposure to PE were associated with memory impairment that was not present in the pregnant state, suggesting neurovascular dysfunction precedes cognitive decline. Our central hypothesis is that the sequela of PE accelerates hypertension-induced cerebrovascular dysfunction that predisposes to intracerebral hemorrhage during pregnancy and its persistence postpartum results in early-onset cognitive decline. We will therefore elucidate mechanisms by which PE accelerates vascular degradation and worsens outcome from hemorrhagic stroke, probing pathways involved in oxidative degradative processes using multi-omics and multivariate analysis (Aim 1). We will also determine underlying molecular mechanisms that cause persistent cerebral microvascular dysfunction and cognitive decline postpartum, including oxidative stress-induced BBB leakage and persistent neuroinflammation that drives potassium channel dysfunction, reduced neurovascular coupling and neurovascular uncoupling (Aim 2). We will also use machine learning approaches together with multi-omics and outcome measures to identify factors and cellular pathways that are most impactful for prediction of intracerebral hemorrhage and cognitive impairment. The ability to predict and prevent devasting neurovascular disorders associated with PE has the potential to have long-lasting impacts on the lives of women with PE.

Improving Disease-Modifying Therapy Uptake among Patients with Multiple Sclerosis

Project Summary/Abstract Recent advances in the epidemiology of multiple sclerosis (MS) indicate that its prevalence is similar among White (238 per 100,000) and Black (226 per 100,000) populations. These data challenge historic assumptions about individuals with northern European heritage having higher risk and prevalence of MS. Evidence also suggests that MS incidence may be higher than previously recognized in the United States and increasing over time with more individuals identified and diagnosed year over year. MS continues to impose significant and growing burden on patients, healthcare systems and society. These health differences in the diagnosis, treatment and symptom management of MS in light of the increasing prevalence of MS in the US are an important public health issue that requires broader urgent research and policy attention to reduce the overall disease burden. In this study, we will use real-world data derived from the electronic health records (EHR) from four large academic medical centers (University of Kentucky, University of Virginia, Virginia Commonwealth University, and University of Southern California). Extracted EHR data from these four medical centers will be deidentified, combined, and harmonized. We will use this combined data set to examine (1) whether there are any differences in the timely treatment of disease modifying therapy (DMT) among different MS populations, (2) any disparities in the management of symptoms and comorbidities, (3) how non-medical factors of health such as income, education, and health insurance status (patientlevel), linguistically appropriate care provision (provider-level), and neighborhood factors (system-level) affect these outcomes and influence disparities across populations, and (4) assess whether disparities exist in the risks of cardiovascular disease CVD and mortality in MS subgroups and examine if these disparities can be reduced with improved treatment of MS and vascular comorbidities. In pursuing these objectives, we will identify clinical solutions (e.g., optimal DMT sequences) and non-medical factors such as neighborhood factors such as poverty, educational achievement, crime rates, civic participation, and housing quality, access to care factors, and cultural and linguistic match between providers and patients that substantially contribute to health disparities. For actionable solutions, we will rank-order these factors by their relative importance in addressing disparities, which will guide decision-making at the policy, system, and provider level. Our long-term objective is to develop public health strategies and scalable solutions to reduce overall burden in the management of MS. This project is expected to help policy makers and health system administrators in prioritizing interventions and to have implications for clinical practice in improving care of all patients with MS in neurology clinics, at the healthcare system level, and for national health policy.

Specific Affinity Requirements for Antibody Somatic Hypermutation

PROJECT SUMMARY Antibodies diversify through two distinct pathways. The first involves the combinatorial assembly of immunoglobulin (Ig) heavy and light chain variable region (V) exons, forming the antigen recognition domains of the B cell receptor (BCR), which is initially expressed as IgM on immature B cells. The second diversification pathway is somatic hypermutation (SHM) of V exons in germinal centers (GCs). In this setting, B cells that acquire mutations enhancing affinity for antigen receive limited cognate T cell help and are selected for clonal expansion, leading to affinity maturation. These primary and secondary diversification systems work together to generate protective antibody responses. The primary, or pre-immune, repertoire provides the foundation for initial antigen recognition. SHM and affinity maturation refine these baseline specificities. While it is well established that SHM improves affinities already present in the primary repertoire, this project explores the hypothesis that SHM can also generate new specificities in B cells that initially lack measurable antigen recognition. This process, termed affinity birth, may enable access to otherwise excluded V gene segments and expand the landscape of antibody evolution. This hypothesis will be tested through two specific aims: (i) To elucidate the extent of SHM-mediated Ig diversification in non-specific or bystander B cells. And, (ii) to define parameters that influence SHM-mediated antibody affinity birth. The significance of this work lies in its potential to reveal previously unappreciated flexibility in the antibody diversification process and to uncover modifiable factors that influence the emergence of new specificities. The proposed studies are innovative in suggesting that B cells possess intrinsic capacity to undergo SHM and selection regardless of their initial antigen specificity. This research may advance understanding of how germinal centers support antibody evolution and inform strategies to design vaccines that anticipate emerging pathogens.

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

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

Transposable element silencing as a regulator of salivary gland immune homeostasis

PROJECT SUMMARY/ABSTRACT Sjogren’s syndrome (SjS) is a chronic autoimmune disorder marked by salivary and lacrimal gland dysfunction, lymphocytic infiltration, and progressive secretory decline. While traditionally viewed as immune cell–driven, emerging evidence suggests that epithelial cells may initiate local inflammation. However, the molecular triggers originating from epithelial cells remain poorly defined. Transposable elements (TEs), including endogenous retroviruses (ERVs) and LINEs, are normally repressed through DNA methylation, histone modifications, and heterochromatin organization. Failure of TE silencing mechanisms due to aging, hormonal changes, or stress results in cytoplasmic dsRNA accumulation, nucleic acid sensor activation, and type I interferon signaling. These TE-derived nucleic acids are increasingly recognized as endogenous triggers of immunological stress that disrupt cellular homeostasis. Our preliminary data show widespread TE derepression and upregulation of interferon-stimulated genes in salivary glands from patients with SjS. To mimic this phenomenon, we will inducibly delete Setdb1, a key histone H3K9 methyltransferase, in defined epithelial compartments of the salivary gland. This will allow us to model compartment-specific TE derepression and assess its impact on both innate immune activation and adaptive immune responses. We will also test how aging and estrogen deficiency disrupt TE repression in basal/ductal versus acinar cells using lineage tracing and epigenomic profiling. Finally, we will evaluate the therapeutic potential of reverse transcriptase inhibitors and chromatin-modifying drugs in attenuating TE-driven inflammation. This exploratory study will uncover how failure of TE silencing contributes to epithelial-driven autoimmunity in SjS and will provide a foundation for future targeted epigenetic manipulations in human tissues and patients.

A Novel Mitochondrial-Targeted Inhibitor of NLRP3 Inflammasome Activation

PROJECT ABSTRACT Inflammasomes are multiprotein complexes of the innate immune system that assemble upon detecting specific molecular patterns associated with pathogens and cellular damage. Once assembled, activated inflammasomes trigger a cascade of downstream events that culminate in cell death and inflammation. Aberrant activation of the NLRP3 inflammasome contributes to the pathogenesis of numerous inflammatory and degenerative diseases, including gout, atherosclerosis, type 2 diabetes, and Alzheimer’s disease. Despite its central role in innate immunity and inflammation, there are no FDA-approved therapies that directly target the NLRP3 inflammasome. Current strategies rely on biologics that inhibit downstream pro-inflammatory cytokines produced from inflammasome activation, such as interleukin-1β (IL-1β), but do not block upstream inflammasome assembly or pyroptotic cell death, highlighting a critical unmet need for selective small-molecule inhibitors with novel mechanisms of action. To address this gap, we identified a covalent small molecule, Compound-2 (C-2), that robustly inhibits NLRP3 inflammasome activation in murine and human immune cells. C-2 suppresses multiple downstream events triggered by inflammasome activation, including IL-1β secretion and pyroptosis, with no apparent toxicity. Chemoproteomic profiling revealed that C-2 interacts with SLC25A3, a mitochondrial phosphate and copper transporter, suggesting a previously unrecognized regulatory node in inflammasome signaling. This R21 project aims to (1) elucidate the mechanism by which C-2 suppresses NLRP3 activation and (2) define the molecular interaction between C-2 and SLC25A3 and its functional consequences. Our studies will integrate biochemical, cellular, and in vivo approaches to uncover a novel mitochondrial mechanism of inflammasome regulation and validate C-2 as a first-in-class inflammasome inhibitor. Successful completion of this project will lay the foundation for future therapeutic development targeting mitochondrial- inflammasome crosstalk in inflammatory disease.

Investigating the role of noncoding RNAs in malaria parasites through targeted Cas13-mediated degradation

Project Summary/Abstract One of the most significant sources of morbidity and mortality throughout large regions of the developing world continues to be malaria caused by infection with mosquito-borne parasites of the genus Plasmodium. The parasite species responsible for the most severe form of the disease is P. falciparum. To avoid antibodies produced by their host and thereby maintain lengthy infections, these parasites undergo a process called antigenic variation by which they can extend an infection for over a year. This results from changes in expression of a protein called PfEMP1, the primary antigenic and virulence determinant expressed on the surface of infected red blood cells. A large, multicopy gene family called var encodes different forms of PfEMP1, and switching expression between var genes enables parasites to evade antibody recognition and destruction by the immune system. The process requires precise and coordinated regulation of transcription of each var gene, however how this is accomplished is unknown. It was recently hypothesized that a family of noncoding RNAs (ncRNAs) plays a key role in controlling the expression of each var gene and in determining the likelihood of activation of any given gene. If correct, this would represent a significant advance in our understanding of how P. falciparum controls antigenic variation and avoids immune clearance. To test this hypothesis, we propose to adapt the CRISPR/Cas13 system of targeted RNA degradation for use in P. falciparum. Similar to the extensively used CRISPR/Cas9 system, CRISPR/Cas13 employes guide RNAs to target a nuclease to a sequence-specific target, however Cas13 targets single stranded RNA rather than DNA. By applying this system to the study of var-related ncRNAs, we will degrade specific ncRNAs and determine the effect on var gene expression. Two classes of ncRNAs previously proposed to regulate var gene expression will be targeted, one called ruf6 and a second encoded by the second exon of all var genes. This will enable us to alter ncRNA expression while leaving the underlying genomic DNA untouched, thereby allowing the unambiguous attribution of any resulting phenotypes to the ncRNAs. Aim 1 will optimize the Cas13 system for P. falciparum by testing different variants of the Cas13 endonuclease for their ability to degrade mRNAs encoding fluorescent reporter proteins. We will determine both the efficiency and sequence specificity of the system. Aim 2 will apply the system to var-associated ncRNAs and quantitatively measure changes in var gene expression and transcriptional switching. If successful, the adaptation of the Cas13 system to P. falciparum will provide the malaria research community with a powerful new tool for manipulating gene expression. In addition, we will gain valuable new insights into how malaria parasites regulate var gene expression, antigenic variation and immune evasion.

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.

Optimizing gamma-delta T cell receptor-mediated signaling to improve cancer immunotherapy

PROJECT SUMMARY The recent development of T cell-based cancer immunotherapies, including checkpoint blockade (anti-PD-1, anti-CTLA-4 and others) or adoptive cell therapy (ACT) using modified patient T cells, has led to improved patient outcomes for a variety of cancers. However, durable responses are observed in only a fraction of patients. Further progress can be made by studying and targeting different T cell subpopulations, such as the gd T cells which are known to possess antitumor activities. Further, gd T cells are mostly independent of MHC-restriction, unconstrained by neoantigen burden, preferential homing to peripheral tissues and possess unique properties of T cells as well as natural killer cells making them an extremely attractive cancer immunotherapy target. One way of gd T cell activation involves the gd T cell receptor (gdTCR)-CD3 signaling pathway. gd T cell recognition of antigen by the gdTCR and the resulting proximal signaling through surrounding CD3 subunits are key steps of gd T cell activation. Even though the individual components of the gdTCR-CD3 and abTCR-CD3 complexes remain the same except for the TCRs, the complete gdTCR-CD3 complex extracellular structure is unknown. Identification of the specific extracellular interactions between the gdTCR and CD3 subunits could offer precise guidance for the development of immunotherapeutic strategies that modulate gdT cell immunity by targeting signaling through the gdTCR-CD3 complex. Our previous data showed that mutating residues in the constant domain of the abTCR resulted in altered ab T cell cytokine responses. Based on this data, our hypothesis is that gdTCR-CD3 signaling can also be modulated by targeting specific regions of the gdTCR by mutagenesis to improve gd T cell antitumor activities. To test our hypothesis, in Aim 1, we will use a novel photo-crosslinking and computational docking methodology to solve the complete extracellular structure of a gdTCR-CD3 complex. Further, we will use an in silico structure-based TCR design approach to identify gdTCR mutants that enhance signaling. In Aim 2, we will use an in vitro retroviral TCR display method using degenerate primers to create gdTCR mutant libraries at specific gdTCR sites such as Cg helix 3 and connecting peptide (CP) regions. In both instances, identified mutants will be tested for improved functionalities in an MHC-independent gd TCR (G115 Vg9Vd2 TCR) using in vitro cytokine and tumor-killing assays. Overall, the newly identified enhanced gd T cell clones could potentially lead to a new wave of effective cancer immunotherapy strategy by leaning into the largely untapped potential of gd T cells.

Autoreactive T cells in lupus

The autoimmune disease systemic lupus erythematosus (SLE) is characterized by loss of adaptive immune tolerance in conjunction with innate immune system hyperactivity. Autoantibodies, produced by plasma cells derived from activated B cells, form proinflammatory immune complexes. These immune complexes drive feed forward loops that sustain a systemic inflammatory environment and deposit in tissues leading to potentially fatal organ damage. B cells receive help from T cells to produce antibodies. They also contribute to disease by shaping T cell responses and secreting cytokines. Recent case reports in which SLE patients were treated with anti-CD19 CAR-T cell therapy to deplete B cells highlight the pathogenic role of B cells in lupus and their value as a therapeutic target. However, a better understanding of how autoreactive B cells interact with autoreactive T cells may reveal more targeted points of therapeutic intervention that specifically block autoreactive responses while sparing protective ones. Antigen specific interactions between CD4+ T cells and B cells are required for the development of autoimmune disease in lupus. However, whether these critical interactions occur in germinal centers, where competition for CD4+ T cell help selects high affinity B cells, or in extrafollicular responses, where B cells may avoid peripheral tolerance checkpoints, is unclear. Gene expression profiles and pathways specific to autoreactive CD4+ T cells, and how they are shaped by their interaction with autoreactive B cells, are also ill defined. CD8+ T cells, which recognize antigen presented on MHC Class I, have also been suggested to modulate the fate of autoreactive B cells. They can directly kill autoreactive B cells as a means of tolerance, and a subset of CD8+ T cells has recently been shown to have B cell helper function. Whether and how such interactions between B and CD8+ T cells enhance or suppress the development of lupus is unknown. Here, we will use genetic and in vivo proximity labeling approaches to address these knowledge gaps. In Aim 1, we will test the hypothesis that antigen specific interactions between B and CD8+ T cells promote B cell activation and autoantibody production in lupus. We will prevent B cells, but not other cells, from undergoing cognate interactions with CD8+ T cells via B cell-specific deletion of B2M, a component of the MHC Class I complex, in two lupus models. In Aim 2, will use the uLIPSTIC in vivo proximity system to label all T cells interacting with B cells in lupus models compared to wild type controls. Features specific to these autoreactive T cells will be defined by flow cytometry, scRNA Seq, and scTCR-Seq. These studies will provide valuable molecular and cellular insight into the mutual activation of B and T cells in lupus. They will set the stage for future mechanistic studies defining the role of autoreactive T cell specific genes and pathways and potentially highlight new therapeutic targets specific to autoreactive B/T interactions.

Structure-Based Development of Nucleotide-Competing Inhibitors Against HIV-1 and LINE-1 Reverse Transcriptases

PROJECT SUMMARY Reverse transcriptases (RTs) from retroviruses and endogenous retroelements are essential polymerases that catalyze RNA- and DNA-dependent DNA synthesis. Nucleoside inhibitors (NIs) remain central to HIV-1 therapy and are also used against other viral infections and in cancer, but toxicity, limited selectivity, pharmacokinetic (PK) liabilities, and the emergence of drug resistance highlight the need for alternative RT inhibitor mechanisms. In contrast to NIs, nucleotide-competing inhibitors (NCIs) block the polymerase active site without requiring incorporation into nucleic acids. Structural studies by PI Ruiz have defined the NCI mechanism of action for HIV- 1 RT and revealed conserved binding modules shared across multiple polymerase families. These advances now enable rational discovery of improved NCIs. LINE-1 (L1) ORF2 RT is an emerging therapeutic target in cancer, autoimmunity, and aging, yet NIs are the only inhibitors known to act against L1 RT. Notably, the NCI-binding region is structurally similar between HIV-1 RT and L1 RT, suggesting that NCI recognition principles may extend across these two biologically distinct polymerases. This R21 seeks to establish proof-of-concept for NCI development against both enzymes. Aim 1 will discover and structurally optimize NCIs targeting HIV-1 RT by combining binding modules from known NCI chemotypes and determining their biochemical activity and co-crystal structures. Aim 2 will determine whether HIV-1 RT NCI principles translate to L1 RT by solving L1 RT/nucleic acid/NCI structures, evaluating enzymatic inhibition, and applying AI-based structure prediction and generative design to propose L1-specific NCI candidates. Cellular retrotransposition assays will test mechanism of action. Aim 3 will develop a fragment library tailored to protein–nucleic acid interfaces and perform fragment screening of HIV-1 and L1 RT/nucleic acid complexes to identify additional chemotypes that engage the NCI binding region. Successful completion will yield NCI scaffolds and mechanistic insights applicable to HIV-1 RT and L1 RT, define structural principles governing NCI recognition across two evolutionarily related polymerases, and establish new avenues for RT inhibitor development. The PI is highly qualified to lead this work, with extensive expertise in RT structural biology, drug design, and fragment-based discovery.

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.

A PROTAC Strategy to Combat Botulinum Neurotoxicity

PROJECT SUMMARY/ABSTRACT Botulinum neurotoxin (BoNT), the causative agent of botulism, is the most potent toxin known to humans. While BoNTs are widely recognized for their therapeutic and cosmetic applications, such as Botox™, their increasing use has raised concerns about iatrogenic botulism. Due to their extreme lethality, ease of production, and history of weaponization, the Centers for Disease Control and Prevention (CDC) classifies BoNTs as a Category A bioterrorism threat. Among the seven major serotypes (A-G), BoNT/A, BoNT/B, and BoNT/E account for over 95% of human botulism cases with A being the most prevalent. Despite the severity of botulism, no approved therapeutic exists to rescue intoxicated neurons. The current treatment, a heptavalent antitoxin, can only slow disease progression and requires early administration and prolonged hospitalization due to the inability of antibodies to penetrate infected cells. In the field of small- molecule inhibitors (SMIs), promising scaffolds targeting BoNT/A have been discovered, offering opportunities for further derivatization to incorporate bifunctional approaches. Developing a clinically viable therapeutic requires inhibiting the zinc (Zn2+) metalloprotease light chain (LC) as well as addressing toxin persistence. Through extensive inhibitor screening, we have identified two classes of small molecules that inhibit BoNT/A with submicromolar affinity and demonstrate efficacy in both cellular and animal models. However, the transient nature of these inhibitors necessitates the need of a sustained clearance approach. To achieve this, we propose integrating our previously identified BoNT/A LC SMIs with a targeted protein degradation (TPD) technology for toxin elimination. Based upon the background outlined, vide supra, our research strategy for the ablation of BoNT/A will be focused upon the following three specific objectives: 1) Structural Optimization – Utilize molecular docking, and structure-activity relationship (SAR) analysis to modify inhibitors for TPD ligand attachment. 2) Degrader Design – Development of ubiquitin-protease system (UPS)-based proteolysis-targeting chimeras (PROTACs) and autophagy-targeting chimeras to enhance degradation efficiency. 3) Cellular Evaluation – Assess enzyme inhibition, toxin clearance, degradation kinetics in cells.

Directing the Evolution of Common Human Precursors into HIV-1 Broadly Neutralizing Antibodies

Project Summary An effective HIV vaccine will likely elicit broadly neutralizing antibodies (bnAbs). Doing so, however, remains a major challenge because bnAbs usually require multiple rare and unusual changes that emerge after years of active infection. It is not clear that a practical number of immunizations can consistently recapitulate this process. Although investigators have successfully expanded defined precursors of known bnAbs, they have not moved these diversified precursors to a specific target in humans. Importantly here, the severity of this problem increases rapidly with the number changes needed. The problem further deepens if the required changes are in slow-to-mutate antibody framework regions or require specific indels. The need to move from precursor to bnAb in the fewest steps motivates our focus on the V2 apex epitope of the HIV-1 envelope glycoprotein (Env). Apex bnAbs are qualitatively different from other bnAb classes. They require far fewer mutations, located in their rapidly evolving heavy-chain CDR3 (HCDR3) regions. These HCDR3s are unusually important to their ability to neutralize virus. For example, we have shown that a diverse repertoire of mouse B cell receptors can be modified with apex bnAb HCDR3s, and the resulting mouse B cells generated potent neutralizing sera. Thus, apex precursors can largely be defined by their HCDR3s alone and are far more common than other defined bnAb precursors. Interestingly, these HCDR3 are very similar to those of another class of antibodies that recognize the CD4-induced co-receptor-binding site (CoRBS). Both antibody classes have unusually long HCDR3s with sulfated tyrosines at their tips. Unlike apex bnAbs, these non-neutralizing CoRBS antibodies are readily elicited through vaccination. We have recently shown that apex precursors also bind the CoRBS, suggesting that some apex bnAbs emerge from CoRBS antibodies. Thus, the first step of sequential vaccine strategies, expanding and diversifying a defined precursor pool, is straightforward. Here we divide the remaining goals into two: moving from a precursor that does not bind Env to one that does so and then broaden it to recognize the majority of circulating isolates. We have already made significant progress in the first step: we have shown in our original mouse vaccine model that we can generate potent apex- specific neutralizing antisera. However the breadth of this sera remains limited. Building on these studies, we will pursue three goals: (1) Define the essential mutations that transform a CoRBS antibody into one that binds the Env apex and then generate antigens that select for these mutations. (2) Define mutations and generate antigens that expand the breadth of these antibodies, transforming them to bnAbs, and (3) Evaluate these antigens in a novel system that models key features of the human apex response in mice, and iteratively refine this process using antibodies and HCDR3s drawn from a wide panel of HIV-naïve persons. In short, these studies develop original concepts and tools that can accelerate development of an HIV-1 vaccine and deepen our understanding of the antibody response to vaccines and pathogens.

Single-neuron correlates of perception and memory in the human medial temporal lobe

The human medial temporal lobe contains neurons that respond selectively to the semantic contents of a presented stimulus. These "concept cells" may respond to very different pictures of a given person and even to their written or spoken name. Their response latency is far longer than necessary for object recognition, they follow subjective, conscious perception, and they are found in brain regions that are crucial for declarative memory formation. It has thus been hypothesized that they may represent the semantic "building blocks" of episodic memories. In this talk I will present data from single unit recordings in the hippocampus, entorhinal cortex, parahippocampal cortex, and amygdala during paradigms involving object recognition and conscious perception as well as encoding of episodic memories in order to characterize the role of concept cells in these cognitive functions.

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.

LLMs and Human Language Processing

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

Principles of Cognitive Control over Task Focus and Task

2024 BACN Mid-Career Prize Lecture Adaptive behavior requires the ability to focus on a current task and protect it from distraction (cognitive stability), and to rapidly switch tasks when circumstances change (cognitive flexibility). How people control task focus and switch-readiness has therefore been the target of burgeoning research literatures. Here, I review and integrate these literatures to derive a cognitive architecture and functional rules underlying the regulation of stability and flexibility. I propose that task focus and switch-readiness are supported by independent mechanisms whose strategic regulation is nevertheless governed by shared principles: both stability and flexibility are matched to anticipated challenges via an incremental, online learner that nudges control up or down based on the recent history of task demands (a recency heuristic), as well as via episodic reinstatement when the current context matches a past experience (a recognition heuristic).

The immunopathogenesis of autoimmune seizure disorders

Immune-mediated mechanisms are increasingly recognised as a cause of epilepsy even in the absence of an immune response against a specifical neuronal antigen. In some cases, these autoimmune processes are clearly pathogenic, for example acute seizures in autoimmune encephalitis, whereas in others this is less clear, for example autoimmune-associated epilepsy. Recent research has provided novel insights into the clinical, paraclinical and immunopathogenetic mechanisms in these conditions. I will provide an overview of clinical and paraclinical features of immune-associated seizures. Furthermore, I will describe specific immunopathogenic examples implicating lymphoid follicular autoimmunisation and intrathecal B cells in these conditions. These insights into immunopathogenesis may help to explain the role of current and immunotherapies in these conditions.

Of glia and macrophages, signaling hubs in development and homeostasis

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

Deepfake Detection in Super-Recognizers and Police Officers

Using videos from the Deepfake Detection Challenge (cf. Groh et al., 2021), we investigated human deepfake detection performance (DDP) in two unique observer groups: Super-Recognizers (SRs) and "normal" officers from within the 18K members of the Berlin Police. SRs were identified either via previously proposed lab-based procedures (Ramon, 2021) or the only existing tool for SR identification involving increasingly challenging, authentic forensic material: beSure® (Berlin Test For Super-Recognizer Identification; Ramon & Rjosk, 2022). Across two experiments we examined deepfake detection performance (DDP) in participants who judged single videos and pairs of videos in a 2AFC decision setting. We explored speed-accuracy trade-offs in DDP, compared DDP between lab-identified SRs and non-SRs, and police officers whose face identity processing skills had been extensively tested using challenging. In this talk I will discuss our surprising findings and argue that further work is needed too determine whether face identity processing is related to DDP or not.

Recognizing Faces: Insights from Group and Individual Differences

Decoding mental conflict between reward and curiosity in decision-making

Humans and animals are not always rational. They not only rationally exploit rewards but also explore an environment owing to their curiosity. However, the mechanism of such curiosity-driven irrational behavior is largely unknown. Here, we developed a decision-making model for a two-choice task based on the free energy principle, which is a theory integrating recognition and action selection. The model describes irrational behaviors depending on the curiosity level. We also proposed a machine learning method to decode temporal curiosity from behavioral data. By applying it to rat behavioral data, we found that the rat had negative curiosity, reflecting conservative selection sticking to more certain options and that the level of curiosity was upregulated by the expected future information obtained from an uncertain environment. Our decoding approach can be a fundamental tool for identifying the neural basis for reward–curiosity conflicts. Furthermore, it could be effective in diagnosing mental disorders.

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

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

Microbial modulation of zebrafish behavior and brain development

There is growing recognition that host-associated microbiotas modulate intrinsic neurodevelopmental programs including those underlying human social behavior. Despite this awareness, the fundamental processes are generally not understood. We discovered that the zebrafish microbiota is necessary for normal social behavior. By examining neuronal correlates of behavior, we found that the microbiota restrains neurite complexity and targeting of key forebrain neurons within the social behavior circuitry. The microbiota is also necessary for both localization and molecular functions of forebrain microglia, brain-resident phagocytes that remodel neuronal arbors. In particular, the microbiota promotes expression of complement signaling pathway components important for synapse remodeling. Our work provides evidence that the microbiota modulates zebrafish social behavior by stimulating microglial remodeling of forebrain circuits during early neurodevelopment and suggests molecular pathways for therapeutic interventions during atypical neurodevelopment.

From cells to systems: multiscale studies of the epileptic brain

It is increasingly recognized that epilepsy affects human brain organization across multiple scales, ranging from cellular alterations in specific regions towards macroscale network imbalances. My talk will overview an emerging paradigm that integrates cellular, neuroimaging, and network modelling approaches to faithful characterize the extent of structural and functional alterations in the common epilepsies. I will also discuss how multiscale framework can help to derive clinically useful biomarkers of dysfunction, and how these methods may guide surgical planning and prognostics.

Off the rails - how pathological patterns of whole brain activity emerge in epileptic seizures

In most brains across the animal kingdom, brain dynamics can enter pathological states that are recognisable as epileptic seizures. Yet usually, brain operate within certain constraints given through neuronal function and synaptic coupling, that will prevent epileptic seizure dynamics from emerging. In this talk, I will bring together different approaches to identifying how networks in the broadest sense shape brain dynamics. Using illustrative examples from intracranial EEG recordings, disorders characterised by molecular disruption of a single neurotransmitter receptor type, to single-cell recordings of whole-brain activity in the larval zebrafish, I will address three key questions - (1) how does the regionally specific composition of synaptic receptors shape ongoing physiological brain activity; (2) how can disruption of this regionally specific balance result in abnormal brain dynamics; and (3) which cellular patterns underly the transition into an epileptic seizure.

Learning to see stuff

Humans are very good at visually recognizing materials and inferring their properties. Without touching surfaces, we can usually tell what they would feel like, and we enjoy vivid visual intuitions about how they typically behave. This is impressive because the retinal image that the visual system receives as input is the result of complex interactions between many physical processes. Somehow the brain has to disentangle these different factors. I will present some recent work in which we show that an unsupervised neural network trained on images of surfaces spontaneously learns to disentangle reflectance, lighting and shape. However, the disentanglement is not perfect, and we find that as a result the network not only predicts the broad successes of human gloss perception, but also the specific pattern of errors that humans exhibit on an image-by-image basis. I will argue this has important implications for thinking about appearance and vision more broadly.

Analyzing artificial neural networks to understand the brain

In the first part of this talk I will present work showing that recurrent neural networks can replicate broad behavioral patterns associated with dynamic visual object recognition in humans. An analysis of these networks shows that different types of recurrence use different strategies to solve the object recognition problem. The similarities between artificial neural networks and the brain presents another opportunity, beyond using them just as models of biological processing. In the second part of this talk, I will discuss—and solicit feedback on—a proposed research plan for testing a wide range of analysis tools frequently applied to neural data on artificial neural networks. I will present the motivation for this approach as well as the form the results could take and how this would benefit neuroscience.

Representations of people in the brain

Faces and voices convey much of the non-verbal information that we use when communicating with other people. We look at faces and listen to voices to recognize others, understand how they are feeling, and decide how to act. Recent research in my lab aims to investigate whether there are similar coding mechanisms to represent faces and voices, and whether there are brain regions that integrate information across the visual and auditory modalities. In the first part of my talk, I will focus on an fMRI study in which we found that a region of the posterior STS exhibits modality-general representations of familiar people that can be similarly driven by someone’s face and their voice (Tsantani et al. 2019). In the second part of the talk, I will describe our recent attempts to shed light on the type of information that is represented in different face-responsive brain regions (Tsantani et al., 2021).

It’s All About Motion: Functional organization of the multisensory motion system at 7T

The human middle temporal complex (hMT+) has a crucial biological relevance for the processing and detection of direction and speed of motion in visual stimuli. In both humans and monkeys, it has been extensively investigated in terms of its retinotopic properties and selectivity for direction of moving stimuli; however, only in recent years there has been an increasing interest in how neurons in MT encode the speed of motion. In this talk, I will explore the proposed mechanism of speed encoding questioning whether hMT+ neuronal populations encode the stimulus speed directly, or whether they separate motion into its spatial and temporal components. I will characterize how neuronal populations in hMT+ encode the speed of moving visual stimuli using electrocorticography ECoG and 7T fMRI. I will illustrate that the neuronal populations measured in hMT+ are not directly tuned to stimulus speed, but instead encode speed through separate and independent spatial and temporal frequency tuning. Finally, I will suggest that this mechanism may play a role in evaluating multisensory responses for visual, tactile and auditory stimuli in hMT+.

Training Dynamic Spiking Neural Network via Forward Propagation Through Time

With recent advances in learning algorithms, recurrent networks of spiking neurons are achieving performance competitive with standard recurrent neural networks. Still, these learning algorithms are limited to small networks of simple spiking neurons and modest-length temporal sequences, as they impose high memory requirements, have difficulty training complex neuron models, and are incompatible with online learning.Taking inspiration from the concept of Liquid Time-Constant (LTCs), we introduce a novel class of spiking neurons, the Liquid Time-Constant Spiking Neuron (LTC-SN), resulting in functionality similar to the gating operation in LSTMs. We integrate these neurons in SNNs that are trained with FPTT and demonstrate that thus trained LTC-SNNs outperform various SNNs trained with BPTT on long sequences while enabling online learning and drastically reducing memory complexity. We show this for several classical benchmarks that can easily be varied in sequence length, like the Add Task and the DVS-gesture benchmark. We also show how FPTT-trained LTC-SNNs can be applied to large convolutional SNNs, where we demonstrate novel state-of-the-art for online learning in SNNs on a number of standard benchmarks (S-MNIST, R-MNIST, DVS-GESTURE) and also show that large feedforward SNNs can be trained successfully in an online manner to near (Fashion-MNIST, DVS-CIFAR10) or exceeding (PS-MNIST, R-MNIST) state-of-the-art performance as obtained with offline BPTT. Finally, the training and memory efficiency of FPTT enables us to directly train SNNs in an end-to-end manner at network sizes and complexity that was previously infeasible: we demonstrate this by training in an end-to-end fashion the first deep and performant spiking neural network for object localization and recognition. Taken together, we out contribution enable for the first time training large-scale complex spiking neural network architectures online and on long temporal sequences.

Behavioral Timescale Synaptic Plasticity (BTSP) for biologically plausible credit assignment across multiple layers via top-down gating of dendritic plasticity

A central problem in biological learning is how information about the outcome of a decision or behavior can be used to reliably guide learning across distributed neural circuits while obeying biological constraints. This “credit assignment” problem is commonly solved in artificial neural networks through supervised gradient descent and the backpropagation algorithm. In contrast, biological learning is typically modelled using unsupervised Hebbian learning rules. While these rules only use local information to update synaptic weights, and are sometimes combined with weight constraints to reflect a diversity of excitatory (only positive weights) and inhibitory (only negative weights) cell types, they do not prescribe a clear mechanism for how to coordinate learning across multiple layers and propagate error information accurately across the network. In recent years, several groups have drawn inspiration from the known dendritic non-linearities of pyramidal neurons to propose new learning rules and network architectures that enable biologically plausible multi-layer learning by processing error information in segregated dendrites. Meanwhile, recent experimental results from the hippocampus have revealed a new form of plasticity—Behavioral Timescale Synaptic Plasticity (BTSP)—in which large dendritic depolarizations rapidly reshape synaptic weights and stimulus selectivity with as little as a single stimulus presentation (“one-shot learning”). Here we explore the implications of this new learning rule through a biologically plausible implementation in a rate neuron network. We demonstrate that regulation of dendritic spiking and BTSP by top-down feedback signals can effectively coordinate plasticity across multiple network layers in a simple pattern recognition task. By analyzing hidden feature representations and weight trajectories during learning, we show the differences between networks trained with standard backpropagation, Hebbian learning rules, and BTSP.

Beyond Biologically Plausible Spiking Networks for Neuromorphic Computing

Biologically plausible spiking neural networks (SNNs) are an emerging architecture for deep learning tasks due to their energy efficiency when implemented on neuromorphic hardware. However, many of the biological features are at best irrelevant and at worst counterproductive when evaluated in the context of task performance and suitability for neuromorphic hardware. In this talk, I will present an alternative paradigm to design deep learning architectures with good task performance in real-world benchmarks while maintaining all the advantages of SNNs. We do this by focusing on two main features – event-based computation and activity sparsity. Starting from the performant gated recurrent unit (GRU) deep learning architecture, we modify it to make it event-based and activity-sparse. The resulting event-based GRU (EGRU) is extremely efficient for both training and inference. At the same time, it achieves performance close to conventional deep learning architectures in challenging tasks such as language modelling, gesture recognition and sequential MNIST.

Shallow networks run deep: How peripheral preprocessing facilitates odor classification

Drosophila olfactory sensory hairs ("sensilla") typically house two olfactory receptor neurons (ORNs) which can laterally inhibit each other via electrical ("ephaptic") coupling. ORN pairing is highly stereotyped and genetically determined. Thus, olfactory signals arriving in the Antennal Lobe (AL) have been pre-processed by a fixed and shallow network at the periphery. To uncover the functional significance of this organization, we developed a nonlinear phenomenological model of asymmetrically coupled ORNs responding to odor mixture stimuli. We derived an analytical solution to the ORNs’ dynamics, which shows that the peripheral network can extract the valence of specific odor mixtures via transient amplification. Our model predicts that for efficient read-out of the amplified valence signal there must exist specific patterns of downstream connectivity that reflect the organization at the periphery. Analysis of AL→Lateral Horn (LH) fly connectomic data reveals evidence directly supporting this prediction. We further studied the effect of ephaptic coupling on olfactory processing in the AL→Mushroom Body (MB) pathway. We show that stereotyped ephaptic interactions between ORNs lead to a clustered odor representation of glomerular responses. Such clustering in the AL is an essential assumption of theoretical studies on odor recognition in the MB. Together our work shows that preprocessing of olfactory stimuli by a fixed and shallow network increases sensitivity to specific odor mixtures, and aids in the learning of novel olfactory stimuli. Work led by Palka Puri, in collaboration with Chih-Ying Su and Shiuan-Tze Wu.

Real-world scene perception and search from foveal to peripheral vision

A high-resolution central fovea is a prominent design feature of human vision. But how important is the fovea for information processing and gaze guidance in everyday visual-cognitive tasks? Following on from classic findings for sentence reading, I will present key results from a series of eye-tracking experiments in which observers had to search for a target object within static or dynamic images of real-world scenes. Gaze-contingent scotomas were used to selectively deny information processing in the fovea, parafovea, or periphery. Overall, the results suggest that foveal vision is less important and peripheral vision is more important for scene perception and search than previously thought. The importance of foveal vision was found to depend on the specific requirements of the task. Moreover, the data support a central-peripheral dichotomy in which peripheral vision selects and central vision recognizes.

General purpose event-based architectures for deep learning

Biologically plausible spiking neural networks (SNNs) are an emerging architecture for deep learning tasks due to their energy efficiency when implemented on neuromorphic hardware. However, many of the biological features are at best irrelevant and at worst counterproductive when evaluated in the context of task performance and suitability for neuromorphic hardware. In this talk, I will present an alternative paradigm to design deep learning architectures with good task performance in real-world benchmarks while maintaining all the advantages of SNNs. We do this by focusing on two main features -- event-based computation and activity sparsity. Starting from the performant gated recurrent unit (GRU) deep learning architecture, we modify it to make it event-based and activity-sparse. The resulting event-based GRU (EGRU) is extremely efficient for both training and inference. At the same time, it achieves performance close to conventional deep learning architectures in challenging tasks such as language modelling, gesture recognition and sequential MNIST

Neuroscience of socioeconomic status and poverty: Is it actionable?

SES neuroscience, using imaging and other methods, has revealed generalizations of interest for population neuroscience and the study of individual differences. But beyond its scientific interest, SES is a topic of societal importance. Does neuroscience offer any useful insights for promoting socioeconomic justice and reducing the harms of poverty? In this talk I will use research from my own lab and others’ to argue that SES neuroscience has the potential to contribute to policy in this area, although its application is premature at present. I will also attempt to forecast the ways in which practical solutions to the problems of poverty may emerge from SES neuroscience. Bio: Martha Farah has conducted groundbreaking research on face and object recognition, visual attention, mental imagery, and semantic memory and - in more recent times - has been at the forefront of interdisciplinary research into neuroscience and society. This deals with topics such as using fMRI for lie detection, ethics of cognitive enhancement, and effects of social deprivation on brain development.

New Insights into the Neural Machinery of Face Recognition

Don't forget the gametes: Neurodevelopmental pathogenesis starts in the sperm and egg

Proper development of the nervous system depends not only on the inherited DNA sequence, but also on proper regulation of gene expression, as controlled in part by epigenetic mechanisms present in the parental gametes. In this presentation an internationally recognized research advocate explains why researchers concerned about the origins of increasingly prevalent neurodevelopmental disorders such as autism and attention deficit hyperactivity disorder should look beyond genetics in probing the origins of dysregulated transcription of brain-related genes. The culprit for a subset of cases, she contends, may lie in the exposure history of the parents, and thus their germ cells. To illustrate how environmentally informed, nongenetic dysfunction may occur, she focuses on the example of parents' histories of exposure to common agents of modern inhalational anesthesia, a highly toxic exposure that in mammalian models has been seen to induce heritable neurodevelopmental abnormality in offspring born of exposed germline.

Feedforward and feedback processes in visual recognition

Progress in deep learning has spawned great successes in many engineering applications. As a prime example, convolutional neural networks, a type of feedforward neural networks, are now approaching – and sometimes even surpassing – human accuracy on a variety of visual recognition tasks. In this talk, however, I will show that these neural networks and their recent extensions exhibit a limited ability to solve seemingly simple visual reasoning problems involving incremental grouping, similarity, and spatial relation judgments. Our group has developed a recurrent network model of classical and extra-classical receptive field circuits that is constrained by the anatomy and physiology of the visual cortex. The model was shown to account for diverse visual illusions providing computational evidence for a novel canonical circuit that is shared across visual modalities. I will show that this computational neuroscience model can be turned into a modern end-to-end trainable deep recurrent network architecture that addresses some of the shortcomings exhibited by state-of-the-art feedforward networks for solving complex visual reasoning tasks. This suggests that neuroscience may contribute powerful new ideas and approaches to computer science and artificial intelligence.

Unchanging and changing: hardwired taste circuits and their top-down control

The taste system detects 5 major categories of ethologically relevant stimuli (sweet, bitter, umami, sour and salt) and accordingly elicits acceptance or avoidance responses. While these taste responses are innate, the taste system retains a remarkable flexibility in response to changing external and internal contexts. Taste chemicals are first recognized by dedicated taste receptor cells (TRCs) and then transmitted to the cortex via a multi-station relay. I reasoned that if I could identify taste neural substrates along this pathway, it would provide an entry to decipher how taste signals are encoded to drive innate response and modulated to facilitate adaptive response. Given the innate nature of taste responses, these neural substrates should be genetically identifiable. I therefore exploited single-cell RNA sequencing to isolate molecular markers defining taste qualities in the taste ganglion and the nucleus of the solitary tract (NST) in the brainstem, the two stations transmitting taste signals from TRCs to the brain. How taste information propagates from the ganglion to the brain is highly debated (i.e., does taste information travel in labeled-lines?). Leveraging these genetic handles, I demonstrated one-to-one correspondence between ganglion and NST neurons coding for the same taste. Importantly, inactivating one ‘line’ did not affect responses to any other taste stimuli. These results clearly showed that taste information is transmitted to the brain via labeled lines. But are these labeled lines aptly adapted to the internal state and external environment? I studied the modulation of taste signals by conflicting taste qualities in the concurrence of sweet and bitter to understand how adaptive taste responses emerge from hardwired taste circuits. Using functional imaging, anatomical tracing and circuit mapping, I found that bitter signals suppress sweet signals in the NST via top-down modulation by taste cortex and amygdala of NST taste signals. While the bitter cortical field provides direct feedback onto the NST to amplify incoming bitter signals, it exerts negative feedback via amygdala onto the incoming sweet signal in the NST. By manipulating this feedback circuit, I showed that this top-down control is functionally required for bitter evoked suppression of sweet taste. These results illustrate how the taste system uses dedicated feedback lines to finely regulate innate behavioral responses and may have implications for the context-dependent modulation of hardwired circuits in general.

The evolution and development of visual complexity: insights from stomatopod visual anatomy, physiology, behavior, and molecules

Bioluminescence, which is rare on land, is extremely common in the deep sea, being found in 80% of the animals living between 200 and 1000 m. These animals rely on bioluminescence for communication, feeding, and/or defense, so the generation and detection of light is essential to their survival. Our present knowledge of this phenomenon has been limited due to the difficulty in bringing up live deep-sea animals to the surface, and the lack of proper techniques needed to study this complex system. However, new genomic techniques are now available, and a team with extensive experience in deep-sea biology, vision, and genomics has been assembled to lead this project. This project is aimed to study three questions 1) What are the evolutionary patterns of different types of bioluminescence in deep-sea shrimp? 2) How are deep-sea organisms’ eyes adapted to detect bioluminescence? 3) Can bioluminescent organs (called photophores) detect light in addition to emitting light? Findings from this study will provide valuable insight into a complex system vital to communication, defense, camouflage, and species recognition. This study will bring monumental contributions to the fields of deep sea and evolutionary biology, and immediately improve our understanding of bioluminescence and light detection in the marine environment. In addition to scientific advancement, this project will reach K-college aged students through the development and dissemination of educational tools, a series of molecular and organismal-based workshops, museum exhibits, public seminars, and biodiversity initiatives.

Brain and behavioural impacts of early life adversity

Abuse, neglect, and other forms of uncontrollable stress during childhood and early adolescence can lead to adverse outcomes later in life, including especially perturbations in the regulation of mood and emotional states, and specifically anxiety disorders and depression. However, stress experiences vary from one individual to the next, meaning that causal relationships and mechanistic accounts are often difficult to establish in humans. This interdisciplinary talk considers the value of research in experimental animals where stressor experiences can be tightly controlled and detailed investigations of molecular, cellular, and circuit-level mechanisms can be carried out. The talk will focus on the widely used repeated maternal separation procedure in rats where rat offspring are repeatedly separated from maternal care during early postnatal life. This early life stress has remarkably persistent effects on behaviour with a general recognition that maternally-deprived animals are susceptible to depressive-like phenotypes. The validity of this conclusion will be critically appraised with convergent insights from a recent longitudinal study in maternally separated rats involving translational brain imaging, transcriptomics, and behavioural assessment.

Functional segregation of rostral and caudal hippocampus in associative memory

It has long been established that the hippocampus plays a crucial role for episodic memory. As opposed to the modular approach, now it is generally assumed that being a complex structure, the HC performs multiplex interconnected functions, whose hierarchical organization provides basis for the higher cognitive functions such as semantics-based encoding and retrieval. However, the «where, when and how» properties of distinct memory aspects within and outside the HC are still under debate. Here we used a visual associative memory task as a probe to test the hypothesis about the differential involvement of the rostral and caudal portions of the human hippocampus in memory encoding, recognition and associative recall. In epilepsy patients implanted with stereo-EEG, we show that at retrieval the rostral HC is selectively active for recognition memory, whereas the caudal HC is selectively active for the associative memory. Low frequency desynchronization and high frequency synchronization characterize the temporal dynamic in encoding and retrieval. Therefore, we describe here anatomical segregation in the hippocampal contributions to associative and recognition memory.

Visualization and manipulation of our perception and imagery by BCI

We have been developing Brain-Computer Interface (BCI) using electrocorticography (ECoG) [1] , which is recorded by electrodes implanted on brain surface, and magnetoencephalography (MEG) [2] , which records the cortical activities non-invasively, for the clinical applications. The invasive BCI using ECoG has been applied for severely paralyzed patient to restore the communication and motor function. The non-invasive BCI using MEG has been applied as a neurofeedback tool to modulate some pathological neural activities to treat some neuropsychiatric disorders. Although these techniques have been developed for clinical application, BCI is also an important tool to investigate neural function. For example, motor BCI records some neural activities in a part of the motor cortex to generate some movements of external devices. Although our motor system consists of complex system including motor cortex, basal ganglia, cerebellum, spinal cord and muscles, the BCI affords us to simplify the motor system with exactly known inputs, outputs and the relation of them. We can investigate the motor system by manipulating the parameters in BCI system. Recently, we are developing some BCIs to visualize and manipulate our perception and mental imagery. Although these BCI has been developed for clinical application, the BCI will be useful to understand our neural system to generate the perception and imagery. In this talk, I will introduce our study of phantom limb pain [3] , that is controlled by MEG-BCI, and the development of a communication BCI using ECoG [4] , that enable the subject to visualize the contents of their mental imagery. And I would like to discuss how much we can control our cortical activities that represent our perception and mental imagery. These examples demonstrate that BCI is a promising tool to visualize and manipulate the perception and imagery and to understand our consciousness. References 1. Yanagisawa, T., Hirata, M., Saitoh, Y., Kishima, H., Matsushita, K., Goto, T., Fukuma, R., Yokoi, H., Kamitani, Y., and Yoshimine, T. (2012). Electrocorticographic control of a prosthetic arm in paralyzed patients. AnnNeurol 71, 353-361. 2. Yanagisawa, T., Fukuma, R., Seymour, B., Hosomi, K., Kishima, H., Shimizu, T., Yokoi, H., Hirata, M., Yoshimine, T., Kamitani, Y., et al. (2016). Induced sensorimotor brain plasticity controls pain in phantom limb patients. Nature communications 7, 13209. 3. Yanagisawa, T., Fukuma, R., Seymour, B., Tanaka, M., Hosomi, K., Yamashita, O., Kishima, H., Kamitani, Y., and Saitoh, Y. (2020). BCI training to move a virtual hand reduces phantom limb pain: A randomized crossover trial. Neurology 95, e417-e426. 4. Ryohei Fukuma, Takufumi Yanagisawa, Shinji Nishimoto, Hidenori Sugano, Kentaro Tamura, Shota Yamamoto, Yasushi Iimura, Yuya Fujita, Satoru Oshino, Naoki Tani, Naoko Koide-Majima, Yukiyasu Kamitani, Haruhiko Kishima (2022). Voluntary control of semantic neural representations by imagery with conflicting visual stimulation. arXiv arXiv:2112.01223.

Biopsychosocial pathways in dementia inequalities

In the United States, racial/ethnic inequalities in Alzheimer's disease and related dementias persist even after controlling for socioeconomic factors and physical health. These persistent and unexplained disparities suggest: (1) there are unrecognized dementia risk factors that are socially patterned and/or (2) known dementia risk factors exhibit differential impact across social groups. Pursuing these research directions with data from multiple longitudinal studies of brain and cognitive aging has revealed several challenges to the study of late-life health inequalities, highlighted evidence for both risk and resilience within marginalized communities, and inspired new data collection efforts to advance the field.

Object recognition by touch and other senses

Cross-modality imaging of the neural systems that support executive functions

Executive functions refer to a collection of mental processes such as attention, planning and problem solving, supported by a frontoparietal distributed brain network. These functions are essential for everyday life. Specifically in the context of patients with brain tumours there is a need to preserve them in order to enable good quality of life for patients. During surgeries for the removal of a brain tumour, the aim is to remove as much as possible of the tumour and at the same time prevent damage to the areas around it to preserve function and enable good quality of life for patients. In many cases, functional mapping is conducted during an awake surgery in order to identify areas critical for certain functions and avoid their surgical resection. While mapping is routinely done for functions such as movement and language, mapping executive functions is more challenging. Despite growing recognition in the importance of these functions for patient well-being in recent years, only a handful of studies addressed their intraoperative mapping. In the talk, I will present our new approach for mapping executive function areas using electrocorticography during awake brain surgery. These results will be complemented by neuroimaging data from healthy volunteers, directed at reliably localizing executive function regions in individuals using fMRI. I will also discuss more broadly challenges ofß using neuroimaging for neurosurgical applications. We aim to advance cross-modality neuroimaging of cognitive function which is pivotal to patient-tailored surgical interventions, and will ultimately lead to improved clinical outcomes.

A biological model system for studying predictive processing

Despite the increasing recognition of predictive processing in circuit neuroscience, little is known about how it may be implemented in cortical circuits. We set out to develop and characterise a biological model system with layer 5 pyramidal cells in the centre. We aim to gain access to prediction and internal model generating processes by controlling, understanding or monitoring everything else: the sensory environment, feed-forward and feed-back inputs, integrative properties, their spiking activity and output. I’ll show recent work from the lab establishing such a model system both in terms of biology as well as tool development.

Why is the suprachiasmatic nucleus such a brilliant circadian time-keeper?

Circadian clocks dominate our lives. By creating and distributing an internal representation of 24-hour solar time, they prepare us, and thereby adapt us, to the daily and seasonal world. Jet-lag is an obvious indicator of what can go wrong when such adaptation is disrupted acutely. More seriously, the growing prevalence of rotational shift-work which runs counter to our circadian life, is a significant chronic challenge to health, presenting as increased incidence of systemic conditions such as metabolic and cardiovascular disease. Added to this, circadian and sleep disturbances are a recognised feature of various neurological and psychiatric conditions, and in some cases may contribute to disease progression. The “head ganglion” of the circadian system is the suprachiasmatic nucleus (SCN) of the hypothalamus. It synchronises the, literally, innumerable cellular clocks across the body, to each other and to solar time. Isolated in organotypic slice culture, it can maintain precise, high-amplitude circadian cycles of neural activity, effectively, indefinitely, just as it does in vivo. How is this achieved: how does this clock in a dish work? This presentation will consider SCN time-keeping at the level of molecular feedback loops, neuropeptidergic networks and neuron-astrocyte interactions.

Multimodal framework and fusion of EEG, graph theory and sentiment analysis for the prediction and interpretation of consumer decision

The application of neuroimaging methods to marketing has recently gained lots of attention. In analyzing consumer behaviors, the inclusion of neuroimaging tools and methods is improving our understanding of consumer’s preferences. Human emotions play a significant role in decision making and critical thinking. Emotion classification using EEG data and machine learning techniques has been on the rise in the recent past. We evaluate different feature extraction techniques, feature selection techniques and propose the optimal set of features and electrodes for emotion recognition.Affective neuroscience research can help in detecting emotions when a consumer responds to an advertisement. Successful emotional elicitation is a verification of the effectiveness of an advertisement. EEG provides a cost effective alternative to measure advertisement effectiveness while eliminating several drawbacks of the existing market research tools which depend on self-reporting. We used Graph theoretical principles to differentiate brain connectivity graphs when a consumer likes a logo versus a consumer disliking a logo. The fusion of EEG and sentiment analysis can be a real game changer and this combination has the power and potential to provide innovative tools for market research.

Hearing in an acoustically varied world

In order for animals to thrive in their complex environments, their sensory systems must form representations of objects that are invariant to changes in some dimensions of their physical cues. For example, we can recognize a friend’s speech in a forest, a small office, and a cathedral, even though the sound reaching our ears will be very different in these three environments. I will discuss our recent experiments into how neurons in auditory cortex can form stable representations of sounds in this acoustically varied world. We began by using a normative computational model of hearing to examine how the brain may recognize a sound source across rooms with different levels of reverberation. The model predicted that reverberations can be removed from the original sound by delaying the inhibitory component of spectrotemporal receptive fields in the presence of stronger reverberation. Our electrophysiological recordings then confirmed that neurons in ferret auditory cortex apply this algorithm to adapt to different room sizes. Our results demonstrate that this neural process is dynamic and adaptive. These studies provide new insights into how we can recognize auditory objects even in highly reverberant environments, and direct further research questions about how reverb adaptation is implemented in the cortical circuit.

What does the primary visual cortex tell us about object recognition?

Object recognition relies on the complex visual representations in cortical areas at the top of the ventral stream hierarchy. While these are thought to be derived from low-level stages of visual processing, this has not been shown, yet. Here, I describe the results of two projects exploring the contributions of primary visual cortex (V1) processing to object recognition using artificial neural networks (ANNs). First, we developed hundreds of ANN-based V1 models and evaluated how their single neurons approximate those in the macaque V1. We found that, for some models, single neurons in intermediate layers are similar to their biological counterparts, and that the distributions of their response properties approximately match those in V1. Furthermore, we observed that models that better matched macaque V1 were also more aligned with human behavior, suggesting that object recognition is derived from low-level. Motivated by these results, we then studied how an ANN’s robustness to image perturbations relates to its ability to predict V1 responses. Despite their high performance in object recognition tasks, ANNs can be fooled by imperceptibly small, explicitly crafted perturbations. We observed that ANNs that better predicted V1 neuronal activity were also more robust to adversarial attacks. Inspired by this, we developed VOneNets, a new class of hybrid ANN vision models. Each VOneNet contains a fixed neural network front-end that simulates primate V1 followed by a neural network back-end adapted from current computer vision models. After training, VOneNets were substantially more robust, outperforming state-of-the-art methods on a set of perturbations. While current neural network architectures are arguably brain-inspired, these results demonstrate that more precisely mimicking just one stage of the primate visual system leads to new gains in computer vision applications and results in better models of the primate ventral stream and object recognition behavior.

From bench to clinic – Translating fundamental neuroscience into real-life healthcare practices, and developing nationally recognised life science companies

Dr. Ryan C.N. D’Arcy is a Canadian neuroscientist, researcher, innovator and entrepreneur. Dr. D'Arcy co-founded HealthTech Connex Inc. and serves as President and Chief Scientific Officer. HealthTech Connex translates neuroscience advances into health technology breakthroughs. D'Arcy is most known for coining the term "brain vital signs" and for leading the research and development of the brain vital signs framework. Dr. D’Arcy also holds a BC Leadership Chair in Medical Technology, is a full Professor at Simon Fraser University, and a member of the DM Centre for Brain Health at the University of British Columbia. He has published more than 260 academic works, attracted more than $85 Million CAD in competitive research and innovation funding, and been recognized through numerous awards and distinctions. Please join us for an exciting virtual talk with Dr. D'Arcy who will speak on some of the current research he is involved in, how he is translating this research into real-life applications, and the development of HealthTech Connects Inc.

If we can make computers play chess, why can't we make them see?

If we can make computers play chess and even Jeopardy and Go, then why can't we make them see like us? How does our brain solve the problem of seeing? I will describe some of our recent insights into understanding object recognition in the brain using behavioral, neuronal and computational methods.

Molecular recognition and the assembly of feature-selective retinal circuits

NMC4 Short Talk: Novel population of synchronously active pyramidal cells in hippocampal area CA1

Hippocampal pyramidal cells have been widely studied during locomotion, when theta oscillations are present, and during short wave ripples at rest, when replay takes place. However, we find a subset of pyramidal cells that are preferably active during rest, in the absence of theta oscillations and short wave ripples. We recorded these cells using two-photon imaging in dorsal CA1 of the hippocampus of mice, during a virtual reality object location recognition task. During locomotion, the cells show a similar level of activity as control cells, but their activity increases during rest, when this population of cells shows highly synchronous, oscillatory activity at a low frequency (0.1-0.4 Hz). In addition, during both locomotion and rest these cells show place coding, suggesting they may play a role in maintaining a representation of the current location, even when the animal is not moving. We performed simultaneous electrophysiological and calcium recordings, which showed a higher correlation of activity between the LFO and the hippocampal cells in the 0.1-0.4 Hz low frequency band during rest than during locomotion. However, the relationship between the LFO and calcium signals varied between electrodes, suggesting a localized effect. We used the Allen Brain Observatory Neuropixels Visual Coding dataset to further explore this. These data revealed localised low frequency oscillations in CA1 and DG during rest. Overall, we show a novel population of hippocampal cells, and a novel oscillatory band of activity in hippocampus during rest.

NMC4 Short Talk: Directly interfacing brain and deep networks exposes non-hierarchical visual processing

A recent approach to understanding the mammalian visual system is to show correspondence between the sequential stages of processing in the ventral stream with layers in a deep convolutional neural network (DCNN), providing evidence that visual information is processed hierarchically, with successive stages containing ever higher-level information. However, correspondence is usually defined as shared variance between brain region and model layer. We propose that task-relevant variance is a stricter test: If a DCNN layer corresponds to a brain region, then substituting the model’s activity with brain activity should successfully drive the model’s object recognition decision. Using this approach on three datasets (human fMRI and macaque neuron firing rates) we found that in contrast to the hierarchical view, all ventral stream regions corresponded best to later model layers. That is, all regions contain high-level information about object category. We hypothesised that this is due to recurrent connections propagating high-level visual information from later regions back to early regions, in contrast to the exclusively feed-forward connectivity of DCNNs. Using task-relevant correspondence with a late DCNN layer akin to a tracer, we used Granger causal modelling to show late-DCNN correspondence in IT drives correspondence in V4. Our analysis suggests, effectively, that no ventral stream region can be appropriately characterised as ‘early’ beyond 70ms after stimulus presentation, challenging hierarchical models. More broadly, we ask what it means for a model component and brain region to correspond: beyond quantifying shared variance, we must consider the functional role in the computation. We also demonstrate that using a DCNN to decode high-level conceptual information from ventral stream produces a general mapping from brain to model activation space, which generalises to novel classes held-out from training data. This suggests future possibilities for brain-machine interface with high-level conceptual information, beyond current designs that interface with the sensorimotor periphery.

Aesthetic preference for art can be predicted from a mixture of low- and high-level visual features

It is an open question whether preferences for visual art can be lawfully predicted from the basic constituent elements of a visual image. Here, we developed and tested a computational framework to investigate how aesthetic values are formed. We show that it is possible to explain human preferences for a visual art piece based on a mixture of low- and high-level features of the image. Subjective value ratings could be predicted not only within but also across individuals, using a regression model with a common set of interpretable features. We also show that the features predicting aesthetic preference can emerge hierarchically within a deep convolutional neural network trained only for object recognition. Our findings suggest that human preferences for art can be explained at least in part as a systematic integration over the underlying visual features of an image.

What neural oscillations can(not) do for syntactic structure building

The question of how syntactic structure can be built at the neural level has come to the forefront of cognitive neuroscience in the last decade. Neural oscillations have been widely recognised as playing an important role in building syntactic representations. In this talk I will review existing oscillatory approaches to syntactic structure building and assess their functionality in light of basic properties of a hierarchical syntactic structure, such as varied length of syntactic phrases, nesting of constituents, overlap in length between different levels of the syntactic hierarchy and others. I will also briefly discuss key requirements on neural structure building mechanisms from the perspective of a real-time parser.

How do we find what we are looking for? The Guided Search 6.0 model

The talk will give a tour of Guided Search 6.0 (GS6), the latest evolution of the Guided Search model of visual search. Part 1 describes The Mechanics of Search. Because we cannot recognize more than a few items at a time, selective attention is used to prioritize items for processing. Selective attention to an item allows its features to be bound together into a representation that can be matched to a target template in memory or rejected as a distractor. The binding and recognition of an attended object is modeled as a diffusion process taking > 150 msec/item. Since selection occurs more frequently than that, it follows that multiple items are undergoing recognition at the same time, though asynchronously, making GS6 a hybrid serial and parallel model. If a target is not found, search terminates when an accumulating quitting signal reaches a threshold. Part 2 elaborates on the five sources of Guidance that are combined into a spatial “priority map” to guide the deployment of attention (hence “guided search”). These are (1) top-down and (2) bottom-up feature guidance, (3) prior history (e.g. priming), (4) reward, and (5) scene syntax and semantics. Finally, in Part 3, we will consider the internal representation of what we are searching for; what is often called “the search template”. That search template is really two templates: a guiding template (probably in working memory) and a target template (in long term memory). Put these pieces together and you have GS6.

Towards a Theory of Human Visual Reasoning

Many tasks that are easy for humans are difficult for machines. In particular, while humans excel at tasks that require generalising across problems, machine systems notably struggle. One such task that has received a good amount of attention is the Synthetic Visual Reasoning Test (SVRT). The SVRT consists of a range of problems where simple visual stimuli must be categorised into one of two categories based on an unknown rule that must be induced. Conventional machine learning approaches perform well only when trained to categorise based on a single rule and are unable to generalise without extensive additional training to tasks with any additional rules. Multiple theories of higher-level cognition posit that humans solve such tasks using structured relational representations. Specifically, people learn rules based on structured representations that generalise to novel instances quickly and easily. We believe it is possible to model this approach in a single system which learns all the required relational representations from scratch and performs tasks such as SVRT in a single run. Here, we present a system which expands the DORA/LISA architecture and augments the existing model with principally novel components, namely a) visual reasoning based on the established theories of recognition by components; b) the process of learning complex relational representations by synthesis (in addition to learning by analysis). The proposed augmented model matches human behaviour on SVRT problems. Moreover, the proposed system stands as perhaps a more realistic account of human cognition, wherein rather than using tools that has been shown successful in the machine learning field to inform psychological theorising, we use established psychological theories to inform developing a machine system.

Encoding and perceiving the texture of sounds: auditory midbrain codes for recognizing and categorizing auditory texture and for listening in noise

Natural soundscapes such as from a forest, a busy restaurant, or a busy intersection are generally composed of a cacophony of sounds that the brain needs to interpret either independently or collectively. In certain instances sounds - such as from moving cars, sirens, and people talking - are perceived in unison and are recognized collectively as single sound (e.g., city noise). In other instances, such as for the cocktail party problem, multiple sounds compete for attention so that the surrounding background noise (e.g., speech babble) interferes with the perception of a single sound source (e.g., a single talker). I will describe results from my lab on the perception and neural representation of auditory textures. Textures, such as a from a babbling brook, restaurant noise, or speech babble are stationary sounds consisting of multiple independent sound sources that can be quantitatively defined by summary statistics of an auditory model (McDermott & Simoncelli 2011). How and where in the auditory system are summary statistics represented and the neural codes that potentially contribute towards their perception, however, are largely unknown. Using high-density multi-channel recordings from the auditory midbrain of unanesthetized rabbits and complementary perceptual studies on human listeners, I will first describe neural and perceptual strategies for encoding and perceiving auditory textures. I will demonstrate how distinct statistics of sounds, including the sound spectrum and high-order statistics related to the temporal and spectral correlation structure of sounds, contribute to texture perception and are reflected in neural activity. Using decoding methods I will then demonstrate how various low and high-order neural response statistics can differentially contribute towards a variety of auditory tasks including texture recognition, discrimination, and categorization. Finally, I will show examples from our recent studies on how high-order sound statistics and accompanying neural activity underlie difficulties for recognizing speech in background noise.

Cortex-wide high density ECoG recordings from rat reveal diverse generators of sleep-spindles with characteristic anatomical topographies and non-stationary subcycle dynamics

Bernstein Conference 2024

Non-Human Recognition of Orthography: How is it implemented and how does it differ from Human orthographic processing

Bernstein Conference 2024

Recognizing relevant information in neural activity

Bernstein Conference 2024

Action recognition best explains neural activity in cuneate nucleus

COSYNE 2022

Do better object recognition models improve the generalization gap in neural predictivity?

COSYNE 2022

Linking neural dynamics across macaque V4, IT, and PFC to trial-by-trial object recognition behavior

COSYNE 2022

Linking neural dynamics across macaque V4, IT, and PFC to trial-by-trial object recognition behavior

COSYNE 2022

How many objects can be recognized under all possible views?

COSYNE 2022

How many objects can be recognized under all possible views?

COSYNE 2022

Distinct roles of excitatory and inhibitory neurons in the macaque IT cortex in object recognition

COSYNE 2023

Leveraging computational and animal models of vision to probe atypical emotion recognition in autism

COSYNE 2023

On-line SEUDO for real-time cell recognition in Calcium Imaging

COSYNE 2023

Spatial-frequency channels for object recognition by neural networks are twice as wide as those of humans

COSYNE 2023

Temporal pattern recognition in retinal ganglion cells is mediated by dynamical inhibitory synapses

COSYNE 2023

Geometric Signatures of Speech Recognition: Insights from Deep Neural Networks to the Brain

COSYNE 2025

Action recognition and kinematic analysis with DeepLabCut

Allosteric modulation of AhR by 3,3’-diindolylmethane can prevent recognition memory impairment caused by binge-ethanol exposure

Brain molecular alterations associated to early recognition deficits in a new mouse model of Alzheimer’s disease

The circuit basis of olfactory mate recognition and localisation

The deficits of recognition and memory in adolescence by neonatal maternal separation

Two distinct ways to form long-term object-recognition memory during sleep and wakefulness

Effect of a high-fat diet on hippocampal area CA2 and social recognition memory

The HIP-mPFC network is a neural resource of object recognition memory

Neurophysiologically-inspired computational model of the visual recognition of social behavior and intent

Physiological synaptic activity and recognition memory require astroglial glutamine

Post-stimulus inactivation of the mammillary bodies impairs performance on spatial recognition tasks

The role of endgonous opoid signaling in social recognition

The role of the CA1 in social and spatial recognition memory in juvenile males and females

Understanding how interneurons in the medial prefrontal cortex modulate associative recognition memory

Unsupervised, frequent and remote: A novel platform for personalised digital phenotyping of spatial working memory and image recognition in humans

The analysis of the OXT-DA interaction causing social recognition deficit in Syntaxin1A KO

FENS Forum 2024

Behavioral impacts of simulated microgravity on male mice: Locomotion, social interactions and memory in a novel object recognition task

FENS Forum 2024

Cortex-wide high-density ECoG and translaminar local field potential recordings reveal rich broad-band spatio-temporal dynamics

FENS Forum 2024

The cortical amygdala mediates individual recognition in mice

FENS Forum 2024

A deep learning approach for the recognition of behaviors in the forced swim test

FENS Forum 2024

Direct electrical stimulation of the human amygdala enhances recognition memory for objects but not scenes

FENS Forum 2024

Two distinct ways to form long-term object recognition memory during sleep and wakefulness

FENS Forum 2024

Early disruption in social recognition and its impact on episodic memory in triple transgenic mice model of Alzheimer’s disease

FENS Forum 2024

ECoG-based functional mapping of the motor cortex in rhesus monkeys

FENS Forum 2024

Evaluation of novel object recognition test results of rats injected with intracerebroventricular streptozocin to develop Alzheimer's disease models

FENS Forum 2024