differences

Cardiorespiratory and autonomic impacts of coolants in e-cigarette aerosols

PROJECT SUMMARY / ABSTRACT Coolants such as menthol, WS-3, and WS-23 are widely used in electronic cigarettes (e-cigs) to reduce irritation and enhance appeal—especially among youth. Despite their prevalence, the cardiopulmonary toxicity of these agents remains poorly characterized. Recent work shows that e-cig aerosols can disrupt autonomic nervous system regulation and cardiac electrophysiology, increasing catecholamine release, enhancing sympathetic regulation of cardiac rhythm, and provoking arrhythmias. Proof is also mounting that nicotine’s sympathomimetic traits mediate these pathogenic effects. Preliminary data from our laboratory show that coolants increase systemic nicotine levels, blunt respiratory reflexes, and potentiate arrhythmias upon exposures to e-cigarette aerosols, suggesting a paradoxical role for coolants in suppressing ventilatory responses while intensifying cardiovascular risk. These findings take on added significance in light of recent case reports of sudden cardiac arrest in young e-cigarette users, including some in otherwise healthy individuals. This project will elucidate how e-cigarette coolants alter exposure to harmful and potentially harmful constituents (HPHCs)—particularly nicotine and aldehydes—concurrent with their effects on cardiovascular and respiratory physiology. Using robust murine models with continuous ECG, blood pressure, and pleural pressure telemetry, we will assess how coolants alter the acute and chronic effects of e-cigarette aerosols on cardiac electrophysiology, autonomic tone, ventilatory function, hemodynamics, and toxicant exposure. We will also evaluate how coolant concentration and device power modulate these effects. In parallel, we will determine whether adolescent mice exhibit heightened susceptibility to these effects compared to adults, with attention to sex differences and the persistence of cardiotoxicity after exposure cessation. This comprehensive, multi-modal approach incorporates novel protocols for arrhythmia inducibility, high-resolution physiologic monitoring, and complementary analyses of biomarkers of exposure and effect. By clarifying how coolants interact with HPHCs—especially nicotine and aldehydes—to drive cardiopulmonary injury across age and sex, this work addresses high-priority research areas identified in RFA-OD-25-001, including the toxicological evaluation of e-cigarette constituents and their cardiopulmonary effects. The results will inform regulatory policy and public health strategies aimed at mitigating cardiovascular risk associated with e-cigarette use, particularly among vulnerable youth.

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.

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

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

Increasing Lung Cancer Screening Uptake Among High-Risk Emergency Department Patients

PROJECT SUMMARY/ABSTRACT Lung cancer is the leading cause of cancer death in the US. Although lung cancer screening (LCS), using low- dose CT scan, decreases lung cancer mortality through early disease identification, fewer than 1 in 6 eligible individuals get screened, with significant differences based on demographic and socio-economic factors. LCS is a process, not just a test. The critical first steps in this process are (1) identification of high-risk individuals who are eligible for LCS, and (2) recruitment of these individuals into an LCS program. The Emergency Department (ED) setting is optimal for an intervention to promote LCS by accomplishing these steps. Individuals at high risk for lung cancer are over-represented in the ED population, including: individuals that smoke, non-White individuals, patients with lower education levels, and the under-insured. In fact, over 2.3 million high-risk people pass through EDs every year who are eligible for LCS but have never been screened. The investigators’ long-term goal is to develop a low-cost, scalable intervention that increases LCS uptake among ED patients and is deployable in any ED with a regionally referrable LCS program. The objective of the proposed randomized clinical trial is to test the efficacies of text messaging and a facilitated referral strategy to promote uptake of LCS in order to achieve this goal. Step 1 of the approach is to identify participants that are eligible for LCS. Step 2 is to randomize eligible participants, using a 2x2 design, among four study arms: (1) basic referral for LCS (i.e. verbal referral with written materials; comprising an enhanced control arm), (2) basic referral plus a subsequent series of text messages, grounded in behavioral change theory, aimed at generating intention and motivation to get screened, (3) facilitated referral for LCS (i.e. submission of a requisition to LCS program by staff), and (4) facilitated referral plus text messages. The investigators’ pilot work demonstrated the feasibility and efficacy of the proposed approach. A total of 1036 individuals eligible for LCS will be recruited from a high-volume urban ED and a low-volume rural ED, randomized among study arms, and followed-up at 120 days to assess interval LCS uptake. The Specific Aims of the proposed project are, (1) Compare LCS program uptake among study arms that receive text messages to study arms that do not, (2) Compare LCS program uptake among study arms with basic referral to study arms with facilitated referral, (3) Investigate the interaction between receipt of text messages (yes/no) and referral type (basic/facilitated), and (4) Evaluate participant feedback on (a) differential barriers to LCS across sub-groups and (b) acceptability and appropriateness of ED-based promotion of LCS. The study team is at the forefront of developing ED-based interventions to promote cancer screening. This project leverages the universal access setting of the ED to identify individuals at greatest risk for lung cancer and get them screened. A scalable ED-based intervention that increases LCS uptake would save lives.

Weak Cell Adhesion is a Prognostic Signature of Invasive Cancer

Project Summary Despite early detection, low-grade and localized breast cancers such as ductal carcinoma in situ (DCIS) can relapse in up to 20% of cases despite standard of care. For DCIS, relapse affects over 12,000 U.S. women annually and has increased 60% in the last 40 years. Current diagnostic assessments including histopathological markers often miss early disseminating cells, lack specificity, or cannot distinguish cancer from non-cancer cells in the stroma. Hence there is an unmet need for cancer diagnostic technologies that employ radically different characterization methods. For example, significant physical differences exist between metastasizing and benign breast cancer cells, owing to metastasizing cells detaching from the primary tumor, migrating through the surrounding stroma, intravasating and extravasating, and ultimately engrafting in distant tissues. We recently demonstrated that cancer cells with weaker adhesion migrate faster and metastasize more frequently in murine breast cancer models than strongly adherent cells. In a small pilot study of human breast tumors, we also observed that the abundance of weakly adherent (WA) cells scales with disease severity; subpopulations from invasive carcinomas were the least adherent. However, a subset of DCIS cases displayed much less adhesion, suggesting that these patients may have a tumor subpopulation that progresses to metastatic disease despite standard-of-care treatment. Weak adhesion is a defining physical characteristic of tumors, but to establish their role in initiation, metastasis, and patient outcomes, we will leverage model systems and our newly patented adhesion technology to answer these fundamental questions of cancer biology and clinical translation. To understand the impact of adhesion on cancer progression, we will evaluate the tumor-initiating potential of WA versus strongly adherent (SA) tumor cells in a murine breast cancer model before confirming how weak adhesion advantages cells to cause secondary disease using bioengineered in vitro models. In dissecting the stages of metastasis where WA cells exhibit advantages, e.g., recapitulating stromal niche, transendothelial migration, and tissue-specific colonization, we will identify mechanisms that enable WA cells to thrive and evaluate therapeutic targets that disrupt these pathways. Finally, we will analyze the adhesion profiles of resected tumors and stroma from 80 breast cancer patients with DCIS or invasive disease. Adhesion data will be correlated with conventional assessment methods and ultimately with patient outcomes, e.g., disease-free and progression-free intervals. We anticipate that the DCIS subpopulation that aligns with the adhesion signature of invasive carcinomas will have shorter intervals and survival time. This integrated study design bridges mouse models, mechanistic bioengineering assays, and human samples to clarify the metastatic potential and prognostic value of WA breast cancer cells. Our use of mouse models in this grant is required to study the interactions among tumor cells, immune cells, vasculature, and stromal tissues that drive tumor formation in vivo. Bioengineered in vitro systems lack the complexity to ask such questions and using injected tumor cells is not possible in humans.

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

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

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

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

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.

Molecular strategies for resolving differential regulation of dopamine subpopulations

Project Summary/Abstract Dopamine neurons in the ventral tegmental area (VTA) fire action potentials in complex patterns of tonic and phasic activity in response to environmental stimuli and during behavioral tasks. Transcriptomic, anatomical, and functional studies have established that VTA dopamine neurons can be divided into multiple subpopulations with variable gene expression, projection patterns, and response profiles. We recently completed a transcriptomic study that identified genetic markers for three distinct subpopulations of VTA dopamine neurons, and also found evidence for variability in ion channel gene expression between populations that correlated with differences in activity-dependent gene expression. However, much remains unknown regarding how specific genes encoding ion channels, receptors, transcription factors, or other signaling components contribute to the variability in baseline physiological properties observed across the VTA. Here we propose to combine slice electrophysiology recordings of VTA dopamine neurons with post-hoc single-cell sequencing analysis (i.e. patch-seq), which will allow us to directly correlate gene expression and physiological properties in order to identify candidate genes that may be key drivers of the variability between subpopulations. We also propose to validate and utilize a novel dual-recombinase CRISPR/Cas9 system for targeted gene mutagenesis in intersectional neuronal populations, which will provide a mechanism for testing gene function with unprecedented precision. We will use this approach to test the function of two candidate ion channel genes, the potassium channels Kcnh5 and Kcnh7, previously identified in our transcriptomic study as potential contributors to dopamine neuron action potential firing properties. We hypothesize that these genes are important for enabling rapid action potential firing in highly excitable dopamine neurons found in specific subpopulations. As a whole, with this proposal we aim to generate a valuable dataset linking gene expression in VTA dopamine neurons with physiology and subpopulation identification, as well as develop an intersectional gene mutagenesis strategy that can be used throughout the brain to precisely target neuronal subpopulations to test gene function. With this approach, we hope to facilitate future precision targeting of the dopamine system and dopamine-dependent behaviors.

Circulating and Mucosal Predictors and Effects of Therapeutic Interleukin-23 Blockade in Crohn's Disease

PROJECT SUMMARY/ABSTRACT Since its discovery 20 years ago, the cytokine interleukin (IL)-23 has increasingly been implicated in the pathogenesis of immune mediated diseases, such as Crohn’s disease (CD). Consequently, four monoclonal antibodies that block IL-23 are currently approved CD therapies, including risankizumab. Although suppression of pathogenic Th17 cells has been widely cited as the mechanism by which IL-23 blockade controls disease, there is a paucity of data to indicate that this is how such therapy works, and a few other immune cell populations expressing the IL-23 receptor could instead be its target. We therefore propose to study how risankizumab affects not only Th17 cells, but also mucosa-associate invariant T (MAIT) cells γδ T cells and (in the colon) type 3 innate lymphoid cells (ILC3s). In addition to quantifying these cells, we will study their gene expression to detect phenotypic differences in treated patients, and in the case of T cells, track their clonal expansion and deletion through their unique T cell receptor sequences. In colon samples, we will use a combination of single cell sequencing of sort-enriched immune cell populations and spatial transcriptomics to characterize cells in situ, at the site of disease, and determine how IL-23 blockade affects their microenvironment in vivo. By contrasting results in patients who do or do not respond therapeutically to IL-23 blockade, we will reveal valuable insights into how this treatment succeeds or fails in CD, in the process identifying predictive biomarkers to guide treatment decisions, and potentially identifying future molecular targets with which to prevent treatment failure.

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

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

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

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

Development of a multi-modal mouse model of cluster headache

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

Spike train structure of cortical transcriptomic populations in vivo

The cortex comprises many neuronal types, which can be distinguished by their transcriptomes: the sets of genes they express. Little is known about the in vivo activity of these cell types, particularly as regards the structure of their spike trains, which might provide clues to cortical circuit function. To address this question, we used Neuropixels electrodes to record layer 5 excitatory populations in mouse V1, then transcriptomically identified the recorded cell types. To do so, we performed a subsequent recording of the same cells using 2-photon (2p) calcium imaging, identifying neurons between the two recording modalities by fingerprinting their responses to a “zebra noise” stimulus and estimating the path of the electrode through the 2p stack with a probabilistic method. We then cut brain slices and performed in situ transcriptomics to localize ~300 genes using coppaFISH3d, a new open source method, and aligned the transcriptomic data to the 2p stack. Analysis of the data is ongoing, and suggests substantial differences in spike time coordination between ET and IT neurons, as well as between transcriptomic subtypes of both these excitatory types.

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

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

Modeling human brain development and disease: the role of primary cilia

Neurodevelopmental disorders (NDDs) impose a global burden, affecting an increasing number of individuals. While some causative genes have been identified, understanding the human-specific mechanisms involved in these disorders remains limited. Traditional gene-driven approaches for modeling brain diseases have failed to capture the diverse and convergent mechanisms at play. Centrosomes and cilia act as intermediaries between environmental and intrinsic signals, regulating cellular behavior. Mutations or dosage variations disrupting their function have been linked to brain formation deficits, highlighting their importance, yet their precise contributions remain largely unknown. Hence, we aim to investigate whether the centrosome/cilia axis is crucial for brain development and serves as a hub for human-specific mechanisms disrupted in NDDs. Towards this direction, we first demonstrated species-specific and cell-type-specific differences in the cilia-genes expression during mouse and human corticogenesis. Then, to dissect their role, we provoked their ectopic overexpression or silencing in the developing mouse cortex or in human brain organoids. Our findings suggest that cilia genes manipulation alters both the numbers and the position of NPCs and neurons in the developing cortex. Interestingly, primary cilium morphology is disrupted, as we find changes in their length, orientation and number that lead to disruption of the apical belt and altered delamination profiles during development. Our results give insight into the role of primary cilia in human cortical development and address fundamental questions regarding the diversity and convergence of gene function in development and disease manifestation. It has the potential to uncover novel pharmacological targets, facilitate personalized medicine, and improve the lives of individuals affected by NDDs through targeted cilia-based therapies.

Dyslexia, Rhythm, Language and the Developing Brain

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

Unifying the mechanisms of hippocampal episodic memory and prefrontal working memory

Remembering events in the past is crucial to intelligent behaviour. Flexible memory retrieval, beyond simple recall, requires a model of how events relate to one another. Two key brain systems are implicated in this process: the hippocampal episodic memory (EM) system and the prefrontal working memory (WM) system. While an understanding of the hippocampal system, from computation to algorithm and representation, is emerging, less is understood about how the prefrontal WM system can give rise to flexible computations beyond simple memory retrieval, and even less is understood about how the two systems relate to each other. Here we develop a mathematical theory relating the algorithms and representations of EM and WM by showing a duality between storing memories in synapses versus neural activity. In doing so, we develop a formal theory of the algorithm and representation of prefrontal WM as structured, and controllable, neural subspaces (termed activity slots). By building models using this formalism, we elucidate the differences, similarities, and trade-offs between the hippocampal and prefrontal algorithms. Lastly, we show that several prefrontal representations in tasks ranging from list learning to cue dependent recall are unified as controllable activity slots. Our results unify frontal and temporal representations of memory, and offer a new basis for understanding the prefrontal representation of WM

Genomic investigation of sex-differential neurodevelopment and risk for autism

The Role of Spatial and Contextual Relations of real world objects in Interval Timing

In the real world, object arrangement follows a number of rules. Some of the rules pertain to the spatial relations between objects and scenes (i.e., syntactic rules) and others about the contextual relations (i.e., semantic rules). Research has shown that violation of semantic rules influences interval timing with the duration of scenes containing such violations to be overestimated as compared to scenes with no violations. However, no study has yet investigated whether both semantic and syntactic violations can affect timing in the same way. Furthermore, it is unclear whether the effect of scene violations on timing is due to attentional or other cognitive accounts. Using an oddball paradigm and real-world scenes with or without semantic and syntactic violations, we conducted two experiments on whether time dilation will be obtained in the presence of any type of scene violation and the role of attention in any such effect. Our results from Experiment 1 showed that time dilation indeed occurred in the presence of syntactic violations, while time compression was observed for semantic violations. In Experiment 2, we further investigated whether these estimations were driven by attentional accounts, by utilizing a contrast manipulation of the target objects. The results showed that an increased contrast led to duration overestimation for both semantic and syntactic oddballs. Together, our results indicate that scene violations differentially affect timing due to violation processing differences and, moreover, their effect on timing seems to be sensitive to attentional manipulations such as target contrast.

Recognizing Faces: Insights from Group and Individual Differences

Neuromodulation of subjective experience

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

Social and non-social learning: Common, or specialised, mechanisms? (BACN Early Career Prize Lecture 2022)

The last decade has seen a burgeoning interest in studying the neural and computational mechanisms that underpin social learning (learning from others). Many findings support the view that learning from other people is underpinned by the same, ‘domain-general’, mechanisms underpinning learning from non-social stimuli. Despite this, the idea that humans possess social-specific learning mechanisms - adaptive specializations moulded by natural selection to cope with the pressures of group living - persists. In this talk I explore the persistence of this idea. First, I present dissociations between social and non-social learning - patterns of data which are difficult to explain under the domain-general thesis and which therefore support the idea that we have evolved special mechanisms for social learning. Subsequently, I argue that most studies that have dissociated social and non-social learning have employed paradigms in which social information comprises a secondary, additional, source of information that can be used to supplement learning from non-social stimuli. Thus, in most extant paradigms, social and non-social learning differ both in terms of social nature (social or non-social) and status (primary or secondary). I conclude that status is an important driver of apparent differences between social and non-social learning. When we account for differences in status, we see that social and non-social learning share common (dopamine-mediated) mechanisms.

Sex hormone regulation of neural gene expression

Gonadal steroid hormones are the principal drivers of sex-variable biology in vertebrates. In the brain, estrogen (17β-estradiol) establishes neural sex differences in many species and modulates mood, behavior, and energy balance in adulthood. To understand the diverse effects of estradiol on the brain, we profiled the genomic binding of estrogen receptor alpha (ERα), providing the first picture of the neural actions of any gonadal hormone receptor. To relate ERα target genes to brain sex differences we assessed gene expression and chromatin accessibility in the posterior bed nucleus of the stria terminalis (BNSTp), a sexually dimorphic node in limbic circuitry that underlies sex-differential social behaviors such as aggression and parenting. In adult animals we observe that levels of ERα are predictive of the extent of sex-variable gene expression, and that these sex differences are a dynamic readout of acute hormonal state. In neonates we find that transient ERα recruitment at birth leads to persistent chromatin opening and male-biased gene expression, demonstrating a true epigenetic mechanism for brain sexual differentiation. Collectively, our findings demonstrate that sex differences in gene expression in the brain are a readout of state-dependent hormone receptor actions, rather than other factors such as sex chromosomes. We anticipate that the ERα targets we have found will contribute to established sex differences in the incidence and etiology of neurological and psychiatric disorders.

How curiosity affects learning and information seeking via the dopaminergic circuit

Over the last decade, research on curiosity – the desire to seek new information – has been rapidly growing. Several studies have shown that curiosity elicits activity within the dopaminergic circuit and thereby enhances hippocampus-dependent learning. However, given this new field of research, we do not have a good understanding yet of (i) how curiosity-based learning changes across the lifespan, (ii) why some people show better learning improvements due to curiosity than others, and (iii) whether lab-based research on curiosity translates to how curiosity affects information seeking in real life. In this talk, I will present a series of behavioural and neuroimaging studies that address these three questions about curiosity. First, I will present findings on how curiosity and interest affect learning differently in childhood and adolescence. Second, I will show data on how inter-individual differences in the magnitude of curiosity-based learning depend on the strength of resting-state functional connectivity within the cortico-mesolimbic dopaminergic circuit. Third, I will present findings on how the level of resting-state functional connectivity within this circuit is also associated with the frequency of real-life information seeking (i.e., about Covid-19-related news). Together, our findings help to refine our recently proposed framework – the Prediction, Appraisal, Curiosity, and Exploration (PACE) framework – that attempts to integrate theoretical ideas on the neurocognitive mechanisms of how curiosity is elicited, and how curiosity enhances learning and information seeking. Furthermore, our findings highlight the importance of curiosity research to better understand how curiosity can be harnessed to improve learning and information seeking in real life.

The Geometry of Decision-Making

Running, swimming, or flying through the world, animals are constantly making decisions while on the move—decisions that allow them to choose where to eat, where to hide, and with whom to associate. Despite this most studies have considered only on the outcome of, and time taken to make, decisions. Motion is, however, crucial in terms of how space is represented by organisms during spatial decision-making. Employing a range of new technologies, including automated tracking, computational reconstruction of sensory information, and immersive ‘holographic’ virtual reality (VR) for animals, experiments with fruit flies, locusts and zebrafish (representing aerial, terrestrial and aquatic locomotion, respectively), I will demonstrate that this time-varying representation results in the emergence of new and fundamental geometric principles that considerably impact decision-making. Specifically, we find that the brain spontaneously reduces multi-choice decisions into a series of abrupt (‘critical’) binary decisions in space-time, a process that repeats until only one option—the one ultimately selected by the individual—remains. Due to the critical nature of these transitions (and the corresponding increase in ‘susceptibility’) even noisy brains are extremely sensitive to very small differences between remaining options (e.g., a very small difference in neuronal activity being in “favor” of one option) near these locations in space-time. This mechanism facilitates highly effective decision-making, and is shown to be robust both to the number of options available, and to context, such as whether options are static (e.g. refuges) or mobile (e.g. other animals). In addition, we find evidence that the same geometric principles of decision-making occur across scales of biological organisation, from neural dynamics to animal collectives, suggesting they are fundamental features of spatiotemporal computation.

The sense of agency as an explorative role in our perception and action

The sense of agency refers to the subjective feeling of controlling one's own behavior and, through them, external events. Why is this subjective feeling important for humans? Is it just a by-product of our actions? Previous studies have shown that the sense of agency can affect the intensity of sensory input because we predict the input from our motor intention. However, my research has found that the sense of agency plays more roles than just predictions. It enhances perceptual processes of sensory input and potentially helps to harvest more information about the link between the external world and the self. Furthermore, our recent research found both indirect and direct evidence that the sense of agency is important for people's exploratory behaviors, and this may be linked to proximal exploitations of one's control in the environment. In this talk, I will also introduce the paradigms we use to study the sense of agency as a result of perceptual processes, and our findings of individual differences in this sense and the implications.

Asymmetric signaling across the hierarchy of cytoarchitecture within the human connectome

Cortical variations in cytoarchitecture form a sensory-fugal axis that shapes regional profiles of extrinsic connectivity and is thought to guide signal propagation and integration across the cortical hierarchy. While neuroimaging work has shown that this axis constrains local properties of the human connectome, it remains unclear whether it also shapes the asymmetric signaling that arises from higher-order topology. Here, we used network control theory to examine the amount of energy required to propagate dynamics across the sensory-fugal axis. Our results revealed an asymmetry in this energy, indicating that bottom-up transitions were easier to complete compared to top-down. Supporting analyses demonstrated that asymmetries were underpinned by a connectome topology that is wired to support efficient bottom-up signaling. Lastly, we found that asymmetries correlated with differences in communicability and intrinsic neuronal time scales and lessened throughout youth. Our results show that cortical variation in cytoarchitecture may guide the formation of macroscopic connectome topology.

Age differences in cortical network flexibility and motor learning ability

Hormonal control of brain sex differences

Mechanisms of relational structure mapping across analogy tasks

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

Modeling shared and variable information encoded in fine-scale cortical topographies

Information is encoded in fine-scale functional topographies that vary from brain to brain. Hyperalignment models information that is shared across brain in a high-dimensional common information space. Hyperalignment transformations project idiosyncratic individual topographies into the common model information space. These transformations contain topographic basis functions, affording estimates of how shared information in the common model space is instantiated in the idiosyncratic functional topographies of individual brains. This new model of the functional organization of cortex – as multiplexed, overlapping basis functions – captures the idiosyncratic conformations of both coarse-scale topographies, such as retinotopy and category-selectivity, and fine-scale topographies. Hyperalignment also makes it possible to investigate how information that is encoded in fine-scale topographies differs across brains. These individual differences in fine-grained cortical function were not accessible with previous methods.

Do large language models solve verbal analogies like children do?

Analogical reasoning –learning about new things by relating it to previous knowledge– lies at the heart of human intelligence and creativity and forms the core of educational practice. Children start creating and using analogies early on, making incredible progress moving from associative processes to successful analogical reasoning. For example, if we ask a four-year-old “Horse belongs to stable like chicken belongs to …?” they may use association and reply “egg”, whereas older children will likely give the intended relational response “chicken coop” (or other term to refer to a chicken’s home). Interestingly, despite state-of-the-art AI-language models having superhuman encyclopedic knowledge and superior memory and computational power, our pilot studies show that these large language models often make mistakes providing associative rather than relational responses to verbal analogies. For example, when we asked four- to eight-year-olds to solve the analogy “body is to feet as tree is to …?” they responded “roots” without hesitation, but large language models tend to provide more associative responses such as “leaves”. In this study we examine the similarities and differences between children's and six large language models' (Dutch/multilingual models: RobBERT, BERT-je, M-BERT, GPT-2, M-GPT, Word2Vec and Fasttext) responses to verbal analogies extracted from an online adaptive learning environment, where >14,000 7-12 year-olds from the Netherlands solved 20 or more items from a database of 900 Dutch language verbal analogies.

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.

Time as its own representation? Exploring a link between timing of cognition and time perception

The way we represent and perceive time has crucial implications for studying temporality in conscious experience. Contrasting positions posit that temporal information is separately abstracted out like any other perceptual property, or that time is represented through representations having temporal properties themselves. To add to this debate, we investigated alterations in felt time in conditions where only conscious visual experience is altered while a bistable figure remains physically unchanged. In this talk, I will discuss two studies that we have done in relation to answering this question. In study 1, we investigated whether perceptual switches in fixed intervals altered felt time. In three experiments we showed that a break in visual experience (via a perceptual switch) also leads to a break in felt time. In study 2, we are currently looking at figure-ground perception in ambigous displays. Here, in experiment 1 we show that differences in flicker frequencies on ambigous regions can induce figure-ground segregation. To see if a reverse complementarity exists for felt time, we ask participants to view ambigous regions as figure/ground and show that they have different temporal resolutions for the same region based on whether it is seen as figure or background. Overall, the two studies provide evidence for temporal mirroring and isomorphism in visual experience, arguing for a link between the timing of experience and time perception.

Learning-to-read and dyslexia: a cross-language computational perspective

How do children learn to read in different countries? How do deficits in various components of the reading network affect learning outcomes? What are the consequences of such deficits in different languages? In this talk, I will present a full-blown developmentally plausible computational model of reading acquisition that has been implemented in English, French, Italian and German. The model can simulate individual learning trajectories and intervention outcomes on the basis of three component skills: orthography, phonology, and vocabulary. I will use the model to show how cross-language differences affect the learning-to-read process in different languages and to investigate to what extent similar deficits will produce similar or different manifestations of dyslexia in different languages.

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.

A mind set in stone: fossil traces of human brain evolution

Brains do not fossilise, but as they grow and expand during fetal and infant development, they leave an imprint in the bony braincase. Such imprints of fossilised braincases provide direct evidence of brain evolution, but the underlying biological changes have remained elusive. Combining data from fossil skulls, ancient genomes, brain imaging and gene expression helps shed light on the evolutionary changes shaping the human brain. I will highlight two examples separated by more than 3 million years: the evolution of brain growth in Lucy and her kind, and differences between modern humans and Neanderthals.

The Learning Salon

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

Semantic Distance and Beyond: Interacting Predictors of Verbal Analogy Performance

Prior studies of A:B::C:D verbal analogies have identified several factors that affect performance, including the semantic similarity between source and target domains (semantic distance), the semantic association between the C-term and incorrect answers (distracter salience), and the type of relations between word pairs (e.g., categorical, compositional, and causal). However, it is unclear how these stimulus properties affect performance when utilized together. Moreover, how do these item factors interact with individual differences such as crystallized intelligence and creative thinking? Several studies reveal interactions among these item and individual difference factors impacting verbal analogy performance. For example, a three-way interaction demonstrated that the effects of semantic distance and distracter salience had a greater impact on performance for compositional and causal relations than for categorical ones (Jones, Kmiecik, Irwin, & Morrison, 2022). Implications for analogy theories and future directions are discussed.

Chemistry of the adaptive mind: lessons from dopamine

The human brain faces a variety of computational dilemmas, including the flexibility/stability, the speed/accuracy and the labor/leisure tradeoff. I will argue that striatal dopamine is particularly well suited to dynamically regulate these computational tradeoffs depending on constantly changing task demands. This working hypothesis is grounded in evidence from recent studies on learning, motivation and cognitive control in human volunteers, using chemical PET, psychopharmacology, and/or fMRI. These studies also begin to elucidate the mechanisms underlying the huge variability in catecholaminergic drug effects across different individuals and across different task contexts. For example, I will demonstrate how effects of the most commonly used psychostimulant methylphenidate on learning, Pavlovian and effortful instrumental control depend on fluctuations in current environmental volatility, on individual differences in working memory capacity and on opportunity cost respectively.

Mismatching clocks: the effect of circadian misalignment on peripheral 24-h rhythms in humans

Night shift work is associated with adverse health effects and leads to misalignment between timing cues from the environment and the endogenous circadian clock. In this presentation, I will discuss the effect of circadian misalignment induced by night shift work on peripheral 24-h rhythms on the transcriptome and metabolome in humans, presenting findings from both controlled laboratory studies and field studies. Furthermore, I will highlight the importance of taking into account interindividual differences in the response to circadian misalignment.

The Learning Salon

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

Sex Differences in Learning from Exploration

Sex-based modulation of cognitive processes could set the stage for individual differences in vulnerability to neuropsychiatric disorders. While value-based decision making processes in particular have been proposed to be influenced by sex differences, the overall correct performance in decision making tasks often show variable or minimal differences across sexes. Computational tools allow us to uncover latent variables that define different decision making approaches, even in animals with similar correct performance. Here, we quantify sex differences in mice in the latent variables underlying behavior in a classic value-based decision making task: a restless two-armed bandit. While male and female mice had similar accuracy, they achieved this performance via different patterns of exploration. Male mice tended to make more exploratory choices overall, largely because they appeared to get ‘stuck’ in exploration once they had started. Female mice tended to explore less but learned more quickly during exploration. Together, these results suggest that sex exerts stronger influences on decision making during periods of learning and exploration than during stable choices. Exploration during decision making is altered in people diagnosed with addictions, depression, and neurodevelopmental disabilities, pinpointing the neural mechanisms of exploration as a highly translational avenue for conferring sex-modulated vulnerability to neuropsychiatric diagnoses.

The evolution of computation in the brain: Insights from studying the retina

The retina is probably the most accessible part of the vertebrate central nervous system. Its computational logic can be interrogated in a dish, from patterns of lights as the natural input, to spike trains on the optic nerve as the natural output. Consequently, retinal circuits include some of the best understood computational networks in neuroscience. The retina is also ancient, and central to the emergence of neurally complex life on our planet. Alongside new locomotor strategies, the parallel evolution of image forming vision in vertebrate and invertebrate lineages is thought to have driven speciation during the Cambrian. This early investment in sophisticated vision is evident in the fossil record and from comparing the retina’s structural make up in extant species. Animals as diverse as eagles and lampreys share the same retinal make up of five classes of neurons, arranged into three nuclear layers flanking two synaptic layers. Some retina neuron types can be linked across the entire vertebrate tree of life. And yet, the functions that homologous neurons serve in different species, and the circuits that they innervate to do so, are often distinct to acknowledge the vast differences in species-specific visuo-behavioural demands. In the lab, we aim to leverage the vertebrate retina as a discovery platform for understanding the evolution of computation in the nervous system. Working on zebrafish alongside birds, frogs and sharks, we ask: How do synapses, neurons and networks enable ‘function’, and how can they rearrange to meet new sensory and behavioural demands on evolutionary timescales?

The Learning Salon

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

The Learning Salon

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

The Synaptome Architecture of the Brain: Lifespan, disease, evolution and behavior

The overall aim of my research is to understand how the organisation of the synapse, with particular reference to the postsynaptic proteome (PSP) of excitatory synapses in the brain, informs the fundamental mechanisms of learning, memory and behaviour and how these mechanisms go awry in neurological dysfunction. The PSP indeed bears a remarkable burden of disease, with components being disrupted in disorders (synaptopathies) including schizophrenia, depression, autism and intellectual disability. Our work has been fundamental in revealing and then characterising the unprecedented complexity (>1000 highly conserved proteins) of the PSP in terms of the subsynaptic architecture of postsynaptic proteins such as PSD95 and how these proteins assemble into complexes and supercomplexes in different neurons and regions of the brain. Characterising the PSPs in multiple species, including human and mouse, has revealed differences in key sets of functionally important proteins, correlates with brain imaging and connectome data, and a differential distribution of disease-relevant proteins and pathways. Such studies have also provided important insight into synapse evolution, establishing that vertebrate behavioural complexity is a product of the evolutionary expansion in synapse proteomes that occurred ~500 million years ago. My lab has identified many mutations causing cognitive impairments in mice before they were found to cause human disorders. Our proteomic studies revealed that >130 brain diseases are caused by mutations affecting postsynaptic proteins. We uncovered mechanisms that explain the polygenic basis and age of onset of schizophrenia, with postsynaptic proteins, including PSD95 supercomplexes, carrying much of the polygenic burden. We discovered the “Genetic Lifespan Calendar”, a genomic programme controlling when genes are regulated. We showed that this could explain how schizophrenia susceptibility genes are timed to exert their effects in young adults. The Genes to Cognition programme is the largest genetic study so far undertaken into the synaptic molecular mechanisms underlying behaviour and physiology. We made important conceptual advances that inform how the repertoire of both innate and learned behaviours is built from unique combinations of postsynaptic proteins that either amplify or attenuate the behavioural response. This constitutes a key advance in understanding how the brain decodes information inherent in patterns of nerve impulses, and provides insight into why the PSP has evolved to be so complex, and consequently why the phenotypes of synaptopathies are so diverse. Our most recent work has opened a new phase, and scale, in understanding synapses with the first synaptome maps of the brain. We have developed next-generation methods (SYNMAP) that enable single-synapse resolution molecular mapping across the whole mouse brain and extensive regions of the human brain, revealing the molecular and morphological features of a billion synapses. This has already uncovered unprecedented spatiotemporal synapse diversity organised into an architecture that correlates with the structural and functional connectomes, and shown how mutations that cause cognitive disorders reorganise these synaptome maps; for example, by detecting vulnerable synapse subtypes and synapse loss in Alzheimer’s disease. This innovative synaptome mapping technology has huge potential to help characterise how the brain changes during normal development, including in specific cell types, and with degeneration, facilitating novel pathways to diagnosis and therapy.

The Learning Salon

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

The Learning Salon

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

Inter-individual variability in reward seeking and decision making: role of social life and consequence for vulnerability to nicotine

Inter-individual variability refers to differences in the expression of behaviors between members of a population. For instance, some individuals take greater risks, are more attracted to immediate gains or are more susceptible to drugs of abuse than others. To probe the neural bases of inter-individual variability  we study reward seeking and decision-making in mice, and dissect the specific role of dopamine in the modulation of these behaviors. Using a spatial version of the multi-armed bandit task, in which mice are faced with consecutive binary choices, we could link modifications of midbrain dopamine cell dynamics with modulation of exploratory behaviors, a major component of individual characteristics in mice. By analyzing mouse behaviors in semi-naturalistic environments, we then explored the role of social relationships in the shaping of dopamine activity and associated beahviors. I will present recent data from the laboratory suggesting that changes in the activity of dopaminergic networks link social influences with variations in the expression of non-social behaviors: by acting on the dopamine system, the social context may indeed affect the capacity of individuals to make decisions, as well as their vulnerability to drugs of abuse, in particular nicotine.

Mapping the Dynamics of the Linear and 3D Genome of Single Cells in the Developing Brain

Three intimately related dimensions of the mammalian genome—linear DNA sequence, gene transcription, and 3D genome architecture—are crucial for the development of nervous systems. Changes in the linear genome (e.g., de novo mutations), transcriptome, and 3D genome structure lead to debilitating neurodevelopmental disorders, such as autism and schizophrenia. However, current technologies and data are severely limited: (1) 3D genome structures of single brain cells have not been solved; (2) little is known about the dynamics of single-cell transcriptome and 3D genome after birth; (3) true de novo mutations are extremely difficult to distinguish from false positives (DNA damage and/or amplification errors). Here, I filled in this longstanding technological and knowledge gap. I recently developed a high-resolution method—diploid chromatin conformation capture (Dip-C)—which resolved the first 3D structure of the human genome, tackling a longstanding problem dating back to the 1880s. Using Dip-C, I obtained the first 3D genome structure of a single brain cell, and created the first transcriptome and 3D genome atlas of the mouse brain during postnatal development. I found that in adults, 3D genome “structure types” delineate all major cell types, with high correlation between chromatin A/B compartments and gene expression. During development, both transcriptome and 3D genome are extensively transformed in the first month of life. In neurons, 3D genome is rewired across scales, correlated with gene expression modules, and independent of sensory experience. Finally, I examined allele-specific structure of imprinted genes, revealing local and chromosome-wide differences. More recently, I expanded my 3D genome atlas to the human and mouse cerebellum—the most consistently affected brain region in autism. I uncovered unique 3D genome rewiring throughout life, providing a structural basis for the cerebellum’s unique mode of development and aging. In addition, to accurately measure de novo mutations in a single cell, I developed a new method—multiplex end-tagging amplification of complementary strands (META-CS), which eliminates nearly all false positives by virtue of DNA complementarity. Using META-CS, I determined the true mutation spectrum of single human brain cells, free from chemical artifacts. Together, my findings uncovered an unknown dimension of neurodevelopment, and open up opportunities for new treatments for autism and other developmental disorders.

Differences between beginning and advanced students using specific analogical stimuli during design-by-analogy

Studies reported the effects of different types and different levels of abstraction of analogical stimuli on designers. However, specific, single visual analogical stimuli on the effects of designers have not been reported. We define this type of stimuli as specific analogical stimuli. We used the extended linkography method to analyze the facilitating and limiting effects of specific analogical stimuli and free association analogical stimuli (nonspecific analogical stimuli) on the students' creativity at different design levels. Through an empirical study, we explored the differences in the effects of specific analogy stimuli on the students at different design levels. It clarifies the use of analogical stimuli in design and the teaching of analogical design methods in design education.

Multi-modal biomarkers improve prediction of memory function in cognitively unimpaired older adults

Identifying biomarkers that predict current and future cognition may improve estimates of Alzheimer’s disease risk among cognitively unimpaired older adults (CU). In vivo measures of amyloid and tau protein burden and task-based functional MRI measures of core memory mechanisms, such as the strength of cortical reinstatement during remembering, have each been linked to individual differences in memory in CU. This study assesses whether combining CSF biomarkers with fMRI indices of cortical reinstatement improves estimation of memory function in CU, assayed using three unique tests of hippocampal-dependent memory. Participants were 158 CU (90F, aged 60-88 years, CDR=0) enrolled in the Stanford Aging and Memory Study (SAMS). Cortical reinstatement was quantified using multivoxel pattern analysis of fMRI data collected during completion of a paired associate cued recall task. Memory was assayed by associative cued recall, a delayed recall composite, and a mnemonic discrimination task that involved discrimination between studied ‘target’ objects, novel ‘foil’ objects, and perceptually similar ‘lure’ objects. CSF Aβ42, Aβ40, and p-tau181 were measured with the automated Lumipulse G system (N=115). Regression analyses examined cross-sectional relationships between memory performance in each task and a) the strength of cortical reinstatement in the Default Network (comprised of posterior medial, medial frontal, and lateral parietal regions) during associative cued recall and b) CSF Aβ42/Aβ40 and p-tau181, controlling for age, sex, and education. For mnemonic discrimination, linear mixed effects models were used to examine the relationship between discrimination (d’) and each predictor as a function of target-lure similarity. Stronger cortical reinstatement was associated with better performance across all three memory assays. Age and higher CSF p-tau181 were each associated with poorer associative memory and a diminished improvement in mnemonic discrimination as target-lure similarity decreased. When combined in a single model, CSF p-tau181 and Default Network reinstatement strength, but not age, explained unique variance in associative memory and mnemonic discrimination performance, outperforming the single-modality models. Combining fMRI measures of core memory functions with protein biomarkers of Alzheimer’s disease significantly improved prediction of individual differences in memory performance in CU. Leveraging multimodal biomarkers may enhance future prediction of risk for cognitive decline.

The Learning Salon

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

The Learning Salon

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

The Learning Salon

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

Reasoning Ability: Neural Mechanisms, Development, and Plasticity

Relational thinking, or the process of identifying and integrating relations between mental representations, is regularly invoked during reasoning. This mental capacity enables us to draw higher-order abstractions and generalize across situations and contexts, and we have argued that it should be included in the pantheon of executive functions. In this talk, I will briefly review our lab's work characterizing the roles of lateral prefrontal and parietal regions in relational thinking. I will then discuss structural and functional predictors of individual differences and developmental changes in reasoning.

The Learning Salon

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

Do we reason differently about affectively charged analogies? Insights from EEG research

Affectively charged analogies are commonly used in literature and art, but also in politics and argumentation. There are reasons to think we may process these analogies differently. Notably, analogical reasoning is a complex process that requires the use of cognitive resources, which are limited. In the presence of affectively charged content, some of these resources might be directed towards affective processing and away from analogical reasoning. To investigate this idea, I investigated effects of affective charge on differences in brain activity evoked by sound versus unsound analogies. The presentation will detail the methods and results for two such experiments, one in which participants saw analogies formed of neutral and negative words and one in which they were created by combining conditioned symbols. I will also briefly discuss future research aiming to investigate the effects of analogical reasoning on brain activity related to affective processing.

How bilingualism modulates the neural mechanisms of selective attention

Learning and using multiple languages places considerable demands on our cognitive system, and has been shown to modulate the mechanisms of selective attention in both children and adults. Yet the nature of these adaptive changes is still not entirely clear. One possibility is that bilingualism boosts the capacity for selective attention; another is that it leads to a different distribution of this finite resource, aimed at supporting optimal performance under the increased processing demands. I will present a series of studies investigating the nature of modifications of selective attention in bilingualism. Using behavioural and neuroimaging techniques, our data confirm that bilingualism modifies the neural mechanisms of selective attention even in the absence of behavioural differences between monolinguals and bilinguals. They further suggest that, instead of enhanced attentional capacity, these neuroadaptive modifications appear to reflect its redistribution, arguably aimed at economising the available resources to support optimal behavioural performance.

The Learning Salon

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

Brain chart for the human lifespan

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight. Here, we built an interactive resource to benchmark brain morphology, www.brainchart.io, derived from any current or future sample of magnetic resonance imaging (MRI) data. With the goal of basing these reference charts on the largest and most inclusive dataset available, we aggregated 123,984 MRI scans from 101,457 participants aged from 115 days post-conception through 100 postnatal years, across more than 100 primary research studies. Cerebrum tissue volumes and other global or regional MRI metrics were quantified by centile scores, relative to non-linear trajectories of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones; showed high stability of individual centile scores over longitudinal assessments; and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared to non-centiled MRI phenotypes, and provided a standardised measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In sum, brain charts are an essential first step towards robust quantification of individual deviations from normative trajectories in multiple, commonly-used neuroimaging phenotypes. Our collaborative study proves the principle that brain charts are achievable on a global scale over the entire lifespan, and applicable to analysis of diverse developmental and clinical effects on human brain structure.

Mapping functional differences across cell types using a group embedding-enhanced transformer

COSYNE 2025

Two autism-related mouse models – differences in the hypothalamic gene expression of synaptic adhesion molecules and inhibitory neurotransmitter markers

Blood brain barrier differences in the nucleus accumbens relate to natural variation in trait anxiety

Central versus Peripheral nervous injuries: What are the transcriptomic differences and similarities, 24 hours after lesion?

Can the brain be taken as a whole when choosing housekeeping genes for studding sex differences?

Cortical interneurons present marked differences in their pH and intracellular chloride levels

Cost function masking artificially inflates group level differences in processing of magnetic resonance imaging data for pathological patient populations

Development of hemispheric specializations: differences in the maturation of the left and right Auditory Cortex

Differences in auditory temporal processing in the left and right auditory cortices of the rat

Sex differences in Autism Spectrum Disorder: a proteome analysis

Sex differences in behavioral and neurochemical effects of amphetamine modulated by vasopressin in the lateral septum

Sex differences in behavioral phenotype and markers of the unfolded protein response (UPR) pathway in a mouse model overexpressing human α-synuclein

Sex differences in chloride homeostasis of c-fiber primary afferents in the spinal cord dorsal horn

Sex differences in epigenetic mechanisms of opioids

Sex differences in motivated behavior: rodent models of effort-based decision-making for sweet reinforcers

Sex differences in a mouse Sensory Preconditioning task

Sex differences in neural representation of threat in ventral hippocampal and prefrontal cortical projections to nucleus accumbens

Differences in population sparsity can explain different memory consolidation speeds

Age Differences in Visuo-proprioceptive Adaptation

Dissociating Recovered from Unrecovered individuals following treatment in an animal model of PTSD reveals differences in excitatory-inhibitory balance in the hippocampus

Exploring age-and sex-related neurological and behavioural differences in a rat model of chronic traumatic encephalopathy (CTE)

The Emotional and Cognitive Differences Between Novices and Beginners Adult Programmers During An Introductory Task On Code.org : Preliminary Results

An fMRI-based brain marker of individual differences in delay discounting predicts overweight and metabolic markers

Functional recovery caused by human adipose tissue mesenchymal stem cell-derived extracellular vesicles administered 24h after stroke in normotensive rats and some differences to hypertensive rats

IGF-1 effect in LPS-derived neuroinflammation is mediated by p110α subunit of PI3K and shows sexual differences

Individual differences in dominant theta-band frequency during associative memory encoding

Individual differences in dopamine neural dynamics predict voluntary alcohol drinking

Individual differences in DNA methylation associated with sensitivity and resistance to the extinction of cocaine memories

How do Interaural Time and Interaural Level Differences Interact in Spatial Hearing with Cochlear Implants?

Investigating sex differences in the developing brain of mice using the Sex Chromosome Trisomy (SCT) mouse model

Linking inter-individual differences in cognitive function to RNAome heterogeneity

Male-female differences in social behaviour of Cntnap2 mutant mice correlate with disrupted synaptic connectivity in the anterior cingulate cortex and increased microglia activity

Neuroendocrine mechanisms governing sex differences in arcuate nucleus neurons signaling for prolactin release control

Neuronal laminar identity markers delineate regional differences during the primate-specific subplate expansion period in the human fetal cortex

Optimized reporters uncover differences in miR-124 expression among neuronal populations in the brain

Position dependent differences in response features of individual cells within the same type- Intracellular double recordings of mechanosensory T cells of the leech

Resting EEG theta activity as an electrophysiological marker of individual differences in efficiency of Temporal Information Processing

RNA-sequencing reveals treatment and sex differences in the brains of Letrozole-treated common marmosets (C. jacchus)

Similarities and differences upon binding of naturally occurring Δ9-tetrahydrocannabinol-derivatives to cannabinoid receptors, possible new terapeutic targets

Contextual inference accounts for differences in motor learning under distinct curricula

COSYNE 2025