transfer

Short-wave infrared Cerenkov imaging to better visualize targeted radiotherapy and diagnostic radiotracers

SUMMARY. The problem: Cerenkov luminescence (CL) imaging (CLI) is a new imaging method that utilizes light emitted during decay of radiotracers. CLI merges optical and nuclear imaging by utilizing affordable yet highly sensitive optical cameras with clinical radiotracers. It provides fast and cheap clinical optical imaging to explore radiotracer distribution in patients. While not tomographic, CLI systems have a lower price, smaller footprint and higher resolution than nuclear imaging scanners. Yet, due to the very low signal intensity of CL its versatility remains limited since CLI requires strict exclusion of ambient light with an enclosure. Therefore, CLI requires novel approaches to make clinical imaging more feasible. We hypothesized that we could explore the short-wave infrared (SWIR) part of CL to enable CLI under ambient light without enclosure, providing improved and facile CLI, particularly of isotopes used for therapy that cannot be imaged otherwise. SWIR imaging (900- 1300 nm) has almost no autofluorescence, absorption or scatter but provides significantly higher depth penetration, yielding images with higher contrast and resolution compared to the visible range. Since typical LEDs do not emit light beyond 850 nm, they do not interfere with the SWIR camera. We can therefore perform CLI in the SWIR range (SWIR-CLI) without the limiting light-tight box and under ambient LED light and also achieve better signal penetration and accuracy. We will investigate if SWIR-CLI can be used to monitor distribution of therapeutic isotopes for targeted radiotherapy (TRT), a fast-expanding field as highlighted by Novartis’ acquisition of Lutathera and Pluvicto for the price of $6 bn. These agents are targeting 177Lu as therapy to neuroendocrine and prostate cancers. For TRT α-emitting isotopes are particularly attractive due to the α- particle’s short path length with high linear energy transfer. However, α-emitters are very difficult to image with conventional equipment. The α-emitter could be swapped with an imaging isotope, but this can alter the agent’s biodistribution. The α-particle itself does not have sufficient energy to produce CL but several daughters in the decay chains of most α-emitters produce electrons with sufficient energy to create CL. We have already imaged the α-emitter 223Ra in patients and have recently shown that CLI of α-emitters in the SWIR is possible. SWIR- CLI could therefore provide a facile imaging approach for α-emitters. We will answer with our three independent Aims the following questions: (1) Can we image diagnostic isotopes with SWIR-CLI? (2) Can we image therapeutic emitters with SWIR-CLI? (3) Can we use SWIR-CLI to image patients undergoing PET and/or TRT? Animal studies will employ established mouse cancer models to optimize imaging parameters and validate findings, directly informing the co-clinical Aim 3 trial. By eliminating the requirement for a light-tight enclosure and enabling CLI under ambient light, SWIR-CLI represents a significant shift in the practical deployment of CLI rather than an incremental improvement. Our study will broaden the reach of CLI by enabling imaging under ambient lighting, unlocking innovative new opportunities for CLI (monitoring TRT) in research & clinical settings.

Defining Microbial and Host Pathways Driving Asymptomatic C. difficile Colonization Associated with Aging and High-Sugar Diets

SUMMARY Clostridioides difficile infection (CDI) is a leading cause of healthcare-associated diarrhea, with rising incidence in community settings and a growing burden of asymptomatic colonization. Asymptomatic car- riers, particularly among the elderly and individuals consuming high-sugar diets, represent a critical but underexplored reservoir for transmission and disease progression. This proposal introduces novel, anti- biotic-independent mouse models demonstrating that both dietary sugar and aging independently pro- mote asymptomatic C. difficile colonization. We hypothesize that these factors disrupt colonization re- sistance (CR) through distinct but overlapping microbial, metabolic, and immune pathways. In Aim 1, we will define how traditional and emerging dietary sugars alter the gut environment to permit C. difficile colonization using in vitro bioreactors and in vivo models. Aim 2 will identify age-associated changes in microbiota and mucosal immunity that impair CR, using longitudinal studies and fecal micro- biota transfer. Aim 3 will functionally validate C. difficile genes upregulated during asymptomatic carriage using CRISPR-Cas9 mutants in both sugar- and age-induced models. This integrative, multi-omics approach will uncover the mechanisms enabling asymptomatic colonization and identify microbial and host targets for intervention. The findings will inform microbiome-based strat- egies to prevent CDI in vulnerable populations and shift current paradigms in CDI risk assessment and prevention.

Systems Biology of Early Atopy: Role of Human Milk (SunBEAm-Milk)

Surprisingly little is known about the effect of breastfeeding (BF) on infant immune system development besides an effect on the gut microbiome, but its impact on metabolites and Tregs could support protection against food allergy (FA). BF is currently recommended to prevent the development of allergic diseases, especially asthma/recurrent wheezing and AD in early childhood, but firm conclusions could not be drawn regarding FA due to high heterogeneity and low quality of studies. Reverse causation, recall bias and the poor accuracy of outcome assessment are significant limitations. Most are inadequately powered to specific FA; however, a recent study showed that exclusively BF infants had lower odds of egg, sesame, and peanut allergies. Importantly, immunomodulatory composition of HM varies between mothers, which has not been taken into consideration. For over two decades we have been developing methods to assess immunomodulatory factors in the complex matrix of HM and their association with infant FA. We have shown that high levels of HM total and specific IgA are associated with protection against cow’s milk allergy, but it is unclear whether HM IgA is responsible for or is a biomarker of the vertical transfer of protection. Infant fecal and systemic IgA levels during breastfeeding and after weaning are also elevated in infants at low risk for atopic disease raising the question of whether HM factors such as cytokines can promote IgA production in infants. Consistent with this, we showed that HM cytokines, such as APRIL, induce IgA production in naïve infant B cells, and infants receiving HM with higher levels of APRIL had lower incidence of allergic disease. Finally, lower levels of several HM fatty acids including short-chain fatty acids and DHA were associated with FA. While some these factors were are associated with maternal atopic disease, several of them are not and suggest a role for diet instead. The System Biology of Early Atopy (SunBEAm) population-based cohort of 2500 mother-infant pairs is >50% recruited and provides an unprecedented opportunity to assess association of HM feeding and immune factors in HM with development of infant immune system and FA/AD. The Common Sample comprises a subset of 100 dyads with FA, 100 with FA+AD, 100 with AD, 100 with no FA or AD and more extensively profiled biological data. Utilizing all 2-month HM samples available in the Common Sample, we will assess levels of immune factors in HM and their association with maternal/infant characteristics (Aim 1). Utilizing data from the whole cohort, we will assess the association between HM vs formula feeding on well-defined FA/AD further adjusted based on high vs low levels of HM immune components in the Common Sample (Aim 2b). Finally, we will examine the immune cell and epithelial effects of HM on infant immune markers and intestinal organoids (Aim 3). Key findings will be validated in an independent birth cohort. The ultimate goal is to uncover protective properties of BF and HM in FA and subsequent design of policies and prevention strategies to address the increasing rates of FA.

Characterization and functional impact of somatic numtogenesis in the human cortex

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

Circulating extracellular vesicles as functional indicators of maternal mental and physical health in pregnancy and postpartum

Women with high levels of adverse childhood experiences (ACEs) are at significantly greater risk for negative health outcomes in pregnancy and postpartum, including gestational diabetes, PTB, and depressed mood. However, we still lack biomarkers or a sufficient understanding of causal mechanisms. Extracellular vesicles (EVs) are one of the most dynamic and abundant biological signals secreted into maternal circulation, largely produced by the placenta – where levels increase 4-5-fold during pregnancy. Similarly, removal of the placenta at delivery produces a dramatic drop in maternal EV concentration. Across species, we and others have identified significant EV changes during pregnancy associated with homeostatic regulation, including glucose and glucocorticoid levels, supporting key roles for EVs in maternal health. However, longitudinal studies in human pregnancy and postpartum have not been conducted. We know little as to the mechanisms controlling EV secretion or the roles for EVs in maternal pregnancy and postpartum health. Our decade’s long work identified the X-linked gene, O-glycosyltransferase (OGT), in mouse and human placenta as a master gage of the maternal milieu, where OGT regulation of annexin A1 (AA1) is key to EV cargo loading and secretion from the placenta. We recently reported that placental OGT levels positively correlate with maternal EV concentration. How this association may contribute toward postpartum health, including regulating maternal stress physiology and mood in humans is not known. We hypothesize that increased ACEs, similar to stress in preclinical models, are negatively associated with a cell’s ability to secrete EVs important to maintain homeostasis in the face of the challenges of pregnancy and postpartum, producing an increasingly unhealthy state. Therefore, the goals of these proposed studies in both mice and humans are as follows: 1) To identify cellular mechanisms involved in EV secretion important to maternal health outcomes utilizing the placenta as a tool to genetically target OGT in mice and examine maternal homeostatic control related to EV concentration and composition during pregnancy; 2) To examine the functional ability for a dynamic elevation in maternal EV concentration to improve homeostatic regulation in pregnancy and postpartum using chemogenetic activation (DREADDs) of placenta trophoblast cells in pregnancy, and by EV transfer by tail vein injection postpartum; and 3) To examine in women changes in maternal EVs in a longitudinal pregnancy and postpartum study in association with maternal glucose and cortisol changes, we will examine markers of physical (glucose challenge test), HPA stress (hair cortisol & stress- stimulated salivary cortisol) and psychological (Hamilton Rating Scale for Depression, Perceived Stress Scale) health across pregnancy and the postpartum period in 150 healthy women with varying degrees of exposure to ACEs as measured using the ACE Questionnaire (ACE-Q).

Validating Causality of Disputed Mitochondrial Variants in Inborn Errors of Metabolism

PROJECT SUMMARY Primary mitochondrial disease (PMD) encompasses multi-systemic disorders caused by impaired mitochondrial function. PMDs arise from pathogenic variants in either nuclear genes encoding mitochondrial proteins, or in the mitochondrial DNA (mtDNA) genome. Clinical diagnosis is challenging due to phenotypic heterogeneity, underscoring the importance of genetic diagnosis. ACMG/AMP guidelines provide a well-established framework for interpreting nuclear DNA variants while diagnosing genetic diseases. Their application to mtDNA variants, however, remains challenging due to unique features of mtDNA: maternal inheritance, heteroplasmy, threshold effects, and effect of transfer or ribosomal RNA rather than coding variants. To address these challenges, the ClinGen Mitochondrial Disease Nuclear and Mitochondrial Variant Curation Expert Panel, co-chaired by the Multi-PIs of this study, developed widely adopted ACMG/AMP revised guidelines for mtDNA variant interpretation. Over the past five years, this global expert panel has curated more than 280 mtDNA variant. Because of the lack of functional data of individual mtDNA variants in the literature, 23 previously reported pathogenic (P) variants were classified as Variants of Uncertain Significance (VUS), hindering definitive PMD diagnoses and therapeutic development. This R01 project aims to resolve the pathogenicity of these 23 mtDNA VUS through functional validation, leveraging advanced mtDNA base editing and single-cell genomics in in vitro and in vivo models. In Aim 1, we will create human 143B cell line models for 20 VUS using cutting-edge mtDNA editing techniques, optimized for efficiency and minimal off-target effects. Single-cell genomics (mtscATAC-seq and scRNA-seq) will assess heteroplasmy and genomic changes, while functional assays will evaluate mitochondrial ATP production, oxidative phosphorylation, membrane potential, and redox stress. Aim 2 will develop zebrafish models for 17 conserved VUS, characterizing phenotypic and mitochondrial outcomes to corroborate in vitro findings and PMD patient phenotypes. This study will clarify longstanding uncertainties regarding the pathogenicity of these mtDNA VUSs which were nonetheless reported to be pathogenic with often strong genetic evidence but limited functional data. The study will also establish valuable cell and zebrafish models and provide mechanistic insights of PMDs. The resulting resources will be shared with the scientific community to accelerate research and therapeutic advancements for novel precision medicine approaches for PMDs.

Transposable element silencing as a regulator of salivary gland immune homeostasis

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

Avian influenza virus prevention in the domestic host by a deactivated vaccine

Abstract Influenza viruses, which affect both birds and mammals, pose a substantial public health concern. An estimated 10% of the global population annually becomes infected, resulting in 300,000 to 600,000 deaths worldwide. Our research objectives are to develop a Hemagglutinin (HA) and Neuraminidase (NA) based rabies-vectored vaccine against highly pathogenic Avian Influenza (HPAI) A virus H5N1. We have already demonstrated the vaccine’s immunogenicity and protective efficacy against HPAI H5N1 Vietnam 1203. To advance this research, we propose to utilize a novel RAVB-based deactivated vaccine that harbors the H5 antigens of the current homologous circulation (clade 2.3.4.4b) and a construct expressing N1. Our first aim will involve comparing the H5 or H5/N1 RABV-based vaccines against challenges by PR8 recombinants carrying H5N1 proteins in mice. We will employ a single immunization and a prime/boost approach, either with or without an adjuvant approved for use in animals and humans (SEPIVAC SWE™). We will assess the role of T cells in the vaccine-induced protection by performing CD4/CD8 depletion before challenge Our second aim will utilize the vaccine approach identified to protect our mouse system in dairy cows. Subsequently, we will assess the vaccine’s efficacy against challenges administered intranasally and intramammary. We will verify the role of the vaccine-induced antibodies in protection against H5N1 by performing passive transfer studies of purified IgG from vaccinated cows before challenge. In summary, this study will evaluate the efficacy and delineate the mechanism of protection of a safe and well- established vaccine platform to protect against HPAI H5 and explore its potential as an animal and human vaccine.

A novel MRI method for noninvasive imaging of bone quality in type 2 diabetes

ABSTRACT: Type 2 diabetes mellitus (T2DM) affects 500 million of the global population, which is expected to increase to 800 million in 20 years. One of the multiple complications involved with T2DM is the significantly increased bone fracture risk and post-fracture mortality. Dual-energy X-ray absorptiometry (DXA) scans are routinely performed to measure bone mineral density (BMD) and associated fracture risk. However, T2DM patients often show preserved or even elevated BMD despite the significantly increased fracture risk. This mismatch between the BMD measurement and actual fracture risk hampers the accurate assessment of fracture risk and the appropriate treatment of T2DM that considers patient bone health. The lack of an accurate fracture risk assessment tool also confounds the evaluation of the bone health effect of antidiabetic drugs, including recently highlighted glucagon-like peptide-1 receptor agonists (e.g., semaglutide) and sodium-glucose cotransporter-2 inhibitors. Previous studies have suggested that bone quality, rather than bone quantity, as represented by BMD, is a crucial factor contributing to fracture risk in T2DM settings. Collagen crosslinking via advanced glycation end-products (AGEs) in cortical bone has been identified as a distinctive bone quality characteristic of T2DM patients, which explains the increased bone fragility. Although this finding is highly promising for improving the bone health management of T2DM patients, currently, no non-invasive method can monitor collagen crosslinking in the bones. This proposal aims to develop an ultrashort echo time (UTE) MRI-based method for measuring the degree of bone collagen crosslinking by quantifying magnetization transfer between water and collagen in the bone. This method, termed UTE-quantitative magnetization transfer (UTE-qMT) MRI, measures not only the quantity of macromolecules (e.g., collagen) in the bone but also the rates of exchange between water and macromolecular protons, which are related to the degree of collagen crosslinking. The proposal will develop and optimize the accelerated UTE-qMT method for reliably measuring the exchange rate in Aim 1. The optimized technique will be validated by correlating exchange rates with AGE-driven collagen crosslinking and subsequent compromise of bone mechanical properties in Aim 2. Finally, the optimized UTE-qMT MRI method will be translated to animal and human studies to demonstrate its clinical feasibility for investigating the effect of antidiabetic drugs on bone health in patients with T2DM in Aim 3. The successful completion of these aims will enable rapid and accurate assessment of bone fracture risk in patients with T2DM. Furthermore, noninvasively probing bone quality can also accurately assess the effect of antidiabetic drugs on bone health and aid in screening novel T2DM therapeutics for their impact on bone health.

Developing a novel technology for studying T cell differentiation in vivo

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

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

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

Neutralizing persistent IFN-I to improve HIV-specific CAR T cell therapy

PROJECT SUMMARY A critical hurdle to further improving the quality of life for people living with HIV (PLWH) is the need to resolve the residual immune activation and inflammation that persists even in those taking effective antiretroviral therapy (ART), which suppresses HIV replication. This unresolved and persistent immune activation is associated with increased type-I interferon (IFN-I) signaling, and increased incidence of comorbidities. Encouragingly, reports demonstrate that blocking IFN-I signaling in animal models of HIV infection can reduce HIV reservoirs and restore T cell immune function. We hypothesize that blocking IFN-I would likewise augment engineered T cell-based therapies against HIV, such as chimeric antigen receptor (CAR) T cells. Our prior work has demonstrated that when engineered to express both the 4-1BB and CD28 costimulatory domains and protected from HIV infection, HIV-specific CD4 ectodomain CAR T cells can reduce acute viremia, prevent CD4+ T cell loss, and reduce viral burden in the tissues of HIV-infected humanized mice. However, the reduction of plasma viral loads was ultimately transient, suggesting that the potency of HIV-specific CAR T cells should be further optimized for clinical translation. Our preliminary data highlights interferon-beta (IFNb) as a key immunosuppressive IFN-I negatively regulating CAR T cell proliferation, and we demonstrate that neutralizing IFNb in vivo enhanced the engraftment and persistence of HIV-specific CAR T cells adoptively transferred into HIV-infected ART- suppressed humanized mice. This proposal will interrogate whether IFNb neutralization augments CAR T cell therapy through 1) identifying the mechanism(s) by which chronic IFNb exposure mediates HIV-specific CAR T cell dysfunction, and 2) determining the effect of neutralizing IFNb on CAR T cell function and persistence in HIV infection in vivo. The proposed aims seek to develop the neutralization of IFNb as a novel immunotherapy approach to maximize the potency of HIV-specific CAR T cells aimed at achieving a functional HIV cure.

Intrinsic and extrinsic mechanisms underlying trigeminal nerve deficits in familial dysautonomia

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

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

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

Computational Mechanisms of Predictive Processing in Brains and Machines

Predictive processing offers a unifying view of neural computation, proposing that brains continuously anticipate sensory input and update internal models based on prediction errors. In this talk, I will present converging evidence for the computational mechanisms underlying this framework across human neuroscience and deep neural networks. I will begin with recent work showing that large-scale distributed prediction-error encoding in the human brain directly predicts how sensory representations reorganize through predictive learning. I will then turn to PredNet, a popular predictive coding inspired deep network that has been widely used to model real-world biological vision systems. Using dynamic stimuli generated with our Spatiotemporal Style Transfer algorithm, we demonstrate that PredNet relies primarily on low-level spatiotemporal structure and remains insensitive to high-level content, revealing limits in its generalization capacity. Finally, I will discuss new recurrent vision models that integrate top-down feedback connections with intrinsic neural variability, uncovering a dual mechanism for robust sensory coding in which neural variability decorrelates unit responses, while top-down feedback stabilizes network dynamics. Together, these results outline how prediction error signaling and top-down feedback pathways shape adaptive sensory processing in biological and artificial systems.

Hippocampal sequences in temporal association memory and information transfer

Diffuse coupling in the brain - A temperature dial for computation

The neurobiological mechanisms of arousal and anesthesia remain poorly understood. Recent evidence highlights the key role of interactions between the cerebral cortex and the diffusely projecting matrix thalamic nuclei. Here, we interrogate these processes in a whole-brain corticothalamic neural mass model endowed with targeted and diffusely projecting thalamocortical nuclei inferred from empirical data. This model captures key features seen in propofol anesthesia, including diminished network integration, lowered state diversity, impaired susceptibility to perturbation, and decreased corticocortical coherence. Collectively, these signatures reflect a suppression of information transfer across the cerebral cortex. We recover these signatures of conscious arousal by selectively stimulating the matrix thalamus, recapitulating empirical results in macaque, as well as wake-like information processing states that reflect the thalamic modulation of largescale cortical attractor dynamics. Our results highlight the role of matrix thalamocortical projections in shaping many features of complex cortical dynamics to facilitate the unique communication states supporting conscious awareness.

Movement planning as a window into hierarchical motor control

The ability to organise one's body for action without having to think about it is taken for granted, whether it is handwriting, typing on a smartphone or computer keyboard, tying a shoelace or playing the piano. When compromised, e.g. in stroke, neurodegenerative and developmental disorders, the individuals’ study, work and day-to-day living are impacted with high societal costs. Until recently, indirect methods such as invasive recordings in animal models, computer simulations, and behavioural markers during sequence execution have been used to study covert motor sequence planning in humans. In this talk, I will demonstrate how multivariate pattern analyses of non-invasive neurophysiological recordings (MEG/EEG), fMRI, and muscular recordings, combined with a new behavioural paradigm, can help us investigate the structure and dynamics of motor sequence control before and after movement execution. Across paradigms, participants learned to retrieve and produce sequences of finger presses from long-term memory. Our findings suggest that sequence planning involves parallel pre-ordering of serial elements of the upcoming sequence, rather than a preparation of a serial trajectory of activation states. Additionally, we observed that the human neocortex automatically reorganizes the order and timing of well-trained movement sequences retrieved from memory into lower and higher-level representations on a trial-by-trial basis. This echoes behavioural transfer across task contexts and flexibility in the final hundreds of milliseconds before movement execution. These findings strongly support a hierarchical and dynamic model of skilled sequence control across the peri-movement phase, which may have implications for clinical interventions.

Epigenetic rewiring in Schinzel-Giedion syndrome

During life, a variety of specialized cells arise to grant the right and timely corrected functions of tissues and organs. Regulation of chromatin in defining specialized genomic regions (e.g. enhancers) plays a key role in developmental transitions from progenitors into cell lineages. These enhancers, properly topologically positioned in 3D space, ultimately guide the transcriptional programs. It is becoming clear that several pathologies converge in differential enhancer usage with respect to physiological situations. However, why some regulatory regions are physiologically preferred, while some others can emerge in certain conditions, including other fate decisions or diseases, remains obscure. Schinzel-Giedion syndrome (SGS) is a rare disease with symptoms such as severe developmental delay, congenital malformations, progressive brain atrophy, intractable seizures, and infantile death. SGS is caused by mutations in the SETBP1 gene that results in its accumulation further leading to the downstream accumulation of SET. The oncoprotein SET has been found as part of the histone chaperone complex INHAT that blocks the activity of histone acetyltransferases suggesting that SGS may (i) represent a natural model of alternative chromatin regulation and (ii) offer chances to study downstream (mal)adaptive mechanisms. I will present our work on the characterization of SGS in appropriate experimental models including iPSC-derived cultures and mouse.

The Neural Race Reduction: Dynamics of nonlinear representation learning in deep architectures

What is the relationship between task, network architecture, and population activity in nonlinear deep networks? I will describe the Gated Deep Linear Network framework, which schematizes how pathways of information flow impact learning dynamics within an architecture. Because of the gating, these networks can compute nonlinear functions of their input. We derive an exact reduction and, for certain cases, exact solutions to the dynamics of learning. The reduction takes the form of a neural race with an implicit bias towards shared representations, which then govern the model’s ability to systematically generalize, multi-task, and transfer. We show how appropriate network architectures can help factorize and abstract knowledge. Together, these results begin to shed light on the links between architecture, learning dynamics and network performance.

Roots of Analogy

Can nonhuman animals perceive the relation-between-relations? This intriguing question has been studied over the last 40 years; nonetheless, the extent to which nonhuman species can do so remains controversial. Here, I review empirical evidence suggesting that pigeons, parrots, crows, and baboons join humans in reliably acquiring and transferring relational matching-to-sample (RMTS). Many theorists consider that RMTS captures the essence of analogy, because basic to analogy is appreciating the ‘relation between relations.’ Factors affecting RMTS performance include: prior training experience, the entropy of the sample stimulus, and whether the items that serve as sample stimuli can also serve as choice stimuli.

Protocols for the social transfer of pain and analgesia in mice

We provide protocols for the social transfer of pain and analgesia in mice. We describe the steps to induce pain or analgesia (pain relief) in bystander mice with a 1-h social interaction with a partner injected with CFA (complete Freund’s adjuvant) or CFA and morphine, respectively. We detail behavioral tests to assess pain or analgesia in the untreated bystander mice. This protocol has been validated in mice and rats and can be used for investigating mechanisms of empathy. Highlights • A protocol for the rapid social transfer of pain in rodents • Detailed requirements for handling and housing conditions • Procedures for habituation, social interaction, and pain induction and assessment • Adaptable for social transfer of analgesia and may be used to study empathy in rodents https://doi.org/10.1016/j.xpro.2022.101756

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

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

Learning by Analogy in Mathematics

Analogies between old and new concepts are common during classroom instruction. While previous studies of transfer focus on how features of initial learning guide later transfer to new problem solving, less is known about how to best support analogical transfer from previous learning while children are engaged in new learning episodes. Such research may have important implications for teaching and learning in mathematics, which often includes analogies between old and new information. Some existing research promotes supporting learners' explicit connections across old and new information within an analogy. In this talk, I will present evidence that instructors can invite implicit analogical reasoning through warm-up activities designed to activate relevant prior knowledge. Warm-up activities "close the transfer space" between old and new learning without additional direct instruction.

Mouse visual cortex as a limited resource system that self-learns an ecologically-general representation

Studies of the mouse visual system have revealed a variety of visual brain areas in a roughly hierarchical arrangement, together with a multitude of behavioral capacities, ranging from stimulus-reward associations, to goal-directed navigation, and object-centric discriminations. However, an overall understanding of the mouse’s visual cortex organization, and how this organization supports visual behaviors, remains unknown. Here, we take a computational approach to help address these questions, providing a high-fidelity quantitative model of mouse visual cortex. By analyzing factors contributing to model fidelity, we identified key principles underlying the organization of mouse visual cortex. Structurally, we find that comparatively low-resolution and shallow structure were both important for model correctness. Functionally, we find that models trained with task-agnostic, unsupervised objective functions, based on the concept of contrastive embeddings were substantially better than models trained with supervised objectives. Finally, the unsupervised objective builds a general-purpose visual representation that enables the system to achieve better transfer on out-of-distribution visual, scene understanding and reward-based navigation tasks. Our results suggest that mouse visual cortex is a low-resolution, shallow network that makes best use of the mouse’s limited resources to create a light-weight, general-purpose visual system – in contrast to the deep, high-resolution, and more task-specific visual system of primates.

Flexible multitask computation in recurrent networks utilizes shared dynamical motifs

Flexible computation is a hallmark of intelligent behavior. Yet, little is known about how neural networks contextually reconfigure for different computations. Humans are able to perform a new task without extensive training, presumably through the composition of elementary processes that were previously learned. Cognitive scientists have long hypothesized the possibility of a compositional neural code, where complex neural computations are made up of constituent components; however, the neural substrate underlying this structure remains elusive in biological and artificial neural networks. Here we identified an algorithmic neural substrate for compositional computation through the study of multitasking artificial recurrent neural networks. Dynamical systems analyses of networks revealed learned computational strategies that mirrored the modular subtask structure of the task-set used for training. Dynamical motifs such as attractors, decision boundaries and rotations were reused across different task computations. For example, tasks that required memory of a continuous circular variable repurposed the same ring attractor. We show that dynamical motifs are implemented by clusters of units and are reused across different contexts, allowing for flexibility and generalization of previously learned computation. Lesioning these clusters resulted in modular effects on network performance: a lesion that destroyed one dynamical motif only minimally perturbed the structure of other dynamical motifs. Finally, modular dynamical motifs could be reconfigured for fast transfer learning. After slow initial learning of dynamical motifs, a subsequent faster stage of learning reconfigured motifs to perform novel tasks. This work contributes to a more fundamental understanding of compositional computation underlying flexible general intelligence in neural systems. We present a conceptual framework that establishes dynamical motifs as a fundamental unit of computation, intermediate between the neuron and the network. As more whole brain imaging studies record neural activity from multiple specialized systems simultaneously, the framework of dynamical motifs will guide questions about specialization and generalization across brain regions.

Analogical retrieval across disparate task domains

Previous experiments have shown that a comparison of two written narratives highlights their shared relational structure, which in turn facilitates the retrieval of analogous narratives from the past (e.g., Gentner, Loewenstein, Thompson, & Forbus, 2009). However, analogical retrieval occurs across domains that appear more conceptually distant than merely different narratives, and the deepest analogies use matches in higher-order relational structure. The present study investigated whether comparison can facilitate analogical retrieval of higher-order relations across written narratives and abstract symbolic problems. Participants read stories which became retrieval targets after a delay, cued by either analogous stories or letter-strings. In Experiment 1 we replicated Gentner et al. who used narrative retrieval cues, and also found preliminary evidence for retrieval between narrative and symbolic domains. In Experiment 2 we found clear evidence that a comparison of analogous letter-string problems facilitated the retrieval of source stories with analogous higher-order relations. Experiment 3 replicated the retrieval results of Experiment 2 but with a longer delay between encoding and recall, and a greater number of distractor source stories. These experiments offer support for the schema induction account of analogical retrieval (Gentner et al., 2009) and show that the schemas abstracted from comparison of narratives can be transferred to non-semantic symbolic domains.

Exploration-Based Approach for Computationally Supported Design-by-Analogy

Engineering designers practice design-by-analogy (DbA) during concept generation to retrieve knowledge from external sources or memory as inspiration to solve design problems. DbA is a tool for innovation that involves retrieving analogies from a source domain and transferring the knowledge to a target domain. While DbA produces innovative results, designers often come up with analogies by themselves or through serendipitous, random encounters. Computational support systems for searching analogies have been developed to facilitate DbA in systematic design practice. However, many systems have focused on a query-based approach, in which a designer inputs a keyword or a query function and is returned a set of algorithmically determined stimuli. In this presentation, a new analogical retrieval process that leverages a visual interaction technique is introduced. It enables designers to explore a space of analogies, rather than be constrained by what’s retrieved by a query-based algorithm. With an exploration-based DbA tool, designers have the potential to uncover more useful and unexpected inspiration for innovative design solutions.

Trading Off Performance and Energy in Spiking Networks

Many engineered and biological systems must trade off performance and energy use, and the brain is no exception. While there are theories on how activity levels are controlled in biological networks through feedback control (homeostasis), it is not clear what the effects on population coding are, and therefore how performance and energy can be traded off. In this talk we will consider this tradeoff in auto-encoding networks, in which there is a clear definition of performance (the coding loss). We first show how SNNs follow a characteristic trade-off curve between activity levels and coding loss, but that standard networks need to be retrained to achieve different tradeoff points. We next formalize this tradeoff with a joint loss function incorporating coding loss (performance) and activity loss (energy use). From this loss we derive a class of spiking networks which coordinates its spiking to minimize both the activity and coding losses -- and as a result can dynamically adjust its coding precision and energy use. The network utilizes several known activity control mechanisms for this --- threshold adaptation and feedback inhibition --- and elucidates their potential function within neural circuits. Using geometric intuition, we demonstrate how these mechanisms regulate coding precision, and thereby performance. Lastly, we consider how these insights could be transferred to trained SNNs. Overall, this work addresses a key energy-coding trade-off which is often overlooked in network studies, expands on our understanding of homeostasis in biological SNNs, as well as provides a clear framework for considering performance and energy use in artificial SNNs.

In pursuit of a universal, biomimetic iBCI decoder: Exploring the manifold representations of action in the motor cortex

My group pioneered the development of a novel intracortical brain computer interface (iBCI) that decodes muscle activity (EMG) from signals recorded in the motor cortex of animals. We use these synthetic EMG signals to control Functional Electrical Stimulation (FES), which causes the muscles to contract and thereby restores rudimentary voluntary control of the paralyzed limb. In the past few years, there has been much interest in the fact that information from the millions of neurons active during movement can be reduced to a small number of “latent” signals in a low-dimensional manifold computed from the multiple neuron recordings. These signals can be used to provide a stable prediction of the animal’s behavior over many months-long periods, and they may also provide the means to implement methods of transfer learning across individuals, an application that could be of particular importance for paralyzed human users. We have begun to examine the representation within this latent space, of a broad range of behaviors, including well-learned, stereotyped movements in the lab, and more natural movements in the animal’s home cage, meant to better represent a person’s daily activities. We intend to develop an FES-based iBCI that will restore voluntary movement across a broad range of motor tasks without need for intermittent recalibration. However, the nonlinearities and context dependence within this low-dimensional manifold present significant challenges.

A new experimental paradigm to study analogy transfer

Analogical reasoning is one of the most complex cognitive functions in humans that allows abstract thinking, high-level reasoning, and learning. Based on analogical reasoning, one can extract an abstract and general concept (i.e., an analogy schema) from a familiar situation and apply it to a new context or domain (i.e., analogy transfer). These processes allow us to solve problems we never encountered before and generate new ideas. However, the place of analogy transfer in problem solving mechanisms is unclear. This presentation will describe several experiments with three main findings. First, we show how analogy transfer facilitates problem-solving, replicating existing empirical data largely based on the radiation/fortress problems with four new riddles. Second, we propose a new experimental task that allows us to quantify analogy transfer. Finally, using science network methodology, we show how restructuring the mental representation of a problem can predict successful solving of an analogous problem. These results shed new light on the cognitive mechanism underlying solution transfer by analogy and provide a new tool to quantify individual abilities.

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.

The role of histone methyltransferase SETDB1 on regulating mood behaviors

Scaffolding up from Social Interactions: A proposal of how social interactions might shape learning across development

Social learning and analogical reasoning both provide exponential opportunities for learning. These skills have largely been studied independently, but my future research asks how combining skills across previously independent domains could add up to more than the sum of their parts. Analogical reasoning allows individuals to transfer learning between contexts and opens up infinite opportunities for innovation and knowledge creation. Its origins and development, so far, have largely been studied in purely cognitive domains. Constraining analogical development to non-social domains may mistakenly lead researchers to overlook its early roots and limit ideas about its potential scope. Building a bridge between social learning and analogy could facilitate identification of the origins of analogical reasoning and broaden its far-reaching potential. In this talk, I propose that the early emergence of social learning, its saliency, and its meaningful context for young children provides a springboard for learning. In addition to providing a strong foundation for early analogical reasoning, the social domain provides an avenue for scaling up analogies in order to learn to learn from others via increasingly complex and broad routes.

Representation transfer and signal denoising through topographic modularity

To prevail in a dynamic and noisy environment, the brain must create reliable and meaningful representations from sensory inputs that are often ambiguous or corrupt. Since only information that permeates the cortical hierarchy can influence sensory perception and decision-making, it is critical that noisy external stimuli are encoded and propagated through different processing stages with minimal signal degradation. Here we hypothesize that stimulus-specific pathways akin to cortical topographic maps may provide the structural scaffold for such signal routing. We investigate whether the feature-specific pathways within such maps, characterized by the preservation of the relative organization of cells between distinct populations, can guide and route stimulus information throughout the system while retaining representational fidelity. We demonstrate that, in a large modular circuit of spiking neurons comprising multiple sub-networks, topographic projections are not only necessary for accurate propagation of stimulus representations, but can also help the system reduce sensory and intrinsic noise. Moreover, by regulating the effective connectivity and local E/I balance, modular topographic precision enables the system to gradually improve its internal representations and increase signal-to-noise ratio as the input signal passes through the network. Such a denoising function arises beyond a critical transition point in the sharpness of the feed-forward projections, and is characterized by the emergence of inhibition-dominated regimes where population responses along stimulated maps are amplified and others are weakened. Our results indicate that this is a generalizable and robust structural effect, largely independent of the underlying model specificities. Using mean-field approximations, we gain deeper insight into the mechanisms responsible for the qualitative changes in the system’s behavior and show that these depend only on the modular topographic connectivity and stimulus intensity. The general dynamical principle revealed by the theoretical predictions suggest that such a denoising property may be a universal, system-agnostic feature of topographic maps, and may lead to a wide range of behaviorally relevant regimes observed under various experimental conditions: maintaining stable representations of multiple stimuli across cortical circuits; amplifying certain features while suppressing others (winner-take-all circuits); and endow circuits with metastable dynamics (winnerless competition), assumed to be fundamental in a variety of tasks.

Improving Communication With the Brain Through Electrode Technologies

Over the past 30 years bionic devices such as cochlear implants and pacemakers, have used a small number of metal electrodes to restore function and monitor activity in patients following disease or injury of excitable tissues. Growing interest in neurotechnologies, facilitated by ventures such as BrainGate, Neuralink and the European Human Brain Project, has increased public awareness of electrotherapeutics and led to both new applications for bioelectronics and a growing demand for less invasive devices with improved performance. Coupled with the rapid miniaturisation of electronic chips, bionic devices are now being developed to diagnose and treat a wide variety of neural and muscular disorders. Of particular interest is the area of high resolution devices that require smaller, more densely packed electrodes. Due to poor integration and communication with body tissue, conventional metallic electrodes cannot meet these size and spatial requirements. We have developed a range of polymer based electronic materials including conductive hydrogels (CHs), conductive elastomers (CEs) and living electrodes (LEs). These technologies provide synergy between low impedance charge transfer, reduced stiffness and an ability to be provide a biologically active interface. A range of electrode approaches are presented spanning wearables, implantables and drug delivery devices. This talk outlines the materials development and characterisation of both in vitro properties and translational in vivo performance. The challenges for translation and commercial uptake of novel technologies will also be discussed.

Swarms for people

As tiny robots become individually more sophisticated, and larger robots easier to mass produce, a breakdown of conventional disciplinary silos is enabling swarm engineering to be adopted across scales and applications, from nanomedicine to treat cancer, to cm-sized robots for large-scale environmental monitoring or intralogistics. This convergence of capabilities is facilitating the transfer of lessons learned from one scale to the other. Cm-sized robots that work in the 1000s may operate in a way similar to reaction-diffusion systems at the nanoscale, while sophisticated microrobots may have individual capabilities that allow them to achieve swarm behaviour reminiscent of larger robots with memory, computation, and communication. Although the physics of these systems are fundamentally different, much of their emergent swarm behaviours can be abstracted to their ability to move and react to their local environment. This presents an opportunity to build a unified framework for the engineering of swarms across scales that makes use of machine learning to automatically discover suitable agent designs and behaviours, digital twins to seamlessly move between the digital and physical world, and user studies to explore how to make swarms safe and trustworthy. Such a framework would push the envelope of swarm capabilities, towards making swarms for people.

Analogy and ethics: opportunities at the intersection

Analogy offers a new interpretation of a common concern in ethics: whether decision making includes or excludes a consideration of moral issues. This is often discussed as the moral awareness of decision makers and considered a motivational concern. The possible new interpretation is that moral awareness is in part a matter of expertise. Some failures of moral awareness can then be understood as stemming from novicehood. Studies of analogical transfer are consistent with the possibility that moral awareness is in part a matter of expertise, that as a result motivation is less helpful than some prior theorizing would predict, and that many adults are not as expert in the domain of ethics as one might hope. The possibility of expert knowledge of ethical principles leads to new questions and opportunities.

Music training effects on multisensory and cross-sensory transfer processing: from cross-sectional to RCT studies

Integration of „environmental“ information in the neuronal epigenome

The inhibitory actions of the heterogeneous collection of GABAergic interneurons tremendously influence cortical information processing, which is reflected by diseases like autism, epilepsy and schizophrenia that involve defects in cortical inhibition. Apart from the regulation of physiological processes like synaptic transmission, proper interneuron function also relies on their correct development. Hence, decrypting regulatory networks that direct proper cortical interneuron development as well as adult functionality is of great interest, as this helps to identify critical events implicated in the etiology of the aforementioned diseases. Thereby, extrinsic factors modulate these processes and act on cell- and stage-specific transcriptional programs. Herein, epigenetic mechanisms of gene regulation, like DNA methylation executed by DNA methyltransferases (DNMTs), histone modifications and non-coding RNAs, call increasing attention in integrating “environmental information” in our genome and sculpting physiological processes in the brain relevant for human mental health. Several studies associate altered expression levels and function of the DNA methyltransferase 1 (DNMT1) in subsets of embryonic and adult cortical interneurons in patients diagnosed with schizophrenia. Although accumulating evidence supports the relevance of epigenetic signatures for instructing cell type-specific development, only very little is known about their functional implications in discrete developmental processes and in subtype-specific maturation of cortical interneurons. Similarly, little is known about the role of DNMT1 in regulating adult interneurons functionality. This talk will provide an overview about newly identified and roles DNMT1 has in orchestrating cortical interneuron development and adult function. Further, this talk will report about the implications of lncRNAs in mediating site-specific DNA methylation in response to discrete external stimuli.

How single neuron dynamics influence network activity and behaviour

To understand how the brain can perform complex tasks such as perception, we have to understand how information enters the brain, how it is transformed and how it is transferred. But, how do we measure information transfer in the brain? This presentation will start with a general introduction of what mutual information is and how to measure it in an experimental setup. Next, the talk will focus on how this can be used to develop brain models at different (spatial) levels, from the microscopic single neuron level to the macroscopic network and behavioural level. How can we incorporate the knowledge about single neurons, that already show complex dynamics, into network activity and link this to behaviour?

Optogenetic silencing of synaptic transmission with a mosquito rhodopsin

Long-range projections link distant circuits in the brain, allowing efficient transfer of information between regions and synchronization of distributed patterns of neural activity. Understanding the functional roles of defined neuronal projection pathways requires temporally precise manipulation of their activity, and optogenetic tools appear to be an obvious choice for such experiments. However, we and others have previously shown that commonly-used inhibitory optogenetic tools have low efficacy and off-target effects when applied to presynaptic terminals. In my talk, I will present a new solution to this problem: a targeting-enhanced mosquito homologue of the vertebrate encephalopsin (eOPN3), which upon activation can effectively suppress synaptic transmission through the Gi/o signaling pathway. Brief illumination of presynaptic terminals expressing eOPN3 triggers a lasting suppression of synaptic output that recovers spontaneously within minutes in vitro and in vivo. The efficacy of eOPN3 in suppressing presynaptic release opens new avenues for functional interrogation of long-range neuronal circuits in vivo.

Herbert Jasper Lecture

There is a long-standing tension between the notion that the hippocampal formation is essentially a spatial mapping system, and the notion that it plays an essential role in the establishment of episodic memory and the consolidation of such memory into structured knowledge about the world. One theory that resolves this tension is the notion that the hippocampus generates rather arbitrary 'index' codes that serve initially to link attributes of episodic memories that are stored in widely dispersed and only weakly connected neocortical modules. I will show how an essentially 'spatial' coding mechanism, with some tweaks, provides an ideal indexing system and discuss the neural coding strategies that the hippocampus apparently uses to overcome some biological constraints affecting the possibility of shipping the index code out widely to the neocortex. Finally, I will present new data suggesting that the hippocampal index code is indeed transferred to layer II-III of the neocortex.

Anterior Cingulate inputs to nucleus accumbens control the social transfer of pain and analgesia

Empathy plays a critical role in social interactions, and many species, including rodents, display evolutionarily conserved behavioral antecedents of empathy. In both humans and rodents, the anterior cingulate cortex (ACC) encodes information about the affective state of others. However, little is known about which downstream targets of the ACC contribute to empathy behaviors. We optimized a protocol for the social transfer of pain behavior in mice and compared the ACC-dependent neural circuitry responsible for this behavior with the neural circuitry required for the social transfer of two related states: analgesia and fear. We found that a 1-hour social interaction between a bystander mouse and a cagemate experiencing inflammatory pain led to congruent mechanical hyperalgesia in the bystander. This social transfer led to activation of neurons in the ACC and several downstream targets, including the nucleus accumbens (NAc), which was revealed by monosynaptic rabies virus tracing to be directly connected to the ACC. Bidirectional manipulation of activity in ACC-to-NAc inputs influenced the acquisition of socially transferred pain. Further, the social transfer of analgesia also depended upon ACC-NAc inputs. By contrast, the social transfer of fear instead required activity in ACC projections to the basolateral amygdala. This shows that mice rapidly adopt the sensory-affective state of a social partner, regardless of the valance of the information (pain, fear, or pain relief). We find that the ACC generates specific and appropriate empathic behavioral responses through distinct downstream targets. More sophisticated understanding of evolutionarily conserved brain mechanisms of empathy will also expedite the development of new therapies for the empathy-related deficits associated with a broad range of neuropsychiatric disorders.

Magnetic Resonance Measures of Brain Blood Vessels, Metabolic Activity, and Pathology in Multiple Sclerosis

The normally functioning blood-brain barrier (BBB) regulates the transfer of material between blood and brain. BBB dysfunction has long been recognized in multiple sclerosis (MS), and there is considerable interest in quantifying functional aspects of brain blood vessels and their role in disease progression. Parenchymal water content and its association with volume regulation is important for proper brain function, and is one of the key roles of the BBB. There is convincing evidence that the astrocyte is critical in establishing and maintaining a functional BBB and providing metabolic support to neurons. Increasing evidence suggests that functional interactions between endothelia, pericytes, astrocytes, and neurons, collectively known as the neurovascular unit, contribute to brain water regulation, capillary blood volume and flow, BBB permeability, and are responsive to metabolic demands. Increasing evidence suggests altered metabolism in MS brain which may contribute to reduced neuro-repair and increased neurodegeneration. Metabolically relevant biomarkers may provide sensitive readouts of brain tissue at risk of degeneration, and magnetic resonance offers substantial promise in this regard. Dynamic contrast enhanced MRI combined with appropriate pharmacokinetic modeling allows quantification of distinct features of BBB including permeabilities to contrast agent and water, with rate constants that differ by six orders of magnitude. Mapping of these rate constants provides unique biological aspects of brain vasculature relevant to MS.

STDP and the transfer of rhythmic signals in the brain

Rhythmic activity in the brain has been reported in relation to a wide range of cognitive processes. Changes in the rhythmic activity have been related to pathological states. These observations raise the question of the origin of these rhythms: can the mechanisms responsible for generation of these rhythms and that allow the propagation of the rhythmic signal be acquired via a process of learning? In my talk I will focus on spike timing dependent plasticity (STDP) and examine under what conditions this unsupervised learning rule can facilitate the propagation of rhythmic activity downstream in the central nervous system. Next, the I will apply the theory of STDP to the whisker system and demonstrate how STDP can shape the distribution of preferred phases of firing in a downstream population. Interestingly, in both these cases STDP dynamics does not relax to a fixed-point solution, rather the synaptic weights remain dynamic. Nevertheless, STDP allows for the system to retain its functionality in the face of continuous remodeling of the entire synaptic population.

Cross Domain Generalisation in Humans and Machines

Recent advances in deep learning have produced models that far outstrip human performance in a number of domains. However, where machine learning approaches still fall far short of human-level performance is in the capacity to transfer knowledge across domains. While a human learner will happily apply knowledge acquired in one domain (e.g., mathematics) to a different domain (e.g., cooking; a vinaigrette is really just a ratio between edible fat and acid), machine learning models still struggle profoundly at such tasks. I will present a case that human intelligence might be (at least partially) usefully characterised by our ability to transfer knowledge widely, and a framework that we have developed for learning representations that support such transfer. The model is compared to current machine learning approaches.

Cellular mechanisms behind stimulus evoked quenching of variability

A wealth of experimental studies show that the trial-to-trial variability of neuronal activity is quenched during stimulus evoked responses. This fact has helped ground a popular view that the variability of spiking activity can be decomposed into two components. The first is due to irregular spike timing conditioned on the firing rate of a neuron (i.e. a Poisson process), and the second is the trial-to-trial variability of the firing rate itself. Quenching of the variability of the overall response is assumed to be a reflection of a suppression of firing rate variability. Network models have explained this phenomenon through a variety of circuit mechanisms. However, in all cases, from the vantage of a neuron embedded within the network, quenching of its response variability is inherited from its synaptic input. We analyze in vivo whole cell recordings from principal cells in layer (L) 2/3 of mouse visual cortex. While the variability of the membrane potential is quenched upon stimulation, the variability of excitatory and inhibitory currents afferent to the neuron are amplified. This discord complicates the simple inheritance assumption that underpins network models of neuronal variability. We propose and validate an alternative (yet not mutually exclusive) mechanism for the quenching of neuronal variability. We show how an increase in synaptic conductance in the evoked state shunts the transfer of current to the membrane potential, formally decoupling changes in their trial-to-trial variability. The ubiquity of conductance based neuronal transfer combined with the simplicity of our model, provides an appealing framework. In particular, it shows how the dependence of cellular properties upon neuronal state is a critical, yet often ignored, factor. Further, our mechanism does not require a decomposition of variability into spiking and firing rate components, thereby challenging a long held view of neuronal activity.

The interaction of sensory and motor information to shape neuronal representations in mouse cortical networks

The neurons in our brain never function in isolation; they are organized into complex circuits which perform highly specialized information processing tasks and transfer information through large neuronal networks. The aim of Janelle Pakan's research group is to better understand how neural circuits function during the transformation of information from sensory perception to behavioural output. Importantly, they also aim to further understand the cell-type specific processes that interrupt the flow of information through neural circuits in neurodegenerative disorders with dementia. The Pakan group utilizes innovative neuroanatomical tracing techniques, advanced in vivo two-photon imaging, and genetically targeted manipulations of neuronal activity to investigate the cell-type specific microcircuitry of the cerebral cortex, the macrocircuitry of cortical output to subcortical structures, and the functional circuitry underlying processes of sensory perception and motor behaviour.

Infant Relational Learning - Interactions with Visual and Linguistic Factors

Humans are incredible learners, a talent supported by our ability to detect and transfer relational similarities between items and events. Spotting these common relations despite perceptual differences is challenging, yet there’s evidence that this ability begins early, with infants as young as 3 months discriminating same and different (Anderson et al., 2018; Ferry et al., 2015). How? To understand the underlying mechanisms, I examine how learning outcomes in the first year correspond with changes in input and in infant age. I discuss the commonalities in this process with that seen in older children and adults, as well as differences due to interactions with other maturing processes like language and visual attention.

Information transfer in (barrel) cortex: from single cell to network

Evaluating different facets of category status for promoting spontaneous transfer

Existing accounts of analogical transfer highlight the importance of comparison-based schema abstraction in aiding retrieval of relevant prior knowledge from memory. In this talk, we discuss an alternative view, the category status hypothesis—which states that if knowledge of a target principle is represented as a relational category, it is easier to activate as a result of categorizing (as opposed to cue-based reminding)—and briefly review supporting evidence. We then further investigate this hypothesis by designing study tasks that promote different facets of category-level representations and assess their impact on spontaneous analogical transfer. A Baseline group compared two analogous cases; the remaining groups experienced comparison plus another task intended to impact the category status of the knowledge representation. The Intension group read an abstract statement of the principle with a supporting task of generating a new case. The Extension group read two more positive cases with the task of judging whether each exemplified the target principle. The Mapping group read a contrast case with the task of revising it into a positive example of the target principle (thereby providing practice moving in both directions between type and token, i.e., evaluating a given case relative to knowledge and using knowledge to generate a revised case). The results demonstrated that both Intension and Extension groups led to transfer improvements over Baseline (with the former demonstrating both improved accessibility of prior knowledge and ability to apply relational concepts). Implications for theories of analogical transfer are discussed.

Virus-like intercellular communication in the nervous system

The neuronal gene Arc is essential for long-lasting information storage in the mammalian brain and mediates various forms of synaptic plasticity. We recently discovered that Arc self-assembles into virus-like capsids that encapsulate RNA. Endogenous Arc protein is released from neurons in extracellular vesicles that mediate the transfer of Arc mRNA into new target cells. Evolutionary analysis indicates that Arc is derived from a vertebrate lineage of Ty3/gypsy retrotransposons, which are also ancestral to retroviruses such as HIV. These findings suggest that Gag retroelements have been repurposed during evolution to mediate intercellular communication in the nervous system that may underlie cognition and memory.

Molecular Biology of the Fragile X Syndrome

Silencing of FMR1 and loss of its gene product, FMRP, results in fragile X syndrome (FXS). FMRP binds brain mRNAs and inhibits polypeptide elongation. Using ribosome profiling of the hippocampus, we find that ribosome footprint levels in Fmr1-deficient tissue mostly reflect changes in RNA abundance. Profiling over a time course of ribosome runoff in wild-type tissue reveals a wide range of ribosome translocation rates; on many mRNAs, the ribosomes are stalled. Sucrose gradient ultracentrifugation of hippocampal slices after ribosome runoff reveals that FMRP co-sediments with stalled ribosomes, and its loss results in decline of ribosome stalling on specific mRNAs. One such mRNA encodes SETD2, a lysine methyltransferase that catalyzes H3K36me3. Chromatin immunoprecipitation sequencing (ChIP-seq) demonstrates that loss of FMRP alters the deployment of this histone mark. H3K36me3 is associated with alternative pre-RNA processing, which we find occurs in an FMRP-dependent manner on transcripts linked to neural function and autism spectrum disorders.

Dynamic computation in the retina by retuning of neurons and synapses

How does a circuit of neurons process sensory information? And how are transformations of neural signals altered by changes in synaptic strength? We investigate these questions in the context of the visual system and the lateral line of fish. A distinguishing feature of our approach is the imaging of activity across populations of synapses – the fundamental elements of signal transfer within all brain circuits. A guiding hypothesis is that the plasticity of neurotransmission plays a major part in controlling the input-output relation of sensory circuits, regulating the tuning and sensitivity of neurons to allow adaptation or sensitization to particular features of the input. Sensory systems continuously adjust their input-output relation according to the recent history of the stimulus. A common alteration is a decrease in the gain of the response to a constant feature of the input, termed adaptation. For instance, in the retina, many of the ganglion cells (RGCs) providing the output produce their strongest responses just after the temporal contrast of the stimulus increases, but the response declines if this input is maintained. The advantage of adaptation is that it prevents saturation of the response to strong stimuli and allows for continued signaling of future increases in stimulus strength. But adaptation comes at a cost: a reduced sensitivity to a future decrease in stimulus strength. The retina compensates for this loss of information through an intriguing strategy: while some RGCs adapt following a strong stimulus, a second population gradually becomes sensitized. We found that the underlying circuit mechanisms involve two opposing forms of synaptic plasticity in bipolar cells: synaptic depression causes adaptation and facilitation causes sensitization. Facilitation is in turn caused by depression in inhibitory synapses providing negative feedback. These opposing forms of plasticity can cause simultaneous increases and decreases in contrast-sensitivity of different RGCs, which suggests a general framework for understanding the function of sensory circuits: plasticity of both excitatory and inhibitory synapses control dynamic changes in tuning and gain.

Synthesizing Machine Intelligence in Neuromorphic Computers with Differentiable Programming

The potential of machine learning and deep learning to advance artificial intelligence is driving a quest to build dedicated computers, such as neuromorphic hardware that emulate the biological processes of the brain. While the hardware technologies already exist, their application to real-world tasks is hindered by the lack of suitable programming methods. Advances at the interface of neural computation and machine learning showed that key aspects of deep learning models and tools can be transferred to biologically plausible neural circuits. Building on these advances, I will show that differentiable programming can address many challenges of programming spiking neural networks for solving real-world tasks, and help devise novel continual and local learning algorithms. In turn, these new algorithms pave the road towards systematically synthesizing machine intelligence in neuromorphic hardware without detailed knowledge of the hardware circuits.

On temporal coding in spiking neural networks with alpha synaptic function

The timing of individual neuronal spikes is essential for biological brains to make fast responses to sensory stimuli. However, conventional artificial neural networks lack the intrinsic temporal coding ability present in biological networks. We propose a spiking neural network model that encodes information in the relative timing of individual neuron spikes. In classification tasks, the output of the network is indicated by the first neuron to spike in the output layer. This temporal coding scheme allows the supervised training of the network with backpropagation, using locally exact derivatives of the postsynaptic spike times with respect to presynaptic spike times. The network operates using a biologically-plausible alpha synaptic transfer function. Additionally, we use trainable synchronisation pulses that provide bias, add flexibility during training and exploit the decay part of the alpha function. We show that such networks can be trained successfully on noisy Boolean logic tasks and on the MNIST dataset encoded in time. The results show that the spiking neural network outperforms comparable spiking models on MNIST and achieves similar quality to fully connected conventional networks with the same architecture. We also find that the spiking network spontaneously discovers two operating regimes, mirroring the accuracy-speed trade-off observed in human decision-making: a slow regime, where a decision is taken after all hidden neurons have spiked and the accuracy is very high, and a fast regime, where a decision is taken very fast but the accuracy is lower. These results demonstrate the computational power of spiking networks with biological characteristics that encode information in the timing of individual neurons. By studying temporal coding in spiking networks, we aim to create building blocks towards energy-efficient and more complex biologically-inspired neural architectures.

Mechanisms of pathogenesis in the tauopathies

The distribution of pathological tau in the brain of patients with AD is highly predicable, and as disease worsens, it spreads transynaptically from initial regions of vulnerability. The reason why only some neurons are vulnerable to the accumulation and propagation of pathological forms of tau, and the mechanisms by which tauopathy spreads through the brain are not well understood. Using a combination of immunohistochemistry and computational analysis we have examined pathway differences between vulnerable and resistant neurons. How tau spreads across a synapse has been examined in vitro using different model systems. Our data show that dysregulation of tau homeostasis determines the cellular and regional vulnerability of specific neurons to tau pathology (H. Fu et al. 2019. Nat. Neuro. 22 (1):47-56) and that deficits in tau homeostasis can exacerbate tau accumulation and propagation. Aging appears to impact similar neuronal populations. Mechanisms and consequences of abnormal tau accumulation within neurons, its transfer between cells, pathology propagation and therapeutic opportunities will be discussed.

Information transfer during dyadic interactions in perceptual decision-making.

Bernstein Conference 2024

Characterization of neuronal resonance and inter-areal transfer using optogenetics

COSYNE 2022

Neural basis of transferable representations for efficient learning

COSYNE 2022

Neural basis of transferable representations for efficient learning

COSYNE 2022

Alternating inference and learning: a thalamocortical model for continual and transfer learning

COSYNE 2023

Inter-animal variability in learning depends on transfer of pre-task experience via the hippocampus

COSYNE 2023

Learning beyond the synapse: activity-dependent myelination, neural correlations, and information transfer

COSYNE 2023

Coordinated Multi-frequency Oscillatory Bursts Enable Time-structured Dynamic Information Transfer

COSYNE 2025

A gated receptivity model of widely broadcast signals for the transfer of graded information

COSYNE 2025

Using transfer learning to identify a neural system's algorithm

COSYNE 2025

Anterograde transneuronal transfer of rabies via novel pseudotyping with HSV-1 glycoproteins gE, gI and US9

Effects of natural and synthetic catechol-O-methyl transferase inhibitors on two in vivo models of Parkinson’s disease

Fecal Microbiota Transfer reduces alcohol preference in stressed rats

From synapses to behavior: The impact of DNA methyltransferase 1 (DNMT1) in parvalbuminergic, murine interneurons

Identification of a developmental switch in information transfer between whisker S1 and S2 cortex in mice

Inflammatory exosomes transfer danger signals and induce glial dysfunctional calcium dynamics in naïve spinal cultured explants

Insular cortex is required to guide action selection in outcome-specific devaluation and Pavlovian-to-Instrumental Transfer

The DNA methyltransferase 1 is important for the migration of MGE-derived interneurons

Passive transfer mouse model of ALS shows differently elevated calcium levels and loss of lumbar motor neurons according to the genetic alterations of the patients

Physiological role of the full-length amyloid precursor protein (APP) in presynaptic plasticity and information transfer within hippocampal CA3 circuits

The psychotomimetic ketamine disrupts the transfer of sensory information in the corticothalamic network

EEG spatial patterns related to rhythm processing accurately classify dyslexia and show transfer learning capabilities across receptive speech tasks

Transfer of a pharmacological neurovascular uncoupling model from mice to rats

Visuomotor defects of achiasmatic mice expressing a transfer-defective Vax1 mutant

Artifact identification in transfer entropy connectivity inference of neuronal cultures

FENS Forum 2024

Cortical circuits for goal-directed cross-modal transfer learning

FENS Forum 2024

Effect of RNA m6A methyltransferase activation on anxiety- and depression-related behaviours, monoamine neurochemistry, and striatal gene expression in the rat

FENS Forum 2024

Endogenous alpha-Synuclein is essential for the transfer of pathology by exosome-enriched extracellular vesicles, following inoculation with preformed fibrils in vivo

FENS Forum 2024

Feasibility of the Pavlovian instrumental transfer task using a full transfer paradigm: A replication and meta-analysis

FENS Forum 2024

Fecal microbiota transfer reduces alcohol preference in stressed rats

FENS Forum 2024

Investigating information transfer at the single-cell level using ultra low-density neuronal networks

FENS Forum 2024

EMG monitoring of central neuroplastic changes after nerve transfer procedures in brachial plexus injury

FENS Forum 2024

Passive transfer of IgG from patients with long-COVID neurological symptoms induces tactile allodynia in mice

FENS Forum 2024

Placental transfer of NMDA receptor autoantibodies impairs correlated network activity by affecting GABAergic neurotransmission

FENS Forum 2024

Postnatal developmental dynamics of choline acetyltransferase (ChAT) and nerve growth factor (NGF) expression in rat oculomotor system

FENS Forum 2024

Transfer Learning from Real to Imagined Motor Actions in ECoG Data

Neuromatch 5