validation

Targeting disulfidptosis in cancer: mechanisms and preclinical translation

Project Summary Studying regulated cell death is critical for our understanding of cellular homeostasis and tumor suppression. We recently discovered disulfidptosis as a new form of regulated cell death induced by disulfide stress under NADPH-depleting conditions in SLC7A11-high cancer cells. However, in contrast to our deep understanding of other cell death modalities such as apoptosis and ferroptosis, the molecular and metabolic underpinnings of disulfidptosis, along with its therapeutic implications, remain largely unexplored. The objectives of this application are to elucidate the mechanisms underlying disulfidptosis and to therapeutically target this form of cell death in SLC7A11-high cancers. The proposed studies will make extensive use of human cancer cell lines and integrated human cellbased molecular analyses, including metabolomics, proteomics, CRISPR screening, and biochemical studies, to define the metabolic and signaling mechanisms governing disulfidptosis. In addition, select in vivo studies are incorporated in the therapeutic validation components of the project, where tumor growth response, systemic drug exposure and tolerability, tumor microenvironmental influences, and host immune/stromal interactions must be evaluated in an organismal context to ensure translational rigor. Alternative in vitro systems such as organoids may provide useful complementary information on tumor-intrinsic responses, but they cannot fully recapitulate the systemic metabolic stress, pharmacologic exposure, and organism-level therapeutic efficacy required for these studies. It is expected that our proposed studies will reveal novel mechanisms underlying disulfidptosis and identify effective therapies to induce this form of cell death in SLC7A11-high cancers. Our proposal is highly innovative because it focuses on a previously unexplored cell death pathway in cancer therapy. Our proposed studies will have significant impact on both our understanding of the fundamental mechanisms of disulfidptosis and our ability to target this cell death pathway in cancer treatment.

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

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

Optimizing CD45-Targeted Astatine-211-Radioimmunotherapy for Malignant and Non-Malignant Blood Disorders

ABSTRACT CD45 is expressed on almost all normal and neoplastic hematopoietic cells but not on non-blood cells and has, therefore, been pursued as a drug target. Initially centered on augmenting conditioning before hematopoietic cell transplantation (HCT) for blood cancers, there is increasing interest in expanding CD45-directed therapies into other settings, with radioimmunotherapy (RIT) being the major therapeutic modality so far. Investigators at our institution pioneered CD45 RIT with b-emitters such as iodine-131 (131I) using the murine monoclonal antibody (mAb), BC8. A phase 3 trial testing 131I-BC8 (131I-apamistamab [Iomab-B]) with allogeneic HCT in older adults with relapsed/refractory acute myeloid leukemia showed improved outcomes over conventional care, validating this approach. More recently, attention has shifted toward a-emitters that deliver substantially higher decay energies over much shorter distances than b-emitters, rendering them more suitable for precise and potent target cell killing. In our work, we focus on astatine-211 (211At) for its ideal half-life and decay without a-emitting daughters. For clinical application, mAbs are conjugated with the bifunctional boron cage molecule, isothiocyantophenethyl-ureido-closo-decaborate(2-) (B10-NCS), to enable stable protein astatination. Three early-phase trials testing 211At-BC8-B10 as augmentation of HCT conditioning for patients with malignant and non-malignant blood disorders are ongoing, with emerging data indicating significant anti-tumor efficacy. Nonetheless, relapses still occur. Other important limitations include marked infusion toxicities and human antimouse antibody (HAMA) responses related to the murine nature of BC8 and dimer formation after 211At labeling of mAb-B10 conjugates with tissue residualization from 211At atom oxidation. The latter may contribute to the risk of liver cell injury, the dose limiting extramedullary toxicity of CD45 RIT. As a first step toward our goal of optimizing CD45 RIT, we have raised new, fully human CD45 mAbs as basis for novel therapeutics. In preliminary in vivo studies in immunodeficient mice, we found some of these mAbs to have greater anti-tumor efficacy than a humanized version of BC8 (HuBC8) we generated as a reference mAb. We will now conduct comparative in vivo CD45+ cell targeting (“biodistribution”) and anti-tumor efficacy studies to select a lead candidate mAb for clinical application and use protein engineering to maximize the selectivity and efficacy of targeted radiation delivery. We will use immunodeficient mice xenotransplanted with human leukemia cells for this purpose as no human approaches are available and in vitro testing is inadequate to measure both the targeting and biologic RIT effects on human leukemia cells. Mice provide the in vivo milieu needed for comprehensive evaluation. Development of improved mAb astatination methodologies to minimize off-target toxicities of 211At-RIT will further increase therapy specificity and reduce toxicity. In parallel, we will conduct genome-scale, unbiased target identification/validation studies to identify partner drugs for rational combination therapies aimed at enhancing the anti-tumor efficacy of 211At-CD45 RIT.

Investigating the nonlinear complex dynamics of the tuft cell-microbiome cross-talk: the impact of feedback loops on immune regulation, microbial modulation and response to tissue insults

Project Abstract Tuft cells (TCs) are specialized chemosensory epithelial cells that are emerging as critical regulators of intestinal homeostasis. Named over 70 years ago based on their distinct morphology, a defined function for TCs was only elucidated in the last decade. TCs in the small intestine sense succinate from helminths to initiate type 2 immune responses that mediate parasite expulsion. Recently, we discovered a novel physiologic function for TCs in the colon, where their role had been considered minimal. Succinate, a key microbial metabolite, is produced by colonic microbiota as both a precursor to other metabolites and a cross-feeding fuel source for pathogens. TCs respond to succinate by secreting interleukin-25 (IL-25), which activates type 2 cytokine- producing lymphocytes (T2Ls), amplifying TC expansion and reinforcing barrier function. We recently demonstrated that this SPB–TC–IL-25–T2L feedback loop is essential for protection against pathogen-induced colitis. Our preliminary data further suggest that TCs actively promote colonization by succinate-producing bacteria (SPBs), establishing positive feedback on TC-supporting microbes, while other epithelial cells such as goblet cells (GCs) and Paneth cells (PCs) may exert complementary or counterbalancing influences. Supported by new modeling insights, we hypothesize that these epithelial–immune–microbiome interactions form coordinated feedback loops that collectively optimize intestinal resilience. These loops may create a dynamic, multi-stable system that flexibly transitions between homeostatic and hyperplastic states, buffering against microbial fluctuations and pathogenic insults while preventing uncontrolled type 2 inflammation. Using a combination of mathematical modeling and experimental validation, we will develop a multi- layered systems framework to explore how epithelial–immune–microbial feedbacks shape resilience or breakdown in clinically relevant models of colonic infection and inflammation. Our three Aims will (1) develop, calibrate, and validate a mathematical model that integrates TCs, GCs, PCs, SPBs, and SCBs; (2) define the immunological circuits governing epithelial–microbiome equilibrium; and (3) determine how epithelial feedbacks regulate microbial community structure and resilience. In line with NIH’s new initiative to prioritize human-based research, our proposal combines computational modeling, human colonic organoids, and complementary mouse models. Organoid experiments will provide human-relevant data for model calibration, while in vivo studies validate systemic predictions, ensuring both rigor and translational relevance while minimizing reliance on animal models. This work will generate interoperable models that integrate epithelial, microbial, and immune networks, providing predictive insight into intestinal outcomes under homeostatic, infectious, and inflammatory conditions and informing therapeutic strategies for microbiome-targeted interventions.

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.

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

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

Role of Two Medial Prefrontal Long-Range Recurrent Networks in Behavior Initiation and Inhibition

Abstract The medial prefrontal cortex (mPFC) is critical for executive function, yet how its dorsal (dmPFC) and ventral (vmPFC) motor-projecting (MP) neurons coordinate behavioral initiation, inhibition, and cognitive flexibility remains poorly understood. This R21 leverages four translational behavioral paradigms (head-fixed Persistent Licking/Shock-Escape; freely moving FED3-based Reversal Learning/Stop-Signal), high-density neural recordings, circuit manipulations, and Brian2 spiking neural network modeling to test our central hypothesis: dmPFC MP neurons drive action initiation and adaptive switching, while vmPFC MP neurons suppress impulsivity and perseveration. In Aim 1a, we quantify behavior using kinematic analyses (jerk, velocity, z-scored) aligned with human executive dysfunction metrics (Action Latency [AL], Reversal Accuracy [RA], Perseveration Errors [PE], Stop-Signal Reaction Time [SSRT]), combined with optogenetic (stGtACR2/ChR2) and chemogenetic (PSAM/varenicline) perturbations. Aim 1b employs optotagging and population analyses (PCA, SVM, Total Spiking Probability Edges) to decode dmPFC/vmPFC MP dynamics across tasks, resolving specialized versus mixed functional roles. Aim 1c integrates these datasets into Brian2 spiking network models to predict neural-behavioral correlations, validated through cross-validation. Exploratory analyses will link murine kinematic signatures to human stop-signal/reversal learning metrics. By elucidating strain-specific (C57BL/6 vs. CD1) circuit mechanisms and delivering translatable biomarkers (AL, RA, PE, SSRT, kinematics), this work addresses a critical gap in understanding neuropsychiatric disorders like ADHD (impulsivity) and schizophrenia (perseveration). The study’s innovative combination of recurrent neural network theory, FED3-based assays, and New Approach Methodology (NAM)-compliant computational modeling pioneers high-risk, high-reward tools for circuit dissection, fully aligning with NIH’s 2025 priorities.

Uncovering genetic determinants of carbapenem resistance in Klebsiella pneumoniae

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

Targeted Prodrug Cytokines for Metastatic Breast Cancer Immunotherapy

Project Summary. Our approach directly addresses key limitations in targeting and treating metastatic breast cancer, where we propose the selective activation of modular immune-modulating cytokines within the hypoxic and ROS-active TME for delivery across the BBB, providing the necessary pre-clinical data for future clinical translation. The in vitro and in vivo investigations of this novel immunotherapeutic in immunocompetent models will allow our team to study the interplay between tumor-driven immune activation, cytokine signaling, and anti-tumor immunity in both primary and metastatic sites, and establish a robust groundwork for subsequent clinical validation within the OSUCCC. This proposal addresses two key challenges in developing a novel immunotherapy strategy for breast cancer by answering two hypotheses: (1) can a modular immunotherapy platform with tumor-selective activation of prodrug recombinant cytokines overcome these limitations in drug delivery, and (2) can the development of nanobody-cytokine fusions that can selectively target primary breast cancer tumors and cross the BBB to reach metastatic tumor sites? The first hypothesis focuses on achieving tumor environment-specific activation of prodrug-based recombinant cytokines. Protein cytokines are highly potent, and while others have tried to block their activity using a fused genetic linker to ‘mask’ functionality, no one has yet attempted to use a non-canonical-based chemical strategy to achieve this inhibition. Immune-modulating cytokines will be recombinantly expressed with integrated ncAAs that block cytokine activity until the function is regenerated in the breast cancer TME. Once the cytokine activity is controlled, our second hypothesis will be to achieve selective delivery of the cytokine via fusion to nanobodies. While success has been found in targeting primary tumors in drug and protein delivery, a key challenge remains in reaching secondary metastatic tumors in hard-to-reach sites (i.e., brain). Engineered nanobodies, with affinity for breast cancer tumors and the ability to bind to BBB transcytosis receptors, will enable selective delivery to metastatic breast-to-brain tumors, resulting in tumor- specific activation, immune responses, and improved therapeutic outcomes. This system can significantly improve therapeutic outcomes for patients with mBC by integrating selective activation and delivery mechanisms to reduce off-target effects and enhance tumor-specific immune responses in both primary and secondary metastatic tumor sites. Optimizing drug delivery systems to tune immune responses could offer more effective and less invasive treatment options when compared to traditional and engineered cell-based approaches. Our momentum towards precision medicine and targeted therapies holds significant promise for improving outcomes for mBC patients, and has the potential to serve as a pan-cancer treatment for aggressive metastatic cancers from the following aims: (1) generating a modular platform for tumor-specific activation of prodrug cytokines, (2) evaluating cytokine delivery and anti-cancer immune phenotypes in mBC.

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

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

Brain Emulation Challenge Workshop

Brain Emulation Challenge workshop will tackle cutting-edge topics such as ground-truthing for validation, leveraging artificial datasets generated from virtual brain tissue, and the transformative potential of virtual brain platforms, such as applied to the forthcoming Brain Emulation Challenge.

Brain Emulation Challenge Workshop

Brain Emulation Challenge workshop will tackle cutting-edge topics such as ground-truthing for validation, leveraging artificial datasets generated from virtual brain tissue, and the transformative potential of virtual brain platforms, such as applied to the forthcoming Brain Emulation Challenge.

Brain Emulation Challenge Workshop

Brain Emulation Challenge workshop will tackle cutting-edge topics such as ground-truthing for validation, leveraging artificial datasets generated from virtual brain tissue, and the transformative potential of virtual brain platforms, such as applied to the forthcoming Brain Emulation Challenge.

Brain Emulation Challenge Workshop

Brain Emulation Challenge workshop will tackle cutting-edge topics such as ground-truthing for validation, leveraging artificial datasets generated from virtual brain tissue, and the transformative potential of virtual brain platforms, such as applied to the forthcoming Brain Emulation Challenge.

Brain Emulation Challenge Workshop

Brain Emulation Challenge workshop will tackle cutting-edge topics such as ground-truthing for validation, leveraging artificial datasets generated from virtual brain tissue, and the transformative potential of virtual brain platforms, such as applied to the forthcoming Brain Emulation Challenge.

Brain network communication: concepts, models and applications

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

NMC4 Short Talk: Decoding finger movements from human posterior parietal cortex

Restoring hand function is a top priority for individuals with tetraplegia. This challenge motivates considerable research on brain-computer interfaces (BCIs), which bypass damaged neural pathways to control paralyzed or prosthetic limbs. Here, we demonstrate the BCI control of a prosthetic hand using intracortical recordings from the posterior parietal cortex (PPC). As part of an ongoing clinical trial, two participants with cervical spinal cord injury were each implanted with a 96-channel array in the left PPC. Across four sessions each, we recorded neural activity while they attempted to press individual fingers of the contralateral (right) hand. Single neurons modulated selectively for different finger movements. Offline, we accurately classified finger movements from neural firing rates using linear discriminant analysis (LDA) with cross-validation (accuracy = 90%; chance = 17%). Finally, the participants used the neural classifier online to control all five fingers of a BCI hand. Online control accuracy (86%; chance = 17%) exceeded previous state-of-the-art finger BCIs. Furthermore, offline, we could classify both flexion and extension of the right fingers, as well as flexion of all ten fingers. Our results indicate that neural recordings from PPC can be used to control prosthetic fingers, which may help contribute to a hand restoration strategy for people with tetraplegia.

Microbiota in the health of the nervous system and the response to stress

Microbes have shaped the evolution of eukaryotes and contribute significantly to the physiology and behavior of animals. Some of these traits are inherited by the progenies. Despite the vast importance of microbe-host communication, we still do not know how bacteria change short term traits or long-term decisions in individuals or communities. In this seminar I will present our work on how commensal and pathogenic bacteria impact specific neuronal phenotypes and decision making. The traits we specifically study are the degeneration and regeneration of neurons and survival behaviors in animals. We use the nematode Caenorhabditis elegans and its dietary bacteria as model organisms. Both nematode and bacteria are genetically tractable, simplifying the detection of specific molecules and their effect on measurable characteristics. To identify these molecules we analyze their genomes, transcriptomes and metabolomes, followed by functional in vivo validation. We found that specific bacterial RNAs and bacterially produced neurotransmitters are key to trigger a survival behavioral and neuronal protection respectively. While RNAs cause responses that lasts for many generations we are still investigating whether bacterial metabolites are capable of inducing long lasting phenotypic changes.

Digitization as a driving force for collaboration in neuroscience

Many of the collaborations we encounter in our scientific careers are centered on a common idea that can be associated with certain resources, such as a dataset, an algorithm, or a model. All partners in a collaboration need to develop a common understanding of these resources, and need to be able to access them in a simple and unambiguous manner in order to avoid incorrect conclusions especially in highly cross-disciplinary contexts. While digital computers have entered to assist scientific workflows in experiment and simulation for many decades, the high degree of heterogeneity in the field had led to a scattered landscape of highly customized, lab-internal solutions to organizing and managing the resources on a project-by-project basis. Only with the availability of modern technologies such as the semantic web, platforms for collaborative coding or the development of data standards overarching different disciplines, we have tools at our disposal to make resources increasingly more accessible, understandable, and usable. However, without overarching standardization efforts and adaptation of such technologies to the workflows and needs of individual researchers, their adoption by the neuroscience community will be impeded. From the perspective of computational neuroscience, which is inherently dependent on leveraging data and methods across the field of neuroscience for inspiration and validation, I will outline my view on past and present developments towards a more rigorous use of digital resources and how they improved collaboration, and introduce emerging initiatives to support this process in the future (e.g., EBRAINS http://ebrains.eu, NFDI-Neuro http://www.nfdi-neuro.de).

Hebbian learning, its inference, and brain oscillation

Despite the recent success of deep learning in artificial intelligence, the lack of biological plausibility and labeled data in natural learning still poses a challenge in understanding biological learning. At the other extreme lies Hebbian learning, the simplest local and unsupervised one, yet considered to be computationally less efficient. In this talk, I would introduce a novel method to infer the form of Hebbian learning from in vivo data. Applying the method to the data obtained from the monkey inferior temporal cortex for the recognition task indicates how Hebbian learning changes the dynamic properties of the circuits and may promote brain oscillation. Notably, recent electrophysiological data observed in rodent V1 showed that the effect of visual experience on direction selectivity was similar to that observed in monkey data and provided strong validation of asymmetric changes of feedforward and recurrent synaptic strengths inferred from monkey data. This may suggest a general learning principle underlying the same computation, such as familiarity detection across different features represented in different brain regions.

Cognitive Psychometrics: Statistical Modeling of Individual Differences in Latent Processes

Many psychological theories assume that qualitatively different cognitive processes can result in identical responses. Multinomial processing tree (MPT) models allow researchers to disentangle latent cognitive processes based on observed response frequencies. Recently, MPT models have been extended to explicitly account for participant and item heterogeneity. These hierarchical Bayesian MPT models provide the opportunity to connect two traditionally isolated disciplines. Whereas cognitive psychology has often focused on the experimental validation of MPT model parameters on the group level, psychometrics provides the necessary concepts and tools for measuring differences in MPT parameters on the item or person level. Moreover, MPT parameters can be regressed on covariates to model latent processes as a function of personality traits or other person characteristics.

Contextual inference underlies the learning of sensorimotor repertoires

Humans spend a lifetime learning, storing and refining a repertoire of motor memories. However, it is unknown what principle underlies the way our continuous stream of sensori-motor experience is segmented into separate memories and how we adapt and use this growing repertoire. Here we develop a principled theory of motor learning based on the key insight that memory creation, updating, and expression are all controlled by a single computation – contextual inference. Unlike dominant theories of single-context learning, our repertoire-learning model accounts for key features of motor learning that had no unified explanation and predicts novel phenomena, which we confirm experimentally. These results suggest that contextual inference is the key principle underlying how a diverse set of experiences is reflected in motor behavior.

Train/test behavioral cross-validation reveals neural correlates in mice

COSYNE 2025

Assessing neural activity of musicians with music performance anxiety: Creation and validation of the fMRI Social-Evaluative Music Performance Task

Development and validation of Arc nanobodies: new tools for probing Arc dynamics and function

Genetic Susceptibility to Acquired Epilepsy affects Seizure Progression after Amygdala Kindling: Validation of the FAST and SLOW rat models

Histological validation of the accuracy of diffusion tensor imaging for tracing fibre tracts in macaque extrastriate visual cortex

MCC-DLPFC network modulation by pathway-specific DREADDs in macaque monkeys: behavioral, resting-state fMRI and histological validations

Validation of the essential role of the 16p11.2 ASD candidate gene QPRT in human stem cell-derived iNeurons

Validation of an innovative millifluidic gut-on-a-chip to challenge the microbiota-gut-brain axis in vitro

Validation of iPSC-derived blood-brain barrier model on microfluidic chip

Validation of IHC markers antibody panel in rat Experimental Autoimmune Encephalomyelitis (EAE) model of multiple sclerosis

Validation of nanoparticle-peptide targeting biomarker in the blood-brain barrier under neuroinflammation related to multiple sclerosis

What can tractography tell us about cortical connectivity: A within-animal and across-validation comparison with tract tracing in macaque

Comparative transcriptome profiling of multiple human induced pluripotent stem cell-derived sensory neuron populations and functional validation of pain targets on automated patch clamp systems

FENS Forum 2024

Cross-validation under different sensory conditions reveals the practical validity of the MVUE model

FENS Forum 2024

Integrating different approaches for establishing a multi-scale functional validation platform for RNA-based drugs in the CNS (MULTIVAL)

FENS Forum 2024

Statistics versus animal welfare: Validation of the experimental unit in the focus of 3R

FENS Forum 2024

Stratification of ALS progression by a combined motor and behavioural tracking approach for preclinical drug validation

FENS Forum 2024

Validation of portable, dry electrode-based electroencephalography device for application in brain–computer interface solutions

FENS Forum 2024

Validation of template-based attenuation correction for in vivo quantification of the serotonin transporter using positron emission tomography

FENS Forum 2024