Project Summary/Abstract. Lung cancer is the leading cause of cancer death in the US, with over 125,000 deaths annually. Radiation therapy (RT) is a critical component of curative lung cancer treatment for many patients. However, radiationinduced pulmonary fibrosis (RIPF) is a common side effect that carries a poor prognosis with limited treatment options. Up to 40% of patients with lung cancer who receive RT may experience RIPF. RIPF is a late effect of RT, typically occurring 3 or more months after treatment. The symptoms of RIPF can include shortness of breath, pleural effusions, decreased lung function, and respiratory failure. Cell surface integrin heterodimers play a key role in the pathogenesis of RIPF. In particular, the integrin αvβ6, which is expressed at a low level in the alveolar epithelium at baseline, is significantly upregulated upon RT damage. The key role of integrin αvβ6 in RIPF is illustrated by studies in which mice lacking integrin αvβ6, or treated with an αvβ6-blocking antibody, do not develop RIPF. Here, we propose to translate this mechanistic understanding of RIPF into novel approaches for monitoring and treating RIPF. We hypothesize that non-invasive αvβ6 PET imaging will be safe and can specifically bind to αvβ6 in patients with RIPF. Additionally, we hypothesize that a novel small-molecule integrin antagonist, IDL2965, can mitigate and treat RIPF in mice. In this project, we are utilizing mice to model RIPF, as mice develop RIPF that mimics human disease. In addition, cellular and in vitro models do not approximate the complex biology leading to the development of RIPF. Our data using [64Cu]Cu-DOTA-αvβ6-BP to detect early RIPF in mice are compelling in both single-fraction high-dose RT and lower dose-larger volume RT models (Lo et. al, IJROBP 2025). However, to progress to clinical trials in patients with cancer, we will obtain data to submit an Investigational New Drug (IND) application to the FDA. Importantly, we propose translating [64Cu]Cu-DOTA-αvβ6-BP PET imaging into patients with lung cancer, allowing us to better identify RIPF and develop a tool to determine the efficacy of IDL-2965 in future clinical studies. The specific aims of the proposal are: (1) Characterize the utility of [64Cu]Cu-DOTA-αvβ6-BP in mice with conventionally fractionated RT and identify circulating biomarkers of RIPF, and determine the in vivo toxicology of [64Cu]Cu-DOTA-αvβ6-BP to prepare and submit an exploratory Investigational New Drug (eIND) application to the FDA, (2) Conduct a first-in-human clinical trial of [64Cu]Cu-DOTA-αvβ6-BP to determine its safety and human dosimetry in patients with evidence of RIPF from computed tomography or in healthy controls, and (3) Determine the effect of integrin antagonism using IDL-2965 on mitigating RIPF in preclinical mouse models. The goals of this proposal are two-fold: (1) demonstrate safety and target specificity for [64Cu]Cu-DOTA-αvβ6-BP so that it can be used in future studies to identify RIPF and evaluate the efficacy of anti-fibrotic therapies, and 2) determine the ability of IDL-2965 to prevent RIPF in preclinical mouse models.

ABSTRACT Gastric cancer represents a significant disease burden and is a leading cause of cancer-related deaths in the United States and globally. Approximately 80% of gastric cancer patients are diagnosed at an advanced stage, with the peritoneum being the most common site of relapse (peritoneal recurrence) after radical surgery. Nearly 50% of patients with advanced-stage gastric cancer develop peritoneal recurrence post-surgery, resulting in a median survival of only 3–6 months and a markedly reduced quality of life. Early peritoneal recurrence is primarily characterized by micro-metastasis, which traditional imaging techniques struggle to detect due to the small size of metastatic nodules. Predicting the likelihood and timing of peritoneal recurrence is crucial for identifying at- risk patients, enabling timely interventions that could improve survival rates and quality of life. Unfortunately, reliable predictive biomarkers and models for peritoneal recurrence in gastric cancer are lacking in clinical practice, highlighting an urgent need for innovative predictive tools. This proposal aims to develop and validate novel predictive models for early peritoneal recurrence in gastric cancer, leveraging advanced deep learning techniques and multimodal integration of clinical, radiological (CT), and histopathological (hematoxylin and eosin, H&E) data. In Aim 1, we will develop a rational approach for predicting peritoneal recurrence by creating a novel deep learning multimodal method guided by genomics knowledge. Additionally, we will integrate both deep learning-extracted features and traditional hand-crafted radiomics features with clinical data to improve prediction accuracy. Aim 2 focuses on developing a robust prediction model of peritoneal recurrence utilizing a pre-trained foundation model from large-scale H&E image data. Aim 3 will combine CT, H&E, and clinical data to further enhance predictive capabilities, employing an innovative cross-modal collaborative optimization approach for multimodal data integration. All models will be trained and internally validated using a retrospective cohort from Atrium Health Wake Forest Baptist Comprehensive Cancer Center and externally validated in two independent cohorts from additional institutions to ensure robustness across populations and imaging protocols. Additionally, we will compare our models with existing methods, including clinical staging and alternative fusion strategies. If successful, these models will enhance risk stratification and prediction of peritoneal recurrence in gastric cancer patients, significantly improving survival rates and quality of life by identifying those likely to develop peritoneal recurrence post-surgery and facilitating timely intervention. Furthermore, they can help avoid the risk of complications and extra medical costs associated with overtreatment. Since the information is derived from routinely examined CT, H&E and clinical data, they could be seamlessly integrated into current clinical workflows. The AI technology developed through this project has the potential to benefit underserved populations in low- resource settings and reduce healthcare disparities in the U.S.

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.

Project Summary The fundamental role of the immune system is to detect self from non-self. The detection and elimination of microbial infection is critical for human survival. One challenge to the immune system is infection from an intracellular microbe because the microbe masks its presence in a host cell. One strategy of the immune system to detect microbes is the sampling of different kinds of antigens, such as peptides, lipids and glycolipids, by antigen presenting molecules. A fundamentally unique arm of the immune system is MR1, which is an antigen presenting molecule that is intracellular, ubiquitously expressed across tissues, and detects small molecules derived from microbial metabolism. These features suggest that MR1 is poised to detect intracellular microbes. MR1 presents antigens to MR1-restricted T cells. These T cells are highly prevalent in the lungs and can kill infected cells. Because MR1 presents small molecule antigens and adopts an intracellular distribution, the mechanisms governing MR1 sampling of the intracellular environment are distinct from other antigen presenting molecules. These mechanisms remain unknown. Our over-arching hypothesis is that intracellular calcium signaling is important for MR1 antigen presentation. We use Mycobacterium tuberculosis (Mtb) as a model for intracellular infection and have identified calcium-sensitive trafficking proteins and calcium channels important for MR1 antigen presentation. Aim 1 of this study will determine the mechanism of two-pore channel 1 in MR1- dependent antigen presentation, with a focus on endoplasmic reticulum-endosome contact sites. Aim 2 will determine the role of specific calcium-sensitive Synaptotagmins and their binding partners. Aim 3 will determine the mechanism behind augmented MR1 antigen presentation following modulation of the of the cystic fibrosis transmembrane conductance regulator. Successful completion of these Aims has the potential to lead to new MR1-based immunotherapies.

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

Project Summary The importance of localizing and treating all upper tract urothelial cancer (UTUC) tumors during a renal sparing, endoscopic treatment is emphasized by the high risk of cancer progression from inadequate tumor treatment. Insufficient treatment necessitates kidney and ureteral removal (i.e., nephroureterectomy). Nephroureterectomy permanently compromises renal function, and increases morbidity and mortality, while negatively impacting a patient’s quality of life. In contrast, endoscopic treatment (i.e., using a laser to ablate only the tumors) improves long-term outcomes by sparing healthy kidney tissue. However, endoscopic treatment is underutilized compared to nephroureterectomy because it is difficult to accomplish. Successful endoscopic treatment is dependent on the surgeon’s ability to create a mental 3D map of the branched, intrarenal endoscopic anatomy intraoperatively from preoperative 2D imaging, which is extremely difficult. Since mental mapping relies on hand-eye coordination, memory, and spatial reasoning, it is inherently imprecise and its impact on accuracy and tumor treatment is dependent on the surgeon’s experience. To make matters worse, even when tumors are successfully visualized, the surgeon often cannot accurately assess the location of tumor margins or infer pathologic grade due to the limited field of view and depth of field (10mm and 6mm on average, respectively) of current scopes. The scopes only provide visualization of a small part of the surgical field at any instant. These inherent challenges prevent many surgeons from attempting endoscopic tumor treatment since incomplete treatment leads to a devastating, oncologic outcome. Our overall goal is to create an enhanced visualization and navigational system that makes endoscopic UTUC tumor treatment easier and more accurate for all surgeons, enabling wider utilization. Toward this goal, our specific objective in this proposal is to test the hypothesis that our system can make endoscopic UTUC surgery more accurate and efficient. To test this hypothesis, we propose three Specific Aims: Aim 1 involves the development of an automatic, real-time segmentation and grading system of UTUC tumors during endoscopic treatment. Aim 2 integrates a 3D navigational map of collecting system anatomy, which includes tumor and endoscope location, during endoscopic surgery. Aim 3 evaluates the system in patients, with zero risk to the human subjects. The endpoint of this R01 will be a fully validated enhanced visualization and navigational system for endoscopic UTUC surgery, which would provide the necessary experimental data towards a large-scale, multi-center clinical trial and future FDA approval. As our system would require only software integration to current endoscopic surgical cameras, all existing endoscopic surgical systems could in principle immediately benefit from the results of this project. In this way, we believe the success of our project will facilitate improved UTUC treatment and mitigate progression to a higher risk extirpative surgery.

PROJECT SUMMARY: Rates of depression continue to rise and the mental health impact of COVID-19 has only accelerated trends. While mental health apps, specifically mindfulness apps, are not a panacea, they are popular tools that millions are turning to today for easy access, affordable, and low-stigma help. But increased reliance on mindfulness apps has not been supported by rigorous scientific evidence exemplified by few studies employing appropriate control conditions. Thus, this research is designed to focus on using 100% remote but robust methodology to assess the efficacy of mindfulness apps by applying a novel precision medicine framework. Our study first assesses the impact of the Digital Working Alliance by matching people with depression with a mindfulness app that may better support their personalized needs. We will compare those randomized to the to this matching condition to a digital placebo to better evaluate the efficacy of these mindfulness apps. For the first six weeks, participants will be asked to use the mindfulness app or digital placebo daily, and if not engaged, will receive reminders, allowing for the analysis of clinical outcomes during ideal usage patterns. For an additional six weeks, participants will be asked to use the app or digital placebo naturally, allowing for the elucidation of naturalistic usage patterns and evaluation if these usage patterns impact clinical outcomes. Across the entire study, we will capture smartphone-based digital phenotypes of behaviors (eg sleep, step, screen time), environments (eg home time, greenspace exposure), and symptoms (longitudinal ecological momentary assessment) to create personalized and predictive models of response that can be utilized to better understand factors impacting the efficacy of mindfulness apps, and in the future, better tailor apps to each person.

Project Summary/Abstract Cerebral small vessel disease (cSVD) is a leading cause of vascular contributions to cognitive impairment and dementia (VCID), which is the 2nd leading cause of dementia and a significant contributor to Alzheimer’s disease (AD). Thus far, the underlying pathogenesis of cSVD is poorly understood. Several lines of evidence, including animal models, postmortem human brain pathology, and systemic inflammatory markers, demonstrated the damaging role of chronic neuroinflammation in cSVD. Direct evidence of neuroinflammation at the tissue level in patients with cSVD is still critically needed. The sphingosine-1-phosphate receptor 1 (S1PR1) regulates neuroinflammation through microglial and astrocyte activation and trafficking and has emerged as a promising target for neuroinflammation. In postmortem brains of patients with cSVD, we observed elevated S1PR1 expression and colocalization of S1PR1 with astrocytes and microglia. A novel 11C-CS1P1 PET radiotracer with high affinity and specificity targeting S1PR1 has been recently developed and validated in animal models and post-mortem human specimens. Under an FDA-approved eIND (IND 146548), we have successfully completed the safety and dosimetry study in healthy participants and performed preliminary studies in patients with cSVD. We found that 11C-CS1P1 PET uptake is significantly associated with WMH lesion burden in patients with cSVD after controlling for age, sex, race, vascular risk factors, and amyloid deposition. We hypothesize that 11C-CS1P1 PET uptake is a tissue-level biomarker of neuroinflammation to provide insight into cSVD severity, progression, and prognosis. We will 1) evaluate the relationship between 11C-CS1P1 PET uptake and cSVD neuroimaging abnormalities and cognitive impairment, 2) evaluate the test-retest repeatability and longitudinal evolution, and 3) determine whether 11C-CS1P1 PET uptake at baseline predict cSVD progression. The successful completion of this study will establish 11C-CS1P1 PET as an neuroinflammation imaging biomarker and investigate the role of neuroinflammation in cSVD pathogenesis and progression. It will lay a foundation for developing future therapies in modulating neuroinflammation.

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

Project Summary Prescribed in up to 40% of pregnancies, antibiotics represent the most commonly used class of medication during pregnancy. Although this practice is often necessary for maternal health, accumulating evidence suggests that antibiotic exposure may have unintended consequences for the mother-infant dyad. Epidemiologic studies associate maternal antibiotic exposure, especially in the absence of infection, with increased risk of neonatal complications including late-onset sepsis (LOS) and necrotizing enterocolitis (NEC), yet the mechanisms driving these associations remain poorly understood. Secretory IgA (sIgA) in milk is an essential component of neonatal mucosal immunity, shaping early gut microbial colonization and providing protection against enteric pathogens. The mechanisms by which maternal physiology regulates the abundance and microbial specificity of these antibodies in milk remain poorly understood. In animal models, the maternal gut–mammary axis governs the generation of milk IgA: IgA-committed lymphocytes from the maternal intestine migrate to the mammary gland during advancing pregnancy via CCL- 28/CCR10 signaling. Our preliminary data suggest that maternal antibiotic exposure disrupts this process leading to a decrease in milk IgA. However, the timing and extent of antibody dysbiosis are undefined; the downstream effects on neonatal intestinal health are unknown; and the underlying mechanisms—whether due to altered microbial stimulation, impaired recruitment of IgA⁺ cells to the mammary gland, or both—remain to be elucidated. Our central hypothesis is that maternal antibiotic exposure reduces pathogen-reactive IgA in milk by impairing gut-to-mammary immune cell trafficking thereby compromising neonatal mucosal immunity and increasing infection susceptibility. We will address this hypothesis through three integrated aims: (1) Determine the magnitude and duration of antibiotic-mediated mammary antibody dysbiosis in women who deliver preterm and at term; (2) Identify microbial targets of mammary antibodies diminished by maternal antibiotic exposure and (3 Determine the role of maternal antibiotics in the disruption of mammary resident IgA+ plasma cells in animal models. This integrative human and animal study will uncover critical mechanisms by which maternal antibiotic use alters the maternal-infant immune axis. The results will provide mechanistic insight into the risks associated with perinatal antibiotic exposure and inform clinical strategies to mitigate risk to neonatal health.

Europe’s leading neuroscience conference, bringing together researchers, clinicians, and innovators across molecular, cellular, systems, cognitive, and clinical neuroscience.

Arterial spin labeling (ASL) MRI has become a vital tool in clinical neuroimaging, enabling noninvasive assessment of cerebral perfusion across a range of conditions including stroke, vascular malformations, and brain tumors. With broader clinical adoption, its practical strengths — as well as important limitations — have become increasingly clear.

Although stress can be considered as an ongoing process that helps an organism to cope with present and future challenges, when it is too intense or uncontrollable, it can lead to adverse consequences for physical and mental health. Social stress specifically, is a highly prevalent traumatic experience, present in multiple contexts, such as war, bullying and interpersonal violence, and it has been linked with increased risk for major depression and anxiety disorders. Nevertheless, not all individuals exposed to strong stressful events develop psychopathology, with the mechanisms of resilience and vulnerability being still under investigation. During this talk, I will identify key gaps in our knowledge about stress vulnerability and I will present our recent data from our contextual fear learning protocol based on social defeat stress in mice.

Current machine learning systems consume vastly more energy than biological brains. Neuromorphic systems aim to overcome this difference by mimicking the brain’s information coding via discrete voltage spikes. However, it remains unclear how both artificial and natural networks of spiking neurons can learn energy-efficient information processing strategies. Here we propose Predictive Coding Light (PCL), a recurrent hierarchical spiking neural network for unsupervised representation learning. In contrast to previous predictive coding approaches, PCL does not transmit prediction errors to higher processing stages. Instead, it suppresses the most predictable spikes and transmits a compressed representation of the input. Using only biologically plausible spike-timing based learning rules, PCL reproduces a wealth of findings on information processing in visual cortex and permits strong performance in downstream classification tasks. Overall, PCL offers a new approach to predictive coding and its implementation in natural and artificial spiking neural networks

Traditionally, touch is associated with exteroception and is rarely considered a relevant sensory cue for controlling movements in space, unlike vision. We developed a technique to isolate and measure tactile involvement in controlling sliding finger movements over a surface. Young adults traced a 2D shape with their index finger under direct or mirror-reversed visual feedback to create a conflict between visual and somatosensory inputs. In this context, increased reliance on somatosensory input compromises movement accuracy. Based on the hypothesis that tactile cues contribute to guiding hand movements when in contact with a surface, we predicted poorer performance when the participants traced with their bare finger compared to when their tactile sensation was dampened by a smooth, rigid finger splint. The results supported this prediction. EEG source analyses revealed smaller current in the source-localized somatosensory cortex during sensory conflict when the finger directly touched the surface. This finding supports the hypothesis that, in response to mirror-reversed visual feedback, the central nervous system selectively gated task-irrelevant somatosensory inputs, thereby mitigating, though not entirely resolving, the visuo-somatosensory conflict. Together, our results emphasize touch’s involvement in movement control over a surface, challenging the notion that vision predominantly governs goal-directed hand or finger movements.

Over the last 20 years, neuroimaging and electrophysiology techniques have become central to understanding the mechanisms that accompany loss and recovery of consciousness. Much of this research is performed in the context of healthy individuals with neurotypical brain dynamics. Yet, a true understanding of how consciousness emerges from the joint action of neurons has to account for how severely pathological brains, often showing phenotypes typical of unconsciousness, can nonetheless generate a subjective viewpoint. In this presentation, I will start from the context of Disorders of Consciousness and will discuss recent work aimed at finding generalizable signatures of consciousness that are reliable across a spectrum of brain electrophysiological phenotypes focusing in particular on the notion of edge-of-chaos criticality.

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.

The Nature versus Nurture debate has generally been considered from the lens of genome versus experience dichotomy and has dominated our thinking about behavioral individuality and personality traits. In contrast, the role of nonheritable noise during brain development in behavioral variation is understudied. Using the Drosophila melanogaster visual system, I will discuss our efforts to dissect how individuality in circuit wiring emerges during development, and how that helps generate individual behavioral variation.

Accurate perception of the environment is a constructive process that requires integration of external bottom-up sensory signals with internally-generated top-down information reflecting past experiences and current aims. Decades of work have elucidated how sensory neocortex processes physical stimulus features. In contrast, examining how memory-related-top-down information is encoded and integrated with bottom-up signals has long been challenging. Here, I will discuss our recent work pinpointing the outermost layer 1 of neocortex as a central hotspot for processing of experience-dependent top-down information threat during perception, one of the most fundamentally important forms of sensation.

Thalamic networks, at the core of thalamocortical and thalamosubcortical communications, underlie processes of perception, attention, memory, emotions, and the sleep-wake cycle, and are disrupted in mental disorders, including schizophrenia and autism. However, the underlying mechanisms of pathology are unknown. I will present novel evidence on key organizational principles, structural, and molecular features of thalamocortical networks, as well as critical thalamic pathway interactions that are likely affected in disorders. This data can facilitate modeling typical and abnormal brain function and can provide the foundation to understand heterogeneous disruption of these networks in sleep disorders, attention deficits, and cognitive and affective impairments in schizophrenia and autism, with important implications for the design of targeted therapeutic interventions

The COSYNE 2025 conference was held in Montreal with post-conference workshops in Mont-Tremblant, continuing to provide a premier forum for computational and systems neuroscience. Attendees exchanged cutting-edge research in a single-track main meeting and in-depth specialized workshops, reflecting Cosyne’s mission to understand how neural systems function.

Each year the Bernstein Network invites the international computational neuroscience community to the annual Bernstein Conference for intensive scientific exchange. Bernstein Conference 2024, held in Frankfurt am Main, featured discussions, keynote lectures, and poster sessions, and has established itself as one of the most renowned conferences worldwide in this field.

Organised by FENS in partnership with the Austrian Neuroscience Association and the Hungarian Neuroscience Society, the FENS Forum 2024 will take place on 25–29 June 2024 in Vienna, Austria. The FENS Forum is Europe’s largest neuroscience congress, covering all areas of neuroscience from basic to translational research.

The COSYNE 2023 conference provided an inclusive forum for exchanging experimental and theoretical approaches to problems in systems neuroscience, continuing the tradition of bringing together the computational neuroscience community. The main meeting was held in Montreal followed by post-conference workshops in Mont-Tremblant, fostering intensive discussions and collaboration.

Neuromatch 5 (Neuromatch Conference 2022) was a fully virtual conference focused on computational neuroscience broadly construed, including machine learning work with explicit biological links. After four successful Neuromatch conferences, the fifth edition consolidated proven innovations from past events, featuring a series of talks hosted on Crowdcast and flash talk sessions (pre-recorded videos) with dedicated discussion times on Reddit.

The annual Cosyne meeting provides an inclusive forum for the exchange of empirical and theoretical approaches to problems in systems neuroscience, in order to understand how neural systems function. The main meeting is single-track, with invited talks selected by the Executive Committee and additional talks and posters selected by the Program Committee based on submitted abstracts. The workshops feature in-depth discussion of current topics of interest in a small group setting.

Neuroinflammation is an early and dynamic feature of preclinical Alzheimer's disease (AD) that may influence amyloid-β (Aβ) pathology. Diffusion-weighted MRI (DWI) offers a sensitive means of probing subtle tissue alterations <em>in vivo</em>, particularly when combined with multi-compartment biophysical modelling. However, clinical datasets are often not acquired with protocols suited for advanced DWI models. Here, we present preliminary evidence of microstructural alterations in cognitively unimpaired preclinical AD participants and evaluate simulation-based inference (SBI) as a flexible framework for diffusion model estimation. DWI data from a subset of participants in the ALFA+ study was analysed. Participants were classified as Aβ-positive or Aβ-negative using cerebrospinal fluid Aβ_42/40cut-offs. Diffusion tensor and free-water imaging metrics were quantified in predefined white matter tracts and AD-signature cortical grey matter regions. Fractional anisotropy (FA), mean diffusivity (MD), and free-water fraction (FW_F) were estimated using conventional weighted least-squares fitting and SBI. Agreement between approaches was assessed using Lin’s concordance correlation coefficient (CCC, voxel-wise agreement) and the structural similarity index (SSIM, spatial correspondence). Following quality control, 179 datasets were available for analysis, including 68 Aβ-positive participants. Aβ-positive participants exhibited lower MD in the uncinate fasciculus and inferior temporal cortex. A significant Aβ-by-C-reactive protein interaction was observed for uncinate FW_F. SBI-derived diffusion maps showed excellent correspondence with conventional fitting (FA: CCC = 0.94, SSIM = 0.94), supporting its validity. These findings identify the uncinate fasciculus as a potential locus of early microstructural change and highlight SBI as a promising framework for future estimation of diffusion indices derived from multi-compartment biophysical DWI models.

COVID-19 during pregnancy can trigger maternal immune activation, potentially disrupting placental homeostasis and fetal neurodevelopment, with effects depending on gestational stage. Given the challenges to source fetal tissue, the brain-placenta axis allows us to use placenta to study neurodevelopment and its molecular mechanisms. This study aimed at exploring genes in fetal neurodevelopment and the placenta of pregnant people with COVID-19 and understanding its differences across two mid-gestation periods. We meta-analyzed six placenta transcriptome datasets from COVID-infected pregnant people. The network medicine-based method NERI integrated the meta-analysis results into the protein-protein interaction (PPI) network BIOGRID and two healthy neurodevelopmental transcriptome datasets with data at the 12-13 post-conceptional weeks (pcw) and 16-21 pcw stages, from regions corresponding to the dorsolateral prefrontal cortex (DLPFC) and the hippocampus. Genes prioritized by NERI were tested for enrichment of genes from the neurodevelopmental disorders (NDD) interactome and Gene Ontology (GO) terms. The meta-analysis identified 43 genes consistently altered in the placenta of COVID-infected pregnant people. Their integration with PPI and fetal transcriptomes selected 193 hippocampal and 192 DLPFC genes (Figure 1A). NDD interactome genes were enriched in the selected genes from both DLPFC (139 genes, p-val = 4.19∙10²⁵) and hippocampus (122 genes, p-val = 4.98∙10²⁰). The GO analyses revealed enrichment of terms related to TORC1, MAPK and NFκβ, and others associated to neurodevelopment (Figure 1B-C). These results indicate that gestational COVID-19 may influence neurodeveloment in a time-dependent manner. Our study presents candidate pathways for future research on how viral infections affect the developmental origins of NDDs.<br><br><img src="https://file.documedias.systems/download/5639736b-7d6e-4503-b338-d4d0759f154a" class="fr-fil fr-dib fr-draggable" alt="Panel A: Workflow showing data integration. Text: six placenta transcriptomes datasets, n = 66 pregnant with COVID-19 versus n = 35 pregnant without COVID-19. Blue arrow, Text: 43 genes consistenly altered across datasets. Blue arrows joining the texts: NERI method, PPI network, fetal neurodevelopment transcriptome at 12 to 13 pcw versus 16 to 21 pcw. Pointing at: Prioritized genes 192 DLPFC and 193 hippocampal. Panels B and C: Greyscale scatter plot showing top 15 Gene Ontology Biological Process terms enriched in the genes pioritized in the dorsolateral prefrontal cortex and the hippocampus. The x-axis indicates fold-enrichment, y-axis indicates the Gene Ontology terms, the grayscale indicates the p-value at the Fisher’s exact test, and dot size indicates the number of genes enriched in that term. Number of genes ranges from 5 to 27. Terms from the DLPFC ordered by fold enrichment (higher to lower): Protein refolding, Cellular response to heat, Negative regulation of autophagy, TORC1 signaling, Cellular response to nutriente levels, MAPK Cascade, Endocytosis, Protein folding, Pos. reg. of canonical NFκβ signal transduction, Chromatin remodeling, Chromatin organization, Neg. reg. of DNA-templated transcription, Protein transport, Neg. reg. Of trancription by RNA PolII, Pos.reg. Of transcription by RNA PolI. Terms from the hipoccampus ordered by fold enrichment (higher to lower): Schwann cell Development, Neg. reg. Of protein-containing complex assembly, Canonical NFκβ signal transduction, Cellular response to UV, TORC1 signaling, Cellular response to nutrient levels, Neg. reg. Of TGF-β receptor signaling pathway, MAPK Cascade, Rhythmic process, Intracellular protein localization, Chromatin remodelling, Neg. reg. Of apoptotic process, Intracellular protein transport, Pos. reg. Of DNA-templated transcription, Pos.reg. Of transcription by RNA PolI.">

Serotonin (5-HT) has a multitude of functions in the central nervous system and, in addition to modulating neuronal function, has also been reported to act as a morphogen during development and affect neurite extension. However, the precise molecular mechanisms that underlie the effect of 5-HT on neurite extension are not well characterised and the interpretation of studies in the intact nervous system is challenging due to the complexity of cellular interactions, which makes it difficult to differentiate between direct and indirect effects. In order to address whether activation of 5HT1A receptors expressed on cortical neurons directly affects neurite extension, we have studied the effect of the highly selective 5HT1A receptor agonist NLX-101 on neurite extension and the morphology of ‘pyramidal-like’ neurons in primary cultures of mouse cortical neurons. Single cell tracings revealed a concentration-dependent subtle, but significant increase in neurite extension. Interestingly, application of SCH-772984 to block the extracellular signal-regulated kinase (ERK) signalling pathway did not appear to prevent the growth promoting effect of 5HT1A receptor activation. Instead, SCH-772984 mimicked the effect of 5HT1A receptor activation, and even on its own promoted neurite extension in primary cortical neurons. Thus, we suggest that 5HT1A receptor effects on neurite growth may not be mediated via the activation of ERK as suggested based on work in non-neuronal cells and immortalised hippocampal HN2-5 cells (Rojas PS and Fiedler JL, 2016, Front. Cell. Neurosci. 10:272. doi: 10.3389/fncel.2016.00272), but via some alternative signalling pathway, likely related to its effect on intracellular cAMP concentrations.

Linking word forms to their meanings is a fundamental aspect of language learning [1]. Transcranial direct current stimulation (tDCS) has shown promise in supporting this process, although its effects have been inconsistent [2]. This study aimed to identify reliable and functionally meaningful cortical targets for tDCS to enhance novel word learning, and to evaluate the test–retest-reliability (TRR) of behavioral and neural measures. Twenty healthy participants completed two functional magnetic resonance imaging (fMRI) sessions with parallel versions of a picture–pseudoword association task, alongside a lexical decision control task. Learning occurred across four blocks. Behavioral performance was analyzed using linear mixed models, and imaging data were examined using whole-brain and region-of-interest analyses. TRR was assessed using intraclass correlation coefficients [3]. Participants successfully acquired novel word forms, as reflected in increasing accuracy and faster response times, with behavioral measures demonstrating good-to-excellent TRR across sessions. The learning task elicited robust language-related brain activity, and changes across learning stages were associated with performance, whereas the control task did not show consistent activity changes. Despite some variability, approximately 81% of voxels within significant clusters demonstrated moderate-to-excellent reliability. Ongoing analyses examine task-based functional connectivity and its relationship to learning. Overall, these findings identify reliable cortical targets for tDCS and support the suitability of this paradigm for future combined tDCS–fMRI research, highlighting the importance of reliability-driven approaches in brain stimulation studies.<br > <br><strong>References</strong><br>[1] Shtyrov et al. (2012). <em>The Neuroscientist</em><br>[2] Meinzer et al. (2024). <em>Frontiers in Neuroscience</em><br>[3] Kocataş et al. (2025). bioRxiv

Cognitive impairment remains a major therapeutic challenge in neurodevelopmental and neuropsychiatric disorders. Targeting both epigenetic regulation and histaminergic signalling has emerged as a promising strategy for cognitive enhancement. A-366, a potent G9a inhibitor with histamine H3 receptor (H3R) antagonistic activity, represents a novel dual-acting compound with therapeutic potential. In this study, we investigated the effects of chronic A-366 administration on cognitive function and oxidative stress in the BTBR T+tf/J mouse model, an idiopathic model of autism.<br >Male BTBR T+tf/J mice were administered A-366 (0.5–2 mg/kg, i.p.) daily for 21 days. Cognitive behaviour was evaluated using the Fear Conditioning Test, Novel Object Recognition Test, and Open Field Test. Following behavioural assessments, hippocampal tissues were collected to measure oxidative stress markers, including superoxide dismutase (SOD) activity and malondialdehyde (MDA) levels.<br>Chronic administration of A-366 significantly improved cognitive performance in BTBR mice, evidenced by enhanced contextual and cued memory in the Fear Conditioning Test, increased discrimination indices in the Novel Object Recognition Test, and maintained exploratory behaviour in the Open Field Test. Biochemical analysis revealed elevated superoxide dismutase (SOD) activity and reduced malondialdehyde (MDA) levels in hippocampal tissue, indicating a reduction in oxidative stress.<br>Dual targeting of G9a and H3R demonstrates significant memory-enhancing effects in BTBR mice, mediated through combined modulation of epigenetic mechanisms and histaminergic signalling, with improved oxidative balance. These findings support the potential therapeutic value of dual-targeting G9a inhibitors/H3R antagonists as a promising class for the development of novel therapies for cognitive impairments associated with neurodevelopmental and neuropsychiatric disorders.

The ventral hippocampus is a key regulator of emotional behavior through its projections to hypothalamic defensive circuits. In particular, inputs to the anterior hypothalamic nucleus (AHN) contribute to anxiety, innate fear, and contextual fear responses. Here, we investigated whether hippocampal neurons projecting to the AHN distribute collateral outputs to further brain regions, supporting an extended integrative role. By injecting into the AHN an AAV retrograde vector carrying the enhanced yellow fluorescent protein (EYFP) reporter gene, we mapped the hippocampal neurons projecting to this region and found that they are primarily located along the rostrocaudal extent of the ventral subiculum. To determine their full projection pattern, we combined AHN-targeted retrograde delivery of Cre recombinase along with the Cre-dependent expression of membrane-bound green fluorescent protein (GFP) and synaptophysin-mRuby in the subiculum, enabling visualization of axonal projections and presynaptic terminals. This approach revealed that AHN-projecting ventral subicular neurons exhibit widespread branching projections to multiple brain regions implicated in memory, motivation, arousal, circadian regulation, neuroendocrine control, and goal-directed behavior. These findings demonstrate that hippocampal outputs to the AHN are embedded within a distributed network rather than a single pathway. Such organization positions ventral subicular neurons to coordinate complex behavioral and physiological responses, highlighting their role as integrative nodes in circuits underlying fear and anxiety behavior.

Prenatal alcohol exposure (PAE) is a major cause of fetal alcohol spectrum disorders (FASDs) and is associated with long-lasting neurodevelopmental impairments. Although alcohol can induce neuroinflammatory responses, whether neuroinflammation contributes to brain alterations induced by PAE remains largely unexplored. Using a well-established mouse model, this study investigates early neuroinflammatory-related changes in the developing brain of male and female embryos. The results suggest that early alcohol exposure is associated with impaired blood–brain barrier integrity, characterized by delayed barrier closure and dysregulation of tight junction protein expression (ZO-1, Occludin). In addition, microglial morphology and the expression of key microglial markers (Iba-1, P2RY12 and CX3CR1) are altered as early as embryonic day E17, suggesting disrupted microglial maturation and homeostasis. These findings support the central hypothesis that PAE has a major effect on the neuroimmune axis, which may contribute to the cognitive and behavioral impairments observed in fetal alcohol spectrum disorders.

Dynamical memory allows neural systems to retain useful internal states over short and intermediate timescales, supporting computations relevant to navigation, motor control, and working memory. Conventional models typically rely on recurrent networks with separate excitatory and inhibitory populations. However, recent observations of localized excitatory activity in the Drosophila head-direction system suggest that such dynamics may in fact be implemented by a handful of neurons. This raises a timely and fundamental question: what is the minimal neural substrate required to generate persistent activity?<br><br>Here, we show, contrary to common intuition, that a single leaky integrate-and-fire (LIF) neuron with recurrent slow self-excitation is sufficient to produce a persistent firing state that is stable and robust to distractors. Alongside numerical solutions, we provide an empirical demonstration using a minimal hardware implementation. We further show that this finding extends to the widely used Izhikevich and AdEx models, in which robust persistent activity can be obtained simply by reversing the sign of the slow adaptation variable. Together, these results identify a minimal substrate for memory-like dynamics and suggest a compact building block for future neuromorphic cognitive architectures.

Stress activates the hypothalamic-pituitary-adrenal (HPA) axis and sympathetic nervous system (SNS), driving glucocorticoid and catecholamine release modulating splenic immune function. Although acute activation is adaptive, chronic or dysregulated signalling may promote inflammation and immune dysfunction. How different physiological stressors remodel splenic neuroimmune signalling, and whether these responses differ by sex, remains poorly defined. We investigated whether ageing, stroke, and caloric restriction (CR) induce patterns of splenic neuroimmune remodelling in murine models. Splenic neurotransmitter receptor expression was quantified by qPCR in spleen tissue and isolated lymphocyte populations, while glucocorticoid and catecholamine concentrations were measured in tissue by ELISA. Whole spleen β2-adrenergic receptor (β2-AR) and glucocorticoid receptor expression remained unchanged across conditions, suggesting that tissue-level analysis may mask changes within immune cell subsets. Consistent with this, β2-AR expression was reduced in CD4⁺ T cells acutely following stroke, indicating stressor-specific changes in adrenergic sensitivity in populations of cells. Glucocorticoid concentrations were significantly elevated after stroke and CR, but not ageing, demonstrating divergent endocrine adaptations. Adrenaline levels were unchanged across groups, whereas noradrenaline showed stressor- and sex-dependent regulation: increased following stroke and CR, but reduced with ageing in females only. These findings demonstrate that physiological stressors do not display a uniform splenic stress response, but instead drive selective neuroimmune remodelling. Stroke was associated with an enhanced sympathetic-endocrine profile, CR with a potentially adaptive regulated response, and ageing with impaired noradrenergic signalling, particularly in females. Ongoing immunohistochemical analyses will determine whether these molecular changes are accompanied by alterations in splenic neural and immune cell architecture.

Dopamine (DA) neurons in the <span style="font-style:italic">substantia nigra pars compacta</span> (SN) are critically involved in locomotor control, while their degeneration is a hallmark of Parkinson’s disease. During embryonic development, cells typically downregulate stemness-promoting microRNAs, such as the miR-290-295 cluster, upon terminal differentiation. Surprisingly, we identified that these embryonic microRNAs are highly and selectively expressed in adult SN DA neurons, though its expression significantly declines with aging. To investigate its physiological role, we generated genetic knockout models. While global deletion of the miR-290-295 cluster led to progressive SN DA neuron loss, the adult DA neuron-specific knock-out led to an early reduction in key DA biogenesis enzymes, including dopa decarboxylase and DA transporter, ultimately manifesting in late-onset locomotor deficits. Mechanistically, we determined that miR-292a-3p, the most abundant member of this cluster, directly targets and represses <span style="font-style:italic">Pten</span>, a primary antagonist of the PI3K-Akt-mTOR signaling pathway, which is essential for translation initiation and conveying protection of DA neurons. Using <span style="font-style:italic">in vivo</span> L-azidohomoalanine labeling to track <span style="font-style:italic">de novo</span> translation, we demonstrated that the loss of this microRNA cluster causes a severe impairment of protein synthesis within mature SN DA neurons. Notably, supplementing miR-292a-3p or silencing <span style="font-style:italic">Pten</span> effectively rescued these cluster knockout-associated decline in cell viability. These findings reveal an unexpected epigenetic mechanism where terminally differentiated neurons repurpose stem cell-specific microRNAs to preserve protein synthesis and maintain long-term neuroprotection.<br><br><img src="https://file.documedias.systems/download/7cba93f1-475b-4663-bb85-ebe4617a02d3" class="fr-fic fr-dib fr-draggable" alt="Selective abundance of the stemness-promoting cluster miR-290-295 within the adult substantia nigra dopamine neurons is neuroprotective via preservation of protein synthesis">