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Biology subjects

Ghosh, S. S.

Publications and source records attributed to Ghosh, S. S..

At least 19 recordsLinked to original sources

Brain states recur across diverse narrative contexts during longitudinal viewing

What does the brain do during the continuous, varied experience of watching a story unfold? One account holds that the brain traverses a finite repertoire of recurring states, but whether that repertoire is a stable property of the individual or is reshaped by each new experience has not been tested across diverse naturalistic content within the same person. We characterized the dynamic brain-state repertoire in six individuals who watched the television series Friends across its six seasons during fMRI (up to [~]146 episodes, [~]54 hours per person). For each individual we fit a sticky hierarchical Dirichlet process hidden Markov model across all episodes, discovering brain states (recurring whole-brain activity patterns with characteristic coupling) without pre-specifying their number. Each individuals brain visited roughly forty-five states arrayed along a continuous recurrence gradient, from states active in nearly every episode to episode-specific ones, with no sharp division between them. The repertoire was heterogeneous in why its states recurred: a minority locked to scan-run structure, the majority remaining eligible for content. Transitions were organized by the functional-connectivity similarity between states (per-individual Spearman {rho} = 0.33-0.55) and, in most individuals, respected resting-state network boundaries. Episode content was associated with which states the brain occupied moment to moment. The recurrence ordering discovered in Friends transferred to state occupancy during other social-narrative films (five of six individuals) and attenuated as stimuli departed from that class, weakening for visual-only reading and audio-only listening. Across diverse narrative experience, the dynamic repertoire is a property of the individual: content varies which states are visited and when, not which states exist.

neuroscience↗

A layered standards framework for integrating single-cell and spatial omics data into brain cell atlases

The BRAIN Initiative Cell Atlas Network (BICAN) is generating large-scale multimodal datasets to profile cell types in the human, non-human primate, and mouse brain. The diversity of single-cell and spatial transcriptomic and epigenomic assays, combined with varied experimental contexts, multiple data-generating laboratories and distributed infrastructure, poses substantial challenges for data integration and reuse in BICAN. To address this, we implemented a standards framework that enables layered integration of these data into knowledge-ready products for interoperable brain cell atlases. This framework organizes data based on three progressively structured layers. First, we introduced an assay-agnostic modeling layer that unifies the representation of single-cell and spatial omics data using a common set of biological entities and processes assessed by diverse experimental techniques. Second, we implemented harmonized metadata standards that capture key experimental features linked to biospecimen provenance across heterogeneous tissue sources, species, and preparations, supporting integration and validation while minimizing burden on data contributors. Third, we present an extensible representation for data-driven cell type taxonomies that integrates molecular data with annotations, ontology mappings, and evidence. Together, these contributions represent an end-to-end framework that transforms heterogeneous datasets into structured, interoperable resources that support broad community reuse via mapping algorithms, annotation systems, and visualization platforms. This approach links biospecimen provenance with cell-level outputs and embeds these in a standardized taxonomy format, enabling downstream applications such as cross-dataset integration, reference mapping, and knowledge-driven analysis. More broadly, our work demonstrates a generalizable strategy for enabling an efficient data-to-knowledge pipeline in a large-scale consortium setting.

genomics↗

Hierarchical X-ray microscopy and mesoscopic diffusion MRI in the same brain reveal the human connectome across scales

We present a multimodal pipeline for 3D imaging of cerebral white-matter archi-tecture across scales, from whole-brain axonal projections down to individual myelinated axons. After diffusion MRI, an adult ex vivo human hemisphere undergoes label-free imaging with Hierarchical Phase-Contrast Tomography (HiP-CT) from 20 {micro}m/voxel in the whole hemisphere to 2 {micro}m/voxel in areas of interest, with intrinsic cross-scale alignment. A 4 cm tissue block extracted from the hemisphere is reimaged with HiP-CT at 0.857 {micro}m/voxel, enabling direct visualisation of single myelinated axons. After osmium staining, micro-CT at 0.364 {micro}m/voxel and electron microscopy at 4 nm/voxel are acquired in biop-sies from the tissue block to validate the presence of myelinated axons in the label-free HiP-CT contrast. Spanning three orders of magnitude in resolution, these co-registered multimodal datasets bridge microscopic wiring and macro-scopic brain organisation, providing a foundation for anatomically grounded whole-brain connectomics.

neuroscience↗

A human subcortical connectome at 400 μm resolution

The fiber pathways of the human subcortex are of particular importance in clinical neuroscience, as they are targeted by neuromodulation therapies, such as deep brain stimulation (DBS) and lesion approaches, in various motor and psychiatric disorders. However, the complexity, size, and position of these pathways make them challenging to image non-invasively with diffusion MRI (dMRI). As a result, recent efforts in atlasing these pathways to guide neuromodulation have resorted to using synthetic data. Here we present the first extensive reconstruction of fiber pathways of the human subcortex with ultra-high-resolution dMRI. We leverage a unique, ex vivo dMRI dataset acquired by the BRAIN CONNECTS center for Large-scale Imaging of Neural Circuits (LINC) on the first-of-its-kind Connectome 2.0 scanner. Our contribution is two-fold. First, by showing the feasibility of reconstructing these pathways with non-invasive neuroimaging at the single subject level, we set the stage for future research into reconstructing them in vivo in individual patients. Second, we provide a high-definition atlas of basal-ganglia-thalamocortical circuits that is readily extensible via our publicly released data and annotations. As a first demonstration of its clinical validity, we align our atlas to "hotspots" from previous DBS studies and identify pathways associated with therapeutic or side effects.

neuroscience↗

Pregistered movie-fMRI analyses reveal altered visual feature encoding in autism in pSTS

Sensory-perceptual differences are widely reported in autism, yet their underlying mechanisms remain unclear. We tested preregistered hypotheses using stacked encoding models applied to naturalistic movie-viewing fMRI from children and adolescents with and without an autism diagnosis from the Healthy Brain Network. We mapped cortical responsiveness to low- and high-level auditory and visual feature spaces. Contrary to enhanced perceptual functioning predictions, autism was not associated with increased low-level encoding in primary sensory cortices. Instead, autistic children and adolescents had reduced high-level visual representations and a relative shift toward low-level over high-level feature encoding in integration and social brain regions including the pSTS and adjacent face/social areas. In pSTS, this high-low weighting tracked Social Responsiveness Scale (SRS) scores. By contrast, audio-visual modality preference and sensory dominance were broadly conserved across groups. Developmentally, encoding exhibited strong, lateralized, modality-congruent age effects. Together, these findings favor weak central coherence accounts over early sensory enhancement, constrain mechanisms to altered visual feature weighting within social/multisensory networks, and demonstrate the value of naturalistic stimuli and encoding models for characterizing sensory-perceptual neurodevelopmental differences.

neuroscience↗

Functional fractionation of large-scale brain networks in the human subcortex

Brain network mapping plays a crucial role in advancing our understanding of human brain organization and the neuroanatomic foundations of cognition. Historically, the identification of large-scale brain networks has focused on the cerebral cortex. Some cortical networks have been fractionated into subnetworks, yielding valuable insights into their domain-specific cognitive functions. In contrast, functional mapping of large-scale brain networks within subcortical regions remains an emerging and challenging field, hindered by a low signal-to-noise ratio in subcortical functional MRI data and an inability to distinguish networks with substantial spatiotemporal overlap. In this study, we fractionated and identified fifteen spatially overlapped and temporally correlated subnetworks, which can be categorized into four large-scale brain networks. The subcortical functional connectivity patterns of these subnetworks exhibited distinct, yet overlapping, spatial configurations, with widely connected hub nodes identified in the caudate, putamen, hippocampus, and thalamus. These subnetworks are highly reproducible across healthy human brains and provide normative functional atlases, which we release here as a resource for the academic community. As a proof-of-principle demonstration of how the atlases can be used to elucidate the pathophysiology of neuropsychiatric disorders, we show that the spatial patterns of the subnetworks predict the level of consciousness in patients with severe traumatic brain injury. These observations indicate a highly conserved and spatially overlapped subcortical functional architecture in the human brain, providing opportunities to elucidate pathophysiologic mechanisms and develop new neuromodulatory therapies for a broad spectrum of neuropsychiatric diseases.

neuroscience↗

Atypical p38 Kinase Signaling in Retinal Vascular Damage and Recovery

Despite the life-changing impact of anti-VEGF therapies, vascular retinopathies continue to affect millions of patients worldwide. To better understand disease progression, it is essential to define alternative mechanisms that contribute to retinal vascular dysregulation. Mitogen-activated protein kinase (MAPK) p38 plays a significant role in regulating vascular homeostasis, angiogenesis, and retinal disease progression, yet effective therapeutic targeting remains elusive. An alternative atypical p38 signaling pathway is mediated by interaction with the adaptor protein TGF-beta activated kinase 1, binding protein 1 (TAB1). Atypical p38 can be activated by ischemia or by inflammatory G protein-coupled receptors (GPCRs) to regulate inflammation and vascular homeostasis. However, atypical signaling has not been investigated in the context of vascular retinopathies, specifically oxygen-induced retinopathy (OIR). Here, we utilized a genetic knock-in mouse to block atypical p38 activity (Tab1KI) to explore retinal damage during OIR. We report that Tab1KI mice display significantly reduced vaso-obliteration and limited neovascularization relative to wild-type C57BL6 controls. Retinal RNAseq analysis revealed distinct transcriptional regulation in the Tab1KI mouse. Suppression of atypical p38 reduced Mef2c signaling, which in turn enhanced microglial activation and inflammation. Despite the upregulation of proangiogenic markers, the increase in endothelial markers does not correlate with the dysregulated growth of blood vessels. Instead, Mef2c suppression reduces pathological angiogenesis, while increasing physiological vascular regrowth. The combined data suggest that the selective inhibition of atypical p38 signaling could block the enhanced pathogenic neovascular tuft formation without impacting blood vessel repair. Further investigation will clarify how atypical p38 controls neovascular responses, potentially revealing new treatment strategies for vascular retinopathies. NEW AND NOTEWORTHYMAPK p38 drives ocular injury and inflammation, but the contribution of Tab1-dependent atypical p38 in retinal injury has not been investigated. Using a mouse genetic knock-in model to block atypical p38 during oxygen-induced retinal injury, we show that atypical p38 enhances vascular damage and neovascular tufting. Blockade of atypical p38 protects mice from retinal vascular damage.

cell biology↗

A naturalistic reinforcement learning task uncovers historical neural representations

Exploration is essential for reinforcement learning (RL) in human development, facilitating cognitive and behavioral adaptation. However, conventional RL paradigms in neuroimaging studies often rely on highly constrained search spaces and artificial, non-naturalistic scenarios, limiting their capacity to reflect the complexity of real-world human exploration and decision- making. To address this gap, we developed a novel, naturalistic RL paradigm called Photographer, which involves a photograph-taking task set in a virtual street-view environment. Participants navigated city scenes and attempted to infer the paradigms covert goal solely from feedback, defined as the conceptual similarity between their photograph and a hidden target sentence. Representational similarity analysis revealed that feedback history, an encoding of recent trial attempts, was robustly represented in the middle orbital gyrus and inferior frontal gyrus. These neural representations were consistently observed across two independent participant groups. Moreover, although the two regions coactivated during exploration, each was associated with a distinct functional network.

neuroscience↗

Context modulates brain state dynamics and behavioral responses during narrative comprehension

Narrative comprehension is inherently context-sensitive, yet the brain and cognitive mechanisms by which brief contextual priming shapes story interpretation remain unclear. Using hidden Markov modeling (HMM) of fMRI data, we identified dynamic brain states as participants listened to an ambiguous spoken story under two distinct narrative contexts (affair vs. paranoia). We identified recurrent states involving auditory, language, and default mode network (DMN) regions that were expressed across both groups, as well as additional states characterized by recruitment of multiple-demand network (MDN) systems, including control, dorsal attention, and salience networks. Bayesian mixed-effects modeling revealed that contextual framing modulated how specific linguistic and character-related features influenced the probability of occupying these states. Complementary behavioral data showed parallel context-sensitive modulation of participants moment-to-moment interpretive judgments. Together, these findings suggest that contextual priming influences narrative comprehension through subtle, feature-dependent adjustments in the engagement of DMN- and MDN-related brain states during naturalistic story listening.

neuroscience↗

AI-readiness for Biomedical Data: Bridge2AI Recommendations

Biomedical research is rapidly adopting artificial intelligence (AI). Yet the inherent complexity of biomedical data preparation requires implementing actionable, robust criteria for ethical and explainable AI (XAI) at the "pre-model" stage, encompassing data acquisition, detailed transformations, and ethical governance. Simple conformance to FAIR (Findable, Accessible, Interoperable, Reusable) Principles is insufficient. Here, we define criteria and practices for reliable AI-readiness of biomedical data, developed by the NIH Bridge to Artificial Intelligence (Bridge2AI) Standards Working Group across seven core dimensions of dataset AI-readiness: FAIRness, Provenance, Characterization, Ethics, Pre-model Explainability, Sustainability, and Computability. Conformance to these criteria provides a basis for pre-model scientific rigor and ethical integrity, mitigating downstream risks of bias and error prior to AI modeling. We apply and evaluate these standards across all four Bridge2AI flagship datasets, spanning functional genomics to clinical medicine, and encode them in machine-actionable metadata bound to the datasets. This framework sets a benchmark for preparing ethical, reusable datasets in biomedical AI and provides standardized methods for reliable pre-model data evaluation.

bioinformatics↗

A reproducible and generalizable software workflow for analysis of large-scale neuroimaging data collections using BIDS Apps

Neuroimaging research faces a crisis of reproducibility. With massive sample sizes and greater data complexity, this problem becomes more acute. Software that operates on imaging data defined using the Brain Imaging Data Structure (BIDS) - BIDS Apps - have provided a substantial advance. However, even using BIDS Apps, a full audit trail of data processing is a necessary prerequisite for fully reproducible research. Obtaining a faithful record of the audit trail is challenging - especially for large datasets. Recently, the FAIRly big framework was introduced as a way to facilitate reproducible processing of large-scale data by leveraging DataLad - a version control system for data management. However, the current implementation of this framework was more of a proof of concept, and could not be immediately reused by other investigators for different use cases. Here we introduce the BIDS App Bootstrap (BABS), a user-friendly and generalizable Python package for reproducible image processing at scale. BABS facilitates the reproducible application of BIDS Apps to large-scale datasets. Leveraging DataLad and the FAIRly big framework, BABS tracks the full audit trail of data processing in a scalable way by automatically preparing all scripts necessary for data processing and version tracking on high performance computing (HPC) systems. Currently, BABS supports jobs submissions and audits on Sun Grid Engine (SGE) and Slurm HPCs with a parsimonious set of programs. To demonstrate its scalability, we applied BABS to data from the Healthy Brain Network (HBN; n=2,565). Taken together, BABS allows reproducible and scalable image processing and is broadly extensible via an open-source development model.

neuroscience↗

OME-Zarr: a cloud-optimized bioimaging file format with international community support

A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself - OME-Zarr - along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain -- the file format that underlies so many personal, institutional, and global data management and analysis tasks.

bioinformatics↗

The BRAIN Initiative Cell Census Data Ecosystem: A User's Guide

Characterizing cellular diversity at different levels of biological organization across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also required to manipulate cell types in controlled ways, and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data generating centers, data archives and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain and demonstration of prototypes for human and non-human primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed, and to accessing and using the BICCN data and its extensive resources, including the BRAIN Cell Data Center (BCDC) which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted by the BICCN toward FAIR (Wilkinson et al. 2016a) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.

bioinformatics↗

Neuroscout, a unified platform for generalizable and reproducible fMRI research

Functional magnetic resonance imaging (fMRI) has revolutionized cognitive neuroscience, but methodological barriers limit the generalizability of findings from the lab to the real world. Here, we present Neuroscout, an end-to-end platform for analysis of naturalistic fMRI data designed to facilitate the adoption of robust and generalizable research practices. Neuroscout leverages state-of-the-art machine learning models to automatically annotate stimuli from dozens of naturalistic fMRI studies, allowing researchers to easily test neuroscientific hypotheses across multiple ecologically-valid datasets. In addition, Neuroscout builds on a robust ecosystem of open tools and standards to provide an easy-to-use analysis builder and a fully automated execution engine that reduce the burden of reproducible research. Through a series of meta-analytic case studies, we validate the automatic feature extraction approach and demonstrate its potential to support more robust fMRI research. Owing to its ease of use and a high degree of automation, Neuroscout makes it possible to overcome modeling challenges commonly arising in naturalistic analysis and to easily scale analyses within and across datasets, democratizing generalizable fMRI research.

neuroscience↗

The Neurodata Without Borders ecosystem for neurophysiological data science

The neurophysiology of cells and tissues are monitored electrophysiologically and optically in diverse experiments and species, ranging from flies to humans. Understanding the brain requires integration of data across this diversity, and thus these data must be findable, accessible, interoperable, and reusable (FAIR). This requires a standard language for data and metadata that can coevolve with neuroscience. We describe design and implementation principles for a language for neurophysiology data. Our open-source software (Neurodata Without Borders, NWB) defines and modularizes the interdependent, yet separable, components of a data language. We demonstrate NWBs impact through unified description of neurophysiology data across diverse modalities and species. NWB exists in an ecosystem, which includes data management, analysis, visualization, and archive tools. Thus, the NWB data language enables reproduction, interchange, and reuse of diverse neurophysiology data. More broadly, the design principles of NWB are generally applicable to enhance discovery across biology through data FAIRness.

neuroscience↗

TemplateFlow: a community archive of imaging templates and atlases for improved consistency in neuroimaging

Reference anatomies of the brain and corresponding atlases play a central role in experimental neuroimaging workflows and are the foundation for reporting standardized results. The choice of such references --i.e., templates-- and atlases is one relevant source of methodological variability across studies, which has recently been brought to attention as an important challenge to reproducibility in neuroscience. TemplateFlow is a publicly available framework for human and nonhuman brain models. The framework combines an open database with software for access, management, and vetting, allowing scientists to distribute their resources under FAIR --findable, accessible, interoperable, reusable-- principles. TemplateFlow supports a multifaceted insight into brains across species, and enables multiverse analyses testing whether results generalize across standard references, scales, and in the long term, species, thereby contributing to increasing the reliability of neuroimaging results.

neuroscience↗

Remote Digital Psychiatry: MindLogger for Mobile Mental Health Assessment and Therapy

BackgroundUniversal access to assessment and treatment of mental health and learning disorders remains a significant and unmet need. There is a vast number of people without access to care because of economic, geographic, and cultural barriers as well as limited availability of clinical experts who could help advance our understanding of mental health. ObjectiveTo create an open, configurable software platform to build clinical measures, mobile assessments, tasks, and interventions without programming expertise. Specifically, our primary requirements include: an administrator interface for creating and scheduling recurring and customized questionnaires where end users receive and respond to scheduled notifications via an iOS or Android app on a mobile device. Such a platform would help relieve overwhelmed health systems, and empower remote and disadvantaged subgroups in need of accurate and effective information, assessment, and care. This platform has potential to advance scientific research by supporting the collection of data with instruments tailored to specific scientific questions from large, distributed, and diverse populations. MethodsWe conducted a search for tools that satisfy the above requirements. We designed and developed a new software platform called "MindLogger" that exceeds the above requirements. To demonstrate the tools configurability, we built multiple "applets" (collections of activities) within the MindLogger mobile application and deployed several, including a comprehensive set of assessments underway in a large-scale, longitudinal, mental health study. ResultsOf the hundreds of products we researched, we found 10 that met our primary requirements above with 4 that support end-to-end encryption, 2 that enable restricted access to individual users data, 1 that provides open source software, and none that satisfy all three. We compared features related to information presentation and data capture capabilities, privacy and security, and access to the product, code, and data. We successfully built MindLogger mobile and web applications, as well as web browser-based tools for building and editing new applets and for administering them to end users. MindLogger has end-to-end encryption, enables restricted access, is open source, and supports a variety of data collection features. One applet is currently collecting data from children and adolescents in our mental health study, and other applets are in different stages of testing and deployment for use in clinical and research settings. ConclusionsWe have demonstrated the flexibility and applicability of the MindLogger platform through its deployment in a large-scale, longitudinal, mobile mental health study, and by building a variety of other mental health-related applets. With this release, we encourage a broad range of users to apply the MindLogger platform to create and test applets to advance health care and scientific research. We hope that increasing availability of applets designed to assess and administer interventions will facilitate access to health care in the general population.

neuroscience↗

Asymmetric Functional Gradients in the Human Subcortex

A central principle in our understanding of cerebral cortical organization is that homotopic left and right areas are functionally linked to each other, and also connected with structures that share similar functions within each cerebral cortical hemisphere. Here we refer to this concept as interhemispheric functional symmetry (IHFS). While multiple studies have described the distribution and variations of IHFS in the cerebral cortex, descriptions of IHFS in the subcortex are largely absent in the neuroscientific literature. Further, the proposed anatomical basis of IHFS is centered on callosal and other commissural tracts. These commissural fibers are present in virtually all cerebral cortical areas, but almost absent in the subcortex. There is thus an important knowledge gap in our understanding of subcortical IHFS. What is the distribution and variations of subcortical IHFS, and what are the anatomical correlates and physiological implications of this important property in the subcortex? Using fMRI functional gradient analyses in a large dataset (Human Connectome Project, n=1003), here we explored IHFS in human thalamus, lenticular nucleus, cerebellar cortex, and caudate nucleus. Our detailed descriptions provide an empirical foundation upon which to build hypotheses for the anatomical and physiological basis of subcortical IHFS. Our results indicate that direct or driver cerebral cortical afferent connectivity, as opposed to indirect or modulatory cerebral cortical afferent connectivity, is associated with stronger subcortical IHFS in thalamus and lenticular nucleus. In cerebellar cortex and caudate, where there is no variability in terms of either direct vs. indirect or driver vs. modulatory cerebral cortical afferent connections, connectivity to cerebral cortical areas with stronger cerebral cortical IHFS is associated with stronger IHFS in the subcortex. These two observations support a close relationship between subcortical IHFS and connectivity between subcortex and cortex, and generate new testable hypotheses that advance our understanding of subcortical organization.

neuroscience↗