A5039259523- Functional Brain Connectivity Studies
- Neural dynamics and brain function
- Mental Health Research Topics
- Brain Tumor Detection and Classification
- EEG and Brain-Computer Interfaces
- Advanced MRI Techniques and Applications
- Meta-analysis and systematic reviews
- Dementia and Cognitive Impairment Research
- Advanced Neuroimaging Techniques and Applications
- Biomedical and Engineering Education
- Blind Source Separation Techniques
- Health disparities and outcomes
- Retinal Imaging and Analysis
- Advanced Memory and Neural Computing
- Neurobiology of Language and Bilingualism
- Medical Imaging and Analysis
- Circadian rhythm and melatonin
- Photoreceptor and optogenetics research
- AI in cancer detection
- Advanced Graph Neural Networks
- Health and Medical Research Impacts
- Acute Ischemic Stroke Management
- Gene expression and cancer classification
- Sleep and Wakefulness Research
- Neonatal and fetal brain pathology
Indian Institute of Technology Hyderabad
2022-2024
Inria Saclay - Île de France
2019-2022
Université Paris-Saclay
2016-2022
Commissariat à l'Énergie Atomique et aux Énergies Alternatives
2016-2022
CEA Paris-Saclay
2016-2022
International Institute of Information Technology, Hyderabad
2022
Institut national de recherche en informatique et en automatique
2020
Population imaging markedly increased the size of functional-imaging datasets, shedding new light on neural basis inter-individual differences. Analyzing these large data entails scalability challenges, computational and statistical. For this reason, brain images are typically summarized in a few signals, for instance reducing voxel-level measures with atlases or functional modes. A good choice corresponding networks is important, as most analyses start from reduced signals. We contribute...
Summary Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The analytic approaches is exemplified fact that no two teams chose identical to analyze data. This resulted sizeable variation hypothesis test even for whose statistical maps were highly correlated at...
Abstract Background Biological aging is revealed by physical measures, e.g., DNA probes or brain scans. In contrast, individual differences in mental function are explained psychological constructs, intelligence neuroticism. These constructs typically assessed tailored neuropsychological tests that build on expert judgement and require careful interpretation. Could machine learning large samples from the general population be used to proxy measures of these do not human intervention? Results...
Previous literature has focused on predicting a diagnostic label from structural brain imaging. Since subtle changes in the precede cognitive decline healthy and pathological aging, our study predicts future as continuous trajectory instead. Here, we tested whether baseline multimodal neuroimaging data improve prediction of aging. Nonbrain (demographics, clinical, neuropsychological scores), MRI, functional connectivity OASIS-3 (N = 662; age 46–96 years) were entered into cross-validated...
Autism spectrum disorder (ASD) is a neurodevelopmental predominantly found in children. The current behavior-based diagnosis of ASD arduous and requires expertise. Therefore, it appealing to develop an accurate computer-aided tool for diagnosing ASD. Although resting-state functional magnetic resonance imaging (rsfMRI) has proven be successful capturing the neural organization brain, automated detection using rsfMRI scans challenging task due heterogeneity dataset limited sample size. This...
Processing neuroimaging data on the cortical surface traditionally requires dedicated heavy-weight software suites. Here, we present an initial support of surfaces in Python within processing toolbox Nilearn. We provide loading and plotting functions for different formats with minimal dependencies, along examples their application. Limitations current implementation potential next steps are discussed.
Resting-state functional Magnetic Resonance Imaging (rs-fMRI) holds the promise of easy-to-acquire and widespectrum biomarkers. However, there are few predictivemodeling studies on resting state, processing pipelines all vary. Here, we systematically study state functionalconnectivity (FC)-based prediction across three different cohorts. Analysis consist four steps: Delineation brain regions interest (ROIs), ROI-level rs-fMRI time series signal extraction, FC estimation linear model...
Abstract Background Biological aging is revealed by physical measures, e . g ., DNA probes or brain scans. Instead, individual differences in mental function are explained psychological constructs, e.g., intelligence neuroticism. These constructs typically assessed tailored neuropsychological tests that build on expert judgement and require careful interpretation. Could machine learning large samples from the general population be used to proxy measures of these do not human intervention?...
Cognitive decline occurs in healthy and pathological aging, both may be preceded by subtle changes the brain — offering a basis for cognitive predictions. Previous work has largely focused on predicting diagnostic label from structural imaging. Our study broadens scope of applications to aging future as continuous trajectory, rather than label. Furthermore, since structure well function it is reasonable expect predictive gains when using multiple imaging modalities. Here, we tested whether...
Autism spectrum disorder (ASD) is a neurodevelopmental that predominantly occurs in children. Previous brain research ASD has mainly studied biomarkers based on the functional connectivity characterized by correlation of static temporal signals. However, dynamic and varies extensively among states. The main aim paper to understand fundamental group differences between patients typically developing (TD) subjects using (dFNC) analysis. In this study, we investigated dFNC 53 independent...
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by varied social cognitive challenges and repetitive behavioral patterns. Identifying reliable brain imaging-based biomarkers for ASD has been persistent challenge due to the spectrum's diverse symptomatology. Existing baselines in field have made significant strides this direction, yet there remains room improvement both performance interpretability. We propose \emph{HyperGALE}, which builds upon hypergraph...
Brain stroke has become a significant burden on global health and thus we need remedies prevention strategies to overcome this challenge. For this, the immediate identification of risk stratification is primary task for clinicians. To aid expert clinicians, automated segmentation models are crucial. In work, consider publicly available dataset ATLAS $v2.0$ benchmark various end-to-end supervised U-Net style models. Specifically, have benchmarked both 2D 3D brain images evaluated them using...