A5072488699- Functional Brain Connectivity Studies
- Neural dynamics and brain function
- Advanced Neuroimaging Techniques and Applications
- EEG and Brain-Computer Interfaces
- Advanced MRI Techniques and Applications
- Mental Health Research Topics
- Blind Source Separation Techniques
- Neurotransmitter Receptor Influence on Behavior
- Neural and Behavioral Psychology Studies
- Complex Network Analysis Techniques
- Heart Rate Variability and Autonomic Control
- Structural Health Monitoring Techniques
- Photoreceptor and optogenetics research
- Gait Recognition and Analysis
- Tryptophan and brain disorders
- Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Nonlinear Dynamics and Pattern Formation
- Complex Systems and Time Series Analysis
- Advanced Memory and Neural Computing
- Ultrasonics and Acoustic Wave Propagation
Center for Translational Research in Neuroimaging and Data Science
2019-2025
Georgia Institute of Technology
2019-2025
Georgia State University
2019-2025
Emory University
2019-2025
Park University
2022
University of New Mexico
2017-2020
Mind Research Network
2017-2020
Sharif University of Technology
2015
Tehran University of Medical Sciences
2015
Abstract Brain maturation through adolescence has been the topic of recent studies. Previous works have evaluated changes in morphometry and also functional connectivity. However, most resting‐state fMRI studies focused on static Here we examine relationship between age/maturity dynamics brain Utilizing a resting dataset comprised 421 subjects ages 3–22 from PING study, first performed group ICA to extract independent components their time courses. Next, dynamic network connectivity (dFNC)...
Abstract Despite the known benefits of data‐driven approaches, lack approaches for identifying functional neuroimaging patterns that capture both individual variations and inter‐subject correspondence limits clinical utility rsfMRI its application to single‐subject analyses. Here, using data from over 100k individuals across private public datasets, we identify replicable multi‐spatial‐scale canonical intrinsic connectivity network (ICN) templates via use multi‐model‐order independent...
We introduce an extension of independent component analysis (ICA), called multiscale ICA, and design approach to capture dynamic functional source interactions within between multiple spatial scales. Multiscale ICA estimates sources at scales without imposing direct constraints on the size sources, overcomes limitation using fixed anatomical locations, eliminates need for model-order selection in analysis. leveraged this study sex-specific sex-common connectivity patterns schizophrenia....
Resting-state functional magnetic resonance imaging is currently the mainstay of neuroimaging and has allowed researchers to identify intrinsic connectivity networks (aka networks) at different spatial scales. However, little known about temporal profiles these whether it best model them as continuous phenomena in both space time or, rather, a set temporally discrete events. Both categories have been supported by series studies with promising findings. critical question focusing only on...
Static and dynamic functional network connectivity (FNC) are typically studied separately, which makes us unable to see the full spectrum of in each analysis. Here, we propose an approach called filter-banked (FBC) estimate while preserving its frequency range subsequently examine both static one unified approach. First, demonstrate that FBC can across multiple frequencies missed by a sliding-window Next, use FNC resting-state fMRI dataset including schizophrenia patients (SZ) typical...
BackgroundSchizophrenia research reveals sex differences in incidence, symptoms, genetic risk factors, and brain function. However, a knowledge gap remains regarding sex-specific schizophrenia alterations Schizophrenia is considered dysconnectivity syndrome, but the dynamic integration segregation of networks are poorly understood. Recent advances resting-state functional magnetic resonance imaging allow us to study spatial dynamics, phenomenon spatially evolving over time. Nevertheless,...
Abstract Resting-state functional magnetic resonance imaging (rsfMRI) has shown considerable promise for improving our understanding of brain function and characterizing various mental cognitive states in the healthy disordered brain. However, lack accurate precise estimations comparable patterns across datasets, individuals, ever-changing a way that captures both individual variation inter-subject correspondence limits clinical utility rsfMRI its application to single-subject analyses. We...
Studies of brain structure have shown that the cortex matures in both a linear and nonlinear manner depending on time window specific region studied. In addition, it has been socioeconomic status can impact development throughout childhood. However, very few studies evaluated these patterns using functional measures. To this end, study we used cross-sectional resting-state magnetic resonance imaging data 368 subjects, age 3–21 years, to examine connectivity. We employed clustering approach...
Brain functional connectivity has been shown to change over time during resting state fMRI experiments. Close examination of temporal changes have revealed a small set whole-brain patterns called dynamic states. Dynamic network (dFNC) studies demonstrated that it is possible replicate the states across several However, estimation and their dynamicity still suffers from noisy imperfect estimations. In regular dFNC implementations, are estimated by comparing through data without considering...
Abstract In neuroimaging research, understanding the intricate dynamics of brain networks over time is paramount for unraveling complexities function. One approach commonly used to explore dynamic nature functional connectivity analysis. However, while offers valuable insights, it fails consider diverse timescales coupling between different regions. This gap in leaves a significant aspect unexplored research. We propose an innovative that delves into coupling/connectivity regions relative...
Abstract Time-resolved functional connectivity (trFC) assesses the time-resolved coupling between brain regions using magnetic resonance imaging (fMRI) data. This study aims to compare two techniques used estimate trFC, investigate their similarities and differences when applied fMRI These are sliding window Pearson correlation (SWPC), an amplitude-based approach, phase synchronization (PS), a phase-based technique. To accomplish our objective, we resting-state data from Human Connectome...
Spatial independent component analysis (sICA) has become an integral part of functional MRI (fMRI) studies, particularly with resting-state fMRI. Early work used low-order ICA between 20 and 45 components, which led to the identification around a dozen reproducible, distributed, large-scale brain networks. While regions within each largescale network are fairly temporally coherent, later studies have shown that distributed can be split into group spatially granular, covarying parcels. Thus,...
Given the dynamic nature of brain, there has always been a motivation to move beyond “static” functional connectivity, which characterizes interactions over an extended period time. Progress in data acquisition and advances analytical neuroimaging methods now allow us assess whole brain’s connectivity (dFC) its network-based analog, network (dFNC) at macroscale (mm) using fMRI. This resulted rapid growth approaches, some are very complex, requiring technical expertise that could daunt...
Representing data using time-resolved networks is valuable for analyzing functional of the human brain. One commonly used method constructing from sliding window Pearson correlation (SWPC). major limitation SWPC that it applies a high-pass filter to activity time series. Therefore, if we select short (desirable estimate rapid changes in connectivity), will remove important low-frequency information. Here, propose an approach based on single sideband modulation (SSB) communication theory....
To capture different aspects of a complex system, the modeling approach should be able to take these effectively into consideration. Two human brain we are quite interested in its interconnected nature and dynamism. One that can two is based on networks change with time go beyond pairwise interactions. Partly because size temporal high-order networks, analyzing visualizing them challenge. In this work, propose pipeline canonical polyadic (CP) decomposition analyze resolved both frequency...
Abstract We introduce an extension of independent component analysis (ICA), called multiscale ICA (msICA), and design approach to capture dynamic functional source interactions within between multiple spatial scales. msICA estimates sources at scales without imposing direct constraints on the size sources, overcomes limitation using fixed anatomical locations, eliminates need for model-order selection in analysis. leveraged this study sex-specific -common connectivity patterns schizophrenia....
Time-resolved functional network connectivity (trFNC) provides a useful tool for representing magnetic resonance imaging (fMRI) data with networks that change time. Partly due to its simplicity, sliding window Pearson correlation (SWPC) is the most widely used method trFNC estimation. In SWPC, size should be selected long enough avoid spurious estimates of values, and short capture meaningful fast variations in estimates. To solve this issue, we propose inspired by single sideband (SSB)...