A5084684839- Functional Brain Connectivity Studies
- Neural dynamics and brain function
- Advanced MRI Techniques and Applications
- Advanced Neuroimaging Techniques and Applications
- EEG and Brain-Computer Interfaces
- Mental Health Research Topics
- Attention Deficit Hyperactivity Disorder
- Neuroscience and Neuropharmacology Research
- Health, Environment, Cognitive Aging
- Neural and Behavioral Psychology Studies
- Optical Imaging and Spectroscopy Techniques
- Dementia and Cognitive Impairment Research
- Image and Signal Denoising Methods
- Bipolar Disorder and Treatment
- Advanced Fluorescence Microscopy Techniques
- Traumatic Brain Injury Research
- Traumatic Brain Injury and Neurovascular Disturbances
- Autism Spectrum Disorder Research
- Photoreceptor and optogenetics research
- Fractal and DNA sequence analysis
- Mitochondrial Function and Pathology
- Atomic and Subatomic Physics Research
- Medical Image Segmentation Techniques
- Blind Source Separation Techniques
- Memory and Neural Mechanisms
Yale University
2016-2025
Wuhan University
2022
University of Colorado Boulder
2004-2008
Best practices are currently being developed for the acquisition and processing of resting-state magnetic resonance imaging data used to estimate brain functional organization—or "functional connectivity." Standards have been proposed based on test–retest reliability, but open questions remain. These include how amount per subject influences whole-brain influence increasing runs versus sessions, spatial distribution reliability multivariate methods, and, crucially, maps onto prediction...
Abstract Resting‐state functional magnetic resonance image (rs‐fMRI) is increasingly used to study brain networks. Nevertheless, variability in these networks due factors such as sex and aging not fully understood. This explored differences normal age trajectories of resting‐state (RSNs) using a novel voxel‐wise measure connectivity, the intrinsic connectivity distribution (ICD). Males females showed differential patterns changing large‐scale RSNs during from early adulthood late middle‐age....
Functional connectivity analyses of functional magnetic resonance imaging data are a powerful tool for characterizing brain networks and how they disrupted in neural disorders. However, many such examine only one or small number priori seed regions. Studies that consider the whole frequently rely on anatomic atlases to define network nodes, which might result mixing distinct activation time-courses within single node. Here, we improve upon previous methods by using data-driven parcellation...
The goal of human brain mapping has long been to delineate the functional subunits in and elucidate role each these regions. Recent work focused on whole-brain parcellation Magnetic Resonance Imaging (fMRI) data identify create a atlas. Functional connectivity approaches understand at network level require such an atlas assess connections between parcels extract properties. While no single emerged as dominant date, there remains underlying assumption that exists. Using fMRI from highly...
Functional connectomes computed from fMRI provide a means to characterize individual differences in the patterns of BOLD synchronization across regions entire brain. Using four resting-state datasets with wide range ages, we show that functional connectome are stable 3 months 1-2 years (and even detectable at above-chance levels years). Medial frontal and frontoparietal networks appear be both unique stable, resulting high ID rates, as did combination these two networks. We conduct analyses...
Abstract Individual differences in brain functional organization track a range of traits, symptoms and behaviours 1–12 . So far, work modelling linear brain–phenotype relationships has assumed that single such relationship generalizes across all individuals, but models do not equally well participants 13,14 A better understanding whom fail why is crucial to revealing robust, useful unbiased relationships. To this end, here we related activity phenotype using predictive models—trained tested...
Abstract Large-scale functional networks have been characterized in both rodent and human brains, typically by analyzing fMRI-BOLD signals. However, the relationship between underlying neural activity is complex incompletely understood, which poses challenges to interpreting network organization obtained using this technique. Additionally, most work has assumed a disjoint (i.e., brain regions belong one only network). Here, we employ wide-field Ca 2+ imaging simultaneously with mice...
Recent years have witnessed an increasing number of multisite MRI functional connectivity (fcMRI) studies. While studies provide efficient way to accelerate data collection and increase sample sizes, especially for rare clinical populations, any effects site or scanner could ultimately limit power weaken results. Little exists on the stability measurements across sites sessions. In this study, we assess influence session resting state in a healthy cohort traveling subjects (8 scanned twice...
Recent work has demonstrated that human whole-brain functional connectivity patterns measured with fMRI contain information about cognitive abilities, including sustained attention. To derive behavioral predictions from patterns, our group developed a connectome-based predictive modeling (CPM) approach (Finn et al., 2015; Rosenberg 2016). Previously using CPM, we defined high-attention network, comprising connections positively correlated performance on attention task, and low-attention...
Abstract Autism is a heterogeneous condition, and functional magnetic resonance imaging-based studies have advanced understanding of neurobiological correlates autistic features. Nevertheless, little work has focused on the optimal brain states to reveal brain-phenotype relationships. In addition, there need better understand relevance attentional abilities in mediating Using connectome-based predictive modelling, we interrogate three datasets determine scanning conditions that can boost...
Terahertz imaging makes it possible to acquire images of objects concealed underneath clothing by measuring the radiometric temperatures different on a human subject. The goal this work is automatically detect and segment in broadband 0.1-1 THz images. Due inherent physical properties passive terahertz associated hardware, have poor contrast low signal noise ratio. Standard segmentation algorithms are unable or objects. Our approach relies two stages. First, we remove from image using...