[HTML][HTML] The dynamic functional connectome: State-of-the-art and perspectives

MG Preti, TAW Bolton, D Van De Ville - Neuroimage, 2017 - Elsevier
Resting-state functional magnetic resonance imaging (fMRI) has highlighted the rich
structure of brain activity in absence of a task or stimulus. A great effort has been dedicated …

The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery

VD Calhoun, R Miller, G Pearlson, T Adalı - Neuron, 2014 - cell.com
Recent years have witnessed a rapid growth of interest in moving functional magnetic
resonance imaging (fMRI) beyond simple scan-length averages and into approaches that …

[HTML][HTML] NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders

Y Du, Z Fu, J Sui, S Gao, Y Xing, D Lin, M Salman… - NeuroImage: Clinical, 2020 - Elsevier
Many mental illnesses share overlapping or similar clinical symptoms, confounding the
diagnosis. It is important to systematically characterize the degree to which unique and …

Benchmarking functional connectome-based predictive models for resting-state fMRI

K Dadi, M Rahim, A Abraham, D Chyzhyk, M Milham… - NeuroImage, 2019 - Elsevier
Functional connectomes reveal biomarkers of individual psychological or clinical traits.
However, there is great variability in the analytic pipelines typically used to derive them from …

Classification and prediction of brain disorders using functional connectivity: promising but challenging

Y Du, Z Fu, VD Calhoun - Frontiers in neuroscience, 2018 - frontiersin.org
Brain functional imaging data, especially functional magnetic resonance imaging (fMRI)
data, have been employed to reflect functional integration of the brain. Alteration in brain …

Dynamic resting-state functional connectivity in major depression

RH Kaiser, S Whitfield-Gabrieli, DG Dillon… - …, 2016 - nature.com
Major depressive disorder (MDD) is characterized by abnormal resting-state functional
connectivity (RSFC), especially in medial prefrontal cortical (MPFC) regions of the default …

Machine learning in resting-state fMRI analysis

M Khosla, K Jamison, GH Ngo, A Kuceyeski… - Magnetic resonance …, 2019 - Elsevier
Abstract Machine learning techniques have gained prominence for the analysis of resting-
state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview …

Dynamic functional network connectivity in idiopathic generalized epilepsy with generalized tonic–clonic seizure

F Liu, Y Wang, M Li, W Wang, R Li, Z Zhang… - Human brain …, 2017 - Wiley Online Library
Idiopathic generalized epilepsy (IGE) has been linked with disrupted intra‐network
connectivity of multiple resting‐state networks (RSNs); however, whether impairment is …

3D-deep learning based automatic diagnosis of Alzheimer's disease with joint MMSE prediction using resting-state fMRI

NT Duc, S Ryu, MNI Qureshi, M Choi, KH Lee, B Lee - Neuroinformatics, 2020 - Springer
We performed this research to 1) evaluate a novel deep learning method for the diagnosis of
Alzheimer's disease (AD) and 2) jointly predict the Mini Mental State Examination (MMSE) …

Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity

B Rashid, MR Arbabshirani, E Damaraju, MS Cetin… - Neuroimage, 2016 - Elsevier
Recently, functional network connectivity (FNC, defined as the temporal correlation among
spatially distant brain networks) has been used to examine the functional organization of …