scholarly journals Deep Learning‐based Classification of Resting‐state fMRI Independent‐component Analysis

2021 ◽  
Author(s):  
Victor Nozais ◽  
Philippe Boutinaud ◽  
Violaine Verrecchia ◽  
Marie-Fateye Gueye ◽  
Pierre-Yves Hervé ◽  
...  
Keyword(s):  
Deep Learning ◽  
Independent Component Analysis ◽  
Resting State ◽  
Resting State Fmri ◽  
Component Analysis ◽  
Independent Component

Combining Spatial Independent Component Analysis with Regression to Identify the Subcortical Components of Resting-State fMRI Functional Networks

2014 ◽  
Vol 4 (3) ◽  
pp. 181-192 ◽  
Author(s):  
Caroline Malherbe ◽  
Arnaud Messé ◽  
Eric Bardinet ◽  
Mélanie Pélégrini-Issac ◽  
Vincent Perlbarg ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Resting State ◽  
Resting State Fmri ◽  
Component Analysis ◽  
Independent Component ◽  
Functional Networks

scholarly journals Deep learning-based classification of resting-state fMRI independent-component analysis

2020 ◽  
Author(s):  
Victor Nozais ◽  
Philippe Boutinaud ◽  
Violaine Verrecchia ◽  
Marie-Fateye Gueye ◽  
Pierre Yves Hervé ◽  
...  
Keyword(s):  
Deep Learning ◽  
Resting State ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Independent Component ◽  
Angular Gyrus ◽  
Automated Classification ◽  
Dimensional Parameter ◽  
Spatial Level

Functional connectivity analyses of fMRI data have shown that the activity of the brain at rest is spatially organized into resting-state networks (RSNs). RSNs appear as groups of anatomically distant but functionally tightly connected brain regions. Inter-RSN intrinsic connectivity analyses may provide an optimal spatial level of integration to analyze the variability of the functional connectome. Here, we propose a deep learning approach to enable the automated classification of individual independent-component (IC) decompositions into a set of predefined RSNs. Two databases were used in this work, BIL&GIN and MRi-Share, with 427 and 1811 participants respectively. We trained a multi-layer perceptron (MLP) to classify each IC as one of 45 RSNs, using the IC classification of 282 participants in BIL&GIN for training and a 5-dimensional parameter grid search for hyperparameter optimization. It reached an accuracy of 92%. Predictions on the remaining individuals in BIL&GIN were tested against the original classification and demonstrated good spatial overlap between the cortical RSNs. As a first application, we created an RSN atlas based on MRi-Share. This atlas defined a brain parcellation in 29 RSNs covering 96% of the gray matter. Second, we proposed an individual-based analysis of the subdivision of the default-mode network into 4 networks. Minimal overlap between RSNs was found except in the angular gyrus and potentially in the precuneus. We thus provide the community with an individual IC classifier that can be used to analyze one dataset or to statistically compare different datasets for RSN spatial definitions.

Inflammation is correlated with abnormal functional connectivity in unmedicated bipolar depression: an independent component analysis study of resting-state fMRI

2021 ◽  
pp. 1-11
Author(s):  
Guixian Tang ◽  
Pan Chen ◽  
Guanmao Chen ◽  
Shuming Zhong ◽  
JiaYing Gong ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Independent Component Analysis ◽  
Resting State ◽  
Bipolar Depression ◽  
Serum Levels ◽  
Resting State Fmri ◽  
Component Analysis ◽  
Independent Component ◽  
Precentral Gyrus ◽  
The Right

Abstract Background Inflammation might play a role in bipolar disorder (BD), but it remains unclear the relationship between inflammation and brain structural and functional abnormalities in patients with BD. In this study, we focused on the alterations of functional connectivity (FC), peripheral pro-inflammatory cytokines and their correlations to investigate the role of inflammation in FC in BD depression. Methods In this study, 42 unmedicated patients with BD II depression and 62 healthy controls (HCs) were enrolled. Resting-state-functional magnetic resonance imaging was performed in all participants and independent component analysis was used. Serum levels of Interleukin-6 (IL-6) and Interleukin-8 (IL-8) were measured in all participants. Correlation between FC values and IL-6 and IL-8 levels in BD was calculated. Results Compared with the HCs, BD II patients showed decreased FC in the left orbitofrontal cortex (OFC) implicating the limbic network and the right precentral gyrus implicating the somatomotor network. BD II showed increased IL-6 (p = 0.039), IL-8 (p = 0.002) levels. Moreover, abnormal FC in the right precentral gyrus were inversely correlated with the IL-8 (r = −0.458, p = 0.004) levels in BD II. No significant correlation was found between FC in the left OFC and cytokines levels. Conclusions Our findings that serum IL-8 levels are associated with impaired FC in the right precentral gyrus in BD II patients suggest that inflammation might play a crucial role in brain functional abnormalities in BD.

scholarly journals Modular and state-relevant connectivity in high-frequency resting-state BOLD fMRI data: An independent component analysis

2020 ◽  
Author(s):  
Thomas DeRamus ◽  
Ashkan Faghiri ◽  
Armin Iraji ◽  
Oktay Agcaoglu ◽  
Victor Vergara ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Resting State ◽  
Network Connectivity ◽  
Resting State Fmri ◽  
Component Analysis ◽  
Independent Component ◽  
Fmri Data ◽  
Bold Signal ◽  
Drift Motion ◽  
Eyes Closed

AbstractResting-state fMRI (rs-fMRI) data are typically filtered at different frequency bins between 0.008∼0.2 Hz (varies across the literature) prior to analysis to mitigate nuisance variables (e.g., drift, motion, cardiac, and respiratory) and maximize the sensitivity to neuronal-mediated BOLD signal. However, multiple lines of evidence suggest meaningful BOLD signal may also be parsed at higher frequencies. To test this notion, a functional network connectivity (FNC) analysis based on a spatially informed independent component analysis (ICA) was performed at seven different bandpass frequency bins to examine FNC matrices across spectra. Further, eyes open (EO) vs. eyes closed (EC) resting-state acquisitions from the same participants were compared across frequency bins to examine if EO vs. EC FNC matrices and randomness estimations of FNC matrices are distinguishable at different frequencies.Results show that FNCs in higher-frequency bins display modular FNC similar to the lowest frequency bin, while r-to-z FNC and FNC-based measures indicating matrix non-randomness were highest in the 0.31-0.46 Hz range relative to all frequency bins above and below this range. As such, the FNC within this range appears to be the most temporally correlated, but the mechanisms facilitating this coherence require further analyses. Compared to EO, EC displayed greater FNC (involved in visual, cognitive control, somatomotor, and auditory domains) and randomness values at lower frequency bins, but this phenomenon flipped (EO > EC) at frequency bins greater than 0.46 Hz, particularly within visual regions.While the effect sizes range from small to large specific to frequency range and resting state (EO vs. EC), with little influence from common artifacts. These differences indicate that unique information can be derived from FNC between BOLD signals at different frequencies relative to a given restingstate acquisition and support the hypothesis meaningful BOLD signal is present at higher frequency ranges.

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