scholarly journals Investigating the BOLD spectral power of the intrinsic connectivity networks in fibromyalgia patients: A resting-state fMRI study

2017 ◽  
Author(s):  
Behnaz Jarrahi ◽  
Katherine T. Martucci ◽  
Aneesha S. Nilakantan ◽  
Sean Mackey
Keyword(s):  
Resting State ◽  
Spectral Power ◽  
Resting State Fmri ◽  
Fmri Study ◽  
Intrinsic Connectivity ◽  
Intrinsic Connectivity Networks

scholarly journals Investigation of Long-Term Reproducibility of Intrinsic Connectivity Network Mapping: A Resting-State fMRI Study

2012 ◽  
Vol 33 (5) ◽  
pp. 833-838 ◽  
Author(s):  
Y.-h. Chou ◽  
L.P. Panych ◽  
C.C. Dickey ◽  
J.R. Petrella ◽  
N.-k. Chen
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Fmri Study ◽  
Intrinsic Connectivity ◽  
Network Mapping

scholarly journals Predicting functional networks from region connectivity profiles in task-based versus resting-state fMRI data

2018 ◽  
Author(s):  
Javier Rasero ◽  
Hannelore Aerts ◽  
Jesus M. Cortes ◽  
Sebastiano Stramaglia ◽  
Daniele Marinazzo
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Blood Oxygenation ◽  
The Other ◽  
Intrinsic Connectivity ◽  
Supervised Classifiers ◽  
Oxygenation Level ◽  
Intrinsic Connectivity Networks ◽  
Hidden Layer ◽  
Simple Neural Network

Intrinsic Connectivity Networks, patterns of correlated activity emerging from "resting-state" Blood Oxygenation Level Dependent time series, are increasingly being associated to cognitive, clinical, and behavioral aspects, and compared with the pattern of activity elicited by specific tasks. We study the reconfiguration of the brain networks between task and resting-state conditions by a machine learning approach, to highlight the Intrinsic Connectivity Networks (ICNs) which are more affected by the change of network configurations in task vs. rest. We use a large cohort of publicly available data in both resting and task-based fMRI paradigms; by trying a battery of different supervised classifiers relying only on task-based measurements, we show that the highest accuracy is reached with a simple neural network of one hidden layer. In addition, when testing the fitted model on resting state measurements, such architecture yields a performance close to 90\% for areas connected to the task performed, which mainly involve the visual and sensorimotor cortex, whilst a relevant decrease of the performance is observed in the other ICNs. On one hand, our results confirm the correspondence of ICNs in both paradigms (task and resting) thus opening a window for future clinical applications to subjects whose participation in a required task cannot be guaranteed. On the other hand it is shown that brain areas not involved in the task display different connectivity patterns in the two paradigms.

scholarly journals Identifying components that vary in space and time from resting-state functional MRI

2021 ◽  
Author(s):  
Gregory Scott ◽  
Robert Leech
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Brain Activity ◽  
Building Blocks ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Time Varying ◽  
Spatiotemporal Evolution ◽  
Intrinsic Connectivity ◽  
Intrinsic Connectivity Networks

A widespread assumption of fMRI-derived large-scale intrinsic connectivity networks (ICNs) is that they are spatially static over time. However, the assumption of spatial stationarity of ICNs has been challenged by a range of techniques that allow for time-varying connectivity between brain regions and demonstration that canonical networks like the default model network (DMN) can be fractionated according to time-varying connectivity relationships of their subcomponents. Previously, we developed a simple spatiotemporal ICA (stICA) technique to allow the discovery of patterns of spatiotemporal evolution in task fMRI data in a way that avoided the traditional constraint of spatial stationarity on brain networks, and we validated the approach in fMRI of task-to-rest transitions. Here, we apply our stICA technique to resting-state fMRI datasets to explore whether spatiotemporally evolving components of brain activity can be identified in the absence of an overt behavioural task. We found that stICA components could generally be described in terms of graded onsets and offsets of ICNs that had been calculated based on techniques that assumed spatial stationarity. Our results suggest that, to a reasonable approximation, stable ICNs can be taken to be building blocks of the spatiotemporal patterns measured with resting-state fMRI.

scholarly journals Development of the default-mode network during childhood and adolescence: A longitudinal resting-state fMRI study

NeuroImage ◽  
2021 ◽  
Vol 226 ◽  
pp. 117581
Author(s):  
Fengmei Fan ◽  
Xuhong Liao ◽  
Tianyuan Lei ◽  
Tengda Zhao ◽  
Mingrui Xia ◽  
...  
Keyword(s):  
Default Mode Network ◽  
Resting State ◽  
Resting State Fmri ◽  
Childhood And Adolescence ◽  
Default Mode ◽  
Fmri Study

Improvement in prefrontal thalamic connectivity during the early course of the illness in recent-onset psychosis: a 12-month longitudinal follow-up resting-state fMRI study

2020 ◽  
pp. 1-9
Author(s):  
Daniel Bergé ◽  
Tyler A. Lesh ◽  
Jason Smucny ◽  
Cameron S. Carter
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Functional Changes ◽  
Recent Onset ◽  
Fmri Study ◽  
Clinical Meaning ◽  
Mixed Pattern ◽  
The Right ◽  
Over Time

Abstract Background Previous research in resting-state functional magnetic resonance imaging (rs-fMRI) has shown a mixed pattern of disrupted thalamocortical connectivity in psychosis. The clinical meaning of these findings and their stability over time remains unclear. We aimed to study thalamocortical connectivity longitudinally over a 1-year period in participants with recent-onset psychosis. Methods To this purpose, 129 individuals with recent-onset psychosis and 87 controls were clinically evaluated and scanned using rs-fMRI. Among them, 43 patients and 40 controls were re-scanned and re-evaluated 12 months later. Functional connectivity between the thalamus and the rest of the brain was calculated using a seed to voxel approach, and then compared between groups and correlated with clinical features cross-sectionally and longitudinally. Results At baseline, participants with recent-onset psychosis showed increased connectivity (compared to controls) between the thalamus and somatosensory and temporal regions (k = 653, T = 5.712), as well as decreased connectivity between the thalamus and left cerebellum and right prefrontal cortex (PFC; k = 201, T = −4.700). Longitudinal analyses revealed increased connectivity over time in recent-onset psychosis (relative to controls) in the right middle frontal gyrus. Conclusions Our results support the concept of abnormal thalamic connectivity as a core feature in psychosis. In agreement with a non-degenerative model of illness in which functional changes occur early in development and do not deteriorate over time, no evidence of progressive deterioration of connectivity during early psychosis was observed. Indeed, regionally increased connectivity between thalamus and PFC was observed.

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