A k-space sharing 3D GRASE pseudocontinuous ASL method for whole-brain resting-state functional connectivity

2012 ◽  
Vol 22 (1) ◽  
pp. 37-43 ◽  
Author(s):  
Xiaoyun Liang ◽  
Jacques-Donald Tournier ◽  
Richard Masterton ◽  
Alan Connelly ◽  
Fernando Calamante
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Resting State Functional Connectivity ◽  
Whole Brain

P.1.i.011 Effect of delta-9-tetrahydrocannabinol on the whole-brain resting state functional connectivity: a dynamic connectivity approach

2015 ◽  
Vol 25 ◽  
pp. S305
Author(s):  
Y. Zaytseva ◽  
J. Horacek ◽  
J. Hlinka ◽  
I. Fajnerova ◽  
R. Androvicova ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Resting State Functional Connectivity ◽  
Whole Brain ◽  
Dynamic Connectivity

Altered Whole-Brain Resting-State Functional Connectivity Patterns Associated With Pediatric Anxiety and Involuntary Attention Capture

2021 ◽  
Vol 89 (9) ◽  
pp. S122-S123
Author(s):  
Michael Perino ◽  
Michael Myers ◽  
Muriah Wheelock ◽  
Qiongru Yu ◽  
Jennifer Harper ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Attention Capture ◽  
Involuntary Attention ◽  
Resting State Functional Connectivity ◽  
Whole Brain ◽  
Pediatric Anxiety ◽  
Connectivity Patterns

scholarly journals Whole brain resting state functional connectivity abnormalities in schizophrenia

2012 ◽  
Vol 139 (1-3) ◽  
pp. 7-12 ◽  
Author(s):  
Archana Venkataraman ◽  
Thomas J. Whitford ◽  
Carl-Fredrik Westin ◽  
Polina Golland ◽  
Marek Kubicki
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Resting State Functional Connectivity ◽  
Whole Brain

scholarly journals Resting-State Functional Connectivity and Scholastic Performance in Preadolescent Children: A Data-Driven Multivoxel Pattern Analysis (MVPA)

2020 ◽  
Vol 9 (10) ◽  
pp. 3198
Author(s):  
Daniel R. Westfall ◽  
Sheeba A. Anteraper ◽  
Laura Chaddock-Heyman ◽  
Eric S. Drollette ◽  
Lauren B. Raine ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Default Mode Network ◽  
Resting State ◽  
Pattern Analysis ◽  
Composite Score ◽  
Resting State Functional Connectivity ◽  
Whole Brain ◽  
Default Mode ◽  
Preadolescent Children ◽  
Scholastic Performance

Scholastic performance is the key metric by which schools measure student’s academic success, and it is important to understand the neural-correlates associated with greater scholastic performance. This study examines resting-state functional connectivity (RsFc) associated with scholastic performance (reading and mathematics) in preadolescent children (7–9 years) using an unbiased whole-brain connectome-wide multi-voxel pattern analysis (MVPA). MVPA revealed four clusters associated with reading composite score, these clusters were then used for whole-brain seed-based RsFc analysis. However, no such clusters were found for mathematics composite score. Post hoc analysis found robust associations between reading and RsFc dynamics with areas involved with the somatomotor, dorsal attention, ventral attention, limbic, frontoparietal, and default mode networks. These findings indicate that reading ability may be associated with a wide range of RsFc networks. Of particular interest, anticorrelations were observed between the default mode network and the somatomotor, dorsal attention, ventral attention, and frontoparietal networks. Previous research has demonstrated the importance of anticorrelations between the default mode network and frontoparietal network associated with cognition. These results extend the current literature exploring the role of network connectivity in scholastic performance of children.

scholarly journals A whole-brain computational modeling approach to explain the alterations in resting-state functional connectivity during progression of Alzheimer's disease

2017 ◽  
Vol 16 ◽  
pp. 343-354 ◽  
Author(s):  
Murat Demirtaş ◽  
Carles Falcon ◽  
Alan Tucholka ◽  
Juan Domingo Gispert ◽  
José Luis Molinuevo ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Computational Modeling ◽  
Resting State ◽  
Resting State Functional Connectivity ◽  
Whole Brain ◽  
Modeling Approach

scholarly journals A Whole-Brain Regression Method to Identify Individual and Group Variations in Functional Connectivity

2020 ◽  
Author(s):  
Yi Zhao ◽  
Brian S. Caffo ◽  
Bingkai Wang ◽  
Chiang-shan R. Li ◽  
Xi Luo
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Statistical Power ◽  
Resting State Fmri ◽  
Regression Method ◽  
Resting State Functional Connectivity ◽  
Whole Brain ◽  
Human Connectome Project ◽  
Group Ica ◽  
Brain Functional Connectivity

AbstractResting-state functional connectivity is an important and widely used measure of individual and group differences. These differences are typically attributed to various demographic and/or clinical factors. Yet, extant statistical methods are limited to linking covariates with variations in functional connectivity across subjects, especially at the voxel-wise level of the whole brain. This paper introduces a generalized linear model method that regresses whole-brain functional connectivity on covariates. Our approach builds on two methodological components. We first employ whole-brain group ICA to reduce the dimensionality of functional connectivity matrices, and then search for matrix variations associated with covariates using covariate assisted principal regression, a recently introduced covariance matrix regression method. We demonstrate the efficacy of this approach using a resting-state fMRI dataset of a medium-sized cohort of subjects obtained from the Human Connectome Project. The results show that the approach enjoys improved statistical power in detecting interaction effects of sex and alcohol on whole-brain functional connectivity, and in identifying the brain areas contributing significantly to the covariate-related differences in functional connectivity.

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