Dataset Information

Task-related concurrent but opposite modulations of overlapping functional networks as revealed by spatial ICA.

ABSTRACT: Animal studies indicate that different functional networks (FNs), each with a unique timecourse, may overlap at common brain regions. For understanding how different FNs overlap in the human brain and how the timecourses of overlapping FNs are modulated by cognitive tasks, we applied spatial independent component analysis (sICA) to functional magnetic resonance imaging (fMRI) data. These data were acquired from healthy participants while they performed a visual task with parametric loads of attention and working memory. sICA identified a total of 14 FNs, and they showed different extents of overlap at a majority of brain regions exhibiting any functional activity. More FNs overlapped at the higher-order association cortex including the anterior and posterior cingulate, precuneus, insula, and lateral and medial frontoparietal cortices (FPCs) than at the primary sensorimotor cortex. Furthermore, overlapping FNs exhibited concurrent but different task-related modulations of timecourses. FNs showing task-related up- vs. down-modulation of timecourses overlapped at both the lateral and medial FPCs and subcortical structures including the thalamus, striatum, and midbrain ventral tegmental area (VTA). Such task-related, concurrent, but opposite changes in timecourses in the same brain regions may not be detected by current analyses based on General-Linear-Model (GLM). The present findings indicate that multiple cognitive processes may associate with common brain regions and exhibit simultaneous but different modulations in timecourses during cognitive tasks.

SUBMITTER: Xu J

PROVIDER: S-EPMC3677796 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

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Task-related concurrent but opposite modulations of overlapping functional networks as revealed by spatial ICA.

Xu Jiansong J Zhang Sheng S Calhoun Vince D VD Monterosso John J Li Chiang-Shan R CS Worhunsky Patrick D PD Stevens Michael M Pearlson Godfrey D GD Potenza Marc N MN

NeuroImage 20130421

Animal studies indicate that different functional networks (FNs), each with a unique timecourse, may overlap at common brain regions. For understanding how different FNs overlap in the human brain and how the timecourses of overlapping FNs are modulated by cognitive tasks, we applied spatial independent component analysis (sICA) to functional magnetic resonance imaging (fMRI) data. These data were acquired from healthy participants while they performed a visual task with parametric loads of atte ...[more]

PMID: 23611864

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Project description:Motor-task functional magnetic resonance imaging (fMRI) is crucial in the study of several clinical conditions, including stroke and Parkinson's disease. However, motor-task fMRI is complicated by task-correlated head motion, which can be magnified in clinical populations and confounds motor activation results. One method that may mitigate this issue is multi-echo independent component analysis (ME-ICA), which has been shown to separate the effects of head motion from the desired BOLD signal but has not been tested in motor-task datasets with high amounts of motion. In this study, we collected an fMRI dataset from a healthy population who performed a hand grasp task with and without task-correlated amplified head motion to simulate a motor-impaired population. We analyzed these data using three models: single-echo (SE), multi-echo optimally combined (ME-OC), and ME-ICA. We compared the models' performance in mitigating the effects of head motion on the subject level and group level. On the subject level, ME-ICA better dissociated the effects of head motion from the BOLD signal and reduced noise. Both ME models led to increased t-statistics in brain motor regions. In scans with high levels of motion, ME-ICA additionally mitigated artifacts and increased stability of beta coefficient estimates, compared to SE. On the group level, all three models produced activation clusters in expected motor areas in scans with both low and high motion, indicating that group-level averaging may also sufficiently resolve motion artifacts that vary by subject. These findings demonstrate that ME-ICA is a useful tool for subject-level analysis of motor-task data with high levels of task-correlated head motion. The improvements afforded by ME-ICA are critical to improve reliability of subject-level activation maps for clinical populations in which group-level analysis may not be feasible or appropriate, for example in a chronic stroke cohort with varying stroke location and degree of tissue damage.

Dataset Information

Task-related concurrent but opposite modulations of overlapping functional networks as revealed by spatial ICA.

Publications

Task-related concurrent but opposite modulations of overlapping functional networks as revealed by spatial ICA.

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