Tags

Type your tag names separated by a space and hit enter

A human brain atlas derived via n-cut parcellation of resting-state and task-based fMRI data.
Magn Reson Imaging 2016; 34(2):209-18MR

Abstract

The growth of functional MRI has led to development of human brain atlases derived by parcellating resting-state connectivity patterns into functionally independent regions of interest (ROIs). All functional atlases to date have been derived from resting-state fMRI data. But given that functional connectivity between regions varies with task, we hypothesized that an atlas incorporating both resting-state and task-based fMRI data would produce an atlas with finer characterization of task-relevant regions than an atlas derived from resting-state alone. To test this hypothesis, we derived parcellation atlases from twenty-nine healthy adult participants enrolled in the Cognitive Connectome project, an initiative to improve functional MRI's translation into clinical decision-making by mapping normative variance in brain-behavior relationships. Participants underwent resting-state and task-based fMRI spanning nine cognitive domains: motor, visuospatial, attention, language, memory, affective processing, decision-making, working memory, and executive function. Spatially constrained n-cut parcellation derived brain atlases using (1) all participants' functional data (Task) or (2) a single resting-state scan (Rest). An atlas was also derived from random parcellation for comparison purposes (Random). Two methods were compared: (1) a parcellation applied to the group's mean edge weights (mean), and (2) a two-stage approach with parcellation of individual edge weights followed by parcellation of mean binarized edges (two-stage). The resulting Task and Rest atlases had significantly greater similarity with each other (mean Jaccard indices JI=0.72-0.85) than with the Random atlases (JI=0.59-0.63; all p<0.001 after Bonferroni correction). Task and Rest atlas similarity was greatest for the two-stage method (JI=0.85), which has been shown as more robust than the mean method; these atlases also better reproduced voxelwise seed maps of the left dorsolateral prefrontal cortex during rest and performing the n-back working memory task (r=0.75-0.80) than the Random atlases (r=0.64-0.72), further validating their utility. We expected regions governing higher-order cognition (such as frontal and anterior temporal lobes) to show greatest difference between Task and Rest atlases; contrary to expectations, these areas had greatest similarity between atlases. Our findings indicate that atlases derived from parcellation of task-based and resting-state fMRI data are highly comparable, and existing resting-state atlases are suitable for task-based analyses. We introduce an anatomically labeled fMRI-derived whole-brain human atlas for future Cognitive Connectome analyses.

Authors+Show Affiliations

Psychiatric Research Institute, University of Arkansas for Medical Sciences, Little Rock, AR. Electronic address: GAJames@uams.edu.Department of Computer Science, University of Arkansas at Little Rock, Little Rock, AR.Department of Computer Science, University of Arkansas at Little Rock, Little Rock, AR.

Pub Type(s)

Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

26523655

Citation

James, George Andrew, et al. "A Human Brain Atlas Derived Via N-cut Parcellation of Resting-state and Task-based fMRI Data." Magnetic Resonance Imaging, vol. 34, no. 2, 2016, pp. 209-18.
James GA, Hazaroglu O, Bush KA. A human brain atlas derived via n-cut parcellation of resting-state and task-based fMRI data. Magn Reson Imaging. 2016;34(2):209-18.
James, G. A., Hazaroglu, O., & Bush, K. A. (2016). A human brain atlas derived via n-cut parcellation of resting-state and task-based fMRI data. Magnetic Resonance Imaging, 34(2), pp. 209-18. doi:10.1016/j.mri.2015.10.036.
James GA, Hazaroglu O, Bush KA. A Human Brain Atlas Derived Via N-cut Parcellation of Resting-state and Task-based fMRI Data. Magn Reson Imaging. 2016;34(2):209-18. PubMed PMID: 26523655.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - A human brain atlas derived via n-cut parcellation of resting-state and task-based fMRI data. AU - James,George Andrew, AU - Hazaroglu,Onder, AU - Bush,Keith A, Y1 - 2015/10/31/ PY - 2015/08/26/received PY - 2015/10/25/accepted PY - 2015/11/3/entrez PY - 2015/11/3/pubmed PY - 2016/10/1/medline KW - Atlas KW - Cognition KW - Parcellation KW - Resting-state KW - Task-based KW - fMRI SP - 209 EP - 18 JF - Magnetic resonance imaging JO - Magn Reson Imaging VL - 34 IS - 2 N2 - The growth of functional MRI has led to development of human brain atlases derived by parcellating resting-state connectivity patterns into functionally independent regions of interest (ROIs). All functional atlases to date have been derived from resting-state fMRI data. But given that functional connectivity between regions varies with task, we hypothesized that an atlas incorporating both resting-state and task-based fMRI data would produce an atlas with finer characterization of task-relevant regions than an atlas derived from resting-state alone. To test this hypothesis, we derived parcellation atlases from twenty-nine healthy adult participants enrolled in the Cognitive Connectome project, an initiative to improve functional MRI's translation into clinical decision-making by mapping normative variance in brain-behavior relationships. Participants underwent resting-state and task-based fMRI spanning nine cognitive domains: motor, visuospatial, attention, language, memory, affective processing, decision-making, working memory, and executive function. Spatially constrained n-cut parcellation derived brain atlases using (1) all participants' functional data (Task) or (2) a single resting-state scan (Rest). An atlas was also derived from random parcellation for comparison purposes (Random). Two methods were compared: (1) a parcellation applied to the group's mean edge weights (mean), and (2) a two-stage approach with parcellation of individual edge weights followed by parcellation of mean binarized edges (two-stage). The resulting Task and Rest atlases had significantly greater similarity with each other (mean Jaccard indices JI=0.72-0.85) than with the Random atlases (JI=0.59-0.63; all p<0.001 after Bonferroni correction). Task and Rest atlas similarity was greatest for the two-stage method (JI=0.85), which has been shown as more robust than the mean method; these atlases also better reproduced voxelwise seed maps of the left dorsolateral prefrontal cortex during rest and performing the n-back working memory task (r=0.75-0.80) than the Random atlases (r=0.64-0.72), further validating their utility. We expected regions governing higher-order cognition (such as frontal and anterior temporal lobes) to show greatest difference between Task and Rest atlases; contrary to expectations, these areas had greatest similarity between atlases. Our findings indicate that atlases derived from parcellation of task-based and resting-state fMRI data are highly comparable, and existing resting-state atlases are suitable for task-based analyses. We introduce an anatomically labeled fMRI-derived whole-brain human atlas for future Cognitive Connectome analyses. SN - 1873-5894 UR - https://www.unboundmedicine.com/medline/citation/26523655/A_human_brain_atlas_derived_via_n_cut_parcellation_of_resting_state_and_task_based_fMRI_data_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S0730-725X(15)00273-8 DB - PRIME DP - Unbound Medicine ER -