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Neuroimage. 2018 Oct 1;179:448-470. doi: 10.1016/j.neuroimage.2018.06.024. Epub 2018 Jun 15.

An approach to directly link ICA and seed-based functional connectivity: Application to schizophrenia.

Author information

1
The Mind Research Network, Albuquerque, NM, 87106, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, 87131, USA. Electronic address: lwu@mrn.org.
2
The Mind Research Network, Albuquerque, NM, 87106, USA.
3
Department of Psychiatry, University of New Mexico, Albuquerque, NM, 87131, USA.
4
The Mind Research Network, Albuquerque, NM, 87106, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, 87131, USA; Department of Psychiatry, University of New Mexico, Albuquerque, NM, 87131, USA.

Abstract

Independent component analysis (ICA) and seed-based analyses are widely used techniques for studying intrinsic neuronal activity in task-based or resting scans. In this work, we show there is a direct link between the two, and show that there are some important differences between the two approaches in terms of what information they capture. We developed an enhanced connectivity-matrix independent component analysis (cmICA) for calculating whole brain voxel maps of functional connectivity, which reduces the computational complexity of voxel-based connectivity analysis on performing many temporal correlations. We also show there is a mathematical equivalency between parcellations on voxel-to-voxel functional connectivity and simplified cmICA. Next, we used this cost-efficient data-driven method to examine the resting state fMRI connectivity in schizophrenia patients (SZ) and healthy controls (HC) on a whole brain scale and further quantified the relationship between brain functional connectivity and cognitive performances measured by the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) battery. Current results suggest that SZ exhibit a wide-range abnormality, primarily a decrease, in functional connectivity both between networks and within different network hubs. Specific functional connectivity decreases were associated with MATRICS performance deficits. In addition, we found that resting state functional connectivity decreases was extensively associated with aging regardless of groups. In contrast, there was no relationship between positive and negative symptoms in the patients and functional connectivity. In sum, we have developed a novel mathematical relationship between ICA and seed-based connectivity that reduces computational complexity, which has broad applicability, and showed a specific application of this approach to characterize connectivity changes associated with cognitive scores in SZ.

KEYWORDS:

Cognitive scores; Functional connectivity; ICA; Matrics; Schizophrenia

PMID:
29894827
PMCID:
PMC6072460
[Available on 2019-10-01]
DOI:
10.1016/j.neuroimage.2018.06.024

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