TY - JOUR
T1 - An approach to directly link ICA and seed-based functional connectivity
T2 - Application to schizophrenia
AU - Wu, Lei
AU - Caprihan, Arvind
AU - Bustillo, Juan
AU - Mayer, Andrew
AU - Calhoun, Vince
N1 - Funding Information:
The authors would like to thank all the principal investigators of COBRE project at MRN ( http://cobre.mrn.org/ ). We greatly appreciate comments and suggestions from Drs. Julia Stephen, Jean Jingyu Liu and Jiayu Chen. Also, thanks to Diana South and MIALAB Auto-analysis crew for the collecting and preprocessing work, Margaret King from the COINs database team ( http://coins.mrn.org/ ) and many other colleagues at MRN for technical help and discussions. This work is funded by the NIH , under grants P20GM103472 , 1R01EB006841 , 1R01EB005846 , R01EB020407 ; NSF # 1539067 and 1P20RR0219 3.
Funding Information:
The authors would like to thank all the principal investigators of COBRE project at MRN (http://cobre.mrn.org/). We greatly appreciate comments and suggestions from Drs. Julia Stephen, Jean Jingyu Liu and Jiayu Chen. Also, thanks to Diana South and MIALAB Auto-analysis crew for the collecting and preprocessing work, Margaret King from the COINs database team (http://coins.mrn.org/) and many other colleagues at MRN for technical help and discussions. This work is funded by the NIH, under grants P20GM103472, 1R01EB006841, 1R01EB005846, R01EB020407; NSF #1539067 and 1P20RR02193.
Publisher Copyright:
© 2018 Elsevier Inc.
PY - 2018/10/1
Y1 - 2018/10/1
N2 - 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.
AB - 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.
KW - Cognitive scores
KW - Functional connectivity
KW - ICA
KW - Matrics
KW - Schizophrenia
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U2 - 10.1016/j.neuroimage.2018.06.024
DO - 10.1016/j.neuroimage.2018.06.024
M3 - Article
C2 - 29894827
AN - SCOPUS:85049304560
SN - 1053-8119
VL - 179
SP - 448
EP - 470
JO - NeuroImage
JF - NeuroImage
ER -