Group-ICA Model Order Highlights Patterns of Functional Brain Connectivity

Resting-state networks (RSNs) can be reliably and reproducibly detected using independent component analysis (ICA) at both individual subject and group levels. Altering ICA dimensionality (model order) estimation can have a significant impact on the spatial characteristics of the RSNs as well as their parcellation into sub-networks. Recent evidence from several neuroimaging studies suggests that the human brain has a modular hierarchical organization which resembles the hierarchy depicted by different ICA model orders. We hypothesized that functional connectivity between-group differences measured with ICA might be affected by model order selection. We investigated differences in functional connectivity using so-called dual regression as a function of ICA model order in a group of unmedicated seasonal affective disorder (SAD) patients compared to normal healthy controls. The results showed that the detected disease-related differences in functional connectivity alter as a function of ICA model order. The volume of between-group differences altered significantly as a function of ICA model order reaching maximum at model order 70 (which seems to be an optimal point that conveys the largest between-group difference) then stabilized afterwards. Our results show that fine-grained RSNs enable better detection of detailed disease-related functional connectivity changes. However, high model orders show an increased risk of false positives that needs to be overcome. Our findings suggest that multilevel ICA exploration of functional connectivity enables optimization of sensitivity to brain disorders

Aberrant functional connectivity in the default mode and central executive networks in subjects with schizophrenia – A whole-brain resting state ICA study

Neurophysiological changes of schizophrenia are currently linked to disturbances in connectivity between functional brain networks. Functional magnetic resonance imaging (fMRI) studies on schizophrenia have focused on a few selected networks. Also previously it has not been possible to discern whether the functional alterations in schizophrenia originate from spatial shifting or amplitude alterations of functional connectivity. In this study we aim to discern the differences in schizophrenia patients with respect to spatial shifting vs. signal amplitude changes in functional connectivity in the whole brain connectome. We used high model order independent component analysis (ICA) to study some 40 resting state networks (RSN) covering the whole cortex. Group differences were analysed with dual regression coupled with y-concat correction for multiple comparisons. We investigated the RSN’s with and without variance normalization in order to discern spatial shifting from signal amplitude changes in 43 schizophrenia patients and matched controls from the Northern Finland 1966 Birth Cohort. Voxel level correction for multiple comparisons revealed 18 RSN’s with altered functional connectivity, six of which had both spatial and signal amplitude changes. After adding the multiple comparison y-concat correction to the analysis for including the 40 RSN’s as well, we found that four RSN’s showed still changes. These robust changes actually seem encompass parcellations of the default mode network (DMN) and central executive networks (CEN). These networks both have spatially shifted connectivity and abnormal signal amplitudes. Interestingly the networks seem to mix their functional representations in areas like left caudate nucleus and dorsolateral pre-frontal cortex. These changes overlapped with areas that have been related to do paminergic alterations in patients with schizophrenia compared to controls