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Abou Elseoud A, Littow H, Remes J, et al. Group-ICA Model Order Highlights Patterns of Functional Brain Connectivity. Front Syst Neurosci. 2011;5:37doi: 10.3389/fnsys.2011.00037.
Abou Elseoud, A., Littow, H., Remes, J., Starck, T., Nikkinen, J., Nissilä, J., Timonen, M., Tervonen, O., & Kiviniemi, V. (2011). Group-ICA Model Order Highlights Patterns of Functional Brain Connectivity. Frontiers in systems neuroscience, 537. https://doi.org/10.3389/fnsys.2011.00037
Abou Elseoud, Ahmed, et al. "Group-ICA Model Order Highlights Patterns of Functional Brain Connectivity." Frontiers in systems neuroscience vol. 5 (2011): 37. doi: https://doi.org/10.3389/fnsys.2011.00037
Abou Elseoud A, Littow H, Remes J, Starck T, Nikkinen J, Nissilä J, Timonen M, Tervonen O, Kiviniemi V. Group-ICA Model Order Highlights Patterns of Functional Brain Connectivity. Front Syst Neurosci. 2011 Jun 03;5:37. doi: 10.3389/fnsys.2011.00037. eCollection 2011. PMID: 21687724; PMCID: PMC3109774.
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Front Syst Neurosci. 2011 Jun 03;5:37. doi: 10.3389/fnsys.2011.00037. eCollection 2011.

Group-ICA Model Order Highlights Patterns of Functional Brain Connectivity.

Frontiers in systems neuroscience

Ahmed Abou Elseoud, Harri Littow, Jukka Remes, Tuomo Starck, Juha Nikkinen, Juuso Nissilä, Markku Timonen, Osmo Tervonen, Vesa Kiviniemi

Affiliations

  1. Department of Diagnostic Radiology, Oulu University Hospital Oulu, Finland.

PMID: 21687724 PMCID: PMC3109774 DOI: 10.3389/fnsys.2011.00037

Abstract

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.

Keywords: ICA; dual regression; fMRI; functional connectivity; model order; modularity; resting-state; seasonal affective disorder

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