Approaches to capture variance differences in rest fMRI networks in the spatial geometric features: Application to schizophrenia
Identification of functionally connected regions while at rest has been at the forefront of research focusing on understanding interactions between different brain regions. Studies have utilized a variety of approaches including seed based as well as data-driven approaches to identifying such networ...
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doaj-3c8216071f2c4ac0a8c4cfa1ba8a20c32020-11-24T21:54:50ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2016-03-011010.3389/fnins.2016.00085180131Approaches to capture variance differences in rest fMRI networks in the spatial geometric features: Application to schizophreniaShruti eGopal0Shruti eGopal1Robyn eMiller2Stefi Alison Baum3Stefi Alison Baum4Vince D Calhoun5Vince D Calhoun6Rochester Institute of TechnologyMind Research NetworkMind Research NetworkRochester Institute of TechnologyUniversity of ManitobaMind Research NetworkUniversity of New MexicoIdentification of functionally connected regions while at rest has been at the forefront of research focusing on understanding interactions between different brain regions. Studies have utilized a variety of approaches including seed based as well as data-driven approaches to identifying such networks. Most such techniques involve differentiating groups based on group mean measures. There has been little work focused on differences in spatial characteristics of resting fMRI data. We present a method to identify between group differences in the variability in the cluster characteristics of network regions within components estimated via independent vector analysis (IVA). IVA is a blind source separation approach shown to perform well in capturing individual subject variability within a group model. We evaluate performance of the approach using simulations and then apply to a relatively large schizophrenia data set (82 schizophrenia patients and 89 healthy controls). We postulate that group differences in the intra-network distributional characteristics of resting state network voxel intensities might indirectly capture important distinctions between the brain function of healthy and clinical populations. Results demonstrate that specific areas of the brain, superior and middle temporal gyrus that are involved in language and recognition of emotions, show greater component level variance in amplitude weights for schizophrenia patients than healthy controls. Statistically significant correlation between component level spatial variance and component volume was observed in 19 of the 27 non-artifactual components implying an evident relationship between the two parameters. Additionally, the greater spread in the distance of the cluster peak of a component from the centroid in schizophrenia patients compared to healthy controls was observed for seven components. These results indicate that there is hidden potential in exploring variance and possibly higher-order measures in resting state networks to better understand diseases such as schizophrenia. It furthers comprehension of how spatial characteristics can highlight previously unexplored differences between populations such as schizophrenia patients and healthy controls.http://journal.frontiersin.org/Journal/10.3389/fnins.2016.00085/fullSchizophreniaresting state fMRIspatial variabilityGeometric featuresIVA |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shruti eGopal Shruti eGopal Robyn eMiller Stefi Alison Baum Stefi Alison Baum Vince D Calhoun Vince D Calhoun |
spellingShingle |
Shruti eGopal Shruti eGopal Robyn eMiller Stefi Alison Baum Stefi Alison Baum Vince D Calhoun Vince D Calhoun Approaches to capture variance differences in rest fMRI networks in the spatial geometric features: Application to schizophrenia Frontiers in Neuroscience Schizophrenia resting state fMRI spatial variability Geometric features IVA |
author_facet |
Shruti eGopal Shruti eGopal Robyn eMiller Stefi Alison Baum Stefi Alison Baum Vince D Calhoun Vince D Calhoun |
author_sort |
Shruti eGopal |
title |
Approaches to capture variance differences in rest fMRI networks in the spatial geometric features: Application to schizophrenia |
title_short |
Approaches to capture variance differences in rest fMRI networks in the spatial geometric features: Application to schizophrenia |
title_full |
Approaches to capture variance differences in rest fMRI networks in the spatial geometric features: Application to schizophrenia |
title_fullStr |
Approaches to capture variance differences in rest fMRI networks in the spatial geometric features: Application to schizophrenia |
title_full_unstemmed |
Approaches to capture variance differences in rest fMRI networks in the spatial geometric features: Application to schizophrenia |
title_sort |
approaches to capture variance differences in rest fmri networks in the spatial geometric features: application to schizophrenia |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Neuroscience |
issn |
1662-453X |
publishDate |
2016-03-01 |
description |
Identification of functionally connected regions while at rest has been at the forefront of research focusing on understanding interactions between different brain regions. Studies have utilized a variety of approaches including seed based as well as data-driven approaches to identifying such networks. Most such techniques involve differentiating groups based on group mean measures. There has been little work focused on differences in spatial characteristics of resting fMRI data. We present a method to identify between group differences in the variability in the cluster characteristics of network regions within components estimated via independent vector analysis (IVA). IVA is a blind source separation approach shown to perform well in capturing individual subject variability within a group model. We evaluate performance of the approach using simulations and then apply to a relatively large schizophrenia data set (82 schizophrenia patients and 89 healthy controls). We postulate that group differences in the intra-network distributional characteristics of resting state network voxel intensities might indirectly capture important distinctions between the brain function of healthy and clinical populations. Results demonstrate that specific areas of the brain, superior and middle temporal gyrus that are involved in language and recognition of emotions, show greater component level variance in amplitude weights for schizophrenia patients than healthy controls. Statistically significant correlation between component level spatial variance and component volume was observed in 19 of the 27 non-artifactual components implying an evident relationship between the two parameters. Additionally, the greater spread in the distance of the cluster peak of a component from the centroid in schizophrenia patients compared to healthy controls was observed for seven components. These results indicate that there is hidden potential in exploring variance and possibly higher-order measures in resting state networks to better understand diseases such as schizophrenia. It furthers comprehension of how spatial characteristics can highlight previously unexplored differences between populations such as schizophrenia patients and healthy controls. |
topic |
Schizophrenia resting state fMRI spatial variability Geometric features IVA |
url |
http://journal.frontiersin.org/Journal/10.3389/fnins.2016.00085/full |
work_keys_str_mv |
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