Benefits of multi-modal fusion analysis on a large-scale dataset: Life-span patterns of inter-subject variability in cortical morphometry and white matter microstructure
Neuroimaging studies have become increasingly multimodal in recent years, with researchers typically acquiring several different types of MRI data and processing them along separate pipelines that provide a set of complementary windows into each subject's brain. However, few attempts have been...
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| Published in | NeuroImage (Orlando, Fla.) Vol. 63; no. 1; pp. 365 - 380 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
15.10.2012
Elsevier Limited |
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| Online Access | Get full text |
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| Abstract | Neuroimaging studies have become increasingly multimodal in recent years, with researchers typically acquiring several different types of MRI data and processing them along separate pipelines that provide a set of complementary windows into each subject's brain. However, few attempts have been made to integrate the various modalities in the same analysis. Linked ICA is a robust data fusion model that takes multi-modal data and characterizes inter-subject variability in terms of a set of multi-modal components. This paper examines the types of components found when running Linked ICA on a large magnetic resonance imaging (MRI) morphometric and diffusion tensor imaging (DTI) data set comprising 484 healthy subjects ranging from 8 to 85years of age. We find several strong global features related to age, sex, and intracranial volume; in particular, one component predicts age to a high accuracy (r=0.95). Most of the remaining components describe spatially localized modes of variability in white or gray matter, with many components including both tissue types. The multimodal components tend to be located in anatomically-related brain areas, suggesting a morphological and possibly functional relationship. The local components show relationships between surface-based cortical thickness and arealization, voxel-based morphometry (VBM), and between three different DTI measures. Further, we report components related to artifacts (e.g. scanner software upgrades) which would be expected in a dataset of this size. Most of the 100 extracted components showed interpretable spatial patterns and were found to be reliable using split-half validation. This work provides novel information about normal inter-subject variability in brain structure, and demonstrates the potential of Linked ICA as a feature-extracting data fusion approach across modalities. This exploratory approach automatically generates models to explain structure in the data, and may prove especially powerful for large-scale studies, where the population variability can be explored in increased detail.
► Linked ICA used to fuse six modalities of MRI data from 484 healthy subjects. ► A strong age-related component shows r=0.95 correlation with age, over 8–85yrs. ► Most features show local, bilateral variability in multiple GM/WM measures. ► Identifies artifact components, including scanner software upgrade effects. |
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| AbstractList | Neuroimaging studies have become increasingly multimodal in recent years, with researchers typically acquiring several different types of MRI data and processing them along separate pipelines that provide a set of complementary windows into each subject's brain. However, few attempts have been made to integrate the various modalities in the same analysis. Linked ICA is a robust data fusion model that takes multi-modal data and characterizes inter-subject variability in terms of a set of multi-modal components. This paper examines the types of components found when running Linked ICA on a large magnetic resonance imaging (MRI) morphometric and diffusion tensor imaging (DTI) data set comprising 484 healthy subjects ranging from 8 to 85years of age. We find several strong global features related to age, sex, and intracranial volume; in particular, one component predicts age to a high accuracy (r=0.95). Most of the remaining components describe spatially localized modes of variability in white or gray matter, with many components including both tissue types. The multimodal components tend to be located in anatomically-related brain areas, suggesting a morphological and possibly functional relationship. The local components show relationships between surface-based cortical thickness and arealization, voxel-based morphometry (VBM), and between three different DTI measures. Further, we report components related to artifacts (e.g. scanner software upgrades) which would be expected in a dataset of this size. Most of the 100 extracted components showed interpretable spatial patterns and were found to be reliable using split-half validation. This work provides novel information about normal inter-subject variability in brain structure, and demonstrates the potential of Linked ICA as a feature-extracting data fusion approach across modalities. This exploratory approach automatically generates models to explain structure in the data, and may prove especially powerful for large-scale studies, where the population variability can be explored in increased detail. Neuroimaging studies have become increasingly multimodal in recent years, with researchers typically acquiring several different types of MRI data and processing them along separate pipelines that provide a set of complementary windows into each subject's brain. However, few attempts have been made to integrate the various modalities in the same analysis. Linked ICA is a robust data fusion model that takes multi-modal data and characterizes inter-subject variability in terms of a set of multi-modal components. This paper examines the types of components found when running Linked ICA on a large magnetic resonance imaging (MRI) morphometric and diffusion tensor imaging (DTI) data set comprising 484 healthy subjects ranging from 8 to 85years of age. We find several strong global features related to age, sex, and intracranial volume; in particular, one component predicts age to a high accuracy (r=0.95). Most of the remaining components describe spatially localized modes of variability in white or gray matter, with many components including both tissue types. The multimodal components tend to be located in anatomically-related brain areas, suggesting a morphological and possibly functional relationship. The local components show relationships between surface-based cortical thickness and arealization, voxel-based morphometry (VBM), and between three different DTI measures. Further, we report components related to artifacts (e.g. scanner software upgrades) which would be expected in a dataset of this size. Most of the 100 extracted components showed interpretable spatial patterns and were found to be reliable using split-half validation. This work provides novel information about normal inter-subject variability in brain structure, and demonstrates the potential of Linked ICA as a feature-extracting data fusion approach across modalities. This exploratory approach automatically generates models to explain structure in the data, and may prove especially powerful for large-scale studies, where the population variability can be explored in increased detail. ► Linked ICA used to fuse six modalities of MRI data from 484 healthy subjects. ► A strong age-related component shows r=0.95 correlation with age, over 8–85yrs. ► Most features show local, bilateral variability in multiple GM/WM measures. ► Identifies artifact components, including scanner software upgrade effects. Neuroimaging studies have become increasingly multimodal in recent years, with researchers typically acquiring several different types of MRI data and processing them along separate pipelines that provide a set of complementary windows into each subject's brain. However, few attempts have been made to integrate the various modalities in the same analysis. Linked ICA is a robust data fusion model that takes multi-modal data and characterizes inter-subject variability in terms of a set of multi-modal components. This paper examines the types of components found when running Linked ICA on a large magnetic resonance imaging (MRI) morphometric and diffusion tensor imaging (DTI) data set comprising 484 healthy subjects ranging from 8 to 85 years of age. We find several strong global features related to age, sex, and intracranial volume; in particular, one component predicts age to a high accuracy (r = 0.95). Most of the remaining components describe spatially localized modes of variability in white or gray matter, with many components including both tissue types. The multimodal components tend to be located in anatomically-related brain areas, suggesting a morphological and possibly functional relationship. The local components show relationships between surface-based cortical thickness and arealization, voxel-based morphometry (VBM), and between three different DTI measures. Further, we report components related to artifacts (e.g. scanner software upgrades) which would be expected in a dataset of this size. Most of the 100 extracted components showed interpretable spatial patterns and were found to be reliable using split-half validation. This work provides novel information about normal inter-subject variability in brain structure, and demonstrates the potential of Linked ICA as a feature-extracting data fusion approach across modalities. This exploratory approach automatically generates models to explain structure in the data, and may prove especially powerful for large-scale studies, where the population variability can be explored in increased detail. Neuroimaging studies have become increasingly multimodal in recent years, with researchers typically acquiring several different types of MRI data and processing them along separate pipelines that provide a set of complementary windows into each subject's brain. However, few attempts have been made to integrate the various modalities in the same analysis. Linked ICA is a robust data fusion model that takes multi-modal data and characterizes inter-subject variability in terms of a set of multi-modal components. This paper examines the types of components found when running Linked ICA on a large magnetic resonance imaging (MRI) morphometric and diffusion tensor imaging (DTI) data set comprising 484 healthy subjects ranging from 8 to 85 years of age. We find several strong global features related to age, sex, and intracranial volume; in particular, one component predicts age to a high accuracy (r=0.95). Most of the remaining components describe spatially localized modes of variability in white or gray matter, with many components including both tissue types. The multimodal components tend to be located in anatomically-related brain areas, suggesting a morphological and possibly functional relationship. The local components show relationships between surface-based cortical thickness and arealization, voxel-based morphometry (VBM), and between three different DTI measures. Further, we report components related to artifacts (e.g. scanner software upgrades) which would be expected in a dataset of this size. Most of the 100 extracted components showed interpretable spatial patterns and were found to be reliable using split-half validation. This work provides novel information about normal inter-subject variability in brain structure, and demonstrates the potential of Linked ICA as a feature-extracting data fusion approach across modalities. This exploratory approach automatically generates models to explain structure in the data, and may prove especially powerful for large-scale studies, where the population variability can be explored in increased detail.Neuroimaging studies have become increasingly multimodal in recent years, with researchers typically acquiring several different types of MRI data and processing them along separate pipelines that provide a set of complementary windows into each subject's brain. However, few attempts have been made to integrate the various modalities in the same analysis. Linked ICA is a robust data fusion model that takes multi-modal data and characterizes inter-subject variability in terms of a set of multi-modal components. This paper examines the types of components found when running Linked ICA on a large magnetic resonance imaging (MRI) morphometric and diffusion tensor imaging (DTI) data set comprising 484 healthy subjects ranging from 8 to 85 years of age. We find several strong global features related to age, sex, and intracranial volume; in particular, one component predicts age to a high accuracy (r=0.95). Most of the remaining components describe spatially localized modes of variability in white or gray matter, with many components including both tissue types. The multimodal components tend to be located in anatomically-related brain areas, suggesting a morphological and possibly functional relationship. The local components show relationships between surface-based cortical thickness and arealization, voxel-based morphometry (VBM), and between three different DTI measures. Further, we report components related to artifacts (e.g. scanner software upgrades) which would be expected in a dataset of this size. Most of the 100 extracted components showed interpretable spatial patterns and were found to be reliable using split-half validation. This work provides novel information about normal inter-subject variability in brain structure, and demonstrates the potential of Linked ICA as a feature-extracting data fusion approach across modalities. This exploratory approach automatically generates models to explain structure in the data, and may prove especially powerful for large-scale studies, where the population variability can be explored in increased detail. |
| Author | Walhovd, Kristine B. Smith, Stephen M. Woolrich, Mark W. Tamnes, Christian K. Fjell, Anders M. Groves, Adrian R. Westlye, Lars T. Douaud, Gwenaëlle |
| Author_xml | – sequence: 1 givenname: Adrian R. surname: Groves fullname: Groves, Adrian R. email: adriang@fmrib.ox.ac.uk, adrian-neuroimage@groves.ca organization: FMRIB (Oxford Centre for Functional Magnetic Resonance Imaging of the Brain), Nuffield Department of Clinical Neurosciences, University of Oxford, OX3 9DU, UK – sequence: 2 givenname: Stephen M. surname: Smith fullname: Smith, Stephen M. organization: FMRIB (Oxford Centre for Functional Magnetic Resonance Imaging of the Brain), Nuffield Department of Clinical Neurosciences, University of Oxford, OX3 9DU, UK – sequence: 3 givenname: Anders M. surname: Fjell fullname: Fjell, Anders M. organization: Center for the Study of Human Cognition, Department of Psychology, University of Oslo, Norway, PO Box 1094 Blindern, 0317 Oslo, Norway – sequence: 4 givenname: Christian K. surname: Tamnes fullname: Tamnes, Christian K. organization: Center for the Study of Human Cognition, Department of Psychology, University of Oslo, Norway, PO Box 1094 Blindern, 0317 Oslo, Norway – sequence: 5 givenname: Kristine B. surname: Walhovd fullname: Walhovd, Kristine B. organization: Center for the Study of Human Cognition, Department of Psychology, University of Oslo, Norway, PO Box 1094 Blindern, 0317 Oslo, Norway – sequence: 6 givenname: Gwenaëlle surname: Douaud fullname: Douaud, Gwenaëlle organization: FMRIB (Oxford Centre for Functional Magnetic Resonance Imaging of the Brain), Nuffield Department of Clinical Neurosciences, University of Oxford, OX3 9DU, UK – sequence: 7 givenname: Mark W. surname: Woolrich fullname: Woolrich, Mark W. organization: FMRIB (Oxford Centre for Functional Magnetic Resonance Imaging of the Brain), Nuffield Department of Clinical Neurosciences, University of Oxford, OX3 9DU, UK – sequence: 8 givenname: Lars T. surname: Westlye fullname: Westlye, Lars T. email: l.t.westlye@psykologi.uio.no organization: Center for the Study of Human Cognition, Department of Psychology, University of Oslo, Norway, PO Box 1094 Blindern, 0317 Oslo, Norway |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/22750721$$D View this record in MEDLINE/PubMed |
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| Copyright | 2012 Elsevier Inc. Copyright © 2012 Elsevier Inc. All rights reserved. Copyright Elsevier Limited Oct 15, 2012 |
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| Issue | 1 |
| Keywords | Surface-based morphometry Bayesian modeling Independent component analysis Voxel-based morphometry Aging Data fusion Diffusion tensor imaging |
| Language | English |
| License | https://www.elsevier.com/tdm/userlicense/1.0 Copyright © 2012 Elsevier Inc. All rights reserved. |
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| PublicationTitle | NeuroImage (Orlando, Fla.) |
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| Title | Benefits of multi-modal fusion analysis on a large-scale dataset: Life-span patterns of inter-subject variability in cortical morphometry and white matter microstructure |
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