Fusion analysis of first episode depression: Where brain shape deformations meet local composition of tissue

Computational neuroanatomical techniques that are used to evaluate the structural correlates of disorders in the brain typically measure regional differences in gray matter or white matter, or measure regional differences in the deformation fields required to warp individual datasets to a standard...

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Main Authors: Mahdi Ramezani, Purang Abolmaesumi, Amir Tahmasebi, Rachael Bosma, Ryan Tong, Tom Hollenstein, Kate Harkness, Ingrid Johnsrude
Format: Article
Language:English
Published: Elsevier 2015-01-01
Series:NeuroImage: Clinical
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2213158214001806
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spelling doaj-ecdff94598b54c07b4d2de5babf609f82020-11-24T22:00:33ZengElsevierNeuroImage: Clinical2213-15822015-01-017C11412110.1016/j.nicl.2014.11.016Fusion analysis of first episode depression: Where brain shape deformations meet local composition of tissueMahdi Ramezani0Purang Abolmaesumi1Amir Tahmasebi2Rachael Bosma3Ryan Tong4Tom Hollenstein5Kate Harkness6Ingrid Johnsrude7Department of Electrical and Computer Engineering, The University of British Columbia, 2329 West Mall, Vancouver, BC V6T 1Z4, CanadaDepartment of Electrical and Computer Engineering, The University of British Columbia, 2329 West Mall, Vancouver, BC V6T 1Z4, CanadaPhilips Research North America, 345 Scarborough Rd., Briarcliff Manor, NY 10510, USADepartment of Psychology, Queen's University, Kingston, ON K7L 3N6, CanadaDepartment of Psychology, Queen's University, Kingston, ON K7L 3N6, CanadaDepartment of Psychology, Queen's University, Kingston, ON K7L 3N6, CanadaDepartment of Psychology, Queen's University, Kingston, ON K7L 3N6, CanadaDepartment of Psychology, Queen's University, Kingston, ON K7L 3N6, Canada Computational neuroanatomical techniques that are used to evaluate the structural correlates of disorders in the brain typically measure regional differences in gray matter or white matter, or measure regional differences in the deformation fields required to warp individual datasets to a standard space. Our aim in this study was to combine measurements of regional tissue composition and of deformations in order to characterize a particular brain disorder (here, major depressive disorder). We use structural Magnetic Resonance Imaging (MRI) data from young adults in a first episode of depression, and from an age- and sex-matched group of non-depressed individuals, and create population gray matter (GM) and white matter (WM) tissue average templates using DARTEL groupwise registration. We obtained GM and WM tissue maps in the template space, along with the deformation fields required to co-register the DARTEL template and the GM and WM maps in the population. These three features, reflecting tissue composition and shape of the brain, were used within a joint independent-components analysis (jICA) to extract spatially independent joint sources and their corresponding modulation profiles. Coefficients of the modulation profiles were used to capture differences between depressed and non-depressed groups. The combination of hippocampal shape deformations and local composition of tissue (but neither shape nor local composition of tissue alone) was shown to discriminate reliably between individuals in a first episode of depression and healthy controls, suggesting that brain structural differences between depressed and non-depressed individuals do not simply reflect chronicity of the disorder but are there from the very outset. http://www.sciencedirect.com/science/article/pii/S2213158214001806DepressionStructural MRIJoint analysisBrain shape deformationsBrain local composition of tissue
collection DOAJ
language English
format Article
sources DOAJ
author Mahdi Ramezani
Purang Abolmaesumi
Amir Tahmasebi
Rachael Bosma
Ryan Tong
Tom Hollenstein
Kate Harkness
Ingrid Johnsrude
spellingShingle Mahdi Ramezani
Purang Abolmaesumi
Amir Tahmasebi
Rachael Bosma
Ryan Tong
Tom Hollenstein
Kate Harkness
Ingrid Johnsrude
Fusion analysis of first episode depression: Where brain shape deformations meet local composition of tissue
NeuroImage: Clinical
Depression
Structural MRI
Joint analysis
Brain shape deformations
Brain local composition of tissue
author_facet Mahdi Ramezani
Purang Abolmaesumi
Amir Tahmasebi
Rachael Bosma
Ryan Tong
Tom Hollenstein
Kate Harkness
Ingrid Johnsrude
author_sort Mahdi Ramezani
title Fusion analysis of first episode depression: Where brain shape deformations meet local composition of tissue
title_short Fusion analysis of first episode depression: Where brain shape deformations meet local composition of tissue
title_full Fusion analysis of first episode depression: Where brain shape deformations meet local composition of tissue
title_fullStr Fusion analysis of first episode depression: Where brain shape deformations meet local composition of tissue
title_full_unstemmed Fusion analysis of first episode depression: Where brain shape deformations meet local composition of tissue
title_sort fusion analysis of first episode depression: where brain shape deformations meet local composition of tissue
publisher Elsevier
series NeuroImage: Clinical
issn 2213-1582
publishDate 2015-01-01
description Computational neuroanatomical techniques that are used to evaluate the structural correlates of disorders in the brain typically measure regional differences in gray matter or white matter, or measure regional differences in the deformation fields required to warp individual datasets to a standard space. Our aim in this study was to combine measurements of regional tissue composition and of deformations in order to characterize a particular brain disorder (here, major depressive disorder). We use structural Magnetic Resonance Imaging (MRI) data from young adults in a first episode of depression, and from an age- and sex-matched group of non-depressed individuals, and create population gray matter (GM) and white matter (WM) tissue average templates using DARTEL groupwise registration. We obtained GM and WM tissue maps in the template space, along with the deformation fields required to co-register the DARTEL template and the GM and WM maps in the population. These three features, reflecting tissue composition and shape of the brain, were used within a joint independent-components analysis (jICA) to extract spatially independent joint sources and their corresponding modulation profiles. Coefficients of the modulation profiles were used to capture differences between depressed and non-depressed groups. The combination of hippocampal shape deformations and local composition of tissue (but neither shape nor local composition of tissue alone) was shown to discriminate reliably between individuals in a first episode of depression and healthy controls, suggesting that brain structural differences between depressed and non-depressed individuals do not simply reflect chronicity of the disorder but are there from the very outset.
topic Depression
Structural MRI
Joint analysis
Brain shape deformations
Brain local composition of tissue
url http://www.sciencedirect.com/science/article/pii/S2213158214001806
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