Local connectome phenotypes predict social, health, and cognitive factors

The unique architecture of the human connectome is defined initially by genetics and subsequently sculpted over time with experience. Thus, similarities in predisposition and experience that lead to similarities in social, biological, and cognitive attributes should also be reflected in the local ar...

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প্রধান লেখক: Michael A. Powell, Javier O. Garcia, Fang-Cheng Yeh, Jean M. Vettel, Timothy Verstynen
বিন্যাস: প্রবন্ধ
ভাষা:English
প্রকাশিত: The MIT Press 2018-03-01
মালা:Network Neuroscience
বিষয়গুলি:
অনলাইন ব্যবহার করুন:https://www.mitpressjournals.org/doi/pdf/10.1162/NETN_a_00031
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author Michael A. Powell
Javier O. Garcia
Fang-Cheng Yeh
Jean M. Vettel
Timothy Verstynen
author_facet Michael A. Powell
Javier O. Garcia
Fang-Cheng Yeh
Jean M. Vettel
Timothy Verstynen
author_sort Michael A. Powell
collection DOAJ
description The unique architecture of the human connectome is defined initially by genetics and subsequently sculpted over time with experience. Thus, similarities in predisposition and experience that lead to similarities in social, biological, and cognitive attributes should also be reflected in the local architecture of white matter fascicles. Here we employ a method known as local connectome fingerprinting that uses diffusion MRI to measure the fiber-wise characteristics of macroscopic white matter pathways throughout the brain. This fingerprinting approach was applied to a large sample (N = 841) of subjects from the Human Connectome Project, revealing a reliable degree of between-subject correlation in the local connectome fingerprints, with a relatively complex, low-dimensional substructure. Using a cross-validated, high-dimensional regression analysis approach, we derived local connectome phenotype (LCP) maps that could reliably predict a subset of subject attributes measured, including demographic, health, and cognitive measures. These LCP maps were highly specific to the attribute being predicted but also sensitive to correlations between attributes. Collectively, these results indicate that the local architecture of white matter fascicles reflects a meaningful portion of the variability shared between subjects along several dimensions. The local connectome is the pattern of fiber systems (i.e., number of fibers, orientation, and size) within a voxel, and it reflects the proximal characteristics of white matter fascicles distributed throughout the brain. Here we show how variability in the local connectome is correlated in a principled way across individuals. This intersubject correlation is reliable enough that unique phenotype maps can be learned to predict between-subject variability in a range of social, health, and cognitive attributes. This work shows, for the first time, how the local connectome has both the sensitivity and the specificity to be used as a phenotypic marker for subject-specific attributes.
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spelling doaj.art-aef56c9c764e454fbef6c0cb15a70a412022-12-22T02:35:37ZengThe MIT PressNetwork Neuroscience2472-17512018-03-01218610510.1162/NETN_a_00031NETN_a_00031Local connectome phenotypes predict social, health, and cognitive factorsMichael A. Powell0Javier O. Garcia1Fang-Cheng Yeh2Jean M. Vettel3Timothy Verstynen4Department of Mathematical Sciences, United States Military Academy, West Point, NY, USAU.S. Army Research Laboratory, Aberdeen Proving Ground, MD, USADepartment of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, USAU.S. Army Research Laboratory, Aberdeen Proving Ground, MD, USADepartment of Psychology and Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA, USAThe unique architecture of the human connectome is defined initially by genetics and subsequently sculpted over time with experience. Thus, similarities in predisposition and experience that lead to similarities in social, biological, and cognitive attributes should also be reflected in the local architecture of white matter fascicles. Here we employ a method known as local connectome fingerprinting that uses diffusion MRI to measure the fiber-wise characteristics of macroscopic white matter pathways throughout the brain. This fingerprinting approach was applied to a large sample (N = 841) of subjects from the Human Connectome Project, revealing a reliable degree of between-subject correlation in the local connectome fingerprints, with a relatively complex, low-dimensional substructure. Using a cross-validated, high-dimensional regression analysis approach, we derived local connectome phenotype (LCP) maps that could reliably predict a subset of subject attributes measured, including demographic, health, and cognitive measures. These LCP maps were highly specific to the attribute being predicted but also sensitive to correlations between attributes. Collectively, these results indicate that the local architecture of white matter fascicles reflects a meaningful portion of the variability shared between subjects along several dimensions. The local connectome is the pattern of fiber systems (i.e., number of fibers, orientation, and size) within a voxel, and it reflects the proximal characteristics of white matter fascicles distributed throughout the brain. Here we show how variability in the local connectome is correlated in a principled way across individuals. This intersubject correlation is reliable enough that unique phenotype maps can be learned to predict between-subject variability in a range of social, health, and cognitive attributes. This work shows, for the first time, how the local connectome has both the sensitivity and the specificity to be used as a phenotypic marker for subject-specific attributes.https://www.mitpressjournals.org/doi/pdf/10.1162/NETN_a_00031Local connectomeWhite matterIndividual differencesBehavior predictionStructural connectivity
spellingShingle Michael A. Powell
Javier O. Garcia
Fang-Cheng Yeh
Jean M. Vettel
Timothy Verstynen
Local connectome phenotypes predict social, health, and cognitive factors
Network Neuroscience
Local connectome
White matter
Individual differences
Behavior prediction
Structural connectivity
title Local connectome phenotypes predict social, health, and cognitive factors
title_full Local connectome phenotypes predict social, health, and cognitive factors
title_fullStr Local connectome phenotypes predict social, health, and cognitive factors
title_full_unstemmed Local connectome phenotypes predict social, health, and cognitive factors
title_short Local connectome phenotypes predict social, health, and cognitive factors
title_sort local connectome phenotypes predict social health and cognitive factors
topic Local connectome
White matter
Individual differences
Behavior prediction
Structural connectivity
url https://www.mitpressjournals.org/doi/pdf/10.1162/NETN_a_00031
work_keys_str_mv AT michaelapowell localconnectomephenotypespredictsocialhealthandcognitivefactors
AT javierogarcia localconnectomephenotypespredictsocialhealthandcognitivefactors
AT fangchengyeh localconnectomephenotypespredictsocialhealthandcognitivefactors
AT jeanmvettel localconnectomephenotypespredictsocialhealthandcognitivefactors
AT timothyverstynen localconnectomephenotypespredictsocialhealthandcognitivefactors