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...
| Autors principals: | , , , , |
|---|---|
| Format: | Article |
| Idioma: | English |
| Publicat: |
The MIT Press
2018-03-01
|
| Col·lecció: | Network Neuroscience |
| Matèries: | |
| Accés en línia: | https://www.mitpressjournals.org/doi/pdf/10.1162/NETN_a_00031 |
| _version_ | 1828320861845716992 |
|---|---|
| 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. |
| first_indexed | 2024-04-13T18:18:19Z |
| format | Article |
| id | doaj.art-aef56c9c764e454fbef6c0cb15a70a41 |
| institution | Directory Open Access Journal |
| issn | 2472-1751 |
| language | English |
| last_indexed | 2024-04-13T18:18:19Z |
| publishDate | 2018-03-01 |
| publisher | The MIT Press |
| record_format | Article |
| series | Network Neuroscience |
| 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 |