Connectotyping: Model Based Fingerprinting of the Functional Connectome

A better characterization of how an individual’s brain is functionally organized will likely bring dramatic advances to many fields of study. Here we show a model-based approach toward characterizing resting state functional connectivity MRI (rs-fcMRI) that is capable of identifying a so-called “con...

Full description

Bibliographic Details
Main Authors:	Miranda-Dominguez, Oscar, Mills, Brian D., Carpenter, Samuel D., Grant, Kathleen A., Kroenke, Christopher D., Nigg, Joel T., Fair, Damien A.
Format:	Online
Language:	English
Published:	Public Library of Science 2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4227655/

id	pubmed-4227655
recordtype	oai_dc
spelling	pubmed-42276552014-11-18 Connectotyping: Model Based Fingerprinting of the Functional Connectome Miranda-Dominguez, Oscar Mills, Brian D. Carpenter, Samuel D. Grant, Kathleen A. Kroenke, Christopher D. Nigg, Joel T. Fair, Damien A. Research Article A better characterization of how an individual’s brain is functionally organized will likely bring dramatic advances to many fields of study. Here we show a model-based approach toward characterizing resting state functional connectivity MRI (rs-fcMRI) that is capable of identifying a so-called “connectotype”, or functional fingerprint in individual participants. The approach rests on a simple linear model that proposes the activity of a given brain region can be described by the weighted sum of its functional neighboring regions. The resulting coefficients correspond to a personalized model-based connectivity matrix that is capable of predicting the timeseries of each subject. Importantly, the model itself is subject specific and has the ability to predict an individual at a later date using a limited number of non-sequential frames. While we show that there is a significant amount of shared variance between models across subjects, the model’s ability to discriminate an individual is driven by unique connections in higher order control regions in frontal and parietal cortices. Furthermore, we show that the connectotype is present in non-human primates as well, highlighting the translational potential of the approach. Public Library of Science 2014-11-11 /pmc/articles/PMC4227655/ /pubmed/25386919 http://dx.doi.org/10.1371/journal.pone.0111048 Text en © 2014 Miranda-Dominguez et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Miranda-Dominguez, Oscar Mills, Brian D. Carpenter, Samuel D. Grant, Kathleen A. Kroenke, Christopher D. Nigg, Joel T. Fair, Damien A.
spellingShingle	Miranda-Dominguez, Oscar Mills, Brian D. Carpenter, Samuel D. Grant, Kathleen A. Kroenke, Christopher D. Nigg, Joel T. Fair, Damien A. Connectotyping: Model Based Fingerprinting of the Functional Connectome
author_facet	Miranda-Dominguez, Oscar Mills, Brian D. Carpenter, Samuel D. Grant, Kathleen A. Kroenke, Christopher D. Nigg, Joel T. Fair, Damien A.
author_sort	Miranda-Dominguez, Oscar
title	Connectotyping: Model Based Fingerprinting of the Functional Connectome
title_short	Connectotyping: Model Based Fingerprinting of the Functional Connectome
title_full	Connectotyping: Model Based Fingerprinting of the Functional Connectome
title_fullStr	Connectotyping: Model Based Fingerprinting of the Functional Connectome
title_full_unstemmed	Connectotyping: Model Based Fingerprinting of the Functional Connectome
title_sort	connectotyping: model based fingerprinting of the functional connectome
description	A better characterization of how an individual’s brain is functionally organized will likely bring dramatic advances to many fields of study. Here we show a model-based approach toward characterizing resting state functional connectivity MRI (rs-fcMRI) that is capable of identifying a so-called “connectotype”, or functional fingerprint in individual participants. The approach rests on a simple linear model that proposes the activity of a given brain region can be described by the weighted sum of its functional neighboring regions. The resulting coefficients correspond to a personalized model-based connectivity matrix that is capable of predicting the timeseries of each subject. Importantly, the model itself is subject specific and has the ability to predict an individual at a later date using a limited number of non-sequential frames. While we show that there is a significant amount of shared variance between models across subjects, the model’s ability to discriminate an individual is driven by unique connections in higher order control regions in frontal and parietal cortices. Furthermore, we show that the connectotype is present in non-human primates as well, highlighting the translational potential of the approach.
publisher	Public Library of Science
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4227655/
_version_	1613154999686135808

Connectotyping: Model Based Fingerprinting of the Functional Connectome

Similar Items