Connectotyping: Model based fingerprinting of the functional connectome

Research output: Contribution to journalArticle

47 Citations (Scopus)

Abstract

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 (rsfcMRI) 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.

Original languageEnglish (US)
Article numbere111048
JournalPLoS One
Volume9
Issue number11
DOIs
StatePublished - Nov 11 2014

Fingerprint

Connectome
Parietal Lobe
Brain
Dermatoglyphics
Frontal Lobe
Primates
Linear Models
Magnetic Resonance Imaging
brain
cortex
linear models

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Connectotyping : Model based fingerprinting of the functional connectome. / Miranda Dominguez, Oscar; Mills, Brian D.; Carpenter, Samuel D.; Grant, Kathleen (Kathy); Kroenke, Christopher (Chris); Nigg, Joel; Fair, Damien.

In: PLoS One, Vol. 9, No. 11, e111048, 11.11.2014.

Research output: Contribution to journalArticle

@article{c7bbdfbd330f4fa5bcfcafb1563f8b96,
title = "Connectotyping: Model based fingerprinting of the functional connectome",
abstract = "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 (rsfcMRI) 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.",
author = "{Miranda Dominguez}, Oscar and Mills, {Brian D.} and Carpenter, {Samuel D.} and Grant, {Kathleen (Kathy)} and Kroenke, {Christopher (Chris)} and Joel Nigg and Damien Fair",
year = "2014",
month = "11",
day = "11",
doi = "10.1371/journal.pone.0111048",
language = "English (US)",
volume = "9",
journal = "PLoS One",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "11",

}

TY - JOUR

T1 - Connectotyping

T2 - Model based fingerprinting of the functional connectome

AU - Miranda Dominguez, Oscar

AU - Mills, Brian D.

AU - Carpenter, Samuel D.

AU - Grant, Kathleen (Kathy)

AU - Kroenke, Christopher (Chris)

AU - Nigg, Joel

AU - Fair, Damien

PY - 2014/11/11

Y1 - 2014/11/11

N2 - 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 (rsfcMRI) 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.

AB - 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 (rsfcMRI) 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.

UR - http://www.scopus.com/inward/record.url?scp=84911469145&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84911469145&partnerID=8YFLogxK

U2 - 10.1371/journal.pone.0111048

DO - 10.1371/journal.pone.0111048

M3 - Article

C2 - 25386919

AN - SCOPUS:84911469145

VL - 9

JO - PLoS One

JF - PLoS One

SN - 1932-6203

IS - 11

M1 - e111048

ER -