Connectotyping: model based fingerprinting of the functional connectome: model based fingerprinting of the functional connectome

Oscar Miranda-Dominguez, Brian D Mills, Samuel D Carpenter, Kathleen A Grant, Christopher D Kroenke, Joel T Nigg, Damien A Fair

Research output: Contribution to journalArticlepeer-review

83 Scopus citations

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 (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.

Original languageEnglish (US)
Pages (from-to)e111048
JournalPloS one
Volume9
Issue number11
DOIs
StatePublished - Nov 2014
Externally publishedYes

Keywords

  • Adult
  • Algorithms
  • Animals
  • Connectome/methods
  • Female
  • Frontal Lobe/physiology
  • Humans
  • Image Processing, Computer-Assisted
  • Macaca fascicularis
  • Magnetic Resonance Imaging
  • Male
  • Models, Theoretical
  • Nerve Net/physiology
  • Neural Pathways/physiology
  • Parietal Lobe/physiology
  • Young Adult

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

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