Identifying Sparse Connectivity Patterns in the brain using resting-state fMRI

Harini Eavani, Theodore D. Satterthwaite, Roman Filipovych, Raquel E. Gur, Ruben C. Gur, Christos Davatzikos

Research output: Contribution to journalArticle

Abstract

The human brain processes information via multiple distributed networks. An accurate model of the brain's functional connectome is critical for understanding both normal brain function as well as the dysfunction present in neuropsychiatric illnesses. Current methodologies that attempt to discover the organization of the functional connectome typically assume spatial or temporal separation of the underlying networks. This assumption deviates from an intuitive understanding of brain function, which is that of multiple, inter-dependent spatially overlapping brain networks that efficiently integrate information pertinent to diverse brain functions. It is now increasingly evident that neural systems use parsimonious formations and functional representations to efficiently process information while minimizing redundancy. Hence we exploit recent advances in the mathematics of sparse modeling to develop a methodological framework aiming to understand complex resting-state fMRI connectivity data. By favoring networks that explain the data via a relatively small number of participating brain regions, we obtain a parsimonious representation of brain function in terms of multiple "Sparse Connectivity Patterns" (SCPs), such that differential presence of these SCPs explains inter-subject variability. In this manner the sparsity-based framework can effectively capture the heterogeneity of functional activity patterns across individuals while potentially highlighting multiple sub-populations within the data that display similar patterns. Our results from simulated as well as real resting state fMRI data show that SCPs are accurate and reproducible between sub-samples as well as across datasets. These findings substantiate existing knowledge of intrinsic functional connectivity and provide novel insights into the functional organization of the human brain.

Original languageEnglish (US)
Pages (from-to)286-299
Number of pages14
JournalNeuroImage
Volume105
DOIs
StatePublished - Jan 5 2015
Externally publishedYes

Fingerprint

Magnetic Resonance Imaging
Brain
Connectome
Data Display
Mathematics
Population

Keywords

  • Functional connectivity
  • Resting state fMRI
  • Sparsity

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Neurology

Cite this

Eavani, H., Satterthwaite, T. D., Filipovych, R., Gur, R. E., Gur, R. C., & Davatzikos, C. (2015). Identifying Sparse Connectivity Patterns in the brain using resting-state fMRI. NeuroImage, 105, 286-299. https://doi.org/10.1016/j.neuroimage.2014.09.058

Identifying Sparse Connectivity Patterns in the brain using resting-state fMRI. / Eavani, Harini; Satterthwaite, Theodore D.; Filipovych, Roman; Gur, Raquel E.; Gur, Ruben C.; Davatzikos, Christos.

In: NeuroImage, Vol. 105, 05.01.2015, p. 286-299.

Research output: Contribution to journalArticle

Eavani, H, Satterthwaite, TD, Filipovych, R, Gur, RE, Gur, RC & Davatzikos, C 2015, 'Identifying Sparse Connectivity Patterns in the brain using resting-state fMRI', NeuroImage, vol. 105, pp. 286-299. https://doi.org/10.1016/j.neuroimage.2014.09.058
Eavani H, Satterthwaite TD, Filipovych R, Gur RE, Gur RC, Davatzikos C. Identifying Sparse Connectivity Patterns in the brain using resting-state fMRI. NeuroImage. 2015 Jan 5;105:286-299. https://doi.org/10.1016/j.neuroimage.2014.09.058
Eavani, Harini ; Satterthwaite, Theodore D. ; Filipovych, Roman ; Gur, Raquel E. ; Gur, Ruben C. ; Davatzikos, Christos. / Identifying Sparse Connectivity Patterns in the brain using resting-state fMRI. In: NeuroImage. 2015 ; Vol. 105. pp. 286-299.
@article{e414f43b2b2c444da98b14d8030e5589,
title = "Identifying Sparse Connectivity Patterns in the brain using resting-state fMRI",
abstract = "The human brain processes information via multiple distributed networks. An accurate model of the brain's functional connectome is critical for understanding both normal brain function as well as the dysfunction present in neuropsychiatric illnesses. Current methodologies that attempt to discover the organization of the functional connectome typically assume spatial or temporal separation of the underlying networks. This assumption deviates from an intuitive understanding of brain function, which is that of multiple, inter-dependent spatially overlapping brain networks that efficiently integrate information pertinent to diverse brain functions. It is now increasingly evident that neural systems use parsimonious formations and functional representations to efficiently process information while minimizing redundancy. Hence we exploit recent advances in the mathematics of sparse modeling to develop a methodological framework aiming to understand complex resting-state fMRI connectivity data. By favoring networks that explain the data via a relatively small number of participating brain regions, we obtain a parsimonious representation of brain function in terms of multiple {"}Sparse Connectivity Patterns{"} (SCPs), such that differential presence of these SCPs explains inter-subject variability. In this manner the sparsity-based framework can effectively capture the heterogeneity of functional activity patterns across individuals while potentially highlighting multiple sub-populations within the data that display similar patterns. Our results from simulated as well as real resting state fMRI data show that SCPs are accurate and reproducible between sub-samples as well as across datasets. These findings substantiate existing knowledge of intrinsic functional connectivity and provide novel insights into the functional organization of the human brain.",
keywords = "Functional connectivity, Resting state fMRI, Sparsity",
author = "Harini Eavani and Satterthwaite, {Theodore D.} and Roman Filipovych and Gur, {Raquel E.} and Gur, {Ruben C.} and Christos Davatzikos",
year = "2015",
month = "1",
day = "5",
doi = "10.1016/j.neuroimage.2014.09.058",
language = "English (US)",
volume = "105",
pages = "286--299",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Academic Press Inc.",

}

TY - JOUR

T1 - Identifying Sparse Connectivity Patterns in the brain using resting-state fMRI

AU - Eavani, Harini

AU - Satterthwaite, Theodore D.

AU - Filipovych, Roman

AU - Gur, Raquel E.

AU - Gur, Ruben C.

AU - Davatzikos, Christos

PY - 2015/1/5

Y1 - 2015/1/5

N2 - The human brain processes information via multiple distributed networks. An accurate model of the brain's functional connectome is critical for understanding both normal brain function as well as the dysfunction present in neuropsychiatric illnesses. Current methodologies that attempt to discover the organization of the functional connectome typically assume spatial or temporal separation of the underlying networks. This assumption deviates from an intuitive understanding of brain function, which is that of multiple, inter-dependent spatially overlapping brain networks that efficiently integrate information pertinent to diverse brain functions. It is now increasingly evident that neural systems use parsimonious formations and functional representations to efficiently process information while minimizing redundancy. Hence we exploit recent advances in the mathematics of sparse modeling to develop a methodological framework aiming to understand complex resting-state fMRI connectivity data. By favoring networks that explain the data via a relatively small number of participating brain regions, we obtain a parsimonious representation of brain function in terms of multiple "Sparse Connectivity Patterns" (SCPs), such that differential presence of these SCPs explains inter-subject variability. In this manner the sparsity-based framework can effectively capture the heterogeneity of functional activity patterns across individuals while potentially highlighting multiple sub-populations within the data that display similar patterns. Our results from simulated as well as real resting state fMRI data show that SCPs are accurate and reproducible between sub-samples as well as across datasets. These findings substantiate existing knowledge of intrinsic functional connectivity and provide novel insights into the functional organization of the human brain.

AB - The human brain processes information via multiple distributed networks. An accurate model of the brain's functional connectome is critical for understanding both normal brain function as well as the dysfunction present in neuropsychiatric illnesses. Current methodologies that attempt to discover the organization of the functional connectome typically assume spatial or temporal separation of the underlying networks. This assumption deviates from an intuitive understanding of brain function, which is that of multiple, inter-dependent spatially overlapping brain networks that efficiently integrate information pertinent to diverse brain functions. It is now increasingly evident that neural systems use parsimonious formations and functional representations to efficiently process information while minimizing redundancy. Hence we exploit recent advances in the mathematics of sparse modeling to develop a methodological framework aiming to understand complex resting-state fMRI connectivity data. By favoring networks that explain the data via a relatively small number of participating brain regions, we obtain a parsimonious representation of brain function in terms of multiple "Sparse Connectivity Patterns" (SCPs), such that differential presence of these SCPs explains inter-subject variability. In this manner the sparsity-based framework can effectively capture the heterogeneity of functional activity patterns across individuals while potentially highlighting multiple sub-populations within the data that display similar patterns. Our results from simulated as well as real resting state fMRI data show that SCPs are accurate and reproducible between sub-samples as well as across datasets. These findings substantiate existing knowledge of intrinsic functional connectivity and provide novel insights into the functional organization of the human brain.

KW - Functional connectivity

KW - Resting state fMRI

KW - Sparsity

UR - http://www.scopus.com/inward/record.url?scp=84918802470&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84918802470&partnerID=8YFLogxK

U2 - 10.1016/j.neuroimage.2014.09.058

DO - 10.1016/j.neuroimage.2014.09.058

M3 - Article

VL - 105

SP - 286

EP - 299

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

ER -