Fused estimation of sparse connectivity patterns from rest fMRI. Application to comparison of children and adult brains

Pascal Zille, Vince Daniel Calhoun, Julia M. Stephen, Tony W. Wilson, Yu Ping Wang

Research output: Contribution to journalArticle

Abstract

In this work, we consider the problem of estimating multiple sparse, co-activated brain regions from functional magnetic resonance imaging (fMRI) observations belonging to different classes. More precisely, we propose a method to analyze similarities and differences in functional connectivity between children and young adults. Often, analysis is conducted on each class separately, and differences across classes are identified with an additional postprocessing step using adequate statistical tools. Here, we propose to rely on a generalized fused Lasso penalty, which allows us to make use of the entire dataset in order to estimate connectivity patterns that are either shared across classes, or specific to a given group. By using the entire population during the estimation, we hope to increase the power of our analysis. The proposed model falls in the category of population-wise matrix decomposition, and a simple and efficient alternating direction method of multipliers (ADMM) algorithm is introduced to solve the associated optimization problem. After validating our approach on simulated data, experiments are performed on resting-state fMRI imaging from the Philadelphia Neurodevelopmental Cohort (PNC) dataset, comprised of normally developing children from ages 8 to 21. Developmental differences were observed in various brain regions, as a total of 3 class-specific resting-state components were identified. Statistical analysis of the estimated subject-specific features, as well as classification results (based on age groups, up to 81% accuracy, n = 583 samples) related to these components demonstrate that the proposed method is able to properly extract meaningful shared and class-specific sub-networks.

Original languageEnglish (US)
JournalIEEE Transactions on Medical Imaging
DOIs
StateAccepted/In press - Jun 29 2017
Externally publishedYes

Fingerprint

Brain
Magnetic Resonance Imaging
Statistical methods
Decomposition
Imaging techniques
Population
Young Adult
Age Groups
Experiments
Datasets
alachlor
Direction compound

Keywords

  • Brain
  • Brain Development
  • Correlation
  • Data mining
  • Estimation
  • Functional Connectivity
  • Joint Lasso Penalty
  • Matrix decomposition
  • Sparse matrices
  • Sparse Models
  • Time series analysis

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Fused estimation of sparse connectivity patterns from rest fMRI. Application to comparison of children and adult brains. / Zille, Pascal; Calhoun, Vince Daniel; Stephen, Julia M.; Wilson, Tony W.; Wang, Yu Ping.

In: IEEE Transactions on Medical Imaging, 29.06.2017.

Research output: Contribution to journalArticle

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