Quantifying Individual Brain Connectivity with Functional Principal Component Analysis for Networks

Alexander Petersen, Jianyang Zhao, Owen Carmichael, Hans Georg Müller

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


In typical functional connectivity studies, connections between voxels or regions in the brain are represented as edges in a network. Networks for different subjects are constructed at a given graph density and are summarized by some network measure such as path length. Examining these summary measures for many density values yields samples of connectivity curves, one for each individual. This has led to the adoption of basic tools of functional data analysis, most commonly to compare control and disease groups through the average curves in each group. Such group differences, however, neglect the variability in the sample of connectivity curves. In this article, the use of functional principal component analysis (FPCA) is demonstrated to enrich functional connectivity studies by providing increased power and flexibility for statistical inference. Specifically, individual connectivity curves are related to individual characteristics such as age and measures of cognitive function, thus providing a tool to relate brain connectivity with these variables at the individual level. This individual level analysis opens a new perspective that goes beyond previous group level comparisons. Using a large data set of resting-state functional magnetic resonance imaging scans, relationships between connectivity and two measures of cognitive function - episodic memory and executive function - were investigated. The group-based approach was implemented by dichotomizing the continuous cognitive variable and testing for group differences, resulting in no statistically significant findings. To demonstrate the new approach, FPCA was implemented, followed by linear regression models with cognitive scores as responses, identifying significant associations of connectivity in the right middle temporal region with both cognitive scores.

Original languageEnglish (US)
Pages (from-to)540-547
Number of pages8
JournalBrain Connectivity
Issue number7
StatePublished - Sep 1 2016
Externally publishedYes


  • connectivity curves
  • episodic memory
  • executive function
  • fMRI
  • functional data analysis
  • functional principal component analysis
  • network
  • network density
  • resting state

ASJC Scopus subject areas

  • Neuroscience(all)
  • Medicine(all)


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