Connectivity in fMRI: Blind Spots and Breakthroughs

Victor Solo, Jean Baptiste Poline, Martin A. Lindquist, Sean L. Simpson, F. Dubois Bowman, Moo K. Chung, Ben Cassidy

Research output: Contribution to journalArticlepeer-review

Abstract

In recent years, driven by scientific and clinical concerns, there has been an increased interest in the analysis of functional brain networks. The goal of these analyses is to better understand how brain regions interact, how this depends upon experimental conditions and behavioral measures and how anomalies (disease) can be recognized. In this paper, we provide, first, a brief review of some of the main existing methods of functional brain network analysis. But rather than compare them, as a traditional review would do, instead, we draw attention to their significant limitations and blind spots. Then, second, relevant experts, sketch a number of emerging methods, which can break through these limitations. In particular we discuss five such methods. The first two, stochastic block models and exponential random graph models, provide an inferential basis for network analysis lacking in the exploratory graph analysis methods. The other three addresses: network comparison via persistent homology, time-varying connectivity that distinguishes sample fluctuations from neural fluctuations, and network system identification that draws inferential strength from temporal autocorrelation.

Original languageEnglish (US)
Pages (from-to)1537-1550
Number of pages14
JournalIEEE transactions on medical imaging
Volume37
Issue number7
DOIs
StatePublished - Jul 2018

Keywords

  • ERGM
  • causality
  • fMRI
  • graph
  • small world network
  • system identification
  • time-varying
  • topological data analysis

ASJC Scopus subject areas

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

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