Dissociating tinnitus patients from healthy controls using resting-state cyclicity analysis and clustering

Benjamin J. Zimmerman, Ivan Abraham, Sara A. Schmidt, Yuliy Baryshnikov, Fatima T. Husain

Research output: Contribution to journalArticlepeer-review

Abstract

Chronic tinnitus is a common and sometimes debilitating condition that lacks scientific consensus on physiological models of how the condition arises as well as any known cure. In this study, we applied a novel cyclicity analysis, which studies patterns of leader-follower relationships between two signals, to resting-state functional magnetic resonance imaging (rs-fMRI) data of brain regions acquired from subjects with and without tinnitus. Using the output from the cyclicity analysis, we were able to differentiate between these two groups with 58–67% accuracy by using a partial least squares discriminant analysis. Stability testing yielded a 70% classification accuracy for identifying individual subjects’ data across sessions 1 week apart. Additional analysis revealed that the pairs of brain regions that contributed most to the dissociation between tinnitus and controls were those connected to the amygdala. In the controls, there were consistent temporal patterns across frontal, parietal, and limbic regions and amygdalar activity, whereas in tinnitus subjects, this pattern was much more variable. Our findings demonstrate a proof-of-principle for the use of cyclicity analysis of rs-fMRI data to better understand functional brain connectivity and to use it as a tool for the differentiation of patients and controls who may differ on specific traits.

Original languageEnglish (US)
Pages (from-to)67-89
Number of pages23
JournalNetwork Neuroscience
Volume3
Issue number1
DOIs
StatePublished - Dec 1 2018

Keywords

  • Classification
  • Cyclicity
  • Resting-state fMRI
  • Tinnitus

ASJC Scopus subject areas

  • Neuroscience(all)
  • Computer Science Applications
  • Artificial Intelligence
  • Applied Mathematics

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