A common brain network among state, trait, and pathological anxiety from whole-brain functional connectivity

Yu Takagi, Yuki Sakai, Yoshinari Abe, Seiji Nishida, Ben J. Harrison, Ignacio Martínez-Zalacaín, Carles Soriano-Mas, Jin Narumoto, Saori C. Tanaka

Research output: Contribution to journalArticleResearchpeer-review

16 Citations (Scopus)

Abstract

© 2018 The Authors Anxiety is one of the most common mental states of humans. Although it drives us to avoid frightening situations and to achieve our goals, it may also impose significant suffering and burden if it becomes extreme. Because we experience anxiety in a variety of forms, previous studies investigated neural substrates of anxiety in a variety of ways. These studies revealed that individuals with high state, trait, or pathological anxiety showed altered neural substrates. However, no studies have directly investigated whether the different dimensions of anxiety share a common neural substrate, despite its theoretical and practical importance. Here, we investigated a brain network of anxiety shared by different dimensions of anxiety in a unified analytical framework using functional magnetic resonance imaging (fMRI). We analyzed different datasets in a single scale, which was defined by an anxiety-related brain network derived from whole brain. We first conducted the anxiety provocation task with healthy participants who tended to feel anxiety related to obsessive-compulsive disorder (OCD) in their daily life. We found a common state anxiety brain network across participants (1585 trials obtained from 10 participants). Then, using the resting-state fMRI in combination with the participants' behavioral trait anxiety scale scores (879 participants from the Human Connectome Project), we demonstrated that trait anxiety shared the same brain network as state anxiety. Furthermore, the brain network between common to state and trait anxiety could detect patients with OCD, which is characterized by pathological anxiety-driven behaviors (174 participants from multi-site datasets). Our findings provide direct evidence that different dimensions of anxiety have a substantial biological inter-relationship. Our results also provide a biologically defined dimension of anxiety, which may promote further investigation of various human characteristics, including psychiatric disorders, from the perspective of anxiety.
Original languageEnglish
Pages (from-to)506-516
JournalNeuroImage
Volume172
DOIs
Publication statusPublished - 15 May 2018

Keywords

  • Anxiety
  • Data-driven approach
  • Dimensional psychiatry
  • Functional connectivity fMRI
  • Human connectome project
  • Machine learning

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