Frontiers in Psychiatry (Jun 2021)

Common Brain Networks Between Major Depressive-Disorder Diagnosis and Symptoms of Depression That Are Validated for Independent Cohorts

  • Ayumu Yamashita,
  • Yuki Sakai,
  • Takashi Yamada,
  • Takashi Yamada,
  • Noriaki Yahata,
  • Noriaki Yahata,
  • Noriaki Yahata,
  • Noriaki Yahata,
  • Akira Kunimatsu,
  • Akira Kunimatsu,
  • Naohiro Okada,
  • Naohiro Okada,
  • Takashi Itahashi,
  • Ryuichiro Hashimoto,
  • Ryuichiro Hashimoto,
  • Ryuichiro Hashimoto,
  • Hiroto Mizuta,
  • Naho Ichikawa,
  • Masahiro Takamura,
  • Go Okada,
  • Hirotaka Yamagata,
  • Kenichiro Harada,
  • Koji Matsuo,
  • Saori C. Tanaka,
  • Mitsuo Kawato,
  • Mitsuo Kawato,
  • Kiyoto Kasai,
  • Kiyoto Kasai,
  • Kiyoto Kasai,
  • Nobumasa Kato,
  • Nobumasa Kato,
  • Hidehiko Takahashi,
  • Hidehiko Takahashi,
  • Yasumasa Okamoto,
  • Okito Yamashita,
  • Okito Yamashita,
  • Hiroshi Imamizu,
  • Hiroshi Imamizu

DOI
https://doi.org/10.3389/fpsyt.2021.667881
Journal volume & issue
Vol. 12

Abstract

Read online

Large-scale neuroimaging data acquired and shared by multiple institutions are essential to advance neuroscientific understanding of pathophysiological mechanisms in psychiatric disorders, such as major depressive disorder (MDD). About 75% of studies that have applied machine learning technique to neuroimaging have been based on diagnoses by clinicians. However, an increasing number of studies have highlighted the difficulty in finding a clear association between existing clinical diagnostic categories and neurobiological abnormalities. Here, using resting-state functional magnetic resonance imaging, we determined and validated resting-state functional connectivity related to depression symptoms that were thought to be directly related to neurobiological abnormalities. We then compared the resting-state functional connectivity related to depression symptoms with that related to depression diagnosis that we recently identified. In particular, for the discovery dataset with 477 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a brain network prediction model of depression symptoms (Beck Depression Inventory-II [BDI] score). The prediction model significantly predicted BDI score for an independent validation dataset with 439 participants from 4 different imaging sites. Finally, we found 3 common functional connections between those related to depression symptoms and those related to MDD diagnosis. These findings contribute to a deeper understanding of the neural circuitry of depressive symptoms in MDD, a hetero-symptomatic population, revealing the neural basis of MDD.

Keywords