AUTHOR=Yamashita Ayumu , Sakai Yuki , Yamada Takashi , Yahata Noriaki , Kunimatsu Akira , Okada Naohiro , Itahashi Takashi , Hashimoto Ryuichiro , Mizuta Hiroto , Ichikawa Naho , Takamura Masahiro , Okada Go , Yamagata Hirotaka , Harada Kenichiro , Matsuo Koji , Tanaka Saori C. , Kawato Mitsuo , Kasai Kiyoto , Kato Nobumasa , Takahashi Hidehiko , Okamoto Yasumasa , Yamashita Okito , Imamizu Hiroshi TITLE=Common Brain Networks Between Major Depressive-Disorder Diagnosis and Symptoms of Depression That Are Validated for Independent Cohorts JOURNAL=Frontiers in Psychiatry VOLUME=12 YEAR=2021 URL=https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2021.667881 DOI=10.3389/fpsyt.2021.667881 ISSN=1664-0640 ABSTRACT=

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.