Disturbed hippocampal intra-network in first-episode of drug-naïve major depressive disorder

Keita Watanabe; Naomichi Okamoto; Issei Ueda; Hirofumi Tesen; Rintaro Fujii; Atsuko Ikenouchi; Reiji Yoshimura; Shingo Kakeda

doi:10.1093/braincomms/fcac323

Disturbed hippocampal intra-network in first-episode of drug-naïve major depressive disorder

Brain Commun. 2022 Dec 8;5(1):fcac323. doi: 10.1093/braincomms/fcac323. eCollection 2023.

Authors

Keita Watanabe¹, Naomichi Okamoto², Issei Ueda³, Hirofumi Tesen², Rintaro Fujii², Atsuko Ikenouchi², Reiji Yoshimura², Shingo Kakeda³

Affiliations

¹ Open Innovation Institute, Kyoto University, Kyoto 6068501, Japan.
² Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu 8078555, Japan.
³ Department of Radiology, Graduate School of Medicine, Hirosaki University, Hirosaki 0368502, Japan.

Abstract

Complex networks inside the hippocampus could provide new insights into hippocampal abnormalities in various psychiatric disorders and dementia. However, evaluating intra-networks in the hippocampus using MRI is challenging. Here, we employed a high spatial resolution of conventional structural imaging and independent component analysis to investigate intra-networks structural covariance in the hippocampus. We extracted the intra-networks based on the intrinsic connectivity of each 0.9 mm isotropic voxel to every other voxel using a data-driven approach. With a total volume of 3 cc, the hippocampus contains 4115 voxels for a 0.9 mm isotropic voxel size or 375 voxels for a 2 mm isotropic voxel of high-resolution functional or diffusion tensor imaging. Therefore, the novel method presented in the current study could evaluate the hippocampal intra-networks in detail. Furthermore, we investigated the abnormality of the intra-networks in major depressive disorders. A total of 77 patients with first-episode drug-naïve major depressive disorder and 79 healthy subjects were recruited. The independent component analysis extracted seven intra-networks from hippocampal structural images, which were divided into four bilateral networks and three networks along the longitudinal axis. A significant difference was observed in the bilateral hippocampal tail network between patients with major depressive disorder and healthy subjects. In the logistic regression analysis, two bilateral networks were significant predictors of major depressive disorder, with an accuracy of 78.1%. In conclusion, we present a novel method for evaluating intra-networks in the hippocampus. One advantage of this method is that a detailed network can be estimated using conventional structural imaging. In addition, we found novel bilateral networks in the hippocampus that were disturbed in patients with major depressive disorders, and these bilateral networks could predict major depressive disorders.

Keywords: hippocampal network; hippocampus; major depression; structural covariance network.