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Abstract
Quantitative susceptibility mapping is a magnetic resonance imaging technique that measures brain tissues’ magnetic susceptibility, including iron deposition and myelination. This study examines the relationship between subcortical volume and magnetic susceptibility and determines specific differences in these measures among patients with major depressive disorder (MDD), patients with schizophrenia, and healthy controls (HCs). This was a cross-sectional study. Sex- and age- matched patients with MDD (n = 49), patients with schizophrenia (n = 24), and HCs (n = 50) were included. Magnetic resonance imaging was conducted using quantitative susceptibility mapping and T1-weighted imaging to measure subcortical susceptibility and volume. The acquired brain measurements were compared among groups using analyses of variance and post hoc comparisons. Finally, a general linear model examined the susceptibility–volume relationship. Significant group-level differences were found in the magnetic susceptibility of the nucleus accumbens and amygdala (p = 0.045). Post-hoc analyses indicated that the magnetic susceptibility of the nucleus accumbens and amygdala for the MDD group was significantly higher than that for the HC group (p = 0.0054, p = 0.0065, respectively). However, no significant differences in subcortical volume were found between the groups. The general linear model indicated a significant interaction between group and volume for the nucleus accumbens in MDD group but not schizophrenia or HC groups. This study showed susceptibility alterations in the nucleus accumbens and amygdala in MDD patients. A significant relationship was observed between subcortical susceptibility and volume in the MDD group’s nucleus accumbens, which indicated abnormalities in myelination and the dopaminergic system related to iron deposition.
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1 The University of Tokyo, Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, Tokyo, Japan (GRID:grid.26999.3d) (ISNI:0000 0001 2151 536X); Juntendo University, Faculty of Health Science, Department of Radiological Technology, Tokyo, Japan (GRID:grid.258269.2) (ISNI:0000 0004 1762 2738); Tokyo Medical University, Department of Radiology, Tokyo, Japan (GRID:grid.410793.8) (ISNI:0000 0001 0663 3325)
2 Nagoya University Graduate School of Medicine, Department of Integrated Health Sciences, Nagoya, Japan (GRID:grid.27476.30) (ISNI:0000 0001 0943 978X)
3 Keio University School of Medicine, Department of Neuropsychiatry, Tokyo, Japan (GRID:grid.26091.3c) (ISNI:0000 0004 1936 9959)
4 The University of Tokyo, Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, Tokyo, Japan (GRID:grid.26999.3d) (ISNI:0000 0001 2151 536X)
5 The University of Tokyo, Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, Tokyo, Japan (GRID:grid.26999.3d) (ISNI:0000 0001 2151 536X); The University of Tokyo, University of Tokyo Institute for Diversity and Adaptation of Human Mind, Tokyo, Japan (GRID:grid.26999.3d) (ISNI:0000 0001 2151 536X)
6 The University of Tokyo, Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, Tokyo, Japan (GRID:grid.26999.3d) (ISNI:0000 0001 2151 536X); The University of Tokyo, University of Tokyo Institute for Diversity and Adaptation of Human Mind, Tokyo, Japan (GRID:grid.26999.3d) (ISNI:0000 0001 2151 536X); University of Tokyo Institutes for Advanced Study (UTIAS), The International Research Center for Neurointelligence, Tokyo, Japan (GRID:grid.143643.7) (ISNI:0000 0001 0660 6861)