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Updated: Nov 6, 2025

Design and Implementation of an fMRI Study Examining Thought Suppression in Young Women with, and At-risk, for Depression
Published on: May 19, 2015
Subtypes of depression characterized by different cognitive decline and brain activity alterations
Xiao Yang1, Shile Qi2, Min Wang1
1Psychiatric Laboratory and Mental Health Center, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China; West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
Abstract:
Depression is characterized by the heterogeneity in anti-depressant treatment response and clinical outcomes. Cognitive impairment may be one of the more practically important aspects of depression. A new approach was to identify neuropsychologically derived depression subtypes based on the trajectory of neuro-cognition such as intelligence quotient (IQ) change. We used a classical premorbid IQ prediction algorithm and then compared predicted premorbid IQ with current IQ. IQ change was used to delineate the patterns of neuropsychological heterogeneity within a large dataset consisting of 131 patients with major depressive disorder (MDD) and 165 healthy controls (HCs). Neurocognitive results from CANTAB and 3 T resting-state fMRI data were compared among the subgroups identified. IQ change heterogeneity identified two subgroups within the MDD group: preserved IQ (PIQ) and deteriorated IQ (DIQ) in MDD. The DIQ subgroup was marked by poorer functioning across multiple cognition domains, including increased impairments in motor speed, cognitive flexibility, and catastrophic thinking when compared to PIQ and HCs. Moreover, cognitive performance of patients with DIQ was correlated with IQ decline. Also, increased brain activity of anterior cingulate cortex and medial prefrontal cortex was found in DIQ but not in PIQ and HCs. IQ-based subgroups of depression may be differentially associated with the extent of neurocognitive impairment and brain activities, which suggests that classifying the cognitive heterogeneity associated with depression may provide a platform to better characterize the neurobiological underpinnings of the disease.
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