Distinct structural deficits in treatment-resistant schizophrenia and their putative neurotransmitter basis: a source-based morphometry analysis

难治性精神分裂症的独特结构缺陷及其假定的神经递质基础:基于来源的形态测量分析

阅读:1

Abstract

Schizophrenia is associated with widespread gray matter reduction. This is influenced by the underlying connectivity, resulting in covarying patterns of structural changes that are more pronounced in treatment-resistant individuals. However, it remains uncertain whether a distinct network of brain regions, with specific neurotransmitter basis, forms the substrate for treatment resistance in schizophrenia. We investigated the structural covariance networks (SCN) in 198 individuals; 55 with treatment-resistant schizophrenia (TRS) and 79 without TRS (non-TRS) in active symptomatic phase, and 64 healthy controls (HC) using Calhoun's Source-Based Morphometry. We mapped the putative neurotransmitter basis of the SCNs using a PET-based chemoarchitectural atlas. Twelve independent components (i.e., SCNs) were identified. A prefrontal-limbic SCN had lower gray matter volume (GMV) in TRS compared to HC and non-TRS (F = 7.757, p < 0.001, FDR-corrected). Spatial correlation with chemoarchitectural atlas revealed predominant contributions from serotonergic [5HT(1b) and 5HT(2a)], glutamatergic [mGluR(5)], histaminergic [H(3)], and opioid [MOR] receptors for this TRS-related SCN (all p(spin-permutation) < 0.05, FDR-corrected). A different SCN comprised of dorsal fronto-temporal and parieto-occipital regions, not associated with  any specific neurotransmitter distribution, exhibited reduced GMV in both TRS and non-TRS groups vs. HC (F = 7.239, p < 0.001, FDR-corrected). Amidst the generic GMV reduction that is shared with non-TRS patients, patients with TRS have specific prefrontal-limbic structural deficits with a unique non-dopaminergic chemoarchitecture. These findings indicate a putative molecular and structural basis for poor treatment response, guiding the development of second- and third-line pharmacotherapies for TRS.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。