The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Schizophr Bull. 2023 Nov 29;49(6):1554-1567. doi: 10.1093/schbul/sbad047.
Schizophrenia is a multidimensional disease. This study proposes a new research framework that combines multimodal meta-analysis and genetic/molecular architecture to solve the consistency in neuroimaging biomarkers of schizophrenia and whether these link to molecular genetics.
We systematically searched Web of Science, PubMed, and BrainMap for the amplitude of low-frequency fluctuations (ALFF) or fractional ALFF, regional homogeneity, regional cerebral blood flow, and voxel-based morphometry analysis studies investigating schizophrenia. The pooled-modality, single-modality, and illness duration-dependent meta-analyses were performed using the activation likelihood estimation algorithm. Subsequently, Spearman correlation and partial least squares regression analyses were conducted to assess the relationship between identified reliable convergent patterns of multimodality and neurotransmitter/transcriptome, using prior molecular imaging and brain-wide gene expression.
In total, 203 experiments comprising 10 613 patients and 10 461 healthy controls were included. Multimodal meta-analysis showed that brain regions of significant convergence in schizophrenia were mainly distributed in the frontotemporal cortex, anterior cingulate cortex, insula, thalamus, striatum, and hippocampus. Interestingly, the analyses of illness-duration subgroups identified aberrant functional and structural evolutionary patterns: Lines from the striatum to the cortical core networks to extensive cortical and subcortical regions. Subsequently, we found that these robust multimodal neuroimaging abnormalities were associated with multiple neurobiological abnormalities, such as dopaminergic, glutamatergic, serotonergic, and GABAergic systems.
This work links transcriptome/neurotransmitters with reliable structural and functional signatures of brain abnormalities underlying disease effects in schizophrenia, which provides novel insight into the understanding of schizophrenia pathophysiology and targeted treatments.
精神分裂症是一种多维疾病。本研究提出了一种新的研究框架，将多模态荟萃分析与遗传/分子结构相结合，以解决精神分裂症神经影像学生物标志物的一致性问题，以及这些标志物是否与分子遗传学相关。
我们系统地在 Web of Science、PubMed 和 BrainMap 中搜索了振幅低频波动（ALFF）或分数 ALFF、局部一致性、局部脑血流和体素形态学分析研究精神分裂症的研究。使用激活似然估计算法进行了联合模态、单一模态和疾病持续时间依赖的荟萃分析。随后，进行了 Spearman 相关和偏最小二乘回归分析，以评估识别的多模态可靠收敛模式与神经递质/转录组之间的关系，使用先前的分子成像和全脑基因表达。
共纳入了 203 项实验，包含 10613 名患者和 10461 名健康对照者。多模态荟萃分析显示，精神分裂症中具有显著一致性的脑区主要分布在前额皮质、前扣带回皮质、岛叶、丘脑、纹状体和海马体。有趣的是，对疾病持续时间亚组的分析确定了异常的功能和结构进化模式：从纹状体到皮质核心网络再到广泛的皮质和皮质下区域的线条。随后，我们发现这些稳健的多模态神经影像学异常与多种神经生物学异常相关，如多巴胺能、谷氨酸能、血清素能和 GABA 能系统。
这项工作将转录组/神经递质与精神分裂症疾病效应下大脑异常的可靠结构和功能特征联系起来，为理解精神分裂症的病理生理学和靶向治疗提供了新的见解。
