Jensen Kyle M, King Tricia Z, Andrés-Camazón Pablo, Calhoun Vince D, Iraji Armin
Georgia State University, Atlanta, GA, USA.
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA.
medRxiv. 2025 Jun 23:2025.06.19.25329865. doi: 10.1101/2025.06.19.25329865.
Schizophrenia is extremely heterogenous, and the underlying brain mechanisms are not fully understood. Many attempts have been made to substantiate and delineate the relationship between schizophrenia and the brain through unbiased exploratory investigations of resting-state functional magnetic resonance imaging (rs-fMRI). The results of numerous data-driven rs-fMRI studies have converged in support of the disconnection hypothesis framework, reporting aberrant connectivity in cortical-subcortical-cerebellar circuitry. However, this model is vague and underspecified, encompassing a vast array of findings across studies. It is necessary to further refine this model to identify consistent patterns and establish stable imaging markers of schizophrenia and psychosis. The organizational structure of the NeuroMark atlas is especially well-equipped for describing functional units derived through independent component analysis (ICA) and uniting findings across studies utilizing data-driven whole-brain functional connectivity (FC) to characterize schizophrenia and psychosis. Towards this goal, a systematic literature review was conducted on primary empirical articles published in English in peer-reviewed journals between January 2019 - February 2025 which utilized cortical-subcortical-cerebellar terminology to describe schizophrenia-control comparisons of whole-brain FC in human rs-fMRI. The electronic databases utilized included Google scholar, PubMed, and APA PsycInfo, and search terms included ("schizophrenia" OR "psychosis") AND "resting-state fMRI" AND ("cortical-subcortical-cerebellar" OR "cerebello-thalamo-cortical"). 10 studies were identified and NeuroMark nomenclature was utilized to describe findings within a common reference space. The most consistent patterns included cerebellar-thalamic hypoconnectivity, cerebellar-cortical (sensorimotor & insular-temporal) hyperconnectivity, subcortical (basal ganglia & thalamic) - cortical (sensorimotor, temporoparietal, insular-temporal, occipitotemporal, & occipital) hyperconnectivity, and cortical-cortical (insular-temporal & occipitotemporal) hypoconnectivity. Patterns implicating prefrontal cortex are largely inconsistent across studies and may not be effective targets for establishing stable imaging markers based on static FC in rs-fMRI. Instead, adapting new analytical strategies, or focusing on nodes in the cerebellum, thalamus, and primary motor and sensory cortex may prove to be a more effective approach.
精神分裂症具有极高的异质性,其潜在的大脑机制尚未完全明确。人们已进行了诸多尝试,通过对静息态功能磁共振成像(rs-fMRI)进行无偏探索性研究,来证实并描绘精神分裂症与大脑之间的关系。大量基于数据驱动的rs-fMRI研究结果均支持断开连接假说框架,报告了皮质-皮质下-小脑回路中存在异常连接。然而,该模型模糊且不够具体,涵盖了众多研究中的大量发现。有必要进一步完善该模型,以识别出一致的模式,并建立精神分裂症和精神病的稳定成像标志物。NeuroMark图谱的组织结构特别适合描述通过独立成分分析(ICA)得出的功能单元,并整合利用数据驱动的全脑功能连接(FC)来表征精神分裂症和精神病的各项研究结果。为实现这一目标,我们对2019年1月至2025年2月期间在同行评审期刊上发表的、使用皮质-皮质下-小脑术语描述人类rs-fMRI中全脑FC的精神分裂症对照比较的英文实证文章进行了系统的文献综述。所使用的电子数据库包括谷歌学术、PubMed和APA PsycInfo,搜索词包括(“精神分裂症”或“精神病”)以及“静息态fMRI”以及(“皮质-皮质下-小脑”或“小脑-丘脑-皮质”)。共识别出10项研究,并使用NeuroMark命名法在共同的参考空间内描述研究结果。最一致的模式包括小脑-丘脑低连接性、小脑-皮质(感觉运动和岛叶-颞叶)高连接性、皮质下(基底神经节和丘脑)-皮质(感觉运动、颞顶叶、岛叶-颞叶、枕颞叶和枕叶)高连接性以及皮质-皮质(岛叶-颞叶和枕颞叶)低连接性。涉及前额叶皮质的模式在各项研究中大多不一致,可能不是基于rs-fMRI中静态FC建立稳定成像标志物的有效靶点。相反,采用新的分析策略,或关注小脑、丘脑以及初级运动和感觉皮层中的节点,可能会是一种更有效的方法。