非负矩阵分解揭示首发izophrenia 谱系障碍的静息态皮质α网络异常。
Non-negative Matrix Factorization Reveals Resting-State Cortical Alpha Network Abnormalities in the First-Episode Schizophrenia Spectrum.
机构信息
Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania.
Clinical Neurophysiology Research Laboratory, Western Psychiatric Institute and Clinic, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
出版信息
Biol Psychiatry Cogn Neurosci Neuroimaging. 2020 Oct;5(10):961-970. doi: 10.1016/j.bpsc.2019.06.010. Epub 2019 Jul 6.
BACKGROUND
Little is known about neural oscillatory dynamics in first-episode psychosis. Pathophysiology of functional connectivity can be measured through network activity of alpha oscillations, reflecting long-range communication between distal brain regions.
METHODS
Resting magnetoencephalographic activity was collected from 31 individuals with first-episode schizophrenia spectrum psychosis and 22 healthy control individuals. Activity was projected to the realistic cortical surface, based on structural magnetic resonance imaging. The first principal component of activity in 40 Brodmann areas per hemisphere was Hilbert transformed within the alpha range. Non-negative matrix factorization was applied to single-trial alpha phase-locking values from all subjects to determine alpha networks. Within networks, energy and entropy were compared.
RESULTS
Four cortical alpha networks were pathological in individuals with first-episode schizophrenia spectrum psychosis. The networks involved the bilateral anterior and posterior cingulate; left auditory, medial temporal, and cingulate cortex; right inferior frontal gyrus and widespread areas; and right posterior parietal cortex and widespread areas. Energy and entropy were associated with the Positive and Negative Syndrome Scale total and thought disorder factors for the first three networks. In addition, the left posterior temporal network was associated with positive and negative factors, and the right inferior frontal network was associated with the positive factor.
CONCLUSIONS
Machine learning network analysis of resting alpha-band neural activity identified several aberrant networks in individuals with first-episode schizophrenia spectrum psychosis, including the left temporal, right inferior frontal, right posterior parietal, and bilateral cingulate cortices. Abnormal long-range alpha communication is evident at the first presentation for psychosis and may provide clues about mechanisms of dysconnectivity in psychosis and novel targets for noninvasive brain stimulation.
背景
对于首发精神分裂症患者的神经振荡动力学，人们知之甚少。功能连接的病理生理学可以通过α振荡的网络活动来测量，这反映了远隔脑区之间的长程通讯。
方法
从 31 名首发精神分裂症谱系精神病患者和 22 名健康对照个体中采集静息脑磁图活动。基于结构磁共振成像，将活动投影到真实的皮质表面。每侧半球 40 个布罗德曼区的活动的第一主成分在α频带内被希尔伯特变换。对所有受试者的单次试验α锁相值应用非负矩阵分解以确定α网络。在网络内，比较能量和熵。
结果
首发精神分裂症谱系精神病患者存在 4 个皮质α网络异常。这些网络涉及双侧前扣带回和后扣带回；左听觉、内侧颞叶和扣带回皮质；右额下回和广泛区域；以及右顶后皮质和广泛区域。能量和熵与阳性和阴性症状量表总分以及前三个网络的思维障碍因子有关。此外，左侧颞后网络与阳性和阴性因子有关，右侧额下回网络与阳性因子有关。
结论
对静息α频带神经活动的机器学习网络分析在首发精神分裂症谱系精神病患者中识别出了几个异常网络，包括左侧颞叶、右侧额下回、右侧顶后皮质和双侧扣带回。在精神病的首次发作时就出现了异常的长程α通讯，这可能为精神病的去连接机制提供线索，并为非侵入性脑刺激提供新的靶点。
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