Worbe Yulia
Department of Neurology, Pitié-Salpêtrière Hospital Sorbonne Universités, UPMC Université Paris 06, ICM, Paris, France.
Curr Opin Neurol. 2015 Aug;28(4):358-64. doi: 10.1097/WCO.0000000000000220.
This review focuses on new findings related to structural and functional changes in large-scale brain networks (i.e., pathoconnectomics) that occur in neuropsychiatric disorders, particularly Parkinson's disease, Alzheimer's disease and Gilles de la Tourette syndrome.
Many neuropsychiatric disorders involve dysfunction in neuronal regions that are defined as hubs; these regions are pivotal regions of information transfer in large-scale brain networks. In neuropsychiatric disorders with neurodevelopmental mechanisms, the common connectivity profiles remain unclear as both hyper- and hypoconnectivity profiles have been reported. Neurodegenerative neuropsychiatric disorders are commonly characterized by a diminished local and global efficiency of large-scale brain networks.
The connectome, which is largely underpinned by the network science and graph theoretical approaches, is relevant to the field of neuropsychiatry and could be successfully used for the differential diagnosis for neuropsychiatric disorders and for predicting the progression of such disorders. The analysis of large-scale brain network dynamics generates new insights into the mechanisms of action of invasive and noninvasive brain stimulation and guides further investigations of new therapeutic targets.
本综述聚焦于神经精神疾病，特别是帕金森病、阿尔茨海默病和抽动秽语综合征中大规模脑网络（即病理连接组学）结构和功能变化的新发现。
许多神经精神疾病涉及被定义为枢纽的神经元区域功能障碍；这些区域是大规模脑网络中信息传递的关键区域。在具有神经发育机制的神经精神疾病中，由于既有超连接性又有低连接性的报道，常见的连接模式仍不明确。神经退行性神经精神疾病的共同特征通常是大规模脑网络的局部和全局效率降低。
连接组在很大程度上以网络科学和图论方法为基础，与神经精神病学领域相关，可成功用于神经精神疾病的鉴别诊断和预测此类疾病的进展。对大规模脑网络动力学的分析为侵入性和非侵入性脑刺激的作用机制提供了新见解，并指导对新治疗靶点的进一步研究。
