Tijms Betty M, Sprooten Emma, Job Dominic, Johnstone Eve C, Owens David G C, Willshaw David, Seriès Peggy, Lawrie Stephen M
Division of Psychiatry, School of Clinical Sciences, University of Edinburgh, Royal Edinburgh Hospital, Morningside, EH10 5HF Edinburgh, UK; School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK; Alzheimer Centre and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Centre, 1081 HZ Amsterdam, The Netherlands.
Division of Psychiatry, School of Clinical Sciences, University of Edinburgh, Royal Edinburgh Hospital, Morningside, EH10 5HF Edinburgh, UK; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Schizophr Res. 2015 Oct;168(1-2):1-8. doi: 10.1016/j.schres.2015.08.025. Epub 2015 Aug 30.
Grey matter brain networks are disrupted in schizophrenia, but it is still unclear at which point during the development of the illness these disruptions arise and whether these can be associated with behavioural predictors of schizophrenia. We investigated if single-subject grey matter networks were disrupted in a sample of people at familial risk of schizophrenia. Single-subject grey matter networks were extracted from structural MRI scans of 144 high risk subjects, 32 recent-onset patients and 36 healthy controls. The following network properties were calculated: size, connectivity density, degree, path length, clustering coefficient, betweenness centrality and small world properties. People at risk of schizophrenia showed decreased path length and clustering in mostly prefrontal and temporal areas. Within the high risk sample, the path length of the posterior cingulate cortex and the betweenness centrality of the left inferior frontal operculum explained 81% of the variance in schizotypal cognitions, which was previously shown to be the strongest behavioural predictor of schizophrenia in the study. In contrast, local grey matter volume measurements explained 48% of variance in schizotypy. The present results suggest that single-subject grey matter networks can quantify behaviourally relevant biological alterations in people at increased risk for schizophrenia before disease onset.
灰质脑网络在精神分裂症中会受到破坏，但目前仍不清楚这些破坏在疾病发展的哪个阶段出现，以及它们是否与精神分裂症的行为预测指标相关。我们调查了精神分裂症家族风险人群样本中的单受试者灰质网络是否受到破坏。从144名高危受试者、32名近期发病患者和36名健康对照的结构MRI扫描中提取单受试者灰质网络。计算了以下网络属性：大小、连接密度、度、路径长度、聚类系数、介数中心性和小世界属性。精神分裂症风险人群在大多数前额叶和颞叶区域的路径长度和聚类减少。在高危样本中，后扣带回皮质的路径长度和左侧额下回岛盖的介数中心性解释了分裂型认知中81%的方差，在该研究中，分裂型认知先前被证明是精神分裂症最强的行为预测指标。相比之下，局部灰质体积测量解释了分裂型人格中48%的方差。目前的结果表明，单受试者灰质网络可以量化疾病发作前精神分裂症风险增加人群中与行为相关的生物学改变。
