Grey matter networks in people at increased familial risk for schizophrenia.
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.
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. Electronic address: betty.tijms@gmail.com.
Betty M
BM
Tijms
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.
Emma
E
Sprooten
Division of Clinical Neurosciences, Brain Research Imaging Centre (BRIC), University of Edinburgh, Edinburgh EH4 2XU, UK.
Dominic
D
Job
Division of Psychiatry, School of Clinical Sciences, University of Edinburgh, Royal Edinburgh Hospital, Morningside, EH10 5HF Edinburgh, UK.
Eve C
EC
Johnstone
Division of Psychiatry, School of Clinical Sciences, University of Edinburgh, Royal Edinburgh Hospital, Morningside, EH10 5HF Edinburgh, UK.
David G C
DG
Owens
School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK.
David
D
Willshaw
School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK.
Peggy
P
Seriès
Division of Psychiatry, School of Clinical Sciences, University of Edinburgh, Royal Edinburgh Hospital, Morningside, EH10 5HF Edinburgh, UK.
Stephen M
SM
Lawrie
D000293:Adolescent; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping; D005260:Female; D066128:Gray Matter / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D009415:Nerve Net / Q000473:pathology*; D062706:Prodromal Symptoms; D011569:Psychiatric Status Rating Scales; D012306:Risk; D012559:Schizophrenia / Q000235:genetics / Q000473:pathology*; D055815:Young Adult
Graph theory; High risk; Neuroimaging; Psychiatry; Schizophrenia; Structural MRI