|Title:||Disruption of brain anatomical networks in schizophrenia: A longitudinal, diffusion tensor imaging based study|
|Alternate Journal:||Schizophrenia research|
|Keywords:||Adult Brain/*diagnostic imaging Case-Control Studies Connectome Diffusion Tensor Imaging/*methods Female Humans Longitudinal Studies Male Middle Aged Neural Pathways/*diagnostic imaging Psychiatric Status Rating Scales Schizophrenia/diagnostic imaging/*pathology Young Adult Anatomical networks Diffusion tensor imaging (DTI) Graph theory Longitudinal Schizophrenia|
|Abstract:||Despite convergent neuroimaging evidence indicating a wide range of brain abnormalities in schizophrenia, our understanding of alterations in the topological architecture of brain anatomical networks and how they are modulated over time, is still rudimentary. Here, we employed graph theoretical analysis of longitudinal diffusion tensor imaging data (DTI) over a 5-year period to investigate brain network topology in schizophrenia and its relationship with clinical manifestations of the illness. Using deterministic tractography, weighted brain anatomical networks were constructed from 31 patients experiencing schizophrenia and 28 age- and gender-matched healthy control subjects. Although the overall small-world characteristics were observed at both baseline and follow-up, a scan-point independent significant deficit of global integration was found in patients compared to controls, suggesting dysfunctional integration of the brain and supporting the notion of schizophrenia as a disconnection syndrome. Specifically, several brain regions (e.g., the inferior frontal gyrus and the bilateral insula) that are crucial for cognitive and emotional integration were aberrant. Furthermore, a significant group-by-longitudinal scan interaction was revealed in the characteristic path length and global efficiency, attributing to a progressive aberration of global integration in patients compared to healthy controls. Moreover, the progressive disruptions of the brain anatomical network topology were associated with the clinical symptoms of the patients. Together, our findings provide insights into the substrates of anatomical dysconnectivity patterns for schizophrenia and highlight the potential for connectome-based metrics as neural markers of illness progression and clinical change with treatment.|
|Notes:||1573-2509 Sun, Yu Chen, Yu Lee, Renick Bezerianos, Anastasios Collinson, Simon L Sim, Kang Journal Article Research Support, Non-U.S. Gov't Netherlands Schizophr Res. 2016 Mar;171(1-3):149-57. doi: 10.1016/j.schres.2016.01.025. Epub 2016 Jan 19.|
|Authors Address:||Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore. Electronic address: email@example.com. Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore. Department of Bioengineering, National University of Singapore, Singapore. Department of Psychology, National University of Singapore, Singapore. Department of General Psychiatry, Institute of Mental Health (IMH), Singapore; Department of Research, Institute of Mental Health (IMH), Singapore.|
|Appears in Collections:||2016|
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