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Network Clustering of Cognition and Clinical Symptoms in Psychosis - 25 May 2022

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Abstract: Unsupervised learning techniques have been applied to psychosis groups in the hope of finding meaningful but undiscovered groupings of patients. A methodological option for unsupervised learning is network-based clustering, which relies on the topology of the data represented as a network. This study used cognitive and symptom data from a cohort of healthy controls and those with a Clinical High Risk of Psychosis to test the validity of graph clustering and to explore the use of a multilayer clustering method for multimodal unsupervised learning. Graph clustering was able to produce results highly similar to k-means clustering and to separate groups into those with significantly different functioning scores. Multilayer clustering was used to tune the similarity of clustering solutions between modalities.

Bio: George Gifford is a postdoctoral researcher working in the Psychosis Studies department. His work involves resting state fMRI, graph analysis, and prediction modelling of multicentre and multimodal data.

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