Ramyead, Avinash. Predicting psychosis in at-risk patients using abnormal neural oscillations and synchrony in conjunctions with machine learning algorithms. 2016, PhD Thesis, University of Basel, Faculty of Psychology.
Official URL: http://edoc.unibas.ch/diss/DissB_11592
The present dissertation covers three different studies which, together, demonstrate that neural oscillations are disturbed in emerging psychosis. The first paper shows that at-risk patients with later transition to psychosis are characterized by abnormal localized brain activity and that inter-cortical areas of the brain are poorly synchronized. The second study shows that machine learning algorithms can detect patterns of abnormal brain activity predictive of later transitions to psychosis with promising accuracy. The third study reveals, in a cross-sectional manner, that patients who already had a first episode of psychosis at inclusion, already demonstrated the same abnormal patterns of brain activity revealed in at-risk patients with later transition to psychosis.
|Faculties and Departments:||03 Faculty of Medicine > Bereich Psychiatrie (Klinik) > Erwachsenenpsychiatrie UPK > Erwachsenenpsychiatrie (Riecher-Rössler)|
|Bibsysno:||Link to catalogue|
|Number of Pages:||1 Online-Ressource (87 Blätter)|
|Last Modified:||09 Aug 2016 13:18|
|Deposited On:||09 Aug 2016 13:18|
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