Applying neuroimaging to detect neuroanatomical dysconnectivity in psychosis. (August 2015)

• Record Type: Journal Article
• Authors: O'Donoghue, S.; Cannon, D. M.; Perlini, C.; Brambilla, P.; McDonald, C.
• Is Part Of: Epidemiology and psychiatric sciences. Volume 24:Number 4(2015)
• Page Start: 298
• Page End: 302
• Publication Date: 2015-08
• Subjects: Epidemiology -- Periodicals; Mental illness -- Periodicals; Community psychiatry -- Periodicals; 362.2
• DOI: 10.1017/S2045796015000074
• Languages: English
• ISSNs: 2045-7960

Abstract

<abstract abstract-type="normal"> <title> <x content-type="archive" xml:space="preserve">Abstract</x> </title> <p>This editorial discusses the application of a novel brain imaging analysis technique in the assessment of neuroanatomical dysconnectivity in psychotic illnesses. There has long been a clinical interest in psychosis as a disconnection syndrome. In recent years graph theory metrics have been applied to functional and structural imaging datasets to derive measures of brain connectivity, which represent the efficiency of brain networks. These metrics can be derived from structural neuroimaging datasets acquired using diffusion imaging whereby cortical structures are parcellated into nodes and white matter tracts represent edges connecting these nodes. Furthermore neuroanatomical measures of connectivity may be decoupled from measures of physiological connectivity as assessed using functional imaging, underpinning the need for multi-modal imaging approaches to probe brain networks. Studies to date have reported a number of structural brain connectivity abnormalities associated with schizophrenia that carry potential as illness biomarkers. Structural connectivity abnormalities have also been reported in well patients with bipolar disorder and in unaffected relatives of patients with schizophrenia. Such connectivity metrics may represent clinically relevant biomarkers in studies employing a longitudinal design of illness course in psychosis.</p> </abstract>