Gray matter alterations related to P300 abnormalities in individuals at high risk for psychosis: longitudinal MRI-EEG study

none10Background: Psychotic disorders are characterized by gray matter and volumetric and electrophysiological abnormalities. The relationship between these factors in the onset of psychotic illness is unclear. Methods: Eighty English-native right-handed subjects (39 subjects at ultra high risk for psychosis "ARMS" and 41 healthy volunteers) were scanned with MRI, and studied using EEG during an oddball task. Both assessments were performed at first clinical presentation. The ARMS subjects were then followed clinically, with the MRI and EEG assessments repeated in a subgroup of each sample. Results: The P300 amplitude at presentation was significantly lower in the ARMS subjects than in controls. At baseline, the ARMS group showed reduced gray matter volume relative to controls in the right superior frontal gyrus, left medial frontal gyrus, left inferior frontal gyrus, right orbital gyrus and right supramarginal gyrus. Transition to psychosis (26%) was associated with reduced gray matter in the right inferior parietal lobule and in the left parahippocampal gyrus. Within the ARMS group, there was a positive correlation between P300 amplitude and gray matter volume in the right supramarginal gyrus. A significant group by P300 by gray matter interaction was detected in the left medial frontal gyrus. Longitudinal assessment revealed progressive gray matter alterations in prefrontal and subcortical areas of the ARMS but no significant changes in P300 amplitude over time. Conclusions: P300 abnormalities in the ARMS are related to alterations in regional gray matter volume and represent a correlate of an increased vulnerability to psychosis. © 2010 Elsevier Inc.noneFusar-Poli P.; Crossley N.; Woolley J.; Carletti F.; Perez-Iglesias R.; Broome M.; Johns L.; Tabraham P.; Bramon E.; McGuire P.Fusar-Poli, P.; Crossley, N.; Woolley, J.; Carletti, F.; Perez-Iglesias, R.; Broome, M.; Johns, L.; Tabraham, P.; Bramon, E.; Mcguire, P

Association between abnormal brain functional connectivity in children and psychopathology:A study based on graph theory and machine learning

<p><b>Objectives:</b> One of the major challenges facing psychiatry is how to incorporate biological measures in the classification of mental health disorders. Many of these disorders affect brain development and its connectivity.</p> <p>In this study, we propose a novel method for assessing brain networks based on the combination of a graph theory measure (eigenvector centrality) and a one-class support vector machine (OC-SVM).</p> <p><b>Methods:</b> We applied this approach to resting-state fMRI data from 622 children and adolescents. Eigenvector centrality (EVC) of nodes from positive- and negative-task networks were extracted from each subject and used as input to an OC-SVM to label individual brain networks as typical or atypical. We hypothesised that classification of these subjects regarding the pattern of brain connectivity would predict the level of psychopathology.</p> <p><b>Results:</b> Subjects with atypical brain network organisation had higher levels of psychopathology (<i>p</i> < 0.001). There was a greater EVC in the typical group at the bilateral posterior cingulate and bilateral posterior temporal cortices; and significant decreases in EVC at left temporal pole.</p> <p><b>Conclusions:</b> The combination of graph theory methods and an OC-SVM is a promising method to characterise neurodevelopment, and may be useful to understand the deviations leading to mental disorders.</p