Non-Negative Matrix Factorization Reveals Resting-State Cortical Alpha Network Abnormalities in the First Episode Schizophrenia-Spectrum

Background: Little is known about neural oscillatory dynamics in first episode psychosis. Pathophysiology of functional connectivity can be measured through network activity of alpha oscillations, reflecting long-range communication between distal brain regions. Methods: Resting magnetoencephalographic activity was collected from 31 first episode schizophrenia-spectrum psychosis individuals (FESz) and 22 healthy controls. Activity was projected to the realistic cortical surface, based on structural MRI. The first principal component of activity in 40 Brodmann areas per hemisphere was Hilbert transformed within the alpha range. Non-negative matrix factorization was applied to single trial alpha phase locking values from all subjects to determine alpha networks. Within networks, energy and entropy were compared. Results: Four cortical alpha networks were pathological in FESz. The networks involved bilateral anterior and posterior cingulate; left auditory, medial temporal, and cingulate cortex; right inferior frontal gyrus and widespread areas; and right posterior parietal cortex and widespread areas. Energy and entropy were associated with the PANSS Total and Thought Disorder factors for the first 3 networks. Additionally, the left posterior temporal network was associated with Positive and Negative factors and the right inferior frontal network was associated with the Positive Factor. Conclusions: Machine learning network analysis of resting alpha-band neural activity identified several aberrant networks in FESz including left temporal, right inferior frontal, right posterior parietal, and bilateral cingulate cortices. Abnormal long-range alpha communication is evident at the first presentation for psychosis and may provide clues about mechanisms of disconnectivity in psychosis and novel targets for non-invasive brain stimulation.