New Zealand Statistical Association 2024 Conference

Khan Buchwald

Auckland University of Technology

Comparisons Between Functional Brain Networks for Schizophrenia and Healthy Case Controls: A Bayesian Network Approach

This is joint work with Matthieu Vignes, Richard Siegert, Ajit Narayanan, Margaret Sandham

The disconnection hypothesis of schizophrenia has been advanced to explain its symptomology from a neurophysiological perspective. Network statistical methods have been used to assess the disconnection hypothesis but not for network complexity. This paper leverages network statistical complexity analysis to assess the disconnection hypothesis in schizophrenia. This study obtained fMRI data from the University of California Los Angles Consortium for Neuropsychiatric Phenomics LA5c Study for people diagnosed with schizophrenia (PDS) and healthy case-controls (HC). We used one hundred bootstrap samples of 45 PDS and 45 HC, for both encoding and retrieval trials, and we fitted a dynamic Bayesian Network to each sample. The dynamic Bayesian network included a model of brain regions across two points in time. The network properties were assessed at various significance thresholds, and an optimal significance threshold for an edge’s inclusion was obtained. After edge pruning, 52.8% of edges were shared between memory encoding in PDS and memory encoding in HC, and 52.7% between retrieval in PDS and retrieval in HC. Our results show that the number of connections is similar between PDS and HC, but PDS differs in the presence or absence of functional connections compared to HC. These results support the dysconnection hypothesis in PDS rather than a loss or a gain in the number of connections.