We utilized a discrete dynamic Bayesian network (dDBN) approach (Burge et al., 2007) to determine differences in brain regions between patients with schizophrenia and healthy controls on a measure of effective connectivity, termed the approximate conditional likelihood score (ACL) (Burge and Lane, 2005). The ACL score represents a class-discriminative measure of effective connectivity by measuring the relative likelihood of the correlation between brain regions in one group versus another. The algorithm is capable of finding non-linear relationships between brain regions because it uses discrete rather than continuous values and attempts to model temporal relationships with a first-order Markov and stationary assumption constraint (Papoulis, 1991). Since Bayesian networks are overly sensitive to noisy data, we introduced an independent component analysis (ICA) filtering approach that attempted to reduce the noise found in fMRI data by unmixing the raw datasets into a set of independent spatial component maps. Components that represented noise were removed and the remaining components reconstructed into the dimensions of the original fMRI datasets. We applied the dDBN algorithm to a group of 35 patients with schizophrenia and 35 matched healthy controls using an ICA filtered and unfiltered approach. We determined that filtering the data significantly improved the magnitude of the ACL score. Patients showed the greatest ACL scores in several regions, most markedly the cerebellar vermis and hemispheres. Our findings suggest that schizophrenia patients exhibit weaker connectivity than healthy controls in multiple regions, including bilateral temporal and frontal cortices, plus cerebellum during an auditory paradigm.

The general linear model (GLM) approach is the most commonly used method in functional magnetic resonance imaging analysis to predict a particular response. Recently, a novel method of analysis, referred to as inter-participant correlation (IPC), was developed that attempts to determine the level of BOLD (Blood Oxygen Level Dependent) synchrony among subjects. The IPC approach enables detection of changes in inter-participant BOLD synchrony in a manner that does not rely on an explicit model of the hemodynamic activity. In this paper, we extend IPC to the case of two groups and derive an approach for thresholding the resulting maps. We demonstrate our approach by comparing 35 patients with paranoid schizophrenia (DSM-IV subtype 295.30) to 35 healthy matched controls during an auditory target detection paradigm. Results showed significantly lower inter-participant BOLD synchrony in patients versus healthy controls in areas including bilateral temporal lobes, medial frontal gyrus, anterior cingulate cortex, dorsolateral prefrontal cortex, thalamus, insula, and cerebellum. The IPC approach is straightforward to use and provides a useful complement to traditional GLM techniques. This approach may also be sensitive to underlying, but unpredictable, changes in inter-participant BOLD synchrony between patients and controls.