Inferring Dysconnection Syndrome Using fMRI Modeling Techniques
Schizophrenia has been considered one of the most complex neurological disorders of the human condition. Diagnostics for this disease have become that of behavioral analysis as opposed to biological understandings. This has created a problematic circumstance in which inaccurate prescriptions and common misconceptions induce, rather than mediate, patient’s stress. Recently, the concept of “dysconnection” has been discovered as an explanation for the neurological phenomenon that occurs in the Schizophrenic brain. Large-scale brain networks are unable to communicate with one another, creating abnormal interactions between specialized regions. This results in the abnormal behavior that is commonly associated with Schizophrenia. A neuroimaging technique has recently been developed in order to capture neuronal activity using fluctuations in oxygen-rich blood levels. This technique, known as fMRI, is able to compare the connections between regions in healthy subjects with the dysconnection in Schizophrenic subjects using statistical modeling. Depending on the variance of connectivity and directionality among fMRI scans, a specific analytical theory is applied. In this discussion, the practices of Granger Causality in the context of functional connectivity and Dynamic Causal Modeling in the context of effective connectivity were utilized. Each technique outputs models from experimental data that highlight the dysconnection in Schizophrenia. Specifically, the cognitive network and its subsequent regions were explored. It was determined that dysconnection between the dorsolateral prefrontal cortex, dorsal anterior cingulate cortex, and hippocampus directly translated to the abnormal cognitive behavior characterized by Schizophrenia.