Development of MEG data recording and signal processing methods has been very successful. Studies of working memory tasks and auditory processing tasks have shown the ability to localize brain activation and to address issues of phasic versus tonic activity. We are investigating phenotype measures that will be applicable in many related studies and specifically the sibling project. MEG recording during cognitive activation has shown the ability to localize in very comparable fashion to fMRI. Specifically beta desynchronization at the cortical level has been found to agree with BOLD activation results. However, the MEG/EEG allows for temporal information not possible with other imaging techniques. Our results show that measures of brain structure and function represent powerful tools to find susceptibility genes. Further studies have demonstrated the ability to localize signals in deeper structures such as the amygdala and to investigate the relation of visual awareness to gamma band signals. We have also found that GABA (gamma-aminobutyric acid) concentration in the anterior cingulate cortex correlates with spatially localized resting MEG beta band power. We expect that further investigation will reveal additional relationships between gamma power and GABA systems. Differences in the degree of activation especially in frontal regions as indexed by beta desynchronization during a working memory task have been found between patients with schizophrenia compared to well siblings and normal control volunteers. Previously we have seen that this activation reveals an interaction with genotype for the well studied COMT marker. We have found there is a modulation of prefrontal cortex activity that occurs in anticipation of the upcoming task demands. We have extended this analysis to a well matched set of patients, siblings and controls. When working memory task performance is controlled patients show a distinct reduced DLPFC activation in apparent distinction to increased BOLD relative to task load. The MEG analysis isolates a working memory component that may reflect a different aspect of cortical processing. We now find that this component has a distinctly different relation to behavior in patients with schizophrenia compared to healthy volunteers. We are now comparing these results across modalities to better understand how these measures reflect brain activation. Differences in network patterns and dynamics are key to understanding underlying pathology in clinical groups. Bassett et al have shown that functional network differences in patient groups can be demonstrated and related to behavioral outcomes on cognitive activities. Rutter et al have shown that even at rest patients with schizophrenia have gamma power reduction compared to normal subjects. It remains to be seen whether these finding relate to state or trait differences and if there are genetic associations. We have found distinct patterns of the temporal sequence of brain regions involved in these memory tasks that show a variety of individual differences across subjects. This has been extended using graph theoretic measures as a way to capture properties of the pattern activity across brain regions. In a face recognition tasks we found a distinct network of regions that interact by cross-coupling of different frequencies of oscillatory. Further studies will examine the difference across clinical groups. Previous work examined whether reorganization of functional brain networks can be seen in response to cognitive remediation strategies using auditory task training in both patients and healthy. We were able to show significant changes in power and coherence in brain activity that were associated with improved behavioral performance. This could form the basis of a biomarker that would allow tracking the outcome of remediation strategies targeting specific cognitive deficits in neuropsychiatric disorders. Recent work has shown that MEG is able to show wide spread critical dynamics that are crucial to optimize information processing in such networks. These optimal dynamics may be essential for the plasticity necessary for appropriate adaptive behavior.
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