Functional MRI Evaluation of Multiple Neural Networks Underlying Auditory Verbal Hallucinations in Schizophrenia Spectrum Disorders.

Functional MRI studies have identified a distributed set of brain activations to be asso­ ciated with auditory verbal hallucinations (AVH). However, very little is known about how activated brain regions may be linked together into AVH-generating networks. Fifteen volunteers with schizophrenia or schizoaffective disorder pressed buttons to indicate onset and offset of AVH during fMRI scanning. When a general linear model was used to compare blood oxygenation level dependence signals during periods in which subjects indicated that they were versus were not experiencing AVH ( AVH-on versus AVH-off ), it revealed AVH-related activity in bilateral inferior frontal and superior temporal regions; the right middle temporal gyrus; and the left insula, supramarginal gyrus, inferior parietal lobule, and extranuclear white matter. In an effort to identify AVH-related networks, the raw data were also processed using independent component analyses (ICAs). Four ICA components were spatially consistent with an a priori network framework based upon published meta-analyses of imaging correlates of AVH. Of these four components, only a network involving bilateral auditory cortices and posterior receptive language areas was significantly and positively correlated to the pattern of AVH-on versus AVH-off. The ICA also identified two additional networks (occipital-temporal and medial prefrontal), not fully matching the meta-analysis framework, but nevertheless containing nodes reported as active in some studies of AVH. Both networks showed significant AVH-related profiles, but both were most active during AVH-off periods. Overall, the data suggest that AVH generation requires specific and selective activation of auditory cortical and posterior language regions, perhaps coupled to a release of indirect influence by occipital and medial frontal structures

Event-related potential studies of post-traumatic stress disorder: a critical review and synthesis

Despite the sparseness of the currently available data, there is accumulating evidence of information processing impairment in post-traumatic stress disorder (PTSD). Studies of event-related potentials (ERPs) are the main tool in real time examination of information processing. In this paper, we sought to critically review the ERP evidence of information processing abnormalities in patients with PTSD. We also examined the evidence supporting the existence of a relationship between ERP abnormalities and symptom profiles or severity in PTSD patients. An extensive Medline search was performed. Keywords included PTSD or post-traumatic stress disorder, electrophysiology or EEG, electrophysiology, P50, P100, N100, P2, P200, P3, P300, sensory gating, CNV (contingent negative variation) and MMN (mismatch negativity). We limited the review to ERP adult human studies with control groups which were reported in the English language. After applying our inclusion-exclusion review criteria, 36 studies were included. Subjects exposed to wide ranges of military and civilian traumas were studied in these reports. Presented stimuli were both auditory and visual. The most widely studied components included P300, P50 gating, N100 and P200. Most of the studies reported increased P300 response to trauma-related stimuli in PTSD patients. A smaller group of studies reported dampening of responses or no change in responses to trauma-related and/or unrelated stimuli. P50 studies were strongly suggestive of impaired gating in patients with PTSD. In conclusion, the majority of reports support evidence of information processing abnormalities in patients with PTSD diagnosis. The predominance of evidence suggests presence of mid-latency and late ERP components differences in PTSD patients in comparison to healthy controls. Heterogeneity of assessment methods used contributes to difficulties in reaching firm conclusions regarding the nature of these differences. We suggest that future ERP-PTSD studies utilize standardized assessment scales that provide detailed information regarding the symptom clusters and the degree of symptom severity. This would allow assessment of electrophysiological indices-clinical symptoms relationships. Based on the available data, we suggest that ERP abnormalities in PTSD are possibly affected by the level of illness severity. If supported by future research, ERP studies may be used for both initial assessment and treatment follow-up

Abnormal oscillatory brain dynamics in schizophrenia: a sign of deviant communication in neural network?

<p>Abstract</p> <p>Background</p> <p>Slow waves in the delta (0.5–4 Hz) frequency range are indications of normal activity in sleep. In neurological disorders, focal electric and magnetic slow wave activity is generated in the vicinity of structural brain lesions. Initial studies, including our own, suggest that the distribution of the focal concentration of generators of slow waves (dipole density in the delta frequency band) also distinguishes patients with psychiatric disorders such as schizophrenia, affective disorders, and posttraumatic stress disorder.</p> <p>Methods</p> <p>The present study examined the distribution of focal slow wave activity (ASWA: abnormal slow wave activity) in116 healthy subjects, 76 inpatients with schizophrenic or schizoaffective diagnoses and 42 inpatients with affective (ICD-10: F3) or neurotic/reactive (F4) diagnoses using a newly refined measure of dipole density. Based on 5-min resting magnetoencephalogram (MEG), sources of activity in the 1–4 Hz frequency band were determined by equivalent dipole fitting in anatomically defined cortical regions.</p> <p>Results</p> <p>Compared to healthy subjects the schizophrenia sample was characterized by significantly more intense slow wave activity, with maxima in frontal and central areas. In contrast, affective disorder patients exhibited less slow wave generators mainly in frontal and central regions when compared to healthy subjects and schizophrenia patients. In both samples, frontal ASWA were related to affective symptoms.</p> <p>Conclusion</p> <p>In schizophrenic patients, the regions of ASWA correspond to those identified for gray matter loss. This suggests that ASWA might be evaluated as a measure of altered neuronal network architecture and communication, which may mediate psychopathological signs.</p

Comparative studies of brain activation with MEG and functional MRI

The past two years have witnessed the emergence of MRI as a functional imaging methodology. Initial demonstrations involved the injection of a paramagnetic contrast agent and required ultrafast echo planar imaging capability to adequately resolve the passage of the injected bolus. By measuring the local reduction in image intensity due to magnetic susceptibility, it was possible to calculate blood volume, which changes as a function of neural activation. Later developments have exploited endogenous contrast mechanisms to monitor changes in blood volume or in venous blood oxygen content. Recently, we and others have demonstrated that it is possible to make such measurements in a clinical imager, suggesting that the large installed base of such machines might be utilized for functional imaging. Although it is likely that functional MRI (fMRI) will subsume some of the clinical and basic neuroscience applications now touted for MEG, it is also clear that these techniques offer different largely complementary, capabilities. At the very least, it is useful to compare and cross-validate the activation maps produced by these techniques. Such studies will be valuable as a check on results of neuromagnetic distributed current reconstructions and will allow better characterization of the relationship between neurophysiological activation and associated hemodynamic changes. A more exciting prospect is the development of analyses that combine information from the two modalities to produce a better description of underlying neural activity than is possible with either technique in isolation. In this paper we describe some results from initial comparative studies and outline several techniques that can be used to treat MEG and fMRI data within a unified computational framework