A primary goal of neuroscience research on psychiatric disorders such as schizophrenia is to enhance the current understanding of underlying biological mechanisms in order to develop novel interventions.
Human brain functions are maintained through activity of large-scale brain networks. Accordingly, deficient perceptual and cognitive processing can be caused by failures of functional integration within networks, as reflected by the disconnection hypothesis of schizophrenia. Various neuroimaging techniques can be applied to study functional brain networks, each having different strengths. Frequently used complementary methods are the electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), which were shown to have a common basis. Given the feasibility of combined EEG and fMRI measurement, EEG signatures of functional networks have been described, providing complimentary information about the functional state of networks. Both at rest and during task completion, many independent EEG and fMRI studies confirmed deficient network connectivity in schizophrenia. However, a rather diffuse picture with hyper- and hypo- activations within and between specific networks was reported. Furthermore, the theory of state dependent information processing argues that spontaneous and prestimulus brain activity interacts with upcoming task-related processes. Consequently, observed network deficits that vary according to task conditions could be caused by differences in resting or prestimulus state in schizophrenia.
Based on that background, the present thesis aimed to increase the understanding of aberrant functional networks in schizophrenia by using simultaneous EEG-fMRI under different conditions. One study investigated integrative mechanisms of networks during eyes-open (EO) resting state using a common-phase synchronization measure in an EEG-informed fMRI analysis (study 3). The other two studies (studies 1&2) used an fMRI-informed EEG analysis: The second study was an extension of the first, which was performed in healthy subjects only. Hence, the same methodologies and analyses were applied in both studies, but in the second study schizophrenia patients were compared to healthy controls. The associations between four temporally coherent networks (TCNs) – the default mode network (DMN), the dorsal attention network (dAN), left and right working memory networks (WMNs) – and power of three EEG frequency bands (theta, alpha, and beta band) during a verbal working memory (WM) task were investigated.
Both resting state and task-related studies performed in schizophrenia patients (studies 2&3) revealed altered activation strength, functional states and interaction of TCNs, especially of the DMN. During rest (study 3), the DMN was differently integrated through common-phase synchronization in the delta (0.5 – 3.5Hz) and beta (13 – 30Hz) band. At prestimulus states of a verbal WM task, however, study 2 did not reveal differences in the activation level of the DMN between groups. Furthermore, from pre-to-post stimulus, the association of the DMN with frontal-midline (FM) theta (3 – 7Hz) band was altered, and a reduced suppression of the DMN during WM retention was detected. Schizophrenia patients also demonstrated abnormal interactions between networks: the DMN and dAN showed a reduced anti-correlation and the WMNs demonstrated an absent lateralization effect (study 2).
The view that schizophrenia patients display TCN deficiencies is supported by the results of the present thesis. Especially the DMN and its interaction to the task-positive dAN showed specific alterations at different mental states and their interaction (during rest and from pre-to-post stimulus). Those alterations might at least partly explain observed symptomatology as attentional orientation deficits in patients. To conclude, functional networks as the DMN might represent promising targets for novel treatment options such as neurofeedback or transcranial direct current stimulation (tDCS)
