Multiple methods yield different results – so the robustness of results is only valid if (a) methods converge to similar results or (b) methods can be vali-dated by external justification. If they do not – no claim can be made, because each method is equally justifiable but leads to different results or, worse, if only one of the appropriate methods would be used, a false claim could be made. In the first study, the multiple comparison problem could be ameliorated by taking the HC vs CHR contrast as a benchmark and evaluate those with an independent FA patient group. By doing so, sensory gating deficits were found to be a robust measure in schizophrenia across patient groups and quantification methods. In the second study, a partial external justification could be found for a simple net-work model, but if the model was extended to other potential brain areas, no claim could be made. For the simple network model of information flows between the strongest auditory and frontal source, an information flow from auditory to frontal was found for healthy controls but not for schizophrenia patients. This information flow was correlated with the independent measure of sensory gating, which served as an external validation.
List of Tables
Table 2.1: Demographic information on samples: healthy controls (HC), chronic schizophrenia patients (CHR) and firstly admitted schizophrenia patients (FA) ... 40 Table 2.2: Metrics and group comparisons for M50 quantifications ... 51
Supplementary Table 2.1: Metrics and group comparisons for adjusted
quantifications ... 59 Supplementary Table 2.2: Metrics and group comparisons for quantifications
with separate S1 and S2 baseline ... 61 Supplementary Table 2.3: Metrics and group comparisons for S1- and
S2-evoked response amplitudes ... 62 Supplementary Table 2.4: Metrics and group comparisons for M100 scores
instead of M50 ... 63 Supplementary Table 2.5: Metrics and group comparisons for quantifications
with separate peak latencies ... 64 Supplementary Table 2.6: Metrics for M40-M50 peak to peak scoring ... 69 Supplementary Table 2.7A: Metrics and group comparisons for M50
quantifications per hemisphere ... 70
Table 3.1: Demographic information for healthy participants and schizophrenia patients ... 77 Table 3. 2: Extended network analysis ... 84
Table 4.1: Aims, results and conclusions of the present dissertation ... 94
List of Figures
Figure 1.1: Stimulus sequence used in the present dissertation ... 4 Figure 1.2: ERF of the sensors surrounding the M50 peak on the right
hemisphere, averaged over all subjects ... 5 Figure 1.3: Preliminary pipeline of tested methods ... 9 Figure 1.4: ERF of the sensors surrounding the M50 peak on the right
hemisphere, averaged over all subjects with different filter settings. ... 17 Figure 1.5: Selected preprocessing and quantification methods for study 1. ... 27 Figure 1.6: Univariate autoregression. ... 29 Figure 1.7: Bivariate autoregression ... 30
Figure 2.1: Topography 40-65 ms (M50, panel A) and 65-130 ms (M100, panel B) after S1 averaged across all participants ... 43 Figure 2.2: S1 minus S2 power in parcellated regions averaged across all
participants... 46 Figure 2.3: Time course of activity for sensor and source analysis averaged
across participants within group. ... 48 Figure 2.4: Metrics combining S1 and S2 scores. ... 52
Figure 3.1: A: Statistical difference between S1 (0-200ms) and S2 (500-700ms) source strength. B: Time course of activity in virtual sensors C: Granger Causality values for CMR and HGR. D: Correlation between prestimulus connectivity and sensory gating. ... 86
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