Symposium: Computational models of MMN
Friday, Sep 11, 2015
14:30-15:30
Hörsaal 3
Modelling MMN in 22q11 deletion syndrome
1Cognitive systems, Technical University of Denmark and Danish Research Center for Magnetic Resonance, Kgs. Lyngby, Denmark
2DTU Compute, Cognitive systems, Technical University of Denmark, Denmark
3Danish Research Center for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Denmark, Denmark
4Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Denmark, Denmark
5Mental Health Centre Sct. Hans, Capital Region of Denmark, Denmark
6Queensland Brain Institute and Centre for Advanced Imaging, Queensland University, Australia
kit.melissa.larsen@gmail.com
Detection of changes in the environment is a fundamental task that the healthy brain masters successfully on a daily basis, even non-intentionally. The mismatch negativity (MMN), a brain marker of this change detection mechanism, is reduced in people with schizophrenia compared to healthy controls. In the search of potential neural biomarkers for schizophrenia we investigated the neural basis of change detection in a group with 22q11 Deletion Syndrome (22q11DS), who have a 30 fold increased risk for developing schizophrenia. We recorded high-density EEG from the 22q11DS sample and a matched control-group while they listened to a sequence of sounds arranged in a roving MMN paradigm. While we found no indication of a significant decreased MMN response in the 22q11DS-group using standard ERP analysis, whole-scalp spatiotemporal analysis revealed a notable group difference in fronto-temporal regions in responses to tones per se, just prior to the typical MMN window. Dynamic Causal Modelling (DCM) revealed group differences in the network structure, indicating that models with feedback connections were favoured in the control-group, whereas models without feedback connections better explained the 22q11DS data. Bayesian Model Averaging (BMA) across the whole model space yielded a similar result whereby decreased top-down connections from superior temporal gyrus to primary auditory cortex were found bilateral in the 22q11 carriers as compared to controls. The observed differences in effective connectivity may present a possible biomarker for the development of schizophrenia, however this would have to be confirmed by a follow up study