Feasibility of resting-state microstates neurofeedback

Despite major progress, currently available treatment options for patients suffering from schizophrenia remain suboptimal. Antipsychotic medication is one such option, and is helpful in acute phases of the disease. However, antipsychotics cause significant side-effects that often require additional medication, and can even trigger the discontinuation of treatment. Taken together, along with the fact that 20-30% of patients are medication-resistant, it is clear that new medical care options should be developed for patients with schizophrenia. Besides medication, an emerging option to treat psychiatric symptoms is through the use of neurofeedback. This technique has proven efficacy for other disorders and, more importantly, has also proven to be feasible in patients with schizophrenia. One of the major advantages of this approach is that it allows for the influence of brain states that otherwise would be inaccessible; i.e. the physiological markers underlying psychotic symptoms. EEG resting-state microstates are a very interesting electrophysiological marker of schizophrenia symptoms. Precisely, a specific class of resting-state microstates, namely microstate class D, has consistently been found to show a temporal shortening in patients with schizophrenia compared to controls, and this shortening is correlated with the presence positive psychotic symptoms. Under the scope of biological psychiatry, appropriate treatment of psychotic symptoms can be expected to modify the underlying physiological markers accompanying behavioral manifestations of a disease. We reason that if abnormal temporal parameters of resting-state microstates seem to be related to positive symptoms in schizophrenia, regulating this EEG feature might be helpful as a treatment for patients. The goal of this thesis was to prove the feasibility of microstate class D contribution self-regulation via neurofeedback. Given that no other study has attempted to regulate microstates via neurofeedback, we first tested its feasibility in a population of healthy subjects. In the first paper we describe the methodological characteristics of the neurofeedback protocol and its implementation. Neurofeedback performance was assessed by means of linear mixed effects modeling, which provided a complete profile of the neurofeedback’s training response within and between-subjects. The protocol included 20 training sessions, and each session contained three conditions: baseline (resting-state) and two active conditions: training (auditory feedback upon self-regulation performance) and transfer (self-regulation with no feedback). With linear modeling we obtained performance indices for each of them as follows: baseline carryover (baseline increments time-dependent) and learning and aptitude for each of the active conditions. Learning refers to the increase/decrease of the microstate class D contribution, time-dependent during each active condition, and aptitude refers to the constant difference of the microstate class D contribution between each active condition and baseline independent of time. The indices provided are discussed in terms of tailoring neurofeedback treatment to individual profiles so that it can be applied in future studies or clinical practice. In our sample of participants, neurofeedback proved feasible, as all participants at least showed positive results in one of the aforementioned learning indices. Furthermore, between-subjects we observed that the contribution of microstate class D across-sessions increased by 0.42% during baseline, 1.93% during training trials, and 1.83% during transfer. This range is expected to be effective in treating psychotic symptoms in patients. In the second paper presented in this thesis, we explored the possible predictors of neurofeedback success among psychological variables measured with questionnaires. An interesting finding was the negative correlation between “motivational incongruence” and some of the neurofeedback performance indices. Even though this finding requires replication, we discuss it in terms of the interfering effects of incompatible psychological processes with neurofeedback training requirements. In the third paper, we present a meta-analysis on all available studies that have related resting-state microstate abnormalities and schizophrenia. We obtained medium effect sizes for two microstate classes, namely C and D. Combining the meta-analysis results with the fact that microstate class D abnormalities are correlated with the presence of positive symptoms in patients with schizophrenia, these results add further support for the training of this precise microstate. Overall, the results obtained in this study encourage the implementation of this protocol in a population of patients with schizophrenia. However, future studies will have to show whether patients will be able to successfully self-regulate the contribution of microstate class D and, if so, whether this regulation will have an impact on symptomatology