Schizophrenia patients show abnormalities in a broad range of task demands. Therefore, an explanation common to all these abnormalities has to be sought independently of any particular task, ideally in the brain dynamics before a task takes place or during resting state. For the neurobiological investigation of such baseline states, EEG microstate analysis is particularly well suited, because it identifies subsecond global states of stable connectivity patterns directly related to the recruitment of different types of information processing modes (e.g., integration of top-down and bottom-up information). Meanwhile, there is an accumulation of evidence that particular microstate networks are selectively affected in schizophrenia. To obtain an overall estimate of the effect size of these microstate abnormalities, we present a systematic meta-analysis over all studies available to date relating EEG microstates to schizophrenia. Results showed medium size effects for two classes of microstates, namely, a class labeled C that was found to be more frequent in schizophrenia and a class labeled D that was found to be shortened. These abnormalities may correspond to core symptoms of schizophrenia, e.g., insufficient reality testing and self-monitoring as during auditory verbal hallucinations. As interventional studies have shown that these microstate features may be systematically affected using antipsychotic drugs or neurofeedback interventions, these findings may help introducing novel diagnostic and treatment options.

We investigated whether the differences in perceptual awareness for stimuli at the threshold of awareness can arise from different global brain states before stimulus onset indexed by the EEG microstate. We used a metacontrast backward masking paradigm in which subjects had to discriminate between two weak stimuli and obtained measures of accuracy and awareness while their EEG was recorded from 256 channels. Comparing targets that were correctly identified with and without awareness allowed us to contrast differences in awareness while keeping performance constant for identical physical stimuli. Two distinct pre-stimulus scalp potential fields (microstate maps) dissociated correct identification with and without awareness, and their estimated intracranial generators were stronger in primary visual cortex before correct identification without awareness. This difference in activity cannot be explained by differences in alpha power or phase which were less reliably linked with differential pre-stimulus activation of primary visual cortex. Our results shed a new light on the function of pre-stimulus activity in early visual cortex in visual awareness and emphasize the importance of trial-by-trials analysis of the spatial configuration of the scalp potential field identified with multichannel EEG.

Spontaneous EEG signal can be parsed into sub-second periods of stable functional states (microstates) that assumingly correspond to brief large scale synchronization events. In schizophrenia, a specific class of microstate (class "D") has been found to be shorter than in healthy controls and to be correlated with positive symptoms. To explore potential new treatment options in schizophrenia, we tested in healthy controls if neurofeedback training to self-regulate microstate D presence is feasible and what learning patterns are observed. Twenty subjects underwent EEG-neurofeedback training to up-regulate microstate D presence. The protocol included 20 training sessions, consisting of baseline trials (resting state), regulation trials with auditory feedback contingent on microstate D presence, and a transfer trial. Response to neurofeedback was assessed with mixed effects modelling. All participants increased the percentage of time spent producing microstate D in at least one of the three conditions (p < 0.05). Significant between-subjects across-sessions results showed an increase of 0.42 % of time spent producing microstate D in baseline (reflecting a sustained change in the resting state), 1.93 % of increase during regulation and 1.83 % during transfer. Within-session analysis (performed in baseline and regulation trials only) showed a significant 1.65 % increase in baseline and 0.53 % increase in regulation. These values are in a range that is expected to have an impact upon psychotic experiences. Additionally, we found a negative correlation between alpha power and microstate D contribution during neurofeedback training. Given that microstate D has been related to attentional processes, this result provides further evidence that the training was to some degree specific for the attentional network. We conclude that microstate-neurofeedback training proved feasible in healthy subjects. The implementation of the same protocol in schizophrenia patients may promote skills useful to reduce positive symptoms by means of EEG-neurofeedback.

Clinical signs in Parkinson’s disease (PD), including parkinsonian gait, are often asymmetric, but mechanisms underlying gait asymmetries in PD remain poorly understood. A translational toolkit, a set of standardized measures to capture gait asymmetries in relevant mouse models and patients, would greatly facilitate research efforts. We validated approaches to quantify asymmetries in placement and timing of limbs in mouse models of parkinsonism and human PD subjects at speeds that are relevant for human walking. In mice, we applied regression analysis to compare left and right gait metrics within a condition. To compare alternation ratios of left and right limbs before and after induction of parkinsonism, we used circular statistics. Both approaches revealed asymmetries in hind- and forelimb step length in a unilateral PD model, but not in bilateral or control models. In human subjects, a similar regression approach showed a step length asymmetry in the PD but not control group. Sub-analysis of cohorts with predominant postural instability-gait impairment and with predominant tremor revealed asymmetries for step length in both cohorts and for swing time only in the former cohort. This translational approach captures asymmetries of gait in mice and patients. Application revealed striking differences between models, and that spatial and temporal asymmetries may occur independently. This approach will be useful to investigate circuit mechanisms underlying the heterogeneity between models.