Psychotic Disorders
Psychotic Disorders affects brain function in measurable ways that QEEG brain mapping can identify. At Peak Brain Institute, we use quantitative EEG to assess individual patterns related to psychotic disorders, then design personalized neurofeedback protocols targeting those specific signatures. Explore our 2 research papers covering this topic.
Research Papers
Pupillometer-based neurofeedback cognitive training to improve processing speed and social functioning in individuals at clinical high risk for psychosis
OBJECTIVE: Among individuals at clinical high risk (CHR) for psychosis, processing speed (PS) has been related to social and role functioning regardless of conversion to schizophrenia. This information processing dysfunction is a gateway to broader behavioral deficits such as difficulty executing social behaviors. We examined the feasibility of improving information processing relevant to social situations in CHR, including its sustainability at 2-month follow-up, and its association with concurrent social function. METHOD: This was a double-blind RCT in which 62 CHR participants were randomized to Processing Speed Training (PST) or an active control matched for training format and the same dose and duration of treatment. PST is a tablet-based program that uses pupillometry-based neurofeedback to continually adjust training parameters for an optimal neurocognitive load and to improve visual scanning efficiency by inhibiting selection of nonessential targets and discriminating figure-ground details. RESULTS: The PST group showed faster motoric and nonmotoric PS at post training and 2-month follow-up. At 2 month follow-up, the PST group reported better overall social adjustment. Changes in PS from baseline to 2 months were correlated with overall social adjustment and social avoidance in the entire sample. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: This is the first study to test focal neurofeedback-based cognitive training for PS deficits in the putatively prodromal phase of schizophrenia to address associated social morbidity. Targeting PS appears to be a promising pathway to decreasing comorbidity and mitigating a risk factor for psychosis. (PsycINFO Database Record
View Full Paper →Towards Using Microstate-Neurofeedback for the Treatment of Psychotic Symptoms in Schizophrenia. A Feasibility Study in Healthy Participants
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.
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