Recent Findings on Neurofeedback Training for Auditory Hallucinations in Schizophrenia

Yoji Hirano; Shunsuke Tamura


Curr Opin Psychiatry. 2021;34(3):245-252. 

In This Article

Abstract and Introduction


Purpose of Review: To provide recent evidence on real-time neurofeedback (NFB) training for auditory verbal hallucinations (AVH) in schizophrenia patients.

Recent Findings: NFB is a promising technique that allows patients to gain control over their AVH by modulating their own speech-related/language-related networks including superior temporal gyrus (STG) and anterior cingulate cortex (ACC) using fMRI, fNIRS and EEG/MEG. A recent limited number of studies showed that while an EEG-based NFB study failed to regulate auditory-evoked potentials and reduce AVH, downregulation of STG hyperactivity and upregulation of ACC activity with fMRI-based NFB appear to alleviate treatment-resistant AVH in schizophrenia patients. A deeper understanding of AVH and development of more effective methodologies are still needed.

Summary: Despite recent innovations in antipsychotics, many schizophrenia patients continue to suffer from treatment-resistant AVH and social dysfunctions. Recent studies suggested that real-time NFB shows promise in enabling patients to gain control over AVH by regulating their own speech-related/language-related networks. Although fMRI-NFB is suitable for regulating localized activity, EEG/MEG-NFB are ideal for regulating the ever-changing AVH. Although there are still many challenges including logistic complexity and burden on patients, we hope that such innovative real-time NFB trainings will help patients to alleviate severe symptoms and improve social functioning.


Among the various functional deficits and symptoms in patients with schizophrenia,[1–4] auditory verbal hallucinations (AVH) are one of the most prevalent and devastating features of the disease.[5] AVH is defined by hearing a voice in the absence of an external stimulus, which is often associated with severe distress and social dysfunction and is experienced by more than 70% of patients with schizophrenia.[6] To better understand the functional architecture of this pathognomonic symptom, recent studies have employed computational approaches, which have led to support for hypotheses and conceptual models, such as deficits in self-monitoring, salience, and predictive-coding as the underlying mechanism of AVH in schizophrenia.[5,7–9]

Symptomatic remission rate by D2 receptor blockade remains 65% in first-episode schizophrenia),[10] and up to 24% of first-episode schizophrenia patients with additional clozapine treatment still experience residual treatment-resistant symptoms, including AVH.[10] In fact, antipsychotic drugs have little or no effect in about 30% of patients with schizophrenia.[11] Furthermore, clozapine, which is a last resort medication for treatment-resistant schizophrenia, is known to have very low affinity for dopamine D2 receptors (unlike conventional drugs), which suggests that dopamine antagonism is not necessarily the main treatment target.[12] In line with this, new empirical evidence has shown that neuropathology of schizophrenia involves neural networks beyond the classical dopaminergic subcortical pathway, such as the gamma-aminobutyric acidergic (GABAergic) and glutamatergic systems.[11–13] Currently, the treatment of schizophrenia requires not only symptom relief but also early diagnosis and intervention and restoration of cognitive and social functions to enable patients to return to society.[1,14–16] This necessitates the development of new hypotheses on the pathophysiology of schizophrenia and novel therapies beyond antipsychotics that are based on the dopamine hypothesis. One of the therapeutic challenges in treatment-resistant schizophrenia is the voluntary control of psychotic symptoms, such as AVH.[17] In this regard, neurofeedback (NFB) training has attracted attention as a new therapeutic approach for schizophrenia.

Technological advances in computational neuroscience have made it possible to conduct sophisticated real-time NFB, which is a method in which brain activity is modulated via self-regulation to improve cognitive performance or reduce symptoms of schizophrenia, such as AVH. Historically, electroencephalogram (EEG) had been commonly used for NFB; however, it suffered from low quality because of deficiencies in devices and analysis techniques. There has been dramatic progress with the advent of functional MRI (fMRI), high-performance digital EEG systems, and magnetoencephalography (MEG).[18–20] Most recent studies using NFB in patients with schizophrenia have predominantly been conducted using fMRI-NFB systems and have shown some degree of effectiveness.[21] However, EEG and MEG have a clear advantage in regard to temporal resolution in the order of milliseconds, which is crucial for real-time NFB.[20,22] Moreover, time--frequency analysis has enabled the evaluation of brain activity in specific frequencies and their corresponding functions during tasks and rest (spontaneous activity).[23,24] Therefore, given the ever-changing nature of AVH, real-time EEG/MEG-based NFB, in addition to fMRI-NFB, has the potential to be the most suitable NFB system as an alternative treatment approach for AVH in schizophrenia patients.

In order to recommend future directions for NFB training in schizophrenia, it is necessary to clarify the strengths and weaknesses of recent findings. The purpose of this review is to summarize recent evidence (mainly from 2015) on NFB training for AVH in patients with schizophrenia. Summary of recent evidence on NFB training for AVH in patients with schizophrenia is demonstrated in Table 1.[]