Secondary Logo

Journal Logo

SCHIZOPHRENIA AND RELATED DISORDERS: Edited by Lynn E. DeLisi and Iris E.C. Sommer

Recent findings on neurofeedback training for auditory hallucinations in schizophrenia

Hirano, Yojia,b; Tamura, Shunsukea

Author Information
Current Opinion in Psychiatry: May 2021 - Volume 34 - Issue 3 - p 245-252
doi: 10.1097/YCO.0000000000000693
  • Open



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. 

Table 1 - Summary of recent findings on NFB training for AVH in schizophrenia
Authors Participants’ characteristics Stage of illness Target region and index Task Training period Summary of key findings
fMRI-based NFB training
 Dyck et al.[35] Three SZ with current treatment-resistant AVH (two females) (one unmedicated) Chronic BOLD amplitude in ACC Up-regulation training of ACC activity Three-day training within 1 week All patients succeeded in up-regulating the ACC activity during the NFB training.Subjective symptoms of AVH improved after the training in all patients.
 Oriov et al.[31▪▪] Twelve SZ or SZAD with current treatment-resistant AVH (two females) (all medicated) NAa BOLD amplitude in left STG Down-regulation training of STG activity 3-day training within 2 weeks The functional connectivity between the brain regions involved in speech production and perception (left STG, left IFG and IPG) increased in accompany with decrease of left STG hyper-activity following the NFB training.The change of functional connectivity was associated with reduction of AVH severity.
 Okano et al.[32▪▪] Ten SZ or SZAD with current treatment-resistant AVH(one female)(all medicated) Chronic BOLD amplitude in STG Down-regulation training of STG activity while ignoring a stranger's voiceUp-regulation training of STG activity while listening to patients’ own voice 1-day training The decrease of STG activity was observed in 8 out of 10 patients in Task 1The NFB training resulted in the reduction of the severity of AVH in all patients.
 Bauer et al.[39▪] Eleven SZ or SZAD with current treatment-resistant AVH b(1 female)(all medicated) Chronic Anticorrelation between DMN and CEN Down-regulation training of DMN and up-regulation training of CEN One-day training The reduction of DMN hyperconnectivity was caused by the NFB training.Successful down-regulation of DMN significantly correlated with the improvement of AVH symptoms.
fNIRS-based NFB training
 Storchak et al.[44] A female SZ patient with current treatment-resistant AVH (medicated) Chronic O2Hb amplitude in temporal area Down-regulation training of temporal area activity when experiencing AVH.Up-regulation training of temporal area activity at the timing of AVH-onset NA (47 sessions) The patient was able to up-regulate bilateral temporal area activities in task 2 (but fail to down-regulate bilateral temporal area activities in task 1)The patient's subjective AVH decreased during the NFB training.
EEG-based NFB training
 Rieger et al.[47▪] 10 SZ or SZAD with current treatment-resistant AVH c(4 females)(unknown medication status)d NA N1 amplitudeP2 amplitude (control condition) Up-regulation training of N1 (P2) amplitude while listening to beep tones Four-day training within 2 weeks Auditory-evoked potentials were not modulated by the NFB training.There was no significant effect of the training on AVH severity (only the learning ‘pattern’ of NFB training was correlated with change in AVH severity).
ACC, anterior cingulate cortex; AVH, auditory verbal hallucinations; CEN, central executive network; DMN, default mode network; IFG, the inferior prefrontal gyrus; IPG, inferior parietal gyrus; NFB, neurofeedback; SZ, schizophrenia; SZAD, schizoaffective disorder.
aOnly mean of duration of illness is presented: 10.8 years (SD 8.4).
bTen out of 11 patients performed the NFB training of this study after that of Okano et al.[32▪▪].
cParticipants were randomly assigned to treatment or control condition.
dOnly mean of medication doses are presented. treatment condition: 333.3 (SD 245.0); control condition: 616.5 (SD 389.1) (chlorpromazine equivalents: mg).

Box 1:
no caption available


Functional MRI-based neurofeedback training of the superior temporal gyrus

There is consistent evidence that the auditory perception-related areas, such as the superior temporal gyrus (STG) and primary and secondary auditory cortices, are involved in the pathophysiology of schizophrenia [2,23,25]. Recently developed neuroimaging techniques have enabled the identification of functional networks associated with AVH, which include the auditory-related and language-related areas in the STG and inferior parietal gyrus (IPG), speech-related areas in the inferior prefrontal gyrus (IFG), the hippocampus and parahippocampal region, and the anterior cingulate cortex (ACC) [21,26–28]. Several studies and meta-analyses have indicated that when schizophrenia patients are actively experiencing AVH, there is increased activity in the STG and temporoparietal language regions [26–28].

On the basis of this evidence, several studies have attempted to modulate the brain activity of schizophrenia patients who experience AVH, with a specific focus on left STG hyperactivity, which is thought to be linked to severity of AVH [26,27,29,30]. Oriov et al.[31▪▪] reported that patients with schizophrenia successfully downregulated left STG hyperactivity over four sessions during a 2-week fMRI-NFB training period. In addition, the downregulation of left STG activity was accompanied by an increase in functional connectivity between the left STG, left IFG, and IPG (frontal and temporal language regions), and this increase was associated with a reduction in AVH severity. More recently, Okano et al.[32▪▪] conducted a 21-min fMRI-NFB training session with schizophrenia patients with treatment-resistant AVH to upregulate STG activity while listening to the patient's own prerecorded voice and downregulate STG activity while ignoring a stranger's voice. This training induced significant reductions in STG (but not motor cortex) activation while ignoring a stranger's voice in 8 out of 10 patients and also decreased the severity of AVH in all patients.

Functional MRI-based neurofeedback training of other regions

Some researchers have suggested that AVH is a result of source misattribution during self-generated thought and inner speech [33,34]. On the basis of this self-monitoring (inner speech) theory of AVH, Dyck et al.[35] targeted the ACC using fMRI-NFB training, based on its involvement in differentiating between inner and external speech [36]. After 3 days of fMRI-NFB training, all three schizophrenia patients with treatment-resistant AVH showed significant upregulation of ACC activity and reported subjective improvement in AVH symptoms. However, the small number of patients (n = 3) does not allow generalization of these findings for fMRI-NFB of the ACC.

The default-mode network (DMN) has also attracted attention as a neurophysiological marker for fMRI-NFB treatment for AVH; DMN abnormalities (hyperconnectivity of the DMN) have been shown to be associated with the positive symptoms of schizophrenia [37,38]. Bauer et al.[39▪] conducted fMRI-NFB training as part of an fMRI-NFB AVH treatment series [32▪▪] to reduce hyperconnectivity of the DMN, centering on the middle prefrontal cortex (MPFC). Results showed that reductions in functional connectivity between the MPFC and STG were accompanied by a decrease in AVH severity.

As described above, the modulation of brain activity centering on the STG using fMRI-NFB training may be a promising treatment for patients with schizophrenia and treatment-resistant AVH. These findings are consistent with repetitive transcranial magnetic stimulation (rTMS) studies that have shown that reducing cortical excitability of the left temporoparietal region improves AVH [40–42]. Although fMRI-NFB and rTMS appear to induce similar effects on AVH, fMRI-NFB may be superior to rTMS because of its ability to visualize the activity that is linked with AVH without the intrusiveness of rTMS.


Functional near-infrared spectroscopy (fNIRS) has also been employed to assess brain function alterations in schizophrenia [43], although there is only one fNIRS-based NFB study attempt to regulate AVH. Storchak et al.[44] reported a single-case study applying a novel fNIRS-based NFB training (instructed to upregulate when expecting and downregulate when experiencing AVH) with a treatment-resistant schizophrenia patient. During the NFB training, the patient only succeeded to up-regulate bilateral temporal area activities at the timing of the AVH onset, which was accompanied by the significant reduction of subjective AVH. The authors argued that the constant successful increase of the O2H amplitude in the target temporal area before the AVH onset may have led to a compensation of neural activity and prevented the emergence of AVH.


Historically, EEG and MEG have been used to identify the neural bases of AVH and language-related functional deficits in schizophrenia because of their superior temporal resolution [45,46]. The fMRI-NFB has an inherent delay in feedback because of the hemodynamic response (approximately 6 s), whereas EEG/MEG-NFB training has no such delay, owing to its excellent temporal resolution. Therefore, EEG/MEG-NFB system is suitable for providing real-time neural dynamics information that underlies AVH. However, to our knowledge, there has only been one study that has used EEG/MEG-NFB training for AVH treatment. Rieger et al.[47▪] hypothesized that decreases in the amplitude of the auditory-evoked N1 is related to AVH in schizophrenia and investigated whether modulating the N1 component using EEG-NFB training affects AVH. Despite the intensive 4-day training within 2 weeks, they found no significant effects of EEG-NFB training on either the N1 amplitude or AVH severity. Instead, they found that the learning pattern of NFB training was correlated with change in AVH severity.

Although the previous study focused on the N1 amplitude, based on the knowledge that the N1 amplitude is attenuated in schizophrenia and is further deteriorated during AVH, there are other promising neurophysiological markers for EEG/MEG-NFB treatment of AVH. The first promising marker is N1 suppression, which is the suppression of auditory cortical activity during vocalization. N1 suppression is thought to reflect the efferent copy/corollary discharge function of the auditory system, which plays a role in the self-monitoring of speech [48,49▪▪]. Therefore, it is conceivable that based on the self-monitoring theory of AVH, N1 suppression abnormalities are closely related to AVH. A previous study supported this hypothesis by showing less N1 suppression in schizophrenia patients with AVH than in non-AVH patients [50]. Therefore, EEG/MEG-NFB training to modulate N1 suppression by targeting the efferent copy/corollary discharge function has potential as a new treatment for treatment-resistant AVH. In addition, a recent study [51▪] showed improvements in abnormal N1 suppression in schizophrenia patients following 40 h of targeted auditory training (TAT) designed to improve brain function for higher order auditory and cognitive processes. The integration of EEG/MEG-NFB training with TAT may improve the efferent copy/corollary discharge function more effectively than TAT alone.

The second promising marker is the increased spontaneous gamma oscillation obtained using EEG, which has been shown to occur during AVH in schizophrenia patients [23]. This phenomenon has been hypothesized to be caused by hypofunction of the N-methyl-D-aspartate receptor of inhibitory GABAergic interneurons [13,52]. There is an underlying assumption that the excitation and inhibition (E/I) imbalance that is predominantly driven by glutamatergic and GABAergic input causes cortical hyperexcitation, which contributes to psychotic symptoms, such as AVH and delusions. Thus, it would be valuable to develop novel treatments that normalize E/I. Although not without challenges, there is potential for tuning neural alterations as an alternative adjunct treatment to alleviate AVH in schizophrenia by regulating and suppressing the increased spontaneous gamma oscillations using EEG/MEG-NFB. Recently, Molina et al.[53▪] revealed that the malleability of gamma oscillatory power in response to auditory steady-state stimulation after 1 h of TAT predicts improvement of positive and negative symptoms after 30 h of TAT. Given that gamma oscillations that are evoked by an external input interact with spontaneous gamma oscillations, TAT would be expected to drive the plasticity of spontaneous gamma oscillations. Therefore, it is conceivable that combining TAT and EEG/MEG-NFB training would be an effective method to modulate spontaneous gamma oscillations that are related to AVH severity in patients with schizophrenia.


Although the discussed NFB treatments have been shown to have promising effects on treatment-resistant AVH in patients with schizophrenia, some caveats should be noted. First, selection of the appropriate AVH model is crucial as there are several models of perceptual disturbances (e.g. bottom-up theory, sensory-gating theory, salience misattribution theory, and predictive-coding theory) [5,9] and AVH (e.g. self-monitoring hypothesis, reduced sense of control, executive and inhibitory control, unstable memories, source monitoring hypothesis, interhemispheric miscommunication, top-down effect and bottom-up prediction deficits, and a hybrid model of spontaneous activations and self-monitoring deficits) in schizophrenia [7,9,54]. The lack of a definitive model of AVH remains a major concern. Of these models, a noteworthy theory-driven approach is the predictive-coding theory, where the brain is defined as a prediction machine that is based on an internal model of the world and interacts with the world using a computational rule of prediction-error minimization [9,55]. Failures in this predictive processing (because of an altered prediction machine) is thought to lead to psychotic symptoms, such as AVH [9]. Next, appropriate selection of device (e.g. fMRI, EEG, or MEG), index (e.g. BOLD signal, event-related potential/field, and components and/or frequency domains), and activity status (evoked or spontaneous/resting) are crucial as they can impact the overall direction of the NFB strategy and its effectiveness. In particular, spontaneous oscillatory activity (EEG/MEG) and connectivity (EEG/MEG/fMRI) that are related to AVH seem to be new targets for suppressing AVH [21,23,24]. The design of the NFB experiment (e.g. symptom-capture design or upregulation/downregulation design) is another notable issue as the appropriate method differs for each AVH model. It is also necessary to consider whether to target a localized area (e.g. STG, IPG, IFG, or ACC) or a network across a wider area related to AVH. Although targeting the STG seems to be a promising localized NFB strategy [31▪▪,32▪▪], targeting the connectivity between the speech motor and speech perception regions of the language network may also be an effective global network approach (e.g. STG-IFG-IFG) [31▪▪]. In addition, the immediacy of the NFB system and its time lag should be noted. If strict immediacy is required, the delay because of the hemodynamic response of fMRI-NFB training will be problematic. In such cases, EEG/MEG-based NFB training would be more suitable for providing real-time NFB, which has no such delays. Another important factor for real-time NFB is the development of state-of-the-art software that is capable of instantaneous and simultaneous computations of large amounts of data. Novel computational neuroscience approaches may shed light on such real-time computations of complex brain networks [9,56,57]. Furthermore, given the limited efficacy of the above small sample studies as well as some negative findings in NFB trainings [21,47▪,58▪], additional randomized controlled trials (RCTs) with larger samples are essential to ensure the effectiveness of NFB as a widely implemented therapeutic intervention for treatment-resistant AVH [58▪]. There is also the issue of cost-effectiveness. Overall, fMRI-NFB training appears to have a temporal effect for AVH reductions. However, inconveniences and high burden on patients because of high equipment costs, discomfort of the MRI scanner environment, and the high sound volume of the scanner, currently preclude its use in clinical settings. In contrast, EEG is available in many hospitals and institutions at a lower cost. Because of the portability of EEG and new wearable MEG [optically pumped magnetometer (OPM)] [19,59], they are more suitable for NFB approaches that require complex behavioral changes. Moreover, the lack of scanner noise in EEG/MEG makes them ideal methods for targeting AVH. Finally, logistic complexities because of the number of devices (fMRI, fNIRS, EEG, and MEG), modalities (e.g. visual, auditory, and proprioceptive), undefined reinforcement schedule (e.g. continuous or periodic, proportional or binary), undefined reward (e.g. percentage or amplitude), complicated online data processing and artifact rejection/correction [e.g. ocular and muscular artifacts (EEG, MEG), and cardiorespiratory and movement artifacts (fMRI)], the lack of uniformity in NFB systems are the critical issues that need to be solved [60▪▪].


Despite recent innovations in antipsychotics, many schizophrenia patients continue to suffer from severe symptoms, including treatment-resistant AVH and social functioning problems. Despite the small number of RCTs conducted to date, with limited efficacy and most studies constituting small sample sizes or case studies, recent research in schizophrenia patients with treatment-resistant AVH has demonstrated that NFB may be useful in helping patients gain control over AVH through self-regulation of brain function. Further larger sample RCTs to test the efficacy of NFB are required to confirm these encouraging findings. Moreover, although fMRI-NFB may be advantageous for regulating localized neuronal activity related to AVH, EEG/MEG-NFB training may be favorable for fast, real-time NFB for regulating ever-changing AVH in schizophrenia. Although various concerns remain, such as optimal model selection, standardized NFB methodologies, logistic complexities in NFB procedures and high burden on patients, NFB is likely to become a new alternative treatment for schizophrenia in the near future. It is our hope that such innovations in NFB training will help to alleviate severe AVH symptoms and improve social functioning in treatment-resistant schizophrenia patients.


We thank Sarina Iwabuchi, PhD, for editing a draft of this manuscript.

Author Contributions: Y.H. prepared the first draft of the manuscript. Y.H. and S.T. edited the manuscript. All authors contributed to and have approved the final manuscript.

Financial support and sponsorship

This research was supported, in part, by AMED (Japan Agency for Medical Research and Development) under Grant Number JP20dm0207069 and GAJJ020620 (JP19dm0107124h0004) (Y.H.); a Grant-in-Aid for Scientific Research C: JP15K09836 (Y.H.), JP18K07604 (Y.H.), JP19H03579 (Y.H.), JP20K22286 (S.T.) and Fund for the Promotion of Joint International Research (Fostering Joint International Research B): JP20KK0193 (Y.H.) from the Japan Society for the Promotion of Science (JSPS); Medical Research Fund (Y/H/) from Takeda Science Foundation; SIRS Research Fund Award (Y.H.) from Schizophrenia International Research Society.

Conflicts of interest

There are no conflicts of interest.


Papers of particular interest, published within the annual period of review, have been highlighted as:

▪ of special interest

▪▪ of outstanding interest


1. McCutcheon RA, Marques TR, Howes OD. Schizophrenia—an overview. JAMA Psychiatry 2020; 77:201–210.
2. Hirano Y, Oribe N, Onitsuka T, et al. Auditory cortex volume and gamma oscillation abnormalities in schizophrenia. Clin EEG and Neurosci 2020; 51:244–251.
3. Yasuda Y, Okada N, Nemoto K, et al. Brain morphological and functional features in cognitive subgroups of schizophrenia. Psychiat Clin Neurosci 2020; 74:191–203.
4. Wolf A, Ueda K, Hirano Y. Recent updates of eye-movement abnormalities in patients with schizophrenia: a scoping review. Psychiat Clin Neurosci 2020; [Online ahead of print].
5. Horga G, Abi-Dargham A. Publisher correction: an integrative framework for perceptual disturbances in psychosis. Nat Rev Neurosci 2020; 21:297.
6. Sartorius N, Jablensky A, Korten A, et al. Early manifestations and first-contact incidence of schizophrenia in different cultures: a preliminary report on the initial evaluation phase of the WHO collaborative study on determinants of outcome of severe mental disorders. Psychol Med 1986; 16:909–928.
7. Ćurčić-Blake B, Ford JM, Hubl D, et al. Interaction of language, auditory and memory brain networks in auditory verbal hallucinations. Prog Neurobiol 2017; 148:1–20.
8. Sterzer P, Adams RA, Fletcher P, et al. The predictive coding account of psychosis. Biol psychiatry 2018; 84:634–643.
9. Smith R, Badcock P, Friston KJ. Recent advances in the application of predictive coding and active inference models within clinical neuroscience. Psychiatry Clin Neurosci 2020; 75:3–13.
10. Kahn RS, Winter van Rossum I, Leucht S, et al. OPTiMiSE study group. Amisulpride and olanzapine followed by open-label treatment with clozapine in first-episode schizophrenia and schizophreniform disorder (OPTiMiSE): a three-phase switching study. Lancet Psychiatry 2018; 5:797–807.
11. Kane JM, Kishimoto T, Correll CU. Nonadherence to medication in patients with psychotic disorders: epidemiology, contributing factors and management strategies. World Psychiatry 2013; 12:216–226.
12. Coyle JT, Ruzicka WB, Balu DT. Fifty years of research on schizophrenia: the ascendance of the glutamatergic synapse. Am J Psychiatry 2020; 177:1119–1128.
13. Uno Y, Coyle JT. Glutamate hypothesis in schizophrenia. Psychiat Clin Neurosci 2019; 73:204–215.
14. Matsuda Y, Makinodan M, Morimoto T, et al. Neural changes following cognitive remediation therapy for schizophrenia. Psychiatry Clin Neurosci 2019; 73:676–684.
15. Lieberman JA, First MB. Psychotic disorders. N Engl J Med 2018; 379:270–280.
16. Marder SR, Cannon TD. Shizophrenia. N Engl J Med 2019; 381:1753–1761.
17. Swyer A, Powers AR. Voluntary control of auditory hallucinations: phenomenology to therapeutic implications. NPJ Schizophr 2020; 6:1–9.
18. Zotev V, Phillips R, Yuan H, et al. Self-regulation of human brain activity using simultaneous real-time fMRI and EEG neurofeedback. NeuroImage 2014; 85 (Pt 3):985–995.
19. Hironaga N, Takei Y, Mitsudo T, et al. Prospects for future methodological development and application of magnetoencephalography devices in psychiatry. Front Psychiatry 2020; 11:863.
20. Bagherzadeh Y, Baldauf D, Pantazis D, Desimone R. Alpha synchrony and the neurofeedback control of spatial attention. Neuron 2020; 105:577.e5–587.e5.
21. Gandara V, Pineda JA, Shu IW, Singh F. A systematic review of the potential use of neurofeedback in patients with schizophrenia. Schizophr Bull Open 2020; 1:sgaa005.
22. Abiri R, Borhani S, Sellers EW, et al. A comprehensive review of EEG-based brain–computer interface paradigms. J Neural Eng 2019; 16:011001.
23. Hirano Y, Oribe N, Kanba S, et al. Spontaneous gamma activity in schizophrenia. JAMA psychiatry 2015; 72:813–821.
24. Javitt DC, Siegel SJ, Spencer KM, et al. A roadmap for development of neuro-oscillations as translational biomarkers for treatment development in neuropsychopharmacology. Neuropsychopharmacology 2020; 45:1411–1422.
25. Javitt DC, Sweet RA. Auditory dysfunction in schizophrenia: integrating clinical and basic features. Nat Rev Neurosci 2015; 16:535–550.
26. Allen P, Larøi F, McGuire PK, Aleman A. The hallucinating brain: a review of structural and functional neuroimaging studies of hallucinations. Neurosci Biobehav Rev 2008; 32:175–191.
27. Jardri R, Pouchet A, Pins D, Thomas P. Cortical activations during auditory verbal hallucinations in schizophrenia: a coordinate-based meta-analysis. Am J Psychiatry 2011; 168:73–81.
28. Mørch-Johnsen L, Nesvåg R, Jørgensen KN, et al. Auditory cortex characteristics in schizophrenia: associations with auditory hallucinations. Schizophr Bull 2017; 43:75–83.
29. Kühn S, Gallinat J. Quantitative meta-analysis on state and trait aspects of auditory verbal hallucinations in schizophrenia. Schizophr Bull 2012; 38:779–786.
30. Homan P, Kindler J, Hauf M, et al. Repeated measurements of cerebral blood flow in the left superior temporal gyrus reveal tonic hyperactivity in patients with auditory verbal hallucinations: a possible trait marker. Front Hum Neurosci 2013; 7:304.
31▪▪. Orlov ND, Giampietro V, O’Daly O, et al. Real-time fMRI neurofeedback to down-regulate superior temporal gyrus activity in patients with schizophrenia and auditory hallucinations: a proof-of-concept study. Transl Psychiatry 2018; 8:1–10.
32▪▪. Okano K, Bauer CC, Ghosh SS, et al. Real-time fMRI feedback impacts brain activation, results in auditory hallucinations reduction: part 1: Superior temporal gyrus-preliminary evidence. Psychiatry Res 2020; 286:112862.
33. Frith CD. The positive and negative symptoms of schizophrenia reflect impairments in the perception and initiation of action. Psychol Med 1987; 17:631–648.
34. Moseley P, Fernyhough C, Ellison A. Auditory verbal hallucinations as atypical inner speech monitoring, and the potential of neurostimulation as a treatment option. Neurosci Biobehav Rev 2013; 37:2794–2805.
35. Dyck MS, Mathiak KA, Bergert S, et al. Targeting treatment-resistant auditory verbal hallucinations in schizophrenia with fMRI-based neurofeedback–exploring different cases of schizophrenia. Front psychiatry 2016; 7:37.
36. Northoff G, Bermpohl F. Cortical midline structures and the self. Trends Cogn Sci 2004; 8:102–107.
37. Whitfield-Gabrieli S, Moran JM, Nieto-Castañón A, et al. Associations and dissociations between default and self-reference networks in the human brain. NeuroImage 2011; 55:225–232.
38. van Lutterveld R, Diederen KM, Otte WM, Sommer HF. Network analysis of auditory hallucinations in nonpsychotic individuals. Hum brain mapp 2014; 35:1436–1445.
39▪. Bauer CC, Okano K, Gosh SS, et al. Real-time fMRI neurofeedback reduces auditory hallucinations and modulates resting state connectivity of involved brain regions: part 2: default mode network-preliminary evidence. Psychiatry Res 2020; 284:112770.
40. Cole JC, Bernacki CG, Helmer A, et al. Efficacy of transcranial magnetic stimulation (TMS) in the treatment of schizophrenia: a review of the literature to date. Innov Clin Neurosci 2015; 12:12–19.
41. Otani VHO, Shiozawa P, Cordeiro Q, Uchida RR. A systematic review and meta-analysis of the use of repetitive transcranial magnetic stimulation for auditory hallucinations treatment in refractory schizophrenic patients. Int J Psychiatry Clin Pract 2015; 19:228–232.
42. Slotema CW, Aleman A, Daskalakis ZJ, Sommer IE. Meta-analysis of repetitive transcranial magnetic stimulation in the treatment of auditory verbal hallucinations: update and effects after one month. Schizophr Res 2012; 142:40–45.
43. Ehlis AC, Barth B, Hudak J, et al. Near-infrared spectroscopy as a new tool for neurofeedback training: applications in psychiatry and methodological considerations. Jpn Psychol Res 2018; 60:225–241.
44. Storchak H, Hudak J, Haeussinger FB, et al. Reducing auditory verbal hallucinations by means of fNIRS neurofeedback-a case study with a paranoid schizophrenic patient. Schizophr Res 2019; 204:401–403.
45. Ford JM, Dierks T, Fisher DJ, et al. Neurophysiological studies of auditory verbal hallucinations. Schizoph Bull 2012; 38:715–723.
46. Hirano S, Spencer KM, Onitsuka T, et al. Language-related neurophysiological deficits in schizophrenia. Clin EEG and Neurosci 2019; 51:222–233.
47▪. Rieger K, Rarra MH, Díaz Hernàndez L, et al. Neurofeedback-based enhancement of single-trial auditory evoked potentials: treatment of auditory verbal hallucinations in schizophrenia. Clin EEG Neurosci 2018; 49:367–378.
48. Ford JM, Roach BJ, Mathalon DH. Assessing corollary discharge in humans using noninvasive neurophysiological methods. Nat Protoc 2010; 5:1160–1168.
49▪▪. Whitford TJ. Speaking-induced suppression of the auditory cortex in humans and its relevance to schizophrenia. Biol Psychiatry Cogn Neurosci Neuroimaging 2019; 4:791–804.
50. Heinks-Maldonado TH, Mathalon DH, Houde JF, et al. Relationship of imprecise corollary discharge in schizophrenia to auditory hallucinations. Arch Gen Psychiatry 2007; 64:286–296.
51▪. Roach BJ, Ford JM, Biagianti B, et al. Efference copy/corollary discharge function and targeted cognitive training in patients with schizophrenia. Int J Psychophysiol 2019; 145:91–98.
52. Uhlhaas PJ, Singer W. Abnormal neural oscillations and synchrony in schizophrenia. Nat Rev Neurosci 2010; 11:100–113.
53▪. Molina JL, Thomas ML, Joshi YB, et al. Gamma oscillations predict pro-cognitive and clinical response to auditory-based cognitive training in schizophrenia. Transl Psychiatry 2020; 10:1–10.
54. Waters F, Allen P, Aleman A, et al. Auditory hallucinations in schizophrenia and nonschizophrenia populations: a review and integrated model of cognitive mechanisms. Schizophr Bull 2012; 38:683–693.
55. Yamashita Y. Psychiatric disorders as failures in the prediction machine. Psychiatry Clin Neurosci 2021; 75:1–2.
56. Davelaar EJ. Mechanisms of neurofeedback: a computation-theoretic approach. Neurosci 2018; 378:175–188.
57. Paret C, Goldway N, Zich C, et al. Current progress in real-time functional magnetic resonance-based neurofeedback: methodological challenges and achievements. NeuroImage 2019; 202:116107.
58▪. Humpston C, Garrison J, Orlov N, et al. Real-time functional magnetic resonance imaging neurofeedback for the relief of distressing auditory-verbal hallucinations: methodological and empirical advances. Schizophr Bull 2020; 46:1409–1417.
59. Hill RM, Boto E, Rea M, et al. Multichannel whole-head OPM-MEG: helmet design and a comparison with a conventional system. NeuroImage 2020; 219:116995.
60▪▪. Ros T, Enriquez-Geppert S, Zotev V, et al. Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf checklist). Brain 2020; 143:1674–1685.

auditory hallucinations; neurofeedback; schizophrenia; treatment-resistant

Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc.