Can electroencephalography-based neurofeedback treat post-traumatic stress disorder? A meta-analysis study

doi:10.21203/rs.3.rs-3644363/v3

Download PDF

Systematic Review

Can electroencephalography-based neurofeedback treat post-traumatic stress disorder? A meta-analysis study

https://doi.org/10.21203/rs.3.rs-3644363/v3

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Objective: Post-traumatic stress disorder (PTSD) remains a significant clinical challenge with limited treatment options. Although electroencephalogram (EEG) neurofeedback has garnered attention as a prospective treatment modality for PTSD, no comprehensive meta-analysis has been conducted to assess its efficacy and compare different treatment protocols. This study aims to provide a multi-variable meta-regression analysis of EEG neurofeedback's impact on PTSD symptoms, while also assessing variables that may influence treatment outcomes.

Methods: A systematic review was performed to identify controlled studies exploring for the efficacy of EEG neurofeedback on PTSD. The overall effectiveness was evaluated through meta-analysis, and a multi-variable meta-regression was employed to discern fact0rs affecting the EEG neurofeedback efficacy.

Results: EEG neurofeedback yielded a statistically significant reduction in PTSD symptoms immediately post-intervention, with sustained effects at one and three months follow-up. A sub-analysis of sham-controlled studies confirmed that outcomes were not driven by placebo effects. Our findings also identified the target frequency and region, as well as feedback modality, as significant factors for treatment success. In contrast, variables related to treatment duration were not found to be significant moderators, suggesting cost-effectiveness.

Conclusions: EEG neurofeedback emerges as a promising and cost-effective treatment modality for PTSD with the potential for long-term benefits. Our findings challenge commonly utilized protocols and advocate for further research into alternative methodologies to improve treatment efficacy.

PTSD

EEG Neurofeedback

Meta-analysis

Meta-regression

Treatment Protocols

1.1 PTSD

Post-Traumatic Stress Disorder (PTSD) is a psychiatric disorder that arises in response to experiencing or witnessing a traumatic event (American Psychiatric Association, 2013). These can include combat exposure, physical or sexual assault, natural disasters, or accident. The hallmark symptoms of PTSD include intrusive memories, flashbacks, avoidance behaviors, negative alterations in cognition and mood, and hyperarousal. The persistence and intensity of these symptoms can significantly impair an individual's daily functioning, quality of life, and interpersonal relationships.

1.2 Current Treatment Approaches for PTSD

Current treatment approaches for PTSD involve psychotherapy and pharmacotherapy (Schrader & Ross, 2021). Cognitive-behavioral therapy (CBT), especially exposure therapy, helps modify maladaptive thought patterns and behaviors by encouraging patients to confront traumatic memories in a controlled environment (Schrader & Ross, 2021). However, its efficacy depends on patient engagement, and the distress of re-experiencing trauma can lead to dropout or worsened symptoms. Pharmacotherapy, mainly selective serotonin reuptake inhibitors (SSRIs), alleviates symptoms of depression and anxiety, stabilizing mood and improving overall functioning (Schrader & Ross, 2021). Limitations include variability in drug efficacy and potential side effects, addressing only symptomatic relief rather than the root cause of PTSD. With these limitations, many individuals still continue to experience chronic symptoms.

1.3 EEG Neurofeedback

Electroencephalogram (EEG) neurofeedback is another, less common therapeutic approach in treating PTSD. EEG neurofeedback aims to modulate brainwave activity through real-time feedback (Marzbani et al., 2016). This non-invasive method involves training individuals to self-regulate their brain activity by monitoring and receiving feedback on their EEG signals. EEG neurofeedback involves placing electrodes on the scalp to measure electrical activity and targeting specific frequency bands based on symptoms. Individuals receive feedback for their brainwave activity, encouraging self-regulation and achieving desired brain states. Repeated sessions can lead to improved self-regulation and symptom reduction.

There is a recent increase of interest in EEG neurofeedback for PTSD treatment. The first study that showed for the first time a significant reduction in PTSD symptoms with the EEG neurofeedback was by Peniston et al. who gave veterans an alpha-theta targeted neurofeedback (Peniston & Kulkosky, 1991). Studies indicate that individuals with PTSD exhibit abnormal EEG patterns, such as increased spectral power of low beta waves, which are correlated with high anxiety levels (Shim et al., 2017) and EEG neurofeedback can normalize these patterns, potentially promoting better emotional and cognitive functioning.

1.4 Limitations and gaps in knowledge

Given the evolving landscape of research on EEG neurofeedback for PTSD, there is a pressing need for a comprehensive meta-analysis that not only assesses its efficacy as a standalone or adjunct treatment but also integrates the wealth of findings emerging from recent studies. While there are four existing meta-analyses on this topic (Choi et al., 2023; Hong & Park, 2022; Russo et al., 2022; Steingrimsson et al., 2020), their scopes and methodologies exhibit significant limitations. The analyses from 2022 (Hong & Park, 2022; Russo et al., 2022) do not account for the surge of recent studies, underscoring the need for an updated review. Another recent analysis (Choi et al., 2023) does not distinguish between EEG-based neurofeedback and fMRI-based neurofeedback. As these two methods are considered to have a fundamental difference in their mechanism, it is necessary to evaluate the effect of EEG neurofeedback on PTSD specifically. The fourth analysis (Steingrimsson et al., 2020) overlooks the impact of critical moderators such as feedback modality and target brain area, which are crucial for understanding treatment efficacy.

Our proposed meta-analysis is distinct in its methodological rigor, aiming to fill these gaps by employing advanced statistical techniques such as meta-regression to explore the impact of various moderators that have been neglected previously. This includes examining the influence of simultaneous treatments, neurofeedback target areas, target frequencies, and feedback modalities on treatment outcomes. Moreover, we conduct detailed subgroup analyses to uncover how demographics, intervention types, and clinical settings differentially affect treatment effectiveness. This approach allows for a comprehensive understanding of EEG neurofeedback’s efficacy across diverse patient populations and treatment environments, moving beyond the generalized findings of prior reviews. Additionally, our study is unique in its effort to connect empirical findings to underlying theoretical frameworks, specifically focusing on the neural network modulation that may underlie the mechanism of neurofeedback effects on PTSD. This integration not only aims to elucidate the therapeutic mechanisms of EEG neurofeedback but also to guide future research and clinical practice by linking observed outcomes to neurobiological processes.

By synthesizing the latest evidence, including the most recent studies, our meta-analysis seeks to overcome the limitations inherent in individual investigations, offering robust conclusions about the effectiveness of EEG neurofeedback for PTSD. Our comprehensive approach aims to evaluate the efficacy of EEG neurofeedback, reveal the most effective feedback protocols and intervention designs, and, crucially, to advance our theoretical understanding of its mechanisms.

2.1 Search strategy

To identify relevant studies, a comprehensive search was conducted across multiple databases, including PubMed, ScienceDirect, ClinicalTrials.gov, and the Wiley Online Library. This search covered the period up to and including December 2023 from December 2012, to ensure the inclusion of all relevant studies published by the end of that year. The entire search and selection process was carried out by the author, utilizing the PICOS framework to guide the search strategy. The PICOS elements defined were: P (Patient) as PTSD patients, I (Intervention) as EEG-based Neurofeedback, C (Comparison) as Control (PTSD patients receiving either no treatment or treatments other than EEG-based neurofeedback), O (Outcomes) as psychometric assessments of PTSD, and S (Study Design) as Controlled Studies. The search terms employed were (trauma OR (PTSD OR post-traumatic stress disorder)) AND (Neurofeedback OR NF) AND (EEG OR Electroencephalography).

2.2 Inclusion and Exclusion Criteria

The search results were combined from all the databases and were initially deleted for duplicates. Studies were included if they met the following criteria:

Language: English

Design: Controlled Study

Intervention: EEG neurofeedback additional with any protocol

Control group: PTSD patients receiving another form of treatment or being waitlisted

Participant: PTSD patients

Evaluation: PTSD assessment scale

Year: 2013 ~ 2023

Studies were excluded if they were conference abstracts, not full texts, didn’t have a control, or didn’t provide their detailed outcome results in numbers. Studies were not limited to a randomized controlled study.

The inclusion years of 2013 to 2023 for our meta-analysis are chosen based on significant trends in the field and technological advancements. Firstly, research on neurofeedback, especially EEG-based studies, notably increased starting in 2013, with minimal publications before this year. Secondly, advancements in EEG technology after 2013 have greatly enhanced study reliability and comparability, making earlier studies less compatible with current research due to technological disparities.

2.3 Quality Evaluation

Risk of bias was assessed at each study level using the Cochrane risk of bias (RoB) tool in randomized controlled trials (RCTs) (Higgins et al., 2011). The criteria included selection bias, allocation bias, detection bias, performance bias, attrition bias, and reporting bias. These risks were evaluated in three levels (i.e. low, unclear, and high) [Fig. 2]. To test for the publication bias, the asymmetry of the funnel plot is assessed by visual inspection and by Egger’s regression test (Lin & Chu, 2018) [Figs. 3 and 4].

2.4 Data Extraction

Data was extracted manually upon reading the paper. The data extraction process was reviewed thoroughly to ensure there was no mistake in the extraction process. The primary outcome measure of each study was extracted as our intervention effect evaluation metric. Data was all rounded to the hundredth degree. The same measurement was used to investigate the effect at the follow-up phase (if applicable). If any studies did not report the standard deviation (SD) of their outcome measure but instead reported the confidence interval (CI), the following formula was used to convert that CI to SD:

$$SD=\sqrt{N}\times \frac{UpperLimit-LowerLimit}{t}$$

N is the sample size of the study and t is a value from the t-distribution with the appropriate tail according to the CI interval and degree of freedom being N-1. For specific measurements and their characteristics, readers are referred to section 3.2.

As well as outcome measurements, basic information about the study itself, study design, neurofeedback protocol characteristics, and participant characteristics were extracted. Readers are referred to Table 1 for the details of all the specific variables other than the outcome measurements extracted from the studies. If a study did not provide information on any of the variables later used for subsequent data analysis, it is left blank and that study is excluded from that specific analysis.

Table 1

Table of variables extracted from the studies other than the outcome measurement
Category	Items	Description
Basic information	Reference	Link to the study
	Authors
	Publication Year
	Journal
	Country of recruited Patient	Where the patients were recruited
	Year of Recruitment
	Year of Research	Recruitment and the research may be done on different years.
Study Design	follow up duration	When was the follow-up conducted after the experiment? (if applicable)
	Losses to follow up	2 = losses to follow up were less than 15% of total cases 1 = losses were greater than 15% 0 = not reported
	Enrollment condition of study	2 = all cases were recruited 1 = just pick up convenient cases 0 = not reported
	All Outcome Measure	All metrics the study utilizes to evaluate their intervention.
	Primary outcome measure	Their primary outcome measure among all.
	Control group intervention
	Additional intervention	Some studies also allowed participants to continue receiving their prescribed treatment.
	Inclusion criteria
	Exclusion criteria
	Distribution of patients	2 = patients are randomized on both methods 1 = each patient underwent both methods 0 = two non-randomized groups of patients
	Control Sample size	Sample size of a control group.
	Experimental group sample size	Sample size of an experimental group.
Feedback Protocol	Feedback duration	Duration of the intervention in weeks.
	Total number of sessions	Total number of sessions held during the whole intervention.
	# of session in a week
	Duration of one session
	Feedback Modality	Modality in which participants receive their feedback (ex. auditory, visual).
	Extracted Feature	Which feature of the brain wave were given feedback on as well as whether it was aimed to enhance or reduce that feature.
	Target Region	The region of electrode which was targeted.
	Neurofeedback threshold	Threshold it was used to determine the feedback.
Characteristics of participants	Age (Year)	Mean and SD for each control and experimental group.
	Gender	Ratio for each control and experimental group.
	Chronicity	1 = Chronic (Treatment Resistant) 0 = Not chronic
	Time since trauma	Mean and SD

2.5 Statistical Analysis

All analyses were conducted and plots generated using the R package “metafor” (Viechtbauer, 2010). The effect of EEG neurofeedback on PTSD symptoms was quantified using Hedge’s g (Lakens, 2013) with a 95% confidence interval. Hedge’s g, a variation of Cohen’s d, corrects for bias and is suitable for small sample sizes. Negative values indicate a beneficial intervention effect, while positive values indicate no effect or a negative intervention effect. Values less than 0.5 are considered small, 0.5 to 0.8 medium, and greater than 0.8 large.

Heterogeneity was assessed using REML estimation (τ²) (Corbeil & Searle, 1976) and I² statistics (Higgins & Thompson, 2002). I² statistics measure the proportion of variance due to heterogeneity rather than sampling variance, with I² > 25%, 50%, and 70% indicating small, medium, and high heterogeneity, respectively. Due to observed heterogeneity, a random effects model (REM) was used to estimate the average treatment effect size. Meta-regressions were conducted to identify variables contributing to the heterogeneity.

3.1 Search Results

The literature search identified 218 records after the removal of duplicates. After reading the abstracts, 172 articles were excluded according to the inclusion and exclusion criteria. The remaining 46 articles were each screened upon further reading into the full text in which subsequent 35 articles were excluded. For detailed reasons for exclusion, readers are referred to Fig. 1. In the end, 11 studies were left to include in our study [Table 2] (Bell et al., 2019; du Bois et al., 2021; Fruchtman-Steinbok et al., 2021; Leem et al., 2021; Nicholson et al., 2023, 2020; Noohi et al., 2016; Shaw et al., 2023; van der Kolk et al., 2016; Yasuko, 2013).

3.2 Study Characteristics

This meta-analysis included 306 participants from eleven studies, eight of which were randomized controlled trials. Most studies used frequency training (n = 8), with one using the LORETA protocol, and another using the AmygdalaEFP protocol.

The primary outcome measure was the change in PTSD symptom assessments, predominantly using the Clinician-Administered PTSD Scale for DSM (CAPS-5) (n = 5) and the PTSD Checklist for DSM (PCL-5) (n = 4). Some studies also investigated changes in anxiety (n = 3), depression (n = 3), emotion (n = 2), executive control (n = 2), alpha amplitude (n = 2), and fMRI signals (n = 2). Most studies allowed simultaneous treatment with the EEG neurofeedback intervention.

Tables 2 and 3 summarize the study characteristics and outcome measures. Detailed summaries of each study can be found in the OSF repository (refer to the code and data availability section).

Table 2

Study characteristics of the included studies other than its outcome measurements. VA = visual + auditory, VAT = visual + auditory + tactile
Author/Year Published/Country of Patient	NF Sample size	Control Sample size	Feedback Duration	Total number of sessions	# of session in one week	Duration of one session	Feedback Modality	Extracted Feature	Target Region	Neurofeedback threshold	Follow up duration	Age (mean year)	Gender (% of Male)	Chronicity	Time since trauma (mean year)
Kelson/2013/USA	5	5	4	20	5	30	VAT	?	?	?	Non	51.8	100.0	?	?
Van der Kolk/2016/USA	22	22	12	24	2	30	VA	Increase alpha (10–13 Hz) and decrease theta/delta & High beta (2–6 Hz, 22–36 Hz)	T4	% of time spent in that certain frequency range	1 month	44.4	14.3	?	?
Noohi/2016/Iran	15	15	4	25	4	35	Auditory	Increase theta waves ratio (4 to 8 Hz) in relative to alpha waves (8 to 12 Hz)	Pz, P3, P4, O1, O2	?	6w	?	100.0	?	?
Askovic/2019/Australia	13	13	46	27	1 or 2	20	Auditory	Increase SMR (12 ~ 15 Hz) and 8 ~ 10/6 ~ 9 Hz	Cz, C4, T4, and P4	?	Non	44.8	65.4	?	?
Bell/2019/USA	12	11	8	15	2	20	VA	Decrease z-score on three network(DMN, CEN, SN)’s amplitude, coherence, phase, phase shift, phase lock	?	40–60% reward	Non	44.2	?	?	?
Nicholson/2020/Canada	18	18	20	20	1	20	VA	Decrease alpha wave	Pz	65% reward	3 months	43.3	27.8	?	?
Fruchtman-Steinbok/2021/Israel	13	13	13	15	2, than 1	30	VA	AmygdalaEFP	?	Determined by (current delta - baseline delta)/SD	3 and 6 months	36.1	50.0	?	8.7
Bois/2021/Rwanda	10	9	?	7	?	30	Visual	Decrease alpha wave	Pz	If it’s at least 60% smaller than the preceding trial	Non	53.7	0.0	?	?
Leem/2021/Korea	10	9	8	16	2	30	Auditory	Increase Alpha(8–12) & Theta(4–7) and decrease Beta	Pz	?	1 month	44.0	10.5	?	?
Nicholson/2023/Canada	20	18	20	20	1	20	VA	Decrease alpha wave	Pz	65% reward	3 months	42.7	28.9	?	?
Shaw/2023/Canada	18	17	20	20	1	20	VA	Decrease alpha wave	Pz	65% reward	3 months	43.1	28.6	?	?

Table 3

Outcome measurements for each study. When applicable, standard deviations are given in brackets()
Author/Year Published/Country of Patient	Primary Outcome Measure	NF : Mean Before	NF : Mean After	NF : Follow-Up	CT : Mean Before	CT : Mean After	CT : Follow-Up	Other outcome measurement	Other Treatment in NF group
Kelson/2013/USA	Original	72.8	44.2(9.55)	/	69.4	78.8(9.01)	/	None	None
Van der Kolk/2016/USA	CAPS-4	80.98	44.12(19.02)	40.23(20.52)	75.18	65.68(20.18)	64.68(21.49)	None	Continue ongoing treatment (psychotherapeutic, medication)
Noohi/2016/Iran	IES-R	47.20(7.63)	30.40(6.23)	30.46(5.20)	51.07(5.37)	51.14(6.18)	51.21(6.25)	Executive control	None
Askovic/2019/Australia	HTQ	2.8(0.4)	1.9(0.5)	/	3.2(0.5)	3.1(0.6)	/	Anxiety and Depression, Cognitive control	Trauma counseling
Bell/2019/USA	PCL-5	46.17(14.23)	18.08(12.65)	/	49.82(10.16)	31.18(13.53)	/	Anxiety	continue ongoing treatment
Nicholson/2020/Canada	CAPS-5	36.86(10.36)	24.39(15.61)	23.58(14.09)	39.94(7.83)	32.78(12.27)	31.78(12.86)	fMRI analysis	Continue ongoing medication, not therapy
Fruchtman-Steinbok/2021/Israel	PCL-5	53.75(16.49)	46.58(14.01)	46.1(16.56)	59.46(11.23)	61.33(10.33)	59.6(17.24)	Anxiety, depression, Emotion	continue ongoing treatment
Bois/2021/Rwanda	PCL-5	38.9(16.7)	11(5.52)		34.44(23.84)	28.66(20.08)		Resilience, Mental wellbeing, Alpha amplitude	Nothing
Leem/2021/Korea	PCL-5	44.3(10.8)	19.4(7.75)	19.4(6.52)	35.1(18.5))	31(14.92)	29.4(13.99)	Anxiety, Depression, Emotion, Cost effectiveness	Continue ongoing treatment
Nicholson/2023/Canada	CAPS-5	36.52(9.71)	23.19(15.37)	23.65(13.71)	39.94(7.83)	32.78(12.27)	31.78(12.89)	Alpha amplitude	Continue ongoing medication, not therapy
Shaw/2023/Canada	CAPS-5	36.86(10.36)	24.42(17.59)	23.59(17.23)	39.64(7.97)	34.04(11.21)	32.82(19.71)	fMRI analysis	Continue ongoing medication, not therapy

3.3 Risk of Bias

Figure 2 summarizes the RoB assessment. All eleven studies showed a low risk of attrition and reporting bias. However, selection bias was high in two studies due to non-random participant allocation. Six studies exhibited a high risk of bias in participant blinding, and four of these also had high detection bias. Some studies claimed randomization but did not detail the process, causing uncertainty in allocation bias.

3.4 Publication Bias

Studies were plotted by their standard error against the standard mean difference (Cohen’s d) to create a funnel plot and assess publication bias [Figure 3]. Statistically significant asymmetry (p < 0.001) using Egger’s regression test indicates potential publication bias (Lin & Chu, 2018). A Contour-Enhanced funnel plot [Figure 4] also suggests publication bias, as fewer studies appear in the areas indicating statistical insignificance (0.1 < p < 1). However, the small number of studies (n = 11) limits the reliability of these tests (Lin & Chu, 2018). Additionally, no correlation between study size and effect size (p = 0.1697) was found, but the small study effect and potential heterogeneity remain considerations (Sterne et al., 2011).

4.1 PTSD symptom decreases immediately after the treatment but with high heterogeneity between studies

All included studies assessed PTSD symptoms immediately after the intervention. Figure 5 shows a high effect size of EEG neurofeedback (Cohen’s d = -1.28, CI: -1.76 to -0.80, p < 0.0001) with the upper confidence interval still negative. However, there is high heterogeneity among the studies (τ² = 0.4488, I² = 71.57%, p < 0.01).

4.2 Placebo effect not found

Three studies included a sham control and were analyzed separately for placebo effects. Figure 6 shows that even with sham control, EEG neurofeedback had a high effect size (Cohen’s d = -0.63, CI: -1.01 to -0.2, p < 0.05) with no heterogeneity (τ² = 0, I² = 0%, p > 0.05). This consistency may be due to using the same neurofeedback protocol (alpha wave down-regulation at Pz) and a similar population (treatment-resistant PTSD patients in Canada).

4.3 EEG neurofeedback yields long-term effects

Three studies conducted a 1-month follow-up, and four studies conducted a 3-month follow-up to test the long-term effects of EEG neurofeedback. Figure 7 shows the meta-analysis results. None of the studies conducted both 1-month and 3-month follow-ups. One study with a 6-week follow-up was included in the 1-month group. The 1-month follow-up showed a substantial effect (Cohen’s d = -1.72, CI: -3.13 to -0.30, p < 0.05) but with high heterogeneity (τ² = 1.3533, I² = 87.17%, p < 0.01). The 3-month follow-up showed a moderate effect size (Cohen’s d = -0.72, CI: -1.07 to -0.37, p < 0.0001) with no heterogeneity. All three of the four 3-month follow-up studies used the same neurofeedback protocol (alpha down-regulation at Pz) and a similar population (Canadian), while one used a different protocol and population (AmygEFP with an Israeli population). This suggests consistent effects of EEG neurofeedback up to 3 months, regardless of the protocol or population, but the small sample size limits the interpretation. Overall, these results support that EEG neurofeedback can improve PTSD symptoms for up to 3 months.

4.4 Moderators

Given the high heterogeneity across studies, several within-study factors may contribute to this.

4.4.1 # of session, # of weeks, duration of session

Figure 8, 9, and 10 shows the meta-regression computed with the total number of EEG-neurofeedback sessions, total number of weeks spent on the intervention, and duration of each session, respectively, as a moderator. For all moderators, there is still a significant amount of heterogeneity left (i.e. 71%, 80%, and 73%) and the effect of the moderator is not statistically significant (p > 0.05). Thus, these indicate that these do not affect the outcome.

4.4.2 Year

Figure 11 shows the meta-regression using the year of publication as a moderator. Despite significant residual heterogeneity (55%, p < 0.05), the moderator effect is statistically significant (p < 0.01). This indicates that more recent studies show a reduced effect size.

4.4.3 Simultaneous Treatment

Three out of eleven studies had an experimental group without additional treatment (e.g., psychotherapy, medication), and the control group received no medical treatment. In the other eight studies, participants continued their ongoing PTSD treatments. Of these, three allowed psychotropic medication but no other psychotherapy, one required traditional trauma counseling alongside EEG neurofeedback for all participants, and four allowed any treatment outside the experiment. All eight studies prohibited changing treatments during the experiment. Figure 12 shows meta-regression results with simultaneous treatment as a moderator. The residual heterogeneity remains significant (49%, p < 0.05), but the moderator test is statistically significant (p < 0.0001). Studies without simultaneous treatment showed a larger estimated effect size (Cohen’s d = -2.3377) compared to those with simultaneous treatment (Cohen’s d = -0.949).

4.4.5 Targeting Pz

Five out of eleven studies targeted the Pz region, located on the midline parietal lobe. Figure 13 shows meta-regression results using this factor as a moderator. Despite significant residual heterogeneity (65%, p < 0.01), the moderator is statistically significant (p < 0.0001). Studies not targeting Pz showed a larger effect size (Cohen’s d = -1.4983) compared to those targeting Pz (Cohen’s d = -0.8562), opposing the current trend to focus on the Pz region.

4.4.6 Pz Targeted down-regulation of Alpha Wave

Four out of five studies targeting the Pz electrode used an alpha down-regulation neurofeedback protocol. Figure 14 shows the meta-regression with this factor as a moderator. Although there is significant residual heterogeneity (τ² = 0.4488, I² = 54.94%, p < 0.05), 45% of the heterogeneity was explained by this moderator (p < 0.0001). The effect size was larger for studies not using the Pz alpha down-regulation protocol (Cohen’s d = -1.4879) compared to those using it (Cohen’s d = -0.7339), despite its frequent use.

4.4.7 Increasing Alpha Wave

Four out of eleven studies used an alpha up-regulation neurofeedback protocol. Figure 15 shows the meta-regression with this factor as a moderator. Despite significant residual heterogeneity (50%, p < 0.05), the moderator effect is statistically significant (p < 0.0001). Interestingly, studies with alpha up-regulation showed about twice the effect size (Cohen’s d = -1.7090) compared to those without it (Cohen’s d = -0.8262), contrary to the trend of down-regulating alpha.

4.4.8 Feedback Modality

The feedback modality was evaluated as a moderator in eleven studies, categorized as visual only, auditory only, visual + auditory (VA), or visual + auditory + tactile (VAT). Figure 16 shows the results, indicating that feedback modality explained 92% of the heterogeneity. The estimated effect sizes are: VAT = -3.3643 (p < 0.001), Auditory = -2.0186 (p < 0.0001), Visual = -1.1756 (p < 0.05), VA = -0.8258 (p < 0.0001). VAT yielded the highest effect, though based on a single study with a small sample size (n = 10). Interestingly, single modalities (auditory or visual) had higher effect sizes than combined modalities (VA).

In the last 10 years, there has been a rapid increase in studies that test the efficiency of EEG neurofeedback for treating PTSD. This meta-analysis demonstrated the importance of neurofeedback protocol in efficacy and overall strongly supports a need for future research.

5.1 EEG neurofeedback can treat PTSD symptoms

Meta-analytic evidence suggests that EEG neurofeedback is a promising treatment for PTSD symptoms, showing high effect sizes that are statistically significant. Sham-controlled studies confirm that these benefits are not due to placebo effects. High effect sizes persist at both one-month and three-month follow-ups, despite broad confidence intervals likely due to small sample sizes, indicating potential long-term therapeutic effects of EEG neurofeedback.

5.2 EEG neurofeedback can alter neural activity

The premise that brainwave patterns can be modulated through neurofeedback, particularly in treating PTSD with EEG neurofeedback, requires validation. Despite its crucial role in treatment efficacy, comprehensive studies substantiating this for PTSD patients are scarce. Three studies (du Bois et al., 2021; Nicholson et al., 2023; Shaw et al., 2023) focused on downregulating alpha wave amplitude in the Pz region and assessing changes during treatment sessions, consistently demonstrating expected alterations in alpha wave amplitude. One study (Nicholson et al., 2023) observed a significant increase in alpha power in the medial frontal gyrus post-intervention, compared to reduced alpha power at baseline. These results support that neurofeedback can modulate brainwave activity.

5.3 Mechanisms underlying EEG neurofeedback might be the normalization of broad neural network

Empirical evidence indicates EEG neurofeedback can modulate brain activity, but specific mechanisms affecting PTSD patients remain unidentified. A prevailing hypothesis suggests targeted manipulation of brainwave patterns modifies neural networks, especially the brain’s intrinsic connectivity networks (ICNs) (Nicholson et al., 2020; Shaw et al., 2023). The ICNs include the central executive network (CEN), default mode network (DMN), and salient network (SN) (Menon, 2011). Consistent indications of dysfunction in these networks among PTSD patients are identified (Akiki et al., 2017; Holmes et al., 2018; Kennis et al., 2016; Shang et al., 2014). Thus, normalization of these networks could potentially alleviate PTSD symptoms.

Among the eleven reviewed studies, two specifically assessed ICNs before and after EEG neurofeedback (Nicholson et al., 2020; Shaw et al., 2023). Both experimental and control groups underwent fMRI assessments pre- and post-intervention to quantify changes in ICN activity and connectivity. The studies consistently employed an EEG neurofeedback protocol targeting alpha wave reduction at the Pz electrode location. Results demonstrated significant normalization in ICNs among PTSD patients who received EEG neurofeedback. The experimental group exhibited reduced connectivity within the DMN, suggesting normalization of hyperactive posterior DMN activity and improved executive control. Within the SN, diminished connectivity was observed between the right anterior insula and SN, supporting the restorative effects of EEG neurofeedback on system connectivity.

5.4 Midline parietal lobe targeted EEG neurofeedback does not yield a higher effect size

This meta-analysis reveals that protocols targeting the Pz electrode, located over the midline parietal lobe, are associated with smaller effect sizes compared to those targeting other regions. Moreover, our specific examination of alpha wave down-regulation, as studied by Kluetsch et al. (Kluetsch et al., 2014), who explored the ‘alpha rebound effect’—where alpha waves return to a baseline level following down-regulation through EEG neurofeedback, showed that while this approach seemed promising for conditions like PTSD, where reduced alpha waves are common (Eidelman-Rothman et al., 2016), it did not yield higher effect sizes. In contrast, protocols aiming to increase alpha wave activity exhibited higher effect sizes. Thus, while Pz-targeted alpha down-regulation has potential, our findings suggest a need for reevaluating targeted protocols to optimize EEG neurofeedback outcomes.

5.5 Different feedback modalities yield different effect size

Among prevalent neurofeedback modalities, visual and auditory feedback are most extensively utilized across various studies. Currently, no definitive guidelines specify which modality is superior. Data from a single study indicates a highest effect size for a multi-sensory modality combining visual, auditory, and tactile feedback. Auditory feedback alone follows in effect size, with visual feedback alone next. Surprisingly, a combined visual and auditory feedback modality yielded the lowest effect size. This finding challenges the notion that multi-sensory feedback inherently enhances neuroplastic learning. These provisional findings suggest future research should focus on the efficacy and cost-effectiveness of single-modal feedback mechanisms.

5.6 More feedback sessions are not more effective

Our analysis revealed no significant impact of the number of training sessions on PTSD treatment outcomes. Neither the duration of individual sessions nor the overall length of the intervention period were significant factors. These findings suggest that EEG neurofeedback can be effective within a brief intervention period, reducing both financial costs for patients and resource burdens for clinics.

5.7 Simultaneous treatment tends to decrease the efficacy of the neurofeedback

Neurofeedback is often used alongside other therapies, but data indicated that studies prohibiting concurrent treatments yielded higher effect sizes. Studies disallowing additional therapies often involved participants from countries with lower medical standards, while those permitting concomitant therapies involved participants from countries with high-quality medical care. This suggests the efficacy of EEG neurofeedback could be higher in populations with limited access to quality medical care. Future research is necessary to explore these relationships.

6.1 Limitations of the Studies

Many studies lack explicit details on neurofeedback protocols. Future research must specify attributes such as feedback modality, target features, target regions, feedback threshold, duration, and frequency of sessions. Moreover, the role of PTSD chronicity is often overlooked, potentially skewing outcomes. Future studies should assess trauma chronology and control for this variable. Sample sizes are also generally small (max = 44), compromising statistical power. A sample size larger than 70 is recommended [44, 80]. Lastly, less than half of the studies used a double-blind design. Adopting double-blind, randomized controlled trials is crucial for rigor and reliability.

6.2 Limitations of this Study

Due to the nascent state of this field and the predominance of studies with limited sample sizes, drawing definitive conclusions remains challenging.

EEG neurofeedback has emerged as a potential treatment for PTSD, with an increasing number of controlled trials evaluating its efficacy. Despite this, no comprehensive meta-analysis has systematically assessed these studies or quantified EEG neurofeedback’s therapeutic impact on PTSD. Furthermore, there is a lack of meta-analytic evaluations comparing different EEG neurofeedback protocols. In this study, we assessed the overall effectiveness of EEG neurofeedback in mitigating PTSD symptoms. A meta-regression determined that EEG neurofeedback significantly impacted PTSD symptoms immediately post-intervention and sustained this effect over the long term (one and three months), not driven by placebo effects. The feedback protocol, specifically target frequency and region, and the feedback modality, were identified as the most influential factors in treatment success. In contrast, treatment duration, number, frequency, and session number did not influence the outcome. Contrary to the prevalent focus on down-regulating alpha waves at the midline parietal region, our meta-regression indicated that alternative protocols might offer better outcomes. Future studies should investigate other protocols. Similarly, our findings suggest that a single-modality approach, especially auditory feedback, may be more effective and cost-efficient than a multi-modality approach. Based on these results, we encourage future research to explore alternative regions and feedback modalities to enhance treatment effectiveness.

Registration and Protocol

This review was not registered. The review protocol was developed based on Tawfik’s guideline of a systematic review and a meta-analysis (Tawfik et al., 2019).

Data Availability: All data generated or analyzed during this study are available from the OSF repository (https://osf.io/bca82/).

Funding Statement: This research was not funded.

Competing Interest: The author declares that they have no competing interests, financial or otherwise, that have influenced the research and the development of this paper.

Acknowledgements: I would like to extend my deepest gratitude to Sophie Rogers of Pennsylvania University for her invaluable guidance on research methodology and result synthesis throughout the course of this research endeavor. Her meticulous review of the final manuscript has been instrumental in enhancing the quality of this work.

Conflict of Interest

The author declares that there are no competing interests in the publication of this paper. No funds, grants, or other support was received. There are no financial, personal, or professional relationships that could potentially influence or bias the work presented herein. All procedures and methodologies were conducted impartially and without any conflict of interest.

Code and Data Availability

All data and code used in the meta-analysis presented in this paper are openly available for the benefit of the scientific community and to ensure the transparency and reproducibility of our work. The datasets, along with the associated code, have been deposited in the Open Science Framework (OSF) repository (bca82). This allows for both the validation of the results reported here and the potential for further analysis by other researchers. We strongly encourage fellow scientists to utilize these resources in the interest of advancing the field.

Akiki, T. J., Averill, C. L., & Abdallah, C. G. (2017). A network-based neurobiological model of PTSD: Evidence from structural and functional neuroimaging studies. Current Psychiatry Reports, 19(11), 81. https://doi.org/10.1007/s11920-017-0840-4.
American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders (DSM-5 (R)) (5th ed.). American Psychiatric Association Publishing. https://doi.org/10.1176/appi.books.9780890425596.
Bell, A., Moss, D., Kallmeyer, R., & NeuroRegulation (2019). 6(2), 54–70. https://doi.org/10.15540/nr.6.2.54.
Choi, Y. J., Choi, E. J., & Ko, E. (2023). Neurofeedback effect on symptoms of posttraumatic stress disorder: A systematic review and meta-analysis. Applied Psychophysiology and Biofeedback, 48(3), 259–274. https://doi.org/10.1007/s10484-023-09593-3.
Corbeil, R. R., & Searle, S. R. (1976). Restricted maximum likelihood (REML) estimation of variance components in the mixed model. Technometrics: A Journal of Statistics for the Physical Chemical and Engineering Sciences, 18(1), 31. https://doi.org/10.2307/1267913.
du Bois, N., Bigirimana, A. D., Korik, A., Kéthina, L. G., Rutembesa, E., Mutabaruka, J., Mutesa, L., Prasad, G., Jansen, S., & Coyle, D. H. (2021). Neurofeedback with low-cost, wearable electroencephalography (EEG) reduces symptoms in chronic Post-Traumatic Stress Disorder. Journal of Affective Disorders, 295, 1319–1334. https://doi.org/10.1016/j.jad.2021.08.071.
Eidelman-Rothman, M., Levy, J., & Feldman, R. (2016). Alpha oscillations and their impairment in affective and post-traumatic stress disorders. Neuroscience and Biobehavioral Reviews, 68, 794–815. https://doi.org/10.1016/j.neubiorev.2016.07.005.
Fruchtman-Steinbok, T., Keynan, J. N., Cohen, A., Jaljuli, I., Mermelstein, S., Drori, G., Routledge, E., Krasnoshtein, M., Playle, R., Linden, D. E. J., & Hendler, T. (2021). Amygdala electrical-finger-print (AmygEFP) NeuroFeedback guided by individually-tailored Trauma script for post-traumatic stress disorder: Proof-of-concept. NeuroImage Clinical, 32(102859), 102859. https://doi.org/10.1016/j.nicl.2021.102859.
Higgins, J. P. T., Altman, D. G., Gøtzsche, P. C., Jüni, P., Moher, D., Oxman, A. D., Savovic, J., Schulz, K. F., Weeks, L., Sterne, J. A. C., & Cochrane Statistical Methods Group., Cochrane Bias Methods Group, & ((2011).). The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials. BMJ (Clinical Research Ed.), 343(oct18 2), d5928. https://doi.org/10.1136/bmj.d5928.
Higgins, J. P. T., & Thompson, S. G. (2002). Quantifying heterogeneity in a meta-analysis. Statistics in Medicine, 21(11), 1539–1558. https://doi.org/10.1002/sim.1186.
Holmes, S. E., Scheinost, D., DellaGioia, N., Davis, M. T., Matuskey, D., Pietrzak, R. H., Hampson, M., Krystal, J. H., & Esterlis, I. (2018). Cerebellar and prefrontal cortical alterations in PTSD: structural and functional evidence. Chronic Stress (Thousand Oaks Calif), 2, 247054701878639. https://doi.org/10.1177/2470547018786390.
Hong, J., & Park, J. H. (2022). Efficacy of neuro-feedback training for PTSD symptoms: A systematic review and meta-analysis. International Journal of Environmental Research and Public Health, 19(20), 13096. https://doi.org/10.3390/ijerph192013096.
Kennis, M., van Rooij, S. J. H., van den Heuvel, M. P., Kahn, R. S., & Geuze, E. (2016). Functional network topology associated with posttraumatic stress disorder in veterans. NeuroImage Clinical, 10, 302–309. https://doi.org/10.1016/j.nicl.2015.12.008.
Kluetsch, R. C., Ros, T., Théberge, J., Frewen, P. A., Calhoun, V. D., Schmahl, C., Jetly, R., & Lanius, R. A. (2014). Plastic modulation of PTSD resting-state networks and subjective wellbeing by EEG neurofeedback. Acta Psychiatrica Scandinavica, 130(2), 123–136. https://doi.org/10.1111/acps.12229.
Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4, 863. https://doi.org/10.3389/fpsyg.2013.00863.
Leem, J., Cheong, M. J., Lee, H., Cho, E., Lee, S. Y., Kim, G. W., & Kang, H. W. (2021). Effectiveness, cost-utility, and safety of neurofeedback self-regulating training in patients with post-traumatic stress disorder: A randomized controlled trial. Healthcare (Basel Switzerland), 9(10), 1351. https://doi.org/10.3390/healthcare9101351.
Lin, L., & Chu, H. (2018). Quantifying publication bias in meta-analysis. Biometrics, 74(3), 785–794. https://doi.org/10.1111/biom.12817.
Marzbani, H., Marateb, H. R., & Mansourian, M. (2016). Neurofeedback: A comprehensive review on system design, methodology and clinical applications. Basic and Clinical Neuroscience, 7(2), 143–158. https://doi.org/10.15412/J.BCN.03070208.
Menon, V. (2011). Large-scale brain networks and psychopathology: a unifying triple network model. Trends in Cognitive Sciences, 15(10), 483–506. https://doi.org/10.1016/j.tics.2011.08.003.
Nicholson, A. A., Densmore, M., Frewen, P. A., Neufeld, R. W. J., Théberge, J., Jetly, R., Lanius, R. A., & Ros, T. (2023). Homeostatic normalization of alpha brain rhythms within the default-mode network and reduced symptoms in post-traumatic stress disorder following a randomized controlled trial of electroencephalogram neurofeedback. Brain Communications, 5(2), fcad068. https://doi.org/10.1093/braincomms/fcad068.
Nicholson, A. A., Ros, T., Densmore, M., Frewen, P. A., Neufeld, R. W. J., Théberge, J., Jetly, R., & Lanius, R. A. (2020). A randomized, controlled trial of alpha-rhythm EEG neurofeedback in posttraumatic stress disorder: A preliminary investigation showing evidence of decreased PTSD symptoms and restored default mode and salience network connectivity using fMRI. NeuroImage Clinical, 28(102490), 102490. https://doi.org/10.1016/j.nicl.2020.102490.
Noohi, S., Nooripour, R., Miraghaie, A. M., & Arabi, A. (2016). Effectiveness of neurofeedback treatment with alpha/theta method on PTSD symptoms and their executing function. 28(5). https://www.researchgate.net/publication/328163862_Effectiveness_of_neurofeedback_treatment_with_alphatheta_method_on_PTSD_symptoms_and_their_executing_function.
Peniston, E., & Kulkosky, P. (1991). Alpha-theta brainwave neuro-feedback for Vietnam veterans with combat related post-traumatic stress disorder. http://braintrainut.com/wp-content/uploads/2013/10/Peniston-alpha-theta-PTSD.pdf.
Russo, G. M., Balkin, R. S., & Lenz, A. S. (2022). A meta-analysis of neurofeedback for treating anxiety‐spectrum disorders. Journal of Counseling and Development: JCD, 100(3), 236–251. https://doi.org/10.1002/jcad.12424.
Schrader, C., & Ross, A. (2021). A review of PTSD and current treatment strategies. Missouri Medicine, 118(6), 546–551. https://pubmed.ncbi.nlm.nih.gov/34924624/.
Shang, J., Lui, S., Meng, Y., Zhu, H., Qiu, C., Gong, Q., Liao, W., & Zhang, W. (2014). Alterations in low-level perceptual networks related to clinical severity in PTSD after an earthquake: a resting-state fMRI study. PloS One, 9(5), e96834. https://doi.org/10.1371/journal.pone.0096834.
Shaw, S. B., Nicholson, A. A., Ros, T., Harricharan, S., Terpou, B., Densmore, M., Theberge, J., Frewen, P., & Lanius, R. A. (2023). Increased top-down control of emotions during symptom provocation working memory tasks following a RCT of alpha-down neurofeedback in PTSD. NeuroImage Clinical, 37(103313), 103313. https://doi.org/10.1016/j.nicl.2023.103313.
Shim, M., Im, C. H., & Lee, S. H. (2017). Disrupted cortical brain network in post-traumatic stress disorder patients: a resting-state electroencephalographic study. Translational Psychiatry, 7(9), e1231. https://doi.org/10.1038/tp.2017.200.
Steingrimsson, S., Bilonic, G., Ekelund, A. C., Larson, T., Stadig, I., Svensson, M., Vukovic, I. S., Wartenberg, C., Wrede, O., & Bernhardsson, S. (2020). Electroencephalography-based neurofeedback as treatment for post-traumatic stress disorder: A systematic review and meta-analysis. European Psychiatry: The Journal of the Association of European Psychiatrists, 63(1), e7. https://doi.org/10.1192/j.eurpsy.2019.7.
Sterne, J. A. C., Sutton, A. J., Ioannidis, J. P. A., Terrin, N., Jones, D. R., Lau, J., Carpenter, J., Rücker, G., Harbord, R. M., Schmid, C. H., Tetzlaff, J., Deeks, J. J., Peters, J., Macaskill, P., Schwarzer, G., Duval, S., Altman, D. G., Moher, D., & Higgins, J. P. T. (2011). Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ (Clinical Research Ed), 343(jul22 1), d4002. https://doi.org/10.1136/bmj.d4002.
Tawfik, G. M., Dila, K. A. S., Mohamed, M. Y. F., Tam, D. N. H., Kien, N. D., Ahmed, A. M., & Huy, N. T. (2019). A step by step guide for conducting a systematic review and meta-analysis with simulation data. Tropical Medicine and Health, 47(1), 46. https://doi.org/10.1186/s41182-019-0165-6.
van der Kolk, B. A., Hodgdon, H., Gapen, M., Musicaro, R., Suvak, M. K., Hamlin, E., & Spinazzola, J. (2016). A randomized controlled study of neurofeedback for chronic PTSD. PloS One, 11(12), e0166752. https://doi.org/10.1371/journal.pone.0166752.
Viechtbauer, W. (2010). Conducting Meta-Analyses inRwith themetaforPackage. Journal of Statistical Software, 36(3), 1–48. https://doi.org/10.18637/jss.v036.i03.
Yasuko, K. C. (2013). Veterans Symptoms of Posttraumatic Stress Disorder (PTSD). ProQuest. -origsite=gscholar&cbl=18750&diss=y. http://search.proquest.com/openview/019272465d8a47db88d836776a6a0ccb/1?pq.

No competing interests reported.

Download PDF

Reviewers invited by journal
10 Jun, 2024
Editor assigned by journal
05 Jun, 2024
Submission checks completed at journal
05 Jun, 2024
First submitted to journal
04 Jun, 2024

You are reading this latest preprint version

Can electroencephalography-based neurofeedback treat post-traumatic stress disorder? A meta-analysis study

Status:

Version 3

Abstract

Figures

1 Introduction

1.1 PTSD

1.2 Current Treatment Approaches for PTSD

1.3 EEG Neurofeedback

1.4 Limitations and gaps in knowledge

2 Method

2.1 Search strategy

2.2 Inclusion and Exclusion Criteria

2.3 Quality Evaluation

2.4 Data Extraction

2.5 Statistical Analysis

3 Methodological Results

3.1 Search Results

3.2 Study Characteristics

3.3 Risk of Bias

3.4 Publication Bias

4 Contextual Results

4.1 PTSD symptom decreases immediately after the treatment but with high heterogeneity between studies

4.2 Placebo effect not found

4.3 EEG neurofeedback yields long-term effects

4.4 Moderators

4.4.1 # of session, # of weeks, duration of session

4.4.2 Year

4.4.3 Simultaneous Treatment

4.4.5 Targeting Pz

4.4.6 Pz Targeted down-regulation of Alpha Wave

4.4.7 Increasing Alpha Wave

4.4.8 Feedback Modality

5 Discussion

5.1 EEG neurofeedback can treat PTSD symptoms

5.2 EEG neurofeedback can alter neural activity

5.3 Mechanisms underlying EEG neurofeedback might be the normalization of broad neural network

5.4 Midline parietal lobe targeted EEG neurofeedback does not yield a higher effect size

5.5 Different feedback modalities yield different effect size

5.6 More feedback sessions are not more effective

5.7 Simultaneous treatment tends to decrease the efficacy of the neurofeedback

6 Limitations

6.1 Limitations of the Studies

6.2 Limitations of this Study

7 Conclusion

Statements and Declarations

References

Additional Declarations

Status:

Version 3