Neurofeedback is a type of biofeedback training where individuals learn to control and normalize patterns of brain activity using real-time displays of neural signals.
By teaching self-regulation of brain function,neurofeedback can be a non-pharmacological treatmentapproach for various mental health conditions like ADHD, anxiety, depression, and more.
Key Points
Rationale
Medications have limited efficacy, with up to a third of children not responding, highlighting the need for alternative treatments (Adler et al., 2006).
Neurofeedback (NF) is a promising non-pharmacological intervention where individuals learn to modulate brain activity patterns. However, NF’s effects on cognition are unclear. Understanding the cognitive mechanisms of NF is critical for treatment planning and personalization.
This study comprises a secondary analysis of a large double-blindrandomized controlled trial (RCT)on NF for ADHD (Neurofeedback Collaborative Group, 2021).
Although the primary outcome of ADHD symptom improvement showed nonspecific gains for all children, moderator analyses indicated NF efficacy depended on comorbidities. Additionally, baseline cognitive signatures predicted NF response (Ging-Jehli et al., 2023).
Given the mixed results and NF’s conceptualization as areinforcement learningtreatment (Lubianiker et al., 2022), investigating NF’s cognitive effects is vital.
This study aimed to assess whether NF improves the efficiency of information processing, a core deficit in ADHD (Ging-Jehli et al., 2021), using computational modeling. The diffusion decision model (DDM) was applied to data from a continuous performance test to quantify latent cognitive components before and after treatment.
Method
Materials/Instruments:
Procedure
Secondary analysis of a double-blind RCT; diffusion decision model (DDM) analysis
The ADHD group received 38 sessions of NF or controlled treatment with assessments at baseline, mid-, and end-treatment. Controls had one assessment.
Sample
Statistical Measures
Results
The key analysis showed the efficiency of integrating auditory information (phi v) improved significantly more over time with NF compared to control treatment, supporting the hypothesis.
Additionally, context sensitivity (cv) improved more over time with NF, indicating more consistent responses across trial types, especially for auditory trials.
Comparing ADHD to healthy controls at baseline revealed significantly lower phi v in ADHD, confirming integration inefficiency as a deficit before treatment. NF also reduced latency of non-decisional processes like encoding and motor execution (Ter) whereas control treatment showed no Ter changes.
However, response cautiousness (a) and bias (z/a) did not significantly differ between groups over time.
Insight
Applying computational modeling provided valuable insights into NF’s effects on cognition.
The significant gains in auditory performance align with previous evidence showing auditory deficits in ADHD (Ging-Jehli et al., 2022).
As auditory stimuli unfold sequentially and require sustained attention, unlike visual input allowing a “second look”, improving auditory processing abilities could promote attention and reduce distraction.
The reductions in non-decision time could also indicate faster encoding and response readiness resulting from NF training.
Together, these cognitive changes likely contribute to behavioral improvements in attention andimpulse controlthat characterize ADHD.
By focusing analyses on component processes, this study illustrates how NF may remediate the underlying pathophysiology as opposed to just alleviating symptoms. Additionally, some ADHD medications target similar cognitive mechanisms (e.g. drift rates), further validating deficient information integration as a core dysfunction (Ging-Jehli et al., 2021).
Applying computational methods could therefore clarify the neurobiological effects of various ADHD treatments.
Strengths
This research had several key strengths:
Limitations
However, this study also had some limitations:
Implications
Even with the limitations, these results have useful real-world implications.
Patients with more difficulty efficiently integrating information and paying auditory attention seem especially likely to improve with NF. The thinking patterns spotted through computer modeling could be used along with symptom ratings to personalize ADHD treatment.
The findings also show auditory processing should get more focus. NF protocols may need to emphasize sound-based tasks more to optimize gains.
Sounds surround us at school, home, and socially, so better auditory skills could really help functioning and quality of life. Doctors should start checking for auditory issues, too, since these are often overlooked.
Additionally, the results support NF as an alternative or extra therapy to stimulant meds. Showing NF impacts brain function like medication does is promising for NF as a non-drug option targeting underlying ADHD issues. Parents and physicians have wanted more evidence-based choices, given many patients struggle with medication side effects and taking it as prescribed.
Lastly, using computer modeling here is an innovative way to study how treatments work. Applying similar testing to understand psychotherapy or new drugs could identify their effects on thinking.
Overall, adding computational methods to clinical trials may speed up the development of personalized therapies.
References
Primary reference
Ging-Jehli, N. R., Painter, Q. A., Kraemer, H. A., Roley-Roberts, M. E., Panchyshyn, C., deBeus, R., & Arnold, L. E. (2023). A diffusion decision model analysis of the cognitive effects of neurofeedback for ADHD.Neuropsychology.Advance online publication.https://doi.org/10.1037/neu0000932
Other references
Adler, L. A., Reingold, L. S., Morrill, M. S., & Wilens, T. E. (2006). Combination pharmacotherapy for adult ADHD.Current Psychiatry Reports, 8(5), 409–415.https://doi.org/10.1007/s11920-006-0044-9
American Psychiatric Association. (2013).Diagnostic and statistical manual of mental disorders(5th ed.).https://doi.org/10.1176/appi.books.9780890425596
Ging-Jehli, N. R., Kraemer, H. C., Eugene Arnold, L., Roley-Roberts, M. E., & deBeus, R. (2023). Cognitive markers for efficacy of neurofeedback for attention-deficit hyperactivity disorder–personalized medicine using computational psychiatry in a randomized clinical trial.Journal of Clinical and Experimental Neuropsychology, 45(2), 118-131.https://doi.org/10.1080/13803395.2023.2206637
Ging-Jehli, N. R., Ratcliff, R., & Arnold, L. E. (2021). Improving neurocognitive testing using computational psychiatry-A systematic review for ADHD.Psychological Bulletin, 147(2), 169-231.https://doi.org/10.1037/bul0000319
Lubianiker, N., Paret, C., Dayan, P., & Hendler, T. (2022). Neurofeedback through the lens of reinforcement learning.Trends in Neurosciences, 45(8), 579-593.https://doi.org/10.1016/j.tins.2022.03.008
Roley-Roberts, M. E., Pan, X., Bergman, R., Tan, Y., Hendrix, K., deBeus, R., Kerson, C., Arns, M., Ging Jehli, N. R., Connor, S., Schrader, C., & Arnold, L. E. (2023). For which children with ADHD is TBR neurofeedback effective? Comorbidity as a moderator.Applied Psychophysiology and Biofeedback, 48(2), 179-188.https://doi.org/10.1007/s10484-022-09575-x
The Neurofeedback Collaborative Group. (2021). Double-blind placebo-controlled randomized clinical trial of neurofeedback for attention-deficit/hyperactivity disorder with 13 month follow-up.Journal of the American Academy of Child & Adolescent Psychiatry, 60(7), 841-855.https://doi.org/10.1016/j.jaac.2020.07.906
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Saul McLeod, PhD
BSc (Hons) Psychology, MRes, PhD, University of Manchester
Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.
Olivia Guy-Evans, MSc
BSc (Hons) Psychology, MSc Psychology of Education
Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.
