The Current Evidence Levels for Biofeedback and Neurofeedback Interventions in Treating Depression: A Narrative Review
Overview
Authors
Affiliations
This article is aimed at showing the current level of evidence for the usage of biofeedback and neurofeedback to treat depression along with a detailed review of the studies in the field and a discussion of rationale for utilizing each protocol. La Vaque et al. criteria endorsed by the Association for Applied Psychophysiology and Biofeedback and International Society for Neuroregulation & Research were accepted as a means of study evaluation. Heart rate variability (HRV) biofeedback was found to be moderately supportable as a treatment of MDD while outcome measure was a subjective questionnaire like Beck Depression Inventory (level 3/5, "probably efficacious"). Electroencephalographic (EEG) neurofeedback protocols, namely, alpha-theta, alpha, and sensorimotor rhythm upregulation, all qualify for level 2/5, "possibly efficacious." Frontal alpha asymmetry protocol also received limited evidence of effect in depression (level 2/5, "possibly efficacious"). Finally, the two most influential real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback protocols targeting the amygdala and the frontal cortices both demonstrate some effectiveness, though lack replications (level 2/5, "possibly efficacious"). Thus, neurofeedback specifically targeting depression is moderately supported by existing studies (all fit level 2/5, "possibly efficacious"). The greatest complication preventing certain protocols from reaching higher evidence levels is a relatively high number of uncontrolled studies and an absence of accurate replications arising from the heterogeneity in protocol details, course lengths, measures of improvement, control conditions, and sample characteristics.
Decoded EEG neurofeedback-guided cognitive reappraisal training for emotion regulation.
Li L, Gui X, Huang G, Zhang L, Wan F, Han X Cogn Neurodyn. 2024; 18(5):2659-2673.
PMID: 39555250 PMC: 11564442. DOI: 10.1007/s11571-024-10108-x.
Reinforcement learning processes as forecasters of depression remission.
Bansal V, McCurry K, Lisinski J, Kim D, Goyal S, Wang J J Affect Disord. 2024; 368:829-837.
PMID: 39271064 PMC: 11573115. DOI: 10.1016/j.jad.2024.09.066.
Li L, Li Y, Li Z, Huang G, Liang Z, Zhang L Cogn Neurodyn. 2024; 18(3):847-862.
PMID: 38826665 PMC: 11143167. DOI: 10.1007/s11571-023-09939-x.
Wider W, Mutang J, Chua B, Pang N, Jiang L, Fauzi M Front Hum Neurosci. 2024; 18:1339444.
PMID: 38799297 PMC: 11116792. DOI: 10.3389/fnhum.2024.1339444.
Wang H, Zhang X, Wang P, Dai G, Liu L, Xu Y Acta Neurol Belg. 2024; 124(3):871-877.
PMID: 38285160 DOI: 10.1007/s13760-023-02471-z.