Application of functional connectivity neurofeedback in patients with treatment-resistant depression: A preliminary report
•We are developing a neurofeedback method for depression treatment.•Efficacy was tested in patients with treatment-resistant depression.•Five of six patients completed the training sessions and showed improvement.•Reducing training burden and optimal patient selection remain challenges. Functional c...
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Veröffentlicht in: | Journal of affective disorders reports 2023-12, Vol.14, p.100644, Article 100644 |
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Sprache: | eng |
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Zusammenfassung: | •We are developing a neurofeedback method for depression treatment.•Efficacy was tested in patients with treatment-resistant depression.•Five of six patients completed the training sessions and showed improvement.•Reducing training burden and optimal patient selection remain challenges.
Functional connectivity neurofeedback (FCNef) is a technique that modulates synchronous neural activity through training and is being investigated as a potential novel treatment for patients suffering from treatment-resistant depression (TRD). An FCNef protocol, based on the analysis of resting-state functional imaging data from a large cohort of depressed individuals, has been proposed to promote negative functional connectivity between the dorsolateral prefrontal cortex and the posterior cingulate cortex (DLPFC-PCC FC). This study aimed to assess the therapeutic efficacy and practicality of the protocol in a small sample of TRD patients. Of the six patients recruited, five completed the FCNef sessions. Depression and rumination scores significantly improved post-treatment, however, there were no significant changes in DLPFC-PCC FC. The study demonstrated efficacy of FCNef in ameliorating depressive symptoms, yet, it also indicated that the training itself may be burdensome for depressed patients, as evidenced by participants reporting fatigue (one of whom dropped out). Thus, a more efficient and less burdensome protocol is needed for future investigations and applications. |
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ISSN: | 2666-9153 2666-9153 |
DOI: | 10.1016/j.jadr.2023.100644 |