Cognitive markers for efficacy of neurofeedback for attention-deficit hyperactivity disorder – personalized medicine using computational psychiatry in a randomized clinical trial

Ging-Jehli, Nadja R.; Kraemer, Helena C.; Arnold, L. Eugene; Roley‐Roberts, Michelle E.; deBeus, Roger

doi:10.1080/13803395.2023.2206637

Cited by 3 publications

(4 citation statements)

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“…This seems particularly relevant when considering that the primary outcome (symptom improvement in inattention rated by parents and teachers) of this RCT showed unspecific improvement for all children, irrespective of whether they received NF or control treatment (The Neurofeedback Collaborative Group, 2021, 2023). Moreover, a recent secondary moderator analysis of this RCT showed that baseline cognitive characteristics identified children that benefitted more from NF than control treatment (Ging-Jehli et al, 2023). These findings therefore emphasize the importance of understanding the cognitive effects (i.e., active components) of NF as a cognitive reinforcement learning intervention.…”

Section: Discussionmentioning

confidence: 99%

“…Hence, we refer to c v as a measure for context sensitivity of processes involved with information integration (with c v = zero representing the optimal level). In so doing, we follow the practice of recent studies (Ging-Jehli et al, 2022, 2023).…”

Section: Methodsmentioning

confidence: 99%

“…However, moderator analyses showed significant effects in comorbidity subgroups; at treatment end, those with comorbid anxiety without oppositional-defiant disorder (ODD) improved significantly more with the control treatment than with NF, and at 13-month follow-up, those with ODD without anxiety were significantly more improved with NF than control treatment (Roley-Roberts et al, 2023). Moreover, baseline cognitive signatures, quantified by applying computational modeling to the baseline neurocognitive data, indicated children who benefitted from receiving NF treatment (Ging-Jehli et al, 2023). Due to these primary and secondary findings and given the circumstance that NF can be seen as a reinforcement learning intervention (Lubianiker et al, 2022), it is critical to understand the cognitive mechanisms of action for personalized treatment planning.…”

Section: The Present Studymentioning

confidence: 99%

“…However, these differences were only detectable when considering comorbidities and DSM -defined presentations. Some researchers have already emphasized the importance of drift bias ( c v ) and that it is a neglected parameter (Ging-Jehli et al, 2023; Kloosterman et al, 2019; Starns et al, 2012).…”

Section: The Present Studymentioning

confidence: 99%

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A diffusion decision model analysis of the cognitive effects of neurofeedback for ADHD.

Ging-Jehli,

Painter,

Kraemer

et al. 2024

Neuropsychology

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Objective: To examine cognitive effects of neurofeedback (NF) for attention-deficit hyperactivity disorder (ADHD) as a secondary outcome of a randomized clinical trial. Method: In a double-blind randomized clinical trial (NCT02251743), 133 7-10-year olds with ADHD received either 38 sessions of NF (n = 78) or control treatment (n = 55) and performed an integrated visual and auditory continuous performance test at baseline, mid-and end-treatment. We used the diffusion decision model to decompose integrated visual and auditory continuous performance test performance at each assessment into cognitive components: efficiency of integrating stimulus information (v), context sensitivity (c v ), response cautiousness (a), response bias (z /a), and nondecision time for perceptual encoding and response execution (T er ). Based on prior findings, we tested whether the components known to be deficient improved with NF and explored whether other cognitive components improved using linear mixed modeling. Results: Before NF, children with ADHD showed main deficits in integrating stimulus information (v), which led to less accurate and slower responses than healthy controls ( p = .008). The NF group showed significantly more improvement in integrating auditory stimulus information (v) than control treatment (significant group-by-time-by-modality effect: p = .044). Conclusions: NF seems to improve v, deficient in ADHD. Key PointsQuestion: How does cognitive processing change in response to neurofeedback? Findings: Neurofeedback improves latent cognitive processes that are involved in integrating stimulus information and that are quantified with sequential sampling models. Importance: It is therefore critical to understand the underlying cognitive effects of neurofeedback, which can help to personalize treatment planning. Next Steps: More studies are needed that use process-oriented computational models, together with mechanistic neurocognitive tests, to disentangle the different therapeutic ingredients of neurofeedback.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%