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

ABSTRACT

Background

Exploring whether cognitive components (identified by baseline cognitive testing and computational modeling) moderate clinical outcome of neurofeedback (NF) for attention-deficit hyperactivity disorder (ADHD).

Method

142 children (aged 7–10) with ADHD were randomly assigned to either NF (n = 84) or control treatment (n = 58) in a double-blind clinical trial (NCT02251743). The NF group received live, self-controlled downtraining of electroencephalographic theta/beta ratio power. The control group received identical-appearing reinforcement from prerecorded electroencephalograms from other children. 133 (78 NF, 55 control) children had cognitive processing measured at baseline with the Integrated Visual and Auditory Continuous Performance Test (IVA2-CPT) and were included in this analysis. A diffusion decision model applied to the IVA2-CPT data quantified two latent cognitive components deficient in ADHD: drift rate and drift bias, indexing efficiency and context sensitivity of cognitive processes involving information integration. We explored whether these cognitive components moderated the improvement in parent- and teacher-rated inattention symptoms from baseline to treatment end (primary clinical outcome).

Results

Baseline cognitive components reflecting information integration (drift rate, drift bias) moderated the improvement in inattention due to NF vs. control treatment (p = 0.006). Specifically, those with either the most or least severe deficits in these components showed more improvement in parent- and teacher-rated inattention when assigned to NF (Cohen’s d = 0.59) than when assigned to control (Cohen’s d = −0.21).

Conclusions

Pre-treatment cognitive testing with computational modeling identified children who benefitted more from neurofeedback than control treatment for ADHD.

KEYWORDS:

• ADHD
• moderators neurofeedback
• computational psychiatry
• personalizing medicine
• RDoC implementation
• diffusion decision model

Acknowledgments

Nadja R. Ging-Jehli was a Graduate Student and thanks the Swiss National Science Foundation for their support of her graduate studies.

Authors’ contributions

N. R. Ging-Jehli, H. C. Kraemer, and L. E. Arnold were involved in data analysis and writing. M. E. Roley-Roberts and R. deBeus were involved in data collection and editing assistance.

Code availability

All analyses were conducted using the common program language R and fortran; and all analyses were described in detail in the Method section.

Disclosure statement

N. R. Ging-Jehli has received research funding from the Swiss National Science Foundation and the neurocognitive tests (IVA2-CPT) to administer to children without ADHD from the IVA testmakers (not part of this study). H. C. Kraemer has nothing to disclose. L. E. Arnold has received research funding from Curemark, Forest, Lilly, Neuropharm, Novartis, Noven, Shire, Supernus, Roche, and YoungLiving (as well as NIH and Autism Speaks), has consulted with CHADD, Gowlings, Neuropharm, Organon, Pfizer, Sigma Tau, Shire, Tris Pharma, and Waypoint, and been on advisory boards for Arbor, Ironshore, Novartis, Noven, Otsuka, Pfizer, Roche, Seaside Therapeutics, Sigma Tau, Shire. M. Roley-Roberts received research funding from Great Plains IDeA Ctr Clinical and Translational Research Pilot Projects Program, National Institute of General Medical Science. R. deBeus received research funding from NIMH; he is on the Board of Directors for the International Society for Neurofeedback and Research and has a clinic in NC where he performs neurofeedback and other clinical services.

Data availability

Additional data are available in the Supplemental Material and all data will be uploaded to NDAR and OSF.

Ethics approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was approved by The Ohio State University’s Institutional Review

Informed consent

Written informed assent and consent were obtained from children and parents/guardians.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/13803395.2023.2206637

Trial registration number and date of registration

submission date of clinical trial NCT02251743 to registry: 17/09/14; recruitment start of participants: 26/09/14

Notes

1. TBR neurofeedback is considered a standard protocol of NF for ADHD (Arnold et al., 2021). Children were required to have a high TBR since past studies showed that those with high TBR would benefit particularly from TBR-lowering neurofeedback (Arns et al., 2012; Monastra et al., 2002).

2. Drift rates for go trials are commonly positive due to their association with the upper response threshold of the DDM. Instead, drift rates for no-go trials are commonly negative due to their association with the lower response threshold of the DDM. To calculate v, we first multiplied all drift rates for no-go trials by minus one, and then averaged drift rates for go and no-go trials across all task conditions, obtaining one v measure per participant. To calculate c_v, we first multiplied all drift rates for no-go trials by minus one, and then calculated the difference between drift rates for go trials and those for no-go trials for each condition. We then averaged these differences across task conditions, obtaining one c_v measure per participant.

Additional information

Funding

This research was supported by the Swiss National Science Foundation awarded to N. R. Ging-Jehli; the National Institute of Mental Health grant # R01-MH100144, Ohio State University College of Medicine Endowment, and the Clinical and Translational Science award 8UL18TR000090-05 from the National Center for Translational Sciences to the Ohio State University.