Cognitive markers for efficacy of neurofeedback for attention-deficit hyperactivity disorder - personalized medicine
Nadja R Ging-Jehli1,2, Helena C Kraemer3, L Eugene Arnold4
1Department of Psychology, The Ohio State University, Columbus, OH, USA.
Insights
Neurofeedback (NF) shows improved attention for children with ADHD, especially those with specific cognitive deficits. Pre-treatment cognitive assessment can predict who benefits most from NF therapy.
Area of Science:
- Neuroscience
- Clinical Psychology
- Computational Psychiatry
Background:
- Attention-deficit hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder characterized by inattention and hyperactivity.
- Neurofeedback (NF) is a non-pharmacological treatment aiming to improve self-regulation in ADHD.
- Identifying predictors of treatment response is crucial for optimizing ADHD management.
Purpose of the Study:
- To investigate whether cognitive components, identified through computational modeling of baseline cognitive testing, moderate the clinical outcomes of NF in children with ADHD.
- To determine if specific cognitive deficits predict differential response to NF compared to a control treatment.
Main Methods:
- A double-blind clinical trial randomized 142 children (aged 7-10) with ADHD to NF or a sham control treatment.
- Cognitive processing was assessed using the Integrated Visual and Auditory Continuous Performance Test (IVA2-CPT).
- A diffusion decision model was applied to quantify cognitive components: drift rate (efficiency) and drift bias (context sensitivity).
Main Results:
- Baseline cognitive components significantly moderated the improvement in inattention symptoms (p = 0.006).
- Children with the most or least severe deficits in drift rate and drift bias showed greater improvement in inattention with NF compared to the control group.
- Effect sizes indicated a moderate to large benefit for NF in these subgroups (Cohen's d = 0.59 vs. -0.21).
Conclusions:
- Pre-treatment cognitive assessment, utilizing computational modeling, can identify children with ADHD who are more likely to benefit from neurofeedback.
- These findings highlight the potential of personalized treatment approaches for ADHD based on cognitive profiles.
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.
