Cognitive and perceptual load have opposing effects on brain network efficiency and behavioral variability in ADHD

Author:

Fisher Jacob T.1,Hopp Frederic R.2,Weber René345ORCID

Affiliation:

1. Department of Communication, Michigan State University, East Lansing, MI, USA

2. Amsterdam School of Communication Research, University of Amsterdam, Amsterdam, Netherlands

3. Department of Communication, Media Neuroscience Lab, University of California Santa Barbara, Santa Barbara, CA, USA

4. Department of Psychological and Brain Sciences, University of California Santa Barbara, Santa Barbara, CA, USA

5. School of Communication and Media, Ewha Womans University, Seoul, South Korea

Abstract

Abstract Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent neurodevelopmental disorder associated with suboptimal outcomes throughout the life-span. Extant work suggests that ADHD-related deficits in task performance may be magnified under high cognitive load and minimized under high perceptual load, but these effects have yet to be systematically examined, and the neural mechanisms that undergird these effects are as yet unknown. Herein, we report results from three experiments investigating how performance in ADHD is modulated by cognitive load and perceptual load during a naturalistic task. Results indicate that cognitive load and perceptual load influence task performance, reaction time variability (RTV), and brain network topology in an ADHD-specific fashion. Increasing cognitive load resulted in reduced performance, greater RTV, and reduced brain network efficiency in individuals with ADHD relative to those without. In contrast, increased perceptual load led to relatively greater performance, reduced RTV, and greater brain network efficiency in ADHD. These results provide converging evidence that brain network efficiency and intraindividual variability in ADHD are modulated by both cognitive and perceptual load during naturalistic task performance.

Funder

Rutherford Fett Brain Imaging Center Research Fund

UCSB Faculty Senate Research Grant

NSF IGERT Innovation Fund

Publisher

MIT Press

Subject

Applied Mathematics,Artificial Intelligence,Computer Science Applications,General Neuroscience

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