TY  - JOUR
A1  - Hilger, Kirsten
A1  - Fiebach, Christian
T1  - ADHD symptoms are associated with the modular structure of intrinsic brain networks in a representative sample of healthy adults
T2  - Network neuroscience
N2  - Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders with significant and often lifelong effects on social, emotional, and cognitive functioning. Influential neurocognitive models of ADHD link behavioral symptoms to altered connections between and within functional brain networks. Here, we investigate whether network-based theories of ADHD can be generalized to understanding variations in ADHD-related behaviors within the normal (i.e., clinically unaffected) adult population. In a large and representative sample, self-rated presence of ADHD symptoms varied widely; only eight out of 291 participants scored in the clinical range. Subject-specific brain-network graphs were modeled from functional MRI resting-state data and revealed significant associations between (non-clinical) ADHD symptoms and region-specific profiles of between-module and within-module connectivity. Effects were located in brain regions associated with multiple neuronal systems including the default-mode network, the salience network, and the central executive system. Our results are consistent with network perspectives of ADHD and provide further evidence for the relevance of an appropriate information transfer between task-negative (default-mode) and task-positive brain regions. More generally, our findings support a dimensional conceptualization of ADHD and contribute to a growing understanding of cognition as an emerging property of functional brain networks.
KW  - ADHD
KW  - Symptom strength
KW  - Nonclinical
KW  - Graph theory
KW  - Modularity
KW  - Brain networks
Y1  - 2019
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/50158
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-501588
SN  - 2472-1751
N1  - Preprint erschienen in: bioRxiv beta, 2019, doi:10.1101/505891
N1  - This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/legalcode.
VL  - 3
IS  - 2
SP  - 567
EP  - 588
PB  - The MIT Press
CY  - Cambridge, MA
ER  -