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Probing the “Default Network Interference Hypothesis” With EEG: An RDoC Approach Focused on Attention


Gerrits, Berrie; Vollebregt, Madelon A; Olbrich, Sebastian; van Dijk, Hanneke; Palmer, Donna; Gordon, Evian; Pascual-Marqui, Roberto; Kessels, Roy P C; Arns, Martijn (2019). Probing the “Default Network Interference Hypothesis” With EEG: An RDoC Approach Focused on Attention. Clinical EEG and Neuroscience, 50(6):404-412.

Abstract

Studies have shown that specific networks (default mode network [DMN] and task positive network [TPN]) activate in an anticorrelated manner when sustaining attention. Related EEG studies are scarce and often lack behavioral validation. We performed independent component analysis (ICA) across different frequencies (source-level), using eLORETA-ICA, to extract brain-network activity during resting-state and sustained attention. We applied ICA to the voxel domain, similar to functional magnetic resonance imaging methods of analyses. The obtained components were contrasted and correlated to attentional performance (omission errors) in a large sample of healthy subjects (N = 1397). We identified one component that robustly correlated with inattention and reflected an anticorrelation of delta activity in the anterior cingulate and precuneus, and delta and theta activity in the medial prefrontal cortex and with alpha and gamma activity in medial frontal regions. We then compared this component between optimal and suboptimal attentional performers. For the latter group, we observed a greater change in component loading between resting-state and sustained attention than for the optimal performers. Following the National Institute of Mental Health Research Domain Criteria (RDoC) approach, we prospectively replicated and validated these findings in subjects with attention deficit/hyperactivity disorder. Our results provide further support for the “default mode interference hypothesis.”

Abstract

Studies have shown that specific networks (default mode network [DMN] and task positive network [TPN]) activate in an anticorrelated manner when sustaining attention. Related EEG studies are scarce and often lack behavioral validation. We performed independent component analysis (ICA) across different frequencies (source-level), using eLORETA-ICA, to extract brain-network activity during resting-state and sustained attention. We applied ICA to the voxel domain, similar to functional magnetic resonance imaging methods of analyses. The obtained components were contrasted and correlated to attentional performance (omission errors) in a large sample of healthy subjects (N = 1397). We identified one component that robustly correlated with inattention and reflected an anticorrelation of delta activity in the anterior cingulate and precuneus, and delta and theta activity in the medial prefrontal cortex and with alpha and gamma activity in medial frontal regions. We then compared this component between optimal and suboptimal attentional performers. For the latter group, we observed a greater change in component loading between resting-state and sustained attention than for the optimal performers. Following the National Institute of Mental Health Research Domain Criteria (RDoC) approach, we prospectively replicated and validated these findings in subjects with attention deficit/hyperactivity disorder. Our results provide further support for the “default mode interference hypothesis.”

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Psychiatric University Hospital Zurich > Clinic for Psychiatry, Psychotherapy, and Psychosomatics
04 Faculty of Medicine > The KEY Institute for Brain-Mind Research
Dewey Decimal Classification:610 Medicine & health
Uncontrolled Keywords:attention, RDoC, ADHD, EEG, eLORETA
Language:English
Date:1 November 2019
Deposited On:29 Jan 2020 16:13
Last Modified:29 Jan 2020 16:13
Publisher:Sage Publications
ISSN:1550-0594
OA Status:Closed
Publisher DOI:https://doi.org/10.1177/1550059419864461
PubMed ID:31322000

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