Probing the "Default Network Interference Hypothesis" with EEG: An RDoC approach focused on attention
Number of pages
SourceClinical EEG and Neuroscience, 50, 5, (2019), pp. 404-412
Article / Letter to editor
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SW OZ DCC NRP
Clinical EEG and Neuroscience
SubjectAll institutes and research themes of the Radboud University Medical Center; Neuropsychology and rehabilitation psychology; Radboudumc 1: Alzheimer`s disease DCMN: Donders Center for Medical Neuroscience; Neuro- en revalidatiepsychologie
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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