No abstract

Resting-state neuroimaging is a dominant paradigm for studying brain function in health and disease. It is attractive for clinical research because of its simplicity for patients, straightforward standardization, and sensitivity to brain disorders. Importantly, non-sensory experiences like mind wandering may arise from ongoing brain activity. However, little is known about the link between ongoing brain activity and cognition, as phenotypes of resting-state cognition—and tools to quantify them—have been lacking. To facilitate rapid and structured measurements of resting-state cognition we developed a 50-item self-report survey, the Amsterdam Resting-State Questionnaire (ARSQ). Based on ARSQ data from 813 participants assessed after 5 min eyes-closed rest in their home, we identified seven dimensions of resting-state cognition using factor analysis: Discontinuity of Mind, Theory of Mind, Self, Planning, Sleepiness, Comfort, and Somatic Awareness. Further, we showed that the structure of cognition was similar during resting-state fMRI and EEG, and that the test-retest correlations were remarkably high for all dimensions. To explore whether inter-individual variation of resting-state cognition is related to health status, we correlated ARSQ-derived factor scores with psychometric scales measuring depression, anxiety, and sleep quality. Mental health correlated positively with Comfort and negatively with Discontinuity of Mind. Finally, we show that sleepiness may partially explain a resting-state EEG profile previously associated with Alzheimer's disease. These findings indicate that the ARSQ readily provides information about cognitive phenotypes and that it is a promising tool for research on the neural correlates of resting-state cognition in health and disease.

Background Insomnia Disorder (ID) is the second-most prevalent mental disorder and a primary risk factor for depression. Inconsistent clinical and biomarker findings suggest heterogeneity and unrecognized subtypes. Previous top-down proposed subtypes had insufficient validity. The present large-scale study aimed to reveal robust subtypes using data-driven analyses on a high-dimensional set of biologically based traits. Methods Netherlands Sleep Registry participants (N=4,322; 2,224 with probable ID) completed up to 34 trait questionnaires. ID subtypes were identified using latent class analyses. Validity was evaluated in an independent sample and by assessing within-subject stability over years. Clinical relevance was extensively assessed in all subtypes for the development of sleep complaints, comorbidities including depression and response to benzodiazepines, and in two subtypes for an EEG biomarker and effectiveness of cognitive behavioral therapy. To facilitate implementation, a concise subtype questionnaire was constructed and validated in an independent sample. Outcomes Five novel ID subtypes were identified: one labelled as highly distressed, two as moderately distressed with either intact or weak responses to pleasurable emotions, and two as low distressed with either high or low reactivity to environment and life time events. A participant could be classified with high probability to only one subtype, and also in an independent replication sample five subtypes were again optimal (posterior probabilities 0•91-1•00). Participants reassessed 4•8±1•6 years later (N=215) maintained their subtype with high probability (0•87); indicating high stability. Clinical relevance showed from subtype differences in developmental etiology, response to treatment, an EEG biomarker, and up to five-fold differing risk of depression. Interpretation High-dimensional data-driven subtyping of people with insomnia solved an unmet need of heterogeneity reduction. Subtyping facilitates progress in finding mechanisms, developing personalized treatment, and selecting cases with the highest risk of depression for inclusion in preventive trials. Funding European Research Council (ERC-ADG-2014-671084-INSOMNIA); Netherlands Organization for Scientific Research (VICI-453-07-001). 4322 Sleep Registry database search for ISI and one additional questionnaire completed 2224 probable ID; Latent Class Analysis 2098 excluded ISI < 10 control reference values 1046 probable ID; subtype profiles and interpretation 1178 excluded for interpretation completed < 10 questionnaires 215 probable ID; Latent Transition Analysis 831 lost to follow-up 614 Sleep Registry database search for completed Insomnia Type Questionnaire and ISI 251 independent non-overlapping probable ID; Latent Class Analysis 363 excluded were included in original probable ID sample to develop model 244 received DSM-5 face-to-face diagnosis to validate ISI cutoff

The human brain frequently generates thoughts and feelings detached from environmental demands. Investigating the rich repertoire of these mind-wandering experiences is challenging, as it depends on introspection and mapping its content requires an unknown number of dimensions. We recently developed a retrospective self-report questionnaire—the Amsterdam Resting-State Questionnaire (ARSQ)—which quantifies mind wandering along seven dimensions: “Discontinuity of Mind,” “Theory of Mind,” “Self,” “Planning,” “Sleepiness,” “Comfort,” and “Somatic Awareness.” Here, we show using confirmatory factor analysis that the ARSQ can be simplified by standardizing the number of items per factor and extending it to a 10-dimensional model, adding “Health Concern,” “Visual Thought,” and “Verbal Thought.” We will refer to this extended ARSQ as the “ARSQ 2.0.” Testing for effects of age and gender revealed no main effect for gender, yet a moderate and significant negative effect for age on the dimensions of “Self,” “Planning,” and “Visual Thought.” Interestingly, we observed stable and significant test-retest correlations across measurement intervals of 3–32 months except for “Sleepiness” and “Health Concern.” To investigate whether this stability could be related to personality traits, we correlated ARSQ scores to proxy measures of Cloninger's Temperament and Character Inventory, revealing multiple significant associations for the trait “Self-Directedness.” Other traits correlated to specific ARSQ dimensions, e.g., a negative association between “Harm Avoidance” and “Comfort.” Together, our results suggest that the ARSQ 2.0 is a promising instrument for quantitative studies on mind wandering and its relation to other psychological or physiological phenomena.