Resting-State Subjective Experience and EEG Biomarkers Are Associated with Sleep-Onset Latency

The impact of forced awakening on morning blood pressure surge

BACKGROUND

Poor sleep quality can cause an increase in morning blood pressure surge (MBPS), an independent risk factor of cardiovascular disease (CVD) events. Awakening induced by external factors such as alarm clocks, may also contribute to increased MBPS.

OBJECTIVES

To (1) compare the MBPS and sleep quality parameters between natural and forced awakenings and (2) examine the potential impact of forced awakening on MBPS, independent of sleep quality.

METHODS

Thirty-two healthy adults participated in this pilot study, which included one night of natural awakening and one night of forced awakening (i.e., sleep was interrupted by an alarm after five hours). Objective and self-reported sleep quality parameters were measured using a multisensory wristband and sleep diaries, respectively, and beat-to-beat blood pressure variability was assessed using a continuous blood pressure monitor. Analyses included a paired t-test (objective 1) and linear mixed models (objective 2).

RESULTS

Participants predominantly consisted of young, healthy, and highly educated Asian adults. During the night of sleep with forced awakening, significantly higher MBPS, lower objective wakefulness after sleep onset, and lower self-reported sleep latency were observed, compared to the night with natural awakening. Forced awakening was significantly associated with increased MBPS after controlling for age, sex, mean arterial pressure, and sleep quality.

CONCLUSIONS

Forced awakening may significantly increase MBPS, consequently heightening the risk of CVD events. Study findings should be validated in a larger sample. Further research is also warranted to examine the impact of forced awakening on MBPS in individuals with CVD.

Global Functional Connectivity is Associated with Mind Wandering Domain of Comfort

The resting-state paradigm is frequently applied to study spontaneous activity of the brain in normal and clinical conditions. To assess the relationship between brain activity and subjective experiences, various questionnaires are used. Previous studies using Amsterdam Resting State Questionnaire were focusing on fMRI functional connectivity or EEG microstates and spectral aspect. Here, we utilized Global Field Synchronization as the parameter to estimate global functional connectivity. By re-analyzing the resting-state data from 226 young healthy participants we showed a strong evidence of relationship between ARSQ domain of Comfort and GFS values in the alpha range (r = 0.210, BF10 = 12.338) and substantial evidence for positive relationship between ARSQ domain of Comfort and GFS in the beta frequency range (r = 196, BF10 = 6.307). Our study indicates the relevance of assessments of spontaneous thought occurring during the resting-state for the understanding of the individual intrinsic electrical brain activity.

Effects of 90 dB pure tone exposure on auditory and cardio-cerebral system functions in macaque monkeys

Excessive noise exposure presents significant health risks to humans, affecting not just the auditory system but also the cardiovascular and central nervous systems. This study focused on three male macaque monkeys as subjects. 90 dB sound pressure level (SPL) pure tone exposure (frequency: 500Hz, repetition rate: 40Hz, 1 min per day, continuously exposed for 5 days) was administered. Assessments were performed before exposure, during exposure, immediately after exposure, and at 7-, 14-, and 28-days post-exposure, employing auditory brainstem response (ABR) tests, electrocardiograms (ECG), and electroencephalograms (EEG). The study found that the average threshold for the Ⅴ wave in the right ear increased by around 30 dB SPL right after exposure (P < 0.01) compared to pre-exposure. This elevation returned to normal within 7 days. The ECG results indicated that one of the macaque monkeys exhibited an RS-type QRS wave, and inverted T waves from immediately after exposure to 14 days, which normalized at 28 days. The other two monkeys showed no significant changes in their ECG parameters. Changes in EEG parameters demonstrated that main brain regions exhibited significant activation at 40Hz during noise exposure. After noise exposure, the power spectral density (PSD) in main brain regions, particularly those represented by the temporal lobe, exhibited a decreasing trend across all frequency bands, with no clear recovery over time. In summary, exposure to 90 dB SPL noise results in impaired auditory systems, aberrant brain functionality, and abnormal electrocardiographic indicators, albeit with individual variations. It has implications for establishing noise protection standards, although the precise mechanisms require further exploration by integrating pathological and behavioral indicators.

Comparing resting state and task-based EEG using machine learning to predict vulnerability to depression in a non-clinical population

Major Depressive Disorder (MDD) affects a large portion of the population and levies a huge societal burden. It has serious consequences like decreased productivity and reduced quality of life, hence there is considerable interest in understanding and predicting it. As it is a mental disorder, neural measures like EEG are used to study and understand its underlying mechanisms. However most of these studies have either explored resting state EEG (rs-EEG) data or task-based EEG data but not both, we seek to compare their respective efficacy. We work with data from non-clinically depressed individuals who score higher and lower on the depression scale and hence are more and less vulnerable to depression, respectively. Forty participants volunteered for the study. Questionnaires and EEG data were collected from participants. We found that people who are more vulnerable to depression had on average increased EEG amplitude in the left frontal channel, and decreased amplitude in the right frontal and occipital channels for raw data (rs-EEG). Task-based EEG data from a sustained attention to response task used to measure spontaneous thinking, an increased EEG amplitude in the central part of the brain for individuals with low vulnerability and an increased EEG amplitude in right temporal, occipital and parietal regions in individuals more vulnerable to depression were found. In an attempt to predict vulnerability (high/low) to depression, we found that a Long Short Term Memory model gave the maximum accuracy of 91.42% in delta wave for task-based data whereas 1D-Convolution neural network gave the maximum accuracy of 98.06% corresponding to raw rs-EEG data. Hence if one has to look at the primary question of which data will be good for predicting vulnerability to depression, rs-EEG seems to be better than task-based EEG data. However, if mechanisms driving depression like rumination or stickiness are to be understood, task-based data may be more effective. Furthermore, as there is no consensus as to which biomarker of rs-EEG is more effective in the detection of MDD, we also experimented with evolutionary algorithms to find the most informative subset of these biomarkers. Higuchi fractal dimension, phase lag index, correlation and coherence features were also found to be the most important features for predicting vulnerability to depression using rs-EEG. These findings bring up new possibilities for EEG-based machine/deep learning diagnostics in the future.

Driving drowsiness detection using spectral signatures of EEG-based neurophysiology

Introduction: Drowsy driving is a significant factor causing dire road crashes and casualties around the world. Detecting it earlier and more effectively can significantly reduce the lethal aftereffects and increase road safety. As physiological conditions originate from the human brain, so neurophysiological signatures in drowsy and alert states may be investigated for this purpose. In this preface, A passive brain-computer interface (pBCI) scheme using multichannel electroencephalography (EEG) brain signals is developed for spatially localized and accurate detection of human drowsiness during driving tasks.

Methods: This pBCI modality acquired electrophysiological patterns of 12 healthy subjects from the prefrontal (PFC), frontal (FC), and occipital cortices (OC) of the brain. Neurological states are recorded using six EEG channels spread over the right and left hemispheres in the PFC, FC, and OC of the sleep-deprived subjects during simulated driving tasks. In post-hoc analysis, spectral signatures of the δ, θ, α, and β rhythms are extracted in terms of spectral band powers and their ratios with a temporal correlation over the complete span of the experiment. Minimum redundancy maximum relevance, Chi-square, and ReliefF feature selection methods are used and aggregated with a Z-score based approach for global feature ranking. The extracted drowsiness attributes are classified using decision trees, discriminant analysis, logistic regression, naïve Bayes, support vector machines, k-nearest neighbors, and ensemble classifiers. The binary classification results are reported with confusion matrix-based performance assessment metrics.

Results: In inter-classifier comparison, the optimized ensemble model achieved the best results of drowsiness classification with 85.6% accuracy and precision, 89.7% recall, 87.6% F1-score, 80% specificity, 70.3% Matthews correlation coefficient, 70.2% Cohen’s kappa score, and 91% area under the receiver operating characteristic curve with 76-ms execution time. In inter-channel comparison, the best results were obtained at the F8 electrode position in the right FC of the brain. The significance of all the results was validated with a p-value of less than 0.05 using statistical hypothesis testing methods.

Conclusions: The proposed scheme has achieved better results for driving drowsiness detection with the accomplishment of multiple objectives. The predictor importance approach has reduced the feature extraction cost and computational complexity is minimized with the use of conventional machine learning classifiers resulting in low-cost hardware and software requirements. The channel selection approach has spatially localized the most promising brain region for drowsiness detection with only a single EEG channel (F8) which reduces the physical intrusiveness in normal driving operation. This pBCI scheme has a good potential for practical applications requiring earlier, more accurate, and less disruptive drowsiness detection using the spectral information of EEG biosignals.

Robin's Viewer: Using deep-learning predictions to assist EEG annotation

Machine learning techniques such as deep learning have been increasingly used to assist EEG annotation, by automating artifact recognition, sleep staging, and seizure detection. In lack of automation, the annotation process is prone to bias, even for trained annotators. On the other hand, completely automated processes do not offer the users the opportunity to inspect the models' output and re-evaluate potential false predictions. As a first step toward addressing these challenges, we developed Robin's Viewer (RV), a Python-based EEG viewer for annotating time-series EEG data. The key feature distinguishing RV from existing EEG viewers is the visualization of output predictions of deep-learning models trained to recognize patterns in EEG data. RV was developed on top of the plotting library Plotly, the app-building framework Dash, and the popular M/EEG analysis toolbox MNE. It is an open-source, platform-independent, interactive web application, which supports common EEG-file formats to facilitate easy integration with other EEG toolboxes. RV includes common features of other EEG viewers, e.g., a view-slider, tools for marking bad channels and transient artifacts, and customizable preprocessing. Altogether, RV is an EEG viewer that combines the predictive power of deep-learning models and the knowledge of scientists and clinicians to optimize EEG annotation. With the training of new deep-learning models, RV could be developed to detect clinical patterns other than artifacts, for example sleep stages and EEG abnormalities.

Data-Driven EEG Theta and Alpha Components Are Associated with Subjective Experience during Resting State

The resting-state paradigm is frequently applied to study spontaneous activity of the brain in normal and clinical conditions. However, the relationship between the ongoing experience of mind wandering and the individual biological signal is still unclear. We aim to estimate associations between subjective experiences measured with the Amsterdam Resting-State Questionnaire and data-driven components of an electroencephalogram extracted by frequency principal component analysis (f-PCA). Five minutes of resting multichannel EEG was recorded in 226 participants and six EEG data-driven components were extracted-three components in the alpha range (peaking at 9, 10.5, and 11.5 Hz) and one each in the delta (peaking at 0.5 Hz), theta (peaking at 5.5 Hz) and beta (peaking at 17 Hz) ranges. Bayesian Pearson's correlation revealed a positive association between the individual loadings of the theta component and ratings for Sleepiness (r = 0.200, BF10 = 7.676), while the individual loadings on one of the alpha components correlated positively with scores for Comfort (r = 0.198, BF10 = 7.115). Our study indicates the relevance of assessments of spontaneous thought occurring during the resting-state for the understanding of the individual intrinsic electrical brain activity.

DelIrium VULnerability in GEriatrics (DIVULGE) study: a protocol for a prospective observational study of electroencephalogram associations with incident postoperative delirium

INTRODUCTION

Delirium is a neurocognitive disorder common in older adults in acute care settings. Those who develop delirium are at an increased risk of dementia, cognitive decline and death. Electroencephalography (EEG) during delirium in older adults is characterised by slowing and reduced functional connectivity, but markers of vulnerability are poorly described. We aim to identify EEG spectral power and event-related potential (ERP) markers of incident delirium in older adults to understand neural mechanisms of delirium vulnerability. Characterising delirium vulnerability will provide substantial theoretical advances and outcomes have the potential to be translated into delirium risk assessment tools.

METHODS AND ANALYSIS

We will record EEG in 90 participants over 65 years of age prior to elective coronary artery bypass grafting (CABG) or transcatheter aortic valve implantation (TAVI). We will record 4-minutes of resting state (eyes open and eyes closed) and a 5-minute frequency auditory oddball paradigm. Outcome measures will include frequency band power, 1/f offset and slope, and ERP amplitude measures. Participants will undergo cognitive and EEG testing before their elective procedures and daily postoperative delirium assessments. Group allocation will be done retrospectively by linking preoperative EEG data according to postoperative delirium status (presence, severity, duration and subtype).

ETHICS AND DISSEMINATION

This study is approved by the Human Research Ethics Committee of the Royal Adelaide Hospital, Central Adelaide Local Health Network and the University of South Australia Human Ethics Committee. Findings will be disseminated through peer-reviewed journal articles and presentations at national and international conferences.

TRIAL REGISTRATION NUMBER

ACTRN12618001114235 and ACTRN12618000799257.

Actigraphy-Derived Sleep Profiles of Children with and without Attention-Deficit/Hyperactivity Disorder (ADHD) over Two Weeks-Comparison, Precursor Symptoms, and the Chronotype

Although sleep problems are common in children with ADHD, their extent, preceding risk factors, and the association between neurocognitive performance and neurobiological processes in sleep and ADHD, are still largely unknown. We examined sleep variables in school-aged children with ADHD, addressing their intra-individual variability (IIV) and considering potential precursor symptoms as well as the chronotype. Additionally, in a subgroup of our sample, we investigated associations with neurobehavioral functioning (n = 44). A total of 57 children (6-12 years) with (n = 24) and without ADHD (n = 33) were recruited in one center of the large ESCAlife study to wear actigraphs for two weeks. Actigraphy-derived dependent variables, including IIV, were analyzed using linear mixed models in order to find differences between the groups. A stepwise regression model was used to investigate neuropsychological function. Overall, children with ADHD showed longer sleep onset latency (SOL), higher IIV in SOL, more movements during sleep, lower sleep efficiency, and a slightly larger sleep deficit on school days compared with free days. No group differences were observed for chronotype or sleep onset time. Sleep problems in infancy predicted later SOL and the total number of movements during sleep in children with and without ADHD. No additional effect of sleep problems, beyond ADHD symptom severity, on neuropsychological functioning was found. This study highlights the importance of screening children with ADHD for current and early childhood sleep disturbances in order to prevent long-term sleep problems and offer individualized treatments. Future studies with larger sample sizes should examine possible biological markers to improve our understanding of the underlying mechanisms.

Relationship between Spatiotemporal Dynamics of the Brain at Rest and Self-Reported Spontaneous Thoughts: An EEG Microstate Approach

Rationale: The resting-state paradigm is frequently applied in electroencephalography (EEG) research; however, it is associated with the inability to control participants’ thoughts. To quantify subjects’ subjective experiences at rest, the Amsterdam Resting-State Questionnaire (ARSQ) was introduced covering ten dimensions of mind wandering. We aimed to estimate associations between subjective experiences and resting-state microstates of EEG. Methods: 5 min resting-state EEG data of 197 subjects was used to evaluate temporal properties of seven microstate classes. Bayesian correlation approach was implemented to assess associations between ARSQ domains assessed after resting and parameters of microstates. Results: Several associations between Comfort, Self and Somatic Awareness domains and temporal properties of neuroelectric microstates were revealed. The positive correlation between Comfort and duration of microstates E showed the strongest evidence (BF₁₀ > 10); remaining correlations showed substantial evidence (10 > BF₁₀ > 3). Conclusion: Our study indicates the relevance of assessments of spontaneous thought occurring during the resting-state for the understanding of the intrinsic brain activity reflected in microstates.

Associations between self-reported spontaneous thought and temporal sequences of EEG microstates

Thought dynamically evolves from one moment to the next even in the absence of external stimulation. The extent to which patterns of spontaneous thought covary with time-varying fluctuations in intrinsic brain activity is of great interest but remains unknown. We conducted novel analyses of data originally reported by Portnova et al. (2019) to examine associations between the intrinsic dynamics of EEG microstates and self-reported thought measured using the Amsterdam Resting-State Questionnaire (ARSQ). Accordingly, the millisecond fluctuations of microstates were associated with specific dimensions of thought. We evaluated the reliability of these findings by combining our results with those of another study using meta-analysis. Importantly, we extended this investigation using multivariate methods to evaluate multidimensional thought profiles of individuals and their links to sequences of successive microstates. Thought profiles were identified based on hierarchical clustering of ARSQ ratings and were distinguished in terms of the temporal ordering of successive microstates based on sequence analytic methods. These findings demonstrate the relevance of assessing spontaneous thought for understanding intrinsic brain activity and the novel use of sequence analysis for characterizing microstate dynamics. Integrating the phenomenological view from within remains crucial for understanding the functional significance of intrinsic large-scale neurodynamics.

Beta Electroencephalographic Oscillation Is a Potential GABAergic Biomarker of Chronic Peripheral Neuropathic Pain

This preliminary investigation aimed to assess beta (β) oscillation, a marker of the brain GABAergic signaling, as a potential objective pain marker, hence contributing at the same time to the mechanistic approach of pain management. This case–control observational study measured β electroencephalographic (EEG) oscillation in 12 right-handed adult male with chronic neuropathic pain and 10 matched controls (∼55 years). Participants were submitted to clinical evaluation (pain visual analog scale, Hospital Anxiety, and Depression scale) and a 24-min high-density EEG recording (BIOSEMI). Data were analyzed using the EEGlab toolbox (MATLAB), SPSS, and R. The global power spectrum computed within the low (Lβ, 13–20 Hz) and the high (Hβ, 20–30 Hz) β frequency sub-bands was significantly lower in patients than in controls, and accordingly, Lβ was negatively correlated to the pain visual analog scale (R = −0.931, p = 0.007), whereas Hβ correlation was at the edge of significance (R = −0.805; p = 0.053). Patients’ anxiety was correlated to pain intensity (R = 0.755; p = 0.003). Normalization of the low and high β global power spectrum (GPS) to the GPS of the full frequency range, while confirming the significant Lβ power decrease in chronic neuropathic pain patients, vanished the significance of the Hβ decrease, as well as the correlation between Lβ power and pain intensity. Our results suggest that the GABAergic Lβ EEG oscillation is affected by chronic neuropathic pain. Confirming the Lβ GPS decrease and the correlation with pain intensity in larger studies would open new opportunities for the clinical application of gamma-aminobutyric acid-modifying therapies.

Adults with autism spectrum disorder show atypical patterns of thoughts and feelings during rest

LAY ABSTRACT

Everyone knows the feeling of letting one's mind wander freely in a quiet moment. The thoughts and feelings experienced in those moments have been shown to influence our well-being-and vice versa. In this study, we looked at which thoughts and feelings are being experienced by adults with autism spectrum disorder and compared them to adults without autism spectrum disorder. In total, 88 adults with autism spectrum disorder and 90 adults without autism spectrum disorder were asked to rest for 5 min with their eyes closed and let their mind wander. Directly after, they filled in the Amsterdam Resting-State Questionnaire, which probes what participants were feeling and thinking during the period of rest. We found that adults with autism spectrum disorder tend to think less about others, felt less comfortable, and had more disrupted thoughts during the rest compared to adults without autism spectrum disorder. Interestingly, autism spectrum disorder participants reporting lower levels of comfort during the rest also reported more autism spectrum disorder symptoms, specifically in social behaviors and skills, attention switching, and imagination. We propose to use the eyes-closed rest condition in combination with the Amsterdam Resting-State Questionnaire more widely to shed light on aberrant thoughts and feelings in brain disorders and to study the effect of therapeutic interventions.

The dynamic modular fingerprints of the human brain at rest

The human brain is a dynamic modular network that can be decomposed into a set of modules, and its activity changes continually over time. At rest, several brain networks, known as Resting-State Networks (RSNs), emerge and cross-communicate even at sub-second temporal scale. Here, we seek to decipher the fast reshaping in spontaneous brain modularity and its relationships with RSNs. We use Electro/Magneto-Encephalography (EEG/MEG) to track the dynamics of modular brain networks, in three independent datasets (N = 568) of healthy subjects at rest. We show the presence of strikingly consistent RSNs, and a splitting phenomenon of some of these networks, especially the default mode network, visual, temporal and dorsal attentional networks. We also demonstrate that between-subjects variability in mental imagery is associated with the temporal characteristics of specific modules, particularly the visual network. Taken together, our findings show that large-scale electrophysiological networks have modularity-dependent dynamic fingerprints at rest.

Association of the retrospective self-report ratings with the dynamics of EEG

The neural underpinnings of subjective experience during resting state remain elusive. Dynamic features of EEG oscillations may provide more understanding of the relationship between the content of inner conscious experience and electrical brain activity. We tested a correlation of rating on the Amsterdam Resting-State Questionnaire (ARSQ) with dynamic parameters of EEG recorded in 49 healthy volunteers during the 10-min resting session. The participants filled ARSQ immediately after the rest. We investigated both linear (1 Hz-band power spectral density - PSD) and dynamic features (standard deviation and frequency of Hilbert envelope) of EEG averaged for the whole resting-state segment. Besides, we conducted a procedure of k-mean clustering based on PSD, localization of components retrieved by independent component analysis for 10-sec EEG epochs to assess spectral and temporal variability of EEG. The correlation analysis showed that the increase of PSD and cluster duration of the high-frequency alpha rhythm (12-13 Hz) in central and frontal areas was positively associated with the rating of experienced thoughts related to Planning (r = 0.44). The time of the presence of low amplitude delta oscillations correlated negatively with Planning (r = -0.52). The participants with higher ARSQ scores of Visual Thoughts had a higher standard deviation of the wideband (1-30 Hz) Hilbert envelope. Our data suggest that the dynamic properties of EEG reflect cognitive states assessed by ARSQ.

Circadian Rhythms in Fractal Features of EEG Signals

Time-of-day modulations affect both performance on a wide range of cognitive tasks and electrical activity of the brain, as recorded by electroencephalography (EEG). The aim of this work was to identify fluctuations of fractal properties of EEG time series due to circadian rhythms. In twenty-one healthy volunteers (all males, age between 20 and 30 years, chronotype: neutral type) high density EEG recordings at rest in open and closed eyes conditions were acquired in 4 times of the day (8.00 a.m., 11.30 a.m., 2.30 p.m., 7.00 p.m.). A vigilance task (Psychomotor Vigilance Test, PVT) was also performed. Detrended fluctuation Analysis (DFA) of envelope of alpha, beta and theta rhythms was performed, as well as Highuchi fractal dimension (HFD) of the whole band EEG. Our results evidenced circadian fluctuations of fractal features of EEG at rest in both eyes closed and eyes open conditions. Lower values of DFA exponent were found in the time T1 in closed eyes condition, likely effect of the sleep inertia. An alpha DFA exponent reduction was found also in central sensory-motor areas at time T3, the day time in which the sleepiness can be present. In eyes open condition, HFD lowered during the day. In eyes closed condition, an HFD increase was observed in central and frontal regions at time T2, the time in which alertness reaches its maximum and homeostatic sleep pressure is low. Complexity and the persistence of temporal correlations of brain rhythms change during daytime, parallel to changes in alertness and performance.

Lab meets real life: A laboratory assessment of spontaneous thought and its ecological validity

People's minds frequently wander towards self-generated thoughts, which are unrelated to external stimuli or demands. These phenomena, referred to as "spontaneous thought" (ST) and "mind wandering" (MW), have previously been linked with both costs and benefits. Current assessments of ST and MW have predominantly been conducted in the laboratory, whereas studies on the ecological validity of such lab-related constructs and their interrelations are rare. The current study examined the stability of ST dimensions assessed in the lab and their predictive value with respect to MW, repetitive negative thought (uncontrollable rumination, RUM), and affect in daily life. Forty-three university students were assessed with the Amsterdam Resting State Questionnaire (2nd version) to assess ten ST dimensions during the resting state in two laboratory sessions, which were separated by five days of electronic ambulatory assessment (AA). During AA, individuals indicated the intensity of MW and RUM, as well as of positive and negative affect in daily life ten times a day. ST dimensions measured in the lab were moderately stable across one week. Five out of ten ST lab dimensions were predicted by mental health-related symptoms or by dispositional cognitive traits. Hierarchical linear models revealed that a number of ST lab dimensions predicted cognitive and affective states in daily life. Mediation analyses showed that RUM, but not MW per se, accounted for the relationship between specific ST lab dimensions and mood in daily life. By using a simple resting state task, we could demonstrate that a number of lab dimensions of spontaneous thought are moderately stable, are predicted by mental health symptoms and cognitive traits, and show plausible associations with categories of self-generated thought and mood in daily life.

Multiple phenotypes of resting-state cognition are altered in insomnia disorder

BACKGROUND

Research has supported the role of cognitive processes in the development and maintenance of insomnia, yet a standardized characterization of mind-wandering experiences in insomniacs is lacking.

OBJECTIVES

The aim was to understand the quantitative nature of thoughts and feelings during mind wandering in insomniacs and healthy controls and their relationship with sleep-related parameters.

METHODS

We used the 5-minute eyes-closed wakeful rest as an experimental model condition of mind wandering. Forty-seven individuals with insomnia disorder according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (48.66±15.62 years; 31 women) and 29 healthy controls (50.66±15.14 years; 17 women) participated in the experiments and completed the Amsterdam Resting-State Questionnaire (ARSQ) immediately after the resting session. Participants also completed the Insomnia Severity Index (ISI), the Pittsburgh Sleep Quality Index (PSQI), the Dysfunctional Beliefs and Attitudes About Sleep Scale (DBAS). Statistical analyses included multiple regression to elucidate the independent determinants of ARSQ phenotypes.

RESULTS

Participants with insomnia presented higher ISI, PSQI, and DBAS scores than did healthy controls. Insomniacs had strikingly different scores on most dimensions of the ARSQ, in particular Discontinuity of Mind, Self, Sleepiness, and Health Concern, that correlated positively with ISI and DBAS. Multiple regressions highlighted that for insomniacs, ISI was the best predictor of both Discontinuity of Mind and Health Concern.

CONCLUSIONS

Resting-state activity in insomnia is altered and it seems to be related to unhelpful beliefs and insomnia severity. Resting-state neuroimaging in combination with the ARSQ could reveal important associations between these aberrant cognitive scores and their underlying systems-level brain mechanisms.