Resting-State fMRI Functional Connectivity Is Associated with Sleepiness, Imagery, and Discontinuity of Mind

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.

The Functional Aspects of Resting EEG Microstates: A Systematic Review

A growing body of clinical and cognitive neuroscience studies have adapted a broadband EEG microstate approach to evaluate the electrical activity of large-scale cortical networks. However, the functional aspects of these microstates have not yet been systematically reviewed. Here, we present an overview of the existing literature and systematize the results to provide hints on the functional role of electrical brain microstates. Studies that evaluated and manipulated the temporal properties of resting-state microstates and utilized questionnaires, task-initiated thoughts, specific tasks before or between EEG session(s), pharmacological interventions, neuromodulation approaches, or localized sources of the extracted microstates were selected. Fifty studies that met the inclusion criteria were included. A new microstate labeling system has been proposed for a comprehensible comparison between the studies, where four classical microstates are referred to as A-D, and the others are labeled by the frequency of their appearance. Microstate A was associated with both auditory and visual processing and links to subjects' arousal/arousability. Microstate B showed associations with visual processing related to self, self-visualization, and autobiographical memory. Microstate C was related to processing personally significant information, self-reflection, and self-referential internal mentation rather than autonomic information processing. In contrast, microstate E was related to processing interoceptive and emotional information and to the salience network. Microstate D was associated with executive functioning. Microstate F is suggested to be a part of the Default Mode Network and plays a role in personally significant information processing, mental simulations, and theory of mind. Microstate G is potentially linked to the somatosensory network.

FEMA: Fast and efficient mixed-effects algorithm for large sample whole-brain imaging data

The linear mixed-effects model (LME) is a versatile approach to account for dependence among observations. Many large-scale neuroimaging datasets with complex designs have increased the need for LME; however LME has seldom been used in whole-brain imaging analyses due to its heavy computational requirements. In this paper, we introduce a fast and efficient mixed-effects algorithm (FEMA) that makes whole-brain vertex-wise, voxel-wise, and connectome-wide LME analyses in large samples possible. We validate FEMA with extensive simulations, showing that the estimates of the fixed effects are equivalent to standard maximum likelihood estimates but obtained with orders of magnitude improvement in computational speed. We demonstrate the applicability of FEMA by studying the cross-sectional and longitudinal effects of age on region-of-interest level and vertex-wise cortical thickness, as well as connectome-wide functional connectivity values derived from resting state functional MRI, using longitudinal imaging data from the Adolescent Brain Cognitive DevelopmentSM Study release 4.0. Our analyses reveal distinct spatial patterns for the annualized changes in vertex-wise cortical thickness and connectome-wide connectivity values in early adolescence, highlighting a critical time of brain maturation. The simulations and application to real data show that FEMA enables advanced investigation of the relationships between large numbers of neuroimaging metrics and variables of interest while considering complex study designs, including repeated measures and family structures, in a fast and efficient manner. The source code for FEMA is available via: https://github.com/cmig-research-group/cmig_tools/.

Neural connectivity moderators and mechanisms of ketamine treatment among treatment-resistant depressed patients: a randomized controlled trial

BACKGROUND

Intravenous (IV) ketamine has emerged as a rapid and effective treatment for TRD. However, the specific neural mechanisms of ketamine's effects in humans remains unclear. Although neuroplasticity is implicated as a mechanism of action in animal models, relatively few randomized controlled trials (RCTs) in TRD patients have examined ketamine's impact on functional connectivity, a posited functional marker of neuroplasticity-particularly in the context of a mood-induction paradigm (termed miFC).

METHODS

152 adults with TRD (63% female; 37% male) were randomly allocated to receive a single infusion of ketamine or saline in a 2:1 ratio. We examined changes in connectivity (from baseline to 24-h post-infusion) that differed by treatment, and whether clinical treatment response at 24-h post-infusion was uniquely related (among patients allocated to ketamine relative to saline) to (1) pre-treatment connectivity and (2) changes in connectivity. We examined both miFC and rsFC, using prefrontal cortex and limbic seed regions. We also conducted a multiverse analysis to examine findings most robust against analytic decisions.

FINDINGS

Across both miFC and rsFC, ketamine was associated with greater in prefrontal/limbic connectivity compared to saline, and lower baseline connectivity of limbic and prefrontal regions predicted greater treatment response in patients receiving ketamine. Greater connectivity increases in participants receiving ketamine was uniquely related to greater treatment response. In addition, certain findings were identified as being reproducible against different analytic decisions in multiverse analyses.

INTERPRETATION

Our findings identify specific neural connectivity patterns impacted by ketamine and were uniquely related to outcomes following ketamine (relative to saline). These findings generally support prominent neuroplasticity models of ketamine's therapeutic efficacy. These findings lay new groundwork for understanding how to enhance and optimize ketamine treatments and develop novel rapid-acting treatments for depression.

FUNDING

This research was supported by NIH grant R01MH113857 and by the Clinical and Translational Sciences Institute at the University of Pittsburgh (UL1-TR-001857).

Modulatory effects of fMRI acquisition time of day, week and year on adolescent functional connectomes across spatial scales: Implications for inference

Metabolic, hormonal, autonomic and physiological rhythms may have a significant impact on cerebral hemodynamics and intrinsic brain synchronization measured with fMRI (the resting-state connectome). The impact of their characteristic time scales (hourly, circadian, seasonal), and consequently scan timing effects, on brain topology in inherently heterogeneous developing connectomes remains elusive. In a cohort of 4102 early adolescents with resting-state fMRI (median age = 120.0 months; 53.1 % females) from the Adolescent Brain Cognitive Development Study, this study investigated associations between scan time-of-day, time-of-week (school day vs weekend) and time-of-year (school year vs summer vacation) and topological properties of resting-state connectomes at multiple spatial scales. On average, participants were scanned around 2 pm, primarily during school days (60.9 %), and during the school year (74.6 %). Scan time-of-day was negatively correlated with multiple whole-brain, network-specific and regional topological properties (with the exception of a positive correlation with modularity), primarily of visual, dorsal attention, salience, frontoparietal control networks, and the basal ganglia. Being scanned during the weekend (vs a school day) was correlated with topological differences in the hippocampus and temporoparietal networks. Being scanned during the summer vacation (vs the school year) was consistently positively associated with multiple topological properties of bilateral visual, and to a lesser extent somatomotor, dorsal attention and temporoparietal networks. Time parameter interactions suggested that being scanned during the weekend and summer vacation enhanced the positive effects of being scanned in the morning. Time-of-day effects were overall small but spatially extensive, and time-of-week and time-of-year effects varied from small to large (Cohen's f ≤ 0.1, Cohen's d<0.82, p < 0.05). Together, these parameters were also positively correlated with temporal fMRI signal variability but only in the left hemisphere. Finally, confounding effects of scan time parameters on relationships between connectome properties and cognitive task performance were assessed using the ABCD neurocognitive battery. Although most relationships were unaffected by scan time parameters, their combined inclusion eliminated associations between properties of visual and somatomotor networks and performance in the Matrix Reasoning and Pattern Comparison Processing Speed tasks. Thus, scan time of day, week and year may impact measurements of adolescent brain's functional circuits, and should be accounted for in studies on their associations with cognitive performance, in order to reduce the probability of incorrect inference.

Resting-state connectivity and neural response to emotional images in patients with severe health anxiety: An fMRI study

BACKGROUND

Severe health anxiety is a disorder characterized by a persistent preoccupation with one's health. In behavioral studies, biases in the processing of health-related stimuli (e.g., pictures, words) are consistently associated with health anxiety symptoms. The neural correlates of the observed behavioral abnormalities remain however poorly understood.

METHODS

In this functional magnetic resonance imaging study, 22 treatment-seeking patients with severe health anxiety and 22 control participants performed a resting-state and a picture matching task. Immediately after the resting-state, participants completed a questionnaire quantifying their thoughts and feelings during rest along several dimensions. The picture task included images of health-related and neutral scenes and of disgusted and neutral faces.

RESULTS

Compared to controls, patients with health anxiety showed increased functional connectivity in the dorsolateral prefrontal cortex within the left fronto-parietal resting-state network, which correlated positively with the self-reported thought dimensions of Self, Health Concern, and Thought Suppression. In the picture matching task, no significant group differences were found in the hypothesised regions (amygdala and insula) or at a whole-brain level in response to either health-related versus neural scenes or disgust versus neutral faces contrasts.

LIMITATIONS

A relatively small sample size and that no information about patients declining to participate was obtained.

CONCLUSIONS

Our findings suggest that behavioral biases in health anxiety may be related to aberrant left dorsolateral prefrontal cortex functional connectivity at rest. In contrast, the health anxiety patients did not show significant hyperactivations of amygdala and insula during processing of emotional stimuli, contrasting findings in other anxiety disorders.

A method for reconstruction of interpretable brain networks from transient synchronization in resting-state BOLD fluctuations

Resting-state (rs) fMRI has been widely used to examine brain-wide large-scale spatiotemporal architectures, known as resting-state networks (RSNs). Recent studies have focused on the temporally evolving characteristics of RSNs, but it is unclear what temporal characteristics are reflected in the networks. To address this issue, we devised a novel method for voxel-based visualization of spatiotemporal characteristics of rs-fMRI with a time scale of tens of seconds. We first extracted clusters of dominant activity-patterns using a region-of-interest approach and then used these temporal patterns of the clusters to obtain voxel-based activation patterns related to the clusters. We found that activation patterns related to the clusters temporally evolved with a characteristic temporal structure and showed mutual temporal alternations over minutes. The voxel-based representation allowed the decoding of activation patterns of the clusters in rs-fMRI using a meta-analysis of functional activations. The activation patterns of the clusters were correlated with behavioral measures. Taken together, our analysis highlights a novel approach to examine brain activity dynamics during rest.

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.

Reduced Cross-Frequency Coupling and Daytime Sleepiness in Obstructive Sleep Apnea Patients

Obstructive sleep apnea (OSA) is associated with sleep-stage- and respiratory-event-specific sensorimotor cortico-muscular disconnection. The modulation of phase−amplitude cross-frequency coupling (PACFC) may influence information processing throughout the brain. We investigated whether sleep-stage-specific PACFC is impaired at the sensorimotor areas in OSA patients. C3 and C4 electrode EEG polysomnography recordings of 170 participants were evaluated. Different frequency band combinations were used to compute CFC modulation index (MI) to assess if MI differs between OSA and non-significant OSA patients in distinct sleep stages. We tested if the CFC-MI could predict daytime sleepiness in OSA. Theta−gamma CFC-MI at cortical sensorimotor areas was significantly reduced during all sleep stages; the delta−alpha CFC-MI was significantly reduced during REM and N1 while increasing during N2 in patients with respiratory disturbance index (RDI) > 15/h compared to those with RDI ≤ 15/h. A sleep stage classification using MI values was achieved in both patient groups. Theta−gamma MI during N2 and N3 could predict RDI and Epworth Sleepiness Scale, while delta−alpha MI during REM predicted RDI. This increase in disconnection at the cortical sensorimotor areas with increasing respiratory distress during sleep supports a cortical motor dysfunction in OSA patients. The MI provides an objective marker to quantify subjective sleepiness and respiratory distress in OSA.

Attention-Deficit/Hyperactivity Disorder, Insomnia, and Sleepiness Symptoms among a Community Adult Sample: The Mediating Effect of Executive Behavioral Regulation and Metacognition Abilities

OBJECTIVE

Adult attention-deficit/hyperactivity disorder (ADHD) is characterized by a dysexecutive syndrome reflected in cognitive, emotional, and behavioral areas. Independently of a formal diagnosis of ADHD, higher ADHD symptoms are associated with higher levels of insomnia and sleepiness symptoms in adult population-based samples. Insomnia and sleepiness are sleep disorders that are both associated with deficits in several aspects of executive functions which in turn are likely to mimic a range of ADHD symptoms. Our objective was to explore the interrelationships between ADHD, insomnia, and sleepiness symptoms and executive functioning in community-dwelling adults.

METHOD

A total of 442 participants (18-89 years, 56% females) underwent a semistructured clinical interview and completed questionnaires for insomnia, sleepiness, and everyday behaviors in which executive functions are implicated. Mediation Models were applied.

RESULTS

Insomnia and sleepiness symptoms did not play a mediating role between ADHD symptoms and executive functioning. Conversely, our results highlighted a mediating effect of daytime insomnia consequences and sleepiness on ADHD symptoms via behavioral regulation executive symptoms (respectively, β = -0.32, p < .001, 95% CI [-0.46, -0.20]; β = 0.09, p < .05, 95% CI [0.02, 0.16]) and metacognitive executive symptoms (respectively, β = -0.30, p < .001, 95% CI [-0.44, -0.18]; β = 0.12, p < .01, 95% CI [0.04, 0.22]).

CONCLUSIONS

Daytime insomnia consequences and sleepiness symptoms could lead to ADHD-like symptoms through their associated executive symptoms expressed in daily life. When faced with symptoms suggestive of ADHD in adults, insomnia and sleepiness should be systematically screened with standardized instruments.

Functional connectivity alterations between default mode network and occipital cortex in patients with obsessive-compulsive disorder (OCD)

Altered brain network connectivity is a potential biomarker for obsessive-compulsive disorder (OCD). A meta-analysis of resting-state MRI studies by Gürsel et al. (2018) described altered functional connectivity in OCD patients within and between the default mode network (DMN), the salience network (SN), and the frontoparietal network (FPN), as well as evidence for aberrant fronto-striatal circuitry. Here, we tested the replicability of these meta-analytic rsfMRI findings by measuring functional connectivity during resting-state fMRI in a new sample of OCD patients (n = 24) and matched controls (n = 33). We performed seed-to-voxel analyses using 30 seed regions from the prior meta-analysis. OCD patients showed reduced functional connectivity between the SN and the DMN compared to controls, replicating previous findings. We did not observe significant group differences of functional connectivity within the DMN, SN, nor FPN. Additionally, we observed reduced connectivity between the visual network to both the DMN and SN in OCD patients, in particular reduced functional connectivity between lateral parietal seeds and the left inferior lateral occipital pole. Furthermore, the right lateral parietal seed (associated with the DMN) was more strongly correlated with a cluster in the right lateral occipital cortex and precuneus (a region partly overlapping with the Dorsal Attentional Network (DAN)) in patients. Importantly, this latter finding was positively correlated to OCD symptom severity. Overall, our study partly replicated prior meta-analytic findings, highlighting hypoconnectivity between SN and DMN as a potential biomarker for OCD. Furthermore, we identified changes between the SN and the DMN with the visual network. This suggests that abnormal connectivity between cortex regions associated with abstract functions (transmodal regions such as the DMN), and cortex regions associated with constrained neural processing (unimodal regions such as the visual cortex), may be important in OCD.

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.

The Interaction Between Caudate Nucleus and Regions Within the Theory of Mind Network as a Neural Basis for Social Intelligence

The organization of socio-cognitive processes is a multifaceted problem for which many sophisticated concepts have been proposed. One of these concepts is social intelligence (SI), i.e., the set of abilities that allow successful interaction with other people. The theory of mind (ToM) human brain network is a good candidate for the neural substrate underlying SI since it is involved in inferring the mental states of others and ourselves and predicting or explaining others’ actions. However, the relationship of ToM to SI remains poorly explored. Our recent research revealed an association between the gray matter volume of the caudate nucleus and the degree of SI as measured by the Guilford-Sullivan test. It led us to question whether this structural peculiarity is reflected in changes to the integration of the caudate with other areas of the brain associated with socio-cognitive processes, including the ToM system. We conducted seed-based functional connectivity (FC) analysis of resting-state fMRI data for 42 subjects with the caudate as a region of interest. We found that the scores of the Guilford-Sullivan test were positively correlated with the FC between seeds in the right caudate head and two clusters located within the right superior temporal gyrus and bilateral precuneus. Both regions are known to be nodes of the ToM network. Thus, the current study demonstrates that the SI level is associated with the degree of functional integration between the ToM network and the caudate nuclei.

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.

Identifying Diurnal Variability of Brain Connectivity Patterns Using Graph Theory

Significant differences exist in human brain functions affected by time of day and by people’s diurnal preferences (chronotypes) that are rarely considered in brain studies. In the current study, using network neuroscience and resting-state functional MRI (rs-fMRI) data, we examined the effect of both time of day and the individual’s chronotype on whole-brain network organization. In this regard, 62 participants (39 women; mean age: 23.97 ± 3.26 years; half morning- versus half evening-type) were scanned about 1 and 10 h after wake-up time for morning and evening sessions, respectively. We found evidence for a time-of-day effect on connectivity profiles but not for the effect of chronotype. Compared with the morning session, we found relatively higher small-worldness (an index that represents more efficient network organization) in the evening session, which suggests the dominance of sleep inertia over the circadian and homeostatic processes in the first hours after waking. Furthermore, local graph measures were changed, predominantly across the left hemisphere, in areas such as the precentral gyrus, putamen, inferior frontal gyrus (orbital part), inferior temporal gyrus, as well as the bilateral cerebellum. These findings show the variability of the functional neural network architecture during the day and improve our understanding of the role of time of day in resting-state functional networks.

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.

Confound modelling in UK Biobank brain imaging

Dealing with confounds is an essential step in large cohort studies to address problems such as unexplained variance and spurious correlations. UK Biobank is a powerful resource for studying associations between imaging and non-imaging measures such as lifestyle factors and health outcomes, in part because of the large subject numbers. However, the resulting high statistical power also raises the sensitivity to confound effects, which therefore have to be carefully considered. In this work we describe a set of possible confounds (including nonlinear effects and interactions that researchers may wish to consider for their studies using such data). We include descriptions of how we can estimate the confounds, and study the extent to which each of these confounds affects the data, and the spurious correlations that may arise if they are not controlled. Finally, we discuss several issues that future studies should consider when dealing with confounds.

Mind-wandering and sleepiness in adults with attention-deficit/hyperactivity disorder

Sleepiness and mind-wandering are frequently experienced by patients with attention-deficit/hyperactivity disorder (ADHD), without ever having been jointly explored. We aimed to investigate the co-occurrence of these two phenomena in ADHD adults. Drug-free ADHD adults (n = 25) and healthy controls (n = 28) underwent an online experience sampling of mind-wandering episodes and subjective sleepiness. Participants completed self-reported measures of mind-wandering and sleepiness in daily life. Higher trait of mind-wandering was observed in ADHD patients compared to controls. On the whole sample, self-reported mind-wandering propensity was strongly associated with the severity of inattentive, impulsive and hyperactive symptoms. During the probes, patients reported more frequent episodes of mind-wandering and mind-blanking, and higher sleepiness. Their mind-wandering episodes were less intentional and belonged less frequently to a structured succession of thoughts. In both groups, mind-wandering and mind-blanking were associated with higher sleepiness. On the SART, patients were less accurate than controls. We provide first initial evidence for higher propensity of mind-wandering and mind-blanking using experience sampling in patients with formal ADHD diagnosis. This propensity was associated with sleepiness without negatively impacting attention performances. Mind-wandering and sleepiness have common determinants potentially involved in ADHD pathophysiology. Correlates of mind-blanking in ADHD adults remain to be characterized.

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.

The neural correlates of "mind blanking": When the mind goes away

Mind blanking (MB) is the state where our minds are seemingly "nowhere," and attention calls no perceptual input into conscious awareness. It is little investigated, perhaps partly because it is difficult to detect the mysterious periods of blanking. In this study, we found that our participants could intentionally produce a state of MB whose neural correlates were deactivation of Broca's area and parts of the default mode network (namely, the hippocampus) which would be active during mind wandering (MW), in addition to activity in another region in the default mode network (namely, anterior cingulate cortex). Because the behavioral finding replicates a previous report of ours, we suggest that the simple instructions that we used to induce MB should be effective. From the neuroimaging data, we conclude that we cannot define the content of our thoughts during MB because our inner speech system does not work at that time. Another possibility is that we actually think of nothing in the MB state. Although more sophisticated studies would be needed to uncover the mechanism of such a phenomenon, the present study provides a methodology and clues for understanding MB and related concepts such as MW, awareness, and metacognitive ability.

Longitudinally consistent estimates of intrinsic functional networks

Increasing numbers of neuroimaging studies are acquiring data to examine changes in brain architecture by investigating intrinsic functional networks (IFN) from longitudinal resting-state functional MRI (rs-fMRI). At the subject level, these IFNs are determined by cross-sectional procedures, which neglect intra-subject dependencies and result in suboptimal estimates of the networks. Here, a novel longitudinal approach simultaneously extracts subject-specific IFNs across multiple visits by explicitly modeling functional brain development as an essential context for seeking change. On data generated by an innovative simulation based on real rs-fMRI, the method was more accurate in estimating subject-specific IFNs than cross-sectional approaches. Furthermore, only group-analysis based on longitudinally consistent estimates identified significant developmental effects within IFNs of 246 adolescents from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) study. The findings were confirmed by the cross-sectional estimates when the corresponding group analysis was confined to the developmental effects. Those effects also converged with current concepts of neurodevelopment.

Investigating the effects of subconcussion on functional connectivity using mass-univariate and multivariate approaches

There are concerns about the effects of subconcussive head impacts in sport, but the effects of subconcussion on brain connectivity are not well understood. We hypothesized that college football players experience changes in brain functional connectivity not found in athletes competing in lower impact sports or healthy controls. These changes may be spatially heterogeneous across participants, requiring analysis methods that go beyond mass-univariate approaches commonly used in functional MRI (fMRI). To test this hypothesis, we analyzed resting-state fMRI data from college football (n = 15), soccer (n = 12), and lacrosse players (n = 16), and controls (n = 29) collected at preseason and postseason time points. Regional homogeneity (ReHo) and degree centrality (DC) were calculated as measures of local and long-range functional connectivity, respectively. Standard voxel-wise analysis and paired support vector machine (SVM) classification studied subconcussion's effects on local and global functional connectivity. Voxel-wise analyses yielded minimal findings, but SVM classification had high accuracy for college football's ReHo (87%, p = 0.009) and no other group. The findings suggest subconcussion results in spatially heterogeneous changes in local functional connectivity that may only be detectible with multivariate analyses. To determine if voxel-wise and SVM analyses had similar spatial patterns, region-average t-statistic and SVM weight values were compared using a measure of ranking distance. T-statistic and SVM weight rankings exhibited significantly low ranking distance values for all groups and metrics, demonstrating that the analyses converged on a similar underlying effect. Overall, this research suggests that subconcussion in football may produce local functional connectivity changes similar to concussion.

Long-Range Temporal Correlations in Alpha Oscillations Stabilize Perception of Ambiguous Visual Stimuli

Ongoing brain dynamics have been proposed as a type of “neuronal noise” that can trigger perceptual switches when viewing an ambiguous, bistable stimulus. However, no prior study has directly quantified how such neuronal noise relates to the rate of percept reversals. Specifically, it has remained unknown whether individual differences in complexity of resting-state oscillations—as reflected in long-range temporal correlations (LRTC)—are associated with perceptual stability. We hypothesized that participants with stronger resting-state LRTC in the alpha band experience more stable percepts, and thereby fewer perceptual switches. Furthermore, we expected that participants who report less discontinuous thoughts during rest, experience less switches. To test this, we recorded electroencephalography (EEG) in 65 healthy volunteers during 5 min Eyes-Closed Rest (ECR), after which they filled in the Amsterdam Resting-State Questionnaire (ARSQ). This was followed by three conditions where participants attended an ambiguous structure-from-motion stimulus—Neutral (passively observe the stimulus), Hold (the percept for as long as possible), and Switch (as often as possible). LRTC of resting-state alpha oscillations predicted the number of switches only in the Hold condition, with stronger LRTC associated with less switches. Contrary to our expectations, there was no association between resting-state Discontinuity of Mind and percept stability. Participants were capable of controlling switching according to task goals, and this was accompanied by increased alpha power during Hold and decreased power during Switch. Fewer switches were associated with stronger task-related alpha LRTC in all conditions. Together, our data suggest that bistable visual perception is to some extent under voluntary control and influenced by LRTC of alpha oscillations.

Correlation of BOLD Signal with Linear and Nonlinear Patterns of EEG in Resting State EEG-Informed fMRI

Concurrent EEG and fMRI acquisitions in resting state showed a correlation between EEG power in various bands and spontaneous BOLD fluctuations. However, there is a lack of data on how changes in the complexity of brain dynamics derived from EEG reflect variations in the BOLD signal. The purpose of our study was to correlate both spectral patterns, as linear features of EEG rhythms, and nonlinear EEG dynamic complexity with neuronal activity obtained by fMRI. We examined the relationships between EEG patterns and brain activation obtained by simultaneous EEG-fMRI during the resting state condition in 25 healthy right-handed adult volunteers. Using EEG-derived regressors, we demonstrated a substantial correlation of BOLD signal changes with linear and nonlinear features of EEG. We found the most significant positive correlation of fMRI signal with delta spectral power. Beta and alpha spectral features had no reliable effect on BOLD fluctuation. However, dynamic changes of alpha peak frequency exhibited a significant association with BOLD signal increase in right-hemisphere areas. Additionally, EEG dynamic complexity as measured by the HFD of the 2-20 Hz EEG frequency range significantly correlated with the activation of cortical and subcortical limbic system areas. Our results indicate that both spectral features of EEG frequency bands and nonlinear dynamic properties of spontaneous EEG are strongly associated with fluctuations of the BOLD signal during the resting state condition.

Investigations into within- and between-subject resting-state amplitude variations

The amplitudes of spontaneous fluctuations in brain activity may be a significant source of within-subject and between-subject variability, and this variability is likely to be carried through into functional connectivity (FC) estimates (whether directly or indirectly). Therefore, improving our understanding of amplitude fluctuations over the course of a resting state scan and variation in amplitude across individuals is of great relevance to the interpretation of FC findings. We investigate resting state amplitudes in two large-scale studies (HCP and UK Biobank), with the aim of determining between-subject and within-subject variability. Between-subject clustering distinguished between two groups of brain networks whose amplitude variation across subjects were highly correlated with each other, revealing a clear distinction between primary sensory and motor regions ('primary sensory/motor cluster') and cognitive networks. Within subjects, all networks in the primary sensory/motor cluster showed a consistent increase in amplitudes from the start to the end of the scan. In addition to the strong increases in primary sensory/motor amplitude, a large number of changes in FC were found when comparing the two scans acquired on the same day (HCP data). Additive signal change analysis confirmed that all of the observed FC changes could be fully explained by changes in amplitude. Between-subject correlations in UK Biobank data showed a negative correlation between primary sensory/motor amplitude and average sleep duration, suggesting a role of arousal. Our findings additionally reveal complex relationships between amplitude and head motion. These results suggest that network amplitude is a source of significant variability both across subjects, and within subjects on a within-session timescale. Future rfMRI studies may benefit from obtaining arousal-related (self report) measures, and may wish to consider the influence of amplitude changes on measures of (dynamic) functional connectivity.

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.

Just a thought: How mind-wandering is represented in dynamic brain connectivity

The neuroscience of mind-wandering has begun to flourish, with roles of brain regions and networks being defined for various components of spontaneous thought. However, most of brain activity does not represent immediately occurring thoughts. Instead, spontaneous, organized network activity largely reflects "intrinsic" functions that are unrelated to the current experience. There remains no consensus on how brain networks represent mind-wandering in parallel to functioning in other ongoing, predominantly unconscious processes. Commonly, in network analysis of functional neuroimaging data, functional connectivity (FC; correlated time series) between remote brain regions is considered over several minutes or longer. In contrast, dynamic functional connectivity (dFC) is a new, promising approach to characterizing spontaneous changes in neural network communication on the faster time-scale at which intra-individual fluctuations in thought contents may occur. Here I describe how a potential relationship between mind-wandering and FC has traditionally been considered in the literature, and I review methods and results pertaining to the study of the dFC-mind-wandering relationship. While acknowledging challenges to the dFC approach and to behaviorally capturing fluctuations in inner experiences, I describe a framework for describing spontaneous thoughts in terms of brain-network activity patterns that are comprised of connections weighted by time-varying relevance to conscious and unconscious processing. This perspective suggests preferential roles of certain anatomical communication avenues (e.g., via the default mode network) in mind-wandering, while also implying that a region's connectivity fluctuates over time in its immediate degree of relevance to conscious contents, ultimately allowing novelty and diversity of thought.

Aberrant Functional Connectivity Architecture in Participants with Chronic Insomnia Disorder Accompanying Cognitive Dysfunction: A Whole-Brain, Data-Driven Analysis

Objectives: Although it is widely observed that chronic insomnia disorder (CID) is associated with cognitive impairment, the neurobiological mechanisms underlying this remain unclear. Prior neuroimaging studies have confirmed that a close correlation exists between functional connectivity and cognitive impairment. Based on this observation, in this study we used resting-state functional magnetic resonance imaging (rs-fMRI) to study the relationship between whole brain functional connectivity and cognitive function in CID. Methods: We included 39 patients with CID and 28 age-, gender-, and education-matched healthy controls (HC). Abnormalities in functional connectivity were identified by comparing the correlation coefficients for each pair of 116 brain regions between CID and HC. Results: Cognitive impairment was associated with reduced subjective insomnia scores after controlling for age, gender, and educational effects. Compared with HC, patients with CID had larger negative correlations within the task-negative network [medial prefrontal cortex (mPFC), precuneus, inferior temporal gyrus, cerebellum, and superior parietal gyrus], and between two intrinsic anti-correlation networks (mPFC and middle temporal gyrus; supplementary motor area and cerebellum). Patients with CID also had decreased positive correlations within the default mode network (DMN), and between the cerebellum and DMN, which mainly comprises the mPFC and posterior cingulated cortex. There were positive correlations of decreased positive connectivity with subjective sleep scores and MMSE scores, and increased negative correlations between the task-negative-network and MMSE scores in CID. Conclusions: Using rs-fMRI, our results support previous observations of cortical disconnection in CID in the prefrontal and DMN networks. Moreover, abnormal correlations within the task-negative network, and between two intrinsically anti-correlation networks, might be important neurobiological indicators of CID and associated cognitive impairment.

Functional reorganization in obstructive sleep apnoea and insomnia: A systematic review of the resting-state fMRI

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Resting state functional MRI studies is a promising non-invasive tool for better understanding of the pathophysiology of sleep disorders.

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The salience network is involved in hyperarousal and affective symptoms in insomnia.

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The posterior default mode network appears to underlie cognitive and depressive symptoms of obstructive sleep apnoea.

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Disruption of intrinsic networks have been demonstrated in major depression, which is a common co-morbidity of sleep disorders.

Functional neuroimaging techniques have accelerated progress in the study of sleep disorders. Considering the striking prevalence of these disorders in the general population, however, as well as their strong bidirectional relationship with major neuropsychiatric disorders, including major depressive disorder, their numbers are still surprisingly low. This review examines the contribution of resting state functional MRI to current understanding of two major sleep disorders, insomnia disorder and obstructive sleep apnoea. An attempt is made to learn from parallels of previous resting state functional neuroimaging findings in major depressive disorder. Moreover, shared connectivity biomarkers are suggested for each of the sleep disorders. Taken together, despite some inconsistencies, the synthesis of findings to date highlights the importance of the salience network in hyperarousal and affective symptoms in insomnia. Conversely, dysfunctional connectivity of the posterior default mode network appears to underlie cognitive and depressive symptoms of obstructive sleep apnoea.

Maintenance and Representation of Mind Wandering during Resting-State fMRI

Major advances in resting-state functional magnetic resonance imaging (fMRI) techniques in the last two decades have provided a tool to better understand the functional organization of the brain both in health and illness. Despite such developments, characterizing regulation and cerebral representation of mind wandering, which occurs unavoidably during resting-state fMRI scans and may induce variability of the acquired data, remains a work in progress. Here, we demonstrate that a decrease or decoupling in functional connectivity involving the caudate nucleus, insula, medial prefrontal cortex and other domain-specific regions was associated with more sustained mind wandering in particular thought domains during resting-state fMRI. Importantly, our findings suggest that temporal and between-subject variations in functional connectivity of above-mentioned regions might be linked with the continuity of mind wandering. Our study not only provides a preliminary framework for characterizing the maintenance and cerebral representation of different types of mind wandering, but also highlights the importance of taking mind wandering into consideration when studying brain organization with resting-state fMRI in the future.

Insomnia heterogeneity: Characteristics to consider for data-driven multivariate subtyping

Meta-analyses and systematic reviews have reported surprisingly few consistent insomnia-characteristics with respect to cognitions, mood, traits, history of life events and family history. One interpretation of this limited consistency is that different subtypes of insomnia exist, each with its own specific multivariate profile of characteristics. Because previously unrecognized subtypes will be differentially represented in individual studies and dilute effect sizes of subtype-dependent characteristics of importance, they are unlikely to be reported consistently in individual studies, let alone in meta-analyses. This review therefore aims to complement meta-analyses by listing previously reported psychometric characteristics of insomnia, irrespective of the degree of consistency over studies. The review clearly indicates that characteristics of insomnia may not be limited to sleep. Reports suggest that at least some individuals with insomnia may deviate from people without sleep complaints with respect to demographics, mental and physical health, childhood trauma, life events, fatigue, sleepiness, hyperarousal, hyperactivity, other sleep disorders, lifetime sleep history, chronotype, depression, anxiety, mood, quality of life, personality, happiness, worry, rumination, self-consciousness, sensitivity, dysfunctional beliefs, self-conscious emotion regulation, coping, nocturnal mentation, wake resting-state mentation, physical activity, food intake, temperature perception and hedonic evaluation. The value of this list of characteristics is that 1) internet has now made it feasible to asses them all in a large sample of people suffering from insomnia, and 2) statistical methods like latent class analysis and community detection can utilize them for a truly bottom-up data-driven search for subtypes. The supplement to this review provides a blueprint of this multivariate approach as implemented in the Sleep registry platform (www.sleepregistry.nl), that allows for bottom-up subtyping and fosters cross-cultural comparison and worldwide collaboration on insomnia subtype finding - and beyond.

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.

Theory of Mind and the Whole Brain Functional Connectivity: Behavioral and Neural Evidences with the Amsterdam Resting State Questionnaire

A topic of common interest to psychologists and philosophers is the spontaneous flow of thoughts when the individual is awake but not involved in cognitive demands. This argument, classically referred to as the "stream of consciousness" of James, is now known in the psychological literature as "Mind-Wandering." Although of great interest, this construct has been scarcely investigated so far. Diaz et al. (2013) created the Amsterdam Resting State Questionnaire (ARSQ), composed of 27 items, distributed in seven factors: discontinuity of mind, theory of mind (ToM), self, planning, sleepiness, comfort, and somatic awareness. The present study aims at: testing psychometric properties of the ARSQ in a sample of 670 Italian subjects; exploring the neural correlates of a subsample of participants (N = 28) divided into two groups on the basis of the scores obtained in the ToM factor. Results show a satisfactory reliability of the original factional structure in the Italian sample. In the subjects with a high mean in the ToM factor compared to low mean subjects, functional MRI revealed: a network (48 nodes) with higher functional connectivity (FC) with a dominance of the left hemisphere; an increased within-lobe FC in frontal and insular lobes. In both neural and behavioral terms, our results support the idea that the mind, which does not rest even when explicitly asked to do so, has various and interesting mentalistic-like contents.