Altered thalamocortical and intra-thalamic functional connectivity during light sleep compared with wake

Network signatures define consciousness state during focal seizures

OBJECTIVE

Epilepsy is a common neurological disorder affecting 1% of the global population. Loss of consciousness in focal impaired awareness seizures (FIASs) and focal-to-bilateral tonic-clonic seizures (FBTCSs) can be devastating, but the mechanisms are not well understood. Although ictal activity and interictal connectivity changes have been noted, the network states of focal aware seizures (FASs), FIASs, and FBTCSs have not been thoroughly evaluated with network measures ictally.

METHODS

We obtained electrographic data from 74 patients with stereoelectroencephalography (SEEG). Sliding window band power, functional connectivity, and segregation were computed on preictal, ictal, and postictal data. Five-minute epochs of wake, rapid eye movement sleep, and deep sleep were also extracted. Connectivity of subcortical arousal structures was analyzed in a cohort of patients with both SEEG and functional magnetic resonance imaging (fMRI). Given that custom neuromodulation of seizures is predicated on detection of seizure type, a convolutional neural network was used to classify seizure types.

RESULTS

We found that in the frontoparietal association cortex, an area associated with consciousness, both consciousness-impairing seizures (FIASs and FBTCSs) and deep sleep had increases in slow wave delta (1-4 Hz) band power. However, when network measures were employed, we found that only FIASs and deep sleep exhibited an increase in delta segregation and a decrease in gamma segregation. Furthermore, we found that only patients with FIASs had reduced subcortical-to-neocortical functional connectivity with fMRI versus controls. Finally, our deep learning network demonstrated an area under the curve of .75 for detecting consciousness-impairing seizures.

SIGNIFICANCE

This study provides novel insights into ictal network measures in FASs, FIASs, and FBTCSs. Importantly, although both FIASs and FBTCSs result in loss of consciousness, our results suggest that ictal network changes in FIASs uniquely resemble those that occur during deep sleep. Our results may inform novel neuromodulation strategies for preservation of consciousness in epilepsy.

Transient brain activity dynamics discriminate levels of consciousness during anesthesia

The awake mammalian brain is functionally organized in terms of large-scale distributed networks that are constantly interacting. Loss of consciousness might disrupt this temporal organization leaving patients unresponsive. We hypothesize that characterizing brain activity in terms of transient events may provide a signature of consciousness. For this, we analyze temporal dynamics of spatiotemporally overlapping functional networks obtained from fMRI transient activity across different anesthetics and levels of anesthesia. We first show a striking homology in spatial organization of networks between monkeys and humans, indicating cross-species similarities in resting-state fMRI structure. We then track how network organization shifts under different anesthesia conditions in macaque monkeys. While the spatial aspect of the networks is preserved, their temporal dynamics are highly affected by anesthesia. Networks express for longer durations and co-activate in an anesthetic-specific configuration. Additionally, hierarchical brain organization is disrupted with a consciousness-level-signature role of the default mode network. In conclusion, large-scale brain network temporal dynamics capture differences in anesthetic-specific consciousness-level, paving the way towards a clinical translation of these cortical signature.

The impact of the human thalamus on brain-wide information processing

The thalamus is a small, bilateral structure in the diencephalon that integrates signals from many areas of the CNS. This critical anatomical position allows the thalamus to influence whole-brain activity and adaptive behaviour. However, traditional research paradigms have struggled to attribute specific functions to the thalamus, and it has remained understudied in the human neuroimaging literature. Recent advances in analytical techniques and increased accessibility to large, high-quality data sets have brought forth a series of studies and findings that (re-)establish the thalamus as a core region of interest in human cognitive neuroscience, a field that otherwise remains cortico-centric. In this Perspective, we argue that using whole-brain neuroimaging approaches to investigate the thalamus and its interaction with the rest of the brain is key for understanding systems-level control of information processing. To this end, we highlight the role of the thalamus in shaping a range of functional signatures, including evoked activity, interregional connectivity, network topology and neuronal variability, both at rest and during the performance of cognitive tasks.

P2X7 receptor-activated microglia in cortex is critical for sleep disorder under neuropathic pain

Neuropathic pain (NP) is associated with sleep disturbances, which may substantially influence the quality of life. Clinical and animal studies demonstrated that neurotransmitter is one of the main contributors to cause sleep disturbances induced by NP. Recently, it was reported that P2X7 receptors (P2X7R) are widely expressed in microglia, which serves crucial role in neuronal activity in the pain and sleep-awake cycle. In this study, we adopted the chronic constriction injury (CCI) model to establish the progress of chronic pain and investigated whether P2X7R of microglia in cortex played a critical role in sleep disturbance induced by NP. At electroencephalogram (EEG) level, sleep disturbance was observed in mice treated with CCI as they exhibited mechanical and thermal hypersensitivity, and inhibition of P2X7R ameliorated these changes. We showed a dramatic high level of P2X7R and Iba-1 co-expression in the cortical region, and the inhibition of P2X7R also adversely affected it. Furthermore, the power of LFPs in ventral posterior nucleus (VP) and primary somatosensory cortex (S1) which changed in the CCI group was adverse after the inhibition of P2X7R. Furthermore, inhibition of P2X7R also decreased the VP-S1 coherence which increased in CCI group. Nuclear magnetic resonance demonstrated inhibition of P2X7R decreased glutamate (Glu) levels in thalamic and cortical regions which were significantly increased in the CCI mice. Our findings provide evidence that NP has a critical effect on neuronal activity linked to sleep and may built up a new target for the development of sleep disturbances under chronic pain conditions.

Sleep Problems in Preschoolers With Autism Spectrum Disorder Are Associated With Sensory Sensitivities and Thalamocortical Overconnectivity

BACKGROUND

Projections between the thalamus and sensory cortices are established early in development and play an important role in regulating sleep as well as in relaying sensory information to the cortex. Atypical thalamocortical functional connectivity frequently observed in children with autism spectrum disorder (ASD) might therefore be linked to sensory and sleep problems common in ASD.

METHODS

Here, we investigated the relationship between auditory-thalamic functional connectivity measured during natural sleep functional magnetic resonance imaging, sleep problems, and sound sensitivities in 70 toddlers and preschoolers (1.5-5 years old) with ASD compared with a matched group of 46 typically developing children.

RESULTS

In children with ASD, sleep problems and sensory sensitivities were positively correlated, and increased sleep latency was associated with overconnectivity between the thalamus and auditory cortex in a subsample with high-quality magnetic resonance imaging data (n = 29). In addition, auditory cortex blood oxygen level-dependent signal amplitude was elevated in children with ASD, potentially reflecting reduced sensory gating or a lack of auditory habituation during natural sleep.

CONCLUSIONS

These findings indicate that atypical thalamocortical functional connectivity can be detected early in development and may play a crucial role in sleep problems and sensory sensitivities in ASD.

Pre- and post-task resting-state differs in clinical populations

Resting-state functional connectivity has generated great hopes as a potential brain biomarker for improving prevention, diagnosis, and treatment in psychiatry. This neuroimaging protocol can routinely be performed by patients and does not depend on the specificities of a task. Thus, it seems ideal for big data approaches that require aggregating data across multiple studies and sites. However, technical variability, diverging data analysis approaches, and differences in data acquisition protocols introduce heterogeneity to the aggregated data. Besides these technical aspects, a prior task that changes the psychological state of participants might also contribute to heterogeneity. In healthy participants, studies have shown that behavioral tasks can influence resting-state measures, but such effects have not yet been reported in clinical populations. Here, we fill this knowledge gap by comparing resting-state functional connectivity before and after clinically relevant tasks in two clinical conditions, namely substance use disorders and phobias. The tasks consisted of viewing craving-inducing and spider anxiety provoking pictures that are frequently used in cue-reactivity studies and exposure therapy. We found distinct pre- vs post-task resting-state connectivity differences in each group, as well as decreased thalamo-cortical and increased intra-thalamic connectivity which might be associated with decreased vigilance in both groups. Our results confirm that resting-state measures can be strongly influenced by prior emotion-inducing tasks that need to be taken into account when pooling resting-state scans for clinical biomarker detection. This demands that resting-state datasets should include a complete description of the experimental design, especially when a task preceded data collection.

A temporal sequence of thalamic activity unfolds at transitions in behavioral arousal state

Awakening from sleep reflects a profound transformation in neural activity and behavior. The thalamus is a key controller of arousal state, but whether its diverse nuclei exhibit coordinated or distinct activity at transitions in behavioral arousal state is unknown. Using fast fMRI at ultra-high field (7 Tesla), we measured sub-second activity across thalamocortical networks and within nine thalamic nuclei to delineate these dynamics during spontaneous transitions in behavioral arousal state. We discovered a stereotyped sequence of activity across thalamic nuclei and cingulate cortex that preceded behavioral arousal after a period of inactivity, followed by widespread deactivation. These thalamic dynamics were linked to whether participants subsequently fell back into unresponsiveness, with unified thalamic activation reflecting maintenance of behavior. These results provide an outline of the complex interactions across thalamocortical circuits that orchestrate behavioral arousal state transitions, and additionally, demonstrate that fast fMRI can resolve sub-second subcortical dynamics in the human brain.

Simultaneous EEG-fMRI: What Have We Learned and What Does the Future Hold?

Simultaneous EEG-fMRI has developed into a mature measurement technique in the past 25 years. During this time considerable technical and analytical advances have been made, enabling valuable scientific contributions to a range of research fields. This review will begin with an introduction to the measurement principles involved in EEG and fMRI and the advantages of combining these methods. The challenges faced when combining the two techniques will then be considered. An overview of the leading application fields where EEG-fMRI has made a significant contribution to the scientific literature and emerging applications in EEG-fMRI research trends is then presented.

Thalamocortical functional connectivity in patients with insomnia using resting-state fMRI

BACKGROUND

Insomnia is a common disorder that affects a vast number of patients; the hyperarousal theory of insomnia postulates that patients with insomnia are physiologically activated not only at nighttime but also during the daytime. We aimed to investigate the differences in the resting-state functional connectivity (RSFC) of the thalamus with cortical areas between patients with insomnia disorder and healthy controls.

METHODS

All participants completed clinical questionnaires and underwent portable polysomnography and resting-state fMRI.

RESULTS

Patients in the insomnia group (n = 50) showed increased RSFC between the thalamus and right medial superior frontal area, bilateral middle temporal areas, left rectus and right parahippocampal areas compared with controls (n = 42) after controlling for age, sex and education level. Among the pairs that showed increased connectivity, several functional connections were negatively correlated with sleep efficiency, measured by polysomnography.Limitations: We used a small sample size.

CONCLUSION

We consider these results on increased thalamocortical hyperactivity in brain areas related to sensory functions as providing evidence for the hyperarousal theory of insomnia.

Imaging the temporal dynamics of brain states with highly sampled fMRI

The spontaneous dynamics of the brain modulate its function from moment to moment, shaping neural computation and cognition. Functional MRI (fMRI), while classically used as a tool for spatial localization, is increasingly being used to identify the temporal dynamics of brain activity. fMRI analyses focused on the temporal domain have revealed important new information about the dynamics underlying states such as arousal, attention, and sleep. Dense temporal sampling – either by using fast fMRI acquisition, or multiple repeated scan sessions within individuals – can further enrich the information present in these studies. This review focuses on recent developments in using fMRI to identify dynamics across brain states, particularly vigilance and sleep states, and the potential for highly temporally sampled fMRI to answer these questions.

Data-driven beamforming technique to attenuate ballistocardiogram artefacts in electroencephalography-functional magnetic resonance imaging without detecting cardiac pulses in electrocardiography recordings

Simultaneous recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a very promising non‐invasive neuroimaging technique. However, EEG data obtained from the simultaneous EEG–fMRI are strongly influenced by MRI‐related artefacts, namely gradient artefacts (GA) and ballistocardiogram (BCG) artefacts. When compared to the GA correction, the BCG correction is more challenging to remove due to its inherent variabilities and dynamic changes over time. The standard BCG correction (i.e., average artefact subtraction [AAS]), require detecting cardiac pulses from simultaneous electrocardiography (ECG) recording. However, ECG signals are also distorted and will become problematic for detecting reliable cardiac peaks. In this study, we focused on a beamforming spatial filtering technique to attenuate all unwanted source activities outside of the brain. Specifically, we applied the beamforming technique to attenuate the BCG artefact in EEG–fMRI, and also to recover meaningful task‐based neural signals during an attentional network task (ANT) which required participants to identify visual cues and respond accurately. We analysed EEG–fMRI data in 20 healthy participants during the ANT, and compared four different BCG corrections (non‐BCG corrected, AAS BCG corrected, beamforming + AAS BCG corrected, beamforming BCG corrected). We demonstrated that the beamforming approach did not only significantly reduce the BCG artefacts, but also significantly recovered the expected task‐based brain activity when compared to the standard AAS correction. This data‐driven beamforming technique appears promising especially for longer data acquisition of sleep and resting EEG–fMRI. Our findings extend previous work regarding the recovery of meaningful EEG signals by an optimized suppression of MRI‐related artefacts.

Increased thalamo-cortical functional connectivity in patients with diabetic painful neuropathy: A resting-state functional MRI study

Functional changes in the brain of patients with painful diabetic neuropathy (PDN) have remained largely elusive. The aim of the present study was to explore changes in thalamo-cortical functional connectivity (FC) of patients with PDN using resting-state functional MRI. A total of 20 patients with type 2 diabetes mellitus (T2DM) with non-painful diabetic neuropathy (Group NDN), 19 patients with T2DM with PDN (Group-PDN) and 13 age-, sex- and education-matched healthy controls were recruited. The differences in thalamo-cortical FC among the three groups were compared. Patients in Group PDN had increased FC in the left thalamus, the right angular gyrus and the occipital gyrus as compared to those in Group NDN. Furthermore, patients in Group PDN had increased FC in the right thalamus and angular gyrus as compared to those in Group NDN. In conclusion, the present results suggested that the thalamo-cortical FC is increased in patients with T2DM and PDN. Furthermore, the increased FC in the thalamic-parietal-occipital connectivity may be a central pathophysiological mechanism for PDN. The study was retrospectively registered at ClinicalTrials.gov on 3 October 2018 (identifier no. NCT03700502).

Cerebral functional networks during sleep in young and older individuals

Even though sleep modification is a hallmark of the aging process, age-related changes in functional connectivity using functional Magnetic Resonance Imaging (fMRI) during sleep, remain unknown. Here, we combined electroencephalography and fMRI to examine functional connectivity differences between wakefulness and light sleep stages (N1 and N2 stages) in 16 young (23.1 ± 3.3y; 7 women), and 14 older individuals (59.6 ± 5.7y; 8 women). Results revealed extended, distributed (inter-between) and local (intra-within) decreases in network connectivity during sleep both in young and older individuals. However, compared to the young participants, older individuals showed lower decreases in connectivity or even increases in connectivity between thalamus/basal ganglia and several cerebral regions as well as between frontal regions of various networks. These findings reflect a reduced ability of the older brain to disconnect during sleep that may impede optimal disengagement for loss of responsiveness, enhanced lighter and fragmented sleep, and contribute to age effects on sleep-dependent brain plasticity.

Bridging the Gap? Altered Thalamocortical Connectivity in Psychotic and Psychedelic States

Psychiatry has a well-established tradition of comparing drug-induced experiences to psychotic symptoms, based on shared phenomena such as altered perceptions. The present review focuses on experiences induced by classic psychedelics, which are substances capable of eliciting powerful psychoactive effects, characterized by distortions/alterations of several neurocognitive processes (e.g., hallucinations). Herein we refer to such experiences as psychedelic states. Psychosis is a clinical syndrome defined by impaired reality testing, also characterized by impaired neurocognitive processes (e.g., hallucinations and delusions). In this review we refer to acute phases of psychotic disorders as psychotic states. Neuropharmacological investigations have begun to characterize the neurobiological mechanisms underpinning the shared and distinct neurophysiological changes observed in psychedelic and psychotic states. Mounting evidence indicates changes in thalamic filtering, along with disturbances in cortico-striato-pallido-thalamo-cortical (CSPTC)-circuitry, in both altered states. Notably, alterations in thalamocortical functional connectivity were reported by functional magnetic resonance imaging (fMRI) studies. Thalamocortical dysconnectivity and its clinical relevance are well-characterized in psychotic states, particularly in schizophrenia research. Specifically, studies report hyperconnectivity between the thalamus and sensorimotor cortices and hypoconnectivity between the thalamus and prefrontal cortices, associated with patients' psychotic symptoms and cognitive disturbances, respectively. Intriguingly, studies also report hyperconnectivity between the thalamus and sensorimotor cortices in psychedelic states, correlating with altered visual and auditory perceptions. Taken together, the two altered states appear to share clinically and functionally relevant dysconnectivity patterns. In this review we discuss recent findings of thalamocortical dysconnectivity, its putative extension to CSPTC circuitry, along with its clinical implications and future directions.

The Role of the Thalamus in the Neurological Mechanism of Subjective Sleepiness: An fMRI Study

PURPOSE

The thalamus, the region that forms the attentional network and transmits external sensory signals to the entire brain, is important for sleepiness. Herein, we examined the relationship between activity in the thalamus-seed brain network and subjective sleepiness.

MATERIALS AND METHODS

Fifteen healthy male participants underwent an experiment comprising a baseline evaluation and two successive interventions, a 9-day sleep extension followed by 1-night total sleep deprivation. Pre- and post-intervention tests included the Karolinska sleepiness scale and neuroimaging for arterial spin labeling and functional connectivity. We examined the association between subjective sleepiness and the functional magnetic resonance imaging indices.

RESULTS

The functional connectivity between the left or right thalamus and various brain regions displayed a significant negative association with subjective sleepiness, and the functional connectivity between the left and right thalamus displayed a significant positive association with subjective sleepiness. The graph theory analysis indicated that the number of positive functional connectivity related to the thalamus showed a strong negative association with subjective sleepiness, and conversely, the number of negative functional connectivity showed a positive association with subjective sleepiness. Arterial spin labeling analysis indicated that the blood flow in both the left and right thalami was significantly negatively associated with subjective sleepiness. Functional connectivity between the anterior cingulate cortex and salience network areas of the left insular cortex, and that between the anterior and posterior cingulate cortices showed a strong positive and negative association with subjective sleepiness, respectively.

CONCLUSION

Subjective sleepiness and the thalamic-cortical network dynamics are strongly related, indicating the application of graph theory to study sleepiness and consciousness. These results also demonstrate that resting functional connectivity largely reflects the "state" of the subject, suggesting that the control of sleep and conscious states is essential when using functional magnetic resonance imaging indices as biomarkers.

NREM sleep stages specifically alter dynamical integration of large-scale brain networks

Functional dissociations in the brain observed during non-rapid eye movement (NREM) sleep have been associated with reduced information integration and impaired consciousness that accompany increasing sleep depth. Here, we explored the dynamical properties of large-scale functional brain networks derived from transient brain activity using functional magnetic resonance imaging. Spatial brain maps generally display significant modifications in terms of their tendency to occur across wakefulness and NREM sleep. Unexpectedly, almost all networks predominated in activity during NREM stage 2 before an abrupt loss of activity is observed in NREM stage 3. Yet, functional connectivity and mutual dependencies between these networks progressively broke down with increasing sleep depth. Thus, the efficiency of information transfer during NREM stage 2 is low despite the high attempt to communicate. Critically, our approach provides relevant data for evaluating functional brain network integrity and our findings robustly support a significant advance in our neural models of human sleep and consciousness.

Altered Coactive Micropattern Connectivity in the Default-Mode Network during the Sleep-Wake Cycle

The default-mode network (DMN) is believed to be associated with levels of consciousness, but how the functional connectivity (FC) of the DMN changes across different states of consciousness is still unclear. In the current work, we addressed this issue by exploring the coactive micropattern (CAMP) networks of the DMN according to the CAMPs of rat DMN activity during the sleep-wake cycle and tracking their topological alterations among different states of consciousness. Three CAMP networks were observed in DMN activity, and they displayed greater FC and higher efficiency than the original DMN structure in all states of consciousness, implying more efficient information processing in the CAMP networks. Furthermore, no significant differences in FC or network properties were found among the three CAMP networks in the waking state. However, the three networks were distinct in their characteristics in two sleep states, indicating that different CAMP networks played specific roles in distinct sleep states. In addition, we found that the changes in the FC and network properties of the CAMP networks were similar to those in the original DMN structure, suggesting intrinsic effects of various states of consciousness on DMN dynamics. Our findings revealed three underlying CAMP networks within the DMN dynamics and deepened the current knowledge concerning FC alterations in the DMN during conscious changes in the sleep-wake cycle.

Altered thalamic connectivity in insomnia disorder during wakefulness and sleep

Insomnia disorder is the most common sleep disorder and has drawn increasing attention. Many studies have shown that hyperarousal plays a key role in the pathophysiology of insomnia disorder. However, the specific brain mechanisms underlying insomnia disorder remain unclear. To elucidate the neuropathophysiology of insomnia disorder, we investigated the brain functional networks of patients with insomnia disorder and healthy controls across the sleep–wake cycle. EEG‐fMRI data from 33 patients with insomnia disorder and 31 well‐matched healthy controls during wakefulness and nonrapid eye movement sleep, including N1, N2 and N3 stages, were analyzed. A medial and anterior thalamic region was selected as the seed considering its role in sleep–wake regulation. The functional connectivity between the thalamic seed and voxels across the brain was calculated. ANOVA with factors “group” and “stage” was performed on thalamus‐based functional connectivity. Correlations between the misperception index and altered functional connectivity were explored. A group‐by‐stage interaction was observed at widespread cortical regions. Regarding the main effect of group, patients with insomnia disorder demonstrated decreased thalamic connectivity with the left amygdala, parahippocampal gyrus, putamen, pallidum and hippocampus across wakefulness and all three nonrapid eye movement sleep stages. The thalamic connectivity in the subcortical cluster and the right temporal cluster in N1 was significantly correlated with the misperception index. This study demonstrated the brain functional basis in insomnia disorder and illustrated its relationship with sleep misperception, shedding new light on the brain mechanisms of insomnia disorder and indicating potential therapeutic targets for its treatment.

Abnormal Thalamic Functional Connectivity During Light Non-Rapid Eye Movement Sleep in Children With Primary Nocturnal Enuresis

OBJECTIVE

To investigate abnormalities of thalamocortical and intrathalamic functional connectivity (FC) in children with primary nocturnal enuresis (PNE) during light non-rapid eye movement (NREM) sleep using a simultaneous electroencephalography (EEG)-functional magnetic resonance imaging (fMRI) method.

METHOD

Polysomnographic and EEG-fMRI data were obtained during sleep from 61 children with PNE (age 10.2 ± 1.7 years, 59% boys) and 61 age-matched controls (age 10.1 ± 1.4 years, 54% boys). All subjects first participated in one overnight video-polysomnographic study. Total sleep time, percentage of total sleep time in each sleep stage, arousal index, and awakening index were calculated. Simultaneous EEG-fMRI studies were then performed using a 3T MRI system with a 32-channel MRI-compatible EEG system. Visual scoring of EEG data permitted sleep staging. Thalamocortical and intrathalamic FCs in the waking state and at different stages of light sleep were calculated and compared.

RESULTS

Children with PNE had a higher percentage of total sleep time in light sleep and a higher arousal index compared with controls. Abnormal thalamocortical FCs were detected in the lateral prefrontal cortex, medial prefrontal cortex, and inferior parietal lobule during light NREM sleep. Abnormal intrathalamic FCs were also detected during light NREM sleep among the motor, occipital, prefrontal, and temporal subdivisions of the thalamus.

CONCLUSION

Abnormal prefrontal and parietal thalamocortical FCs, accompanied by abnormal intrathalamic FCs among the motor, occipital, prefrontal, and temporal subdivision of thalamus during light NREM sleep, may be related to abnormal sleep and enuresis in children with PNE.

COALIA: A Computational Model of Human EEG for Consciousness Research

Understanding the origin of the main physiological processes involved in consciousness is a major challenge of contemporary neuroscience, with crucial implications for the study of Disorders of Consciousness (DOC). The difficulties in achieving this task include the considerable quantity of experimental data in this field, along with the non-intuitive, nonlinear nature of neuronal dynamics. One possibility of integrating the main results from the experimental literature into a cohesive framework, while accounting for nonlinear brain dynamics, is the use of physiologically-inspired computational models. In this study, we present a physiologically-grounded computational model, attempting to account for the main micro-circuits identified in the human cortex, while including the specificities of each neuronal type. More specifically, the model accounts for thalamo-cortical (vertical) regulation of cortico-cortical (horizontal) connectivity, which is a central mechanism for brain information integration and processing. The distinct neuronal assemblies communicate through feedforward and feedback excitatory and inhibitory synaptic connections implemented in a template brain accounting for long-range connectome. The EEG generated by this physiologically-based simulated brain is validated through comparison with brain rhythms recorded in humans in two states of consciousness (wakefulness, sleep). Using the model, it is possible to reproduce the local disynaptic disinhibition of basket cells (fast GABAergic inhibition) and glutamatergic pyramidal neurons through long-range activation of vasoactive intestinal-peptide (VIP) interneurons that induced inhibition of somatostatin positive (SST) interneurons. The model (COALIA) predicts that the strength and dynamics of the thalamic output on the cortex control the local and long-range cortical processing of information. Furthermore, the model reproduces and explains clinical results regarding the complexity of transcranial magnetic stimulation TMS-evoked EEG responses in DOC patients and healthy volunteers, through a modulation of thalamo-cortical connectivity that governs the level of cortico-cortical communication. This new model provides a quantitative framework to accelerate the study of the physiological mechanisms involved in the emergence, maintenance and disruption (sleep, anesthesia, DOC) of consciousness.

Effective degrees of freedom of the Pearson's correlation coefficient under autocorrelation

The dependence between pairs of time series is commonly quantified by Pearson's correlation. However, if the time series are themselves dependent (i.e. exhibit temporal autocorrelation), the effective degrees of freedom (EDF) are reduced, the standard error of the sample correlation coefficient is biased, and Fisher's transformation fails to stabilise the variance. Since fMRI time series are notoriously autocorrelated, the issue of biased standard errors - before or after Fisher's transformation - becomes vital in individual-level analysis of resting-state functional connectivity (rsFC) and must be addressed anytime a standardised Z-score is computed. We find that the severity of autocorrelation is highly dependent on spatial characteristics of brain regions, such as the size of regions of interest and the spatial location of those regions. We further show that the available EDF estimators make restrictive assumptions that are not supported by the data, resulting in biased rsFC inferences that lead to distorted topological descriptions of the connectome on the individual level. We propose a practical "xDF" method that accounts not only for distinct autocorrelation in each time series, but instantaneous and lagged cross-correlation. We find the xDF correction varies substantially over node pairs, indicating the limitations of global EDF corrections used previously. In addition to extensive synthetic and real data validations, we investigate the impact of this correction on rsFC measures in data from the Young Adult Human Connectome Project, showing that accounting for autocorrelation dramatically changes fundamental graph theoretical measures relative to no correction.

Dynamic functional connectivity states characterize NREM sleep and wakefulness

According to recent neuroimaging studies, temporal fluctuations in functional connectivity patterns can be clustered into dynamic functional connectivity (DFC) states and correspond to fluctuations in vigilance. However, whether there consistently exist DFC states associated with wakefulness and sleep stages and what are the characteristics and electrophysiological origin of these states remain unclear. The aims of the current study were to investigate the properties of DFC in different sleep stages and to explore the relationship between the characteristics of DFC and slow‐wave activity. We collected both eyes‐closed wakefulness and sleep data from 48 healthy young volunteers with simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings. EEG data were employed as the gold standard of sleep stage scoring, and DFC states were estimated based on fMRI data. The results demonstrated that DFC states of the fMRI signals consistently corresponded to wakefulness and nonrapid eye movement sleep stages independent of the number of clusters. Furthermore, the mean dwell time of these states significantly correlated with slow‐wave activity. The inclusion or omission of regression of the global signal and the selection of parcellation schemes exerted minimal effects on the current findings. These results provide strong evidence that DFC states underlying fMRI signals match the fluctuations of vigilance and suggest a possible electrophysiological source of DFC states corresponding to vigilance states.

Spectral entropy indicates electrophysiological and hemodynamic changes in drug-resistant epilepsy - A multimodal MREG study

Objective

Epilepsy causes measurable irregularity over a range of brain signal frequencies, as well as autonomic nervous system functions that modulate heart and respiratory rate variability. Imaging dynamic neuronal signals utilizing simultaneously acquired ultra-fast 10 Hz magnetic resonance encephalography (MREG), direct current electroencephalography (DC-EEG), and near-infrared spectroscopy (NIRS) can provide a more comprehensive picture of human brain function. Spectral entropy (SE) is a nonlinear method to summarize signal power irregularity over measured frequencies. SE was used as a joint measure to study whether spectral signal irregularity over a range of brain signal frequencies based on synchronous multimodal brain signals could provide new insights in the neural underpinnings of epileptiform activity.

Methods

Ten patients with focal drug-resistant epilepsy (DRE) and ten healthy controls (HC) were scanned with 10 Hz MREG sequence in combination with EEG, NIRS (measuring oxygenated, deoxygenated, and total hemoglobin: HbO, Hb, and HbT, respectively), and cardiorespiratory signals. After pre-processing, voxelwise SE_MREG was estimated from MREG data. Different neurophysiological and physiological subfrequency band signals were further estimated from MREG, DC-EEG, and NIRS: fullband (0–5 Hz, FB), near FB (0.08–5 Hz, NFB), brain pulsations in very-low (0.009–0.08 Hz, VLFP), respiratory (0.12–0.4 Hz, RFP), and cardiac (0.7–1.6 Hz, CFP) frequency bands. Global dynamic fluctuations in MREG and NIRS were analyzed in windows of 2 min with 50% overlap.

Results

Right thalamus, cingulate gyrus, inferior frontal gyrus, and frontal pole showed significantly higher SE_MREG in DRE patients compared to HC. In DRE patients, SE of cortical Hb was significantly reduced in FB (p = .045), NFB (p = .017), and CFP (p = .038), while both HbO and HbT were significantly reduced in RFP (p = .038, p = .045, respectively). Dynamic SE of HbT was reduced in DRE patients in RFP during minutes 2 to 6. Fitting to the frontal MREG and NIRS results, DRE patients showed a significant increase in SE_EEG in FB in fronto-central and parieto-occipital regions, in VLFP in parieto-central region, accompanied with a significant decrease in RFP in frontal pole and parietal and occipital (O2, Oz) regions.

Conclusion

This is the first study to show altered spectral entropy from synchronous MREG, EEG, and NIRS in DRE patients. Higher SE_MREG in DRE patients in anterior cingulate gyrus together with SE_EEG and SE_NIRS results in 0.12–0.4 Hz can be linked to altered parasympathetic function and respiratory pulsations in the brain. Higher SE_MREG in thalamus in DRE patients is connected to disturbances in anatomical and functional connections in epilepsy. Findings suggest that spectral irregularity of both electrophysiological and hemodynamic signals are altered in specific way depending on the physiological frequency range.

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Simultaneous imaging methods indicate spectral irregularity in neurovascular and electrophysiological brain pulsations in DRE.

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Altered spectral entropy in EEG, NIRS and BOLD indicate dysfunctional brain pulsations in respiratory frequency in epilepsy.

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Spectral irregularity (0-5 Hz) of BOLD in right thalamus supports previous structural and functional findings in epilepsy.

Objective and subjective measures of prior sleep-wake behavior predict functional connectivity in the default mode network during NREM sleep

INTRODUCTION

Prior sleep behavior has been shown to correlate with waking resting-state functional connectivity (FC) in the default mode network (DMN). However, the impact of sleep history on FC during sleep has not been investigated. The aim of this study was to establish whether there is an association between intersubject variability in habitual sleep behaviors and the strength of FC within the regions of the DMN during non-rapid eye movement (NREM) sleep.

METHODS

Wrist actigraphy and sleep questionnaires were used as objective and subjective measures of habitual sleep behavior, and EEG-functional MRI during NREM sleep was used to quantify sleep.

RESULTS

There was a significant, regionally specific association between the interindividual variability in objective (total sleep time on the night before scanning) and subjective (Insomnia Severity Index) measures of prior sleep-wake behavior and the strength of DMN FC during subsequent wakefulness and NREM sleep. In several cases, FC was related to sleep measures independently of sleep stage, suggesting that previous sleep history effects sleep FC globally across the stages.

CONCLUSIONS

This work highlights the need to consider a subject's prior sleep history in studies utilizing FC analysis during wakefulness and sleep, and indicates the complexity of the impact of sleep on the brain both in the short and long term.

Schizophrenia alters intra-network functional connectivity in the caudate for detecting speech under informational speech masking conditions

BACKGROUND

Speech recognition under noisy "cocktail-party" environments involves multiple perceptual/cognitive processes, including target detection, selective attention, irrelevant signal inhibition, sensory/working memory, and speech production. Compared to health listeners, people with schizophrenia are more vulnerable to masking stimuli and perform worse in speech recognition under speech-on-speech masking conditions. Although the schizophrenia-related speech-recognition impairment under "cocktail-party" conditions is associated with deficits of various perceptual/cognitive processes, it is crucial to know whether the brain substrates critically underlying speech detection against informational speech masking are impaired in people with schizophrenia.

METHODS

Using functional magnetic resonance imaging (fMRI), this study investigated differences between people with schizophrenia (n = 19, mean age = 33 ± 10 years) and their matched healthy controls (n = 15, mean age = 30 ± 9 years) in intra-network functional connectivity (FC) specifically associated with target-speech detection under speech-on-speech-masking conditions.

RESULTS

The target-speech detection performance under the speech-on-speech-masking condition in participants with schizophrenia was significantly worse than that in matched healthy participants (healthy controls). Moreover, in healthy controls, but not participants with schizophrenia, the strength of intra-network FC within the bilateral caudate was positively correlated with the speech-detection performance under the speech-masking conditions. Compared to controls, patients showed altered spatial activity pattern and decreased intra-network FC in the caudate.

CONCLUSIONS

In people with schizophrenia, the declined speech-detection performance under speech-on-speech masking conditions is associated with reduced intra-caudate functional connectivity, which normally contributes to detecting target speech against speech masking via its functions of suppressing masking-speech signals.

Reduced resting-state thalamostriatal functional connectivity is associated with excessive daytime sleepiness in persons with and without depressive disorders

BACKGROUND

Excessive daytime sleepiness (EDS) is a common and significant problem encountered in affective illness, however, the biological underpinnings of EDS in persons with psychiatric disorders are not clear. This study evaluated the associations between thalamic connectivity with cortical and subcortical brain regions with EDS in persons with and without depressive disorders (DD).

METHODS

Resting-state functional connectivity magnetic resonance imaging scans from 67 unmedicated young to middle-aged women with current DD (n = 30), remitted DD (n = 13), and healthy controls (n = 24) were utilized to examine the associations between thalamic connectivity with cortical/subcortical structures and EDS.

RESULTS

After correction for multiple comparisons and adjustment for age, habitual sleep duration, and depressive symptomatology, reduced resting-state connectivity between the bilateral thalamus and left rostral striatum (caudate/putamen) was significantly associated with EDS.

LIMITATIONS

Causal inferences between thalamostriatal connectivity and EDS could not be determined.

CONCLUSIONS

These results further implicate the role of the striatum and thalamus as central components of the experience of EDS. Further research is indicated to clarify the specific role these structures play in EDS in psychiatric disorders.

Sleep onset uncovers thalamic abnormalities in patients with idiopathic generalised epilepsy

The thalamus is crucial for sleep regulation and the pathophysiology of idiopathic generalised epilepsy (IGE), and may serve as the underlying basis for the links between the two. We investigated this using EEG-fMRI and a specific emphasis on the role and functional connectivity (FC) of the thalamus. We defined three types of thalamic FC: thalamocortical, inter-hemispheric thalamic, and intra-hemispheric thalamic. Patients and controls differed in all three measures, and during wakefulness and sleep, indicating disorder-dependent and state-dependent modification of thalamic FC. Inter-hemispheric thalamic FC differed between patients and controls in somatosensory regions during wakefulness, and occipital regions during sleep. Intra-hemispheric thalamic FC was significantly higher in patients than controls following sleep onset, and disorder-dependent alterations to FC were seen in several thalamic regions always involving somatomotor and occipital regions. As interactions between thalamic sub-regions are indirect and mediated by the inhibitory thalamic reticular nucleus (TRN), the results suggest abnormal TRN function in patients with IGE, with a regional distribution which could suggest a link with the thalamocortical networks involved in the generation of alpha rhythms. Intra-thalamic FC could be a more widely applicable marker beyond patients with IGE.

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Sleep onset modifies thalamic FC in generalised epilepsy differently to controls.

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Differences are regionally specific.

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Regions connected to somatomotor/occipital cortices are consistently affected.

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Intra-thalamic FC may be a surrogate marker of thalamic reticular nucleus function.

Improving the Test-Retest Reliability of Resting State fMRI by Removing the Impact of Sleep

Resting state functional magnetic resonance imaging (rs-fMRI) provides a powerful tool to examine large-scale neural networks in the human brain and their disturbances in neuropsychiatric disorders. Thanks to its low demand and high tolerance, resting state paradigms can be easily acquired from clinical population. However, due to the unconstrained nature, resting state paradigm is associated with excessive head movement and proneness to sleep. Consequently, the test-retest reliability of rs-fMRI measures is moderate at best, falling short of widespread use in the clinic. Here, we characterized the effect of sleep on the test-retest reliability of rs-fMRI. Using measures of heart rate variability (HRV) derived from simultaneous electrocardiogram (ECG) recording, we identified portions of fMRI data when subjects were more alert or sleepy, and examined their effects on the test-retest reliability of functional connectivity measures. When volumes of sleep were excluded, the reliability of rs-fMRI is significantly improved, and the improvement appears to be general across brain networks. The amount of improvement is robust with the removal of as much as 60% volumes of sleepiness. Therefore, test-retest reliability of rs-fMRI is affected by sleep and could be improved by excluding volumes of sleepiness as indexed by HRV. Our results suggest a novel and practical method to improve test-retest reliability of rs-fMRI measures.

Connectivity-based change point detection for large-size functional networks

Recent understanding that the brain at rest does not remain in a single state but transiently visits multiple states emphasizes the importance of state changes embedded in the brain network. Due to the effectiveness of larger networks in characterizing brain states, there is an increasing need for a network-based change point detection method that is applicable to large-size networks, particularly those with longer time series. This paper presents a fast and efficient method for detecting change points in the large-size functional networks of resting-state fMRI. To detect change points, a statistic for the covariance change at each time point is tested by a local false discovery rate, estimated based on the empirical null principle using a semiparametric mixture model. We present simulations and empirical analyses of task-based and resting-state fMRI data sets with various network sizes up to 300 nodes selected from the Human Connectome Project database. We demonstrate that the proposed method is not only efficient in detecting change points in large samples of large-size networks but also is less sensitive to the window size selection and provides the consequent identification of the changed edges. The covariance-based change point detection method in this study would be very useful in exploring characteristics of dynamic states in long-term large-size resting-state brain networks.

Global Functional Connectivity Differences between Sleep-Like States in Urethane Anesthetized Rats Measured by fMRI

Sleep is essential for nervous system functioning and sleep disorders are associated with several neurodegenerative diseases. However, the macroscale connectivity changes in brain networking during different sleep states are poorly understood. One of the hindering factors is the difficulty to combine functional connectivity investigation methods with spontaneously sleeping animals, which prevents the use of numerous preclinical animal models. Recent studies, however, have implicated that urethane anesthesia can uniquely induce different sleep-like brain states, resembling rapid eye movement (REM) and non-REM (NREM) sleep, in rodents. Therefore, the aim of this study was to assess changes in global connectivity and topology between sleep-like states in urethane anesthetized rats, using blood oxygenation level dependent (BOLD) functional magnetic resonance imaging. We detected significant changes in corticocortical (increased in NREM-like state) and corticothalamic connectivity (increased in REM-like state). Additionally, in graph analysis the modularity, the measure of functional integration in the brain, was higher in NREM-like state than in REM-like state, indicating a decrease in arousal level, as in normal sleep. The fMRI findings were supported by the supplementary electrophysiological measurements. Taken together, our results show that macroscale functional connectivity changes between sleep states can be detected robustly with resting-state fMRI in urethane anesthetized rats. Our findings pave the way for studies in animal models of neurodegenerative diseases where sleep abnormalities are often one of the first markers for the disorder development.