Common and distinct neural representations of imagined and perceived speech.

Humans excel at constructing mental representations of speech streams in the absence of external auditory input: the internal experience of speech imagery. Elucidating the neural processes underlying speech imagery is critical to understanding this higher-order brain function in humans. Here, using functional magnetic resonance imaging, we investigated the shared and distinct neural correlates of imagined and perceived speech by asking participants to listen to poems articulated by a male voice (perception condition) and to imagine hearing poems spoken by that same voice (imagery condition). We found that compared to baseline, speech imagery and perception activated overlapping brain regions, including the bilateral superior temporal gyri and supplementary motor areas. The left inferior frontal gyrus was more strongly activated by speech imagery than by speech perception, suggesting functional specialization for generating speech imagery. Although more research with a larger sample size and a direct behavioral indicator is needed to clarify the neural systems underlying the construction of complex speech imagery, this study provides valuable insights into the neural mechanisms of the closely associated but functionally distinct processes of speech imagery and perception.

State-dependent and region-specific alterations of cerebellar connectivity across stable human wakefulness and NREM sleep states.

Sleep regulation and functioning may rely on systematic coordination throughout the whole brain, including the cerebellum. However, whether and how interactions between the cerebellum and other brain regions vary across sleep stages remain poorly understood. Here, using simultaneous EEG-fMRI recordings captured from 73 participants during wakefulness and non-rapid eye movement (NREM) sleep, we constructed cerebellar connectivity among intrinsic functional networks with intra-cerebellar, neocortical and subcortical regions. We uncovered that cerebellar connectivity exhibited sleep-dependent alterations: slight differences between wakefulness and N1/N2 sleep and greater changes in N3 sleep than other states. Region-specific cerebellar connectivity changes between N2 sleep and N3 sleep were also revealed: general breakdown of intra-cerebellar connectivity, enhancement of limbic-cerebellar connectivity and alterations of cerebellar connectivity with spatially specific neocortices. Further correlation analysis showed that functional connectivity between the cerebellar Control II network and regions (including the insula, hippocampus, and amygdala) correlated with delta power during N3 and beta power during N2 sleep. These findings systematically reveal altered cerebellar connectivity among intrinsic networks from wakefulness to deep sleep and highlight the potential role of the cerebellum in sleep regulation and functioning.

Disrupted neural tracking of sound localization during non-rapid eye movement sleep.

Spatial hearing in humans is a high-level auditory process that is crucial to rapid sound localization in the environment. Both neurophysiological models with animals and neuroimaging evidence from human subjects in the wakefulness stage suggest that the localization of auditory objects is mainly located in the posterior auditory cortex. However, whether this cognitive process is preserved during sleep remains unclear. To fill this research gap, we investigated the sleeping brain's capacity to identify sound locations by recording simultaneous electroencephalographic (EEG) and magnetoencephalographic (MEG) signals during wakefulness and non-rapid eye movement (NREM) sleep in human subjects. Using the frequency-tagging paradigm, the subjects were presented with a basic syllable sequence at 5 Hz and a location change that occurred every three syllables, resulting in a sound localization shift at 1.67 Hz. The EEG and MEG signals were used for sleep scoring and neural tracking analyses, respectively. Neural tracking responses at 5 Hz reflecting basic auditory processing were observed during both wakefulness and NREM sleep, although the responses during sleep were weaker than those during wakefulness. Cortical responses at 1.67 Hz, which correspond to the sound location change, were observed during wakefulness regardless of attention to the stimuli but vanished during NREM sleep. These results for the first time indicate that sleep preserves basic auditory processing but disrupts the higher-order brain function of sound localization.

Changes in white matter functional networks during wakefulness and sleep.

Blood oxygenation level-dependent (BOLD) signals in the white matter (WM) have been demonstrated to encode neural activities by showing structure-specific temporal correlations during resting-state and task-specific imaging of fiber pathways with various degrees of correlations in strength and time delay. Previous neuroimaging studies have shown state-dependent functional connectivity and regional amplitude of signal fluctuations in brain gray matter across wakefulness and nonrapid eye movement (NREM) sleep cycles. However, the functional characteristics of WM during sleep remain unknown. Using simultaneous electroencephalography and functional magnetic resonance imaging data during wakefulness and NREM sleep collected from 66 healthy participants, we constructed 10 stable WM functional networks using clustering analysis. Functional connectivity between these WM functional networks and regional amplitude of WM signal fluctuations across multiple low-frequency bands were evaluated. In general, decreased WM functional connectivity between superficial and middle layer WM functional networks was observed from wakefulness to sleep. In addition, functional connectivity between the deep and cerebellar networks was higher during light sleep and lower during both wakefulness and deep sleep. The regional fluctuation amplitude was always higher during light sleep and lower during deep sleep. Importantly, slow-wave activity during deep sleep negatively correlated with functional connectivity between WM functional networks but positively correlated with fluctuation strength in the WM. These observations provide direct physiological evidence that neural activities in the WM are modulated by the sleep-wake cycle. This study provided the initial mapping of functional changes in WM during sleep.

Functional connectivity of the human hypothalamus during wakefulness and nonrapid eye movement sleep.

Animal experiments indicate that the hypothalamus plays an essential role in regulating the sleep-wake cycle. A recent neuroimaging study conducted under resting wakefulness conditions suggested the presence of a wake-promoting region and a sleep-promoting region in the human posterior hypothalamus and anterior hypothalamus, respectively, and interpreted their anticorrelated organization in resting-state functional networks as evidence for their opposing roles in sleep-wake regulation. However, whether and how the functional networks of the two hypothalamic regions reorganize according to their wake- or sleep-promoting roles during sleep are unclear. Here, we constructed functional networks of the posterior and anterior hypothalamus during wakefulness and nonrapid eye movement (NREM) sleep using simultaneous electroencephalography and functional magnetic resonance imaging data collected from 62 healthy participants. The functional networks of the posterior and anterior hypothalamus exhibited inversely correlated organizations during both wakefulness and NREM sleep. The connectivity strength of the posterior hypothalamic functional network was stronger during wakefulness than during stable sleep. From wakefulness to sleep, the anterior cingulate gyrus, paracingulate gyrus, insular cortex, and fontal operculum cortex showed decreased positive connectivity, while the precentral gyrus and postcentral gyrus showed decreased negative connectivity with the posterior hypothalamus. Additionally, the insular cortex and frontal operculum cortex showed negative connectivity during wakefulness and positive connectivity during sleep with the anterior hypothalamus, exhibiting an increasing trend. These findings provide insights into the correspondence between the functional network organizations and hypothalamic sleep-wake regulation in humans.

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.

EEG microstates are correlated with brain functional networks during slow-wave sleep.

Electroencephalography (EEG) microstates have been extensively studied in wakefulness and have been described as the "atoms of thought". Previous studies of EEG have found four microstates, i.e., microstates A, B, C and D, that are consistent among participants across the lifespan during the resting state. Studies using simultaneous EEG and functional magnetic resonance imaging (fMRI) have provided evidence for correlations between EEG microstates and fMRI networks during the resting state. Microstates have also been found during non-rapid eye movement (NREM) sleep. Slow-wave sleep (SWS) is considered the most restorative sleep stage and has been associated with the maintenance of sleep. However, the relationship between EEG microstates and brain functional networks during SWS has not yet been investigated. In this study, simultaneous EEG-fMRI data were collected during SWS to test the correspondence between EEG microstates and fMRI networks. EEG microstate-informed fMRI analysis revealed that three out of the four microstates showed significant correlations with fMRI data: 1) fMRI fluctuations in the insula and posterior temporal gyrus positively correlated with microstate B, 2) fMRI signals in the middle temporal gyrus and fusiform gyrus negatively correlated with microstate C, and 3) fMRI fluctuations in the occipital lobe negatively correlated with microstate D, while fMRI signals in the anterior cingulate and cingulate gyrus positively correlated with this microstate. Functional brain networks were then assessed using group independent component analysis based on the fMRI data. The group-level spatial correlation analysis showed that the fMRI auditory network overlapped the fMRI activation map of microstate B, the executive control network overlapped the fMRI deactivation of microstate C, and the visual and salience networks overlapped the fMRI deactivation and activation maps of microstate D. In addition, the subject-level spatial correlations between the general linear model (GLM) beta map of each microstate and the individual maps of each component yielded by dual regression also showed that EEG microstates were closely associated with brain functional networks measured using fMRI during SWS. Overall, the results showed that EEG microstates were closely related to brain functional networks during SWS, which suggested that EEG microstates provide an important electrophysiological basis underlying brain functional networks.

Reliability and Individual Specificity of EEG Microstate Characteristics.

Electroencephalography (EEG) microstates (MSs) are defined as quasi-stable topographies that represent global coherent activation. Alternations in EEG MSs have been reported in numerous neuropsychiatric disorders. Transferring the results of these studies into clinical practice requires not only high reliability but also sufficient individual specificity. Nevertheless, whether the amount of data used in microstate analysis influences reliability and how much individual information is provided by EEG MSs are unclear. In the current study, we aimed to assess the within-subject consistency and between-subject differences in the characteristics of EEG MSs. Two sets of eyes-closed resting-state EEG recordings were collected from 54 young, healthy participants on two consecutive days. The Raven Advanced Progressive Matrices test was conducted to assess general fluid intelligence (gF). We obtained four MSs (labeled A, B, C and D) through EEG microstate analysis. EEG MS characteristics including traditional features (the global explained variances, mean durations, coverages, occurrences and transition probabilities), the Hurst exponents and temporal dynamic features (the autocorrelation functions and the partial autocorrelation functions) were calculated and evaluated. The data with a duration greater than 2 min showed moderate to high reliability and individual specificity. The mean duration and coverage of MS C were significantly correlated with the gF score. The dynamic features showed a higher identification accuracy and were more significantly correlated with gF than the traditional MS features. These findings reveal that EEG microstate characteristics are reliably unique in single subjects and possess abundant inter-individual variability.

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.

Increased connectivity of the anterior cingulate cortex is associated with the tendency to awakening during N2 sleep in patients with insomnia disorder.

STUDY OBJECTIVES: To investigate the relationship between sleep transition dynamics and stage-specific functional connectivity (FC) of the anterior cingulate cortex (ACC) in patients with insomnia disorder (ID). METHODS: Simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) data from 37 patients with ID and 30 well-matched healthy controls (HCs) were recorded during wakefulness and different sleep stages and subsequently analyzed. A Markov chain model was used to estimate the transition probability between each stage. The FC between the ACC (set as the seed) and voxels across the whole brain was calculated. A linear mixed effect model was used to determine the group-by-stage interaction of the seed-based connectivity. The correlation between the sleep-stage transition probability and the ACC-based connectivity was explored. RESULTS: Patients with ID exhibited a higher likelihood of transitioning from N2 to wakefulness than HCs. A significant group-by-stage interaction of connectivity with the bilateral ACC was observed in the cerebellar, subcortical, and cortical regions. Moreover, a significant positive correlation was found in patients with ID between the transition probability from N2 to wakefulness and the FC of the ACC with the anterior cerebellum in N2 (r = 0.48). CONCLUSIONS: This exploratory analysis indicates that enhanced FC between the ACC and cerebellum represents a potential neural pathway underlying the greater likelihood of patients with ID waking during N2 sleep. These findings contribute to an emerging framework that reveals the link between sleep maintenance difficulty and ACC function, further highlighting the possibility that N2 sleep is a therapeutic target for meaningfully reducing sleep disruption.

Thalamic network under wakefulness after sleep onset and its coupling with daytime fatigue in insomnia disorder: An EEG-fMRI study.

BACKGROUND: Fatigue is the most common daytime impairment of insomnia disorder (ID). Thalamus is acknowledged as the key brain region closely associated with fatigue. However, the thalamus-based neurobiological mechanisms of fatigue in patients with ID remain unknown. METHODS: Forty-two ID patients and twenty-eight well-matched healthy controls (HCs) underwent simultaneous electroencephalography--functional magnetic resonance imaging. We calculated the functional connectivity (FC) between the thalamic seed and each voxel across the whole brain in two conditions of wakefulness--after sleep onset (WASO) and before sleep onset. A linear mixed effect model was used to determine the condition effect of the thalamic FC. The correlation between daytime fatigue and the thalamic connectivity was explored. RESULTS: After sleep onset, the connectivity with the bilateral thalamus was increased in the cerebellar and cortical regions. Compared with HCs, ID patients showed significantly lower FC between left thalamus and left cerebellum under the WASO condition. Furthermore, thalamic connectivity with cerebellum under the WASO condition was negatively correlated with Fatigue Severity Scale scores in the pooled sample. CONCLUSIONS: These findings contribute to an emerging framework that reveals the link between insomnia-related daytime fatigue and the altered thalamic network after sleep onset, further highlighting the possibility that this neural pathway is a therapeutic target for meaningfully mitigating fatigue.

A-PASS: an automated pipeline to analyze simultaneously acquired EEG-fMRI data for studying brain activities during sleep.

Objective.Concurrent electroencephalography and functional magnetic resonance imaging (EEG-fMRI) signals can be used to uncover the nature of brain activities during sleep. However, analyzing simultaneously acquired EEG-fMRI data is extremely time consuming and experience dependent. Thus, we developed a pipeline, which we named A-PASS, to automatically analyze simultaneously acquired EEG-fMRI data for studying brain activities during sleep.Approach.A deep learning model was trained on a sleep EEG-fMRI dataset from 45 subjects and used to perform sleep stage scoring. Various fMRI indices can be calculated with A-PASS to depict the neurophysiological characteristics across different sleep stages. We tested the performance of A-PASS on an independent sleep EEG-fMRI dataset from 28 subjects. Statistical maps regarding the main effect of sleep stages and differences between each pair of stages of fMRI indices were generated and compared using both A-PASS and manual processing methods.Main results.The deep learning model implemented in A-PASS achieved both an accuracy and F1-score higher than 70% for sleep stage classification on EEG data acquired during fMRI scanning. The statistical maps generated from A-PASS largely resembled those produced from manually scored stages plus a combination of multiple software programs.Significance.A-PASS allowed efficient EEG-fMRI data processing without manual operation and could serve as a reliable and powerful tool for simultaneous EEG-fMRI studies on sleep.

Reorganizations of latency structures within the white matter from wakefulness to sleep.

Previous resting-state functional magnetic resonance imaging (fMRI) studies have revealed highly reproducible latency structures, reflecting the lead/lag relationship of BOLD fMRI signals in white matter (WM). With simultaneous electroencephalography and fMRI data from 35 healthy subjects who were instructed to sleep during imaging, we explored alterations of latency structures in the WM across wakefulness and nonrapid eye movement (NREM) sleep stages. Lagged cross-covariance was computed among voxelwise time series, followed by parabolic interpolation to determine the actual in-between latencies. WM regions, including the brainstem, internal capsule, optic radiation, genu of corpus callosum, and corona radiata, inconsistently changed temporal dynamics with respect to the rest of the WM across wakefulness and NREM sleep stages, as demonstrated when these regions were used as seeds for seed-based latency analysis. Latency analysis of resting-state networks, obtained by applying K-means clustering to a group-level functional connectivity matrix, identified a dominant direction of signaling, starting from the brainstem up to the internal capsule and then the corona radiata during wakefulness, which was reorganized according to stage transitions, e.g., the temporal organization of the internal capsule and corona radiata switched from unidirectional to bidirectional in the wakefulness to N3 transition. These findings suggest that WM BOLD signals are slow, dynamically modulated across wakefulness and NREM sleep stages and that they are involved in maintaining different levels of consciousness.

Sleep discrepancy is associated with alterations in the salience network in patients with insomnia disorder: An EEG-fMRI study.

BACKGROUND: Positron emission tomography - computed tomography (PET-CT) research has shown that sleep discrepancy recorded by self-report and polysomnography (PSG) may be related to the altered metabolic rate of the anterior insula (aINS) during non-rapid eye movement (NREM) sleep in patients with insomnia disorder. We aim to explore the functional connectivity of aINS across wake and NREM sleep in the patients and to reveal the association between aINS connectivity and sleep discrepancy. METHODS: Patients with insomnia disorder (n = 33) and healthy controls (n = 31) underwent simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) during nighttime sleep, and aINS-based connectivity was calculated across wake and NREM sleep. A linear mixed-effects model was used to assess the main effect of group and group-by-stage (wake, NREM stages 1-3) interaction effect on aINS connectivity. Similar mixed models were used to assess the potential correlation between aINS connectivity and the sleep misperception index (MI). RESULTS: A significant group-by-stage interaction effect on aINS-based connectivity was observed in the bilateral frontal gyrus, right inferior temporal gyrus, bilateral middle occipital gyrus and right postcentral gyrus (p < 0.05, corrected). There was also a significant group-by-MI interaction effect on aINS connectivity with the putamen and thalamus during wakefulness (p < 0.05 corrected); MI was significantly associated with aINS-putamen/thalamus connectivity in the control group, whereas the association was weak or even nonsignificant in the patient group. There was no significant main effect of group. CONCLUSION: The waking activity of a neural pathway containing the aINS, putamen, and thalamus may underlie sleep perception, potentially providing important perspectives to reveal complex mechanisms of sleep discrepancy between self-report and PSG.

Spindle-related brain activation in patients with insomnia disorder: An EEG-fMRI study.

Sleep spindles have been implicated in sleep protection, depression and anxiety. However, spindle-related brain imaging mechanism underpinning the deficient sleep protection and emotional regulation in insomnia disorder (ID) remains elusive. The aim of the current study is to investigate the relationship between spindle-related brain activations and sleep quality, symptoms of depression and anxiety in patients with ID. Participants (n = 46, 28 females, 18-60 years) were recruited through advertisements including 16 with ID, according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, and 30 matched controls. Group differences in spindle-related brain activations were analyzed using multimodality data acquired with simultaneous electroencephalography and functional magnetic resonance imaging during sleep. Compared with controls, patients with ID showed significantly decreased bilateral spindle-related brain activations in the cingulate gyrus (familywise error corrected p ˂ 0.05, cluster size 4401 mm3). Activations in the cingulate gyrus were negatively correlated with Pittsburgh Sleep Quality Index scores (r = -0.404, p = 0.005) and Self-Rating Anxiety Scale scores (r = -0.364, p = 0.013), in the pooled sample. These findings underscore the key role of spindle-related brain activations in the cingulate gyrus in subjective sleep quality and emotional regulation in ID.

Functional MRI of arousals in nonrapid eye movement sleep.

Arousals commonly occur during human sleep and have been associated with several sleep disorders. Arousals are characterized as an abrupt electroencephalography (EEG) frequency change to higher frequencies during sleep. However, the human brain regions involved in arousal are not yet clear. Simultaneous EEG and functional magnetic resonance imaging (fMRI) data were recorded during the early portion of the sleep period in healthy young adults. Arousals were identified based on the EEG data, and fMRI signal changes associated with 83 arousals from 19 subjects were analyzed. Subcortical regions, including the midbrain, thalamus, basal ganglia, and cerebellum, were activated with arousal. Cortices, including the temporal gyrus, occipital gyrus, and frontal gyrus, were deactivated with arousal. The activations associated with arousal in the subcortical regions were consistent with previous findings of subcortical involvement in behavioral arousal and consciousness. Cortical deactivations may serve as a mechanism to direct incoming sensory stimuli to specific brain regions, thereby monitoring environmental perturbations during sleep.