Predicting coarse-grained semantic features in language comprehension: evidence from ERP representational similarity analysis and Chinese classifier.

Existing studies demonstrate that comprehenders can predict semantic information during language comprehension. Most evidence comes from a highly constraining context, in which a specific word is likely to be predicted. One question that has been investigated less is whether prediction can occur when prior context is less constraining for predicting specific words. Here, we aim to address this issue by examining the prediction of animacy features in low-constraining context, using electroencephalography (EEG), in combination with representational similarity analysis (RSA). In Chinese, a classifier follows a numeral and precedes a noun, and classifiers constrain animacy features of upcoming nouns. In the task, native Chinese Mandarin speakers were presented with either animate-constraining or inanimate-constraining classifiers followed by congruent or incongruent nouns. EEG amplitude analysis revealed an N400 effect for incongruent conditions, reflecting the difficulty of semantic integration when an incompatible noun is encountered. Critically, we quantified the similarity between patterns of neural activity following the classifiers. RSA results revealed that the similarity between patterns of neural activity following animate-constraining classifiers was greater than following inanimate-constraining classifiers, before the presentation of the nouns, reflecting pre-activation of animacy features of nouns. These findings provide evidence for the prediction of coarse-grained semantic feature of upcoming words.

Predicting phonological information in language comprehension: evidence from ERP representational similarity analysis and Chinese idioms.

Do comprehenders predict the meaning and even the phonological form of upcoming words during language comprehension? With a growing body of evidence suggesting that semantic representations may be predicted, the evidence for phonological prediction is less clear and largely derived from studies conducted in languages utilizing an alphabetic script. In this research, we aim to examine the prediction of phonological information in the processing of Chinese idioms through the use of ERP representational similarity analysis (RSA). The study utilizes four-character Chinese idioms, and phonological overlap was manipulated by varying the syllable at the idiom-final part between idiom pairs so that pairs of idioms share a syllable (i.e. within-pairs) or not (between-pairs). We quantified the similarity between patterns of neural activity of idioms for within- and between-pairs. RSA results revealed greater similarity in neural activity patterns for idioms within-pairs, compared with between-pairs, and critically this similarity effect was observed prior to the presentation of the phonological similarity, providing evidence for the pre-activation of upcoming phonological information, under circumstances that encourage predictive processing.

Classical and non-classical psychedelic drugs induce common network changes in human cortex.

The neurobiology of the psychedelic experience is not fully understood. Identifying common brain network changes induced by both classical (i.e., acting at the 5-HT2 receptor) and non-classical psychedelics would provide mechanistic insight into state-specific characteristics. We analyzed whole-brain functional connectivity based on resting-state fMRI data in humans, acquired before and during the administration of nitrous oxide, ketamine, and lysergic acid diethylamide. We report that, despite distinct molecular mechanisms and modes of delivery, all three psychedelics reduced within-network functional connectivity and enhanced between-network functional connectivity. More specifically, all three drugs increased connectivity between right temporoparietal junction and bilateral intraparietal sulcus as well as between precuneus and left intraparietal sulcus. These regions fall within the posterior cortical "hot zone," posited to mediate the qualitative aspects of experience. Thus, both classical and non-classical psychedelics modulate networks within an area of known relevance for consciousness, identifying a biologically plausible candidate for their subjective effects.

Scale-free dynamics of core-periphery topography.

The human brain's cerebral cortex exhibits a topographic division into higher-order transmodal core and lower-order unimodal periphery regions. While timescales between the core and periphery region diverge, features of their power spectra, especially scale-free dynamics during resting-state and their mdulation in task states, remain unclear. To answer this question, we investigated the ~1/f-like pink noise manifestation of scale-free dynamics in the core-periphery topography during rest and task states applying infra-slow inter-trial intervals up to 1 min falling inside the BOLD's infra-slow frequency band. The results demonstrate (1) higher resting-state power-law exponent (PLE) in the core compared to the periphery region; (2) significant PLE increases in task across the core and periphery regions; and (3) task-related PLE increases likely followed the task's atypically low event rates, namely the task's periodicity (inter-trial interval = 52-60 s; 0.016-0.019 Hz). A computational model and a replication dataset that used similar infra-slow inter-trial intervals provide further support for our main findings. Altogether, the results show that scale-free dynamics differentiate core and periphery regions in the resting-state and mediate task-related effects.

Rest-task modulation of fMRI-derived global signal topography is mediated by transient coactivation patterns.

Recent resting-state functional MRI (fMRI) studies have revealed that the global signal (GS) exhibits a nonuniform spatial distribution across the gray matter. Whether this topography is informative remains largely unknown. We therefore tested rest-task modulation of GS topography by analyzing static GS correlation and dynamic coactivation patterns in a large sample of an fMRI dataset (n = 837) from the Human Connectome Project. The GS topography in the resting state and in seven different tasks was first measured by correlating the GS with the local time series (GSCORR). In the resting state, high GSCORR was observed mainly in the primary sensory and motor regions, whereas low GSCORR was seen in the association brain areas. This pattern changed during the seven tasks, with mainly decreased GSCORR in sensorimotor cortex. Importantly, this rest-task modulation of GSCORR could be traced to transient coactivation patterns at the peak period of GS (GS-peak). By comparing the topography of GSCORR and respiration effects, we observed that the topography of respiration mimicked the topography of GS in the resting state, whereas both differed during the task states; because of such partial dissociation, we assume that GSCORR could not be equated with a respiration effect. Finally, rest-task modulation of GS topography could not be exclusively explained by other sources of physiological noise. Together, we here demonstrate the informative nature of GS topography by showing its rest-task modulation, the underlying dynamic coactivation patterns, and its partial dissociation from respiration effects during task states.

Probiotics-Fermented Grifola frondosa Total Active Components: Better Antioxidation and Microflora Regulation for Alleviating Alcoholic Liver Damage in Mice.

Alcoholic liver damage is caused by long-term drinking, and it further develops into alcoholic liver diseases. In this study, we prepared a probiotic fermentation product of Grifola frondosa total active components (PFGF) by fermentation with Lactobacillus acidophilus, Lactobacillus rhamnosus, and Pediococcus acidilactici. After fermentation, the total sugar and protein content in the PFGF significantly decreased, while the lactic acid level and antioxidant activity of the PFGF increased. Afterward, we investigated the alleviating effect of PFGF on alcoholic liver injury in alcohol-fed mice. The results showed that the PFGF intervention reduced the necrosis of the liver cells, attenuated the inflammation of the liver and intestines, restored the liver function, increased the antioxidant factors of the liver, and maintained the cecum tissue barrier. Additionally, the results of the 16S rRNA sequencing analysis indicated that the PFGF intervention increased the relative abundance of beneficial bacteria, such as Lactobacillus, Ruminococcaceae, Parabacteroids, Parasutterella, and Alistipes, to attenuate intestinal inflammation. These results demonstrate that PFGF can potentially alleviate alcoholic liver damage by restoring the intestinal barrier and regulating the intestinal microflora.

Theoretical Neurobiology of Consciousness Applied to Human Cerebral Organoids.

Organoids and specifically human cerebral organoids (HCOs) are one of the most relevant novelties in the field of biomedical research. Grown either from embryonic or induced pluripotent stem cells, HCOs can be used as in vitro three-dimensional models, mimicking the developmental process and organization of the developing human brain. Based on that, and despite their current limitations, it cannot be assumed that they will never at any stage of development manifest some rudimentary form of consciousness. In the absence of behavioral indicators of consciousness, the theoretical neurobiology of consciousness being applied to unresponsive brain-injured patients can be considered with respect to HCOs. In clinical neurology, it is difficult to discern a capacity for consciousness in unresponsive brain-injured patients who provide no behavioral indicators of consciousness. In such scenarios, a validated neurobiological theory of consciousness, which tells us what the neural mechanisms of consciousness are, could be used to identify a capacity for consciousness. Like the unresponsive patients that provide a diagnostic difficulty for neurologists, HCOs provide no behavioral indicators of consciousness. Therefore, this article discusses how three prominent neurobiological theories of consciousness apply to human cerebral organoids. From the perspective of the Temporal Circuit Hypothesis, the Global Neuronal Workspace Theory, and the Integrated Information Theory, we discuss what neuronal structures and functions might indicate that cerebral organoids have a neurobiological capacity to be conscious.

Temporospatial Nestedness in Consciousness: An Updated Perspective on the Temporospatial Theory of Consciousness.

Time and space are fundamental elements that permeate the fabric of nature, and their significance in relation to neural activity and consciousness remains a compelling yet unexplored area of research. The Temporospatial Theory of Consciousness (TTC) provides a framework that links time, space, neural activity, and consciousness, shedding light on the intricate relationships among these dimensions. In this review, I revisit the fundamental concepts and mechanisms proposed by the TTC, with a particular focus on the central concept of temporospatial nestedness. I propose an extension of temporospatial nestedness by incorporating the nested relationship between the temporal circuit and functional geometry of the brain. To further unravel the complexities of temporospatial nestedness, future research directions should emphasize the characterization of functional geometry and the temporal circuit across multiple spatial and temporal scales. Investigating the links between these scales will yield a more comprehensive understanding of how spatial organization and temporal dynamics contribute to conscious states. This integrative approach holds the potential to uncover novel insights into the neural basis of consciousness and reshape our understanding of the world-brain dynamic.

Early visual exposure primes future cross-modal specialization of the fusiform face area in tactile face processing in the blind.

The fusiform face area (FFA) is a core cortical region for face information processing. Evidence suggests that its sensitivity to faces is largely innate and tuned by visual experience. However, how experience in different time windows shape the plasticity of the FFA remains unclear. In this study, we investigated the role of visual experience at different time points of an individual's early development in the cross-modal face specialization of the FFA. Participants (n = 74) were classified into five groups: congenital blind, early blind, late blind, low vision, and sighted control. Functional magnetic resonance imaging data were acquired when the participants haptically processed carved faces and other objects. Our results showed a robust and highly consistent face-selective activation in the FFA region in the early blind participants, invariant to size and level of abstraction of the face stimuli. The cross-modal face activation in the FFA was much less consistent in other groups. These results suggest that early visual experience primes cross-modal specialization of the FFA, and even after the absence of visual experience for more than 14 years in early blind participants, their FFA can engage in cross-modal processing of face information.

Asymmetric neural dynamics characterize loss and recovery of consciousness.

Anesthetics are known to disrupt neural interactions in cortical and subcortical brain circuits. While the effect of anesthetic drugs on consciousness is reversible, the neural mechanism mediating induction and recovery may be different. Insight into these distinct mechanisms can be gained from a systematic comparison of neural dynamics during slow induction of and emergence from anesthesia. To this end, we used functional magnetic resonance imaging (fMRI) data obtained in healthy volunteers before, during, and after the administration of propofol at incrementally adjusted target concentrations. We analyzed functional connectivity of corticocortical and subcorticocortical networks and the temporal autocorrelation of fMRI signal as an index of neural processing timescales. We found that en route to unconsciousness, temporal autocorrelation across the entire brain gradually increased, whereas functional connectivity gradually decreased. In contrast, regaining consciousness was associated with an abrupt restoration of cortical but not subcortical temporal autocorrelation and an abrupt boost of subcorticocortical functional connectivity. Pharmacokinetic effects could not account for the difference in neural dynamics between induction and emergence. We conclude that the induction and recovery phases of anesthesia follow asymmetric neural dynamics. A rapid increase in the speed of cortical neural processing and subcorticocortical neural interactions may be a mechanism that reboots consciousness.

Higher-order sensorimotor circuit of the brain's global network supports human consciousness.

Consciousness is a mental characteristic of the human mind, whose exact neural features remain unclear. We aimed to identify the critical nodes within the brain's global functional network that support consciousness. To that end, we collected a large fMRI resting state dataset with subjects in at least one of the following three consciousness states: preserved (including the healthy awake state, and patients with a brain injury history (BI) that is fully conscious), reduced (including the N1-sleep state, and minimally conscious state), and lost (including the N3-sleep state, anesthesia, and unresponsive wakefulness state). We also included a unique dataset of subjects in rapid eye movement sleep state (REM-sleep) to test for the presence of consciousness with minimum movements and sensory input. To identify critical nodes, i.e., hubs, within the brain's global functional network, we used a graph-theoretical measure of degree centrality conjoined with ROI-based functional connectivity. Using these methods, we identified various higher-order sensory and motor regions including the supplementary motor area, bilateral supramarginal gyrus (part of inferior parietal lobule), supragenual/dorsal anterior cingulate cortex, and left middle temporal gyrus, that could be important hubs whose degree centrality was significantly reduced when consciousness was reduced or absent. Additionally, we identified a sensorimotor circuit, in which the functional connectivity among these regions was significantly correlated with levels of consciousness across the different groups, and remained present in the REM-sleep group. Taken together, we demonstrated that regions forming a higher-order sensorimotor integration circuit are involved in supporting consciousness within the brain's global functional network. That offers novel and more mechanism-guided treatment targets for disorders of consciousness.

Are intrinsic neural timescales related to sensory processing? Evidence from abnormal behavioral states.

The brain exhibits a complex temporal structure which translates into a hierarchy of distinct neural timescales. An open question is how these intrinsic timescales are related to sensory or motor information processing and whether these dynamics have common patterns in different behavioral states. We address these questions by investigating the brain's intrinsic timescales in healthy controls, motor (amyotrophic lateral sclerosis, locked-in syndrome), sensory (anesthesia, unresponsive wakefulness syndrome), and progressive reduction of sensory processing (from awake states over N1, N2, N3). We employed a combination of measures from EEG resting-state data: auto-correlation window (ACW), power spectral density (PSD), and power-law exponent (PLE). Prolonged neural timescales accompanied by a shift towards slower frequencies were observed in the conditions with sensory deficits, but not in conditions with motor deficits. Our results establish that the spontaneous activity's intrinsic neural timescale is related to the neural capacity that specifically supports sensory rather than motor information processing in the healthy brain.

Pharmacologically informed machine learning approach for identifying pathological states of unconsciousness via resting-state fMRI.

Determining the level of consciousness in patients with disorders of consciousness (DOC) remains challenging. To address this challenge, resting-state fMRI (rs-fMRI) has been widely used for detecting the local, regional, and network activity differences between DOC patients and healthy controls. Although substantial progress has been made towards this endeavor, the identification of robust rs-fMRI-based biomarkers for level of consciousness is still lacking. Recent developments in machine learning show promise as a tool to augment the discrimination between different states of consciousness in clinical practice. Here, we investigated whether machine learning models trained to make a binary distinction between conscious wakefulness and anesthetic-induced unconsciousness would then be capable of reliably identifying pathologically induced unconsciousness. We did so by extracting rs-fMRI-based features associated with local activity, regional homogeneity, and interregional functional activity in 44 subjects during wakefulness, light sedation, and unresponsiveness (deep sedation and general anesthesia), and subsequently using those features to train three distinct candidate machine learning classifiers: support vector machine, Extra Trees, artificial neural network. First, we show that all three classifiers achieve reliable performance within-dataset (via nested cross-validation), with a mean area under the receiver operating characteristic curve (AUC) of 0.95, 0.92, and 0.94, respectively. Additionally, we observed comparable cross-dataset performance (making predictions on the DOC data) as the anesthesia-trained classifiers demonstrated a consistent ability to discriminate between unresponsive wakefulness syndrome (UWS/VS) patients and healthy controls with mean AUC's of 0.99, 0.94, 0.98, respectively. Lastly, we explored the potential of applying the aforementioned classifiers towards discriminating intermediate states of consciousness, specifically, subjects under light anesthetic sedation and patients diagnosed as having a minimally conscious state (MCS). Our findings demonstrate that machine learning classifiers trained on rs-fMRI features derived from participants under anesthesia have potential to aid the discrimination between degrees of pathological unconsciousness in clinical patients.

Altered Global Brain Signal during Physiologic, Pharmacologic, and Pathologic States of Unconsciousness in Humans and Rats.

BACKGROUND: Consciousness is supported by integrated brain activity across widespread functionally segregated networks. The functional magnetic resonance imaging-derived global brain signal is a candidate marker for a conscious state, and thus the authors hypothesized that unconsciousness would be accompanied by a loss of global temporal coordination, with specific patterns of decoupling between local regions and global activity differentiating among various unconscious states. METHODS: Functional magnetic resonance imaging global signals were studied in physiologic, pharmacologic, and pathologic states of unconsciousness in human natural sleep (n = 9), propofol anesthesia (humans, n = 14; male rats, n = 12), and neuropathological patients (n = 21). The global signal amplitude as well as the correlation between global signal and signals of local voxels were quantified. The former reflects the net strength of global temporal coordination, and the latter yields global signal topography. RESULTS: A profound reduction of global signal amplitude was seen consistently across the various unconscious states: wakefulness (median [1st, 3rd quartile], 0.46 [0.21, 0.50]) versus non-rapid eye movement stage 3 of sleep (0.30 [0.24, 0.32]; P = 0.035), wakefulness (0.36 [0.31, 0.42]) versus general anesthesia (0.25 [0.21, 0.28]; P = 0.001), healthy controls (0.30 [0.27, 0.37]) versus unresponsive wakefulness syndrome (0.22 [0.15, 0.24]; P < 0.001), and low dose (0.07 [0.06, 0.08]) versus high dose of propofol (0.04 [0.03, 0.05]; P = 0.028) in rats. Furthermore, non-rapid eye movement stage 3 of sleep was characterized by a decoupling of sensory and attention networks from the global network. General anesthesia and unresponsive wakefulness syndrome were characterized by a dissociation of the majority of functional networks from the global network. This decoupling, however, was dominated by distinct neuroanatomic foci (e.g., precuneus and anterior cingulate cortices). CONCLUSIONS: The global temporal coordination of various modules across the brain may distinguish the coarse-grained state of consciousness versus unconsciousness, while the relationship between the global and local signals may define the particular qualities of a particular unconscious state.

Spontaneous Brain Activity Predicts Task-Evoked Activity During Animate Versus Inanimate Touch.

The spontaneous activity of the brain is characterized by an elaborate temporal structure with scale-free properties as indexed by the power law exponent (PLE). We test the hypothesis that spontaneous brain activity modulates task-evoked activity during interactions with animate versus inanimate stimuli. For this purpose, we developed a paradigm requiring participants to actively touch either animate (real hand) or inanimate (mannequin hand) stimuli. Behaviorally, participants perceived the animate target as closer in space, temporally more synchronous with their own self, and more personally relevant, compared with the inanimate. Neuronally, we observed a modulation of task-evoked activity by animate versus inanimate interactions in posterior insula, in medial prefrontal cortex, comprising anterior cingulate cortex, and in medial superior frontal gyrus. Among these regions, an increased functional connectivity was shown between posterior insula and perigenual anterior cingulate cortex (PACC) during animate compared with inanimate interactions and during resting state. Importantly, PLE during spontaneous brain activity in PACC correlated positively with PACC task-evoked activity during animate versus inanimate stimuli. In conclusion, we demonstrate that brain spontaneous activity in PACC can be related to the distinction between animate and inanimate stimuli and thus might be specifically tuned to align our brain with its animate environment.

Timescales of Intrinsic BOLD Signal Dynamics and Functional Connectivity in Pharmacologic and Neuropathologic States of Unconsciousness.

Environmental events are processed on multiple timescales via hierarchical organization of temporal receptive windows (TRWs) in the brain. The dependence of neural timescales and TRWs on altered states of consciousness is unclear. States of reduced consciousness are marked by a shift toward slowing of neural dynamics (<1 Hz) in EEG/ECoG signals. We hypothesize that such prolongation of intrinsic timescales are also seen in blood-oxygen-level-dependent (BOLD) signals. To test this hypothesis, we measured the timescales of intrinsic BOLD signals using mean frequency (MF) and temporal autocorrelation (AC) in healthy volunteers (n = 23; male/female 14/9) during graded sedation with propofol. We further examined the relationship between the intrinsic timescales (local/voxel level) and its regional connectivity (across neighboring voxels; regional homogeneity, ReHo), global (whole-brain level) functional connectivity (GFC), and topographical similarity (Topo). Additional results were obtained from patients undergoing deep general anesthesia (n = 12; male/female: 5/7) and in patients with disorders of consciousness (DOC) (n = 21; male/female: 14/7). We found that MF, AC, and ReHo increased, whereas GFC and Topo decreased, during propofol sedation. The local alterations occur before changes of distant connectivity. Conversely, all of these parameters decreased in deep anesthesia and in patients with DOC. We conclude that propofol synchronizes local neuronal interactions and prolongs the timescales of intrinsic BOLD signals. These effects may impede communication among distant brain regions. Furthermore, the intrinsic timescales exhibit distinct dynamic signatures in sedation, deep anesthesia, and DOC. These results improve our understanding of the neural mechanisms of unconsciousness in pharmacologic and neuropathologic states.SIGNIFICANCE STATEMENT Information processing in the brain occurs through a hierarchy of temporal receptive windows (TRWs) in multiple timescales. Anesthetic drugs induce a reversible suppression of consciousness and thus offer a unique opportunity to investigate the state dependence of neural timescales. Here, we demonstrate for the first time that sedation with propofol is accompanied by the prolongation of the timescales of intrinsic BOLD signals presumably reflecting enlarged TRWs. We show that this is accomplished by an increase of local and regional signal synchronization, effects that may disrupt information exchange among distant brain regions. Furthermore, we show that the timescales of intrinsic BOLD signals exhibit distinct dynamic signatures in sedation, deep anesthesia, and disorders of consciousness.

The temporal signature of self: Temporal measures of resting-state EEG predict self-consciousness.

The self is the core of our mental life. Previous investigations have demonstrated a strong neural overlap between self-related activity and resting state activity. This suggests that information about self-relatedness is encoded in our brain's spontaneous activity. The exact neuronal mechanisms of such "rest-self containment," however, remain unclear. The present EEG study investigated temporal measures of resting state EEG to relate them to self-consciousness. This was obtained with the self-consciousness scale (SCS) which measures Private, Public, and Social dimensions of self. We demonstrate positive correlations between Private self-consciousness and three temporal measures of resting state activity: scale-free activity as indexed by the power-law exponent (PLE), the auto-correlation window (ACW), and modulation index (MI). Specifically, higher PLE, longer ACW, and stronger MI were related to higher degrees of Private self-consciousness. Finally, conducting eLORETA for spatial tomography, we found significant correlation of Private self-consciousness with activity in cortical midline structures such as the perigenual anterior cingulate cortex and posterior cingulate cortex. These results were reinforced with a data-driven analysis; a machine learning algorithm accurately predicted an individual as having a "high" or "low" Private self-consciousness score based on these measures of the brain's spatiotemporal structure. In conclusion, our results demonstrate that Private self-consciousness is related to the temporal structure of resting state activity as featured by temporal nestedness (PLE), temporal continuity (ACW), and temporal integration (MI). Our results support the hypothesis that self-related information is temporally contained in the brain's resting state. "Rest-self containment" can thus be featured by a temporal signature.

Contrasting variability patterns in the default mode and sensorimotor networks balance in bipolar depression and mania.

Depressive and manic phases in bipolar disorder show opposite constellations of affective, cognitive, and psychomotor symptoms. At a neural level, these may be related to topographical disbalance between large-scale networks, such as the default mode network (DMN) and sensorimotor network (SMN). We investigated topographical patterns of variability in the resting-state signal-measured by fractional SD (fSD) of the BOLD signal-of the DMN and SMN (and other networks) in two frequency bands (Slow5 and Slow4) with their ratio and clinical correlations in depressed (n = 20), manic (n = 20), euthymic (n = 20) patients, and healthy controls (n = 40). After controlling for global signal changes, the topographical balance between the DMN and SMN, specifically in the lowest frequency band, as calculated by the Slow5 fSD DMN/SMN ratio, was significantly increased in depression, whereas the same ratio was significantly decreased in mania. Additionally, Slow5 variability was increased in the DMN and decreased in the SMN in depressed patients, whereas the opposite topographical pattern was observed in mania. Finally, the Slow5 fSD DMN/SMN ratio correlated positively with clinical scores of depressive symptoms and negatively with those of mania. Results were replicated in a smaller independent bipolar disorder sample. We demonstrated topographical abnormalities in frequency-specific resting-state variability in the balance between DMN and SMN with opposing patterns in depression and mania. The Slow5 DMN/SMN ratio was tilted toward the DMN in depression but was shifted toward the SMN in mania. The Slow5 fSD DMN/SMN pattern could constitute a state-biomarker in diagnosis and therapy.

Disrupted neural variability during propofol-induced sedation and unconsciousness.

Variability quenching is a widespread neural phenomenon in which trial-to-trial variability (TTV) of neural activity is reduced by repeated presentations of a sensory stimulus. However, its neural mechanism and functional significance remain poorly understood. Recurrent network dynamics are suggested as a candidate mechanism of TTV, and they play a key role in consciousness. We thus asked whether the variability-quenching phenomenon is related to the level of consciousness. We hypothesized that TTV reduction would be compromised during reduced level of consciousness by propofol anesthetics. We recorded functional magnetic resonance imaging signals of resting-state and stimulus-induced activities in three conditions: wakefulness, sedation, and unconsciousness (i.e., deep anesthesia). We measured the average (trial-to-trial mean, TTM) and variability (TTV) of auditory stimulus-induced activity under the three conditions. We also examined another form of neural variability (temporal variability, TV), which quantifies the overall dynamic range of ongoing neural activity across time, during both the resting-state and the task. We found that (a) TTM deceased gradually from wakefulness through sedation to anesthesia, (b) stimulus-induced TTV reduction normally seen during wakefulness was abolished during both sedation and anesthesia, and (c) TV increased in the task state as compared to resting-state during both wakefulness and sedation, but not anesthesia. Together, our results reveal distinct effects of propofol on the two forms of neural variability (TTV and TV). They imply that the anesthetic disrupts recurrent network dynamics, thus prevents the stabilization of cortical activity states. These findings shed new light on the temporal dynamics of neuronal variability and its alteration during anesthetic-induced unconsciousness.

Breakdown in the temporal and spatial organization of spontaneous brain activity during general anesthesia.

Which temporal features that can characterize different brain states (i.e., consciousness or unconsciousness) is a fundamental question in the neuroscience of consciousness. Using resting-state functional magnetic resonance imaging (rs-fMRI), we investigated the spatial patterns of two temporal features: the long-range temporal correlations (LRTCs), measured by power-law exponent (PLE), and temporal variability, measured by standard deviation (SD) during wakefulness and anesthetic-induced unconsciousness. We found that both PLE and SD showed global reductions across the whole brain during anesthetic state comparing to wakefulness. Importantly, the relationship between PLE and SD was altered in anesthetic state, in terms of a spatial "decoupling." This decoupling was mainly driven by a spatial pattern alteration of the PLE, rather than the SD, in the anesthetic state. Our results suggest differential physiological grounds of PLE and SD and highlight the functional importance of the topographical organization of LRTCs in maintaining an optimal spatiotemporal configuration of the neural dynamics during normal level of consciousness. The central role of the spatial distribution of LRTCs, reflecting temporo-spatial nestedness, may support the recently introduced temporo-spatial theory of consciousness (TTC).