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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.

Recent studies indicate that spontaneous low-frequency fluctuations (LFFs) of resting-state functional magnetic resonance imaging (rs-fMRI) blood oxygen level-dependent (BOLD) signals are driven by the slow (

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.

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.

Functional connectivity magnetic resonance imaging (fcMRI) studies in rat brain show brain reorganization following peripheral nerve injury. Subacute neuroplasticity was observed two weeks following transection of the four major nerves of the brachial plexus. Direct functional magnetic resonance imaging (fMRI) stimulation of the intact radial nerve reveals an activation pattern in the forelimb regions of the sensory and motor cortices that is significantly different from that observed in normal rats. Results of this fMRI experiment were used to determine seed voxel regions for fcMRI analysis. Intrahemispheric connectivities in the sensorimotor forelimb representations in both hemispheres are largely unaffected by deafferentation, whereas substantial disruption of interhemispheric sensorimotor cortical connectivity occurs. In addition, significant intra- and interhemispheric changes in connectivities of thalamic nuclei were found. These are the central findings of the study. They could not have been obtained from fMRI studies alone—both fMRI and fcMRI are needed. The combination provides a general marker for brain plasticity. The rat visual system was studied in the same animals as a control. No neuroplastic changes in connectivities were found in the primary visual cortex upon forelimb deafferentation. Differences were noted in regions responsible for processing multisensory visual-motor information. This incidental discovery is considered to be significant. It may provide insight into phantom limb epiphenomena.

The response of the rat visual system to flashes of blue light has been studied by blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI). The BOLD temporal response is dependent on the number of flashes presented and demonstrates a refractory period that depends on flash frequency. Activated brain regions included the primary and secondary visual cortex, superior colliculus (SC), dorsal lateral geniculate (DLG), and lateral posterior nucleus (LP), which were found to exhibit differing temporal responses. To explain these differences, the BOLD neurovascular response function was modeled. A second-order differential equation was developed and solved numerically to arrive at region-specific response functions. Included in the model are the light input from the diode (duty cycle), a refractory period, a transient response following onset and cessation of stimulus, and a slow adjustment to changes in the average level of the signal. Constants in the differential equation were evaluated for each region by fitting the model to the experimental BOLD response from a single flash, and the equation was then solved for multiple flashes. The simulation mimics the major features of the data; however, remaining differences in the frequency dependence of the response between the cortical and subcortical regions were unexplained. We hypothesized that these discrepancies were due to regional-specific differences in neuronal response to flash frequency. To test this hypothesis, cortical visual evoked potentials (VEPs) were recorded using the same stimulation protocol as the fMRI. Cortical VEPs were more suppressed than subcortical VEPs as flash frequency increased, supporting our hypothesis. This is the first report that regional differences in neuronal activation to the same stimulus lead to differential BOLD activation.