Changes in Intrinsic Connectivity Networks Topology Across Levels of Dexmedetomidine-Induced Alteration of Consciousness.

BACKGROUND: Human consciousness is generally thought to emerge from the activity of intrinsic connectivity networks (resting-state networks [RSNs]) of the brain, which have topological characteristics including, among others, graph strength and efficiency. So far, most functional brain imaging studies in anesthetized subjects have compared wakefulness and unresponsiveness, a state considered as corresponding to unconsciousness. Sedation and general anesthesia not only produce unconsciousness but also phenomenological states of preserved mental content and perception of the environment (connected consciousness), and preserved mental content but no perception of the environment (disconnected consciousness). Unresponsiveness may be seen during unconsciousness, but also during disconnectedness. Deep dexmedetomidine sedation is frequently a state of disconnected consciousness. In this study, we were interested in characterizing the RSN topology changes across 4 different and steady-state levels of dexmedetomidine-induced alteration of consciousness, namely baseline (Awake, drug-free state), Mild sedation (drowsy, still responding), Deep sedation (unresponsive), and Recovery, with a focus on changes occurring between a connected consciousness state and an unresponsiveness state. METHODS: A functional magnetic resonance imaging database acquired in 14 healthy volunteers receiving dexmedetomidine sedation was analyzed using a method combining independent component analysis and graph theory, specifically looking at changes in connectivity strength and efficiency occurring during the 4 above-mentioned dexmedetomidine-induced altered consciousness states. RESULTS: Dexmedetomidine sedation preserves RSN architecture. Unresponsiveness during dexmedetomidine sedation is mainly characterized by a between-networks graph strength alteration and within-network efficiency alteration of lower-order sensory RSNs, while graph strength and efficiency in higher-order RSNs are relatively preserved. CONCLUSIONS: The differential dexmedetomidine-induced RSN topological changes evidenced in this study may be the signature of inadequate processing of sensory information by lower-order RSNs, and of altered communication between lower-order and higher-order networks, while the latter remain functional. If replicated in an experimental paradigm distinguishing, in unresponsive subjects, disconnected consciousness from unconsciousness, such changes would sustain the hypothesis that disconnected consciousness arises from altered information handling by lower-order sensory networks and altered communication between lower-order and higher-order networks, while the preservation of higher-order networks functioning allows for an internally generated mental content (or dream).

Simultaneous estimation of a model-derived input function for quantifying cerebral glucose metabolism with [18F]FDG PET.

BACKGROUND: Quantification of the cerebral metabolic rate of glucose (CMRGlu) by dynamic [18F]FDG PET requires invasive arterial sampling. Alternatives to using an arterial input function (AIF) include the simultaneous estimation (SIME) approach, which models the image-derived input function (IDIF) by a series of exponentials with coefficients obtained by fitting time activity curves (TACs) from multiple volumes-of-interest. A limitation of SIME is the assumption that the input function can be modelled accurately by a series of exponentials. Alternatively, we propose a SIME approach based on the two-tissue compartment model to extract a high signal-to-noise ratio (SNR) model-derived input function (MDIF) from the whole-brain TAC. The purpose of this study is to present the MDIF approach and its implementation in the analysis of animal and human data. METHODS: Simulations were performed to assess the accuracy of the MDIF approach. Animal experiments were conducted to compare derived MDIFs to measured AIFs (n = 5). Using dynamic [18F]FDG PET data from neurologically healthy volunteers (n = 18), the MDIF method was compared to the original SIME-IDIF. Lastly, the feasibility of extracting parametric images was investigated by implementing a variational Bayesian parameter estimation approach. RESULTS: Simulations demonstrated that the MDIF can be accurately extracted from a whole-brain TAC. Good agreement between MDIFs and measured AIFs was found in the animal experiments. Similarly, the MDIF-to-IDIF area-under-the-curve ratio from the human data was 1.02 ± 0.08, resulting in good agreement in grey matter CMRGlu: 24.5 ± 3.6 and 23.9 ± 3.2 mL/100 g/min for MDIF and IDIF, respectively. The MDIF method proved superior in characterizing the first pass of [18F]FDG. Groupwise parametric images obtained with the MDIF showed the expected spatial patterns. CONCLUSIONS: A model-driven SIME method was proposed to derive high SNR input functions. Its potential was demonstrated by the good agreement between MDIFs and AIFs in animal experiments. In addition, CMRGlu estimates obtained in the human study agreed to literature values. The MDIF approach requires fewer fitting parameters than the original SIME method and has the advantage that it can model the shape of any input function. In turn, the high SNR of the MDIFs has the potential to facilitate the extraction of voxelwise parameters when combined with robust parameter estimation methods such as the variational Bayesian approach.

Central autonomic network and early prognosis in patients with disorders of consciousness.

The central autonomic network (CAN) plays a crucial role in modulating the autonomic nervous system. Heart rate variability (HRV) is a valuable marker for assessing CAN function in disorders of consciousness (DOC) patients. We used HRV analysis for early prognosis in 58 DOC patients enrolled within ten days of hospitalization. They underwent a five-minute electrocardiogram during baseline and acoustic/visual stimulation. The coma recovery scale-revised (CRS-R) was used to define the patient's consciousness level and categorize the good/bad outcome at three months. The high-frequency Power Spectrum Density and the standard deviation of normal-to-normal peaks in baseline, the sample entropy during the stimulation, and the time from injury features were used in the support vector machine analysis (SVM) for outcome prediction. The SVM predicted the patients' outcome with an accuracy of 96% in the training test and 100% in the validation test, underscoring its potential to provide crucial clinical information about prognosis.

Optimization of Gradient-Echo Echo-Planar Imaging for T2* Contrast in the Brain at 0.5 T.

Gradient-recalled echo (GRE) echo-planar imaging (EPI) is an efficient MRI pulse sequence that is commonly used for several enticing applications, including functional MRI (fMRI), susceptibility-weighted imaging (SWI), and proton resonance frequency (PRF) thermometry. These applications are typically not performed in the mid-field (<1 T) as longer T2* and lower polarization present significant challenges. However, recent developments of mid-field scanners equipped with high-performance gradient sets offer the possibility to re-evaluate the feasibility of these applications. The paper introduces a metric "T2* contrast efficiency" for this evaluation, which minimizes dead time in the EPI sequence while maximizing T2* contrast so that the temporal and pseudo signal-to-noise ratios (SNRs) can be attained, which could be used to quantify experimental parameters for future fMRI experiments in the mid-field. To guide the optimization, T2* measurements of the cortical gray matter are conducted, focusing on specific regions of interest (ROIs). Temporal and pseudo SNR are calculated with the measured time-series EPI data to observe the echo times at which the maximum T2* contrast efficiency is achieved. T2* for a specific cortical ROI is reported at 0.5 T. The results suggest the optimized echo time for the EPI protocols is shorter than the effective T2* of that region. The effective reduction of dead time prior to the echo train is feasible with an optimized EPI protocol, which will increase the overall scan efficiency for several EPI-based applications at 0.5 T.

I Feel! Therefore, I Am from Pain to Consciousness in DOC Patients.

Pain assessment and management in patients with disorders of consciousness (DOC) is a challenging and important aspect of care, with implications for detecting consciousness and promoting recovery. This narrative review explores the role of pain in consciousness, the challenges of pain assessment, pharmacological treatment in DOC, and the implications of pain assessment when detecting changes in consciousness. The review discusses the Nociception Coma Scale and its revised version, which are behavioral scales used to assess pain in DOC patients, and the challenges and controversies surrounding the appropriate pharmacological treatment of pain in these patients. Moreover, we highlight recent evidence suggesting that an accurate pain assessment may predict changes in the level of consciousness in unresponsive wakefulness syndrome/vegetative state patients, underscoring the importance of ongoing pain management in these patients.

An implementation of integrated information theory in resting-state fMRI.

Integrated Information Theory was developed to explain and quantify consciousness, arguing that conscious systems consist of elements that are integrated through their causal properties. This study presents an implementation of Integrated Information Theory 3.0, the latest version of this framework, to functional MRI data. Data were acquired from 17 healthy subjects who underwent sedation with propofol, a short-acting anaesthetic. Using the PyPhi software package, we systematically analyze how Φmax, a measure of integrated information, is modulated by the sedative in different resting-state networks. We compare Φmax to other proposed measures of conscious level, including the previous version of integrated information, Granger causality, and correlation-based functional connectivity. Our results indicate that Φmax presents a variety of sedative-induced behaviours for different networks. Notably, changes to Φmax closely reflect changes to subjects' conscious level in the frontoparietal and dorsal attention networks, which are responsible for higher-order cognitive functions. In conclusion, our findings present important insight into different measures of conscious level that will be useful in future implementations to functional MRI and other forms of neuroimaging.

Tinnitus and distress: an electroencephalography classification study.

There exist no objective markers for tinnitus or tinnitus disorders, which complicates diagnosis and treatments. The combination of EEG with sophisticated classification procedures may reveal biomarkers that can identify tinnitus and accurately differentiate different levels of distress experienced by patients. EEG recordings were obtained from 129 tinnitus patients and 142 healthy controls. Linear support vector machines were used to develop two classifiers: the first differentiated tinnitus patients from controls, while the second differentiated tinnitus patients with low and high distress levels. The classifier for healthy controls and tinnitus patients performed with an average accuracy of 96 and 94% for the training and test sets, respectively. For the distress classifier, these average accuracies were 89 and 84%. Minimal overlap was observed between the features of the two classifiers. EEG-derived features made it possible to accurately differentiate healthy controls and tinnitus patients as well as low and high distress tinnitus patients. The minimal overlap between the features of the two classifiers indicates that the source of distress in tinnitus, which could also be involved in distress related to other conditions, stems from different neuronal mechanisms compared to those causing the tinnitus pathology itself.

caliPER: A software for blood-free parametric Patlak mapping using PET/MRI input function.

Routine clinical use of absolute PET quantification techniques is limited by the need for serial arterial blood sampling for input function and more importantly by the lack of automated pharmacokinetic analysis tools that can be readily implemented in clinic with minimal effort. PET/MRI provides the ability for absolute quantification of PET probes without the need for serial arterial blood sampling using image-derived input functions (IDIFs). Here we introduce caliPER, a modular and scalable software for simplified pharmacokinetic modeling of PET probes with irreversible uptake or binding based on PET/MR IDIFs and Patlak Plot analysis. caliPER generates regional values or parametric maps of net influx rate (Ki) using reconstructed dynamic PET images and anatomical MRI aligned to PET for IDIF vessel delineation. We evaluated the performance of caliPER for blood-free region-based and pixel-wise Patlak analyses of [18F] FDG by comparing caliPER IDIF to serial arterial blood input functions and its application in imaging brain glucose hypometabolism in Frontotemporal dementia. IDIFs corrected for partial volume errors including spill-out and spill-in effects were similar to arterial blood input functions with a general bias of around 6-8%, even for arteries <5 mm. The Ki and cerebral metabolic rate of glucose estimated using caliPER IDIF were similar to estimates using arterial blood sampling (<2%) and within limits of whole brain values reported in literature. Overall, caliPER is a promising tool for irreversible PET tracer quantification and can simplify the ability to perform parametric analysis in clinical settings without the need for blood sampling.

Exploring electroencephalography with a model inspired by quantum mechanics.

An outstanding issue in cognitive neuroscience concerns how the brain is organized across different conditions. For instance, during the resting-state condition, the brain can be clustered into reliable and reproducible networks (e.g., sensory, default, executive networks). Interestingly, the same networks emerge during active conditions in response to various tasks. If similar patterns of neural activity have been found across diverse conditions, and therefore, different underlying processes and experiences of the environment, is the brain organized by a fundamental organizational principle? To test this, we applied mathematical formalisms borrowed from quantum mechanisms to model electroencephalogram (EEG) data. We uncovered a tendency for EEG signals to be localized in anterior regions of the brain during "rest", and more uniformly distributed while engaged in a task (i.e., watching a movie). Moreover, we found analogous values to the Heisenberg uncertainty principle, suggesting a common underlying architecture of human brain activity in resting and task conditions. This underlying architecture manifests itself in the novel constant KBrain, which is extracted from the brain state with the least uncertainty. We would like to state that we are using the mathematics of quantum mechanics, but not claiming that the brain behaves as a quantum object.

Cognition across the Lifespan: Investigating Age, Sex, and Other Sociodemographic Influences.

Maintaining cognitive health across the lifespan has been the focus of a multi-billion-dollar industry. In order to guide treatment and interventions, a clear understanding of the way that proficiency in different cognitive domains develops and declines in both sexes across the lifespan is necessary. Additionally, there are sex differences in a range of other factors, including psychiatric illnesses such as anxiety, depression, and substance use, that are also known to affect cognition, although the scale of this interaction is unknown. Our objective was to assess differences in cognitive function across the lifespan in men and women in a large, representative sample. Leveraging online cognitive testing, a sample of 9451 men and 9451 women ranging in age from 12 to 69 (M = 28.21) matched on socio-demographic factors were studied. Segmented regression was used to model three cognitive domains-working memory, verbal abilities, and reasoning. Sex differences in all three domains were minimal; however, after broadening the sample in terms of socio-demographic factors, sex differences appeared. These results suggest that cognition across the lifespan differs for men and women, but is greatly influenced by environmental factors. We discuss these findings within a framework that describes sex differences in cognition as likely guided by a complex interplay between biology and environment.

A comparison of diffusion tractography techniques in simulating the generalized Ising model to predict the intrinsic activity of the brain.

Diffusion tractography is a non-invasive technique that is being used to estimate the location and direction of white matter tracts in the brain. Identifying the characteristics of white matter plays an important role in research as well as in clinical practice that relies on finding the relationship between the structure and function of the brain. An Ising model implemented on a structural connectivity (SC) has proven to explain the spontaneous fluctuations in the brain at criticality using brain's structure depicted by white matter tracts. Since the SC is the only input of the model, identifying the tractography technique which provides a SC that delivers the highest prediction of the brain's intrinsic activity via the generalized Ising model (GIM) is essential. Hence an Ising model is simulated on SCs generated using two different acquisition schemes (single and multi-shell) and two different tractography approaches (deterministic and probabilistic) and analyzed at criticality across 69 healthy subjects. Results showed that by introducing the GIM, predictability of the empirical correlation matrix increases on average from 0.2 to 0.6 compared to the predictability using the empirical connectivity matrix directly. It is also observed that the SC generated using deterministic tractography without fractional anisotropy resulted in the highest correlation coefficient value of 0.65 between the simulated and empirical correlation matrices. Additionally, calculated dimensionalities per simulation illustrated that the dimensionality depends upon the method of tractography that has been used to extract the SC.

Nociceptive Response Is a Possible Marker of Evolution in the Level of Consciousness in Unresponsive Wakefulness Syndrome Patients.

The Nociception Coma Scale (NCS) and its revised version (NCS-R) were used to evaluate behavioral responses to pain in non-communicative patients. We hypothesized that if patients demonstrate changes to their NCS(-R) scores over time, their evolving behavioral abilities could indicate a forthcoming diagnostic improvement with the Coma Recovery Scale-Revised (CRS-R). Forty-three Vegetative State/Unresponsive Wakefulness Syndrome (VS/UWS) patients were enrolled in the study. The patients were assessed weekly using the CRS-R and NCS(-R) for four consecutive weeks. The first assessment was within 10 days after hospitalization. The assessments were performed between 09:30 and 11:30 AM in a room with constant levels of humidity, light and temperature, as well as an absence of transient noise. Noxious stimuli were administered using a Newton-meter, with pressure applied to the fingernail bed for a maximum of 5 s unless interrupted by a behavioral response from subjects. Seventeen patients demonstrated improvements in their level of consciousness, 13 of whom showed significant behavioral changes through the NCS(-R) before being diagnosed with a Minimally Conscious State (MCS) according to the CRS-R. The behavioral changes observed using the NCS(-R) corresponded to a high probability of observing an improvement from VS/UWS to MCS. To characterize the increased likelihood of this transition, our results present threshold scores of ≥5 for the NCS (accuracy 86%, sensitivity 87%, and specificity 86%) and ≥3 for the NCS-R (accuracy 77%, sensitivity 89%, and specificity 73%). In conclusion, a careful evaluation of responses to nociceptive stimuli in DOC patients could constitute an effective procedure in assessing their evolving conscious state.

Diagnostic Developments in Differentiating Unresponsive Wakefulness Syndrome and the Minimally Conscious State.

When treating patients with a disorder of consciousness (DOC), it is essential to obtain an accurate diagnosis as soon as possible to generate individualized treatment programs. However, accurately diagnosing patients with DOCs is challenging and prone to errors when differentiating patients in a Vegetative State/Unresponsive Wakefulness Syndrome (VS/UWS) from those in a Minimally Conscious State (MCS). Upwards of ~40% of patients with a DOC can be misdiagnosed when specifically designed behavioral scales are not employed or improperly administered. To improve diagnostic accuracy for these patients, several important neuroimaging and electrophysiological technologies have been proposed. These include Positron Emission Tomography (PET), functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), and Transcranial Magnetic Stimulation (TMS). Here, we review the different ways in which these techniques can improve diagnostic differentiation between VS/UWS and MCS patients. We do so by referring to studies that were conducted within the last 10 years, which were extracted from the PubMed database. In total, 55 studies met our criteria (clinical diagnoses of VS/UWS from MCS as made by PET, fMRI, EEG and TMS- EEG tools) and were included in this review. By summarizing the promising results achieved in understanding and diagnosing these conditions, we aim to emphasize the need for more such tools to be incorporated in standard clinical practice, as well as the importance of data sharing to incentivize the community to meet these goals.

The Emergence of Integrated Information, Complexity, and 'Consciousness' at Criticality.

Integrated Information Theory (IIT) posits that integrated information ( Φ ) represents the quantity of a conscious experience. Here, the generalized Ising model was used to calculate Φ as a function of temperature in toy models of fully connected neural networks. A Monte-Carlo simulation was run on 159 normalized, random, positively weighted networks analogous to small five-node excitatory neural network motifs. Integrated information generated by this sample of small Ising models was measured across model parameter spaces. It was observed that integrated information, as an order parameter, underwent a phase transition at the critical point in the model. This critical point was demarcated by the peak of the generalized susceptibility (or variance in configuration due to temperature) of integrated information. At this critical point, integrated information was maximally receptive and responsive to perturbations of its own states. The results of this study provide evidence that Φ can capture integrated information in an empirical dataset, and display critical behavior acting as an order parameter from the generalized Ising model.

Modeling an auditory stimulated brain under altered states of consciousness using the generalized Ising model.

Propofol is a short-acting medication that results in decreased levels of consciousness and is used for general anesthesia. Although it is the most commonly used anesthetic in the world, much remains unknown about the mechanisms by which it induces a loss of consciousness. Characterizing anesthesia-induced alterations to brain network activity might provide a powerful framework for understanding the neural mechanisms of unconsciousness. The aim of this work was to model brain activity in healthy brains during various stages of consciousness, as induced by propofol, in the auditory paradigm. We used the generalized Ising model (GIM) to fit the empirical fMRI data of healthy subjects while they listened to an audio clip from a movie. The external stimulus (audio clip) is believed to be at least partially driving a synchronization process of the brain activity and provides a similar conscious experience in different subjects. In order to observe the common synchronization among the subjects, a novel technique called the inter subject correlation (ISC) was implemented. We showed that the GIM-modified to incorporate the naturalistic external field-was able to fit the empirical task fMRI data in the awake state, in mild sedation, in deep sedation, and in recovery, at a temperature T* which is well above the critical temperature. To our knowledge this is the first study that captures human brain activity in response to real-life external stimuli at different levels of conscious awareness using mathematical modeling. This study might be helpful in the future to assess the level of consciousness of patients with disorders of consciousness and help in regaining their consciousness.

Training Skills in Minimally Invasive, Robotic and Open Surgery: Brain Activation as an Opportunity for Learning.

INTRODUCTION: The advantages of the robotic approach in surgery are undisputed. However, during surgical training, how this technique influences the learning curve has not been described. We provide a tentative model for analyzing the learning curves associated with observation and active participation in learning different surgical techniques, using functional imaging. METHODS: Forty medical students were enrolled and assigned to 4 groups who underwent training in robotic (ROB), laparoscopic (LAP), or open (OPEN) surgery, and a control group that performed motor training without surgical instruments. Surgical/motor training included six 1-h sessions completed over 6 days of the same week. All subjects underwent functional magnetic resonance imaging (fMRI) scanning sessions, before and after surgical training during. RESULTS: Twenty-three participants completed the study. The 3 surgical groups exhibited different learning curves during training. The main effects of the day of training (p < 0.01) and the group (p < 0.01) as well as a significant interaction of day of training group (p < 0.01) were observed. The performance increased in the first 4 days, reaching a peak at day 4, when all groups were considered together. The OPEN group showed the best performance compared to all other groups (p < 0.04). The OPEN group showed a rapid improvement in performance, which peaked at day 4 and decreased on the last day. Similarly, the LAP group showed a steady increase in the number of exercises they completed, which continued for the entire training period and reached a peak on the last day. However, the participants training in ROB surgery, after a performance initially indistinguishable from that of the LAP group, had a dip in their performance, quickly followed by an improvement and reaching a plateau on day 4. fMRI analysis documented the different involvement of the cortical and subcortical areas based on the type of training. Surgical training modified the activation of some brain regions during both observation and the execution of tasks. CONCLUSIONS: Differences in the learning curves of the 3 surgical groups were noted. Functional brain activity represents an interesting starting point to guide training programs.

Consciousness and the Dimensionality of DOC Patients via the Generalized Ising Model.

The data from patients with severe brain injuries show complex brain functions. Due to the difficulties associated with these complex data, computational modeling is an especially useful tool to examine the structure-function relationship in these populations. By using computational modeling for patients with a disorder of consciousness (DoC), not only we can understand the changes of information transfer, but we also can test changes to different states of consciousness by hypothetically changing the anatomical structure. The generalized Ising model (GIM), which specializes in using structural connectivity to simulate functional connectivity, has been proven to effectively capture the relationship between anatomical structures and the spontaneous fluctuations of healthy controls (HCs). In the present study we implemented the GIM in 25 HCs as well as in 13 DoC patients diagnosed at three different states of consciousness. Simulated data were analyzed and the criticality and dimensionality were calculated for both groups; together, those values capture the level of information transfer in the brain. Ratifying previous studies, criticality was observed in simulations of HCs. We were also able to observe criticality for DoC patients, concluding that the GIM is generalizable for DoC patients. Furthermore, dimensionality increased for the DoC group as compared to healthy controls, and could distinguish different diagnostic groups of DoC patients.

Time-Delay Latency of Resting-State Blood Oxygen Level-Dependent Signal Related to the Level of Consciousness in Patients with Severe Consciousness Impairment.

Recent evidence on resting-state functional magnetic resonance imaging (rs-fMRI) suggests that healthy human brains have a temporal organization represented in a widely complex time-delay structure. This structure seems to underlie brain communication flow, integration/propagation of brain activity, as well as information processing. Therefore, it is probably linked to the emergence of highly coordinated complex brain phenomena, such as consciousness. Nevertheless, possible changes in this structure during an altered state of consciousness remain poorly investigated. In this work, we hypothesized that due to a disruption in high-order functions and alterations of the brain communication flow, patients with disorders of consciousness (DOC) might exhibit changes in their time-delay structure of spontaneous brain activity. We explored this hypothesis by comparing the time-delay projections from fMRI resting-state data acquired in resting state from 48 patients with DOC and 27 healthy controls (HC) subjects. Results suggest that time-delay structure modifies for patients with DOC conditions when compared with HC. Specifically, the average value and the directionality of latency inside the midcingulate cortex (mCC) shift with the level of consciousness. In particular, positive values of latency inside the mCC relate to preserved states of consciousness, whereas negative values change proportionally with the level of consciousness in patients with DOC. These results suggest that the mCC may play a critical role as an integrator of brain activity in HC subjects, but this role vanishes in an altered state of consciousness.

Neuroimaging of Narcolepsy and Primary Hypersomnias.

Advances in neuroimaging open up the possibility for new powerful tools to be developed that potentially can be applied to clinical populations to improve the diagnosis of neurological disorders, including sleep disorders. At present, the diagnosis of narcolepsy and primary hypersomnias is largely limited to subjective assessments and objective measurements of behavior and sleep physiology. In this review, we focus on recent neuroimaging findings that provide insight into the neural basis of narcolepsy and the primary hypersomnias Kleine-Levin syndrome and idiopathic hypersomnia. We describe the role of neuroimaging in confirming previous genetic, neurochemical, and neurophysiological findings and highlight studies that permit a greater understanding of the symptoms of these sleep disorders. We conclude by considering some of the remaining challenges to overcome, the existing knowledge gaps, and the potential role for neuroimaging in understanding the pathogenesis and clinical features of narcolepsy and primary hypersomnias.