Evoked oscillatory cortical activity during acute pain: Probing brain in pain by transcranial magnetic stimulation combined with electroencephalogram.

Temporal dynamics of local cortical rhythms during acute pain remain largely unknown. The current study used a novel approach based on transcranial magnetic stimulation combined with electroencephalogram (TMS-EEG) to investigate evoked-oscillatory cortical activity during acute pain. Motor (M1) and dorsolateral prefrontal cortex (DLPFC) were probed by TMS, respectively, to record oscillatory power (event-related spectral perturbation and relative spectral power) and phase synchronization (inter-trial coherence) by 63 EEG channels during experimentally induced acute heat pain in 24 healthy participants. TMS-EEG was recorded before, during, and after noxious heat (acute pain condition) and non-noxious warm (Control condition), delivered in a randomized sequence. The main frequency bands (α, ß1, and ß2) of TMS-evoked potentials after M1 and DLPFC stimulation were recorded close to the TMS coil and remotely. Cold and heat pain thresholds were measured before TMS-EEG. Over M1, acute pain decreased α-band oscillatory power locally and α-band phase synchronization remotely in parietal-occipital clusters compared with non-noxious warm (all p < .05). The remote (parietal-occipital) decrease in α-band phase synchronization during acute pain correlated with the cold (p = .001) and heat pain thresholds (p = .023) and to local (M1) α-band oscillatory power decrease (p = .024). Over DLPFC, acute pain only decreased ß1-band power locally compared with non-noxious warm (p = .015). Thus, evoked-oscillatory cortical activity to M1 stimulation is reduced by acute pain in central and parietal-occipital regions and correlated with pain sensitivity, in contrast to DLPFC, which had only local effects. This finding expands the significance of α and ß band oscillations and may have relevance for pain therapies.

Dissociations between spontaneous electroencephalographic features and the perturbational complexity index in the minimally conscious state.

The analysis of spontaneous electroencephalogram (EEG) is a cornerstone in the assessment of patients with disorders of consciousness (DoC). Although preserved EEG patterns are highly suggestive of consciousness even in unresponsive patients, moderately or severely abnormal patterns are difficult to interpret. Indeed, growing evidence shows that consciousness can be present despite either large delta or reduced alpha activity in spontaneous EEG. Quantifying the complexity of EEG responses to direct cortical perturbations (perturbational complexity index [PCI]) may complement the observational approach and provide a reliable assessment of consciousness even when spontaneous EEG features are inconclusive. To seek empirical evidence of this hypothesis, we compared PCI with EEG spectral measures in the same population of minimally conscious state (MCS) patients (n = 40) hospitalized in rehabilitation facilities. We found a remarkable variability in spontaneous EEG features across MCS patients as compared with healthy controls: in particular, a pattern of predominant delta and highly reduced alpha power-more often observed in vegetative state/unresponsive wakefulness syndrome (VS/UWS) patients-was found in a non-negligible number of MCS patients. Conversely, PCI values invariably fell above an externally validated empirical cutoff for consciousness in all MCS patients, consistent with the presence of clearly discernible, albeit fleeting, behavioural signs of awareness. These results confirm that, in some MCS patients, spontaneous EEG rhythms may be inconclusive about the actual capacity for consciousness and suggest that a perturbational approach can effectively compensate for this pitfall with practical implications for the individual patient's stratification and tailored rehabilitation.

Unconsciousness or unresponsiveness in akinetic mutism? Insights from a multimodal longitudinal exploration.

The clinical assessment of patients with disorders of consciousness (DoC) relies on the observation of behavioural responses to standardised sensory stimulation. However, several medical comorbidities may directly impair the production of reproducible and appropriate responses, thus reducing the sensitivity of behaviour-based diagnoses. One such comorbidity is akinetic mutism (AM), a rare neurological syndrome characterised by the inability to initiate volitional motor responses, sometimes associated with clinical presentations that overlap with those of DoC. In this paper, we describe the case of a patient with large bilateral mesial frontal lesions, showing prolonged behavioural unresponsiveness and severe disorganisation of electroencephalographic (EEG) background, compatible with a vegetative state/unresponsive wakefulness syndrome (VS/UWS). By applying an unprecedented multimodal battery of advanced imaging and electrophysiology-based techniques (AIE) encompassing spontaneous EEG, evoked potentials, event-related potentials, transcranial magnetic stimulation combined with EEG and structural and functional MRI, we provide the following: (i) a demonstration of the preservation of consciousness despite unresponsiveness in the context of AM, (ii) a plausible neurophysiological explanation for behavioural unresponsiveness and its subsequent recovery during rehabilitation stay and (iii) novel insights into the relationships between DoC, AM and parkinsonism. The present case offers proof-of-principle evidence supporting the clinical utility of a multimodal hierarchical workflow that combines AIEs to detect covert signs of consciousness in unresponsive patients.

Multimodal approaches supporting the diagnosis, prognosis and investigation of neural correlates of disorders of consciousness: A systematic review.

The limits of the standard, behaviour-based clinical assessment of patients with disorders of consciousness (DoC) prompted the employment of functional neuroimaging, neurometabolic, neurophysiological and neurostimulation techniques, to detect brain-based covert markers of awareness. However, uni-modal approaches, consisting in employing just one of those techniques, are usually not sufficient to provide an exhaustive exploration of the neural underpinnings of residual awareness. This systematic review aimed at collecting the evidence from studies employing a multimodal approach, that is, combining more instruments to complement DoC diagnosis, prognosis and better investigating their neural correlates. Following the PRISMA guidelines, records from PubMed, EMBASE and Scopus were screened to select peer-review original articles in which a multi-modal approach was used for the assessment of adult patients with a diagnosis of DoC. Ninety-two observational studies and 32 case reports or case series met the inclusion criteria. Results highlighted a diagnostic and prognostic advantage of multi-modal approaches that involve electroencephalography-based (EEG-based) measurements together with neuroimaging or neurometabolic data or with neurostimulation. Multimodal assessment deepened the knowledge on the neural networks underlying consciousness, by showing correlations between the integrity of the default mode network and the different clinical diagnosis of DoC. However, except for studies using transcranial magnetic stimulation combined with electroencephalography, the integration of more than one technique in most of the cases occurs without an a priori-designed multi-modal diagnostic approach. Our review supports the feasibility and underlines the advantages of a multimodal approach for the diagnosis, prognosis and for the investigation of neural correlates of DoCs.

Acute pain drives different effects on local and global cortical excitability in motor and prefrontal areas: insights into interregional and interpersonal differences in pain processing.

Pain-related depression of corticomotor excitability has been explored using transcranial magnetic stimulation-elicited motor-evoked potentials. Transcranial magnetic stimulation-electroencephalography now enables non-motor area cortical excitability assessments, offering novel insights into cortical excitability changes during pain states. Here, pain-related cortical excitability changes were explored in the dorsolateral prefrontal cortex and primary motor cortex (M1). Cortical excitability was recorded in 24 healthy participants before (Baseline), during painful heat (Acute Pain), and non-noxious warm (Warm) stimulation at the right forearm in a randomized sequence, followed by a pain-free stimulation measurement. Local cortical excitability was assessed as the peak-to-peak amplitude of early transcranial magnetic stimulation evoked potential, whereas global-mean field power measured the global excitability. Relative to the Baseline, Acute Pain decreased the peak-to-peak amplitude in M1 and dorsolateral prefrontal cortex compared with Warm (both P < 0.05). A reduced global-mean field power was only found in M1 during Acute Pain compared with Warm (P = 0.003). Participants with the largest reduction in local cortical excitability under Acute Pain showed a negative correlation between dorsolateral prefrontal cortex and M1 local cortical excitability (P = 0.006). Acute experimental pain drove differential pain-related effects on local and global cortical excitability changes in motor and non-motor areas at a group level while also revealing different interindividual patterns of cortical excitability changes, which can be explored when designing personalized treatment plans.

TMS-EEG and resting-state EEG applied to altered states of consciousness: oscillations, complexity, and phenomenology.

Exploring the neurobiology of the profound changes in consciousness induced by classical psychedelic drugs may require novel neuroimaging methods. Serotonergic psychedelic drugs such as psilocybin produce states of increased sensory-emotional awareness and arousal, accompanied by increased spontaneous electroencephalographic (EEG) signal diversity. By directly stimulating cortical tissue, the altered dynamics and propagation of the evoked EEG activity can reveal drug-induced changes in the overall brain state. We combine Transcranial Magnetic Stimulation (TMS) and EEG to reveal that psilocybin produces a state of increased chaotic brain activity which is not a result of altered complexity in the underlying causal interactions between brain regions. We also map the regional effects of psilocybin on TMS-evoked activity and identify changes in frontal brain structures that may be associated with the phenomenology of psychedelic experiences.

Measuring Consciousness in the Intensive Care Unit.

Early reemergence of consciousness predicts long-term functional recovery for patients with severe brain injury. However, tools to reliably detect consciousness in the intensive care unit are lacking. Transcranial magnetic stimulation electroencephalography has the potential to detect consciousness in the intensive care unit, predict recovery, and prevent premature withdrawal of life-sustaining therapy.

Beyond alpha power: EEG spatial and spectral gradients robustly stratify disorders of consciousness.

Neurophysiological markers can overcome the limitations of behavioural assessments of Disorders of Consciousness (DoC). EEG alpha power emerged as a promising marker for DoC, although long-standing literature reported alpha power being sustained during anesthetic-induced unconsciousness, and reduced during dreaming and hallucinations. We hypothesized that EEG power suppression caused by severe anoxia could explain this conflict. Accordingly, we split DoC patients (n = 87) in postanoxic and non-postanoxic cohorts. Alpha power was suppressed only in severe postanoxia but failed to discriminate un/consciousness in other aetiologies. Furthermore, it did not generalize to an independent reference dataset (n = 65) of neurotypical, neurological, and anesthesia conditions. We then investigated EEG spatio-spectral gradients, reflecting anteriorization and slowing, as alternative markers. In non-postanoxic DoC, these features, combined in a bivariate model, reliably stratified patients and indexed consciousness, even in unresponsive patients identified as conscious by an independent neural marker (the Perturbational Complexity Index). Crucially, this model optimally generalized to the reference dataset. Overall, alpha power does not index consciousness; rather, its suppression entails diffuse cortical damage, in postanoxic patients. As an alternative, EEG spatio-spectral gradients, reflecting distinct pathophysiological mechanisms, jointly provide a robust, parsimonious, and generalizable marker of consciousness, whose clinical application may guide rehabilitation efforts.

TMS combined with EEG: Recommendations and open issues for data collection and analysis.

Transcranial magnetic stimulation (TMS) evokes neuronal activity in the targeted cortex and connected brain regions. The evoked brain response can be measured with electroencephalography (EEG). TMS combined with simultaneous EEG (TMS-EEG) is widely used for studying cortical reactivity and connectivity at high spatiotemporal resolution. Methodologically, the combination of TMS with EEG is challenging, and there are many open questions in the field. Different TMS-EEG equipment and approaches for data collection and analysis are used. The lack of standardization may affect reproducibility and limit the comparability of results produced in different research laboratories. In addition, there is controversy about the extent to which auditory and somatosensory inputs contribute to transcranially evoked EEG. This review provides a guide for researchers who wish to use TMS-EEG to study the reactivity of the human cortex. A worldwide panel of experts working on TMS-EEG covered all aspects that should be considered in TMS-EEG experiments, providing methodological recommendations (when possible) for effective TMS-EEG recordings and analysis. The panel identified and discussed the challenges of the technique, particularly regarding recording procedures, artifact correction, analysis, and interpretation of the transcranial evoked potentials (TEPs). Therefore, this work offers an extensive overview of TMS-EEG methodology and thus may promote standardization of experimental and computational procedures across groups.

Measures of differentiation and integration: One step closer to consciousness.

Interpreting empirical measures of integration and differentiation as indices of cortical performance and memory consolidation during wakefulness rather than consciousness per se is inconsistent with the literature. Recent studies show that these theory-inspired measures can dissociate from such processes and reliably index the brain's capacity for experience. We consider this as a positive trend in consciousness research.

Quantifying arousal and awareness in altered states of consciousness using interpretable deep learning.

Consciousness can be defined by two components: arousal (wakefulness) and awareness (subjective experience). However, neurophysiological consciousness metrics able to disentangle between these components have not been reported. Here, we propose an explainable consciousness indicator (ECI) using deep learning to disentangle the components of consciousness. We employ electroencephalographic (EEG) responses to transcranial magnetic stimulation under various conditions, including sleep (n = 6), general anesthesia (n = 16), and severe brain injury (n = 34). We also test our framework using resting-state EEG under general anesthesia (n = 15) and severe brain injury (n = 34). ECI simultaneously quantifies arousal and awareness under physiological, pharmacological, and pathological conditions. Particularly, ketamine-induced anesthesia and rapid eye movement sleep with low arousal and high awareness are clearly distinguished from other states. In addition, parietal regions appear most relevant for quantifying arousal and awareness. This indicator provides insights into the neural correlates of altered states of consciousness.

Consciousness and complexity: a consilience of evidence.

Over the last years, a surge of empirical studies converged on complexity-related measures as reliable markers of consciousness across many different conditions, such as sleep, anesthesia, hallucinatory states, coma, and related disorders. Most of these measures were independently proposed by researchers endorsing disparate frameworks and employing different methods and techniques. Since this body of evidence has not been systematically reviewed and coherently organized so far, this positive trend has remained somewhat below the radar. The aim of this paper is to make this consilience of evidence in the science of consciousness explicit. We start with a systematic assessment of the growing literature on complexity-related measures and identify their common denominator, tracing it back to core theoretical principles and predictions put forward more than 20 years ago. In doing this, we highlight a consistent trajectory spanning two decades of consciousness research and provide a provisional taxonomy of the present literature. Finally, we consider all of the above as a positive ground to approach new questions and devise future experiments that may help consolidate and further develop a promising field where empirical research on consciousness appears to have, so far, naturally converged.

Detecting the Potential for Consciousness in Unresponsive Patients Using the Perturbational Complexity Index.

The difficulties of behavioral evaluation of prolonged disorders of consciousness (DOC) motivate the development of brain-based diagnostic approaches. The perturbational complexity index (PCI), which measures the complexity of electroencephalographic (EEG) responses to transcranial magnetic stimulation (TMS), showed a remarkable sensitivity in detecting minimal signs of consciousness in previous studies. Here, we tested the reliability of PCI in an independently collected sample of 24 severely brain-injured patients, including 11 unresponsive wakefulness syndrome (UWS), 12 minimally conscious state (MCS) patients, and 1 emergence from MCS patient. We found that the individual maximum PCI value across stimulation sites fell within the consciousness range (i.e., was higher than PCI*, which is an empirical cutoff previously validated on a benchmark population) in 11 MCS patients, yielding a sensitivity of 92% that surpassed qualitative evaluation of resting EEG. Most UWS patients (n = 7, 64%) showed a slow and stereotypical TMS-EEG response, associated with low-complexity PCI values (i.e., ≤PCI*). Four UWS patients (36%) provided high-complexity PCI values, which might suggest a covert capacity for consciousness. In conclusion, this study successfully replicated the performance of PCI in discriminating between UWS and MCS patients, further motivating the application of TMS-EEG in the workflow of DOC evaluation.

Local sleep-like cortical reactivity in the awake brain after focal injury.

The functional consequences of focal brain injury are thought to be contingent on neuronal alterations extending beyond the area of structural damage. This phenomenon, also known as diaschisis, has clinical and metabolic correlates but lacks a clear electrophysiological counterpart, except for the long-standing evidence of a relative EEG slowing over the injured hemisphere. Here, we aim at testing whether this EEG slowing is linked to the pathological intrusion of sleep-like cortical dynamics within an awake brain. We used a combination of transcranial magnetic stimulation and electroencephalography (TMS/EEG) to study cortical reactivity in a cohort of 30 conscious awake patients with chronic focal and multifocal brain injuries of ischaemic, haemorrhagic and traumatic aetiology. We found that different patterns of cortical reactivity typically associated with different brain states (coma, sleep, wakefulness) can coexist within the same brain. Specifically, we detected the occurrence of prominent sleep-like TMS-evoked slow waves and off-periods-reflecting transient suppressions of neuronal activity-in the area surrounding focal cortical injuries. These perilesional sleep-like responses were associated with a local disruption of signal complexity whereas complex responses typical of the awake brain were present when stimulating the contralesional hemisphere. These results shed light on the electrophysiological properties of the tissue surrounding focal brain injuries in humans. Perilesional sleep-like off-periods can disrupt network activity but are potentially reversible, thus representing a principled read-out for the neurophysiological assessment of stroke patients, as well as an interesting target for rehabilitation.

Subcortical atrophy correlates with the perturbational complexity index in patients with disorders of consciousness.

BACKGROUND: The complexity of neurophysiological brain responses to direct cortical stimulation, referred to as the perturbational complexity index (PCI), has been shown able to discriminate between consciousness and unconsciousness in patients surviving severe brain injury as well as several other conditions (e.g., wake, dreamless sleep, sleep and ketamine dreaming, anesthesia). OBJECTIVE: This study asks whether, in patients with a disorder of consciousness (DOC), the complexity of the neurophysiological response to cortical stimulation is preferentially associated with atrophy within specific brain structures. METHODS: We perform a retrospective analysis of 40 DOC patients and correlate their maximal PCI to MR-based measurements of cortical thinning and subcortical atrophy. RESULTS: PCI was systematically and inversely associated with the degree of local atrophy within the globus pallidus, a region previously linked to electrocortical and behavioral arousal. Conversely, we fail to detect any association between variance in cortical ribbon thickness and PCI. CONCLUSION: These findings corroborate the previously reported association between pallidal atrophy and low behavioral arousal and suggest that this region's role in maintaining the overall balance of excitation and inhibition may critically affect the emergence of complex cortical interactions in chronic disorders of consciousness. This finding thus also suggests a target for potential neuromodulatory intervention in DOC patients.

A fast and general method to empirically estimate the complexity of brain responses to transcranial and intracranial stimulations.

BACKGROUND: The Perturbational Complexity Index (PCI) was recently introduced to assess the capacity of thalamocortical circuits to engage in complex patterns of causal interactions. While showing high accuracy in detecting consciousness in brain-injured patients, PCI depends on elaborate experimental setups and offline processing, and has restricted applicability to other types of brain signals beyond transcranial magnetic stimulation and high-density EEG (TMS/hd-EEG) recordings. OBJECTIVE: We aim to address these limitations by introducing PCIST, a fast method for estimating perturbational complexity of any given brain response signal. METHODS: PCIST is based on dimensionality reduction and state transitions (ST) quantification of evoked potentials. The index was validated on a large dataset of TMS/hd-EEG recordings obtained from 108 healthy subjects and 108 brain-injured patients, and tested on sparse intracranial recordings (SEEG) of 9 patients undergoing intracranial single-pulse electrical stimulation (SPES) during wakefulness and sleep. RESULTS: When calculated on TMS/hd-EEG potentials, PCIST performed with the same accuracy as the original PCI, while improving on the previous method by being computed in less than a second and requiring a simpler set-up. In SPES/SEEG signals, the index was able to quantify a systematic reduction of intracranial complexity during sleep, confirming the occurrence of state-dependent changes in the effective connectivity of thalamocortical circuits, as originally assessed through TMS/hd-EEG. CONCLUSIONS: PCIST represents a fundamental advancement towards the implementation of a reliable and fast clinical tool for the bedside assessment of consciousness as well as a general measure to explore the neuronal mechanisms of loss/recovery of brain complexity across scales and models.

Clinical utility and prospective of TMS-EEG.

Concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG) has emerged as a powerful tool to non-invasively probe brain circuits in humans, allowing for the assessment of several cortical properties such as excitability and connectivity. Over the past decade, this technique has been applied to various clinical populations, enabling the characterization and development of potential TMS-EEG predictors and markers of treatments and of the pathophysiology of brain disorders. The objective of this article is to present a comprehensive review of studies that have used TMS-EEG in clinical populations and to discuss potential clinical applications. To provide a technical and theoretical framework, we first give an overview of TMS-EEG methodology and discuss the current state of knowledge regarding the use of TMS-EEG to assess excitability, inhibition, plasticity and connectivity following neuromodulatory techniques in the healthy brain. We then review the insights afforded by TMS-EEG into the pathophysiology and predictors of treatment response in psychiatric and neurological conditions, before presenting recommendations for how to address some of the salient challenges faced in clinical TMS-EEG research. Finally, we conclude by presenting future directions in line with the tremendous potential of TMS-EEG as a clinical tool.

The spectral exponent of the resting EEG indexes the presence of consciousness during unresponsiveness induced by propofol, xenon, and ketamine.

Despite the absence of responsiveness during anesthesia, conscious experience may persist. However, reliable, easily acquirable and interpretable neurophysiological markers of the presence of consciousness in unresponsive states are still missing. A promising marker is based on the decay-rate of the power spectral density (PSD) of the resting EEG. We acquired resting electroencephalogram (EEG) in three groups of healthy participants (n = 5 each), before and during anesthesia induced by either xenon, propofol or ketamine. Dosage of each anesthetic agent was tailored to yield unresponsiveness (Ramsay score = 6). Delayed subjective reports assessed whether conscious experience was present ('Conscious report') or absent/inaccessible to recall ('No Report'). We estimated the decay of the PSD of the resting EEG-after removing oscillatory peaks-via the spectral exponent ß, for a broad band (1-40 Hz) and narrower sub-bands (1-20 Hz, 20-40 Hz). Within-subject anesthetic changes in ß were assessed. Furthermore, based on ß, 'Conscious report' states were discriminated against 'no report' states. Finally, we evaluated the correlation of the resting spectral exponent with a recently proposed index of consciousness, the Perturbational Complexity Index (PCI), derived from a previous TMS-EEG study. The spectral exponent of the resting EEG discriminated states in which consciousness was present (wakefulness, ketamine) from states where consciousness was reduced or abolished (xenon, propofol). Loss of consciousness substantially decreased the (negative) broad-band spectral exponent in each subject undergoing xenon or propofol anesthesia-indexing an overall steeper PSD decay. Conversely, ketamine displayed an overall PSD decay similar to that of wakefulness-consistent with the preservation of consciousness-yet it showed a flattening of the decay in the high-frequencies (20-40 Hz)-consistent with its specific mechanism of action. The spectral exponent was highly correlated to PCI, corroborating its interpretation as a marker of the presence of consciousness. A steeper PSD of the resting EEG reliably indexed unconsciousness in anesthesia, beyond sheer unresponsiveness.