Disruptions in Effective Connectivity within and between Default Mode Network and Anterior Forebrain Mesocircuit in Prolonged Disorders of Consciousness

Neuroimaging biomarkers for the diagnosis and prognosis of patients with disorders of consciousness

The progress in neuroimaging and electrophysiological techniques has shown substantial promise in improving the clinical assessment of disorders of consciousness (DOC). Through the examination of both stimulus-induced and spontaneous brain activity, numerous comprehensive investigations have explored variations in brain activity patterns among patients with DOC, yielding valuable insights for clinical diagnosis and prognostic purposes. Nonetheless, reaching a consensus on precise neuroimaging biomarkers for patients with DOC remains a challenge. Therefore, in this review, we begin by summarizing the empirical evidence related to neuroimaging biomarkers for DOC using various paradigms, including active, passive, and resting-state approaches, by employing task-based fMRI, resting-state fMRI (rs-fMRI), electroencephalography (EEG), and positron emission tomography (PET) techniques. Subsequently, we conducted a review of studies examining the neural correlates of consciousness in patients with DOC, with the findings holding potential value for the clinical application of DOC. Notably, previous research indicates that neuroimaging techniques have the potential to unveil covert awareness that conventional behavioral assessments might overlook. Furthermore, when integrated with various task paradigms or analytical approaches, this combination has the potential to significantly enhance the accuracy of both diagnosis and prognosis in DOC patients. Nonetheless, the stability of these neural biomarkers still needs additional validation, and future directions may entail integrating diagnostic and prognostic methods with big data and deep learning approaches.

The integrity of thalamo-dorsolateral prefrontal cortex tract: a key factor in residual consciousness in disorders of consciousness patients

Background

The mesocircuit model describes a complex network that includes the prefrontal cortical-striatopallidal-thalamo-cortical loop systems and is involved in the mechanism underlying consciousness in patients with disorders of consciousness (DoC). Inhibitory signals to the thalamus become hyperactive in DoC patients, leading to a loss of consciousness. Reactivating this mesocircuit system is important for recovering consciousness in these patients. We investigated how the residual integrity of the thalamo-dorsolateral prefrontal cortex tract (TDLPFCT) influences consciousness in patients with DoC.

Methods

This retrospective case-control study included three groups: prolonged DoC (n = 20), stroke without DoC (n = 20), and healthy controls (n = 20). Diffusion tensor imaging (DTI) was performed at least 4 weeks after the onset. Thalamo-DLPFC tracts were reconstructed using diffusion tensor tractography, and fractional anisotropy (FA) and tract volume (TV) were measured for each hemisphere. Consciousness was assessed using the revised coma recovery scale (CRS-R) within a week of brain imaging.

Results

Significant differences in DLPFCT TV were observed across all three groups, in both affected and less-affected lobes, with the DoC group showing the greatest reduction. A significant correlation was found between the TV of the less-affected TDLPFCT and CRS-R score.

Conclusion

The integrity of the TDLPFCT, particularly in the less affected hemisphere, is associated with consciousness levels in patients with prolonged DoC. This finding suggests its potential importance in assessing prognosis and further developing therapeutic strategies for patients with DoC.

Restoring consciousness with pharmacologic therapy: Mechanisms, targets, and future directions

Severe brain injury impairs consciousness by disrupting a broad spectrum of neurotransmitter systems. Emerging evidence suggests that pharmacologic modulation of specific neurotransmitter systems, such as dopamine, promotes recovery of consciousness. Clinical guidelines now endorse the use of amantadine in individuals with traumatic disorders of consciousness (DoC) based on level 1 evidence, and multiple neurostimulants are used off-label in clinical practice, including methylphenidate, modafinil, bromocriptine, levodopa, and zolpidem. However, the relative contributions of monoaminergic, glutamatergic, cholinergic, GABAergic, and orexinergic neurotransmitter systems to recovery of consciousness after severe brain injury are unknown, and personalized approaches to targeted therapy have yet to be developed. This review summarizes the state-of-the-science in the neurochemistry and neurobiology of neurotransmitter systems involved in conscious behaviors, followed by a discussion of how pharmacologic therapies may be used to modulate these neurotransmitter systems and promote recovery of consciousness. We consider pharmacologic modulation of consciousness at the synapse, circuit, and network levels, with a focus on the mesocircuit model that has been proposed to explain the consciousness-promoting effects of various monoaminergic, glutamatergic, and paradoxically, GABAergic therapies. Though fundamental questions remain about neurotransmitter mechanisms, target engagement and optimal therapy selection for individual patients, we propose that pharmacologic therapies hold great promise to promote recovery and improve quality of life for patients with severe brain injuries.

Lateral frontoparietal effective connectivity differentiates and predicts state of consciousness in a cohort of patients with traumatic disorders of consciousness

Neuroimaging studies have suggested an important role for the default mode network (DMN) in disorders of consciousness (DoC). However, the extent to which DMN connectivity can discriminate DoC states-unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS)-is less evident. Particularly, it is unclear whether effective DMN connectivity, as measured indirectly with dynamic causal modelling (DCM) of resting EEG can disentangle UWS from healthy controls and from patients considered conscious (MCS+). Crucially, this extends to UWS patients with potentially "covert" awareness (minimally conscious star, MCS*) indexed by voluntary brain activity in conjunction with partially preserved frontoparietal metabolism as measured with positron emission tomography (PET+ diagnosis; in contrast to PET- diagnosis with complete frontoparietal hypometabolism). Here, we address this gap by using DCM of EEG data acquired from patients with traumatic brain injury in 11 UWS (6 PET- and 5 PET+) and in 12 MCS+ (11 PET+ and 1 PET-), alongside with 11 healthy controls. We provide evidence for a key difference in left frontoparietal connectivity when contrasting UWS PET- with MCS+ patients and healthy controls. Next, in a leave-one-subject-out cross-validation, we tested the classification performance of the DCM models demonstrating that connectivity between medial prefrontal and left parietal sources reliably discriminates UWS PET- from MCS+ patients and controls. Finally, we illustrate that these models generalize to an unseen dataset: models trained to discriminate UWS PET- from MCS+ and controls, classify MCS* patients as conscious subjects with high posterior probability (pp > .92). These results identify specific alterations in the DMN after severe brain injury and highlight the clinical utility of EEG-based effective connectivity for identifying patients with potential covert awareness.

Behavioral scales variability in patients with prolonged disorders of consciousness

BACKGROUND

The principal conditions differentiating disorders of consciousness (DOC) patients are the unresponsive wakefulness syndrome/vegetative state (UWS/VS) and the minimally conscious state (MCS). Many individuals who suffer from sudden-onset severe brain injury move through stages of UWS/VS and MCS before regaining full awareness. In some patients, the DOC condition is protracted for years (PDOC). In this study, we observed PDOC patients for 6 months to assess possible changes in their level of consciousness.

METHODS

We enrolled 40 PDOC patients, 23 UWS/VS and 17 MCS hosted in a dedicated unit for long-term brain injury care. The time from injury was 472 ± 533 days for UWS/VS and 1090 ± 1079 days for MCS. The Wessex Head Injury Matrix (WHIM), Coma Recovery Scale-R (CRS-R), and Nociception Coma Scale were administered monthly for 6 months.

RESULTS

During the period of assessment, the percentage of UWS/VS shifted from 58 to 45%, while for the MCS, from 42 to 55%. A positive correlation was found for the UWS/VS patients between the months of observation with the CRS-R total score and WHIM total numbers of behaviors (TNB). In the UWS/VS group, the CRS-R auditive and visual subscales correlated positively with the observation time. During the whole period of observation, 8 patients had constant CRS-R total scores while the WHIM TNB changed in 7 of them.

CONCLUSION

Our findings demonstrated that the monthly assessment of PDOC by means of the CRS-R and WHIM was able to detect also subtle changes in consciousness level.

Mesocircuit mechanisms in the diagnosis and treatment of disorders of consciousness

The 'mesocircuit hypothesis' proposes mechanisms underlying the recovery of consciousness following severe brain injuries. The model builds up from a single premise that multifocal brain injuries resulting in coma and subsequent disorders of consciousness produce widespread neuronal death and dysfunction. Considering the general properties of cortical, thalamic, and striatal neurons, a lawful and specific circuit-level mechanism is constructed based on these known anatomical and physiological specializations of neuronal subtypes. The mesocircuit model generates many testable predictions at the mesocircuit, local circuit, and cellular level across multiple cerebral structures to correlate diagnostic measurements and interpret therapeutic interventions. The anterior forebrain mesocircuit is integrally related to the frontal-parietal network, another network demonstrated to show strong correlation with levels of recovery in disorders of consciousness. A further extension known as the "ABCD" model has been used to examine interaction of these models in recovery of consciousness using electrophysiological data types. Many studies have examined predictions of the mesocircuit model; here we first present the model and review the accumulated evidence for several predictions of model across multiple stages of recovery function in human subjects. Recent studies linking the mesocircuit model, the ABCD model, and interactions with the frontoparietal network are reviewed. Finally, theoretical implications of the mesocircuit model at the neuronal level are considered to interpret recent studies of deep brain stimulation in the central lateral thalamus in patients recovering from coma and in new experimental models in the context of emerging understanding of neuronal and local circuit mechanisms underlying conscious brain states.

Characterising stationary and dynamic effective connectivity changes in the motor network during and after tDCS

The exact mechanisms behind the effects of transcranial direct current stimulation (tDCS) at a network level are still poorly understood, with most studies to date focusing on local (cortical) effects and changes in motor-evoked potentials or BOLD signal. Here, we explored stationary and dynamic effective connectivity across the motor network at rest in two experiments where we applied tDCS over the primary motor cortex (M1-tDCS) or the cerebellum (cb-tDCS) respectively. Two cohorts of healthy volunteers (n = 21 and n = 22) received anodal, cathodal, and sham tDCS sessions (counterbalanced) during 20 min of resting-state functional magnetic resonance imaging (fMRI). We used spectral Dynamic Causal Modelling (DCM) and hierarchical Parametrical Empirical Bayes (PEB) to analyze data after (compared to a pre-tDCS baseline) and during stimulation. We also implemented a novel dynamic (sliding windows) DCM/PEB approach to model the nature of network reorganisation across time. In both experiments we found widespread effects of tDCS that extended beyond the targeted area and modulated effective connectivity between cortex, thalamus, and cerebellum. These changes were characterised by unique nonlinear temporal fingerprints across connections and polarities. Our results support growing research challenging the classic notion of anodal and cathodal tDCS as excitatory and inhibitory respectively, as well as the idea of a cumulative effect of tDCS over time. Instead, they described a rich set of changes with specific spatial and temporal patterns. Our work provides a starting point for advancing our understanding of network-level tDCS effects and may guide future work to optimise its cognitive and clinical applications.

Functional networks in prolonged disorders of consciousness

Prolonged disorders of consciousness (DoC) are characterized by extended disruptions of brain activities that sustain wakefulness and awareness and are caused by various etiologies. During the past decades, neuroimaging has been a practical method of investigation in basic and clinical research to identify how brain properties interact in different levels of consciousness. Resting-state functional connectivity within and between canonical cortical networks correlates with consciousness by a calculation of the associated temporal blood oxygen level-dependent (BOLD) signal process during functional MRI (fMRI) and reveals the brain function of patients with prolonged DoC. There are certain brain networks including the default mode, dorsal attention, executive control, salience, auditory, visual, and sensorimotor networks that have been reported to be altered in low-level states of consciousness under either pathological or physiological states. Analysis of brain network connections based on functional imaging contributes to more accurate judgments of consciousness level and prognosis at the brain level. In this review, neurobehavioral evaluation of prolonged DoC and the functional connectivity within brain networks based on resting-state fMRI were reviewed to provide reference values for clinical diagnosis and prognostic evaluation.

Clinical Decision on Disorders of Consciousness After Acquired Brain Injury: Stepping Forward

Major advances have been made over the past few decades in identifying and managing disorders of consciousness (DOC) in patients with acquired brain injury (ABI), bringing the transformation from a conceptualized definition to a complex clinical scenario worthy of scientific exploration. Given the continuously-evolving framework of precision medicine that integrates valuable behavioral assessment tools, sophisticated neuroimaging, and electrophysiological techniques, a considerably higher diagnostic accuracy rate of DOC may now be reached. During the treatment of patients with DOC, a variety of intervention methods are available, including amantadine and transcranial direct current stimulation, which have both provided class II evidence, zolpidem, which is also of high quality, and non-invasive stimulation, which appears to be more encouraging than pharmacological therapy. However, heterogeneity is profoundly ingrained in study designs, and only rare schemes have been recommended by authoritative institutions. There is still a lack of an effective clinical protocol for managing patients with DOC following ABI. To advance future clinical studies on DOC, we present a comprehensive review of the progress in clinical identification and management as well as some challenges in the pathophysiology of DOC. We propose a preliminary clinical decision protocol, which could serve as an ideal reference tool for many medical institutions.

Effectiveness of transcranial direct current stimulation over dorsolateral prefrontal cortex in patients with prolonged disorders of consciousness: A systematic review and meta-analysis

Background

Transcranial direct current stimulation (tDCS) has been widely studied for treatment of patients with prolonged disorders of consciousness (PDOC). The dorsolateral prefrontal cortex (DLPFC) is a hot target for intervention, but some controversies remain.

Purpose

This review aimed to systematically investigate the therapeutic effects of DLPFC-anodal-tDCS for patients with PDOC through a meta-analysis approach.

Data sources

Searches for relevant articles available in English were conducted using EMBASE, Medline, Web of Science, EBSCO, and Cochrane Central Register of Controlled Trials from inception until March 26, 2022.

Study selection

All randomized parallel or cross-over controlled trials comparing the effect of intervention with active-tDCS and Sham-tDCS on Coma Recovery Scale Revised (CRS-R) score in individuals with PDOC were included.

Data extraction

Two authors independently extracted data, assessed the methodological quality, and rated each study.

Data synthesis

Ten randomized parallel or cross-over controlled trials were eligible for systematic review, and eight of the studies involving 165 individuals were identified as eligible for meta-analysis. Compared with Sham-tDCS, the use of anode-tDCS over DLPFC improved the CRS-R score (SMD = 0.71; 95% CI: 0.47–0.95, I² = 10%). Patients with PDOC classified as MCS and clinically diagnosed as CVA or TBI may benefit from anode-tDCS.

Limitations

Failure to evaluate the long-term effects and lack of quantitative analysis of neurological examination are the main limitations for the application of anode-tDCS.

Conclusions

Anodal-tDCS over the left DLPFC may be advantageous to the recovery of patients with MCS and clinically diagnosed with CVA or TBI. There is a lack of evidence to support the duration of the disease course will limit the performance of the treatment. Further studies are needed to explore the diversity of stimulation targets and help to improve the mesocircuit model.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=279391, identifier: CRD42022279391.

Electrophysiological correlates of thalamocortical function in acute severe traumatic brain injury

Tools assaying the neural networks that modulate consciousness may facilitate tracking of recovery after acute severe brain injury. The ABCD framework classifies resting-state EEG into categories reflecting levels of thalamocortical network function that correlate with outcome in post-cardiac arrest coma. In this longitudinal cohort study, we applied the ABCD framework to 20 patients with acute severe traumatic brain injury requiring intensive care (12 of whom were also studied at ≥6-months post-injury) and 16 healthy controls. We tested four hypotheses: 1) EEG ABCD classifications are spatially heterogeneous and temporally variable; 2) ABCD classifications improve longitudinally, commensurate with the degree of behavioral recovery; 3) ABCD classifications correlate with behavioral level of consciousness; and 4) the Coma Recovery Scale-Revised arousal facilitation protocol yields improved ABCD classifications. Channel-level EEG power spectra were classified based on spectral peaks within pre-defined frequency bands: 'A' = no peaks above delta (<4 Hz) range (complete thalamocortical disruption); 'B' = theta (4-8 Hz) peak (severe thalamocortical disruption); 'C' = theta and beta (13-24 Hz) peaks (moderate thalamocortical disruption); or 'D' = alpha (8-13 Hz) and beta peaks (normal thalamocortical function). Acutely, 95% of patients demonstrated 'D' signals in at least one channel but exhibited within-session temporal variability and spatial heterogeneity in the proportion of different channel-level ABCD classifications. By contrast, healthy participants and patients at follow-up consistently demonstrated signals corresponding to intact thalamocortical network function. Patients demonstrated longitudinal improvement in ABCD classifications (p < .05) and ABCD classification distinguished patients with and without command-following in the subacute-to-chronic phase of recovery (p < .01). In patients studied acutely, ABCD classifications improved after the Coma Recovery Scale-Revised arousal facilitation protocol (p < .05) but did not correspond with behavioral level of consciousness. These findings support the use of the ABCD framework to characterize channel-level EEG dynamics and track fluctuations in functional thalamocortical network integrity in spatial detail.

Conventional and Investigational Approaches Leveraging Clinical EEG for Prognosis in Acute Disorders of Consciousness

Prediction of recovery of consciousness after severe brain injury is difficult and limited by a lack of reliable, standardized biomarkers. Multiple approaches for analysis of clinical electroencephalography (EEG) that shed light on prognosis in acute severe brain injury have emerged in recent years. These approaches fall into two major categories: conventional characterization of EEG background and quantitative measurement of resting state or stimulus-induced EEG activity. Additionally, a small number of studies have associated the presence of electrophysiologic sleep features with prognosis in the acute phase of severe brain injury. In this review, we focus on approaches for the analysis of clinical EEG that have prognostic significance and that could be readily implemented with minimal additional equipment in clinical settings, such as intensive care and intensive rehabilitation units, for patients with acute disorders of consciousness.