Frontal and parietal lobes play crucial roles in understanding the disorder of consciousness: A perspective from electroencephalogram studies

Case report: Monitoring consciousness with fNIRS in a patient with prolonged reduced consciousness following hemorrhagic stroke undergoing adjunct taVNS therapy

Disorders of consciousness (DoC) resulting from severe brain injury present substantial challenges in rehabilitation due to disruptions in brain network connectivity, particularly within the frontal-parietal network critical for awareness. Transcutaneous auricular vagus nerve stimulation (taVNS) has emerged as a promising non-invasive intervention; however, the precise mechanisms through which it influences cortical function in DoC patients remain unclear. This study describes the effects of taVNS on fronto-parietal network connectivity and arousal in a 77-year-old female patient with unresponsive wakefulness syndrome (UWS). The patient received bilateral taVNS for 1 h daily over 3 months, with functional connectivity (FC) in the frontoparietal network assessed using functional near-infrared spectroscopy (fNIRS) and behavioral responsiveness evaluated through the Coma Recovery Scale-Revised (CRS-R). After taVNS intervention, mean FC was enhanced from 0.06 (SD = 0.31) to 0.33 (SD = 0.28) in the frontal-parietal network. The frontal-parietal were subdivided into 12 regions of interest (ROIs) and it was determined that the FC between the left dorsolateral prefrontal cortex (DLPFC) and the left prefrontal ROIs was 0.06 ± 0.41 before the intervention and 0.55 ± 0.24 after the intervention. Behavioral improvements were evidenced by an increase in CRS-R scores from 2 to 14, marking the patient's transition from UWS to minimally conscious state plus (MCS+). Additionally, regions associated with auditory and sensory processing showed increased cortical engagement, supporting the positive impact of taVNS on cortical responsiveness. This suggests its value as a non-invasive adjunctive therapy in the rehabilitation of DoC patients. Further studies are necessary to confirm these effects in a wider patient population and to refine the strategy for clinical application of taVNS.

Spectral peak analysis and intrinsic neural timescales as markers for the state of consciousness

Resting state EEG in patients with disorders of consciousness (DOC) is characterized by an increase of power in the delta frequency band and a concurrent decrease in the alpha range, equivalent to a weakening or disappearance of the alpha peak. Prolongation of Intrinsic Neural Timescales (INTs) is also associated with DOCs. Together, this raises the question whether the decreased alpha peak relates to the prolonged INTs and, importantly, how that can be used for diagnosing the state of consciousness in DOC individuals. Analyzing resting state EEG recordings from both healthy subjects and DOC patients, we measure INTs through autocorrelation window (ACW) and utilize peak analysis to quantify the weakening of the alpha peak. First, we replicate previous findings of prolonged ACW in DOC patients. We then identify significantly lower alpha peak measures in DOC compared to controls. Interestingly, spectral peaks shift from the alpha to the theta range in several DOC subjects while such change is absent in healthy controls. Next, our study reveals a close relationship between ACW and alpha peak in both healthy and DOC subjects, a correlation that holds for theta peaks in DOC. Further, the prolonged ACW correlates with the state of consciousness, as quantified by the Coma Recovery Scale-Revised (CRS-R), and mediates the relationship between theta peak and CRS-R. Finally, through split analyses and machine learning, we show that ACW and alpha peak measures conjointly distinguish healthy controls and DOC patients with high accuracy (95.5%). In conclusion, we demonstrate that the prolongation of ACW, together with spectral peak measures, holds promise to serve as additional EEG biomarkers for diagnosing the state of consciousness in DOC subjects.

Explaining recovery from coma with multimodal neuroimaging

The aim of this prospective, observational cohort study was to investigate and assess diverse neuroimaging biomarkers to predict patients' neurological recovery after coma. 32 patients (18-76 years, M = 44.8, SD = 17.7) with disorders of consciousness participated in the study. Multimodal neuroimaging data acquired during the patient's hospitalization were used to derive cortical glucose metabolism (18F-fluorodeoxyglucose positron emission tomography/computed tomography), and structural (diffusion-weighted imaging) and functional connectivity (resting-state functional MRI) indices. The recovery outcome was defined as a continuous composite score constructed from a multivariate neurobehavioral recovery assessment administered upon the discharge from the hospital. Fractional anisotropy-based white matter integrity in the anterior forebrain mesocircuit (r = 0.72, p < .001, 95% CI: 0.87, 0.45), and the functional connectivity between the antagonistic default mode and dorsal attention resting-state networks (r = - 0.74, p < 0.001, 95% CI: - 0.46, - 0.88) strongly correlated with the recovery outcome. The association between the posterior glucose metabolism and the recovery outcome was moderate (r = 0.38, p = 0.040, 95% CI: 0.66, 0.02). Structural (adjusted R2 = 0.84, p = 0.003) or functional connectivity biomarker (adjusted R2 = 0.85, p = 0.001), but not their combination, significantly improved the model fit to predict the recovery compared solely to bedside neurobehavioral evaluation (adjusted R2 = 0.75). The present study elucidates an important role of specific MRI-derived structural and functional connectivity biomarkers in diagnosis and prognosis of recovery after coma and has implications for clinical care of patients with severe brain injury.

Re-awakening the brain: Forcing transitions in disorders of consciousness by external in silico perturbation

A fundamental challenge in neuroscience is accurately defining brain states and predicting how and where to perturb the brain to force a transition. Here, we investigated resting-state fMRI data of patients suffering from disorders of consciousness (DoC) after coma (minimally conscious and unresponsive wakefulness states) and healthy controls. We applied model-free and model-based approaches to help elucidate the underlying brain mechanisms of patients with DoC. The model-free approach allowed us to characterize brain states in DoC and healthy controls as a probabilistic metastable substate (PMS) space. The PMS of each group was defined by a repertoire of unique patterns (i.e., metastable substates) with different probabilities of occurrence. In the model-based approach, we adjusted the PMS of each DoC group to a causal whole-brain model. This allowed us to explore optimal strategies for promoting transitions by applying off-line in silico probing. Furthermore, this approach enabled us to evaluate the impact of local perturbations in terms of their global effects and sensitivity to stimulation, which is a model-based biomarker providing a deeper understanding of the mechanisms underlying DoC. Our results show that transitions were obtained in a synchronous protocol, in which the somatomotor network, thalamus, precuneus and insula were the most sensitive areas to perturbation. This motivates further work to continue understanding brain function and treatments of disorders of consciousness.

Validating the reproducibility of a low-cost single-channel fNIRS device across hierarchical cognitive tasks

This study evaluates a low-cost, single-channel fNIRS device in cognitive neuroscience, aiming to overcome the financial barriers of commercial systems by testing its efficacy in tasks of varying complexity. Twenty-six participants engaged in motor control (finger-tapping), working memory (n-back), and creativity (AUT) tasks while their prefrontal cortex activity was monitored using the device, with behavioral and cerebral blood flow changes recorded. Results showed the device's capability to detect significant blood flow variations across different tasks, thereby supporting its use in cognitive research. The study confirms the potential of single-channel fNIRS as a cost-effective tool for diverse cognitive assessments, from simple motor actions to complex creative thinking.

A new era of current and future treatment applications of transcranial magnetic stimulation

Background

Transcranial magnetic stimulation (TMS) equipment has advanced dramatically over the years thanks to considerable advancements in signal motors, coils, placement devices, and modeling, optimization, and treatment scheduling programs. In this review, a primary assessment of the impact of transcranial magnetic stimulation (TMS) on seizure course in people with and without epilepsy has been done through search in the Embase, PubMed, Scopus, and Web of Science databases. Other proposed roles of TMS in various studies has been reported. The features of TMS protocols for several potential disorders was assessed and the key TMS findings has been documented starting from 1985 until 2023.

Results

More than 500 papers were found that describe various research populations, TMS techniques, and TMS functions in 16 various medical conditions.

Conclusion

After reviewing recent updates in TMS, further researches are needed to improve the technical part of the used TMS protocols and to have definitive results not experimental one with regard to TMS usage in various psychiatric and neurological disorders.

Temporal and spatial variability of dynamic microstate brain network in disorders of consciousness

BACKGROUND

Accurately diagnosing patients with the vegetative state (VS) and the minimally conscious state (MCS) reached a misdiagnosis of approximately 40%.

METHODS

A method combined microstate and dynamic functional connectivity (dFC) to study the spatiotemporal variability of the brain in disorders of consciousness (DOC) patients was proposed. Resting-state EEG data were obtained from 16 patients with MCS and 16 patients with VS. Mutual information (MI) was used to assess the EEG connectivity in each microstate. MI-based features with statistical differences were selected as the total feature subset (TFS), then the TFS was utilized to feature selection and fed into the classifier, obtaining the optimal feature subsets (OFS) in each microstate. Subsequently, an OFS-based MI functional connectivity network (MIFCN) was constructed in the cortex.

RESULTS

The group-average MI connectivity matrix focused on all channels revealed that all five microstates exhibited stronger information interaction in the MCS when comparing with the VS. While OFS-based MIFCN, which only focused on a few channels, revealed greater MI flow in VS patients than in MCS patients under microstates A, B, C, and E, except for microstate D. Additionally, the average classification accuracy of OFS in the five microstates was 96.2%.

CONCLUSION

Constructing features based on microstates to distinguish between two categories of DOC patients had effectiveness.

Effective DBS treatment improves neural information transmission of patients with disorders of consciousness: an fNIRS study

Objective.Deep brain stimulation (DBS) is a potential treatment that promotes the recovery of patients with disorders of consciousness (DOC). This study quantified the changes in consciousness and the neuromodulation effect of DBS on patients with DOC.Approach.Eleven patients were recruited for this study which consists of three conditions: 'Pre' (two days before DBS surgery), 'Post-On' (one month after surgery with stimulation), and 'Post-Off' (one month after surgery without stimulation). Functional near-infrared spectroscopy (fNIRS) was recorded from the frontal lobe, parietal lobe, and occipital lobe of patients during the experiment of auditory stimuli paradigm, in parallel with the coma recovery scale-revised (CRS-R) assessment. The brain hemodynamic states were defined and state transition acceleration was taken to quantify the information transmission strength of the brain network. Linear regression analysis was conducted between the changes in regional and global indicators and the changes in the CRS-R index.Main results.Significant correlation was observed between the changes in the global transition acceleration indicator and the changes in the CRS-R index (slope = 55.910,p< 0.001,R2= 0.732). For the regional indicators, similar correlations were found between the changes in the frontal lobe and parietal lobe indicators and the changes in the CRS-R index (slope = 46.612,p< 0.01,R2= 0.694; slope = 47.491,p< 0.01,R2= 0.676).Significance.Our study suggests that fNIRS-based brain hemodynamics transition analysis can signify the neuromodulation effect of DBS treatment on patients with DOC, and the transition acceleration indicator is a promising brain functional marker for DOC.

Evaluation of consciousness rehabilitation via neuroimaging methods

Accurate evaluation of patients with disorders of consciousness (DoC) is crucial for personalized treatment. However, misdiagnosis remains a serious issue. Neuroimaging methods could observe the conscious activity in patients who have no evidence of consciousness in behavior, and provide objective and quantitative indexes to assist doctors in their diagnosis. In the review, we discussed the current research based on the evaluation of consciousness rehabilitation after DoC using EEG, fMRI, PET, and fNIRS, as well as the advantages and limitations of each method. Nowadays single-modal neuroimaging can no longer meet the researchers` demand. Considering both spatial and temporal resolution, recent studies have attempted to focus on the multi-modal method which can enhance the capability of neuroimaging methods in the evaluation of DoC. As neuroimaging devices become wireless, integrated, and portable, multi-modal neuroimaging methods will drive new advancements in brain science research.

A protocol for a multicenter randomized and personalized controlled trial using rTMS in patients with disorders of consciousness

Background

Improving the functional recovery of patients with DoC remains one of the greatest challenges of the field. Different theories exist about the role of the anterior (prefrontal areas) versus posterior (parietal areas) parts of the brain as hotspots for the recovery of consciousness. Repetitive transcranial magnetic stimulation (rTMS) is a powerful non-invasive brain stimulation technique for the treatment of DoC. However, a direct comparison of the effect of TMS treatment on the front versus the back of the brain has yet to be performed. In this study, we aim to assess the short- and long-term effects of frontal and parietal rTMS on DoC recovery and characterize responders phenotypically.

Methods/design

Ninety patients with subacute and prolonged DoC will be included in a two-part multicenter prospective study. In the first phase (randomized controlled trial, RCT), patients will undergo four rTMS sessions in a crossover design over 10 days, targeting (i) the left dorsolateral prefrontal cortex (DLPFC) and (ii) the left angular gyrus (AG), as well as (iii & iv) their sham alternatives. In the second phase (longitudinal personalized trial), patients will receive personalized stimulations for 20 working days targeting the brain area that showed the best results in the RCT and will be randomly assigned to either active or sham intervention. The effects of rTMS on neurobehavioral and neurophysiological functioning in patients with DoC will be evaluated using clinical biomarkers of responsiveness (i.e., the Coma Recovery Scale-Revised; CRS-R), and electrophysiological biomarkers (e.g., power spectra, functional and effective connectivity, perturbational complexity index before and after intervention). Functional long-term outcomes will be assessed at 3 and 6 months post-intervention. Adverse events will be recorded during the treatment phase.

Discussion

This study seeks to identify which brain region (front or back) is best to stimulate for the treatment of patients with DoC using rTMS, and to characterize the neural correlates of its action regarding recovery of consciousness and functional outcome. In addition, we will define the responders' profile based on patients' characteristics and functional impairments; and develop biomarkers of responsiveness using EEG analysis according to the clinical responsiveness to the treatment.

Clinical Trial Registration

https://clinicaltrials.gov/ct2/show/NCT04401319, Clinicaltrials.gov, n° NCT04401319.