Neural correlates of consciousness and related disorders: From phenotypic descriptors of behavioral and relative consciousness to cortico-subcortical circuitry

Predicting Long-Term Outcome of Prolonged Disorder of Consciousness in Children Through Machine Learning Based on Conventional Structural Magnetic Resonance Imaging

BACKGROUND

The prognosis of prolonged disorders of consciousness (pDoC) in children has consistently posed a formidable challenge in clinical decision-making.

OBJECTIVE

This study aimed to develop a machine learning (ML) model based on conventional structural magnetic resonance imaging (csMRI) to predict outcomes in children with pDoC.

METHODS

A total of 196 children with pDoC were included in this study. Based on the consciousness states 1 year after brain injury, the children were categorized into either the favorable prognosis group or the poor prognosis group. They were then randomly assigned to the training set (n = 138) or the test set (n = 58). Semi-quantitative visual assessments of brain csMRI were conducted and Least Absolute Shrinkage and Selection Operator regression was used to identify significant features predicting outcomes. Based on the selected features, support vector machine (SVM), random forests (RF), and logistic regression (LR) were used to develop csMRI, clinical, and csMRI-clinical-merge models, respectively. Finally, the performances of all models were evaluated.

RESULTS

Seven csMRI features and 4 clinical features were identified as important predictors of consciousness recovery. All models achieved satisfactory prognostic performances (all areas under the curve [AUCs] >0.70). Notably, the csMRI model developed using the SVM exhibited the best performance, with an AUC, accuracy, sensitivity, and specificity of 0.851, 0.845, 0.844, and 0.846, respectively.

CONCLUSIONS

A csMRI-based prediction model for the prognosis of children with pDoC was developed, showing potential to predict recovery of consciousness 1 year after brain injury and is worth popularizing in clinical practice.

Deciphering network dysregulations and temporo-spatial dynamics in disorders of consciousness: insights from minimum spanning tree analysis

Objectives

The neural mechanism associated with impaired consciousness is not fully clear. We aim to explore the association between static and dynamic minimum spanning tree (MST) characteristics and neural mechanism underlying impaired consciousness.

Methods

MSTs were constructed based on full-length functional magnetic resonance imaging (fMRI) signals and fMRI signal segments within each time window. Global and local measures of static MSTs, as well as spatio-temporal interaction characteristics of dynamic MSTs were investigated.

Results

A disruption or an alteration in the functional connectivity, the decreased average coupling strength and the reorganization of hub nodes were observed in patients with minimally conscious state (MCS) and patients with vegetative state (VS). The analysis of global and local measures quantitatively supported altered static functional connectivity patterns and revealed a slower information transmission efficiency in both patient groups. From a dynamic perspective, the spatial distribution of hub nodes exhibited relative stability over time in both normal and patient populations. The increased temporal variability in multiple brain regions within resting-state networks associated with consciousness was detected in MCS patients and VS patients, especially thalamus. As well, the increased spatial variability in multiple brain regions within these resting-state networks was detected in MCS patients and VS patients. In addition, local measure and spatio-temporal variability analysis indicated that the differences in network structure between two groups of patients were mainly in frontoparietal network and auditory network.

Conclusion

Our findings suggest that altered static and dynamic MST characteristics may shed some light on neural mechanism underlying impaired consciousness.

Differential brain activity in patients with disorders of consciousness: a 3-month rs-fMRI study using amplitude of low-frequency fluctuation

Introduction

Disorders of consciousness (DoC) from severe brain injuries have significant impacts. However, further research on nuanced biomarkers is needed to fully understand the condition. This study employed resting-state functional MRI (rs-fMRI) and the amplitude of low-frequency fluctuation (ALFF) to investigate differential brain activity in patients with DoC following spinal cord stimulation (SCS) therapy. It also assessed the predictive value of rs-fMRI and ALFF in determining the consciousness levels at 3 months post-therapy.

Methods

We analyzed rs-fMRI data from 31 patients with traumatic brain injury (TBI) and 22 with non-traumatic brain injury (non-TBI) diagnosed with DoC. ALFF was measured before SCS therapy, and clinical outcomes were assessed 3 months later using the Coma Recovery Scale-Revised.

Results

Patients with TBI showed increased ALFF in the thalamus and anterior cingulate cortex, whereas the middle occipital lobe showed decreased ALFF. In the non-TBI group, a higher ALFF was noted in the precuneus, with a reduced ALFF in the occipital and temporal lobes. Patients with improved consciousness post-SCS exhibited distinct ALFF patterns compared with those with unchanged consciousness, particularly in the posterior cingulate and occipital regions.

Conclusion

The application of ALFF in rs-fMRI may be a predictive tool for post-treatment outcomes in patients with DoC of varying etiologies. Differential ALFF in specific brain regions could indicate the likelihood of improvement in consciousness following SCS therapy.

Clinical trial registration

https://www.chictr.org.cn/, Identifier ChiCTR2300069756.

Perturbational complexity index in assessing responsiveness to rTMS treatment in patients with disorders of consciousness: a cross-over randomized controlled trial study

BACKGROUND

Disorders of Consciousness (DoC) caused by severe brain injuries represent a challenging clinical entity, which is easy to misdiagnosis and lacks effective treatment options. Repetitive Transcranial Magnetic Stimulation (rTMS) is a non-invasive neuroelectric stimulation method that shows promise in improving consciousness for DoC, especially in minimally conscious state (MCS). However, there is little evidence of its effectiveness, especially in RCT studies.

METHODS

Twenty MCS patients participated in a double-blind, randomized, crossover, sham-controlled clinical study to evaluate the safety and efficacy of rTMS for MCS. Subjects were randomized into two groups: one group received rTMS-active for 10 consecutive days (n = 10), and the other group received rTMS-sham for 10 consecutive days (n = 10). After a 10-day washout period, the two groups were crossed over and received the opposite treatment. the rTMS protocol consisted of 2,000 pulses per day in the left dorsolateral prefrontal cortex (L-DLPFC), sent at 10 Hz. The stimulation intensity was 90% of the resting motor threshold. Coma Recovery Scale Revised (CRS-R), the main evaluation index, was evaluated before and after each phase in a double-blind manner. Meanwhile RS-EEG and TMS-EEG data were acquired and relative alpha power (RAP), and perturbational complexity index based on state transitions (PCIst) were caculated.

RESULTS

One-way ANOVA revealed significantly higher scores in rTMS-active treatment compared to rTMS-sham across various measures, including CRS-R total score, RAP, PCIst (all P < 0.05). Among the 20 MCS patients, 7 (35%) were identified as responders following rTMS treatment. Compared to rTMS-sham, responder scores for CRS-R, RAP, and PCIst (all P < 0.05) were significantly elevated after rTMS-active treatment. Conversely, there was no significant difference observed in non-responders. Furthermore, post-hoc analysis revealed that baseline PCIst was significantly higher in responders than non-responders. Upon a 6-month follow-up, CRS-R scores significantly increased in all 20 patients (P = 0.026). However, the responder group exhibited a more favorable prognosis compared to the non-responder group (P = 0.031).

CONCLUSIONS

Applying 10 Hz rTMS to L-DLPFC significantly increased consciousness level in MCS patients. PCIst is a neurophysiological index that has the potential to evaluate and predict therapeutic efficacy.

TRIAL REGISTRATION

www.

CLINICALTRIALS

gov , identifier: NCT05187000.

Structural and Functional Neuroanatomy of Core Consciousness: A Primer for Disorders of Consciousness Clinicians

Understanding the structural and functional neuroanatomy of core consciousness (ie, wakefulness and awareness) is an asset to clinicians caring for persons with disorders of consciousness. This article provides a primer on the structural and functional neuroanatomy of wakefulness and awareness. The neuroanatomical structures supporting these elements of core consciousness functions are reviewed first, after which brief description of the clinically evaluable relationships between disruption of these structures and disorders of consciousness (ie, brain-behavior relationships) are outlined. Consideration of neuroanatomy at the mesoscale (ie, the mesocircuit hypothesis) as well as in relation to several large-scale neural networks is offered.

Effects of simultaneous transcutaneous auricular vagus nerve stimulation and high-definition transcranial direct current stimulation on disorders of consciousness: a study protocol

Background

Non-invasive brain stimulation (NIBS) techniques are now widely used in patients with disorders of consciousness (DOC) for accelerating their recovery of consciousness, especially minimally conscious state (MCS). However, the effectiveness of single NIBS techniques for consciousness rehabilitation needs further improvement. In this regard, we propose to enhance from bottom to top the thalamic-cortical connection by using transcutaneous auricular vagus nerve stimulation (taVNS) and increase from top to bottom cortical-cortical connections using simultaneous high-definition transcranial direct current stimulation (HD-tDCS) to reproduce the network of consciousness.

Methods/design

The study will investigate the effect and safety of simultaneous joint stimulation (SJS) of taVNS and HD-tDCS for the recovery of consciousness. We will enroll 84 MCS patients and randomize them into two groups: a single stimulation group (taVNS and HD-tDCS) and a combined stimulation group (SJS and sham stimulation). All patients will undergo a 4-week treatment. The primary outcome will be assessed using the coma recovery scale-revised (CRS-R) at four time points to quantify the effect of treatment: before treatment (T0), after 1 week of treatment (T1), after 2 weeks of treatment (T2), and after 4 weeks of treatment (T3). At the same time, nociception coma scale-revised (NCS-R) and adverse effects (AEs) will be collected to verify the safety of the treatment. The secondary outcome will involve an analysis of electroencephalogram (EEG) microstates to assess the response mechanisms of dynamic brain networks to SJS. Additionally, CRS-R and AEs will continue to be obtained for a 3-month follow-up (T4) after the end of the treatment.

Discussion

This study protocol aims to innovatively develop a full-time and multi-brain region combined neuromodulation paradigm based on the mesocircuit model to steadily promote consciousness recovery by restoring thalamocortical and cortical-cortical interconnections.

Altered brain functional connectivity in vegetative state and minimally conscious state

Objectives

The pathological mechanism for a disorder of consciousness (DoC) is still not fully understood. Based on traditional behavioral scales, there is a high rate of misdiagnosis for subtypes of DoC. We aimed to explore whether topological characterization may explain the pathological mechanisms of DoC and be effective in diagnosing the subtypes of DoC.

Methods

Using resting-state functional magnetic resonance imaging data, the weighted brain functional networks for normal control subjects and patients with vegetative state (VS) and minimally conscious state (MCS) were constructed. Global and local network characteristics of each group were analyzed. A support vector machine was employed to identify MCS and VS patients.

Results

The average connection strength was reduced in DoC patients and roughly equivalent in MCS and VS groups. Global efficiency, local efficiency, and clustering coefficients were reduced, and characteristic path length was increased in DoC patients (p < 0.05). For patients of both groups, global network measures were not significantly different (p > 0.05). Nodal efficiency, nodal local efficiency, and nodal clustering coefficient were reduced in frontoparietal brain areas, limbic structures, and occipital and temporal brain areas (p < 0.05). The comparison of nodal centrality suggested that DoC causes reorganization of the network structure on a large scale, especially the thalamus. Lobal network measures emphasized that the differences between the two groups of patients mainly involved frontoparietal brain areas. The accuracy, sensitivity, and specificity of the classifier for identifying MCS and VS patients were 89.83, 78.95, and 95%, respectively.

Conclusion

There is an association between altered network structures and clinical symptoms of DoC. With the help of network metrics, it is feasible to differentiate MCS and VS patients.

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.

Brain Metabolic Connectivity Patterns in Patients with Prolonged Disorder of Consciousness after Hypoxic-Ischemic Injury: A Preliminary Study

Understanding the patterns of brain glucose metabolism and connectivity in hypoxic-ischemic encephalopathy (HIE) patients with prolonged disorders of consciousness (DOC) may be of positive significance to the accurate assessment of consciousness and the optimization of neuromodulation strategy. We retrospectively analyzed the brain glucose metabolism pattern and its correlation with clinical Coma Recovery Scale-Revised (CRS-R) score in six HIE patients with prolonged DOC who had undergone 18F-deoxyglucose brain positron emission tomography scanning (FDG-PET). We also compared the differences in global metabolic connectivity patterns and the characteristics of several brain networks between HIE patients and healthy controls (HC). The metabolism of multiple brain regions decreased significantly in HIE patients, and the degree of local metabolic preservation was correlated with CRS-R score. The internal metabolic connectivity of occipital lobe and limbic system in HIE patients decreased, and their metabolic connectivity with frontal lobe, parietal lobe and temporal lobe also decreased. The metabolic connectivity patterns of default mode network, dorsal attention network, salience network, executive control network and subcortex network of HIE also changed compared with HC. The present study suggested that pattern of cerebral glucose metabolism and network connectivity of HIE patients with prolonged DOC were significantly different from those of healthy people.