Recently, the constructions of strongly nonlocal orthogonal states have attracted much attention. However, for these sets of orthogonal states with stronger nonlocality, there has been little research on how to effectively use entanglement to distinguish them by local operations and classical communication (LOCC). The entanglement-assisted discrimination protocols for genuinely nonlocal orthogonal product bases were first given by Rout et al. (Phys. Rev. A 100:032321, 2019). Inspired by their protocols, this paper concentrates on the entanglement-assisted local discrimination of the 6(d-1)^2 strongly nonlocal orthogonal product states (OPSs) in (ℂ^d)^⊗ 3 ( d≥ 3 ) which were constructed by Yuan et al. (Phys. Rev. A 102:042228, 2020). First, we use an average of three 2-qubit maximally entangled states (MESs) to locally identify strongly nonlocal OPSs in (ℂ^4)^⊗ 3 . Subsequently, the discrimination method can be extended to the OPSs in (ℂ^d)^⊗ 3 , proving that multiple copies of 2⊗ 2 MESs can be used to exactly identify them. Our protocol not only indicates the crucial function of MESs in distinguishing strongly nonlocal OPSs but also reveals that the high entanglement cost makes it easier to overcome the states’ strongly nonlocality under the enhanced LOCC.

Recently, Xu et al. [Phys. Rev. A 111, 022416 (2025)] have constructed minimal nonlocal orthogonal product states (OPSs) on n-partite systems (n 3), as well as minimal nonlocal orthogonal entangled states (OESs) on both bipartite and n-partite systems (n 3). A meaningful question is whether entanglement-assisted discrimination protocols with less entanglement than teleportation can be designed for the above OPSs and OESs. This paper provides an affirmative answer to this question and diagrammatically illustrates the entanglement-assisted local discrimination protocols through geometric shapes. First, we present two efficient entanglement-assisted local discrimination protocols for nonlocal OPSs on tripartite systems, and extend both protocols to general n-partite systems (n 3). Furthermore, we implement the efficient local discrimination of nonlocal OESs on both bipartite and tripartite systems by using auxiliary entanglement, and the discrimination method is generalized to n-partite systems (n 3). In particular, when distinguishing tripartite nonlocal OESs, we construct local discrimination protocols with less entanglement resource for d = 4 and d = 5. Our results highlight the essential role of entanglement in identifying minimal nonlocal sets on multipartite systems, revealing the phenomenon of less nonlocality with more entanglement.

BackgroundSpasticity frequently complicates disorders of consciousness (DoC) after severe brain injury, yet effective treatments addressing both conditions remain limited. Spinal cord stimulation (SCS) has shown promise in treating each condition independently, but its application in patients with concurrent DoC and spasticity has rarely been systematically investigated.MethodsWe report a 30-year-old man with prolonged DoC and severe limb spasticity following traumatic brain injury who underwent dual-level percutaneous SCS lead implantation. Frequency screening was performed to identify optimal stimulation parameters, followed by 21 days of continuous stimulation. Assessments included the Coma Recovery Scale–Revised (CRS-R), Modified Ashworth Scale (MAS), Hip Adductor Tone Scale (HATS), surface electromyography (sEMG), electroencephalography (EEG), and transcranial magnetic stimulation (TMS).ResultsFrequency screening identified 80 Hz as the optimal parameter for spasticity reduction. After 21 days of stimulation, the CRS-R score increased from 5 to 10, MAS improved from 3–4 to 1–1+ , and HATS decreased from 3 to 1. sEMG showed reduced muscle activity and spectral shifts consistent with decreased spasticity. EEG demonstrated enhanced frontotemporal functional connectivity and increased spectral power. TMS motor evoked potential latency was significantly shortened. Clinical improvements were sustained at the 6-month follow-up.ConclusionThis case suggests that 80 Hz SCS may be associated with concurrent improvement in consciousness and spasticity in patients with DoC following severe brain injury, accompanied by electrophysiological correlates of altered neural connectivity and corticospinal function.

BACKGROUND:Disorders of consciousness (DoC) lack an integrated framework to guide neuromodulation. This review updates three hierarchical frameworks-ARAS, mesocircuit, and ICNs-and maps therapeutic targets within each. METHODS:We synthesized recent neuroanatomical, optogenetic, neuroimaging, and clinical trial evidence, reevaluating classic frameworks and proposing a hierarchical integration to inform multi-target strategies. RESULTS:Three major updates are identified. In ARAS, glutamatergic neurons in PPN/LDT and parabrachial nucleus, rather than cholinergic neurons, are the primary arousal drivers; clinical translation relies on peripheral nerve stimulations with variable efficacy. The mesocircuit is expanded from a unidirectional GPi-mediated pathway to a bidirectional GPi-GPe balance modulated by dopamine and adenosine; DBS targeting CM-Pf/CL shows ~44.7% response rate (38/85) but lacks RCTs, whereas amantadine remains the sole Level 1 evidence. Within ICNs, the executive control network has the strongest RCT support, though efficacy depends on etiology and consciousness level; salience network stimulation is untested. These frameworks are hierarchically integrated: ARAS sustains wakefulness, mesocircuit enables cortical activation, and ICNs support conscious content. CONCLUSION:Future strategies should adopt multi-target, multi-level approaches grounded in individualized network profiling. Priorities include large-scale RCTs for DBS and stratified protocols based on etiology and consciousness level.

Deep brain stimulation (DBS) targeting the centromedian-parafascicular (CM-pf) thalamic nucleus is a promising yet challenging intervention for patients with disorders of consciousness (DoC). These patients often present with significant cerebral deformations, which render conventional, atlas-based surgical planning inaccurate. This case series reports the novel application of a Thalamic Region-Based Non-Rigid Registration Technique (NRRT) integrated with a surgical robot to overcome this challenge, demonstrating its feasibility and precision for the first time in this specific patient population. We present a series of four patients with chronic DoC (3 males, 1 female; age range 19–66 years) due to varying etiologies (trauma, encephalitis, brainstem hemorrhage/infarction). All patients exhibited minimally conscious state (MCS) and two had significant hydrocephalus causing brain deformation. Each patient underwent robot-assisted DBS implantation in the CM-pf complex. Preoperatively, we compared planned targets from conventional manual methods and the novel NRRT method. Postoperative imaging revealed that the final electrode positions were consistently closer to the NRRT-planned targets than the conventional ones, with a mean vector error of less than 0.4 mm in the X and Y axes. The procedure was safely completed in all cases without surgical complications. The Coma Recovery Scale-Revised (CRS-R) scores improved postoperatively in all patients, with a median score increase of 7 points. This case series provides initial real-world evidence that NRRT-assisted robotic DBS is a feasible and accurate approach for targeting thalamic nuclei in patients with DoC and distorted brain anatomy. The technique allows for personalized surgical planning that may optimize electrode placement. The observed clinical improvements, while encouraging, require cautious interpretation due to the possibility of spontaneous recovery. This report highlights the potential of advanced image registration technologies to address a fundamental problem in stereotactic surgery and warrants further investigation in larger studies. Our study has been verified by the Chinese Clinical Trial Registry with the registration number: ChiCTR2400085855, and the registration date is June 19, 2024.

Consciousness is hypothesized to emerge from a sophisticated balance between functional integration and segregation across large-scale brain networks. However, how these topological properties are disrupted in disorders of consciousness (DoC), and whether they can predict long-term recovery, remain to be established. Using resting-state functional MRI and graph-theoretical modeling in patients with DoC (N = 117) and healthy controls (HCs; N = 30), we mapped the macroscale reconfiguration of the “conscious connectome” to delineate disruptions in functional integration and segregation. Associations between topological features and behavioral consciousness, as measured by the Coma Recovery Scale-Revised, were further investigated. In addition, we developed a machine-learning framework to evaluate the prognostic value of network topology for neurological outcomes. Compared with HCs, patients with DoC exhibit widespread disruptions in both functional integration and segregation, with more pronounced deficits in segregation. Functional integration, but not segregation, is positively correlated with behavioral consciousness, particularly within the somatomotor network. Furthermore, the machine-learning framework leveraging macroscale network topology, notably the default mode network, predicts long-term recovery with a precision–recall area under the curve (PR-AUC) of 0.61. The inclusion of clinical variables further improved model performance (PR-AUC = 0.84). These findings suggest that functional segregation is more sensitive to the topological disruption characterizing pathological conscious state, whereas residual functional integration better explains interindividual variability in conscious behavior; together, they provide a topological scaffold for predicting neurological prognosis. Collectively, this framework provides objective and individualized biomarkers for the assessment and neuroprognostication of patients with DoC. After a severe brain injury, some patients show very limited signs of consciousness. Better tools are needed to understand their condition and estimate their chances of recovery. In this study, we analyzed brain scans from more than one hundred patients with disorders of consciousness. We examined how different brain regions work together and how specialized brain systems remain organized. We found that both types of brain organization were disrupted in patients. However, preserved communication between brain regions was more closely related to remaining signs of consciousness. These brain network features also helped predict long-term recovery. Our findings provide insight into how consciousness is supported by the human brain and may contribute to more accurate diagnosis and prognosis for patients with severe brain injuries. Zhu et al. characterize large-scale brain network topology in disorders of consciousness using resting-state functional MRI and graph-theoretical analyses. They show that disrupted network topology characterizes consciousness impairment, reflects behavioral responsiveness, and predicts long-term recovery in patients.

Introdution: Spinal cord stimulation (SCS) has emerged as a promising neuromodulatory intervention for patients with disorders of consciousness (DoC). However, the identification of optimal stimulation frequencies remains a subject of ongoing debate. Although previous electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) studies have suggested the therapeutic efficacy of 5- and 70-Hz, respectively, the integrative neurovascular mechanisms and frequency-specific network dynamics underlying these effects remain to be elucidated. Objective and Impact Statement: This study aims to characterize frequency-dependent network reconfiguration in DoC using simultaneous EEG-fNIRS recordings and graph theoretical analysis. By delineating distinct neurophysiological and hemodynamic signatures, our findings establish a mechanistic framework for the optimization of SCS parameters, thereby advancing personalized neuromodulation strategies for the promotion of consciousness recovery. Methods: This prospective trial used simultaneous EEG-fNIRS and graph theory in 16 patients with DoC undergoing multifrequency SCS at 5, 20, 70, and 100 Hz to decode frequency-specific network dynamics. Our integrated EEG-fNIRS analysis revealed 3 principal advances. First, multimodal cortical mapping via a unified anatomical atlas quantified frequency-dependent network reconfiguration, generating graph-theoretical metrics (global and nodal efficiency, characteristic path length, and clustering coefficients) from source-localized EEG (delta-gamma bands) and fNIRS (oxyhemoglobin and deoxygenated) data. Second, we identified frequency-dependent neurophysiological profiles. Results: Five-hertz stimulation produced acute enhancement of theta-band global network efficiency coupled with elevated gamma-band nodal efficiency in the right cingulate motor area, indicating immediate frontolimbic engagement. Conversely, 70-Hz stimulation selectively evoked delayed hemodynamic responses in the visual cortices and increased occipital hemoglobin oxygenation without concomitant EEG alterations, suggesting preferential retinotopic pathway recruitment. Conclusion: Multimodal EEG-fNIRS analysis elucidates frequency-specific SCS mechanisms, where 5-Hz stimulation optimizes local information integration through theta and gamma modulation, while 70-Hz enhances long-range connectivity, exposing frequency-specific neural plasticity mechanisms.

BACKGROUND:Improving the efficacy of neuroregulatory treatments for disorders of consciousness (DOCs) remains a significant challenge. Changes in brain spatiotemporal dynamics reveal the underlying mechanisms of brain networks involved in the DOC. This study explored the spatiotemporal dynamics of electroencephalography source-level microstates associated with deep brain stimulation (DBS) neuromodulation in DOC patients under different stimulation frequencies. METHODS:This study included nine patients with chronic DOC. During the DBS stimulation period, different stimulation parameter combinations were applied (stimulation frequencies of 25 Hz, 50 Hz or 100 Hz, with a stimulation voltage of 3.0 V and a pulse width of 120 μs). Changes in source-level microstate indicators were assessed to explore the spatiotemporal dynamics and underlying mechanisms of brain activity under different frequency modulations. RESULTS:Microstate analysis revealed seven optimal microstate types. The mean duration of microstates in the 100 Hz stimulation group was significantly longer than in the 25 Hz and 50 Hz groups, with the left sensorimotor and right temporal regions showing the most pronounced differences. The microstate syntax matrix showed that, compared with baseline, microstate transitions in the 100 Hz stimulation group occurred across a larger number of cortical areas. Additionally, compared with the 25 Hz and 50 Hz groups, microstate transitions were particularly prominent in the left sensorimotor state and right dorsal state transitioning to the right temporal state. CONCLUSIONS:High-frequency brain stimulation elicits a pronounced transition in spatiotemporal dynamics of microstates and induces global brain dynamic alterations centred on right temporal nodes, underscoring the pivotal role of the default mode network in consciousness circuits during the DBS. TRIAL REGISTRATION NUMBER:ChiCTR2400085855.

ObjectiveThis study aimed to enhance the Coma Recovery Scale-Revised (CRS-R) for disorders of consciousness (DoC) by developing a two-dimensional model differentiating cognition and motor function.MethodsWe analyzed 124 DoC patients retrospectively and validated findings using five multicenter datasets (n = 420). CRS-R subscores were decomposed into Consciousness_x (awareness) and Consciousness_y (arousal/motor function) using Projective Non-negative Matrix Factorization. Logistic regression established diagnostic thresholds, evaluated by accuracy, precision, recall, and F1-score.ResultsThe model achieved high accuracy (0.94), precision (0.92), and recall (0.99). Patients with minimally conscious state (MCS) or emerged MCS showed significantly higher scores than vegetative state (VS) patients (p < 0.05). The four-quadrant framework revealed distinct clinical profiles: Quadrant I (high awareness/arousal) identified patients for cognitive rehabilitation; Quadrant II (low awareness/high arousal) suggested arousal-enhancing therapies; Quadrant III (low awareness/arousal) indicated VS requiring basic support; Quadrant IV (high awareness/low arousal) highlighted needs for sensorimotor integration.ConclusionsThe two-dimensionally reduced representation of CRS-R scores maintains diagnostic accuracy while improving DoC classification. The four-quadrant model enables personalized interventions.Trial registrationOur study has been verified by the Chinese Clinical Trial Registry with the registration number: ChiCTR2400085855, and the registration date is June 19, 2024.

Background: Disorders of consciousness (DoC) pose significant challenges in clinical diagnosis and treatment. This study aims to investigate the relationship between consciousness levels and the brainstem-cortical white matter tracts in DoC patients resulting from focal brainstem injury using diffusion tensor imaging (DTI). Methods: DTI data of DoC patients with focal brainstem injury and healthy volunteers were retrospectively collected. White matter tractography was performed to reconstruct brainstem-cortical projections. The number of streamlines, total volume, and fractional anisotropy (FA) were analyzed from the perspective of global brain, physiological pathways, and functional networks. The relationship between these measurements and consciousness levels was investigated. Results: A cohort of 28 DoC patients and 32 healthy controls were included in the analysis. DoC patients exhibited significant reductions in the number of streamlines in global brainstem-cortical projections compared to controls. However, the total volume and FA of these fibers were relatively preserved. Specific pathways such as the corticospinal tract and frontoparietal tract showed marked reductions in streamline counts. Significant reductions in streamline counts were also observed in the somatomotor and frontoparietal networks. No significant changes in mean FA were observed across different physiological pathways and brain networks. Correlation analyses revealed significant associations between consciousness levels and structural connections in the frontoparietal tract and frontoparietal network. Conclusion: This study highlights the impact of focal brainstem injury on global brain structural connectivity in DoC patients. Despite significant reductions in streamline counts, the preservation of FA suggests maintained microstructural integrity in surviving fibers.

Disorders of Consciousness (DOC) are characterized by abnormal function or disrupted connectivity of consciousness-related neural circuits, mainly presenting as Vegetative State/Unresponsive Wakefulness Syndrome (VS/UWS) and Minimally Conscious State (MCS), which impose a heavy burden on patients’ families and society. Non-Invasive Brain Stimulation (NIBS) has emerged as a core research direction for DOC treatment due to its non-invasiveness, ease of operation, and favorable safety profile. Based on the classification of consciousness-related neural circuits, this review systematically summarizes the research progress of central and peripheral non-invasive neuromodulation techniques, including their potential regulatory mechanisms on core circuits (such as the frontoparietal network, cortico-thalamocortical circuit, and ascending reticular activating system), clinical evidence, and synergistic effects of combined therapies. Studies have shown that techniques like Transcranial Magnetic Stimulation (TMS) and Transcranial Direct Current Stimulation (tDCS) targeting the frontoparietal network, Low-Intensity Transcranial Focused Ultrasound (LITUS, also referred to as Transcranial Focused Ultrasound [TUS]/transcranial Focused Ultrasound [tFUS] in the field) and Temporal Interference (TI) regulating the cortico-thalamocortical circuit, and Median Nerve Stimulation (MNS) activating the ascending reticular activating system have demonstrated certain efficacy in improving consciousness in MCS patients, while the evidence for efficacy in VS/UWS patients remains weak due to small sample sizes, lack of control groups and insufficient statistical power. Combined therapies such as TMS + MNS and Transcranial Focused Ultrasound LITUS+TMS exhibit significantly superior synergistic effects compared to monotherapies. By horizontally comparing the advantages and limitations of various techniques, this review proposes personalized treatment recommendations based on the characteristics of neural circuit damage. It also points out that future research should optimize stimulation parameters, clarify the specificity of circuit regulation, and verify long-term efficacy through large-sample randomized controlled trials (RCTs), aiming to provide a reference for the standardized and precise application of NIBS in DOC treatment.

Disorders of consciousness (DoC) present significant challenges in clinical neurology, particularly when caused by brainstem injury. The brainstem’s role, especially its ascending reticular activating system (ARAS), is crucial for maintaining arousal, a fundamental component of consciousness. However, the precise mechanisms by which brainstem injuries lead to DoC remain incompletely understood, and treatment options are limited. This gap in understanding hampers the development of effective therapies and impedes clinical management of these conditions. Here, we provide a comprehensive review of the latest research on the anatomical, neurochemical, and network-based mechanisms linking brainstem injury to DoC. We focus on the brainstem nuclei and neurotransmitter systems, such as serotonin from the dorsal raphe nucleus, norepinephrine from the locus coeruleus, and dopamine from the ventral tegmental area, highlighting their roles in arousal regulation and brainstem–cortical communication. Furthermore, we explore how disruptions in connectivity between the ARAS and cortical networks, as revealed by advanced neuroimaging techniques like diffusion tensor imaging and functional MRI, correlate with the severity of consciousness impairment. Additionally, we discuss therapeutic strategies, including pharmacological interventions and neuromodulation techniques, which aim to restore consciousness by targeting these disrupted networks. This review advances the field by synthesizing current knowledge on the brainstem’s role in consciousness and highlighting the potential of targeted therapies to improve patient outcomes. By elucidating the mechanisms underlying DoC caused by brainstem injury, this review provides a foundation for future research to develop more effective treatments, ultimately contributing to better clinical management and recovery strategies for patients with DoC.

BACKGROUND:The heart rate variability (HRV) of patients with disorders of consciousness (DOC) differs from healthy individuals. However, there is rarely research on HRV among DOC patients following treatment with deep brain stimulation (DBS). This study aims to investigate the modulatory effects of DBS-on the central-autonomic nervous system of DOC based on the study of HRV variations. METHODS:We conducted DBS surgery on eight patients with DOC. Postoperatively, all patients underwent short-duration stimulation for 3 days, with stimulation frequencies of 25 Hz, 50 Hz, and 100 Hz respectively. Each day comprised four cycles, with a stimulation duration of 30 min DBS-on and 90 min DBS-off. We obtained the coma recovery scale-revised (CRS-R) scores and synchronously recorded electrocardiographic data. RESULITS:We analyzed the HRV indices, including time-domain and frequency-domain parameters across various time points for all patients. The HRV exhibited a consistent trend across the three groups with different parameters. Notably, the most pronounced HRV changes were induced by the 100 Hz. Long-term follow-up indicates that high-frequency (HF), low-frequency (LF), and total power (TP) of HRV may serve as predictive indicators in the prognosis of patients. CONCLUSION:Our study reveals that DBS enhances DOC patient consciousness while increasing HRV. Specifically, frequency-domain indices correlate with favorable prognosis.

Recently, three classes of orthogonal product states in ℂ^m⊗ℂ^n(m≥ 3, n≥ 3) which cannot be exactly discriminated by local operations and classical communication (LOCC) have been constructed, respectively, by Xu et al. (Quantum Inf. Process. 20: 128, 2021) and Zhu et al. (Physica A 624: 128956, 2023). However, it is interesting to know, in order to perfectly distinguish these states by LOCC, how much entanglement resources are sufficient and/or necessary and whether it is possible to find a universal auxiliary resource. In this paper, we present that by using only one two-qubit maximally entangled state as a general auxiliary resource, the above locally indistinguishable states can all be perfectly identified by LOCC. And the general process of auxiliary local discrimination using entanglement is discussed in detail. The local distinguishing protocols we designed not only utilize minimal amount of assisted entanglement, but also show that the strength of these nonlocal sets is minimal from the point of view of auxiliary resources.

ObjectiveThis study was to employ 18F-flurodeoxyglucose (FDG-PET) to evaluate the resting-state brain glucose metabolism in a sample of 46 patients diagnosed with disorders of consciousness (DoC). The aim was to identify objective quantitative metabolic indicators and predictors that could potentially indicate the level of awareness in these patients.MethodsA cohort of 46 patients underwent Coma Recovery Scale-Revised (CRS-R) assessments in order to distinguish between the minimally conscious state (MCS) and the unresponsive wakefulness syndrome (UWS). Additionally, resting-state FDG-PET data were acquired from both the patient group and a control group consisting of 10 healthy individuals. The FDG-PET data underwent reorientation, spatial normalization to a stereotaxic space, and smoothing. The normalization procedure utilized a customized template following the methodology outlined by Phillips et al. Mean cortical metabolism of the overall sample was utilized for distinguishing between UWS and MCS, as well as for predicting the outcome at a 1-year follow-up through the application of receiver operating characteristic (ROC) analysis.ResultsWe used Global Glucose Metabolism as the Diagnostic Marker. A one-way ANOVA revealed that there was a statistically significant difference in cortical metabolic index between two groups (F(2, 53) = 7.26, p < 0.001). Multiple comparisons found that the mean of cortical metabolic index was significantly different between MCS (M = 4.19, SD = 0.64) and UWS group (M = 2.74, SD = 0.94,p < 0.001). Also, the mean of cortical metabolic index was significantly different between MCS and healthy group (M = 7.88, SD = 0.80,p < 0.001). Using the above diagnostic criterion, the diagnostic accuracy yielded an area under the curve (AUC) of 0.89 across the pooled cohort (95%CI 0.79–0.99). There was an 85% correct classification between MCS and UWS, with 88% sensitivity and 81% specificity for MCS. The best classification rate in the derivation cohort was achieved at a metabolic index of 3.32 (41% of the mean cortical metabolic index in healthy controls).ConclusionOur findings demonstrate that conscious awareness requires a minimum of 41% of normal cortical activity, as indicated by metabolic rates.

BackgroundAdvances in neuroimaging have significantly enhanced our understanding of brain function, providing critical insights into the diagnosis and management of disorders of consciousness (DoC). Functional near-infrared spectroscopy (fNIRS), with its real-time, portable, and noninvasive imaging capabilities, has emerged as a promising tool for evaluating functional brain activity and nonrecovery potential in DoC patients. This review explores the current applications of fNIRS in DoC research, identifies its limitations, and proposes future directions to optimize its clinical utility.AimThis review examines the clinical application of fNIRS in monitoring DoC. Specifically, it investigates the potential value of combining fNIRS with brain-computer interfaces (BCIs) and closed-loop neuromodulation systems for patients with DoC, aiming to elucidate mechanisms that promote neurological recovery.MethodsA systematic analysis was conducted on 155 studies published between January 1993 and October 2024, retrieved from the Web of Science Core Collection database.ResultsAnalysis of 21 eligible studies on neurological diseases involving 262 DoC patients revealed significant findings. The prefrontal cortex was the most frequently targeted brain region. fNIRS has proven crucial in assessing brain functional connectivity and activation, facilitating the diagnosis of DoC. Furthermore, fNIRS plays a pivotal role in diagnosis and treatment through its application in neuromodulation techniques such as deep brain stimulation (DBS) and spinal cord stimulation (SCS).ConclusionAs a noninvasive, portable, and real-time neuroimaging tool, fNIRS holds significant promise for advancing the assessment and treatment of DoC. Despite limitations such as low spatial resolution and the need for standardized protocols, fNIRS has demonstrated its utility in evaluating residual brain activity, detecting covert consciousness, and monitoring therapeutic interventions. In addition to assessing consciousness levels, fNIRS offers unique advantages in tracking hemodynamic changes associated with neuroregulatory treatments, including DBS and SCS. By providing real-time feedback on cortical activation, fNIRS facilitates optimizing therapeutic strategies and supports individualized treatment planning. Continued research addressing its technical and methodological challenges will further establish fNIRS as an indispensable tool in the diagnosis, prognosis, and treatment monitoring of DoC patients.

ObjectiveThis study aimed to investigate the brain's hemodynamic responses (HRO) and functional connectivity in patients with disorders of consciousness (DoC) in response to acute pressure pain stimulation using near-infrared spectroscopy (NIRS).MethodsPatients diagnosed with DoC underwent pressure stimulation while brain activity was measured using NIRS. Changes in oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) concentrations were monitored across several regions of interest (ROIs), including the primary somatosensory cortex (PSC), primary motor cortex (PMC), dorsolateral prefrontal cortex (dPFC), somatosensory association cortex (SAC), temporal gyrus (TG), and frontopolar area (FPA). Functional connectivity was assessed during pre-stimulation, stimulation, and post-stimulation phases.ResultsNo significant changes in HbO or HbR concentrations were observed during the stimulation vs. baseline or stimulation vs. post-stimulation comparisons, indicating minimal activation of the targeted brain regions in response to the pressure stimulus. However, functional connectivity between key regions, particularly the PSC, PMC, and dPFC, showed significant enhancement during the stimulation phase (r > 0.9, p < 0.001), suggesting greater coordination among sensory, motor, and cognitive regions. These changes in connectivity were not accompanied by significant activation in pain-related brain areas.ConclusionAlthough pain-induced brain activation was minimal in patients with DoC, enhanced functional connectivity during pain stimulation suggests that the brain continues to process pain information through coordinated activity between regions. The findings highlight the importance of assessing functional connectivity as a potential method for evaluating pain processing in patients with DoC.

BACKGROUND:Epidural spinal cord stimulation (eSCS) has emerged as a promising neuromodulation technique for treating movement disorders. The underlying mechanisms of eSCS are still being explored, making it a compelling area for further research. OBJECTIVE:This review aims to provide a comprehensive analysis of the mechanisms of eSCS, its stimulation parameters, and its clinical applications in movement disorders. It seeks to synthesize the current understanding of how eSCS interacts with the central nervous system to enhance motor function and promotes neural plasticity for sustained recovery. METHODS:A literature search was performed in databases such as Web of Science, Scopus, and PubMed to identify studies on eSCS for movement disorders. RESULTS:The therapeutic effects of eSCS are achieved through both immediate facilitative actions and long-term neural reorganization. By activating sensory neurons in the dorsal root, facilitating proprioceptive input and modulating spinal interneurons, eSCS enhances motor neuron excitability. Additionally, eSCS influences corticospinal interactions, increasing cortical excitability and promoting corticospinal circuit remodeling. Neuroplasticity plays a critical role in the long-term efficacy of eSCS, with evidence suggesting that stimulation can enhance axonal sprouting, synaptic formation, and neurotrophic factor expression while reducing neuroinflammation. Its regulation of the sympathetic nervous system further enhances recovery by improving blood flow, muscle tone, and other physiological parameters. CONCLUSIONS:Epidural spinal cord stimulation shows promise in enhancing motor function and promoting neuroplasticity, but further research is needed to optimize treatment protocols and establish long-term efficacy.

In recent years, using entanglement resources to assist the local discrimination of orthogonal quantum states has attracted wide attention. However, many studies mainly focus on entanglement-assisted local discrimination in bipartite systems, and there are relatively few in multipartite states. In this paper, for the nonlocal set of 3d-3 orthogonal product states in d⊗ d⊗ d (d≥ 3) constructed by Zhu et al. (Quantum Inf. Process. 21, 252, 2022), we propose a method of using an ancillary d⊗ d maximally entangled state to realize the local perfect discrimination. Firstly, with a 3⊗ 3 maximally entangled state as an auxiliary resource, we present a method to exactly identify the locally indistinguishable 6 orthogonal product states in 3⊗ 3⊗ 3 by local operations and classical communication (LOCC). Then the distinguishing method can be generalized to the 3d-3 states in d⊗ d⊗ d . These results not only reveal the phenomenon of less nonlocality with more entanglement, but also help us better realize the usefulness of entanglement in the local discrimination of quantum states.

Advanced neuroimaging techniques have revolutionized our ability to decode brain networks in patients with disorders of consciousness (DoC), offering unprecedented insights into the structural and functional underpinnings of consciousness impairment. This review systematically examines and summarizes the clinical applications of modern neuroimaging methodologies—specifically functional MRI and diffusion MRI— for DoC patients from three key perspectives: (1) pathogenic mechanism and theory evolution, (2) accurate diagnosis and prognosis assessment, and (3) treatment strategy and efficacy evaluation. By integrating network neuroscience with clinical insights, we highlight the transformative role of neuroimaging in unraveling network-level damage, refining clinical assessments, and guiding therapeutic innovations. We further outline the potential applicational challenges associated with leveraging neuroimaging techniques to advance both scientific research on consciousness networks and clinical practice in DoC management, hoping to better address these complex conditions.