Experimental validation and comprehensive analysis of m6A methylation regulators in intervertebral disc degeneration subpopulation classification.

Intervertebral disc degeneration (IVDD) is one of the most prevalent causes of chronic low back pain. The role of m6A methylation modification in disc degeneration (IVDD) remains unclear. We investigated immune-related m6A methylation regulators as IVDD biomarkers through comprehensive analysis and experimental validation of m6A methylation regulators in disc degeneration. The training dataset was downloaded from the GEO database and analysed for differentially expressed m6A methylation regulators and immunological features, the differentially regulators were subsequently validated by a rat IVDD model and RT-qPCR. Further screening of key m6A methylation regulators based on machine learning and LASSO regression analysis. Thereafter, a predictive model based on key m6A methylation regulators was constructed for training sets, which was validated by validation set. IVDD patients were then clustered based on the expression of key m6A regulators, and the expression of key m6A regulators and immune infiltrates between clusters was investigated to determine immune markers in IVDD. Finally, we investigated the potential role of the immune marker in IVDD through enrichment analysis, protein-to-protein network analysis, and molecular prediction. By analysising of the training set, we revealed significant differences in gene expression of five methylation regulators including RBM15, YTHDC1, YTHDF3, HNRNPA2B1 and ALKBH5, while finding characteristic immune infiltration of differentially expressed genes, the result was validated by PCR. We then screen the differential m6A regulators in the training set and identified RBM15 and YTHDC1 as key m6A regulators. We then used RBM15 and YTHDC1 to construct a predictive model for IVDD and successfully validated it in the training set. Next, we clustered IVDD patients based on the expression of RBM15 and YTHDC1 and explored the immune infiltration characteristics between clusters as well as the expression of RBM15 and YTHDC1 in the clusters. YTHDC1 was finally identified as an immune biomarker for IVDD. We finally found that YTHDC1 may influence the immune microenvironment of IVDD through ABL1 and TXK. In summary, our results suggest that YTHDC1 is a potential biomarker for the development of IVDD and may provide new insights for the precise prevention and treatment of IVDD.

A predictive model for depression in Chinese middle-aged and elderly people with physical disabilities.

BACKGROUND: Middle-aged and older adults with physical disabilities exhibit more common and severe depressive symptoms than those without physical disabilities. Such symptoms can greatly affect the physical and mental health and life expectancy of middle-aged and older persons with disabilities. METHOD: This study selected 2015 and 2018 data from the China Longitudinal Study of Health and Retirement. After analyzing the effect of age on depression, we used whether middle-aged and older adults with physical disabilities were depressed as the dependent variable and included a total of 24 predictor variables, including demographic factors, health behaviors, physical functioning and socialization, as independent variables. The data were randomly divided into training and validation sets on a 7:3 basis. LASSO regression analysis combined with binary logistic regression analysis was performed in the training set to screen the predictor variables of the model. Construct models in the training set and perform model evaluation, model visualization and internal validation. Perform external validation of the model in the validation set. RESULT: A total of 1052 middle-aged and elderly persons with physical disabilities were included in this study, and the prevalence of depression in the elderly group > middle-aged group. Restricted triple spline indicated that age had different effects on depression in the middle-aged and elderly groups. LASSO regression analysis combined with binary logistic regression screened out Gender, Location of Residential Address, Shortsightedness, Hearing, Any possible helper in the future, Alcoholic in the Past Year, Difficulty with Using the Toilet, Difficulty with Preparing Hot Meals, and Unable to work due to disability constructed the Chinese Depression Prediction Model for Middle-aged and Older People with Physical Disabilities. The nomogram shows that living in a rural area, lack of assistance, difficulties with activities of daily living, alcohol abuse, visual and hearing impairments, unemployment and being female are risk factors for depression in middle-aged and older persons with physical disabilities. The area under the ROC curve for the model, internal validation and external validation were all greater than 0.70, the mean absolute error was less than 0.02, and the recall and precision were both greater than 0.65, indicating that the model performs well in terms of discriminability, accuracy and generalisation. The DCA curve and net gain curve of the model indicate that the model has high gain in predicting depression. CONCLUSION: In this study, we showed that being female, living in rural areas, having poor vision and/or hearing, lack of assistance from others, drinking alcohol, having difficulty using the restroom and preparing food, and being unable to work due to a disability were risk factors for depression among middle-aged and older adults with physical disabilities. We developed a depression prediction model to assess the likelihood of depression in Chinese middle-aged and older adults with physical disabilities based on the above risk factors, so that early identification, intervention, and treatment can be provided to middle-aged and older adults with physical disabilities who are at high risk of developing depression.

M6A-related bioinformatics analysis indicates that LRPPRC is an immune marker for ischemic stroke.

Ischemic stroke (IS) is a common cerebrovascular disease whose pathogenesis involves a variety of immune molecules, immune channels and immune processes. 6-methyladenosine (m6A) modification regulates a variety of immune metabolic and immunopathological processes, but the role of m6A in IS is not yet understood. We downloaded the data set GSE58294 from the GEO database and screened for m6A-regulated differential expression genes. The RF algorithm was selected to screen the m6A key regulatory genes. Clinical prediction models were constructed and validated based on m6A key regulatory genes. IS patients were grouped according to the expression of m6A key regulatory genes, and immune markers of IS were identified based on immune infiltration characteristics and correlation. Finally, we performed functional enrichment, protein interaction network analysis and molecular prediction of the immune biomarkers. We identified a total of 7 differentially expressed genes in the dataset, namely METTL3, WTAP, YWHAG, TRA2A, YTHDF3, LRPPRC and HNRNPA2B1. The random forest algorithm indicated that all 7 genes were m6A key regulatory genes of IS, and the credibility of the above key regulatory genes was verified by constructing a clinical prediction model. Based on the expression of key regulatory genes, we divided IS patients into 2 groups. Based on the expression of the gene LRPPRC and the correlation of immune infiltration under different subgroups, LRPPRC was identified as an immune biomarker for IS. GO enrichment analyses indicate that LRPPRC is associated with a variety of cellular functions. Protein interaction network analysis and molecular prediction indicated that LRPPRC correlates with a variety of immune proteins, and LRPPRC may serve as a target for IS drug therapy. Our findings suggest that LRPPRC is an immune marker for IS. Further analysis based on LRPPRC could elucidate its role in the immune microenvironment of IS.

Variation in brain connectivity during motor imagery and motor execution in stroke patients based on electroencephalography.

Objective: The objective of this study was to analyze the changes in connectivity between motor imagery (MI) and motor execution (ME) in the premotor area (PMA) and primary motor cortex (MA) of the brain, aiming to explore suitable forms of treatment and potential therapeutic targets. Methods: Twenty-three inpatients with stroke were selected, and 21 right-handed healthy individuals were recruited. EEG signal during hand MI and ME (synergy and isolated movements) was recorded. Correlations between functional brain areas during MI and ME were compared. Results: PMA and MA were significantly and positively correlated during hand MI in all participants. The power spectral density (PSD) values of PMA EEG signals were greater than those of MA during MI and ME in both groups. The functional connectivity correlation was higher in the stroke group than in healthy people during MI, especially during left-handed MI. During ME, functional connectivity correlation in the brain was more enhanced during synergy movements than during isolated movements. The regions with abnormal functional connectivity were in the 18th lead of the left PMA area. Conclusion: Left-handed MI may be crucial in MI therapy, and the 18th lead may serve as a target for non-invasive neuromodulation to promote further recovery of limb function in patients with stroke. This may provide support for the EEG theory of neuromodulation therapy for hemiplegic patients.

Pregabalin inhibits purinergic P2Y2 receptor and TRPV4 to suppress astrocyte activation and to relieve neuropathic pain.

BACKGROUNDS: Transient receptor potential vanilloid 4 (TRPV4)-mediated astrocyte activation is critical to neuropathic pain. Pregabalin, a widely used drug to treat chronic pain, is reported to lower the intracellular calcium level. However, the molecular mechanism by which pregabalin decreases the intracellular calcium level remains unknown. Purinergic P2Y2 receptor-a member of the G protein-coupled receptor (GPCR) family-regulates calcium-related signal transduction in astrocyte activation. We investigated whether P2Y2 receptor is involved in the pharmacological effects of pregabalin on neuropathic pain. METHODS: Neuropathic pain was induced by chronic compression of the dorsal root ganglion (CCD) in rats. Paw withdrawal mechanical threshold (PWMT) was used for behavioral testing. Intracellular calcium concentration was measured using a fluorescent calcium indicator (Fluo-4 AM). RESULTS: We found that P2Y2 receptor protein was upregulated and astrocytes were activated in the experimental rats after CCD surgery. Lipopolysaccharide (LPS) increased the intracellular calcium concentration and induced astrocyte activation in cultured astrocytes but was prevented via P2Y2 receptor inhibitor AR-C118925 or pregabalin. Furthermore, plasmid-mediated P2Y2 receptor overexpression induced an elevation of the intracellular calcium levels and inflammation in astrocytes, which was abolished by the TRPV4 inhibitor HC-067047. AR-C118925, HC-067047, and pregabalin relieved neuropathic pain and inflammation in rats after CCD surgery. Finally, plasmid-mediated P2Y2 receptor overexpression induced neuropathic pain in rats, which was abolished by pregabalin administration. CONCLUSIONS: Pathophysiological variables that upregulated the P2Y2 receptor/TRPV4/calcium axis contribute to astrocyte activation in neuropathic pain. Pregabalin exerts an analgesic effect by inhibiting this pathway.

Corrigendum: Efficacy of non-invasive brain stimulation for disorders of consciousness: a systematic review and meta-analysis.

[This corrects the article DOI: 10.3389/fnins.2023.1219043.].

A clinical model to predict the progression of knee osteoarthritis: data from Dryad.

BACKGROUND: Knee osteoarthritis (KOA) is a multifactorial, slow-progressing, non-inflammatory degenerative disease primarily affecting synovial joints. It is usually induced by advanced age and/or trauma and eventually leads to irreversible destruction of articular cartilage and other tissues of the joint. Current research on KOA progression has limited clinical application significance. In this study, we constructed a prediction model for KOA progression based on multiple clinically relevant factors to provide clinicians with an effective tool to intervene in KOA progression. METHOD: This study utilized the data set from the Dryad database which included patients with Kellgren-Lawrence (KL) grades 2 and 3. The KL grades was determined as the dependent variable, while 15 potential predictors were identified as independent variables. Patients were randomized into training set and validation set. The training set underwent LASSO analysis, model creation, visualization, decision curve analysis and internal validation using R language. The validation set is externally validated and F1-score, precision, and recall are computed. RESULT: A total of 101 patients with KL2 and 94 patients with KL3 were selected. We randomly split the data set into a training set and a validation set by 8:2. We filtered "BMI", "TC", "Hypertension treatment", and "JBS3 (%)" to build the prediction model for progression of KOA. Nomogram used to visualize the model in R language. Area under ROC curve was 0.896 (95% CI 0.847-0.945), indicating high discrimination. Mean absolute error (MAE) of calibration curve = 0.041, showing high calibration. MAE of internal validation error was 0.043, indicating high model calibration. Decision curve analysis showed high net benefit. External validation of the metabolic syndrome column-line graph prediction model was performed by the validation set. The area under the ROC curve was 0.876 (95% CI 0.767-0.984), indicating that the model had a high degree of discrimination. Meanwhile, the calibration curve Mean absolute error was 0.113, indicating that the model had a high degree of calibration. The F1 score is 0.690, the precision is 0.667, and the recall is 0.714. The above metrics represent a good performance of the model. CONCLUSION: We found that KOA progression was associated with four variable predictors and constructed a predictive model for KOA progression based on the predictors. The clinician can intervene based on the nomogram of our prediction model. KEY INFORMATION: This study is a clinical predictive model of KOA progression. KOA progression prediction model has good credibility and clinical value in the prevention of KOA progression.

Deep learning for the rapid automatic segmentation of forearm muscle boundaries from ultrasound datasets.

Ultrasound (US) is widely used in the clinical diagnosis and treatment of musculoskeletal diseases. However, the low efficiency and non-uniformity of artificial recognition hinder the application and popularization of US for this purpose. Herein, we developed an automatic muscle boundary segmentation tool for US image recognition and tested its accuracy and clinical applicability. Our dataset was constructed from a total of 465 US images of the flexor digitorum superficialis (FDS) from 19 participants (10 men and 9 women, age 27.4 ± 6.3 years). We used the U-net model for US image segmentation. The U-net output often includes several disconnected regions. Anatomically, the target muscle usually only has one connected region. Based on this principle, we designed an algorithm written in C++ to eliminate redundantly connected regions of outputs. The muscle boundary images generated by the tool were compared with those obtained by professionals and junior physicians to analyze their accuracy and clinical applicability. The dataset was divided into five groups for experimentation, and the average Dice coefficient, recall, and accuracy, as well as the intersection over union (IoU) of the prediction set in each group were all about 90%. Furthermore, we propose a new standard to judge the segmentation results. Under this standard, 99% of the total 150 predicted images by U-net are excellent, which is very close to the segmentation result obtained by professional doctors. In this study, we developed an automatic muscle segmentation tool for US-guided muscle injections. The accuracy of the recognition of the muscle boundary was similar to that of manual labeling by a specialist sonographer, providing a reliable auxiliary tool for clinicians to shorten the US learning cycle, reduce the clinical workload, and improve injection safety.

Efficacy of non-invasive brain stimulation for disorders of consciousness: a systematic review and meta-analysis.

Objective: The aim of this study is to evaluate the efficacy of non-invasive brain stimulation (NIBS) in patients with disorders of consciousness (DoC) and compare differences in efficacy between different stimulation modalities. Methods: We searched the PubMed, Cochrane Library, Web of Science, and EMBASE databases for all studies published in English from inception to April 2023. Literature screening and quality assessment were performed independently by two investigators. Weighted mean differences (WMDs) and 95% confidence intervals (CIs) were used to evaluate the therapeutic effects of NIBS. The Cochrane Q test and I2 statistic were used to evaluate heterogeneity between studies. Subgroup analysis was performed to identify the source of heterogeneity, and differences in efficacy between different stimulation modalities were compared by Bayesian analysis. Results: A total of 17 studies with 377 DoC patients were included. NIBS significantly improved the state of consciousness in DoC patients when compared to sham stimulation (WMD: 0.81; 95% CI: 0.46, 1.17; I2 = 78.2%, p = 0.000). When divided into subgroups according to stimulation modalities, the heterogeneity of each subgroup was significantly lower than before (I2: 0.00-30.4%, p >0.05); different stimulation modalities may be the main source of such heterogeneity. Bayesian analysis, based on different stimulation modalities, indicated that a patient's state of consciousness improved most significantly after repetitive transcranial magnetic stimulation (rTMS) of the left dorsolateral prefrontal cortex (DLPFC). Diagnosis-based subgroup analysis showed that NIBS significantly improved the state of consciousness in patients with a minimal consciousness state (WMD: 1.11; 95% CI: 0.37, 1.86) but not in patients with unresponsive wakefulness syndrome or a vegetative state (WMD: 0.31; 95% CI: -0.09, 0.71). Subgroup analysis based on observation time showed that single treatment did not improve the state of consciousness in DoC patients (WMD: 0.28; 95% CI: -0.27, 0.82) while multiple treatments could (WMD: 1.05; 95% CI: 0.49, 1.61). Furthermore, NIBS had long-term effects on DoC patients (WMD: 0.79; 95% CI: 0.08-1.49). Conclusion: Available evidence suggests that the use of NIBS on patients with DoC is more effective than sham stimulation, and that rTMS of the left DLPFC may be the most prominent stimulation modality.

Corrigendum: Functional networks in prolonged disorders of consciousness.

[This corrects the article DOI: 10.3389/fnins.2023.1113695.].

Exploring barriers to and facilitators of the implementation of home rehabilitation care for older adults with disabilities using the Consolidated Framework for Implementation Research (CFIR).

BACKGROUND: With global aging on the rise, the number of older adults with disabilities was also increasing exponentially. There has been growing international interest in home rehabilitation care as a new method for older adults with disabilities. METHOD: The current study is a descriptive qualitative study. Guided by the Consolidated Framework for Implementation Research (CFIR), semistructured face-to-face interviews were performed to collect data. The interview data were analyzed using a qualitative content analysis method. RESULT: Sixteen nurses with different characteristics from 16 cities participated in the interviews. The findings highlighted 29 implementation determinants of home-based rehabilitation care for older adults with disabilities, including 16 barriers, and 13 facilitators. These influencing factors aligned with all four CFIR domains that were used to guide the analysis and 15 of the 26 CFIR constructs. More barriers were identified in the CFIR domain of characteristics of individuals, intervention characteristics, and the outer setting, while fewer barriers were identified in the inner setting. CONCLUSION: Nurses from the rehabilitation department reported many barriers related to the implementation of home rehabilitation care. They reported facilitators to the implementation of home rehabilitation care despite the barriers, which provided practical recommendations for directions to be explored by researchers in China and elsewhere.

Corrigendum: Prediction of motor function in patients with traumatic brain injury using genetic algorithms modified back propagation neural network: a data-based study.

[This corrects the article DOI: 10.3389/fnins.2022.1031712.].

Residents of Mountainous Areas Have a Higher Low Back Pain Prevalence Than Flat Areas of Chongqing, China: A Cross-Sectional Study.

Background and Purpose: Epidemiological studies on low back pain in residents living in mountainous areas are scarce. The study was aimed at investigating the prevalence and associated factors of low back pain in Chongqing, relatively impoverished mountainous areas of China. Materials and Methods: This was a cross-sectional study conducted at selected community or village health service centers in Chongqing over a 2-month period (May 2021 to June 2021), which included adults of Chongqing aged >18 years with or without low back pain (N=1820) chosen by stratified, cluster-sampling. Associated factors of low back pain including sociodemographic characteristics, lifestyle, and occupational features were collected, along with medical history, Oswestry Disability Questionnaire (ODI), and Numerical Rating Scale (NRS) of patients with low back pain, and carried out for at least 20 minutes per respondent. Univariate and multivariate logistic regression models were utilized for statistical analysis. Results: Overall, 30.5% of 1704 respondents presented with low back pain, with 26.3% living in flat areas and 35.6% in mountainous areas. The associated factors of low back pain were mountainous area residence (OR 1.4, 95% CI 1.1-1.8), advanced age (OR 1.8, 95% CI 1.3-2.5 for those aged 45-59 years, OR 2.3, 95% CI 1.6-3.4 for those aged 60-74 years, and OR 2.1, 95% CI 1.2-3.6 for those aged ≥75 years), married or remarried (OR 1.9, 95% CI 1.1-3.2), divorced or widowed (OR 2.7, 95% CI 14-5.4), moderate labor intensity (OR 1.4, 95% CI 1.1-1.8), frequent stoop (OR 1.6, 95% CI 1.1-2.4), and depressed mood (OR 1.6, 95% CI 1.2-2.1). Residents in the mountainous areas had a higher score on Oswestry Disability Questionnaire (8.3 [SD 6.3] vs 6.2 [SD 4.3]) than those in flat areas. Conclusion: Mountainous areas in Chongqing had higher prevalence of low back pain as 35.6%, compared with 26.3% in flat areas, with more severe dysfunction in low back pain patients. Multifactorial analysis found that the factors associated with low back pain in Chongqing residents included mountain residence, labor intensity, stoop, psychological factors and frequency of exercise.

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.

Ferroptosis-related genes are involved in asthma and regulate the immune microenvironment.

Background: Asthma was a chronic inflammatory illness driven by complicated genetic regulation and environmental exposure. The complex pathophysiology of asthma has not been fully understood. Ferroptosis was involved in inflammation and infection. However, the effect of ferroptosis on asthma was still unclear. The study was designed to identify ferroptosis-related genes in asthma, providing potential therapeutic targets. Methods: We conducted a comprehensive analysis combined with WGCNA, PPI, GO, KEGG, and CIBERSORT methods to identify ferroptosis-related genes that were associated with asthma and regulated the immune microenvironment in GSE147878 from the GEO. The results of this study were validated in GSE143303 and GSE27066, and the hub genes related to ferroptosis were further verified by immunofluorescence and RT-qPCR in the OVA asthma model. Results: 60 asthmatics and 13 healthy controls were extracted for WGCNA. We found that genes in the black module (r = -0.47, p < 0.05) and magenta module (r = 0.51, p < 0.05) were associated with asthma. CAMKK2 and CISD1 were discovered to be ferroptosis-related hub genes in the black and magenta module, separately. We found that CAMKK2 and CISD1 were mainly involved in the CAMKK-AMPK signaling cascade, the adipocytokine signaling pathway, the metal cluster binding, iron-sulfur cluster binding, and 2 iron, 2 sulfur cluster binding in the enrichment analysis, which was strongly correlated with the development of ferroptosis. We found more infiltration of M2 macrophages and less Tregs infiltration in the asthma group compared to healthy controls. In addition, the expression levels of CISD1 and Tregs were negatively correlated. Through validation, we found that CAMKK2 and CISD1 expression were upregulated in the asthma group compared to the control group and would inhibit the occurrence of ferroptosis. Conclusion: CAMKK2 and CISD1 might inhibit ferroptosis and specifically regulate asthma. Moreover, CISD1 might be tied to the immunological microenvironment. Our results could be useful to provide potential immunotherapy targets and prognostic markers for asthma.

Quality of clinical practice guidelines relevant to rehabilitation of knee osteoarthritis: A systematic review.

OBJECTIVE: This systematic review summarized the rehabilitation recommendations for treating and managing knee osteoarthritis (OA) in practice guidelines and evaluated their applicability and quality using the Appraisal of Guidelines for Research and Evaluation (AGREE) II instrument. DATA SOURCES: PubMed, the Cochrane Library, EMBASE, CINAHL, PEDro, Guideline central, Guideline International Network and Agency for Healthcare Research and Quality (AHRQ) were used to search for relevant studies published between 1 January 2008 and 31 May 2022. METHODS: AGREE II was used to evaluate the included guidelines quality, SPSS 25.0 statistical software was used for data analysis, and the intra-group correlation coefficient value was calculated to verify the consistency between the raters. The two-way random effects model was used to calculate concordance scores, and each domain's total scores were calculated. Additionally, the median and interquartile range for domain and total scores were calculated. RESULTS: Twenty-four guidelines recommending knee OA rehabilitation were included. Inter-rater consistency evaluation ranged from 0.62 to 0.90. The domains where the guideline's overall and rehabilitation parts scored highest and lowest were scope and purpose (domain 1) and applicability (domain 5), respectively. The highly recommended rehabilitation opinions included aerobic exercise programs (21/24), weight control (16/24), self-education and management (16/24), gait/walking aids (7/24), and tai chi (6/24). However, the orthopedic insole and hot/cold therapy roles remain controversial. CONCLUSION: The clinical practice guidelines' overall quality for knee OA rehabilitation is good; however, the applicability is slightly poor. Therefore, we should improve the promoting factors and hindering factors, guideline application recommendations, tools, and resources when developing relevant guidelines.

EEG decoding method based on multi-feature information fusion for spinal cord injury.

To develop an efficient brain-computer interface (BCI) system, electroencephalography (EEG) measures neuronal activities in different brain regions through electrodes. Many EEG-based motor imagery (MI) studies do not make full use of brain network topology. In this paper, a deep learning framework based on a modified graph convolution neural network (M-GCN) is proposed, in which temporal-frequency processing is performed on the data through modified S-transform (MST) to improve the decoding performance of original EEG signals in different types of MI recognition. MST can be matched with the spatial position relationship of the electrodes. This method fusions multiple features in the temporal-frequency-spatial domain to further improve the recognition performance. By detecting the brain function characteristics of each specific rhythm, EEG generated by imaginary movement can be effectively analyzed to obtain the subjects' intention. Finally, the EEG signals of patients with spinal cord injury (SCI) are used to establish a correlation matrix containing EEG channel information, the M-GCN is employed to decode relation features. The proposed M-GCN framework has better performance than other existing methods. The accuracy of classifying and identifying MI tasks through the M-GCN method can reach 87.456%. After 10-fold cross-validation, the average accuracy rate is 87.442%, which verifies the reliability and stability of the proposed algorithm. Furthermore, the method provides effective rehabilitation training for patients with SCI to partially restore motor function.

Altered Brain Activity and Effective Connectivity within the Nonsensory Cortex during Stimulation of a Latent Myofascial Trigger Point.

Myofascial trigger point (MTrP), an iconic characteristic of myofascial pain syndrome (MPS), can induce cerebral cortex changes including altered cortical excitability and connectivity. The corresponding characteristically reactive cortex is still ambiguous. Seventeen participants with latent MTrPs underwent functional near-infrared spectroscopy (fNIRS) to collect cerebral oxygenation hemoglobin (Δ[oxy-Hb]) signals. The Δ[oxy-Hb] signals of the left/right prefrontal cortex (L/R PFC), left/right motor cortex (L/R MC), and left/right occipital lobe (L/R OL) of the subjects were measured using functional near-infrared spectroscopy (fNIRS) in the resting state, nonmyofascial trigger point (NMTrP), state and MTrP state. The data investigated the latent MTrP-induced changes in brain activity and effective connectivity (EC) within the nonsensory cortex. The parameter wavelet amplitude (WA) was used to describe cortical activation, EC to show brain network connectivity, and main coupling direction (mCD) to exhibit the dominant connectivity direction in different frequency bands. An increasing trend of WA and a decreasing trend of EC values were observed in the PFC. The interregional mCD was primarily shifted from a unidirectional to bidirectional connection, especially from PFC to MC or OL, when responding to manual stimulation during the MTrP state compared with resting state and NMTrP state in the intervals III, IV, and V. This study demonstrates that the nonsensory cortex PFC, MC, and OL can participate in the cortical reactions induced by stimulation of a latent MTrP. Additionally, the PFC shows nonnegligible higher activation and weakened regulation than other brain regions. Thus, the PFC may be responsible for the central cortical regulation of a latent MTrP. This trial is registered with ChiCTR2100048433.

Comorbid depressive symptoms can aggravate the functional changes of the pain matrix in patients with chronic back pain: A resting-state fMRI study.

Objective: The purposes of this study are to explore (1) whether comorbid depressive symptoms in patients with chronic back pain (CBP) affect the pain matrix. And (2) whether the interaction of depression and CBP exacerbates impaired brain function. Methods: Thirty-two patients with CBP without comorbid depressive symptoms and thirty patients with CBP with comorbid depressive symptoms were recruited. All subjects underwent functional magnetic resonance imaging (fMRI) scans. The graph theory analysis, mediation analysis, and functional connectivity (FC) analysis were included in this study. All subjects received the detection of clinical depressive symptoms and pain-related manifestations. Result: Compared with the CBP group, subjects in the CBP with comorbid depressive symptoms (CBP-D) group had significantly increased FC in the left medial prefrontal cortex and several parietal cortical regions. The results of the graph theory analyses showed that the area under the curve of small-world property (t = -2.175, p = 0.034), gamma (t = -2.332, p = 0.023), and local efficiency (t = -2.461, p = 0.017) in the CBP-D group were significantly lower. The nodal efficiency in the ventral posterior insula (VPI) (t = -3.581, p = 0.0007), and the network efficiency values (t = -2.758, p = 0.008) in the pain matrix were significantly lower in the CBP-D group. Both the topological properties and the FC values of these brain regions were significantly correlated with self-rating depression scale (SDS) scores (all FDR corrected) but not with pain intensity. Further mediation analyses demonstrated that pain intensity had a mediating effect on the relationship between SDS scores and Pain Disability Index scores. Likewise, the SDS scores mediated the relationship between pain intensity and PDI scores. Conclusion: Our study found that comorbid depressive symptoms can aggravate the impairment of pain matrix function of CBP, but this impairment cannot directly lead to the increase of pain intensity, which may be because some brain regions of the pain matrix are the common neural basis of depression and CBP.

Exercise Training for Mild Cognitive Impairment Adults Older Than 60: A Systematic Review and Meta-Analysis.

BACKGROUND: The prevalence of mild cognitive impairment (MCI) continues to increase due to population aging. Exercise has been a supporting health strategy that may elicit beneficial effects on cognitive function and prevent dementia. OBJECTIVE: This study aimed to examine the effects of aerobic, resistance, and multimodal exercise training on cognition in adults aged > 60 years with MCI. METHODS: We searched the Cochrane Library, PubMed, and Embase databases and ClinicalTrials.gov (https://clinicaltrials.gov) up to November 2021, with no language restrictions. We included all published randomized controlled trials (RCTs) comparing the effect of exercise programs on cognitive function with any other active intervention or no intervention in participants with MCI aged > 60 years. RESULTS: Twelve RCTs were included in this review. Meta-analysis results revealed significant improvements in resistance training on measures of executive function (p < 0.05) and attention (p < 0.05); no significant differences were observed between aerobic exercise and controls on any of the cognitive comparisons. CONCLUSION: Exercise training had a small beneficial effect on executive function and attention in older adults with MCI. Larger studies are required to examine the effects of exercise and the possible moderators.