Decreased left amygdala functional connectivity by cognitive-coping therapy in obsessive-compulsive disorder.

It is of great clinical importance to explore more efficacious treatments for OCD. Recently, cognitive-coping therapy (CCT), mainly focusing on recognizing and coping with a fear of negative events, has been reported as an efficacious psychotherapy. However, the underlying neurophysiological mechanism remains unknown. This study of 79 OCD patients collected Yale-Brown Obsessive Compulsive Scale (Y-BOCS) and resting-state functional magnetic resonance imaging (rs-fMRI) scans before and after four weeks of CCT, pharmacotherapy plus CCT (pCCT), or pharmacotherapy. Amygdala seed-based functional connectivity (FC) analysis was performed. Compared post- to pretreatment, pCCT-treated patients showed decreased left amygdala (LA) FC with the right anterior cingulate gyrus (cluster 1) and with the left paracentral lobule/the parietal lobe (cluster 2), while CCT-treated patients showed decreased LA-FC with the left middle occipital gyrus/the left superior parietal/left inferior parietal (cluster 3). The z-values of LA-FC with the three clusters were significantly lower after pCCT or CCT than pretreatment in comparisons of covert vs. overt and of non-remission vs. remission patients, except the z-value of cluster 2 in covert OCD. CCT and pCCT significantly reduced the Y-BOCS score. The reduction in the Y-BOCS score was positively correlated with the z-value of cluster 1. Our findings demonstrate that both pCCT and CCT with large effect sizes lowered LA-FC, indicating that FCs were involved in OCD. Additionally, decreased LA-FC with the anterior cingulate cortex (ACC) or paracentral/parietal cortex may be a marker for pCCT response or a marker for distinguishing OCD subtypes. Decreased LA-FC with the parietal region may be a common pathway of pCCT and CCT. Trial registration: ChiCTR-IPC-15005969.

Study on the Regulation Effect of Optogenetic Technology on LFP of the Basal Ganglia Nucleus in Rotenone-Treated Rats.

Background: Parkinson's disease (PD) is a common neurological degenerative disease that cannot be completely cured, although drugs can improve or alleviate its symptoms. Optogenetic technology, which stimulates or inhibits neurons with excellent spatial and temporal resolution, provides a new idea and approach for the precise treatment of Parkinson's disease. However, the neural mechanism of photogenetic regulation remains unclear. Objective: In this paper, we want to study the nonlinear features of EEG signals in the striatum and globus pallidus through optogenetic stimulation of the substantia nigra compact part. Methods: Rotenone was injected stereotactically into the substantia nigra compact area and ventral tegmental area of SD rats to construct rotenone-treated rats. Then, for the optogenetic manipulation, we injected adeno-associated virus expressing channelrhodopsin to stimulate the globus pallidus and the striatum with a 1 mW blue light and collected LFP signals before, during, and after light stimulation. Finally, the collected LFP signals were analyzed by using nonlinear dynamic algorithms. Results: After observing the behavior and brain morphology, 16 models were finally determined to be successful. LFP results showed that approximate entropy and fractal dimension of rats in the control group were significantly greater than those in the experimental group after light treatment (p < 0.05). The LFP nonlinear features in the globus pallidus and striatum of rotenone-treated rats showed significant statistical differences before and after light stimulation (p < 0.05). Conclusion: Optogenetic technology can regulate the characteristic value of LFP signals in rotenone-treated rats to a certain extent. Approximate entropy and fractal dimension algorithm can be used as an effective index to study LFP changes in rotenone-treated rats.

Effects of the COVID-19 Pandemic on Obsessive-Compulsive Symptoms Among University Students: Prospective Cohort Survey Study.

BACKGROUND: The COVID-19 pandemic is associated with common mental health problems. However, evidence for the association between fear of COVID-19 and obsessive-compulsive disorder (OCD) is limited. OBJECTIVE: This study aimed to examine if fear of negative events affects Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) scores in the context of a COVID-19-fear-invoking environment. METHODS: All participants were medical university students and voluntarily completed three surveys via smartphone or computer. Survey 1 was conducted on February 8, 2020, following a 2-week-long quarantine period without classes; survey 2 was conducted on March 25, 2020, when participants had been taking online courses for 2 weeks; and survey 3 was conducted on April 28, 2020, when no new cases had been reported for 2 weeks. The surveys comprised the Y-BOCS and the Zung Self-Rating Anxiety Scale (SAS); additional items included questions on demographics (age, gender, only child vs siblings, enrollment year, major), knowledge of COVID-19, and level of fear pertaining to COVID-19. RESULTS: In survey 1, 11.3% of participants (1519/13,478) scored ≥16 on the Y-BOCS (defined as possible OCD). In surveys 2 and 3, 3.6% (305/8162) and 3.5% (305/8511) of participants had scores indicative of possible OCD, respectively. The Y-BOCS score, anxiety level, quarantine level, and intensity of fear were significantly lower at surveys 2 and 3 than at survey 1 (P<.001 for all). Compared to those with a lower Y-BOCS score (<16), participants with possible OCD expressed greater intensity of fear and had higher SAS standard scores (P<.001). The regression linear analysis indicated that intensity of fear was positively correlated to the rate of possible OCD and the average total scores for the Y-BOCS in each survey (P<.001 for all). Multiple regressions showed that those with a higher intensity of fear, a higher anxiety level, of male gender, with sibling(s), and majoring in a nonmedicine discipline had a greater chance of having a higher Y-BOCS score in all surveys. These results were redemonstrated in the 5827 participants who completed both surveys 1 and 2 and in the 4006 participants who completed all three surveys. Furthermore, in matched participants, the Y-BOCS score was negatively correlated to changes in intensity of fear (r=0.74 for survey 2, P<.001; r=0.63 for survey 3, P=.006). CONCLUSIONS: Our findings indicate that fear of COVID-19 was associated with a greater Y-BOCS score, suggesting that an environment (COVID-19 pandemic) × psychology (fear and/or anxiety) interaction might be involved in OCD and that a fear of negative events might play a role in the etiology of OCD.

Investigating the Binding Mode of Reversible LSD1 Inhibitors Derived from Stilbene Derivatives by 3D-QSAR, Molecular Docking, and Molecular Dynamics Simulation.

Overexpression of lysine specific demethylase 1 (LSD1) has been found in many cancers. New anticancer drugs targeting LSD1 have been designed. The research on irreversible LSD1 inhibitors has entered the clinical stage, while the research on reversible LSD1 inhibitors has progressed slowly so far. In this study, 41 stilbene derivatives were studied as reversible inhibitors by three-dimensional quantitative structure-activity relationship (3D-QSAR). Comparative molecular field analysis (CoMFA q 2 = 0.623, r 2 = 0.987, r pred 2 = 0.857) and comparative molecular similarity indices analysis (CoMSIA q 2 = 0.728, r 2 = 0.960, r pred 2 = 0.899) were used to establish the model, and the structure-activity relationship of the compounds was explained by the contour maps. The binding site was predicted by two different kinds of software, and the binding modes of the compounds were further explored. A series of key amino acids Val288, Ser289, Gly314, Thr624, Lys661 were found to play a key role in the activity of the compounds. Molecular dynamics (MD) simulations were carried out for compounds 04, 17, 21, and 35, which had different activities. The reasons for the activity differences were explained by the interaction between compounds and LSD1. The binding free energy was calculated by molecular mechanics generalized Born surface area (MM/GBSA). We hope that this research will provide valuable information for the design of new reversible LSD1 inhibitors in the future.

Dense U-net Based on Patch-Based Learning for Retinal Vessel Segmentation.

Various retinal vessel segmentation methods based on convolutional neural networks were proposed recently, and Dense U-net as a new semantic segmentation network was successfully applied to scene segmentation. Retinal vessel is tiny, and the features of retinal vessel can be learned effectively by the patch-based learning strategy. In this study, we proposed a new retinal vessel segmentation framework based on Dense U-net and the patch-based learning strategy. In the process of training, training patches were obtained by random extraction strategy, Dense U-net was adopted as a training network, and random transformation was used as a data augmentation strategy. In the process of testing, test images were divided into image patches, test patches were predicted by training model, and the segmentation result can be reconstructed by overlapping-patches sequential reconstruction strategy. This proposed method was applied to public datasets DRIVE and STARE, and retinal vessel segmentation was performed. Sensitivity (Se), specificity (Sp), accuracy (Acc), and area under each curve (AUC) were adopted as evaluation metrics to verify the effectiveness of proposed method. Compared with state-of-the-art methods including the unsupervised, supervised, and convolutional neural network (CNN) methods, the result demonstrated that our approach is competitive in these evaluation metrics. This method can obtain a better segmentation result than specialists, and has clinical application value.

Design, Fabrication, Simulation and Characterization of a Novel Dual-Sided Microelectrode Array for Deep Brain Recording and Stimulation.

In this paper, a novel dual-sided microelectrode array is specially designed and fabricated for a rat Parkinson's disease (PD) model to study the mechanisms of deep brain stimulation (DBS). The fabricated microelectrode array can stimulate the subthalamic nucleus and simultaneously record electrophysiological information from multiple nuclei of the basal ganglia system. The fabricated microelectrode array has a long shaft of 9 mm and each planar surface is equipped with three stimulating sites (diameter of 100 µm), seven electrophysiological recording sites (diameter of 20 µm) and four sites with diameter of 50 µm used for neurotransmitter measurements in future work. The performances of the fabricated microelectrode array were characterized by scanning electron microscopy (SEM), electrochemical impedance spectroscopy (EIS) and cyclic voltammetry. In addition, the stimulating effects of the fabricated microelectrode were evaluated by finite element modeling (FEM). Preliminary animal experiments demonstrated that the designed microelectrode arrays can record spontaneous discharge signals from the striatum, the subthalamic nucleus and the globus pallidus interna. The designed and fabricated microelectrode arrays provide a powerful research tool for studying the mechanisms of DBS in rat PD models.

In Vivo Neural Recording and Electrochemical Performance of Microelectrode Arrays Modified by Rough-Surfaced AuPt Alloy Nanoparticles with Nanoporosity.

In order to reduce the impedance and improve in vivo neural recording performance of our developed Michigan type silicon electrodes, rough-surfaced AuPt alloy nanoparticles with nanoporosity were deposited on gold microelectrode sites through electro-co-deposition of Au-Pt-Cu alloy nanoparticles, followed by chemical dealloying Cu. The AuPt alloy nanoparticles modified gold microelectrode sites were characterized by scanning electron microscopy (SEM), electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV) and in vivo neural recording experiment. The SEM images showed that the prepared AuPt alloy nanoparticles exhibited cauliflower-like shapes and possessed very rough surfaces with many different sizes of pores. Average impedance of rough-surfaced AuPt alloy nanoparticles modified sites was 0.23 MΩ at 1 kHz, which was only 4.7% of that of bare gold microelectrode sites (4.9 MΩ), and corresponding in vitro background noise in the range of 1 Hz to 7500 Hz decreased to 7.5 µ V rms from 34.1 µ V rms at bare gold microelectrode sites. Spontaneous spike signal recording was used to evaluate in vivo neural recording performance of modified microelectrode sites, and results showed that rough-surfaced AuPt alloy nanoparticles modified microelectrode sites exhibited higher average spike signal-to-noise ratio (SNR) of 4.8 in lateral globus pallidus (GPe) due to lower background noise compared to control microelectrodes. Electro-co-deposition of Au-Pt-Cu alloy nanoparticles combined with chemical dealloying Cu was a convenient way for increasing the effective surface area of microelectrode sites, which could reduce electrode impedance and improve the quality of in vivo spike signal recording.

Electrochemical Co-Reduction Synthesis of AuPt Bimetallic Nanoparticles-Graphene Nanocomposites for Selective Detection of Dopamine in the Presence of Ascorbic Acid and Uric Acid.

In this paper, AuPt bimetallic nanoparticles-graphene nanocomposites were obtained by electrochemical co-reduction of graphene oxide (GO), HAuCl4 and H2PtCl6. The as-prepared AuPt bimetallic nanoparticles-graphene nanocomposites were characterized by scanning electron microscopy (SEM), electrochemical impedance spectroscopy (EIS) and other electrochemical methods. The morphology and composition of the nanocomposite could be easily controlled by adjusting the HAuCl4/H2PtCl6 concentration ratio. The electrochemical experiments showed that when the concentration ratio of HAuCl4/H2PtCl6 was 1:1, the obtained AuPt bimetallic nanoparticles-graphene nanocomposite (denoted as Au1Pt1NPs-GR) possessed the highest electrocatalytic activity toward dopamine (DA). As such, Au1Pt1NPs-GR nanocomposites were used to detect DA in the presence of ascorbic acid (AA) and uric acid (UA) using the differential pulse voltammetry (DPV) technique and on the modified electrode, there were three separate DPV oxidation peaks with the peak potential separations of 177 mV, 130 mV and 307 mV for DA and AA, DA and UA, AA and UA, respectively. The linear range of the constructed DA sensor was from 1.6 µM to 39.7 µM with a detection limit of 0.1 µM (S/N = 3). The obtained DA sensor with good stability, high reproducibility and excellent selectivity made it possible to detect DA in human urine samples.

Construction and validation of a deep learning prognostic model based on digital pathology images of stage III colorectal cancer.

BACKGROUND: TNM staging is the main reference standard for prognostic prediction of colorectal cancer (CRC), but the prognosis heterogeneity of patients with the same stage is still large. This study aimed to classify the tumor microenvironment of patients with stage III CRC and quantify the classified tumor tissues based on deep learning to explore the prognostic value of the developed tumor risk signature (TRS). METHODS: A tissue classification model was developed to identify nine tissues (adipose, background, debris, lymphocytes, mucus, smooth muscle, normal mucosa, stroma, and tumor) in whole-slide images (WSIs) of stage III CRC patients. This model was used to extract tumor tissues from WSIs of 265 stage III CRC patients from The Cancer Genome Atlas and 70 stage III CRC patients from the Sixth Affiliated Hospital of Sun Yat-sen University. We used three different deep learning models for tumor feature extraction and applied a Cox model to establish the TRS. Survival analysis was conducted to explore the prognostic performance of TRS. RESULTS: The tissue classification model achieved 94.4 % accuracy in identifying nine tissue types. The TRS showed a Harrell's concordance index of 0.736, 0.716, and 0.711 in the internal training, internal validation, and external validation sets. Survival analysis showed that TRS had significant predictive ability (hazard ratio: 3.632, p = 0.03) for prognostic prediction. CONCLUSION: The TRS is an independent and significant prognostic factor for PFS of stage III CRC patients and it contributes to risk stratification of patients with different clinical stages.

A gender recognition method based on EEG microstates.

BACKGROUND: Gender carries important information related to male and female characteristics, and a large number of studies have attempted to use physiological measurement methods for gender classification. Although previous studies have shown that there exist statistical differences in some Electroencephalographic (EEG) microstate parameters between males and females, it is still unknown that whether these microstate parameters can be used as potential biomarkers for gender classification based on machine learning. METHODS: We used two independent resting-state EEG datasets: the first dataset included 74 females and matched 74 males, and the second one included 42 males and matched 42 females. EEG microstate analysis based on modified k-means clustering method was applied, and temporal parameter and nonlinear characteristics (sample entropy and Lempel-Ziv complexity) of EEG microstate sequences were extracted to compare between males and females. More importantly, these microstate temporal parameters and complexity were tried to train six machine learning methods for gender classification. RESULTS: We obtained five common microstates for each dataset and each group. Compared with the male group, the female group has significantly higher temporal parameters of microstate B, C, E and lower temporal parameters of microstate A and D, and higher complexity of microstate sequence. When using combination of microstate temporal parameters and complexity or only microstate temporal parameters as classification features in an independent test set (the second dataset), we achieved 95.2% classification accuracy. CONCLUSION: Our research findings indicate that the dynamics of microstate have considerable Gender-specific alteration. EEG microstates can be used as neurophysiological biomarkers for gender classification.

A model for the diagnosis of anxiety in patients with epilepsy based on phase locking value and Lempel-Ziv complexity features of the electroencephalogram.

OBJECTIVE: Anxiety disorders (AD) are critical factors that significantly (about one-fifth) impact the quality of life (QoL) in patients with epilepsy (PWE). Objective diagnostic methods have contributed to the identification of PWE susceptible to AD. This study aimed to identify AD in PWE by constructing a diagnostic model based on the phase locking value (PLV) and Lempel-Ziv Complexity (LZC) features of the electroencephalogram (EEG). METHODS: EEG data from 131 patients with epilepsy (PWE) were enrolled in this study. Patients were divided into two groups, anxiety disorder (AD, n = 61) and non-anxiety disorder (NAD, n = 70), according to the Hamilton Rating Scale for Anxiety (HAM-A). Support vector machine (SVM) and K-Nearest-Neighbor(KNN) algorithms were used to construct three models - the PLVEEG, LZCEEG, and PLVEEG + LZCEEG feature models. Finally, the area under the receiver operating characteristic curve (AUC) and statistical analyses were performed to evaluate the model performance. RESULTS: The efficiency of the KNN-based PLCEEG + LZCEEG feature model was the best, and the accuracy, precision, recall, F1-score, and AUC of the model after five-fold cross-validations scores were 87.89 %, 82.27 %, 98.33 %, 88.95 %, and 0.89, respectively. When the model efficiency was optimal, 29 EEG features were suggested. Further analysis of these features indicated 22 EEG features that were significantly different between the two groups, including 50 % features of the alpha (α)-band. CONCLUSIONS: The PLVEEG + LZCEEG model features can identify AD in PWE. The PLVEEG and LZCEEG characteristics of the α-band may further be explored as potential biomarkers for AD in PWE.

Multi feature fusion network for schizophrenia classification and abnormal brain network recognition.

Schizophrenia classification and abnormal brain network recognition have an important research significance. Researchers have proposed many classification methods based on machine learning and deep learning. However, fewer studies utilized the advantages of complementary information from multi feature to learn the best representation of schizophrenia. In this study, we proposed a multi-feature fusion network (MFFN) using functional network connectivity (FNC) and time courses (TC) to distinguish schizophrenia patients from healthy controls. DNN backbone was adopted to learn the feature map of functional network connectivity, C-RNNAM backbone was designed to learn the feature map of time courses, and Deep SHAP was applied to obtain the most discriminative brain networks. We proved the effectiveness of this proposed model using the combining two public datasets and evaluated this model quantitatively using the evaluation indexes. The results showed that the functional network connectivity generated by independent component analysis has advantage in schizophrenia classification by comparing static and dynamic functional connections. This method obtained the best classification accuracy (ACC=87.30%, SPE=89.28%, SEN=85.71%, F1 =88.23%, and AUC=0.9081), and it demonstrated the superiority of this proposed model by comparing state-of-the-art methods. Ablation experiment also demonstrated that multi feature fusion and attention module can improve classification accuracy. The most discriminative brain networks showed that default mode network and visual network of schizophrenia patients have aberrant connections in brain networks. In conclusion, this method can identify schizophrenia effectively and visualize the abnormal brain network, and it has important clinical application value.

Combining blood pressure variability and heart rate variability to analyze the autonomic nervous function of rotenone induced Parkinson's rat model.

BACKGROUND: Parkinson's patients have significant autonomic dysfunction, early detect the disorder is a major challenge. To assess the autonomic function in the rat model of rotenone induced Parkinson's disease (PD), Blood pressure and ECG signal acquisition are very important. NEW METHOD: we used telemetry to record the electrocardiogram and blood pressure signals from awake rats, with linear and nonlinear analysis techniques calculate the heart rate variability (HRV) and blood pressure variability (BPV). we applied nonlinear analysis methods like sample entropy and detrended fluctuation analysis to analyze blood pressure signals. Particularly, this is the first attempt to apply nonlinear analysis to the blood pressure evaluate in rotenone induced PD model rat. RESULTS: HRV in the time and frequency domains indicated sympathetic-parasympathetic imbalance in PD model rats. Linear BPV analysis didn't reflect changes in vascular function and blood pressure regulation in PD model rats. Nonlinear analysis revealed differences in BPV, with lower sample entropy results and increased detrended fluctuation analysis results in the PD group rats. COMPARISON WITH EXISTING METHODS AND CONCLUSIONS: our experiments demonstrate the ability to evaluate autonomic dysfunction in models of Parkinson's disease by combining the analysis of BPV with HRV, consistent with autonomic impairment in PD patients. Nonlinear analysis by blood pressure signal may help in early detection of the PD. It indicates that the fluctuation of blood pressure in the rats in the rotenone model group tends to be regular and predictable, contributes to understand the PD pathophysiological mechanisms and to find strategies for early diagnosis.

Influencing factors of corticomuscular coherence in stroke patients.

Stroke, also known as cerebrovascular accident, is an acute cerebrovascular disease with a high incidence, disability rate, and mortality. It can disrupt the interaction between the cerebral cortex and external muscles. Corticomuscular coherence (CMC) is a common and useful method for studying how the cerebral cortex controls muscle activity. CMC can expose functional connections between the cortex and muscle, reflecting the information flow in the motor system. Afferent feedback related to CMC can reveal these functional connections. This paper aims to investigate the factors influencing CMC in stroke patients and provide a comprehensive summary and analysis of the current research in this area. This paper begins by discussing the impact of stroke and the significance of CMC in stroke patients. It then proceeds to elaborate on the mechanism of CMC and its defining formula. Next, the impacts of various factors on CMC in stroke patients were discussed individually. Lastly, this paper addresses current challenges and future prospects for CMC.

Construction of machine learning models for recognizing comorbid anxiety in epilepsy patients based on their clinical and quantitative EEG features.

BACKGROUND: This study aimed to construct prediction models for the recognizing of anxiety disorders (AD) in patients with epilepsy (PWEs) by combining clinical features with quantitative electroencephalogram (qEEG) features and using machine learning (ML). METHODS: Nineteen clinical features and 20-min resting-state EEG were collected from 71 PWEs comorbid with AD and another 60 PWEs without AD who met the inclusion-exclusion criteria of this study. The EEG were preprocessed and 684 Phase Locking Value (PLV) and 76 Lempel-Ziv Complexity (LZC) features on four bands were extracted. The Fisher score method was used to rank all the derived features. We constructed four models for recognizing AD in PWEs, whether PWEs based on different combinations of features using eXtreme gradient boosting (XGboost) and evaluated these models using the five-fold cross-validation method. RESULTS: The prediction model constructed by combining the clinical, PLV, and LZC features showed the best performance, with an accuracy of 96.18%, precision of 94.29%, sensitivity of 98.33%, F1-score of 96.06%, and Area Under the Curve (AUC) of 0.96. The Fisher score ranking results displayed that the top ten features were depression, educational attainment, α_P3LZC, α_T6-PzPLV, α_F7LZC, ß_Fp2-O1PLV, θ_T4-CzPLV, θ_F7-PzPLV, α_Fp2LZC, and θ_T4-PzPLV. CONCLUSIONS: The model, constructed by combining the clinical and qEEG features PLV and LZC, efficiently identified the presence of AD comorbidity in PWEs and might have the potential to complement the clinical diagnosis. Our findings suggest that LZC features in the α band and PLV features in Fp2-O1 may be potential biomarkers for diagnosing AD in PWEs.

The potential of the Lempel-Ziv complexity of the EEG in diagnosing cognitive impairment in patients with temporal lobe epilepsy.

AIM: To analyze whether the Lempel-Ziv Complexity (LZC) in quantitative electroencephalogram differs between the temporal lobe epilepsy (TLE) patients with or without cognitive impairment (CI) and explore the diagnostic value of LZC for identifying CI in TLE patients. METHODS: Twenty-two clinical features and 20-min EEG recordings were collected from 48 TLE patients with CI and 27 cognitively normal (CON) TLE patients. Seventy-six LZC features were calculated for 19 leads in four frequency bands (alpha, beta, delta, and theta). The clinical and LZC features were compared between the two groups. A support vector machine (SVM) was subsequently constructed using the leave-one-out method of cross-validation for LZC features with statistical differences. RESULTS: Regarding the clinical features, the level of education (p < .001), hippocampal atrophy and sclerosis (p = .029), and depression (p = .037) were statistically different between the two groups. For the LZC features, there were statistically significant differences in the alpha (Fp1, Fz, Cz, Pz, C3, C4, T3, T4, T5, T6, F3, F4, F7, F8, O1, and O2), beta (Fp2), and theta (F7) oscillations. The mean LZC in the alpha band was higher in the TLE-CI group than that in the CON group, and there were no differences in the remaining bands. The SVM model showed 74.51% accuracy, 79.63% sensitivity, 84.30% F1 score, 68.75% specificity, and .85 area under the curve scores. CONCLUSIONS: The LZC in the alpha band might have the potential to be used as a biomarker for the diagnosis of TLE combined with CI. The TLE-CI group, on the other hand, exhibited a higher degree of complexity in alpha oscillations, which were widespread and occurred in all brain regions.

Extensor digitorum communis compartments with a double compensation synergy strategy for constant force control of the index finger.

Precise sustained force control of the fingers is important for achieving flexible hand movements. However, how neuromuscular compartments within a forearm multi-tendon muscle cooperate to achieve constant finger force remains unclear. This study aimed to investigate the coordination strategies across multiple compartments of the extensor digitorum communis (EDC) during index finger sustained constant extension. Nine subjects performed index finger extensions of 15%, 30%, and 45% maximal voluntary contractions, respectively. High-density surface electromyography signals were recorded from the EDC and then analyzed using non-negative matrix decomposition to extract activation patterns and coefficient curves of EDC compartments. The results showed two activation patterns with stable structures during all tasks: one pattern corresponding to the index finger compartment was named master pattern; whereas the other corresponding to other compartments was named auxiliary pattern. Further, the intensity and stability of their coefficient curves were assessed using the root mean square value (RMS) and coefficient of variation (CV). The RMS and CV values of the master pattern increased and decreased with time, respectively, while the corresponding values of the auxiliary pattern were both negatively correlated with the formers. These findings suggested a special coordination strategy across EDC compartments during index finger constant extension, manifesting as two compensations of the auxiliary pattern for the intensity and stability of the master pattern. The proposed method provides new insight into the synergy strategy across multiple compartments within a forearm multi-tendon during sustained isometric contraction of a single finger and a new approach for constant force control of prosthetic hands.

Causal link between prefrontal cortex and EEG microstates: evidence from patients with prefrontal lesion.

Introduction: At present, elucidating the cortical origin of EEG microstates is a research hotspot in the field of EEG. Previous studies have suggested that the prefrontal cortex is closely related to EEG microstate C and D, but whether there is a causal link between the prefrontal cortex and microstate C or D remains unclear. Methods: In this study, pretrial EEG data were collected from ten patients with prefrontal lesions (mainly located in inferior and middle frontal gyrus) and fourteen matched healthy controls, and EEG microstate analysis was applied. Results: Our results showed that four classical EEG microstate topographies were obtained in both groups, but microstate C topography in patient group was obviously abnormal. Compared to healthy controls, the average coverage and occurrence of microstate C significantly reduced. In addition, the transition probability from microstate A to C and from microstate B to C in patient group was significantly lower than those of healthy controls. Discussion: The above results demonstrated that the damage of prefrontal cortex especially inferior and middle frontal gyrus could lead to abnormalities in the spatial distribution and temporal dynamics of microstate C not D, showing that there is a causal link between the inferior and middle frontal gyrus and the microstate C. The significance of our findings lies in providing new evidence for elucidating the cortical origin of microstate C.

An integrative prediction algorithm of drug-refractory epilepsy based on combined clinical-EEG functional connectivity features.

OBJECTIVE: Although the use of antiepileptic drugs (AEDs) is routine, 30-40% of patients with epilepsy (PWEs) experience drug resistance. Thus, early identification of AED resistance will help optimize treatment regimens and improve patients' prognoses. However, there have been few studies on this topic to date. Here, we try to establish an integrative prediction model of AED resistance for drug-naive PWEs, and to identify the clinical and Electroencephalogram (EEG) factors that affect their outcomes. METHODS: One hundred sixty-four PWEs naive to AEDs treated at a tertiary care center from January 2014 to June 2020 were retrospectively analyzed. A total of 113 of these patients were well controlled and 53 were drug refractory with regular AED treatment for more than one year. Eighty clinical characteristics and 684 EEG functional connectivity variables based on phase lag index before drug initiation were identified. Overall, 80% of each group was chosen to establish a support vector machine (SVM) model with ten-fold cross validation, and the other 20% were used to evaluate the model's performance. Absolute weight value was used to rank the features that had impacts on classification. RESULTS: An integrative algorithm was modeled to predict AED resistance for drug-naive PWEs by SVM based on clinical characteristics and EEG functional connectivity values. The model had an accuracy of 94% [95% confidence interval (CI) 0.85-1.0], sensitivity of 95% [95% CI 0.82-1.0], specificity of 93% [95% CI 0.77-1.0], and an area under the curve (AUC) of 0.98 [95% CI 0.91-1.0]. The p values of accuracy, sensitivity specificity and AUC were calculated as 0.001, 0.001, 0.01 and 0.001, respectively. The δ band fromT4-FZ and T3-PZ, α band from T3-T6 and ß band from F7-CZ and FP2-F3 were the top five EEG features that impacted the SVM classifier. CONCLUSION: We constructed an integrative prediction algorithm of AED resistance for drug-naive PWEs. Its utility in clinical settings should be evaluated in the future.

EEG microstate in first-episode drug-naive adolescents with depression.

A growing number of studies have revealed significant abnormalities in electroencephalography (EEG) microstate in patients with depression, but these findings may be affected by medication. Therefore, how the EEG microstates abnormally change in patients with depression in the early stage and without the influence of medication has not been investigated so far. Resting-state EEG data and Hamilton Depression Rating Scale (HDRS) were collected from 34 first-episode drug-naïve adolescent with depression and 34 matched healthy controls. EEG microstate analysis was applied and nonlinear characteristics of EEG microstate sequences were studied by sample entropy and Lempel-Ziv complexity (LZC). The microstate temporal parameters and complexity were tried to train an SVM for classification of patients with depression. Four typical EEG microstate topographies were obtained in both groups, but microstate C topography was significantly abnormal in depression patients. The duration of microstate B, C, D and the occurrence and coverage of microstate B significantly increased, the occurrence and coverage of microstate A, C reduced significantly in depression group. Sample entropy and LZC in the depression group were abnormally increased and were negatively correlated with HDRS. When the combination of EEG microstate temporal parameters and complexity of microstate sequence was used to classify patients with depression from healthy controls, a classification accuracy of 90.9% was obtained. Abnormal EEG microstate has appeared in early depression, reflecting an underlying abnormality in configuring neural resources and transitions between distinct brain network states. EEG microstate can be used as a neurophysiological biomarker for early auxiliary diagnosis of depression.