Generalized sleep decoding with basal ganglia signals in multiple movement disorders.

Sleep disturbances profoundly affect the quality of life in individuals with neurological disorders. Closed-loop deep brain stimulation (DBS) holds promise for alleviating sleep symptoms, however, this technique necessitates automated sleep stage decoding from intracranial signals. We leveraged overnight data from 121 patients with movement disorders (Parkinson's disease, Essential Tremor, Dystonia, Essential Tremor, Huntington's disease, and Tourette's syndrome) in whom synchronized polysomnograms and basal ganglia local field potentials were recorded, to develop a generalized, multi-class, sleep specific decoder - BGOOSE. This generalized model achieved 85% average accuracy across patients and across disease conditions, even in the presence of recordings from different basal ganglia targets. Furthermore, we also investigated the role of electrocorticography on decoding performances and proposed an optimal decoding map, which was shown to facilitate channel selection for optimal model performances. BGOOSE emerges as a powerful tool for generalized sleep decoding, offering exciting potentials for the precision stimulation delivery of DBS and better management of sleep disturbances in movement disorders.

Predicting postoperative prognosis of pancreatic cancer using a computed tomography-based radio-clinical model: exploring biologic functions.

Computed tomography (CT) imaging has the potential to assist in predicting the prognosis and treatment strategies for pancreatic cancer (PC). This study aimed to develop and validate a radio-clinical model based on preoperative multiphase CT assessments to predict the overall survival (OS) of PC and identify differentially expressed genes associated with OS. METHODS: Patients with PC who had undergone radical pancreatectomy (R0 resection) were divided into development and external validation sets. Independent predictors of OS were identified using Cox regression analyses and included in the nomogram, which was externally validated. The area under the curve was used to measure the model's accuracy in estimating OS probability. RNA sequencing data from The Cancer Genome Atlas were used for gene expression analysis. RESULTS: In the development and external validation sets, survival was estimated respectively for 132 and 27 patients. Multivariate Cox regression analysis identified 5 independent OS predictors: age (P = .049), sex (P = .001), bilirubin level (P = .005), tumor size (P = .020), and venous invasion (P = .041). These variables were incorporated into the nomogram. Patients were divided into high- and low-risk groups for OS and survival curves showed that all patients in the low-risk group had better OS than that of those in the high-risk group (P < .001). Differentially expressed genes in patients with a poor prognosis were involved in neuroactive ligand-receptor interaction. CONCLUSION: The radio-clinical model may be clinically useful for successfully predicting PC prognosis.

Lanthanum-modified tobermorite synthesized from fly ash for efficient phosphate removal.

Phosphate removal from water by lanthanum-modified tobermorite synthesized from fly ash (LTFA) with different lanthanum concentrations was studied. LTFA samples were characterized by X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, and Brunauer‒Emmett‒Teller specific surface area analysis. The results showed that the LTFA samples were mainly composed of mesoporous tobermorite-11 Å, and LTFA1 with a lanthanum concentration of 0.15 M had a high specific surface area (83.82 m2/g) and pore volume (0.6778 cm3/g). The phosphate adsorption capacities of LTFA samples were highest at pH 3 and gradually decreased with increasing pH. The phosphate adsorption kinetics data on LTFA samples were most accurately described by the Elovich model. The adsorption isotherms were in the strongest agreement with the Temkin model, and LTFA1 showed the highest phosphate adsorption capacity (282.51 mg P/g), which was higher than that of most other lanthanum-modified adsorbents. LTFA1 presented highly selective adsorption of phosphate with other coexisting ions (HCO3-, Cl-, SO42-, and NO3-). In addition, phosphate was adsorbed onto LTFA samples by forming inner-sphere phosphate complexes and amorphous lanthanum phosphate. This study provides technical support for development of efficient fly ash-based phosphate adsorbents.

Contribution of basal ganglia activity to REM sleep disorder in Parkinson's disease.

BACKGROUND: Rapid eye movement (REM) sleep behaviour disorder (RBD) is one of the most common sleep problems and represents a key prodromal marker in Parkinson's disease (PD). It remains unclear whether and how basal ganglia nuclei, structures that are directly involved in the pathology of PD, are implicated in the occurrence of RBD. METHOD: Here, in parallel with whole-night video polysomnography, we recorded local field potentials from two major basal ganglia structures, the globus pallidus internus and subthalamic nucleus, in two cohorts of patients with PD who had varied severity of RBD. Basal ganglia oscillatory patterns during RBD and REM sleep without atonia were analysed and compared with another age-matched cohort of patients with dystonia that served as controls. RESULTS: We found that beta power in both basal ganglia nuclei was specifically elevated during REM sleep without atonia in patients with PD, but not in dystonia. Basal ganglia beta power during REM sleep positively correlated with the extent of atonia loss, with beta elevation preceding the activation of chin electromyogram activities by ~200 ms. The connectivity between basal ganglia beta power and chin muscular activities during REM sleep was significantly correlated with the clinical severity of RBD in PD. CONCLUSIONS: These findings support that basal ganglia activities are associated with if not directly contribute to the occurrence of RBD in PD. Our study expands the understanding of the role basal ganglia played in RBD and may foster improved therapies for RBD by interrupting the basal ganglia-muscular communication during REM sleep in PD.

Relationship between the College Student and the Campus Club: An Evolutionary Game Theory Analysis.

In this paper, we use an evolutionary game theory approach to build a relationship model of students and clubs for the purpose of improving student enthusiasm for participating in club activities. First, the process of the model building is introduced, which mainly includes the basic assumptions and the equilibrium point stability analysis. Based on this analysis, we find that the motivation adjustment of students and clubs is a dynamic process and that unilateral efforts alone cannot achieve an ideal result. Then, we use real data from Yanshan University to evaluate the model, the results of which indicate that the model can analyze the relationship between students and clubs effectively. Finally, we provide relevant suggestions based on the model established in this study, whereby we contribute a theoretical basis and practical guidance for how students can actively participate in clubs, as well as how clubs can better develop themselves.

Temporal interference stimulation targets deep primate brain.

Temporal interference (TI) stimulation, a novel non-invasive stimulation strategy, has recently been shown to modulate neural activity in deep brain regions of living mice. Yet, it is uncertain if this method is applicable to larger brains and whether the electric field produced under traditional safety currents can penetrate deep regions as observed in mice. Despite recent model-based simulation studies offering positive evidence at both macro- and micro-scale levels, the absence of electrophysiological data from actual brains hinders comprehensive understanding and potential application of TI. This study aims to directly measure the spatiotemporal properties of the interfered electric field in the rhesus monkey brain and to validate the effects of TI on the human brain. Two monkeys were involved in the measurement, with implantation of several stereo-electroencephalography (SEEG) depth electrodes. TI stimulation was applied to anesthetized monkeys using two pairs of surface electrodes at differing stimulation parameters. Model-based simulations were also conducted and subsequently compared with actual recordings. Additionally, TI stimulation was administered to patients with motor disorders to validate its effects on motor symptoms. Through the integration of computational electric field simulation with empirical measurements, it was determined that the temporally interfering electric fields in the deep central regions are capable of attaining a magnitude sufficient to induce a subthreshold modulation effect on neural signals. Additionally, an improvement in movement disorders was observed as a result of TI stimulation. This study is the first to systematically measure the TI electric field in living non-human primates, offering empirical evidence that TI holds promise as a more focal and precise method for modulating neural activities in deep regions of a large brain. This advancement paves the way for future applications of TI in treating neuropsychiatric disorders.

Subthalamic nucleus deep brain stimulation alleviates oxidative stress via mitophagy in Parkinson's disease.

Subthalamic nucleus deep brain stimulation (STN-DBS) has the potential to delay Parkinson's disease (PD) progression. Whether oxidative stress participates in the neuroprotective effects of DBS and related signaling pathways remains unknown. To address this, we applied STN-DBS to mice and monkey models of PD and collected brain tissue to evaluate mitophagy, oxidative stress, and related pathway. To confirm findings in animal experiments, a cohort of PD patients was recruited and oxidative stress was evaluated in cerebrospinal fluid. When PD mice received STN stimulation, the mTOR pathway was suppressed, accompanied by elevated LC3 II expression, increased mitophagosomes, and a decrease in p62 expression. The increase in mitophagy and balance of mitochondrial fission/fusion dynamics in the substantia nigra caused a marked enhancement of the antioxidant enzymes superoxide dismutase and glutathione levels. Subsequently, fewer mitochondrial apoptogenic factors were released to the cytoplasm, which resulted in a suppression of caspase activation and reservation of dopaminergic neurons. While interfaced with an mTOR activator, oxidative stress was no longer regulated by STN-DBS, with no neuroprotective effect. Similar results to those found in the rodent experiments were obtained in monkeys treated with chronic STN stimulation. Moreover, antioxidant enzymes in PD patients were increased after the operation, however, there was no relation between changes in antioxidant enzymes and motor impairment. Collectively, our study found that STN-DBS was able to increase mitophagy via an mTOR-dependent pathway, and oxidative stress was suppressed due to removal of damaged mitochondria, which was attributed to the dopaminergic neuroprotection of STN-DBS in PD.

Efficacy and safety of combined deep brain stimulation with capsulotomy for comorbid motor and psychiatric symptoms in Tourette's syndrome: Experience and evidence.

OBJECTIVES: To evaluate the efficacy and safety of combined deep brain stimulation (DBS) with capsulotomy for comorbid motor and psychiatric symptoms in patients with Tourette's syndrome (TS). METHODS: This retrospective cohort study consecutively enrolled TS patients with comorbid motor and psychiatric symptoms who were treated with combined DBS and anterior capsulotomy at our center. Longitudinal motor, psychiatric, and cognitive outcomes and quality of life were assessed. In addition, a systematic review and meta-analysis were performed to summarize the current experience with the available evidence. RESULTS: In total, 5 eligible patients in our cohort and 26 summarized patients in 6 cohorts were included. After a mean 18-month follow-up, our cohort reported that motor symptoms significantly improved by 62.4 % (P = 0.005); psychiatric symptoms of obsessive-compulsive disorder (OCD) and anxiety significantly improved by 87.7 % (P < 0.001) and 78.4 % (P = 0.009); quality of life significantly improved by 61.9 % (P = 0.011); and no significant difference was found in cognitive function (all P > 0.05). Combined surgery resulted in greater improvements in psychiatric outcomes and quality of life than DBS alone. The synthesized findings suggested significant improvements in tics (MD: 57.92, 95 % CI: 41.28-74.56, P < 0.001), OCD (MD: 21.91, 95 % CI: 18.67-25.15, P < 0.001), depression (MD: 18.32, 95 % CI: 13.26-23.38, P < 0.001), anxiety (MD: 13.83, 95 % CI: 11.90-15.76, P < 0.001), and quality of life (MD: 48.22, 95 % CI: 43.68-52.77, P < 0.001). Individual analysis revealed that the pooled treatment effects on motor symptoms, psychiatric symptoms, and quality of life were 78.6 %, 84.5-87.9 %, and 83.0 %, respectively. The overall pooled rate of adverse events was 50.0 %, and all of these adverse events were resolved or alleviated with favorable outcomes. CONCLUSIONS: Combined DBS with capsulotomy is effective for relieving motor and psychiatric symptoms in TS patients, and its safety is acceptable. However, the optimal candidate should be considered, and additional experience is still necessary.

The Related Factors and Effect of Electrode Displacement on Motor Outcome of Subthalamic Nuclei Deep Brain Stimulation in Parkinson's Disease.

BACKGROUND: Previous studies have revealed the existence of electrode displacement during subthalamic nucleus deep brain stimulation (STN-DBS). However, the effect of electrode displacement on treatment outcomes is still unclear. In this study, we aimed to analyze the related factors of electrode displacement and assess postoperative electrode displacement in relation to the motor outcomes of STN-DBS. METHODS: A total of 88 patients aged 62.73 ± 6.35 years (55 males and 33 females) with Parkinson's disease undergoing STN-DBS, with comprehensive clinical characterization before and 1 month after surgery, were involved retrospectively and divided into a cross-incision group and cannula puncture group according to different dura opening methods. The electrode displacement, unilateral pneumocephalus volume percent (uPVP), and brain volume percent were estimated. RESULTS: A significant anterior and lateral electrode displacement was observed among all implanted electrodes after pneumocephalus absorption (p < 0.0001). The degree of electrode displacement was positively correlated with the uPVP (p = 0.005) and smaller in females than males (p = 0.0384). Electrode displacement was negatively correlated with motor improvement following STN-DBS in both on-medication and off-medication conditions (p < 0.05). Dural puncture reduced the uPVP (p < 0.0001) and postoperative electrode displacement (p = 0.0086) compared with dural incision. CONCLUSIONS: Electrode displacement had a negative impact on the therapeutic efficacy of STN-DBS. Opening the dura via cannula puncture is recommended to increase the accuracy of the lead implantation.

Invasive neurophysiology and whole brain connectomics for neural decoding in patients with brain implants.

Brain computer interfaces (BCI) provide unprecedented spatiotemporal precision that will enable significant expansion in how numerous brain disorders are treated. Decoding dynamic patient states from brain signals with machine learning is required to leverage this precision, but a standardized framework for identifying and advancing novel clinical BCI approaches does not exist. Here, we developed a platform that integrates brain signal decoding with connectomics and demonstrate its utility across 123 hours of invasively recorded brain data from 73 neurosurgical patients treated for movement disorders, depression and epilepsy. First, we introduce connectomics-informed movement decoders that generalize across cohorts with Parkinson's disease and epilepsy from the US, Europe and China. Next, we reveal network targets for emotion decoding in left prefrontal and cingulate circuits in DBS patients with major depression. Finally, we showcase opportunities to improve seizure detection in responsive neurostimulation for epilepsy. Our platform provides rapid, high-accuracy decoding for precision medicine approaches that can dynamically adapt neuromodulation therapies in response to the individual needs of patients.

Pathological pallidal beta activity in Parkinson's disease is sustained during sleep and associated with sleep disturbance.

Parkinson's disease (PD) is associated with excessive beta activity in the basal ganglia. Brain sensing implants aim to leverage this biomarker for demand-dependent adaptive stimulation. Sleep disturbance is among the most common non-motor symptoms in PD, but its relationship with beta activity is unknown. To investigate the clinical potential of beta activity as a biomarker for sleep quality in PD, we recorded pallidal local field potentials during polysomnography in PD patients off dopaminergic medication and compared the results to dystonia patients. PD patients exhibited sustained and elevated beta activity across wakefulness, rapid eye movement (REM), and non-REM sleep, which was correlated with sleep disturbance. Simulation of adaptive stimulation revealed that sleep-related beta activity changes remain unaccounted for by current algorithms, with potential negative outcomes in sleep quality and overall quality of life for patients.

The clinical application of neuro-robot in the resection of epileptic foci: a novel method assisting epilepsy surgery.

During surgery for foci-related epilepsy, neurosurgeons face significant difficulties in identifying and resecting MRI-negative or deep-seated epileptic foci. Here, we present a neuro-robotic navigation system that is specifically designed for resection of MRI negative epileptic foci. We recruited 52 epileptic patients, and randomly assigned them to treatment group with either neuro-robotic navigation or conventional neuronavigation system. For each patient, in the neuro-robotic navigation group, we integrated multimodality imaging including MRI and PET-CT into the robotic workstation and marked the boundary of foci from the fused image. During surgery, this boundary was delineated by the robotic laser device with high accuracy, guiding resection for the surgeon. For deeply seated foci, we exploited the neuro-robotic navigation system to localize the deepest point with biopsy needle insertion and methylene dye application to locate the boundary of the foci. Our results show that, compared with the conventional neuronavigation, the neuro-robotic navigation system performs equally well in MRI positive epilepsy patients (ENGEL I ratio: 71.4% vs 100%, p = 0.255) systems and show better performance in patients with MRI-negative focal cortical dysplasia (ENGEL I ratio: 88.2% vs 50%, p = 0.0439). At present, there are no documented neurosurgery robots with similar function and application in the field of epilepsy. Our research highlights the added value of using neuro-robotic navigation systems in resection surgery for epilepsy, particularly in cases that involve MRI-negative or deep-seated epileptic foci.

Organic Anion Transporting Polypeptide (OATP) 1B3 is a Significant Transporter for Hepatic Uptake of Conjugated Bile Acids in Humans.

BACKGROUND & AIMS: OATP1B3/SLCO1B3 is a human liver-specific transporter for the clearance of endogenous compounds (eg, bile acid [BA]) and xenobiotics. The functional role of OATP1B3 in humans has not been characterized, as SLCO1B3 is poorly conserved among species without mouse orthologs. METHODS: Slc10a1-knockout (Slc10a1-/-), Slc10a1hSLCO1B3 (endogenous mouse Slc10a1 promoter-driven human-SLCO1B3 expression in Slc10a1-/- mice), and human SLCO1B3 liver-specific transgenic (hSLCO1B3-LTG) mice were generated and challenged with 0.1% ursodeoxycholic-acid (UDCA), 1% cholic-acid (CA) diet, or bile duct ligation (BDL) for functional studies. Primary hepatocytes and hepatoma-PLC/RPF/5 cells were used for mechanistic studies. RESULTS: Serum BA levels in Slc10a1-/- mice were substantially increased with or without 0.1% UDCA feeding compared with wild-type (WT) mice. This increase was attenuated in Slc10a1hSLCO1B3-mice, indicating that OATP1B3 functions as a significant hepatic BA uptake transporter. In vitro assay using primary hepatocytes from WT, Slc10a1-/-, and Slc10a1hSLCO1B3-mice indicated that OATP1B3 has a similar capacity in taking up taurocholate/TCA as Ntcp. Furthermore, TCA-induced bile flow was significantly impaired in Slc10a1-/- mice but partially recovered in Slc10a1hSLC01B3-mice, indicating that OATP1B3 can partially compensate the NTCP function in vivo. Liver-specific overexpression of OATP1B3 markedly increased the level of hepatic conjugated BA and cholestatic liver injury in 1% CA-fed and BDL mice. Mechanistic studies revealed that conjugated BAs stimulated Ccl2 and Cxcl2 in hepatocytes to increase hepatic neutrophil infiltration and proinflammatory cytokine production (eg, IL-6), which activated STAT3 to repress OATP1B3 expression by binding to its promoter. CONCLUSIONS: Human OATP1B3 is a significant BA uptake transporter and can partially compensate Ntcp for conjugated BA uptake in mice. Its downregulation in cholestasis is an adaptive protective response.

Pallidal activities during sleep and sleep decoding in dystonia, Huntington's, and Parkinson's disease.

BACKGROUND: Sleep disturbances are highly prevalent in movement disorders, potentially due to the malfunctioning of basal ganglia structures. Pallidal deep brain stimulation (DBS) has been widely used for multiple movement disorders and been reported to improve sleep. We aimed to investigate the oscillatory pattern of pallidum during sleep and explore whether pallidal activities can be utilized to differentiate sleep stages, which could pave the way for sleep-aware adaptive DBS. METHODS: We directly recorded over 500 h of pallidal local field potentials during sleep from 39 subjects with movement disorders (20 dystonia, 8 Huntington's disease, and 11 Parkinson's disease). Pallidal spectrum and cortical-pallidal coherence were computed and compared across sleep stages. Machine learning approaches were utilized to build sleep decoders for different diseases to classify sleep stages through pallidal oscillatory features. Decoding accuracy was further associated with the spatial localization of the pallidum. RESULTS: Pallidal power spectra and cortical-pallidal coherence were significantly modulated by sleep-stage transitions in three movement disorders. Differences in sleep-related activities between diseases were identified in non-rapid eye movement (NREM) and REM sleep. Machine learning models using pallidal oscillatory features can decode sleep-wake states with over 90% accuracy. Decoding accuracies were higher in recording sites within the internus-pallidum than the external-pallidum, and can be precited using structural (P < 0.0001) and functional (P < 0.0001) whole-brain neuroimaging connectomics. CONCLUSION: Our findings revealed strong sleep-stage dependent distinctions in pallidal oscillations in multiple movement disorders. Pallidal oscillatory features were sufficient for sleep stage decoding. These data may facilitate the development of adaptive DBS systems targeting sleep problems that have broad translational prospects.

Sleep outcomes and related factors in Parkinson's disease after subthalamic deep brain electrode implantation: a retrospective cohort study.

Background: Subthalamic nucleus deep brain stimulation (STN-DBS) improves sleep qualities in Parkinson's disease (PD) patients; however, it remains elusive whether STN-DBS improves sleep by directly influencing the sleep circuit or alleviates other cardinal symptoms such as motor functions, other confounding factors including stimulation intensity may also involve. Studying the effect of microlesion effect (MLE) on sleep after STN-DBS electrode implantation may address this issue. Objective: To examine the influence of MLE on sleep quality and related factors in PD, as well as the effects of regional and lateral specific correlations with sleep outcomes after STN-DBS electrode implantation. Study Design: Case-control study; Level of evidence, 3. Data Sources and Methods: In 78 PD patients who underwent bilateral STN-DBS surgery in our center, we compared the sleep qualities, motor performances, anti-Parkinsonian drug dosage, and emotional conditions at preoperative baseline and postoperative 1-month follow-up. We determined the related factors of sleep outcomes and visualized the electrodes position, simulated the MLE-engendered volume of tissue lesioned (VTL), and investigated sleep-related sweet/sour spots and laterality in STN. Results: MLE improves sleep quality with Pittsburgh Sleep Quality Index (PSQI) by 13.36% and Parkinson's Disease Sleep Scale-2 (PDSS-2) by 17.95%. Motor (P = 0.014) and emotional (P = 0.001) improvements were both positively correlated with sleep improvements. However, MLE in STN associative subregions, as an independent factor, may cause sleep deterioration (r = 0.348, P = 0.002), and only the left STN showed significance (r = 0.327, P = 0.004). Sweet spot analysis also indicated part of the left STN associative subregion is the sour spot indicative of sleep deterioration. Conclusion: The MLE of STN-DBS can overall improve sleep quality in PD patients, with a positive correlation between motor and emotional improvements. However, independent of all other factors, the MLE in the STN associative subregion, particularly the left side, may cause sleep deterioration.

Closed-Loop Adaptive Deep Brain Stimulation in Parkinson's Disease: Procedures to Achieve It and Future Perspectives.

Parkinson's disease (PD) is a neurodegenerative disease with a heavy burden on patients, families, and society. Deep brain stimulation (DBS) can improve the symptoms of PD patients for whom medication is insufficient. However, current open-loop uninterrupted conventional DBS (cDBS) has inherent limitations, such as adverse effects, rapid battery consumption, and a need for frequent parameter adjustment. To overcome these shortcomings, adaptive DBS (aDBS) was proposed to provide responsive optimized stimulation for PD. This topic has attracted scientific interest, and a growing body of preclinical and clinical evidence has shown its benefits. However, both achievements and challenges have emerged in this novel field. To date, only limited reviews comprehensively analyzed the full framework and procedures for aDBS implementation. Herein, we review current preclinical and clinical data on aDBS for PD to discuss the full procedures for its achievement and to provide future perspectives on this treatment.

Online Machine Vision-Based Modeling during Cantaloupe Microwave Drying Utilizing Extreme Learning Machine and Artificial Neural Network.

Online microwave drying process monitoring has been challenging due to the incompatibility of metal components with microwaves. This paper developed a microwave drying system based on online machine vision, which realized real-time extraction and measurement of images, weight, and temperature. An image-processing algorithm was developed to capture material shrinkage characteristics in real time. Constant-temperature microwave drying experiments were conducted, and the artificial neural network (ANN) and extreme learning machine (ELM) were utilized to model and predict the moisture content of materials during the drying process based on the degree of material shrinkage. The results demonstrated that the system and algorithm operated effectively, and ELM provided superior predictive performance and learning efficiency compared to ANN.

Association between Cognitive Impairment and Freezing of Gait in Patients with Parkinson's Disease.

Background: Freezing of gait (FOG) is a common disabling symptom in Parkinson's disease (PD). Cognitive impairment may contribute to FOG. Nevertheless, their correlations remain controversial. We aimed to investigate cognitive differences between PD patients with and without FOG (nFOG), explore correlations between FOG severity and cognitive performance and assess cognitive heterogeneity within the FOG patients. Methods: Seventy-four PD patients (41 FOG, 33 nFOG) and 32 healthy controls (HCs) were included. Comprehensive neuropsychological assessments testing cognitive domains of global cognition, executive function/attention, working memory, and visuospatial function were performed. Cognitive performance was compared between groups using independent t-test and ANCOVA adjusting for age, sex, education, disease duration and motor symptoms. The k-means cluster analysis was used to explore cognitive heterogeneity within the FOG group. Correlation between FOG severity and cognition were analyzed using partial correlations. Results: FOG patients showed significantly poorer performance in global cognition (MoCA, p < 0.001), frontal lobe function (FAB, p = 0.015), attention and working memory (SDMT, p < 0.001) and executive function (SIE, p = 0.038) than nFOG patients. The FOG group was divided into two clusters using the cluster analysis, of which cluster 1 exhibited worse cognition, and with older age, lower improvement rate, higher FOGQ3 score, and higher proportion of levodopa-unresponsive FOG than cluster 2. Further, in the FOG group, cognition was significantly correlated with FOG severity in MoCA (r = -0.382, p = 0.021), Stroop-C (r = 0.362, p = 0.030) and SIE (r = 0.369, p = 0.027). Conclusions: This study demonstrated that the cognitive impairments of FOG were mainly reflected by global cognition, frontal lobe function, executive function, attention and working memory. There may be heterogeneity in the cognitive impairment of FOG patients. Additionally, executive function was significantly correlated with FOG severity.

Fronto-parieto-subthalamic activity decodes motor status in Parkinson's disease.

AIMS: Patients with Parkinson's disease (PD) have various motor difficulties, including standing up, gait initiation and freezing of gait. These abnormalities are associated with cortico-subthalamic dysfunction. We aimed to reveal the characteristics of cortico-subthalamic activity in PD patients during different motor statuses. METHODS: Potentials were recorded in the superior parietal lobule (SPL), the primary motor cortex (M1), premotor cortex (PMC), and the bilateral subthalamic nucleus (STN) in 18 freely walking patients while sitting, standing, walking, dual-task walking, and freezing in medication "off" (Moff) and "on" (Mon) states. Different motor status activities were compared in band power, and a machine learning classifier was used to differentiate the motor statuses. RESULTS: SPL beta power was specifically inhibited from standing to walking, and negatively correlated with walking speed; M1 beta power reflected the degree of rigidity and was reversed by medication; XGBoost algorithm classified the five motor statuses with acceptable accuracy (68.77% in Moff, 60.58% in Mon). SPL beta power ranked highest in feature importance in both Moff and Mon states. CONCLUSION: SPL beta power plays an essential role in walking status classification and could be a physiological biomarker for walking speed, which would aid the development of adaptive DBS.

Estimating the Moisture Ratio Model of Cantaloupe Slices by Maximum Likelihood Principle-Based Algorithms.

As an agricultural plant, the cantaloupe contains rich nutrition and high moisture content. In this paper, the estimation problem of the moisture ratio model during a cantaloupe microwave drying process was considered. First of all, an image processing-based cantaloupe drying system was designed and the expression of the moisture ratio with regard to the shrinkage was built. Secondly, a maximum likelihood principle-based iterative evolution (MLP-IE) algorithm was put forward to estimate the moisture ratio model. After that, aiming at enhancing the model fitting ability of the MLP-IE algorithm, a maximum likelihood principle-based improved iterative evolution (MLP-I-IE) algorithm was proposed by designing the improved mutation strategy, the improved scaling factor, and the improved crossover rate. Finally, the MLP-IE algorithm and MLP-I-IE algorithm were applied for estimating the moisture ratio model of cantaloupe slices. The results showed that both the MLP-IE algorithm and MLP-I-IE algorithm were effective and that the MLP-I-IE algorithm performed better than the MLP-IE algorithm in model estimation and validation.