Preliminary Network Centric Therapy for Machine Learning Classification of Deep Brain Stimulation Status for the Treatment of Parkinson’s Disease with a Conformal Wearable and Wireless Inertial Sensor

The concept of Network Centric Therapy represents an amalgamation of wearable and wireless inertial sensor systems and machine learning with access to a Cloud computing environment. The advent of Network Centric Therapy is highly relevant to the treatment of Parkinson’s disease through deep brain stimulation. Originally wearable and wireless systems for quantifying Parkinson’s disease involved the use a smartphone to quantify hand tremor. Although originally novel, the smartphone has notable issues as a wearable application for quantifying movement disorder tremor. The smartphone has evolved in a pathway that has made the smartphone progressively more cumbersome to mount about the dorsum of the hand. Furthermore, the smartphone utilizes an inertial sensor package that is not certified for medical analysis, and the trial data access a provisional Cloud computing environment through an email account. These concerns are resolved with the recent development of a conformal wearable and wireless inertial sensor system. This conformal wearable and wireless system mounts to the hand with the profile of a bandage by adhesive and accesses a secure Cloud computing environment through a segmented wireless connectivity strategy involving a smartphone and tablet. Additionally, the conformal wearable and wireless system is certified by the FDA of the United States of America for ascertaining medical grade inertial sensor data. These characteristics make the conformal wearable and wireless system uniquely suited for the quantification of Parkinson’s disease treatment through deep brain stimulation. Preliminary evaluation of the conformal wearable and wireless system is demonstrated through the differentiation of deep brain stimulation set to “On” and “Off” status. Based on the robustness of the acceleration signal, this signal was selected to quantify hand tremor for the prescribed deep brain stimulation settings. Machine learning classification using the Waikato Environment for Knowledge Analysis (WEKA) was applied using the multilayer perceptron neural network. The multilayer perceptron neural network achieved considerable classification accuracy for distinguishing between the deep brain stimulation system set to “On” and “Off” status through the quantified acceleration signal data obtained by this recently developed conformal wearable and wireless system. The research achievement establishes a progressive pathway to the future objective of achieving deep brain stimulation capabilities that promote closed-loop acquisition of configuration parameters that are uniquely optimized to the individual through extrinsic means of a highly conformal wearable and wireless inertial sensor system and machine learning with access to Cloud computing resources.

Mechanistic roles of the subthalamic nucleus and internal globus pallidus:evidence from local field potentials and deep brain stimulation

Deep brain stimulation (DBS) has become an effective therapeutic option for neurological and psychiatric disorders such as Parkinson’s disease (PD), dystonia, and obsessive-compulsive disorder. The subthalamic nucleus (STN) and internal globus pallidus (GPi) are by far the most commonly used targets for DBS in the treatment of PD. However, STN/GPi stimulation sometimes causes side effects, including motor fluctuations, cognitive declines, and worse emotional experience, which affect patients’ postoperative quality of life. Recent invasive electrophysiological studies are driven by the desire to better understand the mechanisms of therapeutic actions and side effects of STN/GPi stimulation. These studies investigated the function of the STN and GPi in motor, cognitive and affective processes by recording single- neuron firing patterns during the surgery or local field potentials after the surgery. Here we review the relevant studies to provide an integrative picture of the functional roles of the STN and GPi within the basal ganglia loops for motor, cognition, and emotion. Previous studies suggested that STN and GPi gamma oscillations encode the strength and speed of voluntary movements (execution), whereas beta oscillations reflect the effort and demand of potential movements (preparation). In the cognitive domain, oscillatory beta activity in the STN is involved when people have to stop an inappropriate action or to suppress salient but task-irrelevant information, whereas theta/delta activity is associated with the adjustment of decision thresholds and cost-benefit trade-off. In the affective domain, STN activity in the alpha band may represent the valence and arousal of emotional information.

Implementation of a Smartphone as a Wearable and Wireless Accelerometer and Gyroscope Platform for Ascertaining Deep Brain Stimulation Treatment Efficacy of Parkinson’s Disease through Machine Learning Classification

Parkinson’s disease manifests in movement disorder symptoms, such as hand tremor. There exists an assortment of therapy interventions. In particular deep brain stimulation offers considerable efficacy for the treatment of Parkinson’s disease. However, a considerable challenge is the convergence toward an optimal configuration of tuning parameters. Quantified feedback from a wearable and wireless system consisting of an accelerometer and gyroscope can be enabled through a novel software application on a smartphone. The smartphone with its internal accelerometer and gyroscope can record the quantified attributes of Parkinson’s disease and tremor through mounting the smartphone about the dorsum of the hand. The recorded data can be then wirelessly transmitted as an email attachment to an Internet derived resource for subsequent post-processing. The inertial sensor data can be consolidated into a feature set for machine learning classification. A multilayer perceptron neural network has been successfully applied to attain considerable classification accuracy between deep brain stimulation “On” and “Off” scenarios for a subject with Parkinson’s disease. The findings establish the foundation for the broad objective of applying wearable and wireless systems for the development of closed-loop optimization of deep brain stimulation parameters in the context of cloud computing with machine learning classification.

Brain Encoding and Decoding in fMRI with Bidirectional Deep Generative Models

Brain encoding and decoding via functional magnetic resonance imaging(fMRI)are two important aspects of visual perception neuroscience.Although previous researchers have made significant advances in brain encoding and decoding models,existing methods still require improvement using advanced machine learning techniques.For example,traditional methods usually build the encoding and decoding models separately,and are prone to overfitting on a small dataset.In fact,effectively unifying the encoding and decoding procedures may allow for more accurate predictions.In this paper,we first review the existing encoding and decoding methods and discuss the potential advantages of a“bidirectional”modeling strategy.Next,we show that there are correspondences between deep neural networks and human visual streams in terms of the architecture and computational rules.Furthermore,deep generative models(e.g.,variational autoencoders(VAEs)and generative adversarial networks(GANs))have produced promising results in studies on brain encoding and decoding.Finally,we propose that the dual learning method,which was originally designed for machine translation tasks,could help to improve the performance of encoding and decoding models by leveraging large-scale unpaired data.

A liquid chromatography with tandem mass spectrometry method for quantitating total and unbound ceritinib in patient plasma and brain tumor

A rapid, sensitive, and robust reversed-phase liquid chromatography with tandem mass spectrometry method was developed and validated for the determination of total and unbound ceritinib, a secondgeneration ALK inhibitor, in patient plasma and brain tumor tissue samples. Sample preparation involved simple protein precipitation with acetonitrile. Chromatographic separation was achieved on a Waters ACQUITY UPLC BEH C_(18) column using a 4-min gradient elution consisting of mobile phase A(0.1% formic acid in water) and mobile phase B(0.1% formic acid in acetonitrile), at a flow rate of 0.4 m L/min. Ceritinib and the internal standard([^(13)C_6]ceritinib) were monitored using multiple reaction monitoring mode under positive electrospray ionization. The lower limit of quantitation(LLOQ) was 1 n M of ceritinib in plasma. The calibration curve was linear over ceritinib concentration range of 1–2000 n M in plasma. The intra-and interday precision and accuracy were within the generally accepted criteria for bioanalytical method( o15%).The method was successfully applied to assess ceritinib brain tumor penetration, as assessed by the unbound drug brain concentration to unbound drug plasma concentration ratio, in patients with brain tumors.

Imaging for deep brain stimulation:The zona incerta at 7 Tesla

AIM:To evaluate different promising magnetic resonance imaging(MRI) methods at 7.0 Tesla(T) for the pre-stereotactic visualization of the zona incerta(ZI).METHODS:Two neuroradiologists qualitatively and quantitatively examined T2-turbo spin-echo(T2-TSE),T1-weighted gradient-echo,as well as FLASH2D-T2Star and susceptibility-weighted imaging(SWI) for the visualization of the ZI at 7.0 T MRI.Delineation and image quality for the ZI were independently examined using a 6-scale grading system.Inter-rater reliability using Cohen's kappa coefficient(κ) were assessed.Contrast-tonoise ratios(CNR),and signal-to-noise ratios(SNR) for the ZI were calculated for all sequences.Differences in delineation,SNR,and CNR between the sequences were statistically assessed using a paired t-test.For the anatomic validation the coronal FLASH2D-T2Star images were co-registered with a stereotactic atlas(Schaltenbrand-Wahren).RESULTS:The rostral part of the ZI(rZI) could easily be identified and was best and reliably visualized in the coronal FLASH2D-T2Star images.The caudal part was not definable in any of the sequences.No major artifacts in the rZI were observed in any of the scans.FLASH2D-T2Star and SWI imaging offered significant higher CNR values for the rZI compared to T2-TSE images(P > 0.05).The co-registration of the coronal FLASH2D-T2Star images with the stereotactic atlas schema(Schaltenbrand-Wahren) confirmed the correct localization of the ZI in all cases.CONCLUSION:FLASH2D-T2Star imaging(particularly coronal view) provides the reliable and currently optimal visualization of the rZI at 7.0 T.These results can facilitate a better and more precise targeting of the caudal part of the ZI than ever before.

Role of the Dorsal Anterior Cingulate Cortex in Relational Memory Formation: A Deep Brain Activity Index Study

The dorsal area of the anterior cingulate cortex (ACC) constructs the salience network associated with the anterior insular cortex. Conventional brain imaging studies, such as functional magnetic resonance imaging (fMRI), have demonstrated that relational memory formation occurs in the ACC. However, how such memory is encoded and retrieved remains unknown due to limited time resolution of conventional fMRI. This study aimed to investigate temporal dynamics of the dorsal ACC (dACC) during word-pair tasks based on a newly developed event-related deep brain activity (ER-DBA) method using occipital electroencephalogram (EEG) signal powers. The method assesses dACC activity at a temporal resolution of approximately 0.3 s beyond the conventional resolution limit. We found that transient deactivation of dACC during the presentation of the second word of each pair was essential for encoding success regardless of whether the words were related or unrelated. We also found that memory accuracy was not affected by the intervention of inter-trials until the recall trial. Taken together, these findings suggest that dACC deactivation for encoding success is accompanied with short-term potentiation essential for durability of memory. We further found that false memory formation associated with the presentation of word pairs was occasionally committed. In such cases, dACC exhibited a similar transient deactivation although false memory commission was independent of related or unrelated conditions. Our findings suggest that encoding and retrieval of associates are paralleled and that simultaneous production of associates seems to be an essential strategy for successful relational memory formation. The study was limited to the assessment of dACC activity and did not account for other regional brain activities or receptor regulation related to short-term potentiation. We detected fast behavior of dACC during relational memory formation using the novel ER-DBA method. Such temporal dynamics will be important for eliciting underlying mechanisms of memory dysfunctions.

Excitatory amino acid changes in the brains of rhesus monkeys following selective cerebral deep hypothermia and blood flow occlusion

Selective cerebral deep hypothermia and blood flow occlusion can enhance brain tolerance to ischemia and hypoxia and reduce cardiopulmonary complications in monkeys. Excitotoxicity induced by the release of a large amount of excitatory amino acids after cerebral ischemia is the major mechanism underlying ischemic brain injury and nerve cell death. In the present study, we used selective cerebral deep hypothermia and blood flow occlusion to block the bilateral common carotid arteries and/or bilateral vertebral arteries in rhesus monkey, followed by reperfusion using Ringer＇s solution at 4~C. Microdialysis and transmission electron microscope results showed that selective cerebral deep hypothermia and blood flow occlusion inhibited the release of glutamic acid into the extracellular fluid in the brain frontal lobe and relieved pathological injury in terms of the ultrastructure of brain tissues after severe cerebral ischemia. These findings indicate that cerebral deep hypothermia and blood flow occlusion can inhibit cytotoxic effects and attenuate ischemic/ hypoxic brain injury through decreasing the release of excitatory amino acids, such as glutamic acid.

Classification of Brain Tumors Using Hybrid Feature Extraction Based on Modified Deep Learning Techniques

According to the World Health Organization(WHO),Brain Tumors(BrT)have a high rate of mortality across the world.The mortality rate,however,decreases with early diagnosis.Brain images,Computed Tomography(CT)scans,Magnetic Resonance Imaging scans(MRIs),segmentation,analysis,and evaluation make up the critical tools and steps used to diagnose brain cancer in its early stages.For physicians,diagnosis can be challenging and time-consuming,especially for those with little expertise.As technology advances,Artificial Intelligence(AI)has been used in various domains as a diagnostic tool and offers promising outcomes.Deep-learning techniques are especially useful and have achieved exquisite results.This study proposes a new Computer-Aided Diagnosis(CAD)system to recognize and distinguish between tumors and non-tumor tissues using a newly developed middleware to integrate two deep-learning technologies to segment brain MRI scans and classify any discovered tumors.The segmentation mechanism is used to determine the shape,area,diameter,and outline of any tumors,while the classification mechanism categorizes the type of cancer as slow-growing or aggressive.The main goal is to diagnose tumors early and to support the work of physicians.The proposed system integrates a Convolutional Neural Network(CNN),VGG-19,and Long Short-Term Memory Networks(LSTMs).A middleware framework is developed to perform the integration process and allow the system to collect the required data for the classification of tumors.Numerous experiments have been conducted on different five datasets to evaluate the presented system.These experiments reveal that the system achieves 97.98%average accuracy when the segmentation and classification functions were utilized,demonstrating that the proposed system is a powerful and valuable method to diagnose BrT early using MRI images.In addition,the system can be deployed in medical facilities to support and assist physicians to provide an early diagnosis to save patients’lives and avoid the high cost of treatments.

Cognitive Control and Brain Network Dynamics during Word Generation Tasks Predicted Using a Novel Event-Related Deep Brain Activity Method

There is a growing interest in the diagnosis and treatment of patients with dementia and cognitive impairment at an early stage. Recent imaging studies have explored neural mechanisms underlying cognitive dysfunction based on brain network architecture and functioning. The dorsal anterior cingulate cortex (dACC) is thought to regulate large-scale intrinsic brain networks, and plays a primary role in cognitive processing with the anterior insular cortex (aIC), thus providing salience functions. Although neural mechanisms have been elucidated at the connectivity level by imaging studies, their understanding at the activity level still remains unclear because of limited time-based resolution of conventional imaging techniques. In this study, we investigated temporal activity of the dACC during word (verb) generation tasks based on our newly developed event-related deep brain activity (ER-DBA) method using occipital electroencephalogram (EEG) alpha-2 powers with a time resolution of a few hundred milliseconds. The dACC exhibited dip-like temporal waveforms indicating deactivation in an initial stage of each trial when appropriate verbs were successfully generated. By contrast, monotonous increase was observed for incorrect responses and a decrease was detected for no responses. The dip depth was correlated with the percentage of success. Additionally, the dip depth linearly increased with increasing slow component of the DBA index at rest across all subjects. These findings suggest that dACC deactivation is essential for cognitive processing, whereas its activation is required for goal-oriented behavioral outputs, such as cued speech. Such dACC functioning, represented by the dip depth, is supported by the activity of the upper brainstem region including monoaminergic neural systems.

Advancements in Liver Tumor Detection:A Comprehensive Review of Various Deep Learning Models

Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment planning.Liver Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and challenging.This review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and resilience.Performance metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical contexts.The review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging conditions.It suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular methods.This paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

Mobile Phone Usage and the Risk of Brain Tumors

Purpose: This literature review investigated the possible association between the use of mobile phones and brain tumors. Methods: In brief, 11 publications were retrieved from JSTOR, PubMed, Google Scholar and Summon in order to compare the association between the usage of mobile phones in patients with a brain tumor and those without. Papers published in English, and after 2001 were selected for. There was no limit on age, gender, geographical location and type of brain tumor. Results: For regular mobile phone usage, the combined odds ratios (OR) (95% confidence intervals) for three studies are: 1.5 (1.2 - 1.8), 1.3 (0.95 - 1.9), and 1.1 (0.8 - 1.4), respectively. Furthermore, the odds ratio did not increase, regardless of mobile phone use duration. Additionally, Lonn et al. (2005) observed that the risk also did not significantly increase when assessing the laterality (ipsilateral or contralateral) of the tumor in relation to side of head used for the mobile phone. Kan et al. (2007) observed an OR of 1.22 when comparing analog phone to digital phone use. Conclusion: This review concludes that there is no current association between mobile phone use and the development of brain tumors. Although certain studies speak in favor of an increased risk, many are plagued with either: sampling bias, misclassification bias, or issues concerning risk estimates. Further research needs to be done in order to evaluate the long-term effect of mobile phone usage on the risk of developing a brain tumor.

Voltage adjustment improves rigidity and tremor in Parkinson's disease patients receiving deep brain stimulation

Deep brain stimulation of the subthalamic nucleus is recognized as the most effective treatment for moderate and advanced Parkinson＇s disease. Programming of the stimulation parameters is important for maintaining the efficacy of deep brain stimulation. Voltage is consid- ered to be the most effective programming parameter. The present study is a retrospective analysis of six patients with Parkinson＇s disease （four men and two women, aged 37-65 years）, who underwent bilateral deep brain stimulation of the subthalamic nucleus at the First Affiliated Hospital of Sun Yat-sen University, China, and who subsequently adjusted only the stimulation voltage. We evaluated motor symptom severity using the Unified Parkinson＇s Disease Rating Scale Part III, symptom progression using the Hoehn and Yahr scale, and the levodopa equivalent daily dose, before surgery and 1 and 2 years after surgery. The 2-year follow-up results show that rigidity and tremor improved, and clinical symptoms were reduced, while pulse width was maintained at 60 ps and frequency at 130 Hz. Voltage adjust- ment alone is particularly suitable for patients who cannot tolerate multiparameter program adjustment. Levodopa equivalent daily dose was markedly reduced 1 and 2 years after surgery compared with baseline. Our results confirm that rigidity, tremor and bradykinesia can be best alleviated by voltage adjustment. The trial was registered at ClinicalTrials.gov （identifier： NCT01934881）.

Endoscopic and surgical resection of T1a/T1b esophageal neoplasms: A systematic review

AIM: To investigate potential therapeutic recommendations for endoscopic and surgical resection of T1a/ T1b esophageal neoplasms. METHODS: A thorough search of electronic databases MEDLINE, Embase, Pubmed and Cochrane Library, from 1997 up to January 2011 was performed. An analysis was carried out, pooling the effects of outcomes of 4241 patients enrolled in 80 retrospective studies. For comparisons across studies, each reporting on only one endoscopic method, we used a random effects meta-regression of the log-odds of the outcome of treatment in each study. "Neural networks" as a data mining technique was employed in order to establish a prediction model of lymph node status in superficial submucosal esophageal carcinoma. Another data mining technique, the "feature selection and root cause analysis", was used to identify the most impor-tant predictors of local recurrence and metachronous cancer development in endoscopically resected patients, and lymph node positivity in squamous carcinoma (SCC) and adenocarcinoma (ADC) separately in surgically resected patients. RESULTS: Endoscopically resected patients: Low grade dysplasia was observed in 4% of patients, high grade dysplasia in 14.6%, carcinoma in situ in 19%, mucosal cancer in 54%, and submucosal cancer in 16% of patients. There were no significant differences between endoscopic mucosal resection and endoscopic submucosal dissection (ESD) for the following parameters: complications, patients submitted to surgery, positive margins, lymph node positivity, local recurrence and metachronous cancer. With regard to piecemeal resection, ESD performed better since the number of cases was significantly less [coefficient: -7.709438, 95%CI: (-11.03803, -4.380844), P < 0.001]; hence local recurrence rates were significantly lower [coefficient: -4.033528, 95%CI: (-6.151498, -1.915559),P < 0.01]. A higher rate of esophageal stenosis was observed following ESD [coefficient: 7.322266, 95%CI: (3.810146, 10.83439), P < 0.001]. A significantly greater number of SCC patients were submitted to surgery (log-odds, ADC: -2.1206 ± 0.6249 vs SCC: 4.1356 ± 0.4038, P < 0.05). The odds for re-classification of tumor stage after endoscopic resection were 53% and 39% for ADC and SCC, respectively. Local tumor recurrence was best predicted by grade 3 differentiation and piecemeal resection, metachronous cancer development by the carcinoma in situ component, and lymph node positivity by lymphovascular invasion. With regard to surgically resected patients: Significant differences in patients with positive lymph nodes were observed between ADC and SCC [coefficient: 1.889569, 95%CI: (0.3945146, 3.384624), P<0.01). In contrast, lymphovascular and microvascular invasion and grade 3 patients between histologic types were comparable, the respective rank order of the predictors of lymph node positivity was: Grade 3, lymphovascular invasion (L+), microvascular invasion (V+), submucosal (Sm) 3 invasion, Sm2 invasion and Sm1 invasion. Histologic type (ADC/SCC) was not included in the model. The best predictors for SCC lymph node positivity were Sm3 invasion and (V+). For ADC, the most important predictor was (L+). CONCLUSION: Local tumor recurrence is predicted by grade 3, metachronous cancer by the carcinoma insitu component, and lymph node positivity by L+. T1b cancer should be treated with surgical resection.

An experimental study on the expression of SV40 large tumor antigen in human brain tumors

Objective: To study the role of SV40 early region gene coding product large tumor antigen(Tag) expression and the interaction between Tag and tumor suppressors p53 and pRb in human brain tumorigenesis. Methods: Tag was investigated by immunoprecipitation followed by silver staining and Western blot in 65 cases of human brain tumors and 8 cases of normal brain tissues. Tag-p53 and Tag-pRb complexes were screened by immunoprecipitation and Western blot in 18 and 15 Tag positive tumor tissues respectively. Results: SV40 Tag was expressed generally in human brain tumors, its positive rate was 66. 2% (43 /65). However, Eight normal brain tissues were all negative for Tag, there was significant difference between them(P < 0. 05). Tag-p53 complex was detected in all of 18 Tag positive tumors as well as Tag-pRb complex in all of 15 Tag positive tumors. Conclnsion: SV40 Tag expression is associated with human brain tumorigenesis. The inactivation of p53 and pRh due to the formation of Tag-p53 and Tag-pRb complexes is possibly an important mechanism in the etiopathogenesis of human brain tumors.

Diffusion tensor tractography of the arcuate fasciculus in patients with brain tumors: Comparison between deterministic and probabilistic models

Purpose: The purpose of this study was to compare the deterministic and probabilistic tracking methods of diffusion tensor white matter fiber tractography in patients with brain tumors. Materials and Methods: We identified 29 patients with left brain tumors ciculus was reconstructed using a deterministic Fiber Assignment by Continuous Tracking (FACT) algorithm and a probabilistic method based on an extended Monte Carlo Random Walk algorithm. Tracking was controlled using two ROIs corresponding to Broca’s and Wernicke’s areas. Tracts in tumoraffected hemispheres were examined for extension between Broca’s and Wernicke’s areas, anterior-posterior length and volume, and compared with the normal contralateral tracts. Results: Probabilistic tracts displayed more complete anterior extension to Broca’s area than did FACT tracts on the tumor-affected and normal sides (p rs.

Distinction of an Assortment of Deep Brain Stimulation Parameter Configurations for Treating Parkinson’s Disease Using Machine Learning with Quantification of Tremor Response through a Conformal Wearable and Wireless Inertial Sensor

Deep brain stimulation offers an advanced means of treating Parkinson’s disease in a patient specific context. However, a considerable challenge is the process of ascertaining an optimal parameter configuration. Imperative for the deep brain stimulation parameter optimization process is the quantification of response feedback. As a significant improvement to traditional ordinal scale techniques is the advent of wearable and wireless systems. Recently conformal wearable and wireless systems with a profile on the order of a bandage have been developed. Previous research endeavors have successfully differentiated between deep brain stimulation “On” and “Off” status through quantification using wearable and wireless inertial sensor systems. However, the opportunity exists to further evolve to an objectively quantified response to an assortment of parameter configurations, such as the variation of amplitude, for the deep brain stimulation system. Multiple deep brain stimulation amplitude settings are considered inclusive of “Off” status as a baseline, 1.0 mA, 2.5 mA, and 4.0 mA. The quantified response of this assortment of amplitude settings is acquired through a conformal wearable and wireless inertial sensor system and consolidated using Python software automation to a feature set amenable for machine learning. Five machine learning algorithms are evaluated: J48 decision tree, K-nearest neighbors, support vector machine, logistic regression, and random forest. The performance of these machine learning algorithms is established based on the classification accuracy to distinguish between the deep brain stimulation amplitude settings and the time to develop the machine learning model. The support vector machine achieves the greatest classification accuracy, which is the primary performance parameter, and K-nearest neighbors achieves considerable classification accuracy with minimal time to develop the machine learning model.

Vascular changes caused by deep brain stimulation using double-dose gadolinium-enhanced brain MRI

We retrospectively analyzed the clinical data of 32 patients with medically intractable idiopathic Parkinson＇s disease who had undergone staged bilateral deep brain stimulation of the subtha-lamic nuclei from January 2007 to May 2011. The vascularture of the patients who received two deep brain stimulations was detected using double-dose gadolinium-enhanced brain MRI. The dimensions of straight sinus, superior sagittal sinus, ipsilateral internal cerebral vein in the tha- lamic branch and ipsilateral anterior caudate vein were reduced. These findings demonstrate that bilateral deep brain stimulation of the subthalamic nuclei affects cerebral venous blood flow.

Training stem cells for treatment of malignant brain tumors

The treatment of malignant brain tumors remains a challenge. Stem cell technology has been applied in the treatment of brain tumors largely because of the ability of some stem cells to infiltrate into regions within the brain where tumor cells migrate as shown in preclinical studies. However, not all of these efforts can translate in the effective treatment that improves the quality of life for pa-tients. Here, we perform a literature review to identify the problems in the field. Given the lack of efficacy of most stem cell-based agents used in the treatment of malignant brain tumors, we found that stem cell distribution(i.e., only a fraction of stem cells applied capable of targeting tumors) are among the limiting factors. We provide guidelines for potential improvements in stem cell distribution. Specifically, we use an engineered tissue graft platform that replicates the in vivo microenvironment, and provide our data to validate that this culture platform is viable for producing stem cells that have better stem cell distribution than with the Petri dish culture system.

Partial improvement in performance of patients with severe Alzheimer's disease at an early stage of fornix deep brain stimulation

Deep brain stimulation is a therapy for Alzheimer＇s disease（AD） that has previously been used for mainly mild to moderate cases. This study provides the first evidence of early alterations in performance induced by stimulation targeted at the fornix in severe AD patients. The performance of the five cases enrolled in this study was scored with specialized assessments including the Mini-Mental State Examination and Clinical Dementia Rating, both before and at an early stage after deep brain stimulation. The burden of caregivers was also evaluated using the Zarit Caregiver Burden Interview. As a whole, the cognitive performance of patients remained stable or improved to varying degrees, and caregiver burden was decreased. Individually, an improved mental state or social performance was observed in three patients, and one of these three patients showed remarkable improvement in long-term memory. The conditions of another patient deteriorated because of inappropriate antipsychotic medications that were administered by his caregivers. Taken together, deep brain stimulation was capable of improving some cognitive aspects in patients with severe AD, and of ameliorating their emotional and social performance, at least at an early stage. However, long-term effects induced by deep brain stimulation in patients with severe AD need to be further validated. More research should focus on clarifying the mechanism of deep brain stimulation. This study was registered with ClinicalTrials.gov（NCT03115814） on April 14, 2017.