RVDLAHA: An RISC-V DLA Hardware Architecture for On-Device Real-Time Seizure Detection and Personalization in Wearable Applications.

Epilepsy is a globally distributed chronic neurological disorder that may pose a threat to life without warning. Therefore, the use of wearable devices for real-time detection and treatment of epilepsy is crucial. Additionally, personalizing disease detection algorithms for individual users is also a challenge in clinical applications. Some studies have proposed seizure detection algorithms with convolutional neural networks (CNNs) and programmable hardware architectures for speeding up the process of CNN inference. However, personalizing seizure detection algorithms could still not be performed on these hardware architectures. Consequently, this study proposes three key contributions to address the challenges: a real-time seizure detection and personalization algorithm, a programmable reduced instruction set computer-V (RISC-V) deep learning accelerator (DLA) hardware architecture (RVDLAHA), and a dedicated RISC-V DLA (RVDLA) compiler. In animal experiments with lab rats, the proposed CNN-based seizure detection algorithm obtains an accuracy of 99.5% for a 32-bit floating point and an accuracy of 99.3% for a 16-bit fixed point. Additionally, the proposed personalization algorithm increases the testing accuracy across different databases from 85.0% to 92.9%. The RVDLAHA is implemented on Xilinx PYNQ-Z2, with a power consumption of only 0.107 W at an operating frequency of 1 MHz. Each step, including raw data input, preprocessing, detection, and personalization, requires only 17.8, 1.0, 1.1, and 1.3 ms, respectively. With the hardware architecture, the seizure detection and personalization algorithm can provide on-device real-time monitoring.

The distinct and potentially conflicting effects of tDCS and tRNS on brain connectivity, cortical inhibition, and visuospatial memory.

Noninvasive brain stimulation (NIBS) techniques, including transcranial direct current stimulation (tDCS) and transcranial random noise stimulation (tRNS), are emerging as promising tools for enhancing cognitive functions by modulating brain activity and enhancing cognitive functions. Despite their potential, the specific and combined effects of tDCS and tRNS on brain functions, especially regarding functional connectivity, cortical inhibition, and memory performance, are not well-understood. This study aims to explore the distinct and combined impacts of tDCS and tRNS on these neural and cognitive parameters. Using a within-subject design, ten participants underwent four stimulation conditions: sham, tDCS, tRNS, and combined tDCS + tRNS. We assessed the impact on resting-state functional connectivity, cortical inhibition via Cortical Silent Period (CSP), and visuospatial memory performance using the Corsi Block-tapping Test (CBT). Our results indicate that while tDCS appears to induce brain lateralization, tRNS has more generalized and dispersive effects. Interestingly, the combined application of tDCS and tRNS did not amplify these effects but rather suggested a non-synergistic interaction, possibly due to divergent mechanistic pathways, as observed across fMRI, CSP, and CBT measures. These findings illuminate the complex interplay between tDCS and tRNS, highlighting their non-additive effects when used concurrently and underscoring the necessity for further research to optimize their application for cognitive enhancement.

Charge-mode Neural Stimulator with a Capacitor-reuse Residual Charge Detector and Active Charge Balancing for Epileptic Seizure Suppression.

This study proposes a charge-mode neural stimulator for electrical stimulation systems that utilizes a capacitor-reuse technique with a residual charge detector and achieves active charge balancing simultaneously. The design is mainly used for epilepsy suppression systems to achieve real-time symptom relief during seizures. A charge-mode stimulator is adopted in consideration of the complexity of circuit design, the high voltage tolerance of transistors, and system integration requirements in the future. The residual charge detector allows users to understand the current stimulus situation, enabling them to make optimal adjustments to the stimulation parameters. On the basis of the information on actual stimulation charge, active charge balancing can effectively prevent the accumulation of mismatched charges on electrode impedance. The capacitor- and phase-reuse techniques help realize high integration of the overall stimulator circuit in consideration of the commonality of the use of a capacitor and charging/discharging phase in the stimulation circuit and charge detector. The proposed charge-mode neural stimulator is implemented in a TSMC 0.18 µm 1P6M CMOS process with a core area of 0.2127 mm2. Measurement results demonstrate the accuracy of the stimulation's functionality and the programmable stimulus parameters. The effectiveness of the proposed charge-mode neural stimulator for epileptic seizure suppression is verified through animal experiments.

Nerve excitability test and lead toxicity: a case-control study.

BACKGROUND: Although conventional electrophysiological parameters have been proposed as clinical indicators for monitoring lead neuropathies, their correlations with blood lead level are weak. In this study, we investigated the applicability of nerve excitability tests (NETs) to evaluate lead intoxication. METHODS: Fourteen workers who were exposed to lead with an elevated blood level ranging from 17.8 to 64.9 µg/dL and 20 healthy controls with similar ages and body heights were enrolled. Both workers and controls underwent nerve conduction studies (NCSs), motor evoked potentials (MEPs) with transcranial magnetic stimulation (TMS), and NETs. RESULTS: NCSs showed prolonged distal latencies and decreased motor nerve conduction velocity of median nerves in the workers but without significant correlation to blood lead level (BLL). Significantly prolonged MEP latency was observed in the workers (+ 6 ms). NETs demonstrated hyperpolarized resting membrane potentials in stimulus-response curves and changes in the property of potassium channels under a hyperpolarized current in threshold electrotonus, implying that lead hyperpolarized nerves by interfering with potassium channels. NETs also showed a better correlation with BLL than conventional electrophysiological parameters. CONCLUSIONS: Axonal hyperpolarization and central conduction delay are more apparently reflecting elevated BLL than NCS. NET may have the potential for early detection of lead neuropathy.

New biophysical rate-based modeling of long-term plasticity in mean-field neuronal population models.

The formation of customized neural networks as the basis of brain functions such as receptive field selectivity, learning or memory depends heavily on the long-term plasticity of synaptic connections. However, the current mean-field population models commonly used to simulate large-scale neural network dynamics lack explicit links to the underlying cellular mechanisms of long-term plasticity. In this study, we developed a new mean-field population model, the plastic density-based neural mass model (pdNMM), by incorporating a newly developed rate-based plasticity model based on the calcium control hypothesis into an existing density-based neural mass model. Derivation of the plasticity model was carried out using population density methods. Our results showed that the synaptic plasticity represented by the resulting rate-based plasticity model exhibited Bienenstock-Cooper-Munro-like learning rules. Furthermore, we demonstrated that the pdNMM accurately reproduced previous experimental observations of long-term plasticity, including characteristics of Hebbian plasticity such as longevity, associativity and input specificity, on hippocampal slices, and the formation of receptive field selectivity in the visual cortex. In conclusion, the pdNMM is a novel approach that can confer long-term plasticity to conventional mean-field neuronal population models.

Hemodynamics and Tissue Optical Properties in Bimodal Infarctions Induced by Middle Cerebral Artery Occlusion.

Various infarct sizes induced by middle cerebral artery occlusion (MCAO) generate inconsistent outcomes for stroke preclinical study. Monitoring cerebral hemodynamics may help to verify the outcome of MCAO. The aim of this study was to investigate the changes in brain tissue optical properties by frequency-domain near-infrared spectroscopy (FD-NIRS), and establish the relationship between cerebral hemodynamics and infarct variation in MCAO model. The rats were undergone transient MCAO using intraluminal filament. The optical properties and hemodynamics were measured by placing the FD-NIRS probes on the scalp of the head before, during, and at various time-courses after MCAO. Bimodal infarction severities were observed after the same 90-min MCAO condition. Significant decreases in concentrations of oxygenated hemoglobin ([HbO]) and total hemoglobin ([HbT]), tissue oxygenation saturation (StO2), absorption coefficient (µa) at 830 nm, and reduced scattering coefficient (µs') at both 690 and 830 nm were detected during the occlusion in the severe infarction but not the mild one. Of note, the significant increases in [HbO], [HbT], StO2, and µa at both 690 and 830 nm were found on day 3; and increases in µs' at both 690 and 830 nm were found on day 2 and day 3 after MCAO, respectively. The interhemispheric correlation coefficient (IHCC) was computed from low-frequency hemodynamic oscillation of both hemispheres. Lower IHCCs standing for interhemispheric desynchronizations were found in both mild and severe infarction during occlusion, and only in severe infarction after reperfusion. Our finding supports that sequential FD-NIRS parameters may associated with the severity of the infarction in MCAO model, and the consequent pathologies such as vascular dysfunction and brain edema. Further study is required to validate the potential use of FD-NIRS as a monitor for MCAO verification.

Using Constrained Square-Root Cubature Kalman Filter for Quantifying the Severity of Epileptic Activities in Mice.

(1) Background: Quantification of severity of epileptic activities, especially during electrical stimulation, is an unmet need for seizure control and evaluation of therapeutic efficacy. In this study, a parameter ratio derived from constrained square-root cubature Kalman filter (CSCKF) was formulated to quantify the excitability of local neural network and compared with three commonly used indicators, namely, band power, Teager energy operator, and sample entropy, to objectively determine their effectiveness in quantifying the severity of epileptiform discharges in mice. (2) Methods: A set of one normal and four types of epileptic EEGs was generated by a mathematical model. EEG data of epileptiform discharges during two types of electrical stimulation were recorded in 20 mice. Then, EEG segments of 5 s in length before, during and after the real and sham stimulation were collected. Both simulated and experimental data were used to compare the consistency and differences among the performance indicators. (3) Results: For the experimental data, the results of the four indicators were inconsistent during both types of electrical stimulation, although there was a trend that seizure severity changed with the indicators. For the simulated data, when the simulated EEG segments were used, the results of all four indicators were similar; however, this trend did not match the trend of excitability of the model network. In the model output which retained the DC component, except for the CSCKF parameter ratio, the results of the other three indicators were almost identical to those using the simulated EEG. For CSCKF, the parameter ratio faithfully reflected the excitability of the neural network. (4) Conclusion: For common EEG, CSCKF did not outperform other commonly used performance indicators. However, for EEG with a preserved DC component, CSCKF had the potential to quantify the excitability of the neural network and the associated severity of epileptiform discharges.

Evaluating interhemispheric synchronization and cortical activity in acute stroke patients using optical hemodynamic oscillations.

Objective.An understanding of functional interhemispheric asymmetry in ischemic stroke patients is a crucial factor in the designs of efficient programs for post-stroke rehabilitation. This study evaluates interhemispheric synchronization and cortical activities in acute stroke patients with various degrees of severity and at different post-stroke stages.Approach.Twenty-three patients were recruited to participate in the experiments, including resting-state and speed finger-tapping tasks at week-1 and week-3 post-stroke. Multichannel near-infrared spectroscopy (NIRS) was used to measure the changes in hemodynamics in the bilateral prefrontal cortex (PFC), the supplementary motor area (SMA), and the sensorimotor cortex (SMC). The interhemispheric correlation coefficient (IHCC) measuring the synchronized activities in time and the wavelet phase coherence (WPCO) measuring the phasic activity in time-frequency were used to reflect the symmetry between the two hemispheres within a region. The changes in oxyhemoglobin during the finger-tapping tasks were used to present cortical activation.Main results.IHCC and WPCO values in the severe-stroke were significantly lower than those in the minor-stroke at low frequency bands during week-3 post-stroke. Cortical activation in all regions in the affected hemisphere was significantly lower than that in the unaffected hemisphere in the moderate-severe stroke measured in week-1, however, the SMC activation on the affected hemisphere was significantly enhanced in week-3 post-stroke.Significance.In this study, non-invasive NIRS was used to observe dynamic synchronization in the resting-state based on the IHCC and WPCO results as well as hemodynamic changes in a motor task in acute stroke patients. The findings suggest that NIRS could be used as a tool for early stroke assessment and evaluation of the efficacy of post-stroke rehabilitation.

Modulation of Interhemispheric Synchronization and Cortical Activity in Healthy Subjects by High-Definition Theta-Burst Electrical Stimulation.

Background: Various forms of theta-burst stimulation (TBS) such as intermittent TBS (iTBS) and continuous TBS (cTBS) have been introduced as novel facilitation/suppression schemes during repetitive transcranial magnetic stimulation (rTMS), demonstrating a better efficacy than conventional paradigms. Herein, we extended the rTMS-TBS schemes to electrical stimulation of high-definition montage (HD-TBS) and investigated its neural effects on the human brain. Methods: In a within-subject design, fifteen right-handed healthy adults randomly participated in 10 min and 2 mA HD-TBS sessions: unilateral (Uni)-iTBS, bilateral (Bi)-cTBS/iTBS, and sham stimulation over primary motor cortex regions. A 20-channel near-infrared spectroscopy (NIRS) system was covered on the bilateral prefrontal cortex (PFC), sensory motor cortex (SMC), and parietal lobe (PL) for observing cerebral hemodynamic responses in the resting-state and during fast finger-tapping tasks at pre-, during, and poststimulation. Interhemispheric correlation coefficient (IHCC) and wavelet phase coherence (WPCO) from resting-state NIRS and concentration of oxyhemoglobin during fast finger-tapping tasks were explored to reflect the symmetry between the two hemispheres and cortical activity, respectively. Results: The IHCC and WPCO of NIRS data in the SMC region under Bi-cTBS/iTBS showed relatively small values at low-frequency bands III (0.06-0.15 Hz) and IV (0.02-0.06), indicating a significant desynchronization in both time and frequency domains. In addition, the SMC activation induced by fast finger-tapping exercise was significantly greater during Uni-iTBS as well as during and post Bi-cTBS/iTBS sessions. Conclusions: It appears that a 10 min and 2 mA Bi-cTBS/iTBS applied over two hemispheres within the primary motor cortex region could effectively modulate the interhemispheric synchronization and cortical activation in the SMC of healthy subjects. Our study demonstrated that bilateral HD-TBS approaches is an effective noninvasive brain stimulation scheme which could be a novel therapeutic for inducing effects of neuromodulation on various neurological disorders caused by ischemic stroke or traumatic brain injuries.

The Proper Use and Reporting of Survival Analysis and Cox Regression.

BACKGROUND: Survival analyses are heavily used to analyze data in which the time to event is of interest. The purpose of this paper is to introduce some fundamental concepts for survival analyses in medical studies. METHODS: We comprehensively review current survival methodologies, such as the nonparametric Kaplan-Meier method used to estimate survival probability, the log-rank test, one of the most popular tests for comparing survival curves, and the Cox proportional hazard model, which is used for building the relationship between survival time and specific risk factors. More advanced methods, such as time-dependent receiver operating characteristic, restricted mean survival time, and time-dependent covariates are also introduced. RESULTS: This tutorial is aimed toward covering the basics of survival analysis. We used a neurosurgical case series of surgically treated brain metastases from non-small cell lung cancer patients as an example. The survival time was defined from the date of craniotomy to the date of patient death. CONCLUSIONS: This work is an attempt to encourage more investigators/medical practitioners to use survival analyses appropriately in medical research. We highlight some statistical issues, make recommendations, and provide more advanced survival modeling in this aspect.

Real-Time Internet of Medical Things System for Detecting Blood Leakage during Hemodialysis Using a Novel Multiple Concentric Ring Sensor.

Venous needle dislodgement (VND) is a major healthcare safety concern in patients undergoing hemodialysis. Although VND is uncommon, it can be life-threatening. The main objective of this study was to implement a real-time multi-bed monitoring system for VND by combining a novel leakage-detection device and IoMT (Internet of Medical Things) technology. The core of the system, the Acusense IoMT platform, consisted of a novel leakage-detection patch comprised of multiple concentric rings to detect blood leakage and quantify the leaked volume. The performance of the leakage-detection system was evaluated on a prosthetic arm and clinical study. Patients with a high risk of blood leakage were recruited as candidates. The system was set up in a hospital, and the subjects were monitored for 2 months. During the pre-clinical simulation experiment, the system could detect blood leakage volumes from 0.3 to 0.9 mL. During the test of the IoMT system, the overall success rate of tests was 100%, with no lost data packets. A total of 701 dialysis sessions were analyzed, and the accuracy and sensitivity were 99.7% and 90.9%, respectively. Evaluation questionnaires showed that the use of the system after training changed attitudes and reduced worry of the nursing staff. Our results show the feasibility of using a novel detector combined with an IoMT system to automatically monitor multi-bed blood leakage. The innovative concentric-circle design could more precisely control the warning blood-leakage threshold in any direction to achieve clinical cost-effectiveness. The system reduced the load on medical staff and improved patient safety. In the future, it could also be applied to home hemodialysis for telemedicine during the era of COVID-19.

GGC Repeat Expansion of NOTCH2NLC in Taiwanese Patients With Inherited Neuropathies.

BACKGROUND AND OBJECTIVES: The GGC repeat expansion in the 5' untranslated region of NOTCH2NLC was recently identified as the cause of neuronal intranuclear inclusion disease (NIID), which may manifest with peripheral neuropathy. The aim of this study is to investigate its contribution to inherited neuropathy. METHODS: This cohort study screened patients with molecularly undiagnosed Charcot-Marie-Tooth disease (CMT) and healthy controls for the GGC repeat expansion in NOTCH2NLC using repeat-primed PCR and fragment analysis. The clinical and electrophysiologic features of the patients harboring the GGC repeat expansion were scrutinized. Skin biopsy with immunohistochemistry staining and electric microscopic imaging were performed. RESULTS: One hundred twenty-seven unrelated patients with CMT, including 66 cases with axonal CMT (CMT2), and 200 healthy controls were included. Among them, 7 patients with CMT carried a variant NOTCH2NLC allele with GGC repeat expansion, but it was absent in controls. The sizes of the expanded GGC repeats ranged from 80 to 104 repeats. All 7 patients developed sensory predominant neuropathy with an average age at disease onset of 37.1 years (range 21-55 years). Electrophysiologic studies revealed mild axonal sensorimotor polyneuropathy. Leukoencephalopathy was absent in the 5 patients who received a brain MRI. Skin biopsy from 2 patients showed eosinophilic, ubiquitin- and p62-positive intranuclear inclusions in the sweat gland cells and dermal fibroblasts. Two of the 7 patients had a family history of NIID. DISCUSSION: The NOTCH2NLC GGC repeat expansions are an underdiagnosed and important cause of inherited neuropathy. The expansion accounts for 10.6% (7 of 66) of molecularly unassigned CMT2 cases in the Taiwanese CMT cohort. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that in Taiwanese patients with genetically undiagnosed CMT, 10.6% of the CMT2 cases have the GGC repeat expansion in NOTCH2NLC.

Memantine Protects against Paclitaxel-Induced Cognitive Impairment through Modulation of Neurogenesis and Inflammation in Mice.

Chemotherapy-induced cognitive impairment (CICI) is an adverse side effect of cancer treatment with increasing awareness. Hippocampal damage and related neurocognitive impairment may mediate the development of CICI, in which altered neurogenesis may play a role. In addition, increased inflammation may be related to chemotherapy-induced hippocampal damage. Memantine, an uncompetitive N-methyl-D-aspartate (NMDA) receptor antagonist that may enhance neurogenesis and modulate inflammation, may be useful for treating CICI. To test this hypothesis, paclitaxel was administered to eight-week-old male B6 mice to demonstrate the relationship between CICI and impaired neurogenesis, and then, we evaluated the impact of different memantine regimens on neurogenesis and inflammation in this CICI model. The results demonstrated that both the pretreatment and cotreatment regimens with memantine successfully reversed impaired neurogenesis and spatial memory impairment in behavior tests. The pretreatment regimen unsuccessfully inhibited the expression of peripheral and central TNF-α and IL-1ß and did not improve the mood alterations following paclitaxel treatment. However, the cotreatment regimen led to a better modulatory effect on inflammation and restoration of mood disturbance. In conclusion, this study illustrated that impaired neurogenesis is one of the mechanisms of paclitaxel-induced CICI. Memantine may serve as a potential treatment for paclitaxel-induced CICI, but different treatment strategies may lead to variations in the treatment efficacy.

A novel density-based neural mass model for simulating neuronal network dynamics with conductance-based synapses and membrane current adaptation.

Despite its success in understanding brain rhythms, the neural mass model, as a low-dimensional mean-field network model, is phenomenological in nature, so that it cannot replicate some of rich repertoire of responses seen in real neuronal tissues. Here, using a colored-synapse population density method, we derived a novel neural mass model, termed density-based neural mass model (dNMM), as the mean-field description of network dynamics of adaptive exponential integrate-and-fire (aEIF) neurons, in which two critical neuronal features, i.e., voltage-dependent conductance-based synaptic interactions and adaptation of firing rate responses, were included. Our results showed that the dNMM was capable of correctly estimating firing rate responses of a neuronal population of aEIF neurons receiving stationary or time-varying excitatory and inhibitory inputs. Finally, it was also able to quantitatively describe the effect of spike-frequency adaptation in the generation of asynchronous irregular activity of excitatory-inhibitory cortical networks. We conclude that in terms of its biological reality and calculation efficiency, the dNMM is a suitable candidate to build significantly large-scale network models involving multiple brain areas, where the neuronal population is the smallest dynamic unit.

RISC-V CNN Coprocessor for Real-Time Epilepsy Detection in Wearable Application.

Epilepsy is a common clinical disease. Severe epilepsy can be life-threatening in certain unexpected conditions, so it is important to detect seizures instantly with a wearable device and to provide treatment within the golden window. The observation of the electroencephalography (EEG) signal is an imperative method to assist correct epilepsy diagnosis. To detect and classify EEG signals, a convolutional neural network (CNN) is an intuitive and appropriate method that borrows expertise from neurologists. However, the computational cost of training and inference on artificial intelligence (AI)-based solutions make software-only and hardware-only solutions incompetent for real-time monitoring on embedded devices. Hence, this study proposes three key contributions for the challenge, namely, an algorithm framework to provide real-time epilepsy detection, a dedicated coprocessor chip implementing this framework to enable real time epilepsy detection to offload and accelerate detection algorithm, and a custom interface with the coprocessor and reduced instruction set computer-V (RISC-V) instructions to reconfigure the coprocessor and transfer data. The epilepsy detection framework is implemented in 11-layer CNN. The proposed epilepsy detection algorithm performs 97.8% accuracy for floating-point and 93.5% for fixed-point operations through animal experiments with lab rats. The RISC-V CNN coprocessor is fabricated in the TSMC 0.18-µm CMOS process. For each classification, the coprocessor consumes 51 nJ/class. and 0.9 µJ/class. energy on data transfer and inference, respectively. The detection latency on the chip is 0.012 s. With the integration of the hardware coprocessor, AI algorithms can be applied to epilepsy detection for real-time monitoring.

Targeting lysosomal cysteine protease cathepsin S reveals immunomodulatory therapeutic strategy for oxaliplatin-induced peripheral neuropathy.

Rationale: Oxaliplatin-induced peripheral neuropathy (OIPN) is a common adverse effect that causes delayed treatment and poor prognosis among colorectal cancer (CRC) patients. However, its mechanism remains elusive, and no effective treatment is available. Methods: We employed a prospective cohort study of adult patients with pathologically confirmed stage III CRC receiving adjuvant chemotherapy with an oxaliplatin-based regimen for investigating OIPN. To further validate the clinical manifestations and identify a potential therapeutic strategy, animal models, and in vitro studies on the mechanism of OIPN were applied. Results: Our work found that (1) consistent with clinical findings, OIPN was observed in animal models. Targeting the enzymatic activity of cathepsin S (CTSS) by pharmacological blockade and gene deficiency strategy alleviates the manifestations of OIPN. (2) Oxaliplatin treatment increases CTSS expression by enhancing cytosol translocation of interferon response factor 1 (IRF1), which then facilitates STIM-dependent store-operated Ca2+ entry homeostasis. (3) The cytokine array demonstrated an increase in anti-inflammatory cytokines and suppression of proinflammatory cytokines in mice treated with RJW-58. (4) Mechanistically, inhibiting CTSS facilitated olfactory receptors transcription factor 1 release from P300/CBP binding, which enhanced binding to the interleukin-10 (IL-10) promoter region, driving IL-10 downstream signaling pathway. (5) Serum CTSS expression is increased in CRC patients with oxaliplatin-induced neurotoxicity. Conclusions: We highlighted the critical role of CTSS in OIPN, which provides a therapeutic strategy for the common adverse side effects of oxaliplatin.

Real-world evidence on the safety and effectiveness of fingolimod in patients with multiple sclerosis from Taiwan.

BACKGROUND/PURPOSE: Multiple sclerosis is classified as a rare disease in Taiwan. This study evaluated the safety and effectiveness of fingolimod in patients with relapsing-remitting multiple sclerosis (RRMS) from routine clinical practice in Taiwan. METHODS: In this retrospective, multicentre, observational study, we collected clinical data of patients treated with fingolimod 0.5 mg/day in routine clinical practice between September 2012 and December 2015. Primary outcome was the overall safety of fingolimod; secondary outcome was the annualized relapse rate (ARR). RESULTS: Overall, 62/69 (86.1%) patients were on fingolimod by the end of data collection period. Mean age (±standard deviation [SD]) at inclusion was 37.7 ± 10.10 years; mean duration of MS was 5.4 ± 4.52 years and mean duration of fingolimod exposure was 135.8 patient-years. The most common adverse events (AEs) were bradycardia (21.7%; first-dose related), upper respiratory tract infection, dizziness, and hypoaesthesia (numbness) (11.6% each), followed by urinary tract infection and back pain (7.2% each). Seven patients had liver enzyme-related AEs. Eight patients had absolute lymphocyte counts <0.2 × 103/uL over the study period. One patient developed second degree AV block after first-dosing. Serious AEs were observed in 11 patients (15.9%; mild-to-moderate). No newly developed macular oedema was detected. The ARR was 0.3 ± 0.74 in fingolimod-treated patients and 66.7% of patients were relapse-free. The mean (SD) change from baseline in expanded disability status scale score was -0.30 ± 1.353. CONCLUSION: Fingolimod 0.5 mg/day treatment with an average of 2 years of exposure was associated with a manageable safety profile, and maintained/improved effectiveness in RRMS patients from Taiwan.

Using Artificial Neural Network to Discriminate Parkinson's Disease from Other Parkinsonisms by Focusing on Putamen of Dopamine Transporter SPECT Images.

BACKGROUND: The challenge of differentiating, at an early stage, Parkinson's disease from parkinsonism caused by other disorders remains unsolved. We proposed using an artificial neural network (ANN) to process images of dopamine transporter single-photon emission computed tomography (DAT-SPECT). METHODS: Abnormal DAT-SPECT images of subjects with Parkinson's disease and parkinsonism caused by other disorders were divided into training and test sets. Striatal regions of the images were segmented by using an active contour model and were used as the data to perform transfer learning on a pre-trained ANN to discriminate Parkinson's disease from parkinsonism caused by other disorders. A support vector machine trained using parameters of semi-quantitative measurements including specific binding ratio and asymmetry index was used for comparison. RESULTS: The predictive accuracy of the ANN classifier (86%) was higher than that of the support vector machine classifier (68%). The sensitivity and specificity of the ANN classifier in predicting Parkinson's disease were 81.8% and 88.6%, respectively. CONCLUSIONS: The ANN classifier outperformed classical biomarkers in differentiating Parkinson's disease from parkinsonism caused by other disorders. This classifier can be readily included into standalone computer software for clinical application.

Feasibility study of greater occipital nerve blocks by focused ultrasound - an animal study.

OBJECTIVE: Greater occipital nerve (GON) block may provide substantial relief for headache in the occipital location. This study tested the feasibility of focused ultrasound (FUS) to induce the conduction block of GONs in rats. APPROACH: For in vitro experiments, the nerve was dissected and cut from C2 to the site near the ear of the rats and preserved in Ringer's solution. Pulsed FUS was used for the block, and sensory action potentials were recorded in the GON. For in vivo experiments, the GONs of the rats were surgically exposed for precise ultrasonic treatment. All data are expressed as the mean ± the standard deviation. MAIN RESULTS: A single ultrasonic treatment temporarily suppressed the amplitude of action potentials of the in vitro nerves to 42 ± 14% of the baseline values, and the time to recovery was 55 min. The in vivo results showed that FUS acutely inhibited the amplitude of action potentials to 41 ± 8% of the baseline value in rat GONs, and the time to recovery was 67 min. Histological examination revealed no appreciable changes in the nerve morphology caused by FUS. Therefore, FUS reversibly blocked the conduction of the rat GON when the sonication parameters were appropriate. SIGNIFICANCE: Noninvasive FUS may be a novel treatment paradigm for occipital headache by blocking the occipital nerve, and the procedure is repeatable if indicated.

Innovative Head-Mounted System Based on Inertial Sensors and Magnetometer for Detecting Falling Movements.

This work presents a fall detection system that is worn on the head, where the acceleration and posture are stable such that everyday movement can be identified without disturbing the wearer. Falling movements are recognized by comparing the acceleration and orientation of a wearer's head using prespecified thresholds. The proposed system consists of a triaxial accelerometer, gyroscope, and magnetometer; as such, a Madgwick's filter is adopted to improve the accuracy of the estimation of orientation. Moreover, with its integrated Wi-Fi module, the proposed system can notify an emergency contact in a timely manner to provide help for the falling person. Based on experimental results concerning falling movements and activities of daily living, the proposed system achieved a sensitivity of 96.67% in fall detection, with a specificity of 98.27%, and, therefore, is suitable for detecting falling movements in daily life.