2008 Progress Report on Brain Research

Highlights new research on various disorders, nervous system injuries, neuroethics, neuroimmunology, pain, sense and body function, stem cells and neurogenesis, and thought and memory. Includes essays on arts and cognition and on deep brain stimulation

Intelligent IoT Framework for Indoor Healthcare Monitoring of Parkinson's Disease Patient

Parkinson’s disease is associated with high treatment costs, primarily attributed to the needs of hospitalization and frequent care services. A study reveals annual per-person healthcare costs for Parkinson’s patients to be 21,482,withanadditional29,695 burden to society. Due to the high stakes and rapidly rising Parkinson’s patients’ count, it is imperative to introduce intelligent monitoring and analysis systems. In this paper, an Internet of Things (IoT) based framework is proposed to enable remote monitoring, administration, and analysis of patient’s conditions in a typical indoor environment. The proposed infrastructure offers both static and dynamic routing, along with delay analysis and priority enabled communications. The scheme also introduces machine learning techniques to detect the progression of Parkinson’s over six months using auditory inputs. The proposed IoT infrastructure and machine learning algorithm are thoroughly evaluated and a detailed analysis is performed. The results show that the proposed scheme offers efficient communication scheduling, facilitating a high number of users with low latency. The proposed machine learning scheme also outperforms state-of-the-art techniques in accurately predicting the Parkinson’s progression

파킨슨병에서 시상하핵 뇌심부자극술의 미세전극기록으로부터 딥러닝을 이용한 임상 결과 예측

학위논문(석사)--서울대학교 대학원 :의과대학 의학과,2019. 8. 백선하.(Objectives) Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment to improve the motor symptoms of advanced Parkinson disease (PD). Accurate positioning of the stimulation electrodes to STN is mandatory for better clinical outcomes. However, the precise identification of the STN during the microelectrode recording (MER) is not easy. In this study, we analyzed deep learning based MER signals to better predict the clinical outcome of motor function improvement after bilateral STN DBS in patients with advanced PD. (Methods) 696 left MER segments of 4 seconds length from 34 PD patients with advanced PD who underwent bilateral STN DBS surgery under general anesthesia were included in this study. The datasets of thirty patients were assigned to the training set, and the datasets of four patients were assigned to the test set. The wavelet transformed MER and the ratio of DBS on and off Unified Parkinson's Disease Rating Scale(UPDRS) Part III score of the off-medication state were applied for deep learning. According to the ratio, the patients were divided into two groups, high-responder and moderate-responder group. Visual Geometry Group(VGG)-16 model with multi-task learning algorithm was used to estimate the bilateral effect of DBS. To apply the effect of the contralateral score more than ipsilateral score, the ratio of the loss function was varied. Gradient class activation map was used to marking the lesion of interest of CNN. (Results) When we divided MER according to the frequency band and transformed to wavelets, the maximal accuracy was the highest in the 50-500 Hz group, compared with 1-50 Hz and 500-5,000Hz groups. In addition, when the multitask-learning method was applied to 50-500Hz group, the stability of the model was prominently improved. The max accuracy was the highest(80.2%) when the loss ratio of right to left was given as 5:1 or 6:1 in the model. Area under the curve(AUC) was 0.88 in the receiver-operating characteristic(ROC) curve. Gradient class activation map showed that 80-200Hz band was the most commonly referenced area. (Conclusion) We confirmed that the clinical improvement of PD patients who underwent bilateral STN DBS could be predicted based on multi-task deep learning based MER analysis. The deep learning based MER analysis could be helpful for determining the position of the electrode, by predicting motor function improvement.연구 배경 시상하핵의 뇌심부자극술은 진행된 파킨슨병에서 운동 증상을 호전시키는 효과적인 치료이다. 좋은 임상적인 결과를 위해 자극 전극을 정확하게 위치시키는 것이 필요하다. 하지만 미세전극측정을 통해서도 시상하핵을 정확하게 식별하는 것이 쉽지 않다. 이 연구에서는 진행된 파킨슨병 환자에서 딥러닝을 기반으로 미세전극측정을 분석하여 양측 시상하핵 뇌심부자극술 후의 운동기능 호전 정도를 예측하였다. 연구 방법 이 연구에는 전신마취 하에서 양측 시상하핵 뇌심부자극술을 시행받은 34명의 환자로부터 측정된 4초 길이의 좌측 미세전극측정 분절이 포함되었다. 30명의 환자는 훈련군으로 4명의 환자는 실험군으로 구분하였다. 웨이브릿(wavelet) 변환된 미세전극측정 자료와 UPDRS(Unified Parkinson's Disease Rating Scale) 파트 III 중 오프-약물(Off-medication) 시기의 뇌심부자극/비자극 점수가 딥러닝에 사용되었다. 그 비율에 따라 고반응군과 중반응군으로 분류하였다. 다중작업학습 알고리즘을 이용한 VGG-16 모델이 DBS의 양측성 효과를 추정하기 위해 사용되었다. 동측의 점수보다 반대측의 점수를 크게 반영하도록 하기 위해 손실함수(loss function)의 비율을 다양하게 적용 하였다. CNN이 참조한 영역을 표시하기 위해 Grad-CAM을 사용하였다. 연구 결과 미세전극측정신호를 주파수 대역 별로 나누어 웨이브릿 변환하였을 때, 최대정확도는 1-50Hz와 500-5,000Hz와 비교하여 50-500Hz에서 가장 높았다. 게다가 다중작업학습을 적용하였을 때 모델의 안정도가 더 개선되었다. 최대 정확도는 좌우 손실함수의 비율이 5:1과 6:1 때 80.2%로 가장 높았다. 수신자 조작 특성 곡선(ROC curve)에서 곡선하 면적(AUC) 값은 0.88이었다. Grad-CAM에서는 80-200Hz 대역을 가장 흔히 참조한 것을 보여주었다. 연구 결론 미세전극측정의 다중작업학습을 통한 분석으로 파킨슨병 환자에서 양측 시상하핵 뇌심부자극술 시행 후 임상적 호전에 관한 예측이 가능할 것으로 판단하였다. 딥러닝으로 미세전극측정신호를 분석하여 수술 후 운동기능향상을 예측함으로써, 전극의 위치를 결정하는 데에 도움이 될 것으로 기대한다.Introduction 5 PD - DBS - UPDRS 5 Signal - CNN - Clinical outcome 5 Methods 7 Subjects 7 Surgical procedure 8 Microelectrode Recordings 9 Wavelet Transformation 9 Training set and Test set 10 Deep learning 11 Multi-task learning 12 Gradient class activation map 13 Statistical analysis 14 IRB 14 Results 15 Patient Data 15 MER & clinical outcome relation 21 Gradient Class Activation Map 24 Discussion 26 Single-task Learning 26 Multi-task Learning 27 Gradient Class Activation Map 28 Expected Clinical Usefulness 28 Limitation 29 Conclusion 31 References 32Maste

Customizable Intraoperative Neural Stimulator and Recording System for Deep Brain Stimulation Research and Surgery.

Intraoperative targeting systems provide neurosurgeons with raw electrophysiological data through microelectrodes used for determining location in the brain. There are significant deficits to the available targeting systems, limiting the use in both clinical and research applications. The work presented in this dissertation is of the development and validation of an intraoperative neural stimulator and recording system for use in deep brain stimulation (DBS) surgeries. This intraoperative data acquisition system (IODA) was validated in three applications to ensure efficacy and improvements in research and clinical studies. The first application investigated was a clinical study illustrating the improvement IODA had on the targeting accuracy of DBS leads in the subthalamic nucleus (STN) over current targeting methods. It was demonstrated that the novel navigation algorithm developed for use with IODA targeted microelectrode probe locations significantly closer to final DBS lead positions compared to preoperatively planned trajectory positions. The second study investigated a clinical science application. There are considerable differences in recently published studies for the optimal chronic stimulation site in the STN region. It was shown, using beta oscillations of local field potentials (LFP) recorded by IODA, that optimal stimulation sites were significantly correlated with locations of peak beta activity when DBS leads were medial to the STN midpoint. While DBS lead trajectories lateral of the STN midpoint were significantly correlated with the dorsal border of the STN. The third study explored a basic science application involving the role of the STN in movement inhibition. Through wideband recordings made with IODA, it was shown that the STN is significantly activated during movement and movement inhibition cues as seen in the theta, alpha, and beta bands and single unit activity. Overall the results indicate the utility and adaptability of this system for use within DBS surgeries. There are many applications of IODA for use in research for other neurodegenerative disease including Essential Tremor and Depression. The use of this system has enables neurosurgeons to reduce surgical time, risk, and error for DBS procedures and made entry for those less experienced in this procedure easier.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99771/1/dodani_1.pd

Unsupervised feature extraction with autoencoder : for the representation of parkinson´s disease patients

Dissertation presented as partial requirement for obtaining the Master’s degree in Information Management, with a specialization in Knowledge Management and Business IntelligenceData representation is one of the fundamental concepts in machine learning. An appropriate representation is found by discovering a structure and automatic detection of patterns in data. In many domains, representation or feature learning is a critical step in improving the performance of machine learning algorithms due to the multidimensionality of data that feeds the model. Some tasks may have different perspectives and approaches depending on how data is represented. In recent years, deep artificial neural networks have provided better solutions to several pattern recognition problems and classification tasks. Deep architectures have also shown their effectiveness in capturing latent features for data representation. In this document, autoencoders will be examined to obtain the representation of Parkinson's disease patients and compared with conventional representation learning algorithms. The results will show whether the proposed method of feature selection leads to the desired accuracy for predicting the severity of Parkinson’s disease

BIOMECHANICAL MARKERS AS INDICATORS OF POSTURAL INSTABILITY PROGRESSION IN PARKINSON'S DISEASE

The long term objective of this research is to identify quantitative biomechanical parameters of postural instability in patients with Parkinson’s disease (PD) that can in turn be used to assess fall risk. Currently, clinical assessments in PD are not sufficiently sensitive to predict fall risk, making a history of falls to be the best predictor of a future fall. Identifying biomechanical measures to predict risk of falls in PD would provide a quantitative justification to implement fall-reducing therapies prior to a first fall and help prevent the associated debilitating fractures or even morbidity. While past biomechanical studies have shown the presence of balance deficits in PD patients, which often include a broad spectrum of disease stages, compared to healthy controls (HC), no studies have assessed whether such parameters can distinguish the onset of postural instability prior to clinical presentation, and if such parameters persist following clinical presentation of postural instability. Toward this end this study had three goals: • Determine if biomechanical assessment of a quasi-static task, postural sway, could provide preclinical indication of postural instability in PD. • Define a mathematical model (based on principal component analysis, PCA) with biomechanical and clinical measures as inputs to quantitatively score earlier postural instability presence and progression in PD. • Investigate if biomechanical assessment of a dynamic task, gait initiation, could provide preclinical indication of postural instability in PD. Specific Aim 1 determined that some biomechanical postural sway variables showed evidence of preclinical postural instability and increased with PD progression. This aim distinguished mild PD (Hoehn and Yahr stage (H&Y) 2, without postural deficits) compared to HC suggesting preclinical indication of postural instability, and confirmed these parameters persisted in moderate PD (H&Y 3, with postural deficits). Specifically, trajectory, variation, and peak measures of the center of pressure (COP) during postural sway showed significant differences (p < .05) in mild PD compared to healthy controls, and these differences persisted in moderate PD. Schwab and England clinical score best correlated with the COP biomechanical measures. These results suggest that postural sway COP measures may provide preclinical indication of balance deficits in PD and increase with clinical PD progression. Specific Aim 2 defined a PCA model based on biomechanical measures of postural sway and clinical measures in mild PD, moderate PD, and HC. PCA modeling based on a correlation matrix structure identified both biomechanical and clinical measures as the primary drivers of variation in the data set. Further, a PCA model based on these selected parameters was able to significantly differentiate (p < .05) all 3 groups, suggesting PCA scores may help with preclinical indication of postural instability (mild PD versus HC) and could be sensitive to clinical disease progression (mild PD versus moderate PD and moderate PD versus HC). AP sway path length and a velocity parameter were the 2 primary measures that explained the variability in the data set, suggesting further investigation of these parameters and mathematical models for scoring postural instability progression is warranted. Specific Aim 3 determined that a velocity measure from biomechanical assessment of gait initiation (peak COP velocity towards the swing foot during locomotion) showed evidence of preclinical postural instability in PD. Because balance is a complex task, having a better understanding of both quasi-static (postural sway) and dynamic (gait initiation) tasks can provide further insight about balance deficits resulting from PD. Several temporal and kinematic parameters changed with increasing disease progression, with significant difference in moderate PD versus HC, but missed significance in mild PD compared to HC. Total Unified Parkinson’s Disease Rating Scale (UPDRS) and Pull Test clinical scores best correlated with the biomechanical measures of the gait initiation response. These results suggest dynamic biomechanical assessment may provide additional information in quantifying preclinical postural instability and progression in PD. In summary, reducing fall risk in PD is a high priority effort to maintain quality of life by allowing continued independence and safe mobility. Since no effective screening method exists to measure fall risk, our team is developing a multi-factorial method to detect postural instability through clinical balance assessment, and in doing so, provide the justification for implementing fall reducing therapies before potentially debilitating falls begin

Deep Brain Stimulation—A new treatment for tinnitus

AbstractIntractable tinnitus can lead to serious consequences. Study evidence indicates that the central nervous system is involved in generation and maintenance of chronic tinnitus and that tinnitus and other neurologic symptoms such as chronic pain may share similar mechanisms. Brain ablation and stimulation are used to treat chronic pain with success. Recent studies showed that ablation and stimulation in non-auditory areas resulted in tinnitus improvement. Deep brain stimulation (DBS) may be an alternative treatment for intractable tinnitus and deserves further study

Oculomotor Control in Patients with Parkinson\u27s Disease

There have been few studies investigating the eye movement behavior of Parkinson’s disease patients during fixation. This study objectively measured the eye movements of 36 patients with Parkinson’s disease, and 20 age matched controls. Stimuli consisted of ten standardized text passages first organized by Miller and Coleman. In addition, subjects followed a randomly displaced step jump target motion. Pendular nystagmus was found in all Parkinson’s subjects, with an average frequency of 7.44 Hz. Saccadic peak velocity and duration along the main sequence were not statistically different from controls. A slower rate of reading was also noted in the Parkinson’s group in terms of characters per minute, but with no more regressions than normal. Rate of square wave jerks was also found to be normal. This suggests that the hallmark feature of eye movements in Parkinson’s disease is a pendular nystagmus during fixation, and all saccadic activity to be normal