Electroencephalogram-Driven Machine-Learning Scenario for Assessing Impulse Control Disorder Comorbidity in Parkinson's Disease Using a Low-Cost, Custom LEGO-Like Headset.

Patients with Parkinson's disease (PD) may develop cognitive symptoms of impulse control disorders (ICDs) when chronically treated with dopamine agonist (DA) therapy for motor deficits. Motor and cognitive comorbidities critically increase the disability and mortality of the affected patients. This study proposes an electroencephalogram (EEG)-driven machine-learning scenario to automatically assess ICD comorbidity in PD. We employed a classic Go/NoGo task to appraise the capacity of cognitive and motoric inhibition with a low-cost, custom LEGO-like headset to record task-relevant EEG activity. Further, we optimized a support vector machine (SVM) and support vector regression (SVR) pipeline to learn discriminative EEG spectral signatures for the detection of ICD comorbidity and the estimation of ICD severity, respectively. With a dataset of 21 subjects with typical PD, 9 subjects with PD and ICD comorbidity (ICD), and 25 healthy controls (HC), the study results showed that the SVM pipeline differentiated subjects with ICD from subjects with PD with an accuracy of 66.3% and returned an around-chance accuracy of 53.3% for the classification of PD versus HC subjects without the comorbidity concern. Furthermore, the SVR pipeline yielded significantly higher severity scores for the ICD group than for the PD group and resembled the ICD vs. PD distinction according to the clinical questionnaire scores, which was barely replicated by random guessing. Without a commercial, high-precision EEG product, our demonstration may facilitate deploying a wearable computer-aided diagnosis system to assess the risk of DA-triggered cognitive comorbidity in patients with PD in their daily environment.

DGA3-Net: A parameter-efficient deep learning model for ASPECTS assessment for acute ischemic stroke using non-contrast computed tomography.

Detecting the early signs of stroke using non-contrast computerized tomography (NCCT) is essential for the diagnosis of acute ischemic stroke (AIS). However, the hypoattenuation in NCCT is difficult to precisely identify, and accurate assessments of the Alberta Stroke Program Early CT Score (ASPECTS) are usually time-consuming and require experienced neuroradiologists. To this end, this study proposes DGA3-Net, a convolutional neural network (CNN)-based model for ASPECTS assessment via detecting early ischemic changes in ASPECTS regions. DGA3-Net is based on a novel parameter-efficient dihedral group CNN encoder to exploit the rotation and reflection symmetry of convolution kernels. The bounding volume of each ASPECTS region is extracted from the encoded feature, and an attention-guided slice aggregation module is used to aggregate features from all slices. An asymmetry-aware classifier is then used to predict stroke presence via comparison between ASPECTS regions from the left and right hemispheres. Pre-treatment NCCTs of suspected AIS patients were collected retrospectively, which consists of a primary dataset (n = 170) and an external validation dataset (n = 90), with expert consensus ASPECTS readings as ground truth. DGA3-Net outperformed two expert neuroradiologists in regional stroke identification (F1 = 0.69) and ASPECTS evaluation (Cohen's weighted Kappa = 0.70). Our ablation study also validated the efficacy of the proposed model design. In addition, class-relevant areas highlighted by visualization techniques corresponded highly with various well-established qualitative imaging signs, further validating the learned representation. This study demonstrates the potential of deep learning techniques for timely and accurate AIS diagnosis from NCCT, which could substantially improve the quality of treatment for AIS patients.

Deep Learning-Based Automatic Detection of ASPECTS in Acute Ischemic Stroke: Improving Stroke Assessment on CT Scans.

(1) Background: The Alberta Stroke Program Early CT Score (ASPECTS) is a standardized scoring tool used to evaluate the severity of acute ischemic stroke (AIS) on non-contrast CT (NCCT). Our aim in this study was to automate ASPECTS. (2) Methods: We utilized a total of 258 patient images with suspected AIS symptoms. Expert ASPECTS readings on NCCT were used as ground truths. A deep learning-based automatic detection (DLAD) algorithm was developed for automated ASPECTS scoring based on 168 training patient images using a convolutional neural network (CNN) architecture. An additional 90 testing patient images were used to evaluate the performance of the DLAD algorithm, which was then compared with ASPECTS readings on NCCT as performed by physicians. (3) Results: The sensitivity, specificity, and accuracy of DLAD for the prediction of ASPECTS were 65%, 82%, and 80%, respectively. These results demonstrate that the DLAD algorithm was not inferior to radiologist-read ASPECTS on NCCT. With the assistance of DLAD, the individual sensitivity of the ER physician, neurologist, and radiologist improved. (4) Conclusion: The proposed DLAD algorithm exhibits a reasonable ability for ASPECTS scoring on NCCT images in patients presenting with AIS symptoms. The DLAD algorithm could be a valuable tool to improve and accelerate the decision-making process of front-line physicians.

Assessment of High Risk for Alzheimer's Disease Using Plasma Biomarkers in Subjects with Normal Cognition in Taiwan: A Preliminary Study.

BACKGROUND: In Alzheimer's disease (AD), cognitive impairment begins 10-15 years later than neurodegeneration in the brain. Plasma biomarkers are promising candidates for assessing neurodegeneration in people with normal cognition. It has been reported that subjects with the concentration of plasma amyloid-ß 1-42×total tau protein higher than 455 pg2/ml2 are assessed as having a high risk of amnesic mild impairment or AD, denoted as high risk of AD (HRAD). OBJECTIVE: The prevalence of high-risk for dementia in cognitively normal controls is explored by assaying plasma biomarkers. METHODS: 422 subjects with normal cognition were enrolled around Taiwan. Plasma Aß1-40, Aß1-42, and T-Tau levels were assayed using immunomagnetic reduction to assess the risk of dementia. RESULTS: The results showed that 4.6% of young adults (age: 20-44 years), 8.5% of middle-aged adults (age: 45-64 years), and 7.3% of elderly adults (age: 65-90 years) had HRAD. The percentage of individuals with HRAD dramatically increased in middle-aged and elderly adults compared to young adults. CONCLUSION: The percentage of HRAD in cognitively normal subjects are approximately 10%, which reveals that the potentially public-health problem of AD in normal population. Although the subject having abnormal levels of Aß or tau is not definitely going on to develop cognitive declines or AD, the risk of suffering cognitive impairment in future is relatively high. Suitable managements are suggested for these high-risk cognitively normal population. Worth noting, attention should be paid to preventing cognitive impairment due to AD, not only in elderly adults but also middle-aged adults.

Objective assessment of impulse control disorder in patients with Parkinson's disease using a low-cost LEGO-like EEG headset: a feasibility study.

BACKGROUND: Patients with Parkinson's disease (PD) can develop impulse control disorders (ICDs) while undergoing a pharmacological treatment for motor control dysfunctions with a dopamine agonist (DA). Conventional clinical interviews or questionnaires can be biased and may not accurately diagnose at the early stage. A wearable electroencephalogram (EEG)-sensing headset paired with an examination procedure can be a potential user-friendly method to explore ICD-related signatures that can detect its early signs and progression by reflecting brain activity. METHODS: A stereotypical Go/NoGo test that targets impulse inhibition was performed on 59 individuals, including healthy controls, patients with PD, and patients with PD diagnosed by ICDs. We conducted two Go/NoGo sessions before and after the DA-pharmacological treatment for the PD and ICD groups. A low-cost LEGO-like EEG headset was used to record concurrent EEG signals. Then, we used the event-related potential (ERP) analytical framework to explore ICD-related EEG abnormalities after DA treatment. RESULTS: After the DA treatment, only the ICD-diagnosed PD patients made more behavioral errors and tended to exhibit the deterioration for the NoGo N2 and P3 peak amplitudes at fronto-central electrodes in contrast to the HC and PD groups. Particularly, the extent of the diminished NoGo-N2 amplitude was prone to be modulated by the ICD scores at Fz with marginal statistical significance (r = - 0.34, p = 0.07). CONCLUSIONS: The low-cost LEGO-like EEG headset successfully captured ERP waveforms and objectively assessed ICD in patients with PD undergoing DA treatment. This objective neuro-evidence could provide complementary information to conventional clinical scales used to diagnose ICD adverse effects.

Neuropsychiatric symptoms in Parkinson's disease: association with caregiver distress and disease severity.

OBJECTIVES: The condition of caregivers is important to the quality of care received by people with Parkinson's disease (PD), especially at the late disease stages. This study addresses the distress placed on caregivers by participants' neuropsychiatric symptoms at different stages of PD in Taiwan. METHODS: This prospective study enrolled 108 people with PD. All participants were examined with the Unified Parkinson's Disease Rating Scale (UPDRS), Neuropsychiatric Inventory (NPI), Mini-Mental State Examination (MMSE), Cognitive Abilities Screening Instrument (CASI), and Clinical Dementia Rating (CDR) scale. Caregiver distress was measured using the Neuropsychiatric Inventory Caregiver Distress Scale (NPI-D). Statistical analysis was used to explore the PD-related factors that contribute to caregiver distress. RESULTS: The mean follow-up interval in the 108 PD participants were 24.0 ± 10.2 months with no participant lost to follow-up due to death. NPI-distress (the sum of NPI caregiver distress scale across the 12 domains of the NPI) was positively correlated with NPI-sum (the total score across the 12 domains of the NPI) (r = 0.787, p < 0.001), CDR (r = 0.403, p < 0.001), UPRDS (r = 0.276, p = 0.004), and disease duration (r = 0.246, p = 0.002), but negatively correlated with CASI (r = -0.237, p = 0.043) and MMSE (r = -0.281, p < 0.001). Multiple linear regression analysis showed that only NPI-sum and disease duration were independently correlated with NPI-distress. CONCLUSION: The disease duration and NPI-sum are independent predictors of caregiver distress in Taiwanese populations with PD. Early detection and reduction of neuropsychiatric symptoms in people with PD can help decrease caregiver distress.

Spontaneous infective spondylitis and mycotic aneurysm: incidence, risk factors, outcome and management experience.

Infective spondylitis occurring concomitantly with mycotic aneurysm is rare. A retrospective record review was conducted in all cases of mycotic aneurysm from January 1995 to December 2004, occurring in a primary care and tertiary referral center. Spontaneous infective spondylitis and mycotic aneurysm were found in six cases (10.3% of 58 mycotic aneurysm patients). Neurological deficit (50% vs. 0; P < 0.001) is the significant clinical manifestation in patients with spontaneous infective spondylitis and mycotic aneurysm. The presence of psoas abscess on computed tomography (83.3% vs. 0; P < 0.001) and endplate destruction on radiography (50% vs. 0; P < 0.001) are predominated in patients with spontaneous infective spondylitis and mycotic aneurysm. Of these six patients, four with Salmonella infection received surgical intervention and all survived. Another two patients (one with Streptococcus pyogenes, another with Staphylococcus aureus) received conservative therapy and subsequently died from rupture of aneurysm or septic shock. Paravertebral soft tissue swelling, presence of psoas abscess and/or unclear soft tissue plane between the aorta and vertebral body in relation to mycotic aneurysm may indicate a concomitant infection in the spine. In contrast, if prevertebral mass is found in the survey of spine infection, coexisting mycotic aneurysm should be considered.