Towards Monitoring Parkinson's Disease Following Drug Treatment: CGP Classification of rs-MRI Data

Background and Objective: It is commonly accepted that accurate monitoring of neurodegenerative diseases is crucial for effective disease management and delivery of medication and treatment. This research develops automatic clinical monitoring techniques for PD, following treatment, using the novel application of EAs. Specifically, the research question addressed was: Can accurate monitoring of PD be achieved using EAs on rs-fMRI data for patients prescribed Modafinil (typically prescribed for PD patients to relieve physical fatigue)? Methods: This research develops novel clinical monitoring tools using data from a controlled experiment where participants were administered Modafinil versus placebo, examining the novel application of EAs to both map and predict the functional connectivity in participants using rs-fMRI data. Specifically, CGP was used to classify DCM analysis and timeseries data. Results were validated with two other commonly used classification methods (ANN and SVM) and via k-fold cross-validation. Results: Findings revealed a maximum accuracy of 74.57% for CGP. Furthermore, CGP provided comparable performance accuracy relative to ANN and SVM. Nevertheless, EAs enable us to decode the classifier, in terms of understanding the data inputs that are used, more easily than in ANN and SVM. Conclusions: These findings underscore the applicability of both DCM analyses for classification and CGP as a novel classification technique for brain imaging data with medical implications for medication monitoring. Furthermore, classification of fMRI data for research typically involves statistical modelling techniques being often hypothesis driven, whereas EAs use data-driven explanatory modelling methods resulting in numerous benefits. DCM analysis is novel for classification and advantageous as it provides information on the causal links between different brain regions.Comment: arXiv admin note: substantial text overlap with arXiv:1910.0537

Using the theoretical domains framework to explore behavioural determinants for medication taking in patients following percutaneous coronary intervention.

The aim of this study was to investigate relationships between factors influencing medication taking and behavioural determinants in patients who have undergone percutaneous coronary intervention (PCI). A cross-sectional survey was undertaken, using a postal questionnaire distributed to PCI patients. The questionnaire was iteratively developed by the research team, with reference to the Theoretical Domains Framework (TDF) of behavioural determinants, reviewed for face and content validity, and piloted. Data were analysed using descriptive and Principal Component Analysis (PCA). Inferential analysis explored relationships between PCA component scores and factors influencing medicating taking behaviour. The adjusted response rate was 62.4% (325/521). PCA gave 3 components: (C1) Self-perceptions of knowledge and abilities in relation to medication taking; (C2) Aspects relating to activities and support in medication taking; (C3) Emotional aspects in taking medication. Generally, respondents held very positive views. Statistically significant relationships between all three components and self-reported chest pain/discomfort indicated patients with ongoing chest pain/discomfort post-PCI are more likely to have behavioural determinants and beliefs which make medication-taking challenging. Respondents who were on 10 or more medications had lower levels of agreement to the C2 and C3 statements indicating challenges associated with their activities / support and anxieties in medication taking. The study concluded that PCI patients show links between TDF behavioural determinants and factors influencing medication taking for those reporting chest pain or polypharmacy. Further research needs to explore the effective design and implementation of behavioural change interventions to reduce the challenge of medication-taking

Characterization and Classification of Adherent Cells in Monolayer Culture using Automated Tracking and Evolutionary Algorithms

This paper presents a novel method for tracking and characterizing adherent cells in monolayer culture. A system of cell tracking employing computer vision techniques was applied to time-lapse videos of replicate normal human uro-epithelial cell cultures exposed to different concentrations of adenosine triphosphate (ATP) and a selective purinergic P2X antagonist (PPADS), acquired over a 24 hour period. Subsequent analysis following feature extraction demonstrated the ability of the technique to successfully separate the modulated classes of cell using evolutionary algorithms. Specifically, a Cartesian Genetic Program (CGP) network was evolved that identified average migration speed, in-contact angular velocity, cohesivity and average cell clump size as the principal features contributing to the separation. Our approach not only provides non-biased and parsimonious insight into modulated class behaviors, but can be extracted as mathematical formulae for the parameterization of computational models

Mixed-method exploratory study of general practitioner and nurse perceptions of a new community based nurse-led heart failure service

Introduction: The treatment of patients with chronic heart failure (CHF) remains sub-optimal. Specialist CHF nurses are proven to improve care and reduce admission but developing such services, especially in remote areas, can be difficult. This study aimed: first, to assess the perceived acceptability and effectiveness of a new community based nurse-led heart failure service by general practitioners (GPs) in an area with a dispersed population; second, to assess the knowledge and learning needs of GPs; and third, to assess perceptions of the use of national guidelines and telehealth on heart failure management. Methods: The study was conducted in the Scottish Highlands, a large geographical area in the north of the UK which includes both rural and urban populations. The area has a total population of 240 000, approximately 60% of whom are within 1 hour travel time of the largest urban centre. A postal survey of all GPs (n = 260) and structured email survey of all CHF specialist nurses (n = 3) was performed. All responses were entered into a Microsoft Excel spreadsheet, summarised and subjected to thematic analysis. Differences between GPs in ‘rural’, ‘urban’ or both ‘urban & rural’ was investigated using an F-test for continuous variables and a three-sample test for equality of proportions for nominal data. Results: Questionnaires were returned from 83 GPs (32%) and all three CHF specialist nurses. In this sample there were only a few differences between GPs from ‘rural’, ‘urban’ and ‘urban & rural’. There also appeared to be little difference in responses between those who had the experience of the CHF nurse service and those who had not. Overall, 32 GPs (39%) wished better, local access to echocardiography, while 63 (76%) wished access to testing for brain natriuretic peptide (BNP). Only 27 GPs (33%) referred all patients with CHF to hospital. A number of GPs stated that this was dependant on individual circumstances and the patient’s ability to travel. The GPs were confident to initiate standard heart failure drugs although only 54 (65%) were confident in the initiation of beta-blockers. Most GPs (69%) had had experience of the CHF specialist nurse service and the responses were mixed. The GPs who had experienced the service appeared less confident that it would lead to reduced admission of patients to hospital (51% vs 77%, p = 0.046). Three main themes emerged from the nurse responses: service planning, communication and attitudinal changes after service embedment. Conclusions: This study demonstrates that a community based heart failure nurse service was not universally valued. Differences between urban and rural localities (communication) suggest that models of care derived from evidence based practice in urban areas may not be directly transferable to remote areas. Clearly, good communication among staff groups at all stages of implementation is important; however, despite best efforts and clinical trial evidence, specialist nurse services will not be welcomed by all doctors. Service providers and commissioners should be cognisant of the different roles of urban and rural GPs when designing such services. Among GPs there was a high degree of confidence with initiation and titration of drugs for heart failure with the exception of beta-blockers so clearly this is an area of ongoing educational need and support. Education and support should focus on ensuring that all doctors who care for patients with CHF have the skills and confidence to use medical therapies and specialist services as appropriate

Use of administrative data for the surveillance of mental disorders in 5 provinces

To evaluate the usefulness of administrative data for the surveillance of mental illness in Canada using databases in the following 5 provinces: British Columbia, Ontario, Quebec, Nova Scotia, and Alberta. We used a population-based record-linkage analysis with data from physician billings, hospital discharge abstracts, and community-based clinics. The following diagnostic codes from the International Classification of Diseases, Ninth Edition, were used to define cases: 290 to 319, inclusive. The prevalence of treated psychiatric disorder was similar in Nova Scotia, British Columbia, Alberta, and Ontario at about 15%. The prevalence for Quebec was slightly lower at 12%. Findings from the provinces showed remarkable consistency across age and sex, despite variations in data coding. Women tended to show a higher prevalence overall of treated mental disorders than men. Prevalence increased steadily to middle age, declining in the 50s and 60s, and then increasing again after age 70 years. Provincial and territorial administrative data can provide a useful, reliable, and economical source of information for the surveillance of treated mental disorders. Such a surveillance system can provide longitudinal data at little cost to support health service provision and planning

Prediction of Cognitive Decline in Healthy Older Adults using fMRI

Few studies have examined the extent to which structural and functional MRI, alone and in combination with genetic biomarkers, can predict future cognitive decline in asymptomatic elders. This prospective study evaluated individual and combined contributions of demographic information, genetic risk, hippocampal volume, and fMRI activation for predicting cognitive decline after an 18-month retest interval. Standardized neuropsychological testing, an fMRI semantic memory task (famous name discrimination), and structural MRI (sMRI) were performed on 78 healthy elders (73% female; mean age = 73 years, range = 65 to 88 years). Positive family history of dementia and presence of one or both apolipoprotein E (APOE) ε4 alleles occurred in 51.3% and 33.3% of the sample, respectively. Hippocampal volumes were traced from sMRI scans. At follow-up, all participants underwent a repeat neuropsychological examination. At 18 months, 27 participants (34.6%) declined by at least 1 SD on one of three neuropsychological measures. Using logistic regression, demographic variables (age, years of education, gender) and family history of dementia did not predict future cognitive decline. Greater fMRI activity, absence of an APOE ε4 allele, and larger hippocampal volume were associated with reduced likelihood of cognitive decline. The most effective combination of predictors involved fMRI brain activity and APOE ε4 status. Brain activity measured from task-activated fMRI, in combination with APOE ε4 status, was successful in identifying cognitively intact individuals at greatest risk for developing cognitive decline over a relatively brief time period. These results have implications for enriching prevention clinical trials designed to slow AD progression

Using Echo State Networks for Classification : A Case Study in Parkinson's Disease Diagnosis

Despite having notable advantages over established machine learning methods for time series analysis, reservoir computing methods, such as echo state networks (ESNs), have yet to be widely used for practical data mining applications. In this paper, we address this deficit with a case study that demonstrates how ESNs can be trained to predict disease labels when stimulated with movement data. Since there has been relatively little prior research into using ESNs for classification, we also consider a number of different approaches for realising input-output mappings. Our results show that ESNs can carry out effective classification and are competitive with existing approaches that have significantly longer training times, in addition to performing similarly with models employing conventional feature extraction strategies that require expert domain knowledge. This suggests that ESNs may prove beneficial in situations where predictive models must be trained rapidly and without the benefit of domain knowledge, for example on high-dimensional data produced by wearable medical technologies. This application area is emphasized with a case study of Parkinson’s Disease patients who have been recorded by wearable sensors while performing basic movement tasks

Comparison of Semantic and Episodic Memory BOLD fMRI Activation in Predicting Cognitive Decline in Older Adults

Previous studies suggest that task-activated functional magnetic resonance imaging (fMRI) can predict future cognitive decline among healthy older adults. The present fMRI study examined the relative sensitivity of semantic memory (SM) versus episodic memory (EM) activation tasks for predicting cognitive decline. Seventy-eight cognitively intact elders underwent neuropsychological testing at entry and after an 18-month interval, with participants classified as cognitively “Stable” or “Declining” based on ≥1.0 SD decline in performance. Baseline fMRI scanning involved SM (famous name discrimination) and EM (name recognition) tasks. SM and EM fMRI activation, along with Apolipoprotein E (APOE) ε4 status, served as predictors of cognitive outcome using a logistic regression analysis. Twenty-seven (34.6%) participants were classified as Declining and 51 (65.4%) as Stable. APOE ε4 status alone significantly predicted cognitive decline (R2 = .106; C index = .642). Addition of SM activation significantly improved prediction accuracy (R2 = .285; C index = .787), whereas the addition of EM did not (R2 = .212; C index = .711). In combination with APOE status, SM task activation predicts future cognitive decline better than EM activation. These results have implications for use of fMRI in prevention clinical trials involving the identification of persons at-risk for age-associated memory loss and Alzheimer\u27s disease. (JINS, 2012, 18, 1–11

Recognition of Famous Names Predicts Cognitive Decline in Healthy Elders

Objective: The ability to recognize familiar people is impaired in both Mild Cognitive Impairment (MCI) and Alzheimer’s Dementia (AD). In addition, both groups often demonstrate a time-limited temporal gradient (TG) in which well known people from decades earlier are better recalled than those learned recently. In this study, we examined the TG in cognitively intact elders for remote famous names (1950–1965) compared to more recent famous names (1995–2005). We hypothesized that the TG pattern on a famous name recognition task (FNRT) would predict future cognitive decline, and also show a significant correlation with hippocampal volume. Method: Seventy-eight healthy elders (ages 65–90) with age-appropriate cognitive functioning at baseline were administered a FNRT. Follow-up testing 18 months later produced two groups: Declining (≥ 1 SD reduction on at least one of three measures) and Stable (\u3c 1 SD). Results: The Declining group (N = 27) recognized fewer recent famous names than the Stable group (N = 51), although recognition for remote names was comparable. Baseline MRI volumes for both the left and right hippocampi were significantly smaller in the Declining group than the Stable group. Smaller baseline hippocampal volume was also significantly correlated with poorer performance for recent, but not remote famous names. Logistic regression analyses indicated that baseline TG performance was a significant predictor of group status (Declining vs. Stable) independent of chronological age and APOE ε4 inheritance. Conclusions: The TG for famous name recognition may serve as an early preclinical cognitive marker of cognitive decline in healthy older individual

Deep Learning Based Fall Detection using WiFi Channel State Information

Falls have always been one of the major threats to the health and well-being of elderly people, particularly for those living alone. Both wearable and non-wearable fall detection systems have already been developed. However, the fall detection systems using WiFi channel state information (CSI) have attracted a significant interest from researchers due to their non-intrusive and low-cost nature. There are existing machine learning (ML) based fall detection systems using WiFi CSI; however, most systems trained with comprehensive datasets tend to achieve relatively lower accuracy compared to that of the systems trained with less inclusive datasets. To address these issues, we propose a novel, deep learning based fall detection technique. First, we implement different WiFi CSI collection tools and evaluate their potential for fall detection. To develop a highly accurate fall detection technique, we construct a comprehensive dataset, which consists of over 700 CSI samples including different types of falls and other daily activities, performed in four different indoor environments on and off the dominant paths. With this dataset, we then develop a deep learning based classifier using an image classification algorithm. The proposed technique, unlike the other fall detection systems, only requires down sampling and reshaping in pre-processing. The proposed fall detection system is evaluated with the constructed dataset, and it outperforms two other existing systems. It achieves over 96% accuracy for CSI collected in all four environments and 99% accuracy for CSI collected in certain combinations of the environments