296 research outputs found

    A neuroimaging investigation of bipolar disorder and the neurocognitive effects of 5-HT7 antagonists

    Get PDF
    Bipolar disorder is a psychiatric disorder characterised by pathological mood states, but there is growing recognition of the role of cognitive impairment and dysfunction of emotional processes, which has a profound impact on quality of life. Many people with bipolar disorders exhibit brain volume impairment associated with cognitive dysfunction and an increased risk of dementia. In this thesis, I conducted a systematic review to understand the relationships between mood disorders and the 5-HT7 receptor. The 5-HT7 receptor is related to depression and anxiety, but the relationship between 5-HT7 and mania remains unclear; in addition, sleep and memory were also related to the 5-HT7 receptor. Followed by these findings, in the next two chapters, I examined the effects of 5-HT7 antagonists, using JNJ-18038683, on emotional and cognitive functioning, as well as their neural substrates. I then reported on neuroimaging investigations examining the effects of 5-HT7 antagonists on emotional processing and cognitive function in healthy volunteers to gain insight into their potential mode of action and utility for bipolar disorder. In fMRI analyses, the drug acted on 5-HT7 receptors potentially improving cognitive performance by modulating the function of the Cognitive Control Network in healthy controls. In the above-mentioned chapters, I gained a better understanding of the 5-HT7 antagonist, JNJ-18038683, and the putative promising effects for pharmacological treatments. However, the approach taken has some limitations, including a small sample size, potential participant bias, and a lack of systematic control of medication dose and duration of administration. In addition, in Chapter 5, I explored the brain basis of bipolar disorder and its links to cognitive and emotional dysfunction using a new ‘brain age’ approach. Individuals with bipolar disorder were found to have increased brain age compared to healthy controls. I hope that these findings can be applied to pharmacological treatment for individuals with bipolar disorder, ultimately allowing patients to benefit from the drug in the future

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

    Get PDF
    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

    Get PDF
    Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden

    Evaluating footwear “in the wild”: Examining wrap and lace trail shoe closures during trail running

    Get PDF
    Trail running participation has grown over the last two decades. As a result, there have been an increasing number of studies examining the sport. Despite these increases, there is a lack of understanding regarding the effects of footwear on trail running biomechanics in ecologically valid conditions. The purpose of our study was to evaluate how a Wrap vs. Lace closure (on the same shoe) impacts running biomechanics on a trail. Thirty subjects ran a trail loop in each shoe while wearing a global positioning system (GPS) watch, heart rate monitor, inertial measurement units (IMUs), and plantar pressure insoles. The Wrap closure reduced peak foot eversion velocity (measured via IMU), which has been associated with fit. The Wrap closure also increased heel contact area, which is also associated with fit. This increase may be associated with the subjective preference for the Wrap. Lastly, runners had a small but significant increase in running speed in the Wrap shoe with no differences in heart rate nor subjective exertion. In total, the Wrap closure fit better than the Lace closure on a variety of terrain. This study demonstrates the feasibility of detecting meaningful biomechanical differences between footwear features in the wild using statistical tools and study design. Evaluating footwear in ecologically valid environments often creates additional variance in the data. This variance should not be treated as noise; instead, it is critical to capture this additional variance and challenges of ecologically valid terrain if we hope to use biomechanics to impact the development of new products

    Prehabilitation for the management of rotator cuff surgery

    Full text link
    Rotator cuff tears are a common cause of shoulder pain in the general community. Approximately one-third of patients with rotator cuff tears proceed to surgery following the failure of conservative treatments such as physiotherapy, non-steroidal anti- inflammatory drugs, opioid analgesics, and cortisone injections. However, rotator cuff tears continue to develop over time, and the burden of illness for patients awaiting rotator cuff repair is substantial, resulting in loss of strength, functional status, and poor quality of life. This dissertation proposes a three-stage approach for the management of rotator tears in patients awaiting surgery, which includes an accurate and reliable evaluation of shoulder range of motion (ROM) and strength, a pre-operative intervention to improve function and quality of life, and an appraisal of potential prognostic factors that can lead to better future clinical outcomes. Therefore, the organisation of this thesis is divided into three sections covering shoulder assessment, intervention, and prognosis. Chapter 1 introduces the concept of prehabilitation, a rapid systematic review, evidence gaps in the literature, and the rationale for shoulder prehabilitation. Prehabilitation is defined as enhancing a patient's functional ability before surgery to improve clinical outcomes following surgery. The rapid systematic review included only high-quality studies based on the National Health and Medical Research Council (Australia) evidence guidelines and the Physiotherapy Evidence Database (PEDro) rating scale. Only pre-operative exercise intervention studies for surgical knee and hip populations were identified. To date, no studies have investigated the efficacy of prehabilitation for patients scheduled for shoulder surgery. This finding necessitated a review of the considerable body of research on rotator cuff tears. Chapter 2 provides a synthesis of the current literature regarding shoulder anatomy, biomechanics of the rotator cuff, epidemiology, aetiology and classification of rotator cuff tears, shoulder assessment methods, an overview of management options, evidence for post-operative rehabilitation, and prognostic factors and potential predictors of outcome associated with rotator cuff surgery. Chapter 3 presents a published study examining the intra- and inter-rater reliability of a variety of testing protocols to measure ROM and strength in healthy participants. The objective measurement of ROM and strength is an integral part of the physical examination of patients with rotator cuff tears and is vital in quantifying improvement after conservative or surgical intervention. Correctly evaluating and interpreting objective shoulder measurements informs the clinical reasoning underlying treatment. Since pre- operative ROM and strength are potentially modifiable predictors for rotator cuff repair success, a precise assessment using reliable instruments and testing methods is essential. The outcomes of this study supported the selection of assessment methods for a randomised controlled trial (Chapter 7) on shoulder prehabilitation. Chapter 4 presents a published systematic review and meta-analysis on the reliability of the Kinect and ambulatory motion-tracking devices to measure shoulder ROM. According to our reliability study findings in Chapter 3, existing methods for evaluating shoulder ROM are less reliable. Emerging inertial sensor technologies and optical markerless motion-tracking systems are valid alternatives to standard ROM assessment methods. However, reliability must also be established before this technology can be used routinely in clinical settings. Chapter 5 presents a published validity and reliability study on the HumanTrak system to measure shoulder ROM in healthy subjects. Based on our findings in Chapter 4, we evaluated the clinical potential of using a movement analysis system that combines inertial sensors with the Microsoft Kinect (HumanTrak) to measure shoulder ROM reliably and accurately. Chapter 6 is a systematic review and meta-analysis of prehabilitation for the management of orthopaedic surgery. The initial rapid systematic review in Chapter 1 only identified orthopaedic prehabilitation programmes for patients undergoing lower limb joint arthroplasty, anterior cruciate ligament reconstruction, and spinal surgery. Given the growing research and clinical adoption of prehabilitation over the past decade, we undertook an updated and more comprehensive systematic review to identify and critically appraise the content and reporting of prehabilitation programmes for all orthopaedic surgeries. Exercise therapy is commonly first line treatment for older patients with non-traumatic rotator cuff tears. Despite growing evidence that exercise therapy and surgery can achieve comparable clinical outcomes, there is a paucity of high-quality studies on the impact of pre-operative exercise or education for patients awaiting rotator cuff surgery. Hence, the main aim of this thesis is to investigate the efficacy of a combined pre-operative exercise and education programme on function and quality of life before and after rotator cuff surgery. Chapter 7 is a randomised control trial (RCT) investigating whether the addition of a pre-operative exercise and education programme to usual care for patients awaiting rotator cuff surgery is more effective than usual care alone. Fifty patients with unilateral rotator cuff tears received either an 8-week shoulder exercise and education prehabilitation (SPrEE) programme or usual care (UC). The SPrEE programme compared to UC resulted in superior and statistically significant improvements in the primary outcomes of SPADI, WORC and SF-36 in the pre-operative phase. The SPrEE program was not more effective than UC alone in improving primary outcomes at 3-, 6- or 12 month follow-up timepoints. There were no statistically significant between-group differences in SPrEE and UC secondary outcomes for surgical or non-surgical patients. Chapter 8 investigated any correlations between pre-operative magnetic resonance imaging (MRI) characteristics and patient-reported outcome measures for patients who underwent rotator cuff repair or no surgery and received either prehabilitation or usual care in the RCT (Chapter 7). Prognosis-based prehabilitation can effectively identify patients who will derive the greatest benefit. Chapter 9 summarises thesis findings, strengths, and directions for future research to optimise function and quality of life prior to rotator cuff surgery

    On Computer Mouse Pointing Model Online Identification and Endpoint Prediction

    Get PDF
    International audienceThis paper proposes a new simplified pointing model as a feedback-based dynamical system, including both human and computer sides of the process. It takes into account the commutation between the correction and ballistic phases in pointing tasks. We use the mouse position increment signal from noisy experimental data to achieve our main objectives: to estimate the model parameters online and predict the task endpoint. Some estimation tools and validation results, applying linear regression techniques on the experimental data are presented. We also compare with a similar prediction algorithm to show the potential of our algorithm's implementation
    corecore