8,345 research outputs found

    The performance of domain-based feature extraction on EEG, ECG, and fNIRS for Huntington’s disease diagnosis via shallow machine learning

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    Introduction: The early detection of Huntington’s disease (HD) can substantially improve patient quality of life. Current HD diagnosis methods include complex biomarkers such as clinical and imaging factors; however, these methods have high time and resource demands.Methods: Quantitative biomedical signaling has the potential for exposing abnormalities in HD patients. In this project, we attempted to explore biomedical signaling for HD diagnosis in high detail. We used a dataset collected at a clinic with 27 HD-positive patients, 36 controls, and 6 unknowns with EEG, ECG, and fNIRS. We first preprocessed the data and then presented a comprehensive feature extraction procedure for statistical, Hijorth, slope, wavelet, and power spectral features. We then applied several shallow machine learning techniques to classify HD-positives from controls.Results: We found the highest accuracy was achieved by the extremely randomized trees algorithm, with an ROC AUC of 0.963 and accuracy of 91.353%.Discussion: The results provide improved performance over competing methodologies and also show promise for biomedical signals for early prognosis of HD

    Fostering Graduate Student Creative Problem Solving in a Professional Military Education Context

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    In military contexts, a tension exists between the need for rapid, unquestioning obedience to orders, especially early in one’s career, and the need for senior leaders to solve complex problems creatively. For officers in the Marine Corps, a key milestone in their careers is the Marine Corps’ Command and Staff College, an intermediate-level professional military education master’s degree program. In 2015, the College, and the wider Marine Corps University community, established a plan to improve student creative problem solving; however, the plan did not meet its outcome goals by 2021. The purpose of this study is twofold. First, using a convergent parallel mixed methods design, this study examined factors related to creative problem solving and their application to Command and Staff College curriculum. Key results of interviews, surveys, and secondary data analysis included the perceived need for additional time for students to think creatively, and the need to address the tension between authoritarian thinking and the imperative to develop new creative solutions. The second part of this study examined an intervention designed to give students more time to think and to give them structural, metacognitive supports for their thinking. Using a quasi-experimental design, the two key factors of concern for the study were metacognition and creative problem solving. Improvements in the students’ metacognitive abilities were expected to lead to improvements in their creative problem-solving ability. Quantitative results showed no significant improvement in creative problem solving while there was actually a significant decrease in perceived metacognitive ability for both the comparison and intervention groups. According to explanatory interviews, one key factor in these results may have been the use of a perception survey, in which decreases in one’s perception of one’s metacognitive ability might mask actual improvements in real metacognitive ability. Another factor that emerged from the explanatory interviews was the need for the intervention to be more fully integrated across the whole curriculum. This study underscores the difficulty of making significant changes to student creative problem solving, especially in a military community. Further study could examine the relationship between perceptions of metacognitive ability and actual metacognitive ability

    The Structure and Function of the Retina in Multiple Sclerosis

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    Background: Multiple sclerosis (MS) is a complex heterogenous autoimmune inflammatory disease with a prolonged and variable time course. The visual system is frequently implicated, either as the presenting symptom, or, with advancement of the disease. This has been documented in the literature with changes in visual acuity (VA) that are accompanied by functional changes in the optic nerve, measured with the visual evoked potential (VEP) and possible retrograde degeneration involving the retinal ganglion cells in the retina, measured with the pattern reversal electroretinogram (PERG). However, inflammatory episodes may be clinical or subclinical in nature and may go unrecognised. Originating from the same embryological origins, the effect of inflammation in MS on the on the retina is less well known. The research hypothesis was that there is a measurable difference in the function of retinal cells in patients with newly diagnosed multiple sclerosis, suggestive of inflammatory retinopathy compared to healthy controls. The overall aim was to investigate any differences in the electrophysiological function of the visual pathway of patients newly diagnosed with MS compared to healthy controls. Methods: The visual system is explored with clinical (VA), electrophysiology (VEP and electroretinography (ERG – pattern and flash) and structural (OCT) measures, in patients presenting with symptoms suggestive of MS to a specialist service. This prospective case control study investigates the visual pathway at the earliest stage of the disease to look for differences in structure and function between patients and healthy volunteers that might serve as a biomarker in the future. Results: There were a number of variables that were significantly different between the two groups, logistic regression analysis found that VA (p 0.038) and VEP P100 peak-time (p 0.014) from the right eye as significant. Dividing the participants by prolongation of the VEP P100 peak-time as defined in clinical practice, found a number of ERG amplitude variables as well as VA that were consistently different between the groups regardless of symptoms. Conclusion: The study confirms optic nerve involvement in MS with VEP and VA abnormalities consistent with the literature in this cohort. Additionally, VA and some ERG amplitude variables were significantly reduced in participants with MS, when grouped according to VEP P100 peak-time, suggesting inner and outer retinal changes. Further work would be required to confirm these findings. No OCT structural changes were found in any of the analysis that included the macula thickness, ganglion cell layer or retinal nerve fibre layer. Keywords: multiple sclerosis (MS), visual evoked potential (VEP), pattern electroretinogram (PERG), electroretinogram (ERG), optical coherence tomography (OCT

    Life on a scale:Deep brain stimulation in anorexia nervosa

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    Anorexia nervosa (AN) is a severe psychiatric disorder marked by low body weight, body image abnormalities, and anxiety and shows elevated rates of morbidity, comorbidity and mortality. Given the limited availability of evidence-based treatments, there is an urgent need to investigate new therapeutic options that are informed by the disorder’s underlying neurobiological mechanisms. This thesis represents the first study in the Netherlands and one of a limited number globally to evaluate the efficacy, safety, and tolerability of deep brain stimulation (DBS) in the treatment of AN. DBS has the advantage of being both reversible and adjustable. Beyond assessing the primary impact of DBS on body weight, psychological parameters, and quality of life, this research is novel in its comprehensive approach. We integrated evaluations of efficacy with critical examinations of the functional impact of DBS in AN, including fMRI, electroencephalography EEG, as well as endocrinological and metabolic assessments. Furthermore, this work situates AN within a broader theoretical framework, specifically focusing on its manifestation as a form of self-destructive behavior. Finally, we reflect on the practical, ethical and philosophical aspects of conducting an experimental, invasive procedure in a vulnerable patient group. This thesis deepens our understanding of the neurobiological underpinnings of AN and paves the way for future research and potential clinical applications of DBS in the management of severe and enduring AN

    The role of upper airway morphology in obstructive sleep apnea

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    Obstructive sleep apnea (OSA) is a highly prevalent sleep-related breathing disorder, characterized by repetitive complete and/or partial obstructions of the upper airway during sleep. It is suggested that impaired upper airway morphology is a fundamental pathophysiological trait of OSA. However, the exact role of the upper airway morphology in the pathogenesis and treatment of OSA is still not well known. Therefore, the general aim of this thesis was to evaluate the role of upper airway morphology in the pathogenesis of different OSA phenotypes and in the effects of mandibular advancement device (MAD) therapy. Upper airway morphology was investigated by cone beam computed tomography (CBCT). No significant differences in the upper airway morphology between positional and non-positional OSA (chapter 2), nor between Dutch and Chinese patients with mild to moderate OSA (chapter 3) were found. Further, miniscrew-assisted orthodontic treatment with premolar extractions increased upper airway dimensions in young adults with Class II malocclusion (chapter 4). Finally, no significant differences in the changes in upper airway dimensions between two types of MADs in situ (chapter 5), nor between responders and non-responders (chapter 6) with mild to moderate OSA were found. Therefore, it was concluded that the upper airway morphology does not play a significant role in the pathogenesis of different OSA phenotypes and in the treatment effects of MADs. Future research involving both anatomical and non-anatomical factors is needed to better understand the pathogenesis and treatment outcomes of OSA

    Classification of temporal lobe epilepsy based on neuropsychological tests and exploration of its underlying neurobiology

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    ObjectiveTo assist improving long-term postoperative seizure-free rate, we aimed to use machine learning algorithms based on neuropsychological data to differentiate temporal lobe epilepsy (TLE) from extratemporal lobe epilepsy (extraTLE), as well as explore the relationship between magnetic resonance imaging (MRI) and neuropsychological tests.MethodsTwenty-three patients with TLE and 23 patients with extraTLE underwent neuropsychological tests and MRI scans before surgery. The least absolute shrinkage and selection operator were firstly employed for feature selection, and a machine learning approach with neuropsychological tests was employed to classify TLE using leave-one-out cross-validation. A generalized linear model was used to analyze the relationship between brain alterations and neuropsychological tests.ResultsWe found that logistic regression with the selected neuropsychological tests generated classification accuracies of 87.0%, with an area under the receiver operating characteristic curve (AUC) of 0.89. Three neuropsychological tests were acquired as significant neuropsychological signatures for the diagnosis of TLE. We also found that the Right-Left Orientation Test difference was related to the superior temporal and the banks of the superior temporal sulcus (bankssts). The Conditional Association Learning Test (CALT) was associated with the cortical thickness difference in the lateral orbitofrontal area between the two groups, and the Component Verbal Fluency Test was associated with the cortical thickness difference in the lateral occipital cortex between the two groups.ConclusionThese results showed that machine learning-based classification with the selected neuropsychological data can successfully classify TLE with high accuracy compared to previous studies, which could provide kind of warning sign for surgery candidate of TLE patients. In addition, understanding the mechanism of cognitive behavior by neuroimaging information could assist doctors in the presurgical evaluation of TLE

    Improving diagnostic procedures for epilepsy through automated recording and analysis of patients’ history

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    Transient loss of consciousness (TLOC) is a time-limited state of profound cognitive impairment characterised by amnesia, abnormal motor control, loss of responsiveness, a short duration and complete recovery. Most instances of TLOC are caused by one of three health conditions: epilepsy, functional (dissociative) seizures (FDS), or syncope. There is often a delay before the correct diagnosis is made and 10-20% of individuals initially receive an incorrect diagnosis. Clinical decision tools based on the endorsement of TLOC symptom lists have been limited to distinguishing between two causes of TLOC. The Initial Paroxysmal Event Profile (iPEP) has shown promise but was demonstrated to have greater accuracy in distinguishing between syncope and epilepsy or FDS than between epilepsy and FDS. The objective of this thesis was to investigate whether interactional, linguistic, and communicative differences in how people with epilepsy and people with FDS describe their experiences of TLOC can improve the predictive performance of the iPEP. An online web application was designed that collected information about TLOC symptoms and medical history from patients and witnesses using a binary questionnaire and verbal interaction with a virtual agent. We explored potential methods of automatically detecting these communicative differences, whether the differences were present during an interaction with a VA, to what extent these automatically detectable communicative differences improve the performance of the iPEP, and the acceptability of the application from the perspective of patients and witnesses. The two feature sets that were applied to previous doctor-patient interactions, features designed to measure formulation effort or detect semantic differences between the two groups, were able to predict the diagnosis with an accuracy of 71% and 81%, respectively. Individuals with epilepsy or FDS provided descriptions of TLOC to the VA that were qualitatively like those observed in previous research. Both feature sets were effective predictors of the diagnosis when applied to the web application recordings (85.7% and 85.7%). Overall, the accuracy of machine learning models trained for the threeway classification between epilepsy, FDS, and syncope using the iPEP responses from patients that were collected through the web application was worse than the performance observed in previous research (65.8% vs 78.3%), but the performance was increased by the inclusion of features extracted from the spoken descriptions on TLOC (85.5%). Finally, most participants who provided feedback reported that the online application was acceptable. These findings suggest that it is feasible to differentiate between people with epilepsy and people with FDS using an automated analysis of spoken seizure descriptions. Furthermore, incorporating these features into a clinical decision tool for TLOC can improve the predictive performance by improving the differential diagnosis between these two health conditions. Future research should use the feedback to improve the design of the application and increase perceived acceptability of the approach

    Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives

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    Deep learning has demonstrated remarkable performance across various tasks in medical imaging. However, these approaches primarily focus on supervised learning, assuming that the training and testing data are drawn from the same distribution. Unfortunately, this assumption may not always hold true in practice. To address these issues, unsupervised domain adaptation (UDA) techniques have been developed to transfer knowledge from a labeled domain to a related but unlabeled domain. In recent years, significant advancements have been made in UDA, resulting in a wide range of methodologies, including feature alignment, image translation, self-supervision, and disentangled representation methods, among others. In this paper, we provide a comprehensive literature review of recent deep UDA approaches in medical imaging from a technical perspective. Specifically, we categorize current UDA research in medical imaging into six groups and further divide them into finer subcategories based on the different tasks they perform. We also discuss the respective datasets used in the studies to assess the divergence between the different domains. Finally, we discuss emerging areas and provide insights and discussions on future research directions to conclude this survey.Comment: Under Revie
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