EVIDENCE-BASED MUSIC THERAPY TREATMENT TO ELEVATE MOOD DURING ACUTE STROKE CARE

Stroke is the fifth leading cause of death in the U.S. with approximately 795,000 Americans experiencing a stroke each year. In addition to common difficulties with communication and physical impairments following stroke, psychosocial impacts warrant assessment and treatment. Experiencing a stroke can lead to depression, mood disorders, and difficulties with emotion regulation. It is well documented that post-stroke depression (PSD) affects a third of stroke survivors. Higher levels of depression and depressive symptoms are associated with a less efficient use of rehabilitation services, poor functional outcomes, greater odds of hospital readmission, negative impacts on social participation, and increased mortality. The acute phase of stroke recovery may be a key factor in influencing the depression trajectory with early depression predicting poor longitudinal outcomes. The current approach to treating PSD is medication. However, psychotherapy approaches have demonstrated more promise in preventing PSD. Investigations into music-based treatments have shown encouraging results following acquired brain injuries with active music therapy interventions demonstrating large effect sizes for mood improvement. Therefore, the purpose of this three-part dissertation was to examine the effects of active music therapy on mood and describe the clinical decision making process of using music therapy to target mood elevation for hospitalized adults following a first-time acute ischemic stroke. The first study examined the effect of one treatment of active music therapy on mood following a first-time ischemic stroke during acute hospitalization. Active music therapy was defined as music making interventions that elicit and encourage active participation from participants. The Faces Scale was used to assess mood immediately prior to and following treatment. Forty-four adults received at least one treatment. A significant change in mood was found following one treatment. Comment analysis indicated that participants viewed music therapy as a positive experience. The second study investigated the impact of receiving two treatments of active music therapy on mood as compared to one. No significant difference was found between those who received one treatment and those who received two. Both dosing groups demonstrated significant mood improvement; however Group 2 (two treatments) had more severe strokes and did not improve until the second session. The purpose of the third study was to describe the clinical decision-making (CDM) process of a music therapist targeting mood elevation for hospitalized patients following a first-time acute ischemic stroke. The Three Phase Process Model of Collaborative Self-Study was selected as a guiding qualitative methodological framework. Data was collected from four sources: (a) electronic medical records, (b) audio recordings of eight music therapy treatments, (c) a researcher journal, and (d) patient and caregiver/visitor comments. Results indicate that factors influencing CDM included progression through a four-stage treatment process, use of a variety of music-based and therapy-based techniques, and the monitoring and influencing of participant levels of arousal, affect, salience, and engagement. In conclusion, active music therapy during acute hospitalization following a first-time ischemic stroke is effective in significantly improving mood. Components of clinical decision making to elevate mood are illustrated in a provided conceptual framework. Continued investigation is warranted with consideration of stroke severity, dosing amounts, and additional outcomes of interest. Longitudinal investigation is needed to evaluate the impact of treatment on the trajectory of post-stroke depression

Developing and Applying CAD-generated Image Markers to Assist Disease Diagnosis and Prognosis Prediction

Developing computer-aided detection and/or diagnosis (CAD) schemes has been an active research topic in medical imaging informatics (MII) with promising results in assisting clinicians in making better diagnostic and/or clinical decisions in the last two decades. To build robust CAD schemes, we need to develop state-of-the-art image processing and machine learning (ML) algorithms to optimize each step in the CAD pipeline, including detection and segmentation of the region of interest, optimal feature generation, followed by integration to ML classifiers. In my dissertation, I conducted multiple studies investigating the feasibility of developing several novel CAD schemes in the field of medicine concerning different purposes. The first study aims to investigate how to optimally develop a CAD scheme of contrast-enhanced digital mammography (CEDM) images to classify breast masses. CEDM includes both low energy (LE) and dual-energy subtracted (DES) images. A CAD scheme was applied to segment mass regions depicting LE and DES images separately. Optimal segmentation results generated from DES images were also mapped to LE images or vice versa. After computing image features, multilayer perceptron-based ML classifiers integrated with a correlation-based feature subset evaluator and leave-one-case-out cross-validation method were built to classify mass regions. The study demonstrated that DES images eliminated the overlapping effect of dense breast tissue, which helps improve mass segmentation accuracy. By mapping mass regions segmented from DES images to LE images, CAD yields significantly improved performance. The second study aims to develop a new quantitative image marker computed from the pre-intervention computed tomography perfusion (CTP) images and evaluate its feasibility to predict clinical outcome among acute ischemic stroke (AIS) patients undergoing endovascular mechanical thrombectomy after diagnosis of large vessel occlusion. A CAD scheme is first developed to pre-process CTP images of different scanning series for each study case, perform image segmentation, quantify contrast-enhanced blood volumes in bilateral cerebral hemispheres, and compute image features related to asymmetrical cerebral blood flow patterns based on the cumulative cerebral blood flow curves of two hemispheres. Next, image markers based on a single optimal feature and ML models fused with multi-features are developed and tested to classify AIS cases into two classes of good and poor prognosis based on the Modified Rankin Scale. The study results show that ML model trained using multiple features yields significantly higher classification performance than the image marker using the best single feature (p<0.01). This study demonstrates the feasibility of developing a new CAD scheme to predict the prognosis of AIS patients in the hyperacute stage, which has the potential to assist clinicians in optimally treating and managing AIS patients. The third study aims to develop and test a new CAD scheme to predict prognosis in aneurysmal subarachnoid hemorrhage (aSAH) patients using brain CT images. Each patient had two sets of CT images acquired at admission and prior to discharge. CAD scheme was applied to segment intracranial brain regions into four subregions, namely, cerebrospinal fluid (CSF), white matter (WM), gray matter (GM), and extraparenchymal blood (EPB), respectively. CAD then computed nine image features related to 5 volumes of the segmented sulci, EPB, CSF, WM, GM, and four volumetrical ratios to sulci. Subsequently, 16 ML models were built using multiple features computed either from CT images acquired at admission or prior to discharge to predict eight prognosis related parameters. The results show that ML models trained using CT images acquired at admission yielded higher accuracy to predict short-term clinical outcomes, while ML models trained using CT images acquired prior to discharge had higher accuracy in predicting long-term clinical outcomes. Thus, this study demonstrated the feasibility of predicting the prognosis of aSAH patients using new ML model-generated quantitative image markers. The fourth study aims to develop and test a new interactive computer-aided detection (ICAD) tool to quantitatively assess hemorrhage volumes. After loading each case, the ICAD tool first segments intracranial brain volume, performs CT labeling of each voxel. Next, contour-guided image-thresholding techniques based on CT Hounsfield Unit are used to estimate and segment hemorrhage-associated voxels (ICH). Next, two experienced neurology residents examine and correct the markings of ICH categorized into either intraparenchymal hemorrhage (IPH) or intraventricular hemorrhage (IVH) to obtain the true markings. Additionally, volumes and maximum two-dimensional diameter of each sub-type of hemorrhage are also computed for understanding ICH prognosis. The performance to segment hemorrhage regions between semi-automated ICAD and the verified neurology residents’ true markings is evaluated using dice similarity coefficient (DSC). The data analysis results in the study demonstrate that the new ICAD tool enables to segment and quantify ICH and other hemorrhage volumes with higher DSC. Finally, the fifth study aims to bridge the gap between traditional radiomics and deep learning systems by comparing and assessing these two technologies in classifying breast lesions. First, one CAD scheme is applied to segment lesions and compute radiomics features. In contrast, another scheme applies a pre-trained residual net architecture (ResNet50) as a transfer learning model to extract automated features. Next, the principal component algorithm processes both initially computed radiomics and automated features to create optimal feature vectors. Then, several support vector machine (SVM) classifiers are built using the optimized radiomics or automated features. This study indicates that (1) CAD built using only deep transfer learning yields higher classification performance than the traditional radiomic-based model, (2) SVM trained using the fused radiomics and automated features does not yield significantly higher AUC, and (3) radiomics and automated features contain highly correlated information in lesion classification. In summary, in all these studies, I developed and investigated several key concepts of CAD pipeline, including (i) pre-processing algorithms, (ii) automatic detection and segmentation schemes, (iii) feature extraction and optimization methods, and (iv) ML and data analysis models. All developed CAD models are embedded with interactive and visually aided graphical user interfaces (GUIs) to provide user functionality. These techniques present innovative approaches for building quantitative image markers to build optimal ML models. The study results indicate the underlying CAD scheme's potential application to assist radiologists in clinical settings for their assessments in diagnosing disease and improving their overall performance

Computational neurorehabilitation: modeling plasticity and learning to predict recovery

Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling – regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity

The Empirical Foundations of Teleradiology and Related Applications: A Review of the Evidence

Introduction: Radiology was founded on a technological discovery by Wilhelm Roentgen in 1895. Teleradiology also had its roots in technology dating back to 1947 with the successful transmission of radiographic images through telephone lines. Diagnostic radiology has become the eye of medicine in terms of diagnosing and treating injury and disease. This article documents the empirical foundations of teleradiology. Methods: A selective review of the credible literature during the past decade (2005?2015) was conducted, using robust research design and adequate sample size as criteria for inclusion. Findings: The evidence regarding feasibility of teleradiology and related information technology applications has been well documented for several decades. The majority of studies focused on intermediate outcomes, as indicated by comparability between teleradiology and conventional radiology. A consistent trend of concordance between the two modalities was observed in terms of diagnostic accuracy and reliability. Additional benefits include reductions in patient transfer, rehospitalization, and length of stay.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140295/1/tmj.2016.0149.pd

DEVELOPING NOVEL COMPUTER-AIDED DETECTION AND DIAGNOSIS SYSTEMS OF MEDICAL IMAGES

Reading medical images to detect and diagnose diseases is often difficult and has large inter-reader variability. To address this issue, developing computer-aided detection and diagnosis (CAD) schemes or systems of medical images has attracted broad research interest in the last several decades. Despite great effort and significant progress in previous studies, only limited CAD schemes have been used in clinical practice. Thus, developing new CAD schemes is still a hot research topic in medical imaging informatics field. In this dissertation, I investigate the feasibility of developing several new innovative CAD schemes for different application purposes. First, to predict breast tumor response to neoadjuvant chemotherapy and reduce unnecessary aggressive surgery, I developed two CAD schemes of breast magnetic resonance imaging (MRI) to generate quantitative image markers based on quantitative analysis of global kinetic features. Using the image marker computed from breast MRI acquired pre-chemotherapy, CAD scheme enables to predict radiographic complete response (CR) of breast tumors to neoadjuvant chemotherapy, while using the imaging marker based on the fusion of kinetic and texture features extracted from breast MRI performed after neoadjuvant chemotherapy, CAD scheme can better predict the pathologic complete response (pCR) of the patients. Second, to more accurately predict prognosis of stroke patients, quantifying brain hemorrhage and ventricular cerebrospinal fluid depicting on brain CT images can play an important role. For this purpose, I developed a new interactive CAD tool to segment hemorrhage regions and extract radiological imaging marker to quantitatively determine the severity of aneurysmal subarachnoid hemorrhage at presentation and correlate the estimation with various homeostatic/metabolic derangements and predict clinical outcome. Third, to improve the efficiency of primary antibody screening processes in new cancer drug development, I developed a CAD scheme to automatically identify the non-negative tissue slides, which indicate reactive antibodies in digital pathology images. Last, to improve operation efficiency and reliability of storing digital pathology image data, I developed a CAD scheme using optical character recognition algorithm to automatically extract metadata from tissue slide label images and reduce manual entry for slide tracking and archiving in the tissue pathology laboratories. In summary, in these studies, we developed and tested several innovative approaches to identify quantitative imaging markers with high discriminatory power. In all CAD schemes, the graphic user interface-based visual aid tools were also developed and implemented. Study results demonstrated feasibility of applying CAD technology to several new application fields, which has potential to assist radiologists, oncologists and pathologists improving accuracy and consistency in disease diagnosis and prognosis assessment of using medical image

Non-communicable Diseases, Big Data and Artificial Intelligence

This reprint includes 15 articles in the field of non-communicable Diseases, big data, and artificial intelligence, overviewing the most recent advances in the field of AI and their application potential in 3P medicine

Technology-supported training of arm-hand skills in stroke

Impaired arm-hand performance is a serious consequence of stroke that is associated with reduced self-efficacy and poor quality of life. Task-oriented arm training is a therapy approach that is known to improve skilled arm-hand performance, even in chronic stages after stroke. At the start of this project, little knowledge had been consolidated regarding taskoriented arm training characteristics, especially in the field of technology-supported rehabilitation. The feasibility and effects of technology-supported client-centred task-oriented training on skilled arm-hand performance had not been investigated but to a very limited degree. Reviewing literature on rehabilitation and motor learning in stroke led to the identification of therapy oriented criteria for rehabilitation technology aiming to influence skilled arm-hand performance (chapter 2). Most rehabilitation systems reported in literature to date are robotic systems that are aimed at providing an engaging exercise environment and feedback on motor performance. Both, feedback and engaging exercises are important for motivating patients to perform a high number of exercise repetitions and prolonged training, which are important factors for motor learning. The review also found that current rehabilitation technology is focussed mainly on providing treatment at a function level, thereby improving joint range of motion, muscle strength and parameters such as movement speed and smoothness of movement during analytical movements. However, related research has found no effects of robot-supported training at the activity level. The review concluded that a challenge exists for upper extremity rehabilitation technology in stroke patients to also provide more patienttailored task-oriented arm-hand training in natural environments to support the learning of skilled arm-hand performance. Besides mapping the strengths of different technological solutions, the use of outcome measures and training protocols needs to become more standardized across similar interventions, in order to help determine which training solutions are most suitable for specific patient categories. Chapter 4 contributes towards such a standardization of outcome measurement. A concept is introduced which may guide the clinician/researcher to choose outcome measures for evaluating specific and generalized training effects. As an initial operationalization of this concept, 28 test batteries that have been used in 16 task-oriented training interventions were rated as to whether measurement components were measured by the test. Future research is suggested that elaborates the concept with information on the relative weighing of components in each test, with more test batteries (which may lead to additional components) and by adding more test properties into the concept (e.g. psychometric properties of the tests, possible floor- or ceiling effects). Task-oriented training is one of the training approaches that has been shown to be beneficial for skilled arm-hand performance after stroke. Important mechanisms for motor learning that are identified are patient motivation for such training, and the learning of efficient goaloriented movement strategies and task-specific problem solving. In this thesis we operationalize task-oriented training in terms of 15 components (chapter 3). A systematic review that included 16 randomized controlled trials using task-oriented training in stroke patients, evaluated the effects of these training components on skilled arm-hand performance. The number of training components used in an intervention aimed at improving arm-hand performance after stroke was not associated with the post-treatment effect size. Distributed practice and feedback were associated with the largest post-intervention effect sizes. Random practice and use of clear functional training goals were associated with the largest follow-up effect sizes. It may be that training components that optimize the storage of learned motor performance in the long-term memory are associated with larger treatment effects. Unfortunately, feedback, random practice and distributed practice were reported in very few of the included randomized controlled trials (in only 6,3 and 1 out of the 17 studies respectively). Client-centred training, i.e. training on exercises that support goals that are selected by the patients themselves, improves patient motivation for training. Motivation in turn has proven to positively influence motor learning in stroke patients, as attention during training is heightened and storage of information in the long-term memory improves. Chapter 5 reports on an interview of 40 stroke patients, investigating into training preferences. A list of 46 skills, ranked according to descending training preference scores, was provided that can be used for implementation of exercises in rehabilitation technology, in order for technologysupported training to be client-centred. Chapter 6 introduces T-TOAT, a technology supported task-oriented arm training method that was developed together with colleagues at Adelante (Hoensbroek, NL). T-TOAT enables the implementation of exercises that support task-oriented training in rehabilitation technology. The training method is applicable for different technological systems, e.g. robot and sensor systems, or in combination with functional electrical stimulation, etc. To enable the use of TTOAT for training with the Haptic Master Robot (MOOG-FCS, NL), special software named Haptic TOAT was developed in Adelante together with colleagues at the Centre of Technology in Care of Zuyd University (chapter 6). The software enables the recording of the patient’s movement trajectories, given task constraints and patient possibilities, using the Haptic Master as a recording device. A purpose-made gimbal was attached to the endeffector, leaving the hand free for the use and manipulating objects. The recorded movement can be replayed in a passive mode or in an active mode (active, active-assisted or activeresisted). Haptic feedback is provided when the patient deviates from the recorded movement trajectory, as the patient receives the sensation of bouncing into a wall, as well as feeling a spring that pulls him/her back to the recorded path. The diameter of the tunnel around the recorded trajectory (distance to the wall), and the spring force can be adjusted for each patient. An ongoing clinical trial in which chronic stroke patients train with Haptic-TOAT examines whether Haptic Master provides additional value compared to supporting the same exercises by video-instruction only. Together with Philips Research Europe (Eindhoven,Aachen), the T-TOAT method has been implemented in a sensor based prototype, called Philips Stroke Rehabilitation Exerciser. This system included movement tracking sensors and an exercise board interacting with real life objects. A very strong feature of the system is that feedback is provided to patients (real-time and after exercise performance), based on a comparison of the patient’s exercise performance to individual targets set by the therapist. Chapter 7 reports on a clinical trial investigating arm-hand treatment outcome and patient motivation for technology-supported task-oriented training in chronic stroke patients. It was found that 8 weeks of T-TOAT training improved arm-hand performance in chronic stroke patients significantly on Fugl-Meyer, Action Research Arm Test, and Motor Activity Log. An improvement was found in health-related quality of life. Training effects lasted at least 6 months post-training. Participants reported feeling intrinsically motivated and competent to use the system. The results of this study showed that T-TOAT is feasible. Despite the small number of stroke patients tested (n=9), significant and clinically relevant improvements in skilled arm-hand performance were found. In conclusion, this thesis has made several contributions. It motivated the need for clientcentred task-oriented training, which it has operationalized in terms of 15 components. Four of these 15 components were identified as most beneficial for the patient. A prioritized inventory of arm-hand training preferences of stroke patients was compiled by means of an interview study of 40 subacute and chronic stroke patients. T-TOAT, a method for technology-supported, client-centred, task-oriented training, was conceived and implemented in two target technologies (Haptic Master and Philips Stroke Rehabilitation Exerciser). Its feasibility was demonstrated in a clinical trial showing substantial and durable benefits for the stroke patients. Finally, the thesis contributes towards the standardization of outcome measures which is necessary for charting progress and guiding future developments of technology-supported stroke rehabilitation. Methodological considerations were discussed and several suggestions for future research were presented. The variety of treatment approaches and the various ways of support and challenge that are offered by existing rehabilitation technologies hold a large potential for offering a variety of extra training opportunities to stroke patients that may improve their arm-hand performance. Such solutions will be of increasing importance, to alleviate therapists and reduce economic pressure on the health care system, as the stroke incidence is increasing rapidly over the coming decades

Aphasia Compendium

Aphasia is an acquired central disorder of language that impairs a person’s ability to understand and/or produce spoken or writing language. The study of aphasia is important in different clinical and fundamental areas, including neurology, psychology, linguistics, and speech-language pathology. This book presents comprehensive information on the diagnosis and treatment of aphasias. Chapters cover such topics as linguistics and the study of aphasias, different types of aphasias, treatment approaches, imaging, and much more