Gait analysis in neurological populations: Progression in the use of wearables

Gait assessment is an essential tool for clinical applications not only to diagnose different neurological conditions but also to monitor disease progression as it contributes to the understanding of underlying deficits. There are established methods and models for data collection and interpretation of gait assessment within different pathologies. This narrative review aims to depict the evolution of gait assessment from observation and rating scales to wearable sensors and laboratory technologies, and provide possible future directions. In this context, we first present an extensive review of current clinical outcomes and gait models. Then, we demonstrate commercially available wearable technologies with their technical capabilities along with their use in gait assessment studies for various neurological conditions. In the next sections, a descriptive knowledge for existing inertial based algorithms and a sign based guide that shows the outcomes of previous neurological gait assessment studies are presented. Finally, we state a discussion for the use of wearables in gait assessment and speculate the possible research directions by revealing the limitations and knowledge gaps in the literature

【研究分野別】シーズ集 [英語版]

[英語版

Effects of concussive impact injury assessed in a new murine neurotrauma model

Postmortem brains from young athletes with a history of repetitive concussive head injury and military service personnel with history of blast neurotrauma revealed evidence of parenchymal contusion, myelinated axonopathy, microvasculopathy, neuroinflammation, neurodegeneration, and phosphorylated tauopathy consistent with chronic traumatic encephalopathy (CTE) (L. E. Goldstein et al., 2012). The mechanisms by which head trauma induces acute concussion and chronic sequelae are unknown. To elucidate the mechanistic connection between traumatic brain injury (TBI), acute concussion and chronic sequelae, including CTE, require the use of animal models. This doctoral dissertation investigated the hypothesis that closed-head impact injury in mice triggers acute neurological signs associated with sport-related concussion as well as brain pathologies and functional sequelae associated with CTE. To test this hypothesis, we developed a mouse model of impact neurotrauma that utilizes a momentum transfer device to induce non-skull deforming head acceleration, triggering transient neurological signs consistent with acute concussion and traumatic brain injury (TBI) in unanesthetized C57BL/6 mice. The Boston University Concussion Scale (BUCS) was developed to assess neurological signs that are consistent with acute concussion in humans. Mice exhibited contralateral circling and limb weakness, locomotor abnormalities, and impaired gait and balance that recapitulate acute concussion in humans. Concussed mice recovered neurological function within three hours, but demonstrated persistent myelinated axonopathy, microvasculopathy, neuroinflammation, and phosphorylated tauopathy consistent with early CTE. Concussive impact injury also induced blood-brain barrier disruption, neuroinflammation (including infiltration peripheral monocytes and activation microglia), impaired hippocampal axonal conduction, and defective long-term potentiation (LTP) of synaptic transmission in medial prefrontal cortex. Kinematic analysis during impact injury revealed head acceleration of sufficient intensity to induce acute concussion, traumatic brain injury (TBI), early CTE-linked pathology, and related chronic sequelae. Surprisingly, the presence or degree of concussion measured by BUCS did not correlate with brain injury. Moreover, concussion was observed following impact injury but not blast exposure under conditions that induce comparable head kinematics. Empirical pressure measurements and dynamic modeling revealed greater pressure on the head and compression wave loading in the brain during impact compared to blast neurotrauma. These findings suggest acute concussion is triggered by focal loading of energy that transit the brain before onset of macroscopic head motion. By contrast, the forces associated with rapid head motion is sufficient to induce CTE-linked pathology. Our results indicate that while acute concussion and chronic sequelae may be triggered by the same insult, the pathophysiological responses underpinning these effects are engaged through distinct mechanisms and time domains. Our results indicate that concussion is neither necessary nor sufficient to induce acute brain injury or chronic sequelae, including CTE.2018-02-17T00:00:00

Cognitive-Motor Integration In Normal Aging And Preclinical Alzheimer's Disease: Neural Correlates And Early Detection

The objectives of the studies included in this dissertation were to characterize how the ability to integrate cognition into action is disrupted by both normal and pathological aging, to evaluate the effectiveness of kinematic measures in discriminating between individuals who are and are not at increased Alzheimer’s disease (AD) risk, and to examine the structural and functional neural correlates of cognitive-motor impairment in individuals at increased AD risk. The underlying hypothesis, based on previous research, is that measuring visuomotor integration under conditions that place demands on visual-spatial and cognitive-motor processing may provide an effective behavioural means for the early detection of brain alterations associated with AD risk. To this end, the first study involved testing participants both with and without AD risk factors on visuomotor tasks using a dual-touchscreen tablet. Comparisons between high AD risk participants and both young and old healthy control groups revealed significant performance disruptions in at-risk participants in the most cognitively demanding task. Furthermore, a stepwise discriminant analysis was able to distinguish between high and low AD risk participants with a classification accuracy of 86.4%. Based on the prediction that the impairments observed in high AD risk participants reflect disruption to the intricate reciprocal communication between hippocampal, parietal, and frontal brain regions required to successfully prepare and update complex reaching movements, the second and third studies were designed to examine the underlying structural and functional connectivity associated with cognitive-motor performance. Young adult and both low AD risk and high AD risk older adult participants underwent anatomical, diffusion-weighted, and resting-state functional connectivity scans. These data revealed significant age-related declines in white matter integrity that were more pronounced in the high AD risk group. Decreased functional connectivity in the default mode network (DMN) was also found in high AD risk participants. Furthermore, measures of white matter integrity and resting-state functional connectivity with DMN seed-regions were significantly correlated with task performance. These data support our hypothesis that disease-related disruptions in visuomotor control are associated with identifiable brain alterations, and thus behavioural assessments incorporating both cognition and action together may be useful in identifying individuals at increased AD risk

Non-Invasive Techniques for the Detection and Diagnosis of Dementia

It is estimated that there are currently fifty million people living with dementia worldwide. An accurate and early diagnosis of dementia is important in order to initiate appropriate treatment programs as soon as possible. Common methods of neuropsychological assessment can be sensitive to external factors which may compromise accuracy. The aim of this thesis was to investigate techniques that have the potential for the detection of dementia that avoid some of the external influences. The thesis looked at measurements of (i) postural stability (ii) facial analysis and (iii) fully-immersive virtual reality in cognitively-healthy individuals. These techniques were chosen as postural stability and facial analysis change in dementia and whilst virtual reality has previously been used in dementia research, fully-immersive virtual reality measures have not been established. To see if the measurements were associated with cognitive function, participants completed a series of cognitive tests. Results indicate that all techniques explored shared a relationship with memory performance, with lower anteroposterior postural sway (F(1,22) = 17.76, p < 0.01), number of activities participated in that involve a posture element (F(2, 39) = 3.77, p < .05; Wilk's Λ = 0.84, partial η2 = 0.16), the greater the frequency of negative facial expressions (F(2, 18) = 4.49, p < .05; Wilk's Λ = 0.67, partial η2 = 0.33.), and low blink rate (t(11.02) = 2.62 p < .05) all showing better scores on memory tests. Moreover, better scores on the fullyimmersive virtual reality task predicted better scores on with short-term memory (F(1,22) = 20.20 p < 0.01), LTM (F(1,22) = 09.10 p < .01), associative learning (F(1,22) = 08.75 p < .01), and a dual–task test (F(1,22) = 04.64 p < .05). The novel findings that elements such as postural stability, participation in sports, facial expressions of emotion, blink rates, and spatial memory as assessed in fully-immersive virtual reality highlight that non-invasive techniques can provide measurements that correspond to cognitive ability. This may hold implications for dementia diagnoses. Future research should assess whether these relationships can also be found in an older adult population. If this relationship is found in older adults, it could justify further research into how these techniques could be applied in a clinical context

DEEP-AD: The deep learning model for diagnostic classification and prognostic prediction of alzheimer's disease

In terms of context, the aim of this dissertation is to aid neuroradiologists in their clinical judgment regarding the early detection of AD by using DL. To that aim, the system design research methodology is suggested in this dissertation for achieving three goals. The first goal is to investigate the DL models that have performed well at identifying patterns associated with AD, as well as the accuracy so far attained, limitations, and gaps. A systematic review of the literature (SLR) revealed a shortage of empirical studies on the early identification of AD through DL. In this regard, thirteen empirical studies were identified and examined. We concluded that three-dimensional (3D) DL models have been generated far less often and that their performance is also inadequate to qualify them for clinical trials. The second goal is to provide the neuroradiologist with the computer-interpretable information they need to analyze neuroimaging biomarkers. Given this context, the next step in this dissertation is to find the optimum DL model to analyze neuroimaging biomarkers. It has been achieved in two steps. In the first step, eight state-of-the-art DL models have been implemented by training from scratch using end-to-end learning (E2EL) for two binary classification tasks (AD vs. CN and AD vs. stable MCI) and compared by utilizing MRI scans from the publicly accessible datasets of neuroimaging biomarkers. Comparative analysis is carried out by utilizing efficiency-effects graphs, comprehensive indicators, and ranking mechanisms. For the training of the AD vs. sMCI task, the EfficientNet-B0 model gets the highest value for the comprehensive indicator and has the fewest parameters. DenseNet264 performed better than the others in terms of evaluation matrices, but since it has the most parameters, it costs more to train. For the AD vs. CN task by DenseNet264, we achieved 100% accuracy for training and 99.56% accuracy for testing. However, the classification accuracy was still only 82.5% for the AD vs. sMCI task. In the second step, fusion of transfer learning (TL) with E2EL is applied to train the EfficientNet-B0 for the AD vs. sMCI task, which achieved 95.29% accuracy for training and 93.10% accuracy for testing. Additionally, we have also implemented EfficientNet-B0 for the multiclass AD vs. CN vs. sMCI classification task with E2EL to be used in ensemble of models and achieved 85.66% training accuracy and 87.38% testing accuracy. To evaluate the model&#8217;s robustness, neuroradiologists must validate the implemented model. As a result, the third goal of this dissertation is to create a tool that neuroradiologists may use at their convenience. To achieve this objective, this dissertation proposes a web-based application (DEEP-AD) that has been created by making an ensemble of Efficient-Net B0 and DenseNet 264 (based on the contribution of goal 2). The accuracy of a DEEP-AD prototype has undergone repeated evaluation and improvement. First, we validated 41 subjects of Spanish MRI datasets (acquired from HT Medica, Madrid, Spain), achieving an accuracy of 82.90%, which was later verified by neuroradiologists. The results of these evaluation studies showed the accomplishment of such goals and relevant directions for future research in applied DL for the early detection of AD in clinical settings.En términos de contexto, el objetivo de esta tesis es ayudar a los neurorradiólogos en su juicio clínico sobre la detección precoz de la AD mediante el uso de DL. Para ello, en esta tesis se propone la metodología de investigación de diseño de sistemas para lograr tres objetivos. El segundo objetivo es proporcionar al neurorradiólogo la información interpretable por ordenador que necesita para analizar los biomarcadores de neuroimagen. Dado este contexto, el siguiente paso en esta tesis es encontrar el modelo DL óptimo para analizar biomarcadores de neuroimagen. Esto se ha logrado en dos pasos. En el primer paso, se han implementado ocho modelos DL de última generación mediante entrenamiento desde cero utilizando aprendizaje de extremo a extremo (E2EL) para dos tareas de clasificación binarias (AD vs. CN y AD vs. MCI estable) y se han comparado utilizando escaneos MRI de los conjuntos de datos de biomarcadores de neuroimagen de acceso público. El análisis comparativo se lleva a cabo utilizando gráficos de efecto-eficacia, indicadores exhaustivos y mecanismos de clasificación. Para el entrenamiento de la tarea AD vs. sMCI, el modelo EfficientNet-B0 obtiene el valor más alto para el indicador exhaustivo y tiene el menor número de parámetros. DenseNet264 obtuvo mejores resultados que los demás en términos de matrices de evaluación, pero al ser el que tiene más parámetros, su entrenamiento es más costoso. Para la tarea AD vs. CN de DenseNet264, conseguimos una accuracy del 100% en el entrenamiento y del 99,56% en las pruebas. Sin embargo, la accuracy de la clasificación fue sólo del 82,5% para la tarea AD vs. sMCI. En el segundo paso, se aplica la fusión del aprendizaje por transferencia (TL) con E2EL para entrenar la EfficientNet-B0 para la tarea AD vs. sMCI, que alcanzó una accuracy del 95,29% en el entrenamiento y del 93,10% en las pruebas. Además, también hemos implementado EfficientNet-B0 para la tarea de clasificación multiclase AD vs. CN vs. sMCI con E2EL para su uso en conjuntos de modelos y hemos obtenido una accuracy de entrenamiento del 85,66% y una precisión de prueba del 87,38%. Para evaluar la solidez del modelo, los neurorradiólogos deben validar el modelo implementado. Como resultado, el tercer objetivo de esta disertación es crear una herramienta que los neurorradiólogos puedan utilizar a su conveniencia. Para lograr este objetivo, esta disertación propone una aplicación basada en web (DEEP-AD) que ha sido creada haciendo un ensemble de Efficient-Net B0 y DenseNet 264 (basado en la contribución del objetivo 2). La accuracy del prototipo DEEP-AD ha sido sometida a repetidas evaluaciones y mejoras. En primer lugar, validamos 41 sujetos de conjuntos de datos de MRI españoles (adquiridos de HT Medica, Madrid, España), logrando una accuracy del 82,90%, que posteriormente fue verificada por neurorradiólogos. Los resultados de estos estudios de evaluación mostraron el cumplimiento de dichos objetivos y las direcciones relevantes para futuras investigaciones en DL, aplicada en la detección precoz de la AD en entornos clínicos.Escuela de DoctoradoDoctorado en Tecnologías de la Información y las Telecomunicacione