Cognitive rehabilitation in a visual variant of Alzheimer's disease

Alzheimer's disease (AD) is commonly associated with marked memory deficits; however, nonamnestic variants have been consistently described as well. Posterior cortical atrophy (PCA) is a progressive degenerative condition in which posterior regions of the brain are predominantly affected, therefore resulting in a pattern of distinctive and marked visuospatial symptoms, such as apraxia, alexia, and spatial neglect. Despite the growing number of studies on cognitive and neural bases of the visual variant of AD, intervention studies remain relatively sparse. Current pharmacological treatments offer modest efficacy. Also, there is a scarcity of complementary nonpharmacological interventions with only two previous studies of PCA. Here we describe a highly educated 57-year-old patient diagnosed with a visual variant of AD who participated in a cognitive intervention program (comprising reality orientation, cognitive stimulation, and cognitive training exercises). Neuropsychological assessment was performed across moments (baseline, postintervention, follow-up) and consisted mainly of verbal and visual memory. Baseline neuropsychological assessment showed deficits in perceptive and visual-constructive abilities, learning and memory, and temporal orientation. After neuropsychological rehabilitation, we observed small improvements in the patient's cognitive functioning, namely in verbal memory, attention, and psychomotor abilities. This study shows evidence of small beneficial effects of cognitive intervention in PCA and is the first report of this approach with a highly educated patient in a moderate stage of the disease. Controlled studies are needed to assess the potential efficacy of cognition-focused approaches in these patients, and, if relevant, to grant their availability as a complementary therapy to pharmacological treatment and visual aids.

EFNS guidelines on cognitive rehabilitation: report of an EFNS task force.

Disorders of language, spatial perception, attention, memory, calculation and praxis are a frequent consequence of acquired brain damage [in particular, stroke and traumatic brain injury (TBI)] and a major determinant of disability. The rehabilitation of aphasia and, more recently, of other cognitive disorders is an important area of neurological rehabilitation. We report here a review of the available evidence about effectiveness of cognitive rehabilitation. Given the limited number and generally low quality of randomized clinical trials (RCTs) in this area of therapeutic intervention, the Task Force considered, besides the available Cochrane reviews, evidence of lower classes which was critically analysed until a consensus was reached. In particular, we considered evidence from small group or single cases studies including an appropriate statistical evaluation of effect sizes. The general conclusion is that there is evidence to award a grade A, B or C recommendation to some forms of cognitive rehabilitation in patients with neuropsychological deficits in the post-acute stage after a focal brain lesion (stroke, TBI). These include aphasia therapy, rehabilitation of unilateral spatial neglect (ULN), attentional training in the post-acute stage after TBI, the use of electronic memory aids in memory disorders, and the treatment of apraxia with compensatory strategies. There is clearly a need for adequately designed studies in this area, which should take into account specific problems such as patient heterogeneity and treatment standardization.

Cognitive rehabilitation of working memory in juvenile multiple sclerosis-effects on cognitive functioning, functional MRI and network related connectivity.

PURPOSE To assess possible effects of working memory (WM) training on cognitive functionality, functional MRI and brain connectivity in patients with juvenile MS. METHODS Cognitive status, fMRI and inter-network connectivity were assessed in 5 cases with juvenile MS aged between 12 and 18 years. Afterwards they received a computerized WM training for four weeks. Primary cognitive outcome measures were WM (visual and verbal) and alertness. Activation patterns related to WM were assessed during fMRI using an N-Back task with increasing difficulty. Inter-network connectivity analyses were focused on fronto-parietal (left and right), default-mode (dorsal and ventral) and the anterior salience network. Cognitive functioning, fMRI and inter-network connectivity were reassessed directly after the training and again nine months following training. RESULTS Response to treatment was seen in two patients. These patients showed increased performance in WM and alertness after the training. These behavioural changes were accompanied by increased WM network activation and systematic changes in inter-network connectivity. The remaining participants were non-responders to treatment. Effects on cognitive performance were maintained up to nine months after training, whereas effects observed by fMRI disappeared. CONCLUSIONS Responders revealed training effects on all applied outcome measures. Disease activity and general intelligence may be factors associated with response to treatment.

2D-Tasks for Cognitive Rehabilitation

Neuropsychological Rehabilitation is a complex clinic process which tries to restore or compensate cognitive and behavioral disorders in people suffering from a central nervous system injury. Information and Communication Technologies (ICTs) in Biomedical Engineering play an essential role in this field, allowing improvement and expansion of present rehabilitation programs. This paper presents a set of cognitive rehabilitation 2D-Tasks for patients with Acquired Brain Injury (ABI). These tasks allow a high degree of personalization and individualization in therapies, based on the opportunities offered by new technologies.

Handmade Task Tracking Applied to Cognitive Rehabilitation

This article presents research focused on tracking manual tasks that are applied in cognitive rehabilitation so as to analyze the movements of patients who suffer from Apraxia and Action Disorganization Syndrome (AADS). This kind of patients find executing Activities of Daily Living (ADL) too difficult due to the loss of memory and capacity to carry out sequential tasks or the impossibility of associating different objects with their functions. This contribution is developed from the work of Universidad Politécnica de Madrid and Technical University of Munich in collaboration with The University of Birmingham. The KinectTM for Windows© device is used for this purpose. The data collected is compared to an ultrasonic motion capture system. The results indicate a moderate to strong correlation between signals. They also verify that KinectTM is very suitable and inexpensive. Moreover, it turns out to be a motion-capture system quite easy to implement for kinematics analysis in ADL.

Data mining applied to the cognitive rehabilitation of patients with acquired brain injury

Acquired brain injury (ABI) is one of the leading causes of death and disability in the world and is associated with high health care costs as a result of the acute treatment and long term rehabilitation involved. Different algorithms and methods have been proposed to predict the effectiveness of rehabilitation programs. In general, research has focused on predicting the overall improvement of patients with ABI. The purpose of this study is the novel application of data mining (DM) techniques to predict the outcomes of cognitive rehabilitation in patients with ABI. We generate three predictive models that allow us to obtain new knowledge to evaluate and improve the effectiveness of the cognitive rehabilitation process. Decision tree (DT), multilayer perceptron (MLP) and general regression neural network (GRNN) have been used to construct the prediction models. 10-fold cross validation was carried out in order to test the algorithms, using the Institut Guttmann Neurorehabilitation Hospital (IG) patients database. Performance of the models was tested through specificity, sensitivity and accuracy analysis and confusion matrix analysis. The experimental results obtained by DT are clearly superior with a prediction average accuracy of 90.38%, while MLP and GRRN obtained a 78.7% and 75.96%, respectively. This study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients.

Artificial metaplasticity prediction model for cognitive rehabilitation outcome in acquired brain injury patients

Objective The main purpose of this research is the novel use of artificial metaplasticity on multilayer perceptron (AMMLP) as a data mining tool for prediction the outcome of patients with acquired brain injury (ABI) after cognitive rehabilitation. The final goal aims at increasing knowledge in the field of rehabilitation theory based on cognitive affectation. Methods and materials The data set used in this study contains records belonging to 123 ABI patients with moderate to severe cognitive affectation (according to Glasgow Coma Scale) that underwent rehabilitation at Institut Guttmann Neurorehabilitation Hospital (IG) using the tele-rehabilitation platform PREVIRNEC©. The variables included in the analysis comprise the neuropsychological initial evaluation of the patient (cognitive affectation profile), the results of the rehabilitation tasks performed by the patient in PREVIRNEC© and the outcome of the patient after a 3–5 months treatment. To achieve the treatment outcome prediction, we apply and compare three different data mining techniques: the AMMLP model, a backpropagation neural network (BPNN) and a C4.5 decision tree. Results The prediction performance of the models was measured by ten-fold cross validation and several architectures were tested. The results obtained by the AMMLP model are clearly superior, with an average predictive performance of 91.56%. BPNN and C4.5 models have a prediction average accuracy of 80.18% and 89.91% respectively. The best single AMMLP model provided a specificity of 92.38%, a sensitivity of 91.76% and a prediction accuracy of 92.07%. Conclusions The proposed prediction model presented in this study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients. The ability to predict treatment outcomes may provide new insights toward improving effectiveness and creating personalized therapeutic interventions based on clinical evidence.

A graphical tool for designing interactive video cognitive rehabilitation therapies

Acquired Brain Injury (ABI) has become one of the most common causes of neurological disability in developed countries. Cognitive disorders result in a loss of independence and therefore patients? quality of life. Cognitive rehabilitation aims to promote patients? skills to achieve their highest degree of personal autonomy. New technologies such as interactive video, whereby real situations of daily living are reproduced within a controlled virtual environment, enable the design of personalized therapies with a high level of generalization and a great ecological validity. This paper presents a graphical tool that allows neuropsychologists to design, modify, and configure interactive video therapeutic activities, through the combination of graphic and natural language. The tool has been validated creating several Activities of Daily Living and a preliminary usability evaluation has been performed showing a good clinical acceptance in the definition of complex interactive video therapies for cognitive rehabilitation.

Intelligent Therapy Assistant (ITA) for cognitive rehabilitation in patients with Acquired Brain Injury

This paper presents the design, development and first evaluation of an algorithm, named Intelligent Therapy Assistant (ITA), which automatically selects, configures and schedules rehabilitation tasks for patients with cognitive impairments after an episode of Acquired Brain Injury. The ITA is integrated in "Guttmann, Neuro Personal Trainer" (GNPT), a cognitive tele-rehabilitation platform that provides neuropsychological services.

Improving brain injury cognitive rehabilitation by personalized telerehabilitation services: Guttmann neuropersonal trainer

Cognitive rehabilitation aims to remediate or alleviate the cognitive deficits appearing after an episode of acquired brain injury (ABI). The purpose of this work is to describe the telerehabilitation platform called Guttmann Neuropersonal Trainer (GNPT) which provides new strategies for cognitive rehabilitation, improving efficiency and access to treatments, and to increase knowledge generation from the process. A cognitive rehabilitation process has been modeled to design and develop the system, which allows neuropsychologists to configure and schedule rehabilitation sessions, consisting of set of personalized computerized cognitive exercises grounded on neuroscience and plasticity principles. It provides remote continuous monitoring of patient's performance, by an asynchronous communication strategy. An automatic knowledge extraction method has been used to implement a decision support system, improving treatment customization. GNPT has been implemented in 27 rehabilitation centers and in 83 patients' homes, facilitating the access to the treatment. In total, 1660 patients have been treated. Usability and cost analysis methodologies have been applied to measure the efficiency in real clinical environments. The usability evaluation reveals a system usability score higher than 70 for all target users. The cost efficiency study results show a relation of 1-20 compared to face-to-face rehabilitation. GNPT enables brain-damaged patients to continue and further extend rehabilitation beyond the hospital, improving the efficiency of the rehabilitation process. It allows customized therapeutic plans, providing information to further development of clinical practice guidelines.

Non-invasive user monitoring in daily tasks for cognitive rehabilitation

El uso de técnicas para la monitorización del movimiento humano generalmente permite a los investigadores analizar la cinemática y especialmente las capacidades motoras en aquellas actividades de la vida cotidiana que persiguen un objetivo concreto como pueden ser la preparación de bebidas y comida, e incluso en tareas de aseo. Adicionalmente, la evaluación del movimiento y el comportamiento humanos en el campo de la rehabilitación cognitiva es esencial para profundizar en las dificultades que algunas personas encuentran en la ejecución de actividades diarias después de accidentes cerebro-vasculares. Estas dificultades están principalmente asociadas a la realización de pasos secuenciales y al reconocimiento del uso de herramientas y objetos. La interpretación de los datos sobre la actitud de este tipo de pacientes para reconocer y determinar el nivel de éxito en la ejecución de las acciones, y para ampliar el conocimiento en las enfermedades cerebrales, sus consecuencias y severidad, depende totalmente de los dispositivos usados para la captura de esos datos y de la calidad de los mismos. Más aún, existe una necesidad real de mejorar las técnicas actuales de rehabilitación cognitiva contribuyendo al diseño de sistemas automáticos para crear una especie de terapeuta virtual que asegure una vida más independiente de estos pacientes y reduzca la carga de trabajo de los terapeutas. Con este objetivo, el uso de sensores y dispositivos para obtener datos en tiempo real de la ejecución y estado de la tarea de rehabilitación es esencial para también contribuir al diseño y entrenamiento de futuros algoritmos que pudieran reconocer errores automáticamente para informar al paciente acerca de ellos mediante distintos tipos de pistas como pueden ser imágenes, mensajes auditivos o incluso videos. La tecnología y soluciones existentes en este campo no ofrecen una manera totalmente robusta y efectiva para obtener datos en tiempo real, por un lado, porque pueden influir en el movimiento del propio paciente en caso de las plataformas basadas en el uso de marcadores que necesitan sensores pegados en la piel; y por otro lado, debido a la complejidad o alto coste de implantación lo que hace difícil pensar en la idea de instalar un sistema en el hospital o incluso en la casa del paciente. Esta tesis presenta la investigación realizada en el campo de la monitorización del movimiento de pacientes para proporcionar un paso adelante en términos de detección, seguimiento y reconocimiento del comportamiento de manos, gestos y cara mediante una manera no invasiva la cual puede mejorar la técnicas actuales de rehabilitación cognitiva para la adquisición en tiempo real de datos sobre el comportamiento del paciente y la ejecución de la tarea. Para entender la importancia del marco de esta tesis, inicialmente se presenta un resumen de las principales enfermedades cognitivas y se introducen las consecuencias que tienen en la ejecución de tareas de la vida diaria. Más aún, se investiga sobre las metodologías actuales de rehabilitación cognitiva. Teniendo en cuenta que las manos son la principal parte del cuerpo para la ejecución de tareas manuales de la vida cotidiana, también se resumen las tecnologías existentes para la captura de movimiento de manos. Una de las principales contribuciones de esta tesis está relacionada con el diseño y evaluación de una solución no invasiva para detectar y seguir las manos durante la ejecución de tareas manuales de la vida cotidiana que a su vez involucran la manipulación de objetos. Esta solución la cual no necesita marcadores adicionales y está basada en una cámara de profundidad de bajo coste, es robusta, precisa y fácil de instalar. Otra contribución presentada se centra en el reconocimiento de gestos para detectar el agarre de objetos basado en un sensor infrarrojo de última generación, y también complementado con una cámara de profundidad. Esta nueva técnica, y también no invasiva, sincroniza ambos sensores para seguir objetos específicos además de reconocer eventos concretos relacionados con tareas de aseo. Más aún, se realiza una evaluación preliminar del reconocimiento de expresiones faciales para analizar si es adecuado para el reconocimiento del estado de ánimo durante la tarea. Por su parte, todos los componentes y algoritmos desarrollados son integrados en un prototipo simple para ser usado como plataforma de monitorización. Se realiza una evaluación técnica del funcionamiento de cada dispositivo para analizar si es adecuada para adquirir datos en tiempo real durante la ejecución de tareas cotidianas reales. Finalmente, se estudia la interacción con pacientes reales para obtener información del nivel de usabilidad del prototipo. Dicha información es esencial y útil para considerar una rehabilitación cognitiva basada en la idea de instalación del sistema en la propia casa del paciente al igual que en el hospital correspondiente. ABSTRACT The use of human motion monitoring techniques usually let researchers to analyse kinematics, especially in motor strategies for goal-oriented activities of daily living, such as the preparation of drinks and food, and even grooming tasks. Additionally, the evaluation of human movements and behaviour in the field of cognitive rehabilitation is essential to deep into the difficulties some people find in common activities after stroke. This difficulties are mainly associated with sequence actions and the recognition of tools usage. The interpretation of attitude data of this kind of patients in order to recognize and determine the level of success of the execution of actions, and to broaden the knowledge in brain diseases, consequences and severity, depends totally on the devices used for the capture of that data and the quality of it. Moreover, there is a real need of improving the current cognitive rehabilitation techniques by contributing to the design of automatic systems to create a kind of virtual therapist for the improvement of the independent life of these stroke patients and to reduce the workload of the occupational therapists currently in charge of them. For this purpose, the use of sensors and devices to obtain real time data of the execution and state of the rehabilitation task is essential to also contribute to the design and training of future smart algorithms which may recognise errors to automatically provide multimodal feedback through different types of cues such as still images, auditory messages or even videos. The technology and solutions currently adopted in the field don't offer a totally robust and effective way for obtaining real time data, on the one hand, because they may influence the patient's movement in case of marker-based platforms which need sensors attached to the skin; and on the other hand, because of the complexity or high cost of implementation, which make difficult the idea of installing a system at the hospital or even patient's home. This thesis presents the research done in the field of user monitoring to provide a step forward in terms of detection, tracking and recognition of hand movements, gestures and face via a non-invasive way which could improve current techniques for cognitive rehabilitation for real time data acquisition of patient's behaviour and execution of the task. In order to understand the importance of the scope of the thesis, initially, a summary of the main cognitive diseases that require for rehabilitation and an introduction of the consequences on the execution of daily tasks are presented. Moreover, research is done about the actual methodology to provide cognitive rehabilitation. Considering that the main body members involved in the completion of a handmade daily task are the hands, the current technologies for human hands movements capture are also highlighted. One of the main contributions of this thesis is related to the design and evaluation of a non-invasive approach to detect and track user's hands during the execution of handmade activities of daily living which involve the manipulation of objects. This approach does not need the inclusion of any additional markers. In addition, it is only based on a low-cost depth camera, it is robust, accurate and easy to install. Another contribution presented is focused on the hand gesture recognition for detecting object grasping based on a brand new infrared sensor, and also complemented with a depth camera. This new, and also non-invasive, solution which synchronizes both sensors to track specific tools as well as recognize specific events related to grooming is evaluated. Moreover, a preliminary assessment of the recognition of facial expressions is carried out to analyse if it is adequate for recognizing mood during the execution of task. Meanwhile, all the corresponding hardware and software developed are integrated in a simple prototype with the purpose of being used as a platform for monitoring the execution of the rehabilitation task. Technical evaluation of the performance of each device is carried out in order to analyze its suitability to acquire real time data during the execution of real daily tasks. Finally, a kind of healthcare evaluation is also presented to obtain feedback about the usability of the system proposed paying special attention to the interaction with real users and stroke patients. This feedback is quite useful to consider the idea of a home-based cognitive rehabilitation as well as a possible hospital installation of the prototype.

Modelling ecological cognitive rehabilitation therapies for building virtual environments in brain injury

Brain Injury (BI) has become one of the most common causes of neurological disability in developed countries. Cognitive disorders result in a loss of independence and patients? quality of life. Cognitive rehabilitation aims to promote patients? skills to achieve their highest degree of personal autonomy. New technologies such as virtual reality or interactive video allow developing rehabilitation therapies based on reproducible Activities of Daily Living (ADLs), increasing the ecological validity of the therapy. However, the lack of frameworks to formalize and represent the definition of this kind of therapies can be a barrier for widespread use of interactive virtual environments in clinical routine.

PREVIRNEC A new platform for cognitive tele-rehabilitation

Acquired Brain Injury (ABI), either caused by vascular or traumatic nature, is one of the most important causes for neurological disabilities. People who suffer ABI see how their quality of life decreases, due to the affection of one or some of the cognitive functions (memory, attention, language or executive functions). The traditional cognitive rehabilitation protocols are too expensive, so every help carried out in this area is justified. PREVIRNEC is a new platform for cognitive tele-rehabilitation that allows the neuropsychologist to schedule rehabilitation sessions consisted of specifically designed tasks, plus offering an additional way of communication between neuropsychologists and patients. Besides, the platform offers a knowledge management module that allows the optimization of the cognitive rehabilitation to this kind of patients.