Cognitive Rehabilitation Tasks Research Articles

Toward Personalized Web-Based Cognitive Rehabilitation for Patients With Ischemic Stroke: Elo Rating Approach.

BackgroundStroke is a worldwide cause of disability; 40% of stroke survivors sustain cognitive impairments, most of them following inpatient rehabilitation at specialized clinical centers. Web-based cognitive rehabilitation tasks are extensively used in clinical settings. The impact of task execution depends on the ratio between the skills of the treated patient and the challenges imposed by the task itself. Thus, treatment personalization requires a trade-off between patients’ skills and task difficulties, which is still an open issue. In this study, we propose Elo ratings to support clinicians in tasks assignations and representing patients’ skills to optimize rehabilitation outcomes.ObjectiveThis study aims to stratify patients with ischemic stroke at an early stage of rehabilitation into three levels according to their Elo rating; to show the relationships between the Elo rating levels, task difficulty levels, and rehabilitation outcomes; and to determine if the Elo rating obtained at early stages of rehabilitation is a significant predictor of rehabilitation outcomes.MethodsThe PlayerRatings R library was used to obtain the Elo rating for each patient. Working memory was assessed using the DIGITS subtest of the Barcelona test, and the Rey Auditory Verbal Memory Test (RAVLT) was used to assess verbal memory. Three subtests of RAVLT were used: RAVLT learning (RAVLT075), free-recall memory (RAVLT015), and recognition (RAVLT015R). Memory predictors were identified using forward stepwise selection to add covariates to the models, which were evaluated by assessing discrimination using the area under the receiver operating characteristic curve (AUC) for logistic regressions and adjusted R2 for linear regressions.ResultsThree Elo levels (low, middle, and high) with the same number of patients (n=96) in each Elo group were obtained using the 50 initial task executions (from a total of 38,177) for N=288 adult patients consecutively admitted for inpatient rehabilitation in a clinical setting. The mid-Elo level showed the highest proportions of patients that improved in all four memory items: 56% (54/96) of them improved in DIGITS, 67% (64/96) in RAVLT075, 58% (56/96) in RAVLT015, and 53% (51/96) in RAVLT015R (P<.001). The proportions of patients from the mid-Elo level that performed tasks at difficulty levels 1, 2, and 3 were 32.1% (3997/12,449), 31.% (3997/12,449), and 36.9% (4595/12,449), respectively (P<.001), showing the highest match between skills (represented by Elo level) and task difficulties, considering the set of 38,177 task executions. Elo ratings were significant predictors in three of the four models and quasi-significant in the fourth. When predicting RAVLT075 and DIGITS at discharge, we obtained R2=0.54 and 0.43, respectively; meanwhile, we obtained AUC=0.73 (95% CI 0.64-0.82) and AUC=0.81 (95% CI 0.72-0.89) in RAVLT075 and DIGITS improvement predictions, respectively.ConclusionsElo ratings can support clinicians in early rehabilitation stages in identifying cognitive profiles to be used for assigning task difficulty levels.

Neuropsychological Assessments of Patients With Acquired Brain Injury: A Cluster Analysis Approach to Address Heterogeneity in Web-Based Cognitive Rehabilitation.

We aimed to (1) apply cluster analysis techniques to mixed-type data (numerical and categorical) from baseline neuropsychological standard and widely used assessments of patients with acquired brain injury (ABI) (2) apply state-of-the-art cluster validity indexes (CVI) to assess their internal validity (3) study their external validity considering relevant aspects of ABI rehabilitation such as functional independence measure (FIM) in activities of daily life assessment (4) characterize the identified profiles by using demographic and clinically relevant variables and (5) extend the external validation of the obtained clusters to all cognitive rehabilitation tasks executed by the participants in a web-based cognitive rehabilitation platform (GNPT). We analyzed 1,107 patients with ABI, 58.1% traumatic brain injury (TBI), 21.8% stroke and 20.1% other ABIs (e.g., brain tumors, anoxia, infections) that have undergone inpatient GNPT cognitive rehabilitation from September 2008 to January 2021. We applied the k-prototypes algorithm from the clustMixType R package. We optimized seven CVIs and applied bootstrap resampling to assess clusters stability (fpc R package). Clusters' post hoc comparisons were performed using the Wilcoxon ranked test, paired t-test or Chi-square test when appropriate. We identified a three-clusters optimal solution, with strong stability (>0.85) and structure (e.g., Silhouette > 0.60, Gamma > 0.83), characterized by distinctive level of performance in all neuropsychological tests, demographics, FIM, response to GNPT tasks and tests normative data (e.g., the 3 min cut-off in Trail Making Test-B). Cluster 1 was characterized by severe cognitive impairment (N = 254, 22.9%) the mean age was 47 years, 68.5% patients with TBI and 22% with stroke. Cluster 2 was characterized by mild cognitive impairment (N = 376, 33.9%) mean age 54 years, 53.5% patients with stroke and 27% other ABI. Cluster 3, moderate cognitive impairment (N = 477, 43.2%) mean age 33 years, 83% patients with TBI and 14% other ABI. Post hoc analysis on cognitive FIM supported a significant higher performance of Cluster 2 vs. Cluster 3 (p < 0.001), Cluster 2 vs. Cluster 1 (p < 0.001) and Cluster 3 vs. Cluster 1 (p < 0.001). All patients executed 286,798 GNPT tasks, with performance significantly higher in Cluster 2 and 3 vs. Cluster 1 (p < 0.001).

Personalized Web-Based Cognitive Rehabilitation Treatments for Patients with Traumatic Brain Injury: Cluster Analysis.

BackgroundTraumatic brain injury (TBI) is a leading cause of disability worldwide. TBI is a highly heterogeneous disease, which makes it complex for effective therapeutic interventions. Cluster analysis has been extensively applied in previous research studies to identify homogeneous subgroups based on performance in neuropsychological baseline tests. Nevertheless, most analyzed samples are rarely larger than a size of 100, and different cluster analysis approaches and cluster validity indices have been scarcely compared or applied in web-based rehabilitation treatments.ObjectiveThe aims of our study were as follows: (1) to apply state-of-the-art cluster validity indices to different cluster strategies: hierarchical, partitional, and model-based, (2) to apply combined strategies of dimensionality reduction by using principal component analysis and random forests and perform stability assessment of the final profiles, (3) to characterize the identified profiles by using demographic and clinically relevant variables, and (4) to study the external validity of the obtained clusters by considering 3 relevant aspects of TBI rehabilitation: Glasgow Coma Scale, functional independence measure, and execution of web-based cognitive tasks.MethodsThis study was performed from August 2008 to July 2019. Different cluster strategies were executed with Mclust, factoextra, and cluster R packages. For combined strategies, we used the FactoMineR and random forest R packages. Stability analysis was performed with the fpc R package. Between-group comparisons for external validation were performed using 2-tailed t test, chi-square test, or Mann-Whitney U test, as appropriate.ResultsWe analyzed 574 adult patients with TBI (mostly severe) who were undergoing web-based rehabilitation. We identified and characterized 3 clusters with strong internal validation: (1) moderate attentional impairment and moderate dysexecutive syndrome with mild memory impairment and normal spatiotemporal perception, with almost 66% (111/170) of the patients being highly educated (P<.05); (2) severe dysexecutive syndrome with severe attentional and memory impairments and normal spatiotemporal perception, with 49.2% (153/311) of the patients being highly educated (P<.05); (3) very severe cognitive impairment, with 45.2% (42/93) of the patients being highly educated (P<.05). We externally validated them with severity of injury (P=.006) and functional independence assessments: cognitive (P<.001), motor (P<.001), and total (P<.001). We mapped 151,763 web-based cognitive rehabilitation tasks during the whole period to the 3 obtained clusters (P<.001) and confirmed the identified patterns. Stability analysis indicated that clusters 1 and 2 were respectively rated as 0.60 and 0.75; therefore, they were measuring a pattern and cluster 3 was rated as highly stable.ConclusionsCluster analysis in web-based cognitive rehabilitation treatments enables the identification and characterization of strong response patterns to neuropsychological tests, external validation of the obtained clusters, tailoring of cognitive web-based tasks executed in the web platform to the identified profiles, thereby providing clinicians a tool for treatment personalization, and the extension of a similar approach to other medical conditions.

The development of a cognitive rehabilitation task for mice

Obesity, neurodegenerative diseases, and injury can all lead to cognitive deficits, which can be improved clinically with the implementation of cognitive rehabilitation. Due to a lack of effective cognitive rehabilitation tools in mice, we re-designed a cognitive task utilized to detect problem-solving deficits, to develop a cognitive rehabilitation paradigm for mice. In this study, we developed a modified the Puzzle Box task by exposing B6 mice to a variety of obstacles and assessing the escape latencies. We then combined obstacles in order to create a “complex obstacle” for the problem-solving task. We determined that our task was reproducible in different cohorts of mice. Furthermore, with repetition the mice display an improvement in the performance, evident by a shorter escape latency and the ability to maintain this improvement in performance, indicative of long-term memory. Given that this approach is new, we validated whether this task could successfully detect deficits in a mouse model of cognitive impairment, the high-fat diet mouse. We demonstrate that high-fat diet mice have longer escape latencies when exposed to the complex obstacle compared to standard diet control mice. Taken together, these data suggest that the Puzzle Box is a valid task for cognitive rehabilitation in mice.

Capturing Expert Knowledge for the Personalization of Cognitive Rehabilitation: Study Combining Computational Modeling and a Participatory Design Strategy.

BackgroundCognitive impairments after stroke are not always given sufficient attention despite the critical limitations they impose on activities of daily living (ADLs). Although there is substantial evidence on cognitive rehabilitation benefits, its implementation is limited because of time and human resource’s demands. Moreover, many cognitive rehabilitation interventions lack a robust theoretical framework in the selection of paper-and-pencil tasks by the clinicians. In this endeavor, it would be useful to have a tool that could generate standardized paper-and-pencil tasks, parameterized according to patients' needs.ObjectiveIn this study, we aimed to present a framework for the creation of personalized cognitive rehabilitation tasks based on a participatory design strategy.MethodsWe selected 11 paper-and-pencil tasks from standard clinical practice and parameterized them with multiple configurations. A total of 67 tasks were assessed according to their cognitive demands (attention, memory, language, and executive functions) and overall difficulty by 20 rehabilitation professionals.ResultsAfter assessing the internal consistency of the data—that is, alpha values from .918 to .997—we identified the parameters that significantly affected cognitive functions and proposed specific models for each task. Through computational modeling, we operationalized the tasks into their intrinsic parameters and developed a Web tool that generates personalized paper-and-pencil tasks—the Task Generator (TG).ConclusionsOur framework proposes an objective and quantitative personalization strategy tailored to each patient in multiple cognitive domains (attention, memory, language, and executive functions) derived from expert knowledge and materialized in the TG app, a cognitive rehabilitation Web tool.

Computer-based cognitive rehabilitation: the CoRe system

Purpose: This work aims at providing a tool for supporting cognitive rehabilitation. This is a wide field, that includes a variety of diseases and related clinical pictures; for this reason the need arises to have a tool available that overcomes the difficulties entailed by what currently is the most common approach, that is, the so-called pen and paper rehabilitation. Methods: We first organized a big number of stimuli in an ontology that represents concepts, attributes and a set of relationships among concepts. Stimuli may be words, sounds, 2D and 3D images. Then, we developed an engine that automatically generates exercises by exploiting that ontology. The design of exercises has been carried on in synergy with neuropsychologists and speech therapists. Solutions have been devised aimed at personalizing the exercises according to both patients’ preferences and performance. Results: Exercises addressed to rehabilitation of executive functions and aphasia-related diseases have been implemented. The system has been tested on both healthy volunteers (n = 38) and patients (n = 9), obtaining a favourable rating and suggestions for improvements. Conclusions: We created a tool able to automate the execution of cognitive rehabilitation tasks. We hope the variety and personalization of exercises will allow to increase compliance, particularly from elderly people, usually neither familiar with technology nor particularly willing to rely on it. The next step involves the creation of a telerehabilitation tool, to allow therapy sessions to be undergone from home, thus guaranteeing continuity of care and advantages in terms of time and costs for the patients and the National Healthcare System (NHS).Implications for rehabilitationCognitive impairments can greatly impact an individual's existence, appreciably reducing his abilities and autonomy, as well as sensibly lowering his quality of life. Cognitive rehabilitation can be used to restore lost brain function or slow down degenerative diseases.Computerization of rehabilitation entails many advantages, but patients – especially elderly people – might be less prone to the use of technology and consequently reluctant towards this innovative therapeutic approach.Our software system, CoRe, supports a therapist during the administration of rehabilitation sessions: exercises can be generated dynamically, thus reducing repetitivity, and patients' performance trends automatically analysed to facilitate the assessment of their progress.Tests performed on both healthy subjects and patients provided useful information that allowed us to define an implementation strategy able to reduce patients' resistance to computerized rehabilitation as much as possible.

Oxyhemoglobin Changes During Cognitive Rehabilitation After Traumatic Brain Injury Using Near Infrared Spectroscopy

To investigate cerebral reactions to cognitive rehabilitation tasks, oxyhemoglobin changes were compared in 9 patients with cognitive impairments after traumatic brain injury (TBI) and 47 healthy controls using functional near infrared spectroscopy (fNIRS) during nine cognitive rehabilitation tasks employed at Nagoya City Rehabilitation Center. Forty-seven measurement channels were placed on the frontal to temporal cortices, and organized into seven channel regions. Oxyhemoglobin changes were normalized based on the mean oxyhemoglobin value at the resting state, and integrated throughout a task. Statistical analyses of the differences between the TBI patients and controls were performed with the two-sided Mann-Whitney U test. Oxyhemoglobin changes were high for both controls and TBI patients in the lateral frontal regions. Oxyhemoglobin changes in TBI patients tended to be higher than controls in the medial frontal regions for most training tasks, and significant differences (p < 0.05) were seen for two tasks in the medial frontal regions. Different regions were activated during the tasks in TBI patients compared to controls. fNIRS measurement is useful in the evaluation of changes of neuronal activities during rehabilitation tasks in TBI patients.

Cognitive Rehabilitation Manual: Translating Evidence-Based Recommendations into Practice

The Cognitive Rehabilitation Manual was developed by the Cognitive Rehabilitation Task Force of the Brain Injury Interdisciplinary Special Interest Group (BI-ISIG) of the American Congress of Rehabilitation Medicine. The manual is intended to guide clinicians in planning and administering evidence-based cognitive rehabilitation treatments. It would be most suitable for beginning or intermediate clinicians who are treating patients in the post-acute phase of rehabilitation. Chapter 1 provides a general framework for organizing interventions into three distinct stages of treatment and recognizes the continuum of learning and support necessary to help patients acquire and ultimately generalize strategies and skills to real world contexts. Chapters 2–6 apply this framework to the rehabilitation of executive functions, memory, attention, hemispatial neglect, and social communication. The rehabilitation chapters begin with general descriptions of the impairments followed by broad treatment recommendations from the BI-ISIG. This is followed by descriptions of specific treatment protocols that have been endorsed in the literature. The manual is both ambitious and straightforward. Ambitious as it covers a wide range of cognitive impairments and different types of therapy approaches and distills the content into the most essential information. The handbook is straightforward in that it is procedural and uses list format to describe clinical protocols. Several features make it a very practical guide for busy clinicians. There are a number of generic and specific treatment forms that clinicians can photocopy and use to record treatment data. The provision of forms helps concretize the treatment procedures as clinicians have a structure for collecting and using treatment data. Another very practical feature is the provision of sample long-term and short-term treatment goals for the different treatment areas. Similarly, a number of the chapters provide scripts for how to introduce or explain treatment. Having exemplar wording for written treatment goals and discussions with clients is illustrative especially for clinicians somewhat new to cognitive rehabilitation. Another helpful feature is graphic decision trees that provide flowcharts for making clinician decisions and help to ensure that individual client profiles are accommodated. For example, for some interventions, a prerequisite skill would be intact awareness; the decision tree ensures a clinician will consider this information when selecting a treatment. Chapter 2, Rehabilitation for Impairments in Executive Functions, is particularly comprehensive. It describes strategy training for impairments in executive functions, problem solving, and behavioral and emotional dysregulation, including awareness deficits. Several treatment protocols are detailed in an easy-to-follow format and the rationale for the treatment components is explained well. The treatments described in the manual are selected based on a series of reviews supported by the BI-ISIG group of experts. At times, this results in a somewhat piecemeal collection of interventions that are not theoretically well linked. It also can result in a cursory overview of complex rehabilitation practices. Clinicians will need to have background information on neuropsychological functioning and assessment in order to select interventions best matched to client profiles. The manual does not address assistive technology which has become integral to cognitive rehabilitation; however, the evidence-based principles of training a memory notebook and the protocols that involve errorless learning and spaced retrieval could be applied to the more commonly used computer devices and electronic applications. This manual is set up as a handbook to facilitate administration of a range of cognitive interventions that have been described in the literature. It is neither intended to provide a comprehensive review of the field nor is it intended to be a one-size-fits-all cookbook. It addresses the “how to” questions more than “why” questions behind rehabilitation. Although it reviews interventions that have been experimentally evaluated, it also provides practical therapeutic suggestions that recognize rehabilitation involves a dynamic interaction between the clinician and the patient. The step-by-step techniques with sample protocols will make this a valuable resource for clinicians treating clients with acquired cognitive impairments.