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Query: UMLS:C0038454 (stroke)
 147,016 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The basal ganglia are important for movement and executive function, but its contribution to language is less understood. This study explored language outcomes associated with childhood basal ganglia stroke. A detailed language coding scheme, which examined expressive and receptive language, verbal fluency, narrative discourse, pragmatic/applied language, and academics, was developed from qualitative and quantitative data acquired from neuropsychological testing and reports. Overall intellectual functioning and verbal comprehension was in the average range. Twelve participants had psychological diagnoses, including Learning Disorder. No one had a Language Disorder diagnosis. Among the 18 children who did not receive a diagnosis, many exhibited language issues in the mild to severe range according to our coding scheme. These children had higher-order language difficulties in verbal fluency, narrative, and pragmatic language rather than overt expressive difficulties noted in Diagnostic and Statistical Manual (DSM) diagnostic criteria. There was an association between infarct size and ESL/immersion education, math performance, and presence of a psychological diagnosis. Psychological diagnosis was also associated with literacy skills. The results highlight that language issues following basal ganglia stroke may not be fully captured by standardized neuropsychological tests and psychological diagnoses. Findings reinforce the need to integrate quantitative and qualitative findings when examining language functioning.
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PMID:Characterizing language outcomes following childhood basal ganglia stroke. 3100 75

Stroke causes behavioral deficits in multiple cognitive domains and there is a growing interest in predicting patient performance from neuroimaging data using machine learning techniques. Here, we investigated a deep learning approach based on convolutional neural networks (CNNs) for predicting the severity of language disorder from 3D lesion images from magnetic resonance imaging (MRI) in a heterogeneous sample of stroke patients. CNN performance was compared to that of conventional (shallow) machine learning methods, including ridge regression (RR) on the images' principal components and support vector regression. We also devised a hybrid method based on re-using CNN's high-level features as additional input to the RR model. Predictive accuracy of the four different methods was further investigated in relation to the size of the training set and the level of redundancy across lesion images in the dataset, which was evaluated in terms of location and topological properties of the lesions. The Hybrid model achieved the best performance in most cases, thereby suggesting that the high-level features extracted by CNNs are complementary to principal component analysis features and improve the model's predictive accuracy. Moreover, our analyses indicate that both the size of training data and image redundancy are critical factors in determining the accuracy of a computational model in predicting behavioral outcome from the structural brain imaging data of stroke patients.
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PMID:A Comparison of Shallow and Deep Learning Methods for Predicting Cognitive Performance of Stroke Patients From MRI Lesion Images. 3141 88

Neurologic sequelae of heat stroke are prevalent among patients with severe heat stroke who require admission to an intensive care unit. Radiologic diagnosis of the condition is challenging because not every patient with clinical deficits shows abnormalities in computed tomography or magnetic resonance imaging. In this case review, we report a patient who had been diagnosed with a severe heat stroke and showed gait disturbance, language disorder, and cognitive impairment although conventional magnetic resonance imaging did not reveal significant findings that correlated with his symptoms. Diffusion tensor tractography has been reported to be a useful tool for evaluating the neural status of white matter tracts across a wide range of conditions. The corticospinal tract, the corticoreticular pathway, the cingulum, the fornix, the medial lemniscus, and the arcuate fasciculus of the patient were reconstructed using diffusion tensor tractography. A narrowing, discontinuation, and decreased fractional anisotropy and fiber volume of the examined neural tracts were observed, which correlated well with his symptoms. These results suggest that diffusion tensor tractography might be a useful tool for the detection of neurologic deficits even when conventional brain magnetic resonance imaging reveals no significant abnormality and in establishing appropriate rehabilitation strategies for patients with neurologic symptoms after a heat stroke.
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PMID:Neural Tract Injuries Revealed by Diffusion Tensor Tractography in a Patient With Severe Heat Stroke. 3146 56

Language impairment, or aphasia, is a disabling symptom that affects at least one third of individuals after stroke. Some affected individuals will spontaneously recover partial language function. However, despite a growing number of investigations, our understanding of how and why this recovery occurs is very limited. This Review proposes that existing hypotheses about language recovery after stroke can be conceptualized as specific examples of two fundamental principles. The first principle, degeneracy, dictates that different neural networks are able to adapt to perform similar cognitive functions, which would enable the brain to compensate for damage to any individual network. The second principle, variable neuro-displacement, dictates that there is spare capacity within or between neural networks, which, to save energy, is not used under standard levels of performance demand, but can be engaged under certain situations. These two principles are not mutually exclusive and might involve neural networks in both hemispheres. Most existing hypotheses are descriptive and lack a clear mechanistic account or concrete experimental evidence. Therefore, a better neurocomputational, mechanistic understanding of language recovery is required to inform research into new therapeutic interventions.
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PMID:The neural and neurocomputational bases of recovery from post-stroke aphasia. 3177 39

Introduction: Aphasia is a debilitating language disorder and even mild forms of aphasia can negatively affect functional outcomes, mood, quality of life, social participation, and the ability to return to work. Language deficits after post-stroke aphasia are heterogeneous. Areas covered: The first part of this manuscript reviews the traditional syndrome-based classification approach as well as recent advances in aphasia classification that incorporate automatic speech recognition for aphasia classification. The second part of this manuscript reviews the behavioral approaches to aphasia treatment and recent advances such as noninvasive brain stimulation techniques and pharmacotherapy options to augment the effectiveness of behavioral therapy. Expert opinion: Aphasia diagnosis has largely evolved beyond the traditional approach of classifying patients into specific syndromes and instead focuses on individualized patient profiles. In the future, there is a great need for more large scale randomized, double-blind, placebo-controlled clinical trials of behavioral treatments, noninvasive brain stimulation, and medications to boost aphasia recovery.
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PMID:Diagnosing and managing post-stroke aphasia. 3323 Nov 17

In the setting of shortened hospitalization periods, periods of confinement and social isolation, limited resources, and accessibility, technology can be leveraged to enhance opportunities for rehabilitative care (1). In the current manuscript, we focus on the use of tablet-based rehabilitation for individuals with aphasia, a language disorder that frequently arises post-stroke. Aphasia treatment that targets naming through effortful and errorful instances of lexical retrieval, where corrective feedback is generated on every trial, may enhance retention and generalizability of gains (2, 3). This pilot evaluation explored how six individuals with aphasia interacted with a tablet-based therapy application that targeted lexical retrieval. Participants with aphasia either (1) autonomously engaged with the therapy tasks or (2) received systematic encouragement to effortfully retrieve words. Behaviors of response latency and cue use were examined to gain insights into the behavioral patterns of both groups, as well as analyses of task accuracy and outcomes on standardized cognitive-linguistic assessments. Despite some variability, initial observations suggest that participants who received systematic training refrained from using cues to complete tasks and spent longer on each trial, which ultimately co-occurred with increased independent engagement with therapy and improved standardized outcomes. Preliminary results present an alternative means of leveraging technology to implement best-practice recommendations in the context of aphasia telerehabilitation.
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PMID:The Application of Lexical Retrieval Training in Tablet-Based Speech-Language Intervention. 3328 21


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