Artificial intelligence in stroke rehabilitation: From acute care to long-term recovery

 Artificial intelligence does nothing if the underlying research for 100% recovery is not there! Artificial intelligence is NOT the answer to stroke recovery. 

You're putting the cart before the horse. ARE YOU THAT BLITHERINGLY STUPID?

Here is my attempt with ChatGPT, useless!

Here is my run through asking for 100% recovery from stroke:

ChatGPT on 100% recovery from stroke Nothing worthwhile!

Artificial intelligence in stroke rehabilitation: From acute care to long-term recovery

Author links open overlay panel, , , , , , , , , , , ,

https://doi.org/10.1016/j.neuroscience.2025.03.017

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Highlights

• •
  AI-driven imaging techniques improve early diagnosis, ischemic penumbra identification, and personalized therapeutic interventions.
• •
  Robotics, AI-powered exoskeletons, and VR/AR technologies provide precise, adaptive, and immersive rehabilitation experiences.
• •
  Machine learning models predict recovery outcomes, while wearable devices enable continuous monitoring and home-based rehabilitation.

  Recovery prediction DOES NOTHING to get survivors recovered!

• •
  Ethical considerations, data privacy, and interdisciplinary collaboration are critical for the successful integration of AI in stroke rehabilitation.

Abstract

Stroke is a leading cause of disability worldwide, driving the need for advanced rehabilitation strategies. The integration of Artificial Intelligence (AI) into stroke rehabilitation presents significant advancements across the continuum of care, from acute diagnosis to long-term recovery. This review explores AI’s role in stroke rehabilitation, highlighting its impact on early diagnosis, motor recovery, and cognitive rehabilitation. AI-driven imaging techniques, such as deep learning applied to CT and MRI scans, improve early diagnosis and identify ischemic penumbra, enabling timely, personalized interventions. AI-assisted decision support systems optimize acute stroke treatment, including thrombolysis and endovascular therapy. In motor rehabilitation, AI-powered robotics and exoskeletons provide precise, adaptive assistance, while AI-augmented Virtual and Augmented Reality environments offer immersive, tailored recovery experiences. Brain-Computer Interfaces utilize AI for neurorehabilitation through neural signal processing, supporting motor recovery. Machine learning models predict functional recovery outcomes and dynamically adjust therapy intensities. Wearable technologies equipped with AI enable continuous monitoring and real-time feedback, facilitating home-based rehabilitation. AI-driven tele-rehabilitation platforms overcome geographic barriers by enabling remote assessment and intervention. The review also addresses the ethical, legal, and regulatory challenges associated with AI implementation, including data privacy and technical integration. Future research directions emphasize the transformative potential of AI in stroke rehabilitation, with case studies and clinical trials illustrating the practical benefits and efficacy of AI technologies in improving patient recovery.

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Machine learning techniques for independent gait recovery prediction in acute anterior circulation ischemic stroke

 Recovery prediction DOES NOTHING to get survivors recovered! Useless research, you're fired!

Machine learning techniques for independent gait recovery prediction in acute anterior circulation ischemic stroke

• Jiangping Ma &
• Yuanjie Xie 

Journal of NeuroEngineering and Rehabilitation volume 22, Article number: 19 (2025) Cite this article

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Abstract

Objective

This study aimed to develop and validate a machine learning-based predictive model for gait recovery in patients with acute anterior circulation ischemic stroke.

Methods

Between May and November 2023, 237 patients with acute anterior circulation ischemic stroke were enrolled. Patients were randomly divided into training and validation sets at a 7:3 ratio. Thirty-one medical characteristics were collected, and the Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied to screen predictor variables. Predictive models were developed using the Random Survival Forest (RSF) and COX regression methods. The optimal model was identified based on C-index values. The SHapley Additive exPlanations (SHAP) method was employed to interpret the RSF model globally and locally.

Results

Ten predictors were identified through LASSO regression, including age, gender, periventricular white matter hyperintensities (PVWMH), Montreal Cognitive Assessment (MoCA), National Institutes of Health Stroke Scale (NIHSS), enlarged perivascular spaces in basal ganglia (BG-EPVS), lacunes, parietal infarction, basal ganglia infarction, and Timed Up & Go (TUG) test score. The C-index values of the COX regression and RSF models were 0.741 and 0.761 in the training set and 0.705 and 0.725 in the validation set, respectively. SHAP analysis of the RSF model identified BG-EPVS, TUG, MoCA, age, and PVWMH as the top five most influential predictors of gait recovery.

Conclusion

The RSF model demonstrated superior performance to the COX regression model in predicting gait recovery, offering a reliable tool for clinical decision-making regarding stroke patients’ prognoses.

Introduction

Ischemic stroke, with a lifetime global risk of 18.3%, is the third leading cause of disability among adults [1]. Stroke frequently leads to long-term and debilitating gait impairments, significantly affecting functional independence and quality of life [2]. Walking capacity is a crucial indicator of functional independence and long-term survival in stroke patients [3]. Accurate early prediction of gait disturbances in stroke patients is critical for formulating treatment plans and allocating rehabilitation resources effectively [4, 5]. Predicting gait recovery early also helps clinicians set realistic rehabilitation goals for post-discharge stroke patients. Despite its importance, gait recovery after stroke remains underexplored in predictive modeling [6,7,8]. Therefore, urgent clinical research is needed to establish robust models for predicting gait recovery in stroke patients.

Previous studies have employed COX regression models to evaluate gait recovery in stroke patients [9]. However, these methods rely on linear assumptions, limiting their ability to model the complex, non-linear relationships between prognostic variables in biological systems, thus reducing predictive accuracy. Novel solutions capable of handling these potentially non-linear variables are highly needed for accurate prognostic prediction.

Machine learning (ML) is a computational approach that uses data-driven algorithms to identify patterns and improve prediction accuracy [10]. In recent years, ML algorithms have been commonly applied to predict functional outcomes following stroke [11,12,13,14,15]. Unlike COX regression models, ML approaches can account for non-linear functions and complex variable interactions, enhancing predictive performance [16]. However, the complexity of ML models, often referred to as “black boxes,” poses interpretability challenges, which are critical in the medical field to ensure patient safety and effective treatment planning [17]. By interpreting predicted results, physicians can better understand the rationale for treatment and thus make accurate clinical decisions.

This study aimed to collect demographic features, clinical features, infarct region characteristics, and magnetic resonance imaging (MRI) features to develop predictive models for gait recovery in patients with acute anterior circulation stroke using COX regression and Random Survival Forest (RSF) models. The study also employed SHapley Additive exPlanations (SHAP) to evaluate the contribution of individual predictors, facilitating model interpretability and enabling early intervention opportunities.

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Iconic Purdue basketball coach Gene Keady suffers stroke, 'expected to make full recovery'

 Really, you think this doctor knows one damn thing about stroke recovery? Like this doctor?

Under what objective criteria can anyone say that Gene Keady

will be one of the 10% who fully recover? 

Or is this the likely outcome?

Mercury astronaut Scott Carpenter suffers stroke; full recovery expected

Oops! 

 Scott Carpenter - Obituary

 

Iconic Purdue basketball coach Gene Keady suffers stroke, 'expected to make full recovery'

WEST LAFAYETTE − Purdue basketball practice began Monday with a new crop of Boilermakers running up and down Gene Keady Court.

The man whose name is emblazoned on the hardwood inside Mackey Arena was not there.

Last week, Keady suffered a minor stroke and was taken to the hospital for evaluation.

Structural Integrity of the Cerebellar Outflow Tract Predicts Long-Term Motor Function After Middle Cerebral Artery Ischemic Stroke

This is still useless since you're predicting stuff rather than delivering EXACT REHAB PROTOCOLS!

They'll want those recovery protocols

when they are the 1 in 4 per WHO that has a stroke!

 

When you have a stroke you'll want recovery and you'll kick yourself for not doing that job when you could.

Structural Integrity of the Cerebellar Outflow Tract Predicts Long-Term Motor Function After Middle Cerebral Artery Ischemic Stroke

Yeun Jie Yoo, MD, Seong Hoon Lim, MD, PhD https://orcid.org/0000-0002-5475-4153, […], and Geun-Young Park, MD, PhD https://orcid.org/0000-0003-0084-2403 rootpmr@catholic.ac.kr+5View all authors and affiliations

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https://doi.org/10.1177/15459683231177607

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Abstract

Background

The cerebellum plays a crucial role in functional movement by influencing sensorimotor coordination and learning. However, the effects of cortico-cerebellar connectivity on the recovery of upper extremity motor function after stroke have not been investigated. We hypothesized that the integrity of the cortico-cerebellar connections would be reduced in patients with a subacute middle cerebral artery (MCA) stroke, and that this reduction may help to predict chronic upper extremity motor function.

Methods

We retrospectively analyzed the diffusion-tensor imaging of 25 patients with a subacute MCA stroke (mean age: 62.2 ± 2.7 years; 14 females) and 25 age- and sex-matched healthy controls. We evaluated the microstructural integrity of the corticospinal tract (CST), dentatothalamocortical tract (DTCT), and corticopontocerebellar tract (CPCT). Furthermore, we created linear regression models to predict chronic upper extremity motor function based on the structural integrity of each tract.

Results

In stroke patients, the affected DTCT and CST showed significantly impaired structural integrity compared to unaffected tracts and the tracts in controls. When all models were compared, the model that used the fractional anisotropy (FA) asymmetry indices of CST and DTCT as independent variables best predicted chronic upper extremity motor function (R² = .506, P = .001). The extent of structural integrity of the CPCT did not significantly differ between hemispheres or groups and was not predictive of motor function.

Conclusions

We found evidence that microstructural integrity of the DTCT in the subacute phase of an MCA stroke helped to predict chronic upper extremity motor function, independent of CST status.

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Association of Dual-Task Gait Cost and White Matter Hyperintensity Burden Poststroke: Results From the ONDRI

 This is still useless since you're predicting stuff rather than delivering EXACT REHAB PROTOCOLS!

Association of Dual-Task Gait Cost and White Matter Hyperintensity Burden Poststroke: Results From the ONDRI

Frederico Pieruccini-Faria, PhD https://orcid.org/0000-0002-7659-4285 frederico.faria@sjhc.london.on.ca, Benjamin Cornish, MSc, […], and the ONDRI Investigators+40View all authors and affiliations

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https://doi.org/10.1177/15459683231177606

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Abstract

Background

Acute change in gait speed while performing a mental task [dual-task gait cost (DTC)], and hyperintensity magnetic resonance imaging signals in white matter are both important disability predictors in older individuals with history of stroke (poststroke). It is still unclear, however, whether DTC is associated with overall hyperintensity volume from specific major brain regions in poststroke.

Methods

This is a cohort study with a total of 123 older (69 ± 7 years of age) participants with history of stroke were included from the Ontario Neurodegenerative Disease Research Initiative. Participants were clinically assessed and had gait performance assessed under single- and dual-task conditions. Structural neuroimaging data were analyzed to measure both, white matter hyperintensity (WMH) and normal appearing volumes. Percentage of WMH volume in frontal, parietal, occipital, and temporal lobes as well as subcortical hyperintensities in basal ganglia + thalamus were the main outcomes. Multivariate models investigated associations between DTC and hyperintensity volumes, adjusted for age, sex, years of education, global cognition, vascular risk factors, APOE4 genotype, residual sensorimotor symptoms from previous stroke and brain volume.

Results

There was a significant positive global linear association between DTC and hyperintensity burden (adjusted Wilks’ λ = .87, P = .01). Amongst all WMH volumes, hyperintensity burden from basal ganglia + thalamus provided the most significant contribution to the global association (adjusted β = .008, η² = .03; P = .04), independently of brain atrophy.

Conclusions

In poststroke, increased DTC may be an indicator of larger white matter damages, specifically in subcortical regions, which can potentially affect the overall cognitive processing and decrease gait automaticity by increasing the cortical control over patients’ locomotion.

Introduction

Mobility and cognitive impairments co-exist and have intertwined relationships in patients who experience vascular white matter lesions caused by a stroke.¹ Current theoretical framework suggests that mobility and cognition share common brain networks that may be damaged by cerebrovascular disease.² Dual-task gait cost (DTC), or the acute change in gait performance when simultaneously performing a mental task,^3,4 was found to be higher in individuals with cognitive and motor syndromes. Furthermore, DTC is a predictor of motor^5,6 and cognitive^7,8 disabilities in individuals with history of stroke (poststroke) and accelerated brain degeneration.^10-12 However, there is insufficient evidence of an independent association between DTC and hyperintensity signals (white matter lesions),¹³ particularly regarding its location, in Poststroke.

Increased DTC has been related to reduced cognitive^5,6,14 and neural efficiency¹⁵ in frontal lobe regions in poststroke. A recent study, in healthy older adults, suggested that global microstructural white matter defects mediates the relationship between neural efficiency in prefrontal cortex and dual-task gait performance.¹⁶ White matter lesions are expected to reduce processing speed¹⁷ which consequently may affect the ability to walk and talk due to increased processing demands for task switching, as suggested by previous studies in Poststroke.^6,14 White matter hyperintensity (WMH) is a typical macrostructural brain tissue alteration, detected with magnetic resonance imaging, in poststroke,^11,12,18-22 and a strong predictor of fast progression to dementias,^18-21 particularly when concentrated in the frontal lobe.^11,12,22 Previous studies in older adults also have found global association between slow gait performance under dual-task conditions and larger WMH volumes and mostly focused on cortical regions.^23,24 Notably, the investigation on the association between WMH burden in the parietal, occipital, temporal lobes and gait performance have been neglected, although pathology in these cortical regions can be directly or indirectly linked with mobility impairments.²⁵ Moreover, despite subcortical regions, including basal ganglia and thalamus be known for its strong involvement with subconscious/well-learnt/automatic sensorimotor processing,^26-28 studies also have shown that these subcortical regions are important to support cognitive processes occurring in frontal lobe regions during complex goal-directed actions.^26,27,29 Hence, an independent association between subcortical WMH burden and increased DTC would suggest increased cognitive load for gait control driven by subcortical dysfunction (ie, decreased motor automaticity). However, it is unknown whether and how WMH burden in these regions would be associated with gait and DTC.

We hypothesize that increased DTC (ie, percentage of gait slowing from single- to dual-task testing conditions), will be cross-sectionally associated with larger WMH volumes in cortical and subcortical regions while controlling associations for potential confounders including atrophy.³⁰ It is expected that increased DTC will be globally associated with larger WMH burden with greater contributions from aforementioned brain regions closely involved with complex thinking and sensorimotor processing for gait control. Importantly, because brain atrophy is quite prevalent in poststroke³¹ and strongly linked with worse gait performance in individuals with cerebrovascular disease,³⁰ we also tested whether the normal appearing volumes would mediate the expected association between DTC and WMH. To test these hypotheses, we examined the cerebrovascular disease (CVD) cohort from the Ontario Neurodegenerative Disease Research Initiative (ONDRI),³² which is exclusively composed by patients with clinically confirmed history of stroke.

 

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Accurate prediction of persistent upper extremity impairment in patients with ischemic stroke

I'd fire anyone who came to me with useless recovery prediction research like this.

 Accurate prediction of persistent upper extremity impairment in patients with ischemic stroke

Archives of Physical Medicine and Rehabilitation , Volume 103(5) , Pgs. 964-969.

NARIC Accession Number: J89032.  What's this?
ISSN: 0003-9993.
Author(s): de Havenon, Adam; Heitsch, Laura; Sunmonu, Abimbola; Braun, Robynne; Lohse, Keith R.; Cole, John W.; Mistry, Eva; Lindgren, Arne; Worrall, Bradford B.; Cramer, Steven C..
Publication Year: 2022.
Number of Pages: 6.
Abstract: Study developed a simple and effective risk score for predicting which patients will have persistent impairment of upper-extremity motor function at 90 days post stroke. Data were analyzed for clinical trial patients hospitalized with acute ischemic stroke who were followed for 90 days to determine functional outcome; the cohort was divided into balanced derivation and validation samples. The primary outcome was persistent arm impairment, defined as a National Institutes of Health Stroke Scale (NIHSS) arm domain score of 2 to 4 at 90 days in patients who had a 24-hour NIHSS arm score of 1 or more. Least absolute shrinkage and selection operator regression were used to determine the elements of the persistent upper-extremity impairment (PUPPI) index. Analyses included 1,653 patients (827 derivation, 826 validation), of whom 803 (48.6 percent) had persistent arm impairment. The PUPPI index gives 1 point each for age 55 years or older and NIHSS values of worse arm, worse leg, facial palsy, and total NIHSS (≥10). The optimal cut point for the PUPPI index was 3 or greater, at which the area under the curve was greater than 0.75 for the derivation and validation cohorts and when using NIHSS values from either 24 hours or in a subacute or discharge time window. Results were similar across different levels of stroke severity. The PUPPI index can be administered in minutes and could be used as inclusion criterion in recovery-related clinical trials or, with additional development, as a prognostic tool for patients, caregivers, and clinicians.
Descriptor Terms: EVALUATION TECHNIQUES, FUNCTIONAL LIMITATIONS, LIMBS, MOBILITY IMPAIRMENTS, MOTOR SKILLS, OUTCOMES, PREDICTION, STROKE.


Can this document be ordered through NARIC's document delivery service*?: Y.

Citation: de Havenon, Adam, Heitsch, Laura, Sunmonu, Abimbola, Braun, Robynne, Lohse, Keith R., Cole, John W., Mistry, Eva, Lindgren, Arne, Worrall, Bradford B., Cramer, Steven C.. (2022). Accurate prediction of persistent upper extremity impairment in patients with ischemic stroke.  Archives of Physical Medicine and Rehabilitation , 103(5), Pgs. 964-969. Retrieved 7/24/2022, from REHABDATA database.

The prognostic utility of electroencephalography in stroke recovery: A systematic review and meta-analysis

PROGNOSTICATION  DOES ABSOLUTELY NOTHING TO GET PATIENTS RECOVERED. Will you please do some useful research? I'd have you all fired.

The prognostic utility of electroencephalography in stroke recovery: A systematic review and meta-analysis

Neurorehabilitation and Neural Repair (NNR) , Volume 36(4-5) , Pgs. 255-268.

NARIC Accession Number: J88715.  What's this?
ISSN: 1545-9683.
Author(s): Vatinno, Amanda A.; Simpson, Annie; Ramakrishnan, Viswanathan; Bonilha, Heather S.; Bonilha, Leonardo; Seo, Na Jin.
Publication Year: 2022.
Number of Pages: 14.

Abstract: 

Study examined the evidence for the prognostic utility of electroencephalography (EEG) in stroke recovery. EEG provides a direct measure of the functional neuroelectricactivity in the brain that forms the basis for neuroplasticity and recovery, and thus may increase prognostic ability. PubMed (Medline), Scopus, and CINAHL electronic databases were searched for peer-reviewed journal articles that examined the relationship between EEG and subsequent clinical outcome(s) in stroke. Seventy-five articles met the inclusion criteria and were synthesized for the systematic review. Two independent researchers extracted data for synthesis. Linear meta-regressions were performed across subsets of papers with common outcome measures to quantify the association between EEG and outcome. Association between EEG and clinical outcomes was seen not only early post-stroke, but more than 6 months post-stroke. The most studied prognostic potential of EEG was in predicting independence and stroke severity in the standard acute stroke care setting. The meta-analysis showed that EEG was associated with subsequent clinical outcomes measured by the Modified Rankin Scale, National Institutes of Health Stroke Scale, and Fugl-Meyer Upper Extremity Assessment. EEG improved prognostic abilities beyond prediction afforded by standard clinical assessments. However, the EEG variables examined were highly variable across studies and did not converge. Findings indicate that EEG shows potential to predict post-stroke recovery outcomes. However, evidence is largely explorative, primarily due to the lack of a definitive set of EEG measures to be used for prognosis.
Descriptor Terms: BRAIN, ELECTROPHYSIOLOGY, LITERATURE REVIEWS, MOBILITY, MOTOR SKILLS, OUTCOMES, PREDICTION, REHABILITATION, STROKE.


Can this document be ordered through NARIC's document delivery service*?: Y.

Citation: Vatinno, Amanda A., Simpson, Annie, Ramakrishnan, Viswanathan, Bonilha, Heather S., Bonilha, Leonardo, Seo, Na Jin. (2022). The prognostic utility of electroencephalography in stroke recovery: A systematic review and meta-analysis.  Neurorehabilitation and Neural Repair (NNR) , 36(4-5), Pgs. 255-268. Retrieved 6/23/2022, from REHABDATA database.

Baseline Predictors of Response to Repetitive Task Practice in Chronic Stroke

 

Baseline Predictors of Response to Repetitive Task Practice in Chronic Stroke

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Michael A. Dimyan, MD, Stacey Harcum, MPH, MS, OTR/L, Elsa Ermer, PhD, ...

First Published May 26, 2022 Research Article Find in PubMed 

https://doi.org/10.1177/15459683221095171

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Abstract

Background

Repetitive task practice reduces mean upper extremity motor impairment in populations of patients with chronic stroke, but individual response is highly variable. A method to predict meaningful reduction in impairment in response to training based on biomarkers and other data collected prior to an intervention is needed to establish realistic rehabilitation goals(So you assholes are using the tyranny of low expectations to justify your failure to know exactly how to get to 100% recovery. I'd have you fired for not doing your job.) and to effectively allocate resources.

Objectives

To identify prognostic factors and better understand the biological substrate for reductions in arm impairment in response to repetitive task practice among patients with chronic (≥6 months) post-stroke hemiparesis.

Methods

The intervention is a form of repetitive task practice using a combination of robot-assisted therapy and functional arm use in real-world tasks. Baseline measures include the Fugl-Meyer Assessment, Wolf Motor Function Test, Action Research Arm Test, Stroke Impact Scale, questionnaires on pain and expectancy, MRI, transcranial magnetic stimulation, kinematics, accelerometry, and genomic testing.

Results

Mean increase in FM-UE was 4.6 ± 1.0 SE, median 2.5. Approximately one-third of participants had a clinically meaningful response to the intervention, defined as an increase in FM ≥ 5. The selected logistic regression model had a receiver operating curve with AUC = .988 (Std Error = .011, 95% Wald confidence limits: .967–1) showed little evidence of overfitting. Six variables that predicted response represented impairment, functional, and genomic measures.

Conclusion

A simple weighted sum of 6 baseline factors can accurately predict clinically meaningful impairment reduction after outpatient intensive practice intervention in chronic stroke. Reduction of impairment may be a critical first step to functional improvement. Further validation and generalization of this model will increase its utility in clinical decision-making.

Keywords

stroke, prognosis, TMS, MRI, robot, rehabilitation

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The TWIST Tool Predicts When Patients Will Recover Independent Walking After Stroke: An Observational Study

 You goddamn lazy assholes. Survivors want 100% recovery in walking, NOT YOUR TYRANNY OF LOW EXPECTATIONS!

The TWIST Tool Predicts When Patients Will Recover Independent Walking After Stroke: An Observational Study

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Marie-Claire Smith, PhD, Alan P. Barber, FRACP, Benjamin J. Scrivener, MHlthLd, ...

First Published May 18, 2022 Research Article 

https://doi.org/10.1177/15459683221085287

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Abstract

Background

The likelihood of regaining independent walking after stroke influences rehabilitation and hospital discharge planning.

Objective

This study aimed to develop and internally validate a tool to predict whether and when a patient will walk independently in the first 6 months post-stroke.

Methods

Adults with stroke were recruited if they had new lower limb weakness and were unable to walk independently. Clinical assessments were completed one week post-stroke. The primary outcome was time post-stroke by which independent walking (Functional Ambulation Category score ≥ 4) was achieved. Cox hazard regression identified predictors for achieving independent walking by 4, 6, 9, 16, or 26 weeks post-stroke. The cut-off and weighting for each predictor was determined using β-coefficients. Predictors were assigned a score and summed for a final TWIST score. The probability of achieving independent walking at each time point for each TWIST score was calculated.

Results

We included 93 participants (36 women, median age 71 years). Age < 80 years, knee extension strength Medical Research Council grade ≥ 3/5, and Berg Balance Test < 6, 6 to 15, or ≥ 16/56, predicted independent walking and were combined to form the TWIST prediction tool. The TWIST prediction tool was at least 83% accurate for all time points.

Conclusions

The TWIST tool combines routine bedside tests at one week post-stroke to accurately predict the probability of an individual patient achieving independent walking by 4, 6, 9, 16, or 26 weeks post-stroke. If externally validated, the TWIST prediction tool may benefit patients and clinicians by informing rehabilitation decisions and discharge planning.

Keywords

stroke, rehabilitation, walking, prediction, recovery

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Genetic and Neurophysiological Biomarkers of Neuroplasticity Inform Post-Stroke Language Recovery

My conclusion is that your prognostication is totally fucking worthless to survivors. This did nothing useful.

Genetic and Neurophysiological Biomarkers of Neuroplasticity Inform Post-Stroke Language Recovery

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Haley C. Dresang, PhD*, Denise Y. Harvey, PhD, Sharon X. Xie, PhD, ...

First Published April 15, 2022 Research Article 

https://doi.org/10.1177/15459683221096391

Article information

Abstract

Background

There is high variability in post-stroke aphasia severity and predicting recovery remains imprecise. Standard prognostics do not include neurophysiological indicators or genetic biomarkers of neuroplasticity, which may be critical sources of variability.

Objective

To evaluate whether a common polymorphism (Val⁶⁶Met) in the gene for brain-derived neurotrophic factor (BDNF) contributes to variability in post-stroke aphasia, and to assess whether BDNF polymorphism interacts with neurophysiological indicators of neuroplasticity (cortical excitability and stimulation-induced neuroplasticity) to improve estimates of aphasia severity.

Methods

Saliva samples and motor-evoked potentials (MEPs) were collected from participants with chronic aphasia subsequent to left-hemisphere stroke. MEPs were collected prior to continuous theta burst stimulation (cTBS; index for cortical excitability) and 10 minutes following cTBS (index for stimulation-induced neuroplasticity) to the right primary motor cortex. Analyses assessed the extent to which BDNF polymorphism interacted with cortical excitability and stimulation-induced neuroplasticity to predict aphasia severity beyond established predictors.

Results

Val⁶⁶Val carriers showed less aphasia severity than Val⁶⁶Met carriers, after controlling for lesion volume and time post-stroke. Furthermore, Val⁶⁶Val carriers showed expected effects of age on aphasia severity, and positive associations between severity and both cortical excitability and stimulation-induced neuroplasticity. In contrast, Val⁶⁶Met carriers showed weaker effects of age and negative associations between cortical excitability, stimulation-induced neuroplasticity and aphasia severity.

Conclusions

Neurophysiological indicators and genetic biomarkers of neuroplasticity improved aphasia severity predictions. Furthermore, BDNF polymorphism interacted with cortical excitability and stimulation-induced neuroplasticity to improve predictions. These findings provide novel insights into mechanisms of variability in stroke recovery and may improve aphasia prognostics.

Keywords

aphasia, neuroplasticity, language recovery, cortical excitability, neurostimulation, brain-derived neurotrophic factor

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