Uninjured Brain Age Holds Key to Language Recovery Following Stroke

 Does your doctor have two neurons to rub together to update their aphasia protocols with this? 

They don't have any? PURE INCOMPETENCE!

Uninjured Brain Age Holds Key to Language Recovery Following Stroke

Summary:

A new study demonstrates that accelerated biological aging in areas of the brain spared by a stroke strongly influences language impairment and long-term rehabilitation outcomes. Researchers found that structural brain age in uninjured tissue predicted aphasia severity and forecast language recovery six months after speech therapy paired with brain stimulation.

Key Facts:

• Impact of Non-Injured Tissue: Biological aging patterns in the hemisphere opposite the stroke lesion accounted for aphasia severity independently of the stroke lesion’s actual size or location.
• Predicting Recovery Success: Structural brain aging metrics gathered prior to intervention reliably predicted language improvements six months after patients completed speech therapy paired with noninvasive brain stimulation.
• Accessible Clinical Translation: The predictive framework relies solely on standard, routine brain scans evaluated via a free, open-access online tool trained on normative human aging datasets.

Source: Society for Neuroscience / University of South Carolina Floyd School of Medicine

Following an ischemic or hemorrhagic stroke, neurological damage is rarely restricted strictly to the primary lesion site. Even brain regions that escape direct ischemic injury can exhibit hallmarks of accelerated structural aging. This secondary vulnerability is especially evident in post-stroke aphasia, a debilitating language impairment characterized by vast individual variability in both baseline severity and long-term responsiveness to rehabilitation.

Historically, clinicians have attempted to forecast recovery by mapping the focal stroke injury itself: measuring lesion volume and tracking specific damaged language tracts. However, these metrics often fail to explain why two individuals with nearly identical lesions experience drastically different recovery trajectories.

Now, a study published in The Journal of Neuroscience (JNeurosci) led by Nicholas Riccardi, Leonardo Bonilha, and colleagues from the University of South Carolina Floyd School of Medicine establishes that post-stroke language outcomes depend significantly on the biological age and resilience of uninjured brain tissue.

Machine Learning Reveals the Brain Age Gap

To quantify subtle structural changes across the whole brain, the research team implemented an online machine-learning platform trained on extensive, normative human brain aging datasets. This computational model compares an individual’s structural MRI scan against expected benchmarks to detect biological deviations from chronological aging.

The investigators evaluated 188 post-stroke patients presenting with varying degrees of aphasia. Strikingly, structural aging markers within the hemisphere not directly damaged by the stroke explained aphasia severity independently of classical variables, such as lesion volume or anatomical location.

Furthermore, the team assessed patients undergoing an intensive rehabilitation regimen combining speech-language therapy with noninvasive brain stimulation. Baseline brain aging metrics recorded prior to treatment accurately predicted the extent of sustained language gains measured six months after therapy concluded.

Accessible, Low-Cost Rehabilitation Biomarkers

The findings establish a critical link between baseline biological aging models and post-stroke rehabilitation success, offering an objective framework for tailoring individualized recovery protocols.

Importantly, because the computational model requires only a standard, non-contrast clinical MRI and an accessible, free computational algorithm, the methodology avoids the high technical and financial hurdles that typically stall advanced neuroimaging biomarkers.

“This work suggests that recovery potential after stroke depends on the health of the rest of the brain, which is partly shaped by treatable factors like cardiovascular health,” said lead author Nicholas Riccardi. “Second, because everything here came from a single routine scan and a free online tool, this could realistically reach a variety of clinical or research settings one day.”

Targeting modifiable systemic health factors, such as blood pressure, metabolic markers, and exercise habits, could serve to protect global brain resilience, ensuring that uninjured neural networks remain primed to support post-stroke neuroplasticity and functional recovery.

Editorial Notes:

• This article was edited by a Neuroscience News editor.
• Journal paper will be reviewed in full upon release.
• Additional context added by our staff.

About this neurology Research:

• Media Contact: SfN Media
• Source: SfN
• Image Credit: Image credited to Neuroscience News
• Original Research is Open Access: The findings will be published in Journal of Neuroscience

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability

 Predicting shit like this is useless, Survivors want recovery! DELIVER THAT!

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability

About the Authors

Qian Ye

Roles Data curation, Writing – original draft

‡ QY, GF also contributed equally to this work and share first authorship.

Affiliation Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China

https://orcid.org/0009-0006-7823-5899

Guilin Fang

Roles Data curation, Formal analysis

‡ QY, GF also contributed equally to this work and share first authorship.

Affiliation Department of Nephrology, Nanjing Jinling Hospital, General Hospital of Eastern Theatre Command, Nanjing, Jiangsu, China

Liping Li

Roles Data curation

Affiliation Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China

Qinggui Li

Roles Investigation

Affiliation Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China

Yun Yang

Roles Investigation

Affiliation Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China

Lingling Liu

Roles Formal analysis, Methodology, Writing – review & editing

* E-mail: 1209674467@qq.com

Affiliation Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China

https://orcid.org/0000-0003-4392-3676

Competing Interests

The authors have declared that no competing interests exist.

Abstract

Purpose

We aimed to develop a machine learning model to predict activities of daily living (ADL) at discharge in stroke patients and identify key predictors to guide rehabilitation decisions.

Materials and methods

Data of 589 stroke inpatients (2019–2024) were split into good (BI ≥ 60) and poor (BI < 60) ADL groups. Continuous variables were processed using Z-score normalization, followed by preliminary univariate regression screening (P < 0.05) and final feature selection via LASSO regression (lambda.1se = 0.0488). The screened features were used to train and validate ten machine learning algorithms; 30% of the dataset (n = 177) was allocated as an independent test set for model evaluation, and SHAP analysis was performed to interpret the optimal model.

Results

Six of 41 features were retained. Random forest achieved the best performance (AUC = 0.958; accuracy = 0.936; sensitivity = 0.934; specificity = 0.950). SHAP identified the top drivers: admission Barthel Index, standing balance, Brunnstrom stages (upper and lower limb), dressing, and grooming abilities.

Conclusion

The ADL risk prediction model constructed using machine learning, particularly the random forest model, shows excellent predictive performance and clinical interpretability, making it valuable for individualized risk assessment of daily living skills in stroke patients at discharge.

Figures

  

Citation: Ye Q, Fang G, Li L, Li Q, Yang Y, Liu L (2026) Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability. PLoS One 21(7): e0351468. https://doi.org/10.1371/journal.pone.0351468

Editor: Fan Zhang, Longhua Hospital Shanghai University of Traditional Chinese Medicine, CHINA

Received: November 28, 2025; Accepted: May 26, 2026; Published: July 2, 2026

Copyright: © 2026 Ye et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data for this study are publicly available from the Zenodo repository (https://doi.org/10.5281/zenodo.20629742).

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Modified small vessel disease score as the top predictor of stroke outcome after thrombectomy: a CT-based machine learning study

 

Why are your predicting failure to recover RATHER THAN DELIVERING RECOVERY?

Laziness? Incompetence? Or just don't care? NO leadership? NO strategy? Not my job? Not my Problem!

You're all fired! You need to create EXACT RECOVERY PROTOCOLS! 

Prediction crapola like this does nothing to get survivors recovered! Your comeuppance when you have a stroke and don't recover will be a bitter pill for you to swallow.

Modified small vessel disease score as the top predictor of stroke outcome after thrombectomy: a CT-based machine learning study

• 

  Thiago Oscar Goulart 1† *

• 

  Rui Kleber do Vale Martins-Filho 2

• 

  Millene Rodrigues Camilo 2

• 

  Daniel Giansante Abud 2

• 

  Octávio Marques Pontes-Neto 2

• 1. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States

• 2. Department of Neuroscience and Behavioral Sciences, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo, Brazil

Abstract

Background: 

Mechanical thrombectomy (MT) improves outcomes in ischemic stroke (IS) due to large vessel occlusion (LVO), but ~50% of patients fail to achieve functional independence.

Objectives: 

We investigated whether cerebral small vessel disease (cSVD), assessed by the modified Small Vessel Disease (mSVD) score and Brain Frailty Score (BFS), outperforms individual CT markers in predicting 90-day outcomes after MT.

Design: 

Prospective cohort with retrospective analysis.

Methods: 

We included 351 patients with anterior circulation LVO treated with MT. Admission CT was used to score cSVD markers (leukoaraiosis, atrophy, lacunes) and compute mSVD and BFS. Eight logistic regression models and a Random Forest algorithm were used to predict poor outcome [modified Rankin Scale (mRS) 3–6]. Model performance was evaluated using AUC-ROC and compared via DeLong tests.

Results: 

Poor outcomes were associated with older age, higher NIHSS, systolic blood pressure, glycemia, and more severe leukoaraiosis and atrophy. Severe mSVD (score = 3) independently predicted poor outcomes (OR = 3.267; CI: 1.731–6.168; p = 0.009). mSVD outperformed BFS and individual CT markers (AUC = 0.904 vs. 0.889/0.898; DeLong p < 0.05) and ranked as the top predictor in Random Forest (importance = 42.05). Treatment efficacy declined with increasing mSVD: the probability of a favorable outcome was 15.53% and poor outcome was 84.47% for mSVD = 3, compared to 89.23% and 10.77%, respectively, for mSVD = 0. A secondary model incorporating 24h NIHSS and hemorrhagic transformation improved discrimination (AUC = 0.954), but mSVD remained a key independent predictor.

Conclusions: 

In this prospective study in a middle-income country, mSVD score was the strongest predictor of post-thrombectomy outcome, outperforming BFS and isolated imaging markers. While cSVD does not contraindicate MT, it reflects reduced cerebrovascular resilience. Integrating mSVD into baseline CT evaluation may enhance risk stratification and treatment guidance.

More at link.

Development and validation of a machine learning model for predicting stroke-associated pneumonia in older patients with acute ischemic stroke

 What fucking stupidity; predicting pneumonia rather that creating a protocol to prevent it! You're all fired!

You've known about this problem for a long time. SOLVE IT! 

Just maybe this vaccine!

Pneumonia Vaccine (3 posts to July 2020)

• 11% Stroke-associated pneumonia (17 posts to October 2020)

Development and validation of a machine learning model for predicting stroke-associated pneumonia in older patients with acute ischemic stroke

• W

  Wen-Jie Chu 1

• S

  Si-Ran Zhang 2*

• Q

  Qi-Lun Lai 2

• J

  Jing-Ying Yu 2

• Y

  Yi-Qian Xu 2

• 1. Department of Hospital Infections, Zhejiang Hospital, Hangzhou, China

• 2. Department of Neurology, Zhejiang Hospital, Hangzhou, China

Abstract

Objective: 

Stroke-associated pneumonia (SAP) is a common and serious complication in older patients with acute ischemic stroke (AIS). However, early identification of high-risk patients remains challenging. This study aimed to develop and validate an interpretable machine learning model for predicting SAP risk in older AIS patients.

Methods: 

This retrospective study included 1,011 eligible patients (aged ≥65 years) with AIS who were consecutively admitted to Zhejiang Hospital in China from September 1, 2018, to December 31, 2023. A total of 1,011 patients were randomly divided into training and testing sets (7:3 ratio). Demographics, comorbidities, laboratory test results, and admission assessments were collected to evaluate the risk of SAP. The synthetic minority oversampling technique (SMOTE) was used to address the imbalanced training data. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to filter the predictive features. Eight machine learning models, including Logistic Regression (LR), Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Gradient Boosting Decision Tree (GBDT), Multi-layer Perceptron (MLP), and Random Forest (RF), were applied to identify the best prediction model. The optimal model was interpreted using the SHapley Additive exPlanations (SHAP).

Results: 

SAP incidence was 18.79%. LASSO identified 12 predictive features. The SVM demonstrated acceptable and stable predictive performance, achieving an accuracy of 0.773, sensitivity of 0.667, specificity of 0.798, F1 score of 0.524, Brier score of 0.156, and AUC of 0.794 (95% CI: 0.748–0.839) in the test set. SHAP analysis identified key factors influencing model predictions. An online platform was developed for clinical use.

Conclusion: 

This study demonstrates that an interpretable SVM-based machine learning model can effectively predict the risk of SAP in older patients with AIS using routinely available clinical and laboratory data. SHAP analysis further improved the model’s clinical interpretability by elucidating feature contributions. Our online prediction platform could serve as a promising tool for identifying high-risk older patients and facilitating the early prophylactic management of SAP.

Development and validation of machine learning models for predicting functional outcome after low-dose alteplase in the extended time window for acute ischemic stroke

 Predicting failure to recover never got anyone recovered! Get the hell out of stroke!

Development and validation of machine learning models for predicting functional outcome after low-dose alteplase in the extended time window for acute ischemic stroke

• H

  Huiru Chen 1†

• 

  Qian Gui 1†

• 
  

• 
  

• 
  

• 
  

• 

  Kangxiang Ji 2

• M

  Mengfan Ye 3

• 
  
• 
  
• 
  
• 
  
• 
  
• 
  
• 
  
• 
  
• 
  
• 
  
• 
  
• 
  
• 
  
• 
  
• J

  Jieji Zhao 4

• Y

  Yan Kong 2*

• 
  

• 
  

• 
  

• 
  

• 
  

• 

  Guanhui Wu 1*

• 
  

• 
  

• 
  

• 
  

• 
  

• 

  Xin Tan 1*

• 1. Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, Jiangsu, China

• 2. Department of Neurology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China

Abstract

Background:

This study aims to develop machine learning (ML) models to predict 90-day functional outcomes for acute ischemic stroke (AIS) patients receiving thrombolysis with low-dose alteplase at 0.6 mg/kg between 4.5 and 9 h after symptom onset.

Methods:

We conducted a retrospective analysis of AIS patients receiving thrombolysis between August 1, 2019 and August 31, 2023. Eligible patients were randomly divided into training and validation sets in a 7:3 ratio. Good functional prognosis at 90 days were defined as modified Rankin scale score (mRS) ≤2. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to select optimal features. Five ML algorithms were employed to construct prediction models. Model performance was evaluated using receiver operating characteristic (ROC) curves, area under the curve (AUC) value, decision curve analysis (DCA), and calibration curves. SHapley Additive exPlanations (SHAP) plot was applied to interpret the model predictions.

Results:

A total of 202 patients were randomly divided into training (n = 142) and validation (n = 60) sets. The rate of poor functional prognosis at 90 days was 56.34% in the training set and 56.67% in the validation set. Random Forest (RF) model showed the best discriminative ability with the highest AUC of 0.854 in the validation set. Key predictive features included age, baseline systolic blood pressure, white blood cell count, baseline National Institutes of Health Stroke Scale (NIHSS) score, wake-up stroke, the absolute difference volume between the ischemic infarct and the penumbra, intracranial hemorrhage, hemorrhagic transformation classification, and occurrence of pneumonia.

Conclusion:

The RF-based ML model demonstrated clinical utility for post-intravenous thrombolysis risk stratification by identifying patients at higher risk of poor functional outcomes.