The Influence of Contralesional Brain Age Gradients on Chronic Stroke Aphasia Severity and Treatment Response.
Paper
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The authors' code
Python · 40 lines · 1.2 KB · GPL-3.0
- import pandas as pd
- # Step 1: Load factor score coefficients
- coefs_df = pd.read_csv("factor_coefs.csv") # columns: Region, Factor1, Factor2, ...
- coefs_df.set_index("Region", inplace=True)
- # Step 2: Load external means and STDs
- # Format: columns = Region, Mean, SD
- means_df = pd.read_csv("means_stds.csv")
- means_df.set_index("Region", inplace=True)
- # Step 3: Load user data
- user_df = pd.read_csv("github_test.csv")
- # Step 4: Match regions
- region_cols = coefs_df.index.tolist()
- # Check for missing regions
- missing = [r for r in region_cols if r not in user_df.columns]
- if missing:
- raise ValueError(f"Missing region(s) in user data: {missing}")
- # Subset and order user data to match coefficient matrix
- X = user_df[region_cols].copy()
- # Step 5: Z-score using external means and SDs
- X_z = (X - means_df.loc[region_cols, "Mean"].values) / means_df.loc[region_cols, "SD"].values
- # Step 6: Compute factor scores
- B = coefs_df
- factor_scores = X_z.dot(B)
- # Step 7: Combine with SubjectID if it exists
- if "SubjectID" in user_df.columns:
- output = pd.concat([user_df[["SubjectID"]], factor_scores], axis=1)
- else:
- output = factor_scores.copy()
- output.to_csv("factor_scores_output.csv", index=False)
- print("Factor scores computed and saved to 'factor_scores_output.csv'")
compute_factors.py at commit 09eee0a, under GPL-3.0 · at the source
Overview
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, South Carolina 29208
- Center for Stroke Research Berlin, Charité – Universitätsmedizin Berlin, Berlin 10117, Germany
- Department of Psychology, University of South Carolina, Columbia, South Carolina 29208
- Department of Neurology, School of Medicine Columbia, Columbia, South Carolina 29208
Abstract
The adult brain follows normative aging trajectories, yet stroke abruptly disrupts this process. Beyond the focal lesion, undamaged regions can deviate from expected aging due to diaschisis or pre-existing vulnerabilities, reflecting tissue health that may constrain recovery. Chronic post-stroke aphasia, a persistent language disorder, shows wide individual variability in severity and treatment response, and these differences are not fully captured by lesion characteristics or demographic factors. Further, prior imaging biomarkers have been difficult to scale because they rely on specialized acquisitions or nonstandardized analytical pipelines. Here, we used a standardized, lifespan-informed brain age model (volBrain) trained on large public datasets to quantify regional deviations from normative aging using only routine structural MRI (T1-weighted). In 188 individuals with chronic stroke (67 female, 121 male), regional aging deviations in the contralesional hemisphere explained aphasia severity beyond lesion volume, lesion location, and basic demographics. In a treated subgroup, baseline brain aging patterns also predicted language gains that persisted 6 months after therapy, indicating prognostic value. By characterizing how individual brains diverge from expected aging, this approach captures variation in tissue integrity that is relevant for both impairment and recovery. This provides a scalable framework that can be integrated across sites and studies, linking normative aging models with post-stroke prognostics and informing precision neurorehabilitation.
Reproduced under the paper's license (CC BY), from the paper cited above.
Repository
Its files are read in the Code ↔ Paper reader above.
NickRiccardi/brain_age_factors
09eee0a929ef096d338922c479812a3bd7482d83, 8 July 2025Availability: 1 check, the latest on 24 October 2026: the link answers
- 24 October 2026: the link answers
3 files
- compute_factors.py, Python, 40 lines
- LICENSE, License, 674 lines
- README.md, Text, 5 lines
The paper's code and data availability statement is in the Data section.
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Data
Datasets cited
- doi:10.18112/
openneuro.ds004884.v1.0. , at OpenNeuro; found in “Data Availability”1
Data Availability
Neuroimaging data is publicly available: doi:10.18112/
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 1, 24 October 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 6 keywords, 14 MeSH terms, 1 funder, 74 references.
Cite
This paper
Riccardi, N., Martin, A., Rangus, I., Busby, N., Rorden, C., Fridriksson, J., & Bonilha, L. (2026). The Influence of Contralesional Brain Age Gradients on Chronic Stroke Aphasia Severity and Treatment Response. The Journal of neuroscience : the official journal of the Society for Neuroscience, 46(42), e2058252026. https://
BibTeX
@article{riccardi2026inf
author = {Riccardi, Nicholas and Martin, Ansley and Rangus, Ida and Busby, Natalie and Rorden, Chris and Fridriksson, Julius and Bonilha, Leonardo},
title = {{The Influence of Contralesional Brain Age Gradients on Chronic Stroke Aphasia Severity and Treatment Response}},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
year = {2026},
month = oct,
volume = {46},
number = {42},
pages = {e2058252026},
publisher = {Society for Neuroscience},
issn = {0270-6474},
doi = {10.1523/
url = {https://
pmid = {42805781},
pmcid = {PMC13689985}
}
RIS
TY - JOUR
AU - Riccardi, Nicholas
AU - Martin, Ansley
AU - Rangus, Ida
AU - Busby, Natalie
AU - Rorden, Chris
AU - Fridriksson, Julius
AU - Bonilha, Leonardo
TI - The Influence of Contralesional Brain Age Gradients on Chronic Stroke Aphasia Severity and Treatment Response
T2 - The Journal of neuroscience : the official journal of the Society for Neuroscience
J2 - J Neurosci
PY - 2026
DA - 2026/
VL - 46
IS - 42
SP - e2058252026
SN - 0270-6474
PB - Society for Neuroscience
DO - 10.1523/
UR - https://
LA - en
ER -
CSL-JSON
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