{
  "abstract": "Background: Following stroke, motor and cognitive impairments often limit functional independence. Personalized rehabilitation requires comprehensive clinical assessments, which are costly and time-consuming. Imaging scans are routinely collected, but their predictive value in determining post-stroke function remains unclear. Objectives: To determine the ability of clinical assessments, lesion features with medical chart information, and a combined clinical and biomedical model to predict kinematic outcomes from a robot-based tracking task in chronic stroke individuals. Methods: Eighteen participants (70+/-6 years) completed a robot-based tracking task and a battery of motor and cognitive assessments. Three elastic net regression models were developed: 1) clinical assessments only; 2) lesion metrics and chart information only; and 3) a combined model. Models were evaluated using leave-one-subject-out cross-validation, bootstrapping, and permutation testing with false discovery rate correction. Robot-based outcomes included tracking error, normalized distance, velocity error response, and delay. Results: The clinical assessment model significantly predicted tracking error (R^2=0.45), delay (R^2=0.22), velocity error response (R^2=0.18), and normalized distance (R^2=0.12). The combined model significantly predicted tracking error (R^2=0.37), delay (R^2=0.19), and velocity error response (R^2=0.11). The imaging model did not significantly predict any outcome. Box and Blocks Test and Color Trails 1 were stable features. The right dorsal attention network was a stable predictor in several combined models. Conclusion: Clinical assessments outperformed lesion features in predicting tracking performance after chronic stroke. Attention- and motor-related measures were the strongest predictors, highlighting the importance of cognitive-motor interactions in function. A larger sample and more granular lesion features may improve prediction from imaging biomarkers.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "Reem M. Azar",
      "orcid": "0009-0004-0788-546X"
    },
    {
      "name": "Erica L. Waters",
      "orcid": "0000-0003-1495-8423"
    },
    {
      "name": "Joana Opong-Duah",
      "orcid": "0009-0006-4455-1180"
    },
    {
      "name": "Rochelle J. Mendonca",
      "orcid": "0000-0002-7706-2133"
    },
    {
      "name": "Pamela Z. Cacchione",
      "orcid": "0000-0003-1805-7091"
    },
    {
      "name": "Michelle J. Johnson",
      "orcid": "0000-0002-7964-0304"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6kqk997842s8x.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.28.26366424",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.10.28.26366424v1.full",
  "id": "6kqk997842s8x",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.10.28.26366424v1",
  "language": "en",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Article"
    ],
    [
      "medrxiv",
      "rehabilitation medicine and physical therapy"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.10.28.26366424v1.full.pdf",
  "pmcid": "PMC13734195",
  "pmid": "42990705",
  "published": "2026-10-28",
  "publisher": "Cold Spring Harbor Laboratory Preprints",
  "ris": "https://research.draconitic.com/paper/6kqk997842s8x.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:05Z",
      "native_id": "10.64898/2026.10.28.26366424",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.10.28.26366424v1"
    },
    {
      "first_seen": "2026-11-08T12:23:49Z",
      "native_id": "PMC13734195",
      "source": "pmc",
      "url": "https://doi.org/10.64898/2026.10.28.26366424"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:rehabilitation medicine and physical therapy",
      "subfield": "general"
    }
  ],
  "title": "Predicting Robot-Based Tracking Performance After Stroke Using Lesion Features and Clinical Assessments: A Pilot Study",
  "title_text": "Predicting Robot-Based Tracking Performance After Stroke Using Lesion Features and Clinical Assessments: A Pilot Study",
  "type": "preprint",
  "updated_at": "2026-11-08T12:23:49Z",
  "url": "https://research.draconitic.com/paper/6kqk997842s8x",
  "venue": "medRxiv",
  "versions": []
}