Longitudinal prediction of motor dysfunction after stroke: a disconnectome study

  1. Lilit Dulyan
  2. Lia Talozzi
  3. Valentina Pacella
  4. Maurizio Corbetta
  5. Stephanie J. Forkel
  6. Michel Thiebaut de Schotten

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Abstract

Motricity is the most commonly affected ability after a stroke. While many clinical studies attempt to predict motor symptoms at different chronic time points after a stroke, longitudinal acute-to-chronic studies remain scarce. Taking advantage of recent advances in mapping brain disconnections, we predict motor outcomes in 62 patients assessed longitudinally two weeks, three months, and one year after their stroke. Results indicate that brain disconnection patterns accurately predict motor impairments. However, disconnection patterns leading to impairment differ between the three-time points and between left and right motor impairments. These results were cross-validated using resampling techniques. In sum, we demonstrated that while some neuroplasticity mechanisms exist changing the structure–function relationship, disconnection patterns prevail when predicting motor impairment at different time points after stroke.

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  1. Version published to 10.1007/s00429-022-02589-5
  2. Version published to 10.1101/2021.12.01.21267129v2 on medRxiv
  3. PeerRef

    Peer review report

    Reviewer: Veena Nair Institution: UW Madison email: vnair@uwhealth.org

    Section 1 – Serious concerns

    • Do you have any serious concerns about the manuscript such as fraud, plagiarism, unethical or unsafe practices? No
    • Have authors’ provided the necessary ethics approval (from authors’ institution or an ethics committee)? Yes

    Section 2 – Language quality

    • How would you rate the English language quality? High quality

    Section 3 – validity and reproducibility

    • Does the work cite relevant and sufficient literature? Yes
    • Is the study design appropriate and are the methods used valid? Yes
    • Are the methods documented and analysis provided so that the study can be replicated?

    See comments below at the end of Section 3; some more methodological details could be provided.

    • Is the statistical analysis and its interpretation appropriate? Yes
    • Is quality of the figures and tables satisfactory? No

    In the demographics table, could you please add number of left versus right stroke patients, distribution of stroke location too. Thank you.

    In figure 1 I don’t see a L for left; perhaps missing in the pdf I have.

    • Are there any objective errors or fundamental flaws that make the research invalid? How could the author improve the study?

    It would be helpful to see the demographic characteristics of the normative database. Do the subjects in that database have a similar distribution of age and sex.

    Scanner variance is a known confound that studies must deal with; should we be concerned that the normative database was collected on a 7T whereas the current study’s data were all collected on a 3T? are there ways to do some kind of harmonization? Likewise, the multimodal Glasser atlas is based off of 21-35 old young healthy adults; but the variability in the current study population is large.

    In the Disconnectome map, what does each ‘map’ represent? Is it a structural connectivity map? What do the connections represent (fiber cross-sectional area, number of fibers etc.?). More details would be helpful.

    Based on Figure 4, prediction accuracy on the test set is low, at 22%; can this be improved by improvements in normalization? Perhaps by using a more age-appropriate template than the MNI152? [see for e.g., Mayo clinic template for older adults].

    Section 4 – Suggestions

    • Do you have any other feedback or comments for the Author?

    This is a very interesting study and the research question is an important one and very relevant to the field. Thank you.

    Section 5 – Decision

    Verified with reservations:The content is scientifically sound but has shortcomings that could be improved by further studies and/or minor revisions.

  4. PeerRef

    SciScore rigor report

    Sciscore is an AI platform that assesses the rigor of the methods used in the manuscript. SciScore assists expert referees by finding and presenting information scattered throughout a manuscript in a simple format.

    Not required = Field is not applicable to this study

    Not detected = Field is applicable to this study, but not included.

    Ethics

    IRB: Institutional Review Board and all participants gave their signed informed consent.

    Consent: Institutional Review Board and all participants gave their signed informed consent.

    Inclusion and Exclusion Criteria

    83 years; 34 males; 57 righthanded , see Table 1 ) met the inclusion criteria: All patients were older than 18 years , presented with first-ever ischemic ( 83 % ) or haemorrhagic ( 17 % ) stroke and behavioural deficits as assessed by a neurological examination.

    Attrition

    Patients who had a history of neurological or psychiatric presentations ( e.g. transient ischemic attack) , multifocal or bilateral strokes , or had MRI contraindications ( e.g. claustrophobia , ferromagnetic objects ) were excluded from the analysis ( n = 131 patients , see the enrollment flowchart in the supplementary materials from Corbetta et al. 2015).

    Sex as a biological variable

    Handedness ( % right-handed ) 91.94 Sex ( % female ) 45.16 Abbreviations: SD = standard deviation It is made available under a CC-BY-NC 4.0 International license.

    Subject Demographics

    Age: 83 years; 34 males; 57 righthanded , see Table 1 ) met the inclusion criteria: All patients were older than 18 years , presented with first-ever ischemic ( 83 % ) or haemorrhagic ( 17 % ) stroke and behavioural deficits as assessed by a neurological examinatio .

    Randomization

    The task instructions require patients to place and remove the nine pegs one at a time and in random order as quickly as possible ( Mathiowetz et al. 1985; Oxford Grice et al. 2003).

    Blinding

    Two boardcertified neurologists ( Drs Corbetta and Carter ) reviewed all segmentations blinded to the individual behavioural data .

    Power Analysis

    We believe that adding other factors ( e.g. demographic , clinical , socioeconomic variables ) that likely interact with the recovery of patients can help us increase the model’s predictive power.

    Replication

    not required.

    Cell Line Authentication

    Authentication: However , most of the studies fall into one of the pitfalls that were described above ( i.e. overfitting , generalisability , and diaschisis ) as the models are not validated in an independent dataset.

    Code Information

    Identifiers: This procedure is available as supplementary code with the manuscript ( see https://github.com/lidulyan/Hierarchical-Linear- Regression-R- ).

    https://github.com/lidulyan/Hierarchical-Linear- Regression-R-

    Data Information

    Availability: Handedness ( % right-handed ) 91.94 Sex ( % female ) 45.16 Abbreviations: SD = standard deviation It is made available under a CC-BY-NC 4.0 International license .

    Identifiers: preprint doi: https:// doi.org/10.1101/2021.12.01.21267129; this version posted December 2 , 2021.

    https://doi.org/10.1101/2021.12.01.21267129

  5. Version published to 10.1101/2021.12.01.21267129v1 on medRxiv