Proposed Methodology for Reducing Bias in Structural MRI Analysis in the Presence of Lesions: Data from a Pediatric Traumatic Brain Injury Cohort


Traumatic brain injury can lead to multiple pathologic features, including brain lesions, which are visible on magnetic resonance imaging (MRI). These resulting heterogenous lesions can present a difficulty for several standard approaches to neuroimaging, resulting in bias and error in subsequent quantitative measurements. Thus, cases presenting with lesions on MRI may be excluded from analyses, biasing samples across the research field. We outline a potential solution to this issue in the case of Freesurfer, a popular neuroimaging tool for surface-based segmentation of brain tissue from structural MRI. The proposed solution involves two-steps, a) Pre-processing: Enantiomorphic Lesion-Filling and b) Post-processing: Lesion Labelling. We applied this methodology to 14 pediatric TBI cases which presented with lesions on T1w MRI. Following qualitative inspection of these cases after implementation of the approach, 8 out of 14 cases were retained as being of sufficient quality. In brief, we have presented here an adapted pipeline for processing structural MRI (sMRI) of patients who have experienced a TBI using the Freesurfer software package. This approach aims to mitigate potential lesion-induced biases that exist beyond the locality of the pathological tissue, even in the contralesioned hemisphere.

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Divisions: College of Health & Life Sciences > Aston Institute of Health & Neurodevelopment (AIHN)
College of Health & Life Sciences > School of Psychology
College of Health & Life Sciences
Additional Information: Copyright 2023 The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Last Modified: 26 Jan 2024 08:02
Date Deposited: 02 Mar 2023 14:03
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Related URLs: ... .02.12.528180v1 (Publisher URL)
PURE Output Type: ["eprint_fieldname_pure_output_type_workingpaper/preprint" not defined]
Published Date: 2023-02-13
Authors: Griffiths-King, Daniel (ORCID Profile 0000-0001-5797-9203)
Shephard, Adam
Novak, Jan (ORCID Profile 0000-0001-5173-3608)
Catroppa, Cathy
Anderson, Vicki A.
Wood, Amanda (ORCID Profile 0000-0002-1537-6858)

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