Evidence map›Paper›PMID 38978662›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Cortical Lesion Expansion in Chronic Traumatic Brain Injury.

Holly J Freeman, Alexander S Atalay, Jian Li, Evie Sobczak, Natalie Gilmore, Samuel B Snider, Brian C Healy, Holly Carrington, Enna Selmanovic, Ariel Pruyser and 9 more

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

19 authors.

Holly J FreemanCenter for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA.ORCID 0009-0004-8217-2937
Alexander S AtalayCenter for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA.ORCID 0009-0009-1511-7782
Jian LiCenter for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA.
Evie SobczakCenter for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA.
Natalie GilmoreCenter for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA.ORCID 0000-0002-2935-8386
Samuel B SniderDepartment of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
Brian C HealyMassachusetts General Hospital Biostatistics Center, Massachusetts General Hospital, Boston, MA.
Holly CarringtonDepartment of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY.
Enna SelmanovicDepartment of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY.ORCID 0000-0002-5350-4566
Ariel PruyserDepartment of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY.
Lisa BuraDepartment of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY.
David SheppardDepartment of Rehabilitation Medicine, University of Washington School of Medicine, Seattle, WA.
David HuntDepartment of Neurological Surgery, University of Washington, Seattle, WA.
Alan C SeifertBiomedical Engineering and Imaging Institute, Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY.
Yelena G BodienCenter for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA.
Jeanne M HoffmanDepartment of Rehabilitation Medicine, University of Washington School of Medicine, Seattle, WA.ORCID 0000-0002-3525-3839
Christine L Mac DonaldDepartment of Neurological Surgery, University of Washington, Seattle, WA.
Kristen Dams-O'ConnorDepartment of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY.
Brian L EdlowCenter for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA.ORCID 0000-0001-7235-8456

Funding

The Late Effects of TBI (LETBI) Study: Injury Patterns and Biological Markers of Post-Traumatic NeurodegenerationRF1NS115268 · NINDS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Kristen Dams-O'Connor · 2019 to 2026
$10.3M
Neuropathology of CTE and Delayed Effects of TBI: Toward In-Vivo DiagnosticsU01NS086625 · NINDS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI DAMS-O'CONNOR, KRISTEN · 2014 to 2017
$6.1M
Clinical & biological signatures of post-traumatic neurodegeneration: Leveraging the TBI Model Systems of Care to accelerate in vivo diagnosis of the late effects of TBI (LETBI)RF1NS128961 · NINDS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI DAMS-O'CONNOR, KRISTEN · 2022 to 2022
$5.0M
Clinical & biological signatures of post-traumatic neurodegeneration: Leveraging the TBI Model Systems of Care to accelerate in vivo diagnosis of the late effects of TBI (LETBI)R01NS128961 · NINDS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Kristen Dams-O'Connor · 2025 to 2026
$3.3M
A Connectome-Based Clinical Trial Platform to Promote Early Recovery of Consciousness after Traumatic ComaDP2HD101400 · NICHD · MASSACHUSETTS GENERAL HOSPITAL · PI EDLOW, BRIAN L. · 2019 to 2019
$2.5M
NICHD NIH HHS DP2 HD101400NINDS NIH HHS R01 NS128961NINDS NIH HHS RF1 NS115268NINDS NIH HHS RF1 NS128961NINDS NIH HHS U01 NS086625
6 · The paper itself

Abstract

Traumatic brain injury (TBI) is a risk factor for neurodegeneration and cognitive decline, yet the underlying pathophysiologic mechanisms are incompletely understood. This gap in knowledge is in part related to a lack of reliable and efficient methods for measuring cortical lesions in neuroimaging studies. The objective of this study was to develop a semi-automated lesion detection tool and apply it to an investigation of longitudinal changes in brain structure among individuals with chronic TBI. We identified 24 individuals with chronic moderate-to-severe TBI enrolled in the Late Effects of TBI (LETBI) study who had cortical lesions detected by T1-weighted MRI and underwent two MRI scans at least two years apart. Initial MRI scans were performed more than one year post-injury, and follow-up scans were performed 3.1 (IQR=1.7) years later. We leveraged FreeSurfer parcellations of T1-weighted MRI volumes and a recently developed super-resolution technique, SynthSR, to automate the identification of cortical lesions in this longitudinal dataset. Trained raters received the data in a randomized order and manually edited the automated lesion segmentations, yielding a final semi-automated lesion mask for each scan at each time point. Inter-rater variability was assessed in an independent cohort of 10 additional LETBI subjects with cortical lesions. The semi-automated lesion segmentations showed a high level of accuracy compared to "ground truth" lesion segmentations performed via manual segmentation by a separate blinded rater. In a longitudinal analysis of the semi-automated segmentations, lesion volume increased between the two time points with a median volume change of 4.91 (IQR=12.95) mL (p<0.0001). Lesion volume significantly expanded in 40 of 61 measured lesions (65.6%), as defined by a longitudinal volume increase that exceeded inter-rater variability. Longitudinal analyses showed similar changes in lesion volume using the ground-truth lesion segmentations. Inter-scan duration was not associated with the magnitude of lesion growth. Reliable and efficient semi-automated lesion segmentation is feasible in studies of chronic TBI, creating opportunities to elucidate mechanisms of post-traumatic neurodegeneration.

Identifiers

PMID38978662
PMCPMC11230300

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.