Longitudinal Multimodal Neuroimaging After Traumatic Brain Injury.
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Python · 164 lines · 6.1 KB · no license
ANCOVA_analysis_fig2.py at commit abdfbc0, no license · at the source
Overview
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
- Department of Computational Biology, Cornell University, Ithaca, New York, USA
- Department of Mathematics, Howard University, Washington, DC, USA
Abstract
Traumatic brain injury is a major cause of long‐term cognitive impairment, yet the mechanisms underlying recovery remain poorly understood. Neuroimaging methods such as diffusion magnetic resonance imaging (MRI), functional MRI (fMRI), and positron emission tomography (PET) provide insight into micro‐ and macro‐scale changes post‐traumatic brain injury (TBI), but the relationships between regional cellular and functional alterations remain unclear. In this exploratory study, we conducted a longitudinal, multimodal neuroimaging analysis quantifying TBI‐related pathologies in four biomarkers, namely flumazenil PET derived binding potential, diffusion MRI (dMRI)‐derived structural connectivity, and resting‐state fMRI‐derived functional connectivity and fractional amplitude of low‐frequency fluctuations in individuals with complicated mild‐to‐severe brain injury at the subacute (4–6 months post‐injury) and chronic (1‐year post‐injury) stages. The TBI sample consisted of 41 fMRI, 40 dMRI, and nine PET subjects, with 16 fMRI and dMRI and seven PET longitudinal measurements. The control sample consisted of 14 dMRI and fMRI and 19 PET subjects scanned at a single time point for comparison with TBI subjects at both time points. Most of the PET and MRI subjects are overlapping in both TBI and control groups. Brain injury related regional pathologies, and their changes over time in TBI subjects, were correlated across the four biomarkers. Our results reveal complex, dynamic changes over time. We found that flumazenil‐PET binding potential was significantly reduced in frontal and thalamic regions in brain‐injured subjects, consistent with neural loss and dysfunction, with partial recovery over time. Functional hyperconnectivity was observed in brain injured subjects initially but declined while remaining elevated compared to non‐injured controls, whereas cortical structural hypoconnectivity persisted. Importantly, we observed that brain injury‐related alterations across MRI modalities became more strongly correlated with flumazenil‐PET at the chronic stage. Regions with chronic reductions in flumazenil‐PET binding also showed weaker structural node strength and lower amplitude of low‐frequency fluctuations, a relationship that was not found at the subacute stage. This observation could suggest a progressive convergence of structural and functional disruptions with neuronal dysfunction and loss over time. Additionally, regions with declining structural node strength also exhibited decreases in functional node strength, while these same regions showed increased amplitude of low‐frequency fluctuations over time. This pattern suggests that heightened intrinsic regional activity may serve as a compensatory mechanism in regions increasingly disconnected due to progressive axonal degradation. Altogether, these findings advance our understanding of how multimodal neuroimaging captures the evolving interplay between neuronal integrity, structural connectivity, and functional dynamics after brain injury. Given the exploratory nature of this study, stemming from the modest sample size, future work in larger cohorts will be essential to validate and refine these preliminary associations as well as the inclusion of multiple measures of healthy controls. Clarifying these interrelationships could inform prognostic models and enhance knowledge of degenerative, compensatory, and recovery mechanisms in traumatic brain injury.
Reproduced under the paper's license (CC BY), from the paper cited above.
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anaradanovic/MultiMod_TBI
abdfbc012a64bb490220cbf774d460d352d74661, 25 April 2026Availability: 1 check, the latest on 28 September 2026: the link answers
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- Figure_2/
ANCOVA_analysis_fig2.py — Python, 164 lines, shown from its source - Figure_2/
Viz_ANCOVA_group_results — Python, 50 lines, shown from its source_brainmontage_fig2.py - Figure_3/
ANCOVA_analysis_fig3.py — Python, 231 lines, shown from its source - Figure_3/
LMER_TBI_bothses.R — R, 134 lines, shown from its source - Figure_3/
Viz_ANCOVA_group_results — Python, 67 lines, shown from its source_brainmontage_fig3.py - Figure_4/
ANCOVA_analysis_fig4.py — Python, 193 lines, shown from its source - Figure_4/
LMER_TBI_bothses.R — R, 132 lines, shown from its source - Figure_4/
plot_multimodal_correlat — R, 954 lines, shown from its sourceions_PET.R - Figure_4/
spearman_corr_multimodal — Python, 285 lines, shown from its source_permutations.py - Supplementary_Figures/
Figure_1/ — R, 287 lines, shown from its sourceplot_correlations_PET.R - Supplementary_Figures/
Figure_1/ — Python, 106 lines, shown from its sourcespearman_corr_multimodal .py - Supplementary_Figures/
Figure_2/ — R, 44 lines, shown from its sourceViz_fALFF_SC_corr.R - Supplementary_Figures/
Figure_2/ — Python, 160 lines, shown from its sourcecorr_fALFF_SC.py - Supplementary_Figures/
Figure_3/ — Python, 101 lines, shown from its sourceViz_etasq_ANCOVA.py - Supplementary_Figures/
Figure_3/ — Python, 213 lines, shown from its sourcefstat_etasq_ANCOVA.py - Supplementary_Figures/
Figure_4/ — Python, 782 lines, shown from its sourcePET_ANT_BEH_analysis.py - Supplementary_Figures/
Figure_4/ — R, 190 lines, shown from its sourceViz_neurcog_corr_network s.R - Supplementary_Figures/
Figure_5/ — Python, 427 lines, shown from its sourceANCOVA-analysis-rapidtid e.py - Supplementary_Figures/
Figure_5/ — R, 28 lines, shown from its sourceLMER_TBI_bothses_rapidti de.R - Supplementary_Figures/
Figure_5/ — Python, 44 lines, shown from its sourceViz_ANCOVA_group_results _rapidtide_brainmontage. py - Supplementary_Figures/
Figure_6/ — R, 288 lines, shown from its sourceLMER_TBI_bothses_1plusda yssub.R - Supplementary_Figures/
Figure_6/ — Python, 119 lines, shown from its sourceLMER_spearman_corr.py - Supplementary_Figures/
Figure_6/ — Python, 95 lines, shown from its sourceViz_LMER_TBI_bothses_ses -effect_days.py - Supplementary_Figures/
Figure_6/ — R, 304 lines, shown from its sourceviz_LMER_spearman_corr.R - __init__.py — Python, 1 line, shown from its source
- utils.py — Python, 699 lines, shown from its source
- README.md — Text, 67 lines, shown from its source
The paper's code and data availability statement is in the Data section.
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Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. Code supporting statistical analyses and data visualization are available here (https://
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Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 4 keywords, 15 MeSH terms, 5 funders, 99 references.
Cite
This paper
Radanovic, A., Jamison, K. W., Kang, Y., Tozlu, C., Shah, S. A., & Kuceyeski, A. (2026). Longitudinal Multimodal Neuroimaging After Traumatic Brain Injury. Human brain mapping, 47(6), e70534. https://
BibTeX
@article{radanovic2026lo
author = {Radanovic, Ana and Jamison, Keith W and Kang, Yeona and Tozlu, Ceren and Shah, Sudhin A and Kuceyeski, Amy},
title = {{Longitudinal Multimodal Neuroimaging After Traumatic Brain Injury}},
journal = {Human brain mapping},
year = {2026},
month = apr,
volume = {47},
number = {6},
pages = {e70534},
publisher = {Wiley},
issn = {1065-9471},
doi = {10.1002/
url = {https://
pmid = {42046146},
pmcid = {PMC13121098}
}
RIS
TY - JOUR
AU - Radanovic, Ana
AU - Jamison, Keith W
AU - Kang, Yeona
AU - Tozlu, Ceren
AU - Shah, Sudhin A
AU - Kuceyeski, Amy
TI - Longitudinal Multimodal Neuroimaging After Traumatic Brain Injury
T2 - Human brain mapping
J2 - Hum Brain Mapp
PY - 2026
DA - 2026/
VL - 47
IS - 6
SP - e70534
SN - 1065-9471
PB - Wiley
DO - 10.1002/
UR - https://
LA - en
ER -
CSL-JSON
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