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Longitudinal Multimodal Neuroimaging After Traumatic Brain Injury.

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The authors' code

Python · 164 lines · 6.1 KB · no license

  1. #%%
  2. from pathlib import Path
  3. import sys
  4. import scipy.io as sio
  5. import pandas as pd
  6. import numpy as np
  7. import ast
  8. from scipy import stats
  10. # Ensure project root is importable when running this file directly.
  11. sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
  12. import utils as ut
  15. # %%
  16. #load DATA
  17. tbi_attn = sio.loadmat("../../../tbiattn_Xmeasure_20230412.mat",squeeze_me = True)
  19. #subset loaded data to have the values of interest
  20. cols_needed = ["Subject", "isTBI", "Session", "Age", "Sex"]
  21. tbi_attn_filt = {key: tbi_attn[key] for key in cols_needed}
  22. tbi_attn_demo = pd.DataFrame.from_dict(tbi_attn_filt)
  24. #create individual DFs to use
  25. mods = ["fALFF",
  26. "FC_posdeg",
  27. "ifod2_volnorm",
  28. "BPND"]
  30. individual_df_dict = {}
  32. for mod in mods :
  33. individual_df_dict[mod] = pd.DataFrame(tbi_attn[mod])
  36. rename_dict = {'fALFF':'fALFF', 'FC_posdeg':'FC', 'ifod2_volnorm':'SC',
  37. 'BPND':'BPND'}
  39. individual_df_dict = dict((rename_dict[key], value) for (key, value) in individual_df_dict.items())
  41. #only doing fs86 analysis
  42. for mod in individual_df_dict.keys():
  43. individual_df_dict[mod] = individual_df_dict[mod][individual_df_dict[mod].columns[0:86]]
  45. #concat w demo so that we can run ancova w age and sex
  46. unfiltered_dfs = {}
  47. for mod in individual_df_dict.keys():
  48. unfiltered_dfs[mod] = pd.concat([tbi_attn_demo,
  49. individual_df_dict[mod]],
  50. axis = 1)
  51. unfiltered_dfs[mod]["Subject"] = unfiltered_dfs[mod]["Subject"].apply(
  52. lambda x: ut.convert_subject_mri(x) if isinstance(x, str) else x
  53. )
  55. ##### session 1 ####
  56. ses_01_lrg = {} #figure 2 and 3 (MRI ONLY for 3)
  57. ses_01_lrg_subjects = pd.read_csv("../subject_lists.csv")
  58. ses_01_lrg_subjects["session"] = pd.to_numeric(ses_01_lrg_subjects["session"], errors="coerce")
  60. for mod in ["FC", "fALFF", "SC", "BPND"]:
  61. matching_row = ses_01_lrg_subjects[
  62. (ses_01_lrg_subjects["df_type"] == "ses_01_lrg") &
  63. (ses_01_lrg_subjects["modality"] == mod) &
  64. (ses_01_lrg_subjects["session"] == 1)
  65. ]
  67. csv_subjects = ast.literal_eval(matching_row.iloc[0]["subjects"])
  69. mod_df = unfiltered_dfs[mod]
  70. ses_01_lrg[mod] = mod_df[
  71. (mod_df["Session"] == 1) &
  72. (mod_df["Subject"].isin(csv_subjects))
  73. ]
  77. #%%
  78. #load lobe assignments info
  80. lobe_assignments = pd.read_csv("../fs86_yeo7_lobe.txt")
  81. lobe_assignments["Name"]= lobe_assignments["Name"].str.replace("-", "_")
  82. non_ctx_assignments = lobe_assignments[lobe_assignments["7Networks"]=="subcortex"]
  83. ctx_only_assignments = lobe_assignments[lobe_assignments["7Networks"]!="subcortex"]
  85. #create dict mapping for column names that are currently just 0-85
  86. mapping = {str(i): name for i, name in enumerate(lobe_assignments['Name'])}
  88. #rename columns to match lobe assignments, convert sex to 0/1, and convert age to integer
  89. for mod in ses_01_lrg.keys():
  90. ses_01_lrg[mod].columns = ses_01_lrg[mod].columns.astype(str)
  91. ses_01_lrg[mod].columns = [mapping[col] if col.isdigit() else col for col in ses_01_lrg[mod].columns]
  93. ses_01_lrg[mod]["Sex"] = ses_01_lrg[mod]["Sex"].replace({"M": 0, "F": 1})
  94. ses_01_lrg[mod]["Age"] = pd.to_numeric(ses_01_lrg[mod]["Age"], downcast="integer")
  96. #get the cortex only data
  97. ctx_ses_01_lrg = {}
  98. for mod in ses_01_lrg.keys():
  99. ctx_ses_01_lrg[mod] = ses_01_lrg[mod].drop(non_ctx_assignments["Name"].to_list(), inplace= False,
  100. axis=1)
  101. #%% ''' Run ancovas for unimodal analysis (and multimodal SC-fMRI analyses)
  102. # , whole brain and ctx only'''
  103. ses_01_lrg_ancova = {}
  104. ses_01_lrg_group = {} #figure 2
  105. ses_01_lrg_Age = {}
  106. ses_01_lrg_Sex = {}
  108. ses_01_lrg_SC_filtered_group = {}
  109. ctx_ses_01_lrg_SC_filtered_group = {}
  111. ses_01_lrg_SC = ses_01_lrg["SC"]["Subject"]
  112. ses_01_lrg_fMRI = ses_01_lrg["fALFF"]["Subject"]
  113. SC_fMRI_ses_01_lrg_subs = set(ses_01_lrg_SC).intersection(ses_01_lrg_fMRI)
  115. #run ancovas for unimodal analysis (and multimodal SC-fMRI analyses)
  116. for mod in ses_01_lrg.keys():
  117. ses_01_lrg_ancova[mod]= ut.run_ancova(ses_01_lrg[mod], lobe_assignments["Name"])
  118. res = ses_01_lrg_ancova[mod]
  119. ses_01_lrg_group[mod] = ut.pull_group_anvoca_res(res)
  120. ses_01_lrg_Age[mod] = ut.pull_ancova_res(res, "Age")
  121. ses_01_lrg_Sex[mod] = ut.pull_ancova_res(res,"Sex")
  123. #run ancovas for multimodal SC-fMRI analyses
  124. if mod in ["FC", "fALFF"]:
  125. ses_01_lrg_SC_filtered = ses_01_lrg[mod][ses_01_lrg[mod]["Subject"].isin(SC_fMRI_ses_01_lrg_subs)]
  126. res = ut.run_ancova(ses_01_lrg_SC_filtered, lobe_assignments["Name"])
  127. ses_01_lrg_SC_filtered_group[mod] = ut.pull_group_anvoca_res(res)
  129. ctx_ses_01_lrg_SC_filtered = ses_01_lrg_SC_filtered.drop(non_ctx_assignments["Name"].to_list(),
  130. inplace= False,
  131. axis=1)
  132. res = ut.run_ancova(ctx_ses_01_lrg_SC_filtered, ctx_only_assignments["Name"])
  133. ctx_ses_01_lrg_SC_filtered_group[mod] = ut.pull_group_anvoca_res(res)
  135. ctx_ses_01_lrg_ancova = {}
  136. ctx_ses_01_lrg_group = {}
  137. ctx_ses_01_lrg_Age = {}
  138. ctx_ses_01_lrg_Sex = {}
  140. #run ancovas for unimodal analysis (and multimodal SC-fMRI analyses)
  141. for mod in ses_01_lrg.keys():
  142. ctx_ses_01_lrg_ancova[mod]= ut.run_ancova(ctx_ses_01_lrg[mod], ctx_only_assignments["Name"])
  143. res = ctx_ses_01_lrg_ancova[mod]
  144. ctx_ses_01_lrg_group[mod] = ut.pull_group_anvoca_res(res)
  145. ctx_ses_01_lrg_Age[mod] = ut.pull_ancova_res(res, "Age")
  146. ctx_ses_01_lrg_Sex[mod] = ut.pull_ancova_res(res,"Sex")
  150. #%% ### save csv of results
  151. #session 1
  152. ancova_output_path_s1 = "ANCOVA_res/ses-01/group_res/"
  153. ancova_output_path_s1_SC_filtered = "multimodal_ANCOVA/"
  155. for mod in ses_01_lrg_group.keys():
  156. ses_01_lrg_group[mod].to_csv(ancova_output_path_s1+ f"{mod}_s1res.csv")
  157. ctx_ses_01_lrg_group[mod].to_csv(ancova_output_path_s1+ f"ctx_{mod}_s1res.csv")
  159. if mod in ["FC","fALFF"]:
  160. ses_01_lrg_SC_filtered_group[mod].to_csv(ancova_output_path_s1_SC_filtered + f"{mod}_s1_lrg_SC_filtered_res.csv")
  161. ctx_ses_01_lrg_SC_filtered_group[mod].to_csv(ancova_output_path_s1_SC_filtered+f"ctx_{mod}_s1_lrg_SC_filtered_res.csv")
  164. # %%

ANCOVA_analysis_fig2.py at commit abdfbc0, no license · at the source

Overview

Authors: Ana Radanovic1, Keith W Jamison1,2, Yeona Kang3, Ceren Tozlu1, Sudhin A Shah1, Amy Kuceyeski1,2
ORCID iDs: Ana Radanovic, Ceren Tozlu
  1. Department of Radiology, Weill Cornell Medicine, New York, New York, USA
  2. Department of Computational Biology, Cornell University, Ithaca, New York, USA
  3. Department of Mathematics, Howard University, Washington, DC, USA
Institutions: Weill Cornell Medicine (United States); Cornell University (United States); Howard University (United States)
Journal: Human brain mapping, volume 47, issue 6, article e70534
Dates: received 26 June 2025; accepted 11 April 2026; published online 27 April 2026; in print April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/hbm.70534 · PMID 42046146 · PMCID PMC13121098 · OpenAlex W4409209520
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.04.04.647315
Status: code verified
Categories: structural MRI / diffusion (modality), fMRI (modality), PET / SPECT (modality), human (organism), traumatic brain injury (population), clinical / translational (subfield)
Methods: Connectivity, Statistics, Machine learning, Spectral & time-frequency, fMRI & imaging
Keywords: flumazenil PET, longitudinal study, multimodal MRI, traumatic brain injury
MeSH: Brain*, Brain Injuries, Traumatic*, Neuroimaging*, Adult, Diffusion Magnetic Resonance Imaging, Female, Flumazenil, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Multimodal Imaging, Positron-Emission Tomography, Young Adult (* major topic)
Topic: Traumatic Brain Injury Research (Epidemiology, Medicine), according to OpenAlex
Funding: NINDS NIH HHS (R01 NS102646, R01 NS134646); NIH HHS (RF1 MH123232, R01 AG077576, R01 NS134646-01A1, R01 NS102646, UL1TR024996); NIA NIH HHS (R01 AG077576); National Institutes of Health (R01 NS134646‐01A1, UL1TR024996, R01 AG077576, R01 NS102646, RF1 MH123232); NIMH NIH HHS (RF1 MH123232)
Citations: not cited yet (Europe PMC); 100 references in the paper

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.

Repository

Its files are read in the Code ↔ Paper reader above.

anaradanovic/MultiMod_TBI

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: abdfbc012a64bb490220cbf774d460d352d74661, 25 April 2026
Languages: Python (17), R (9)
Size: 176 files, 26 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README, environment (requirements.txt)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (16 files), pandas (16 files), SciPy (16 files), tidyverse (9 files), Matplotlib (8 files), statsmodels (8 files), ggplot2 (5 files), seaborn (5 files), lme4 (4 files), lmerTest (4 files), Pingouin (2 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
27 files
At the source: github.com/anaradanovic/MultiMod_TBI.git

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 26 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

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://github.com/anaradanovic/MultiMod_TBI.git).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 28 September 2026: the first record

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://doi.org/10.1002/hbm.70534

BibTeX

@article{radanovic2026longitudinal,
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/hbm.70534},
url = {https://doi.org/10.1002/hbm.70534},
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 - 2026/04/01
VL - 47
IS - 6
SP - e70534
SN - 1065-9471
PB - Wiley
DO - 10.1002/hbm.70534
UR - https://doi.org/10.1002/hbm.70534
LA - en
ER -

CSL-JSON

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"volume": "47",
"issue": "6",
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"DOI": "10.1002/hbm.70534",
"PMID": "42046146",
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