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Investigating the amyloid-tau-neurodegeneration framework in Alzheimer's disease using semi-supervised multimodal imaging data fusion.

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Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Shell · 67 lines · 2.8 KB · MIT

  1. #!/bin/bash
  2. #SBATCH --job-name=SupBigFLICA # Job name
  3. #SBATCH --output=%x_%j.out # Standard output file name (%x: job name, %j: job ID)
  4. #SBATCH --error=%x_%j.err # Standard error file name
  5. #SBATCH --cpus-per-task=2 # Number of CPUs per task (reduced based on low CPU utilization)
  6. #SBATCH --time=72:00:00 # Time limit (hh:mm:ss)
  7. #SBATCH --mem=100G # Memory requirement (increased based on observed utilization)
  8. #SBATCH --mail-type=ALL # Email notifications for job start, end, and failure
  9. #SBATCH --mail-user=[email hidden]
  10. #SBATCH --partition=defq
  11. #SBATCH --gres=gpu:1
  13. # Set up output directory
  14. BASE_OUTPUT_DIR="/data/qnilab/AD_NPS_R01_2022/ADNI_Data_Fusion/SBF_Outputs/SBF_ADNI_CDR/CDRSB" # Replace with your desired directory for output
  15. OUTPUT_DIR="$BASE_OUTPUT_DIR/job_$(date +'%Y%m%d_%H%M%S')"
  16. mkdir -p "$OUTPUT_DIR" # Create the directory if it doesn't exist
  17. echo "All outputs will be saved to: $OUTPUT_DIR"
  19. # Generate a log file name with the current date and time
  20. LOG_DIR="/home/ycheng23/SuperBigFLICA_python/Latest_Versions_14Nov2024/time_logs" # Replace with your desired directory for logs
  21. mkdir -p "$LOG_DIR" # Create the directory if it doesn't exist
  22. LOG_FILE="$LOG_DIR/run_log_$(date +'%Y%m%d_%H%M%S').log"
  23. ERROR_FILE="$OUTPUT_DIR/error_log$(date +'%Y%m%d_%H%M%S').err"
  25. # Record the start time
  26. START_TIME=$(date +"%Y-%m-%d %H:%M:%S")
  27. echo "Script started at: $START_TIME" | tee -a "$LOG_FILE"
  29. # Activate virtual environment
  30. # VENV_DIR="/data/qnilab/Virt_Envs/bigflica"
  31. VENV_DIR="/data/qnilab/AD_NPS_R01_2022/ADNI_Data_Fusion/.myenv"
  33. source "$VENV_DIR/bin/activate"
  34. echo "Virtual environment activated: $VENV_DIR" | tee -a "$LOG_FILE"
  36. # Load required module (only when module command exists)
  37. if command -v module >/dev/null 2>&1; then
  38. module purge
  39. module add fsl # Load FSL (version 6.0.7.4 assumed)
  40. module add freesurfer # Load FreeSurfer (version 7.3.2 assumed)
  41. module add shared
  42. module add cuda11.8/toolkit/11.8.0
  43. echo "Modules fsl, freesurfer, cuda loaded." | tee -a "$LOG_FILE"
  44. else
  45. echo "Module command not found; skipping module loads." | tee -a "$LOG_FILE"
  46. fi
  48. # Required for deterministic CuBLAS behavior when torch.use_deterministic_algorithms(True)
  49. export CUBLAS_WORKSPACE_CONFIG=:4096:8
  51. # Start a timer and run the Python script
  52. SECONDS=0 # Initialize a timer
  54. # Uncomment the Python script you want to run
  55. python3 -u SBF.py
  58. # Record the end time
  59. END_TIME=$(date +"%Y-%m-%d %H:%M:%S")
  60. echo "Script ended at: $END_TIME" | tee -a "$LOG_FILE"
  62. # Calculate and log the elapsed time
  63. ELAPSED_TIME=$SECONDS
  64. echo "Total computing time: $(($ELAPSED_TIME / 3600)) hours, $((($ELAPSED_TIME / 60) % 60)) minutes, $(($ELAPSED_TIME % 60)) seconds" | tee -a "$LOG_FILE"
  66. # Notify the user where the log file is saved
  67. echo "Log file saved to: $LOG_FILE"

RUN_SBF_sbatch.sh at commit cb0b2e7, under MIT · at the source

Overview

Authors: You Cheng1,2,3, Adrián Medina1,2, Cole Korponay1,2,3, Christian F Beckmann4,5, David Harper1,2,3, Lisa Nickerson1,2,3, for the Alzheimer's Disease Neuroimaging Initiative
ORCID iDs: You Cheng
  1. McLean Hospital, Belmont, Massachusetts, USA
  2. Mass General Brigham, Boston, Massachusetts, USA
  3. Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA
  4. Donders Institute for Brain, Cognition and Behaviour, Department of Medical Neuroscience, Radboud University Medical Centre, Nijmegen, the Netherlands
  5. Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
Institutions: Harvard University (United States); McLean Hospital (United States); Mass General Brigham (United States); Radboud University Nijmegen (Netherlands); Radboud University Medical Center (Netherlands); University of Oxford (United Kingdom); Wellcome Centre for Integrative Neuroimaging (United Kingdom); Donders Institute for Brain, Cognition and Behaviour (Netherlands)
Journal: Alzheimer's & dementia (Amsterdam, Netherlands), volume 18, issue 2, article e70360
Dates: received 4 December 2025; accepted 19 April 2026; published online 21 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/dad2.70360 · PMID 42255959 · PMCID PMC13239803 · OpenAlex W7161954574
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: other condition (population), Alzheimer's / dementia (population)
Methods: Smoothing, state filtering, decompositions, Machine learning, Statistics, Preprocessing
Keywords: Alzheimer's disease, amyloid–tau–neurodegeneration framework, latent components, multimodal data fusion, multimodal neuroimaging, semi‐supervised learning
Topic: Dementia and Cognitive Impairment Research (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: Wellcome Trust (215573/Z/19/Z); Dutch Research Council (NWO) (17854); NIA NIH HHS (RF1 AG078304, U01 AG024904)
Citations: not cited yet (Europe PMC); 42 references in the paper

Abstract

INTRODUCTION: Alzheimer's disease (AD) heterogeneity complicates diagnosis and prognosis. Uncovering amyloid–tau–neurodegeneration (A–T–N) patterns may improve diagnostic prediction.

METHODS: We applied SuperBigFLICA (SBF), a semi‐supervised multimodal fusion method, to gray matter density, cortical thickness (CT), pial surface area, amyloid and tau positron emission tomography maps from 274 Alzheimer's Disease Neuroimaging Initiative 3 participants to derive 50 latent components predictive of cognitive decline. Subject loadings were then used to predict diagnosis (cognitively normal, mild cognitive impairment, dementia) and apolipoprotein E (APOE) ε4 status via least absolute shrinkage and selection operator logistic regression, compared to demographic, single‐modality, and naïve fusion comparator models.

RESULTS: SBF modestly predicted out‐of‐sample concurrent clinical severity (Clinical Dementia Rating Sum of Boxes; r = 0.21), yet models using SBF‐derived loadings were among the strongest comparator models (area under the receiver operating characteristic curve; = 0.80 for diagnosis; 0.83 for APOE ε4). Amyloid alterations in sensory areas best separated dementia, while a tri‐modal tau–neurodegeneration pattern related to disease progression. Loadings were validated through cerebrospinal fluid correlations.

DISCUSSION: SBF improves prediction and reveals interpretable patterns that better classify clinical diagnoses and APOE ε4 than traditional approaches.

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

Repository

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

ANSR-laboratory/SuperBigFLICA_McL

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: cb0b2e7d07526d203204df2b8822107eab78d971, 1 July 2026
Languages: Python (2), Shell (1)
Size: 19 files, 3 scripts
Software Heritage: not archived
Found in: “CODE AVAILABILITY”
Holds: README, license file, environment (requirements.macos.txt, requirements.txt)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: FreeSurfer (2 files), FSL (2 files), NiBabel (2 files), NumPy (2 files), pandas (2 files), PyTorch (2 files), SciPy (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
5 files
At the source: github.com/ANSR-laboratory/SuperBigFLICA_McL

Code availability

All code for multimodal data fusion and model training, along with the corresponding model cards, is publicly available on GitHub https://github.com/ANSR‐laboratory/SuperBigFLICA_McL.

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

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;
  • 3 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

Neuroimaging and clinical data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI; http://adni.loni.usc.edu) and can be accessed upon approval of a data use application.

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 6 keywords, 3 funders, 39 references.

Cite

This paper

Cheng, Y., Medina, A., Korponay, C., Beckmann, C. F., Harper, D., Nickerson, L., & for the Alzheimer's Disease Neuroimaging Initiative. (2026). Investigating the amyloid-tau-neurodegeneration framework in Alzheimer's disease using semi-supervised multimodal imaging data fusion. Alzheimer's & dementia (Amsterdam, Netherlands), 18(2), e70360. https://doi.org/10.1002/dad2.70360

BibTeX

@article{cheng2026investigating,
author = {Cheng, You and Medina, Adrián and Korponay, Cole and Beckmann, Christian F and Harper, David and Nickerson, Lisa and {for the Alzheimer's Disease Neuroimaging Initiative}},
title = {{Investigating the amyloid-tau-neurodegeneration framework in Alzheimer's disease using semi-supervised multimodal imaging data fusion}},
journal = {Alzheimer's \& dementia (Amsterdam, Netherlands)},
year = {2026},
month = apr,
volume = {18},
number = {2},
pages = {e70360},
publisher = {Wiley},
issn = {2352-8729},
doi = {10.1002/dad2.70360},
url = {https://doi.org/10.1002/dad2.70360},
pmid = {42255959},
pmcid = {PMC13239803}
}

RIS

TY - JOUR
AU - Cheng, You
AU - Medina, Adrián
AU - Korponay, Cole
AU - Beckmann, Christian F
AU - Harper, David
AU - Nickerson, Lisa
AU - for the Alzheimer's Disease Neuroimaging Initiative
TI - Investigating the amyloid-tau-neurodegeneration framework in Alzheimer's disease using semi-supervised multimodal imaging data fusion
T2 - Alzheimer's & dementia (Amsterdam, Netherlands)
J2 - Alzheimers Dement (Amst)
PY - 2026
DA - 2026/04/01
VL - 18
IS - 2
SP - e70360
SN - 2352-8729
PB - Wiley
DO - 10.1002/dad2.70360
UR - https://doi.org/10.1002/dad2.70360
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Investigating the amyloid-tau-neurodegeneration framework in Alzheimer's disease using semi-supervised multimodal imaging data fusion",
"container-title": "Alzheimer's & dementia (Amsterdam, Netherlands)",
"author": [
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"family": "Cheng",
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"DOI": "10.1002/dad2.70360",
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"date-parts": [
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1
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]
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}

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