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Introduction <p>Recent research has suggested that neuroinflammation may be important in the pathogenesis of neurodegenerative diseases. Free-water diffusion (FWD) has been proposed as a non-invasive neuroimaging-based biomarker for neuroinflammation.</p> Methods <p>Free-water maps were generated using diffusion MRI data in 367 patients from the Ontario Neurodegenerative Disease Research Initiative (108 Alzheimer’s Disease/Mild Cognitive Impairment, 42 Frontotemporal Dementia, 37 Amyotrophic Lateral Sclerosis, 123 Parkinson’s Disease, and 58 vascular disease-related Cognitive Impairment). The ability of FWD to predict neuroinflammation and neurodegeneration from biofluids was estimated using plasma glial fibrillary-associated protein (GFAP) and neurofilament light chain (NfL), respectively.</p> Results <p>Recursive Feature Elimination (RFE) performed the strongest out of all feature selection algorithms used and revealed regional specificity for areas that are the most important features for predicting GFAP over NfL concentration. Deep learning models using selected features and demographic information revealed better prediction of GFAP over NfL.</p> Discussion <p>Based on feature selection and deep learning methods, FWD was found to be more strongly related to GFAP concentration (measure of astrogliosis) over NfL (measure of neuro-axonal damage), across neurodegenerative disease groups, in terms of predictive performance. Non-invasive markers of neurodegeneration such as MRI structural imaging that can reveal neurodegeneration already exist, while non-invasive markers of neuroinflammation are not available. Our results support the use of FWD as a non-invasive neuroimaging-based biomarker for neuroinflammation.</p>

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Regional free-water diffusion is more strongly related to neuroinflammation than neurodegeneration

  • 期刊论文

  • 发表日期：

    2025年6月25日

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  • Vishaal Sumra,
  • Mohsen Hadian,
  • Allison A. Dilliott,
  • Sali M. K. Farhan,
  • Andrew R. Frank,
  • Anthony E. Lang,
  • Angela C. Roberts,
  • Angela Troyer,
  • Stephen R. Arnott,
  • Connie Marras,
  • David F. Tang-Wai,
  • Elizabeth Finger,
  • Ekaterina Rogaeva,
  • Joseph B. Orange,
  • Joel Ramirez,
  • Lorne Zinman,
  • Malcolm Binns,
  • Michael Borrie,
  • Morris Freedman,
  • Miracle Ozzoude,
  • Robert Bartha,
  • Richard H. Swartz,
  • David Munoz,
  • Mario Masellis,
  • Sandra E. Black,
  • Roger A. Dixon,
  • Dar Dowlatshahi,
  • David Grimes,
  • Ayman Hassan,
  • Robert A. Hegele,
  • Sanjeev Kumar,
  • Stephen Pasternak,
  • Bruce Pollock,
  • Tarek Rajji,
  • Demetrios Sahlas,
  • Gustavo Saposnik,
  • Maria Carmela Tartaglia

摘要

Introduction

Recent research has suggested that neuroinflammation may be important in the pathogenesis of neurodegenerative diseases. Free-water diffusion (FWD) has been proposed as a non-invasive neuroimaging-based biomarker for neuroinflammation.

Methods

Free-water maps were generated using diffusion MRI data in 367 patients from the Ontario Neurodegenerative Disease Research Initiative (108 Alzheimer’s Disease/Mild Cognitive Impairment, 42 Frontotemporal Dementia, 37 Amyotrophic Lateral Sclerosis, 123 Parkinson’s Disease, and 58 vascular disease-related Cognitive Impairment). The ability of FWD to predict neuroinflammation and neurodegeneration from biofluids was estimated using plasma glial fibrillary-associated protein (GFAP) and neurofilament light chain (NfL), respectively.

Results

Recursive Feature Elimination (RFE) performed the strongest out of all feature selection algorithms used and revealed regional specificity for areas that are the most important features for predicting GFAP over NfL concentration. Deep learning models using selected features and demographic information revealed better prediction of GFAP over NfL.

Discussion

Based on feature selection and deep learning methods, FWD was found to be more strongly related to GFAP concentration (measure of astrogliosis) over NfL (measure of neuro-axonal damage), across neurodegenerative disease groups, in terms of predictive performance. Non-invasive markers of neurodegeneration such as MRI structural imaging that can reveal neurodegeneration already exist, while non-invasive markers of neuroinflammation are not available. Our results support the use of FWD as a non-invasive neuroimaging-based biomarker for neuroinflammation.