Metabolic connectivity alterations in amyotrophic lateral sclerosis: Individual network analysis based on Wasserstein distances.
Overview
- Nuclear Medicine and Molecular Imaging, Imaging and Pathology, KU Leuven, Leuven, Belgium
- Laboratory of Neurobiology (KU Leuven), Department of Neurosciences, Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium
- Neurology department, University Hospitals Leuven, Leuven, Belgium
- Division of Nuclear Medicine, University Hospitals Leuven, Leuven, Belgium
Abstract
Molecular connectivity analysis with positron emission tomography (PET) imaging offers a promising approach for characterising brain network alterations in neurodegenerative disorders. In this study, we introduce Wasserstein distance (WD) as an alternative to Kullback–Leibler divergence similarity estimation (KLSE) for constructing single-subject metabolic connectivity networks. Using 18F-FDG PET data from 167 individuals with amyotrophic lateral sclerosis (ALS), 36 healthy volunteers (HV), and 25 ALS mimics, we generated WD- and KLSE-based connectivity matrices across 77 atlas-defined brain regions and evaluated corresponding graph theory-based nodal metrics. WD- and KLSE-derived nodal measures were strongly correlated, indicating methodological consistency. Compared with HVs, age-matched subjects in the ALS group (ALSamHV) showed significant alterations in frontal, temporal, cerebellar, and occipital network nodes, with WD-based metrics revealing differences across more brain regions than the KLSE-based approach. Support vector machine classification of ALSamHV vs. HV demonstrated that both connectivity approaches matched voxel-wise PET performance with accuracy higher than 0.80 (yet significantly lower than voxel-wise data, p<0.05 ), while significantly outperforming image-based classification for ALS vs. ALS mimics (p<0.01 ), with WD achieving the highest accuracy of 0.65 (p<0.001 ). These findings support WD-based metabolic connectivity as a sensitive, data-efficient framework for detecting disease-related network alterations and motivate its application to a broader range of PET tracers and cohorts.
Reproduced under the paper's license (CC BY), from the paper cited above.
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The datasets generated and analysed during the study are not publicly available due to GDPR, but are available from the corresponding authors on reasonable request.
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Versions
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Version 2, 28 September 2026
- Funding: added Universitaire Ziekenhuizen Leuven, KU Leuven
Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, pages, dates, 5 authors, 5 keywords, 48 references.
Cite
This paper
Tang, C., De Vocht, J., Van Damme, P., Van Laere, K., & Koole, M. (2026). Metabolic connectivity alterations in amyotrophic lateral sclerosis: Individual network analysis based on Wasserstein distances. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1343. https://
BibTeX
@article{tang2026metabol
author = {Tang, Chunmeng and De Vocht, Joke and Van Damme, Philip and Van Laere, Koen and Koole, Michel},
title = {{Metabolic connectivity alterations in amyotrophic lateral sclerosis: Individual network analysis based on Wasserstein distances}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = aug,
volume = {4},
pages = {IMAG.a.1343},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/
url = {https://
pmid = {42630952},
pmcid = {PMC13494962}
}
RIS
TY - JOUR
AU - Tang, Chunmeng
AU - De Vocht, Joke
AU - Van Damme, Philip
AU - Van Laere, Koen
AU - Koole, Michel
TI - Metabolic connectivity alterations in amyotrophic lateral sclerosis: Individual network analysis based on Wasserstein distances
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/
VL - 4
SP - IMAG.a.1343
SN - 2837-6056
PB - MIT Press
DO - 10.1162/
UR - https://
LA - en
ER -
CSL-JSON
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