Multimodal MRI accurately identifies amyloid status in unbalanced cohorts in Alzheimer’s disease continuum

Amyloid-<i>β</i> (A<i>β</i>) plaques in conjunction with hyperphosphorylated tau proteins in the form of neurofibrillary tangles are the two neuropathological hallmarks of Alzheimer's disease. It is well-known that the identification of individuals with A<i>β</i> positivity could enable early diagnosis. In this work, we aim at capturing the A<i>β</i> positivity status in an unbalanced cohort enclosing subjects at different disease stages, exploiting the underlying structural and connectivity disease-induced modulations as revealed by structural, functional, and diffusion MRI. Of note, due to the unbalanced cohort, the outcomes may be guided by those factors rather than amyloid accumulation. The partial views provided by each modality are integrated in the model, allowing to take full advantage of their complementarity in encoding the effects of the A<i>β</i> accumulation, leading to an accuracy of 0.762 ± 0.04. The specificity of the information brought by each modality is assessed by post hoc explainability analysis (guided backpropagation), highlighting the underlying structural and functional changes. Noteworthy, well-established biomarker key regions related to A<i>β</i> deposition could be identified by all modalities, including the hippocampus, thalamus, precuneus, and cingulate gyrus, witnessing in favor of the reliability of the method as well as its potential in shedding light on modality-specific possibly unknown A<i>β</i> deposition signatures.

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Publication Details

Journal
Network Neuroscience
Published
2024-11-20
DOI
https://doi.org/10.1162/netn_a_00423
Citations
4
Primary Topic
Brain Tumor Detection and Classification
Type
article
Field-Weighted Citation Impact
0.99

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article

Multimodal MRI accurately identifies amyloid status in unbalanced cohorts in Alzheimer’s disease continuum

Lorenza Brusini, Vince D. Calhoun, Gloria Menegaz, Giorgio Dolci et al.
4 citations
Network Neuroscience
Brain Tumor Detection and Classification
0.99
article

Multimodal MRI accurately identifies amyloid status in unbalanced cohorts in Alzheimer’s disease continuum

Lorenza Brusini, Vince D. Calhoun, Gloria Menegaz, Giorgio Dolci, Ilaria Boscolo Galazzo, Charles A. Ellis, Federica Cruciani, Anees Abrol
article en
4 citations

Abstract

Amyloid-<i>β</i> (A<i>β</i>) plaques in conjunction with hyperphosphorylated tau proteins in the form of neurofibrillary tangles are the two neuropathological hallmarks of Alzheimer's disease. It is well-known that the identification of individuals with A<i>β</i> positivity could enable early diagnosis. In this work, we aim at capturing the A<i>β</i> positivity status in an unbalanced cohort enclosing subjects at different disease stages, exploiting the underlying structural and connectivity disease-induced modulations as revealed by structural, functional, and diffusion MRI. Of note, due to the unbalanced cohort, the outcomes may be guided by those factors rather than amyloid accumulation. The partial views provided by each modality are integrated in the model, allowing to take full advantage of their complementarity in encoding the effects of the A<i>β</i> accumulation, leading to an accuracy of 0.762 ± 0.04. The specificity of the information brought by each modality is assessed by post hoc explainability analysis (guided backpropagation), highlighting the underlying structural and functional changes. Noteworthy, well-established biomarker key regions related to A<i>β</i> deposition could be identified by all modalities, including the hippocampus, thalamus, precuneus, and cingulate gyrus, witnessing in favor of the reliability of the method as well as its potential in shedding light on modality-specific possibly unknown A<i>β</i> deposition signatures.

Network NeuroscienceVol. 9(1)
University of Verona (IT), Georgia Institute of Technology (US), Emory University (US), Georgia State University (US), Center for Translational Research in Neuroimaging and Data Science (US)
National Science Foundation, Ministero dell’Istruzione, dell’Università e della Ricerca
Good health and well-being
Openalex Percentile: Top 23%
Brain Tumor Detection and Classification
0.99
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