Publications

Authors:

Dolci, Giorgio; Ellis, Charles A.; Cruciani, Federica; Brusini, Lorenza; Abrol, Anees; Galazzo, Ilaria Boscolo; Menegaz, Gloria; Calhoun, Vince D.; Null, Null

Title:

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

Year:

2024

Type of item:

Articolo in Rivista

Tipologia ANVUR:

Articolo su rivista

Language:

Inglese

Format:

Elettronico

Referee:

No

Name of journal:

NETWORK NEUROSCIENCE

ISSN of journal:

2472-1751

Page numbers:

1-21

Keyword:

Alzheimer’s disease, Amyloid-beta, Connectivity, Deep learning, Explainable AI

Short description of contents:

Amyloid-β (Aβ) 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 Aβ positivity could enable early diagnosis. In this work, we aim at capturing the Aβ 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 Aβ 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 Aβ 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 Aβ deposition signatures.

Web page:

https://direct.mit.edu/netn/article/doi/10.1162/netn_a_00423/125112

Product ID:

142880

Handle IRIS:

11562/1146107

Last Modified:

December 2, 2024

Bibliographic citation:

Dolci, Giorgio; Ellis, Charles A.; Cruciani, Federica; Brusini, Lorenza; Abrol, Anees; Galazzo, Ilaria Boscolo; Menegaz, Gloria; Calhoun, Vince D.; Null, Null, Multimodal MRI accurately identifies amyloid status in unbalanced cohorts in Alzheimer’s disease continuum «NETWORK NEUROSCIENCE» , 2024 , pp. 1-21

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