Stage Detection of Mild Cognitive Impairment: Region-dependent Graph Representation Learning on Brain Morphable Meshes

Jiaqi Guo, Emanuel Azcona, Santiago Lopez-Tapia, Aggelos Katsaggelos
Medical Imaging with Deep Learning, PMLR 227:888-904, 2024.

Abstract

Mild cognitive impairment (MCI), as a transitional state between normal cognition and Alzheimer’s disease (AD), is crucial for taking preventive interventions in order to slow down AD progression. Given the high relevance of brain atrophy and the neurodegeneration process of AD, we propose a novel mesh-based pooling module, RegionPool, to investigate the morphological changes in brain shape regionally. We then present a geometric deep learning framework with the RegionPool and graph attention convolutions to perform binary classification on MCI subtypes (EMCI/LMCI). Our model does not require feature engineering and relies only on the relevant geometric information of T1-weighted magnetic resonance imaging (MRI) signals. Our evaluation reveals the state-of-the-art classification capabilities of our network and shows that current empirically derived MCI subtypes cannot identify heterogeneous patterns of cortical atrophy at the MCI stage. The class activation maps (CAMs) generated from the correct predictions provide additional visual evidence for our model’s decisions and are consistent with the atrophy patterns reported by the relevant literature.

Cite this Paper

BibTeX
@InProceedings{pmlr-v227-guo24b, title = {Stage Detection of Mild Cognitive Impairment: Region-dependent Graph Representation Learning on Brain Morphable Meshes}, author = {Guo, Jiaqi and Azcona, Emanuel and Lopez-Tapia, Santiago and Katsaggelos, Aggelos}, booktitle = {Medical Imaging with Deep Learning}, pages = {888--904}, year = {2024}, editor = {Oguz, Ipek and Noble, Jack and Li, Xiaoxiao and Styner, Martin and Baumgartner, Christian and Rusu, Mirabela and Heinmann, Tobias and Kontos, Despina and Landman, Bennett and Dawant, Benoit}, volume = {227}, series = {Proceedings of Machine Learning Research}, month = {10--12 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v227/guo24b/guo24b.pdf}, url = {https://proceedings.mlr.press/v227/guo24b.html}, abstract = {Mild cognitive impairment (MCI), as a transitional state between normal cognition and Alzheimer’s disease (AD), is crucial for taking preventive interventions in order to slow down AD progression. Given the high relevance of brain atrophy and the neurodegeneration process of AD, we propose a novel mesh-based pooling module, RegionPool, to investigate the morphological changes in brain shape regionally. We then present a geometric deep learning framework with the RegionPool and graph attention convolutions to perform binary classification on MCI subtypes (EMCI/LMCI). Our model does not require feature engineering and relies only on the relevant geometric information of T1-weighted magnetic resonance imaging (MRI) signals. Our evaluation reveals the state-of-the-art classification capabilities of our network and shows that current empirically derived MCI subtypes cannot identify heterogeneous patterns of cortical atrophy at the MCI stage. The class activation maps (CAMs) generated from the correct predictions provide additional visual evidence for our model’s decisions and are consistent with the atrophy patterns reported by the relevant literature.} }
Endnote
%0 Conference Paper %T Stage Detection of Mild Cognitive Impairment: Region-dependent Graph Representation Learning on Brain Morphable Meshes %A Jiaqi Guo %A Emanuel Azcona %A Santiago Lopez-Tapia %A Aggelos Katsaggelos %B Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2024 %E Ipek Oguz %E Jack Noble %E Xiaoxiao Li %E Martin Styner %E Christian Baumgartner %E Mirabela Rusu %E Tobias Heinmann %E Despina Kontos %E Bennett Landman %E Benoit Dawant %F pmlr-v227-guo24b %I PMLR %P 888--904 %U https://proceedings.mlr.press/v227/guo24b.html %V 227 %X Mild cognitive impairment (MCI), as a transitional state between normal cognition and Alzheimer’s disease (AD), is crucial for taking preventive interventions in order to slow down AD progression. Given the high relevance of brain atrophy and the neurodegeneration process of AD, we propose a novel mesh-based pooling module, RegionPool, to investigate the morphological changes in brain shape regionally. We then present a geometric deep learning framework with the RegionPool and graph attention convolutions to perform binary classification on MCI subtypes (EMCI/LMCI). Our model does not require feature engineering and relies only on the relevant geometric information of T1-weighted magnetic resonance imaging (MRI) signals. Our evaluation reveals the state-of-the-art classification capabilities of our network and shows that current empirically derived MCI subtypes cannot identify heterogeneous patterns of cortical atrophy at the MCI stage. The class activation maps (CAMs) generated from the correct predictions provide additional visual evidence for our model’s decisions and are consistent with the atrophy patterns reported by the relevant literature.
APA
Guo, J., Azcona, E., Lopez-Tapia, S. & Katsaggelos, A.. (2024). Stage Detection of Mild Cognitive Impairment: Region-dependent Graph Representation Learning on Brain Morphable Meshes. Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 227:888-904 Available from https://proceedings.mlr.press/v227/guo24b.html.

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