Multimodal Neuroimaging Attention-Based architecture for Cognitive
Decline Prediction
- URL: http://arxiv.org/abs/2401.06777v1
- Date: Thu, 21 Dec 2023 00:56:51 GMT
- Title: Multimodal Neuroimaging Attention-Based architecture for Cognitive
Decline Prediction
- Authors: Jamie Vo, Naeha Sharif and Ghulam Mubashar Hassan
- Abstract summary: Early detection of Alzheimer's Disease is imperative to ensure early treatment and improve patient outcomes.
There is very small literature in predicting the conversion to AD and MCI from normal cognitive condition.
We propose a novel multimodal neuroimaging attention-based CNN architecture, MNA-net, to predict whether cognitively normal (CN) individuals will develop MCI or AD within a period of 10 years.
- Score: 1.4548651568912518
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The early detection of Alzheimer's Disease is imperative to ensure early
treatment and improve patient outcomes. There has consequently been extenstive
research into detecting AD and its intermediate phase, mild cognitive
impairment (MCI). However, there is very small literature in predicting the
conversion to AD and MCI from normal cognitive condition. Recently, multiple
studies have applied convolutional neural networks (CNN) which integrate
Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) to
classify MCI and AD. However, in these works, the fusion of MRI and PET
features are simply achieved through concatenation, resulting in a lack of
cross-modal interactions. In this paper, we propose a novel multimodal
neuroimaging attention-based CNN architecture, MNA-net, to predict whether
cognitively normal (CN) individuals will develop MCI or AD within a period of
10 years. To address the lack of interactions across neuroimaging modalities
seen in previous works, MNA-net utilises attention mechanisms to form shared
representations of the MRI and PET images. The proposed MNA-net is tested in
OASIS-3 dataset and is able to predict CN individuals who converted to MCI or
AD with an accuracy of 83%, true negative rate of 80%, and true positive rate
of 86%. The new state of the art results improved by 5% and 10% for accuracy
and true negative rate by the use of attention mechanism. These results
demonstrate the potential of the proposed model to predict cognitive impairment
and attention based mechanisms in the fusion of different neuroimaging
modalities to improve the prediction of cognitive decline.
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