Predicting Clinical Outcomes of Alzheimer’s Disease from Complex Brain Networks

Li, Xingjuan; Liu, Yu; Li, Xue

doi:10.1007/978-3-319-69179-4_36

Cited by 12 publications

(6 citation statements)

References 14 publications

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“…Furthermore, compared with feature-based methods with fusion of the same sMRI and DTI modalities in [10] the CNN classifiers confirm their better performance. As far as full-brain approaches are concerned, such as [37], [47], the consensus cannot be obtained, as the best performances are shown for the work of Payan and Montana with quite a large dataset on a single sMRI modality [5]. Her we should also notice that full-brain schemes require much stronger computational resources as the full resolution 3D scans have to be submitted to the network architecture at once.…”

Section: Discussionmentioning

confidence: 99%

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3D Inception-based CNN with sMRI and MD-DTI data fusion for Alzheimer's Disease diagnostics

Khvostikov¹,

Aderghal²,

Krylov³

et al. 2018

Preprint

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In the last decade, computer-aided early diagnostics of Alzheimers Disease (AD) and its prodromal form, Mild Cognitive Impairment (MCI), has been the subject of extensive research. Some recent studies have shown promising results in the AD and MCI determination using structural and functional Magnetic Resonance Imaging (sMRI, fMRI), Positron Emission Tomography (PET) and Diffusion Tensor Imaging (DTI) modalities. Furthermore, fusion of imaging modalities in a supervised machine learning framework has shown promising direction of research.In this paper we first review major trends in automatic classification methods such as feature extraction based methods as well as deep learning approaches in medical image analysis applied to the field of Alzheimer's Disease diagnostics. Then we propose our own design of a 3D Inception-based Convolutional Neural Network (CNN) for Alzheimer's Disease diagnostics. The network is designed with an emphasis on the interior resource utilization and uses sMRI and DTI modalities fusion on hippocampal ROI. The comparison with the conventional AlexNet-based network using data from the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset (http://adni.loni.usc.edu) demonstrates significantly better performance of the proposed 3D Inception-based CNN.

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Section: Discussionmentioning

confidence: 99%

“…A so-called spectral convolutional neural network was proposed in [47]. It combines classical convolutions with the ability to learn some topological brain features.…”

Section: Other Networkmentioning

confidence: 99%

3D Inception-based CNN with sMRI and MD-DTI data fusion for Alzheimer's Disease diagnostics

Khvostikov¹,

Aderghal²,

Krylov³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…A so-called spectral convolutional neural network was proposed in [34]. It combines classical convolutions with the ability to learn some topological brain features.…”

Section: Other Networkmentioning

confidence: 99%

3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies

Khvostikov¹,

Aderghal²,

Benois‐Pineau³

et al. 2018

Preprint

View full text Add to dashboard Cite

Computer-aided early diagnosis of Alzheimers Disease (AD) and its prodromal form, Mild Cognitive Impairment (MCI), has been the subject of extensive research in recent years. Some recent studies have shown promising results in the AD and MCI determination using structural and functional Magnetic Resonance Imaging (sMRI, fMRI), Positron Emission Tomography (PET) and Diffusion Tensor Imaging (DTI) modalities. Furthermore, fusion of imaging modalities in a supervised machine learning framework has shown promising direction of research.In this paper we first review major trends in automatic classification methods such as feature extraction based methods as well as deep learning approaches in medical image analysis applied to the field of Alzheimer's Disease diagnostics. Then we propose our own algorithm for Alzheimer's Disease diagnostics based on a convolutional neural network and sMRI and DTI modalities fusion on hippocampal ROI using data from the Alzheimers Disease Neuroimaging Initiative (ADNI) database (http://adni. loni.usc.edu). Comparison with a single modality approach shows promising results. We also propose our own method of data augmentation for balancing classes of different size and analyze the impact of the ROI size on the classification results as well.

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“…Many studies have been conducted to relate brain networks to behavioral, clinical measures or demographical variables and identify the most predictive network features (Eichele et al, 2008 ; Uddin et al, 2013 ; Brown et al, 2017 ; Beaty et al, 2018 ; Tang et al, 2019 , 2022 ; Li C. et al, 2020 ). However, most of these studies (Chennu et al, 2017 ; Li et al, 2017 ; Warren et al, 2017 ; Du et al, 2019 ; D́ıaz-Arteche and Rakesh, 2020 ; Kuo et al, 2020 ) focus on exploring correlations between the pre-defined network features (e.g., clustering coefficient, small-worldness, characteristic path length, etc.) and the measures to be predicted (such as cognitive impairment, biological variables, behavior profile, psychopathological scores, etc.).…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Graph Learning Model for Brain Network Regression Analysis

Tang

Guo

et al. 2022

Front. Neurosci.

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Brain networks have attracted increasing attention due to the potential to better characterize brain dynamics and abnormalities in neurological and psychiatric conditions. Recent years have witnessed enormous successes in deep learning. Many AI algorithms, especially graph learning methods, have been proposed to analyze brain networks. An important issue for existing graph learning methods is that those models are not typically easy to interpret. In this study, we proposed an interpretable graph learning model for brain network regression analysis. We applied this new framework on the subjects from Human Connectome Project (HCP) for predicting multiple Adult Self-Report (ASR) scores. We also use one of the ASR scores as the example to demonstrate how to identify sex differences in the regression process using our model. In comparison with other state-of-the-art methods, our results clearly demonstrate the superiority of our new model in effectiveness, fairness, and transparency.

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Predicting Clinical Outcomes of Alzheimer’s Disease from Complex Brain Networks

Cited by 12 publications

References 14 publications

3D Inception-based CNN with sMRI and MD-DTI data fusion for Alzheimer's Disease diagnostics

3D Inception-based CNN with sMRI and MD-DTI data fusion for Alzheimer's Disease diagnostics

3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies

A Hierarchical Graph Learning Model for Brain Network Regression Analysis

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