Deep Learning of Static and Dynamic Brain Functional Networks for Early MCI Detection

Kam, Tae-Eui; Zhang, Han; Jiao, Zhicheng; Shen, Dinggang

doi:10.1109/tmi.2019.2928790

Cited by 119 publications

(67 citation statements)

References 77 publications

(116 reference statements)

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“…The rapid development of deep learning has become a good supplement to traditional machine learning algorithms, and has also provided new means for the quantification and prediction of various neurodegenerative diseases, and has been increasingly used in the field of neuroimaging ( Kam et al, 2019 ). However, deep learning algorithms still have certain limitations in neuroimaging research.…”

Section: Related Workmentioning

confidence: 99%

Quantification of Cognitive Function in Alzheimer’s Disease Based on Deep Learning

Zhou

et al. 2021

Front. Neurosci.

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Alzheimer disease (AD) is mainly manifested as insidious onset, chronic progressive cognitive decline and non-cognitive neuropsychiatric symptoms, which seriously affects the quality of life of the elderly and causes a very large burden on society and families. This paper uses graph theory to analyze the constructed brain network, and extracts the node degree, node efficiency, and node betweenness centrality parameters of the two modal brain networks. The T test method is used to analyze the difference of graph theory parameters between normal people and AD patients, and brain regions with significant differences in graph theory parameters are selected as brain network features. By analyzing the calculation principles of the conventional convolutional layer and the depth separable convolution unit, the computational complexity of them is compared. The depth separable convolution unit decomposes the traditional convolution process into spatial convolution for feature extraction and point convolution for feature combination, which greatly reduces the number of multiplication and addition operations in the convolution process, while still being able to obtain comparisons. Aiming at the special convolution structure of the depth separable convolution unit, this paper proposes a channel pruning method based on the convolution structure and explains its pruning process. Multimodal neuroimaging can provide complete information for the quantification of Alzheimer’s disease. This paper proposes a cascaded three-dimensional neural network framework based on single-modal and multi-modal images, using MRI and PET images to distinguish AD and MCI from normal samples. Multiple three-dimensional CNN networks are used to extract recognizable information in local image blocks. The high-level two-dimensional CNN network fuses multi-modal features and selects the features of discriminative regions to perform quantitative predictions on samples. The algorithm proposed in this paper can automatically extract and fuse the features of multi-modality and multi-regions layer by layer, and the visual analysis results show that the abnormally changed regions affected by Alzheimer’s disease provide important information for clinical quantification.

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Section: Related Workmentioning

confidence: 99%

Quantification of Cognitive Function in Alzheimer’s Disease Based on Deep Learning

Zhou

et al. 2021

Front. Neurosci.

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“…To measure the performance of the classification model, the frequently used metrics for binary classification are ACC, F1-score, AUC, etc. Significantly, false positive (FP), false negative (FN), true negative (TN), and true positive (TP) are defined using the confusion matrix, as shown in Figure 4 [31,32].…”

Section: Evaluation Criteriamentioning

confidence: 99%

Towards Effective Classification of aMCI Based on Resting‐State Multiscale Brain Features and Machine Learning Approaches

Cai

Yan

Zhou

et al. 2021

Wireless Communications and Mobile Computing

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Smart healthcare has undergone new opportunities and challenges with the arrival of the Industry 4.0 era. The intelligent imaging diagnosis system is a staple part of smart healthcare, helping doctors make clinical decisions. Nevertheless, intelligent diagnosis analysis is still confronted with the issue that it is challenging to extract effective features from the limited and high-dimensional data, particularly in resting-state data of amnesic mild cognitive impairment (aMCI). Furthermore, the intelligent imaging diagnosis system for aMCI is conductive to make timely predicting groups that may convert to Alzheimer’s disease (AD). To improve the system’s detection performance and reduce its data redundancy, we first develop an adaptive structure feature generation strategy (ASFGS) based on the Laplacian matrix and sparse autoencoder to obtain the structural features of brain functional network (BFN). Concurrently, we present a multiscale local feature detection strategy (MLFDS) to overcome the low utilization of local features of BFN. And finally, multiscale features, including structural features and multiscale local features, are fused by concatenation method to further improve the detection performance of aMCI system. Support vector machine based on radial basis function (RBF-SVM) for small data learning is adopted to evaluate the effectiveness of the proposed features. Besides, we employ leave-one-out cross-validation strategy to avoid the overfitting problem of classifier training process. The experiment results elucidate that the accuracy (ACC) and the area under the curve (AUC) in this work provide 86.57% and 86.36%, respectively, which outperforms the traditional methods and offers new insights for accuracy requirements of the aMCI system.

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“…As a result, this method may overlook the complex and dynamic interaction patterns among ROIs, which are essentially time-varying (since the phase is not locked for every subject). In order to overcome this issue, Non-stationary methods have been proposed, which result in more complex networks and also known as dynamic functional connectivity (dFC) (Leonardi and Van De Ville, 2015;Kam et al, 2019). The most common and straightforward way to investigate dFC is using windowed FC, which consists of calculating a given FC measure, for example, the Pearson correlation coefficient, over consecutive windowed segments of the data (Zalesky et al, 2014).…”

Section: Related Workmentioning

confidence: 99%

A Tensor-Based Framework for rs-fMRI Classification and Functional Connectivity Construction

Noroozi

Rezghi

2020

Front. Neuroinform.

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Recently, machine learning methods have gained lots of attention from researchers seeking to analyze brain images such as Resting-State Functional Magnetic Resonance Imaging (rs-fMRI) to obtain a deeper understanding of the brain and such related diseases, for example, Alzheimer's disease. Finding the common patterns caused by a brain disorder through analysis of the functional connectivity (FC) network along with discriminating brain diseases from normal controls have long been the two principal goals in studying rs-fMRI data. The majority of FC extraction methods calculate the FC matrix for each subject and then use simple techniques to combine them and obtain a general FC matrix. In addition, the state-of-the-art classification techniques for finding subjects with brain disorders also rely on calculating an FC for each subject, vectorizing, and feeding them to the classifier. Considering these problems and based on multi-dimensional nature of the data, we have come up with a novel tensor framework in which a general FC matrix is obtained without the need to construct an FC matrix for each sample. This framework also allows us to reduce the dimensionality and create a novel discriminant function that rather than using FCs works directly with each sample, avoids vectorization in any step, and uses the test data in the training process without forcing any prior knowledge of its label into the classifier. Extensive experiments using the ADNI dataset demonstrate that our proposed framework effectively boosts the fMRI classification performance and reveals novel connectivity patterns in Alzheimer's disease at its early stages.

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Deep Learning of Static and Dynamic Brain Functional Networks for Early MCI Detection

Cited by 119 publications

References 77 publications

Quantification of Cognitive Function in Alzheimer’s Disease Based on Deep Learning

Quantification of Cognitive Function in Alzheimer’s Disease Based on Deep Learning

Towards Effective Classification of aMCI Based on Resting‐State Multiscale Brain Features and Machine Learning Approaches

A Tensor-Based Framework for rs-fMRI Classification and Functional Connectivity Construction

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