1
|
Song L, Ren Y, Xu S, Hou Y, He X. A hybrid spatiotemporal deep belief network and sparse representation-based framework reveals multilevel core functional components in decoding multitask fMRI signals. Netw Neurosci 2023; 7:1513-1532. [PMID: 38144693 PMCID: PMC10745082 DOI: 10.1162/netn_a_00334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 08/17/2023] [Indexed: 12/26/2023] Open
Abstract
Decoding human brain activity on various task-based functional brain imaging data is of great significance for uncovering the functioning mechanism of the human mind. Currently, most feature extraction model-based methods for brain state decoding are shallow machine learning models, which may struggle to capture complex and precise spatiotemporal patterns of brain activity from the highly noisy fMRI raw data. Moreover, although decoding models based on deep learning methods benefit from their multilayer structure that could extract spatiotemporal features at multiscale, the relatively large populations of fMRI datasets are indispensable, and the explainability of their results is elusive. To address the above problems, we proposed a computational framework based on hybrid spatiotemporal deep belief network and sparse representations to differentiate multitask fMRI (tfMRI) signals. Using a relatively small cohort of tfMRI data as a test bed, our framework can achieve an average classification accuracy of 97.86% and define the multilevel temporal and spatial patterns of multiple cognitive tasks. Intriguingly, our model can characterize the key components for differentiating the multitask fMRI signals. Overall, the proposed framework can identify the interpretable and discriminative fMRI composition patterns at multiple scales, offering an effective methodology for basic neuroscience and clinical research with relatively small cohorts.
Collapse
Affiliation(s)
- Limei Song
- School of Information Science and Technology, Northwest University, Xi’an, China
| | - Yudan Ren
- School of Information Science and Technology, Northwest University, Xi’an, China
| | - Shuhan Xu
- School of Information Science and Technology, Northwest University, Xi’an, China
| | - Yuqing Hou
- School of Information Science and Technology, Northwest University, Xi’an, China
| | - Xiaowei He
- School of Information Science and Technology, Northwest University, Xi’an, China
| |
Collapse
|
2
|
Jiang X, Yan J, Zhao Y, Jiang M, Chen Y, Zhou J, Xiao Z, Wang Z, Zhang R, Becker B, Zhu D, Kendrick KM, Liu T. Characterizing functional brain networks via Spatio-Temporal Attention 4D Convolutional Neural Networks (STA-4DCNNs). Neural Netw 2023; 158:99-110. [PMID: 36446159 DOI: 10.1016/j.neunet.2022.11.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 08/17/2022] [Accepted: 11/03/2022] [Indexed: 11/11/2022]
Abstract
Characterizing individualized spatio-temporal patterns of functional brain networks (FBNs) via functional magnetic resonance imaging (fMRI) provides a foundation for understanding complex brain function. Although previous studies have achieved promising performances based on either shallow or deep learning models, there is still much space to improve the accuracy of spatio-temporal pattern characterization of FBNs by optimally integrating the four-dimensional (4D) features of fMRI. In this study, we introduce a novel Spatio-Temporal Attention 4D Convolutional Neural Network (STA-4DCNN) model to characterize individualized spatio-temporal patterns of FBNs. Particularly, STA-4DCNN is composed of two subnetworks, in which the first Spatial Attention 4D CNN (SA-4DCNN) models the spatio-temporal features of 4D fMRI data and then characterizes the spatial pattern of FBNs, and the second Temporal Guided Attention Network (T-GANet) further characterizes the temporal pattern of FBNs under the guidance of the spatial pattern together with 4D fMRI data. We evaluate the proposed STA-4DCNN on seven different task fMRI and one resting state fMRI datasets from the publicly released Human Connectome Project. The experimental results demonstrate that STA-4DCNN has superior ability and generalizability in characterizing individualized spatio-temporal patterns of FBNs when compared to other state-of-the-art models. We further apply STA-4DCNN on another independent ABIDE I resting state fMRI dataset including both autism spectrum disorder (ASD) and typical developing (TD) subjects, and successfully identify abnormal spatio-temporal patterns of FBNs in ASD compared to TD. In general, STA-4DCNN provides a powerful tool for FBN characterization and for clinical applications on brain disease characterization at the individual level.
Collapse
Affiliation(s)
- Xi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiadong Yan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yu Zhao
- Syngo Innovation, Siemens Healthineers, Malvern, PA 19355, USA
| | - Mingxin Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuzhong Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jingchao Zhou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhenxiang Xiao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zifan Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Rong Zhang
- Neuroscience Research Institute, Key Laboratory for Neuroscience, Ministry of Education of China, China; Key Laboratory for Neuroscience, National Committee of Health and Family Planning of China, China; Department of Neurobiology, School of Basic Medical Sciences, Peking University, Beijing, China
| | - Benjamin Becker
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Dajiang Zhu
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA
| | - Keith M Kendrick
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, USA.
| |
Collapse
|
3
|
Liu F, Meamardoost S, Gunawan R, Komiyama T, Mewes C, Zhang Y, Hwang E, Wang L. Deep learning for neural decoding in motor cortex. J Neural Eng 2022; 19. [PMID: 36148535 DOI: 10.1088/1741-2552/ac8fb5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 09/06/2022] [Indexed: 11/12/2022]
Abstract
Objective. Neural decoding is an important tool in neural engineering and neural data analysis. Of various machine learning algorithms adopted for neural decoding, the recently introduced deep learning is promising to excel. Therefore, we sought to apply deep learning to decode movement trajectories from the activity of motor cortical neurons.Approach. In this paper, we assessed the performance of deep learning methods in three different decoding schemes, concurrent, time-delay, and spatiotemporal. In the concurrent decoding scheme where the input to the network is the neural activity coincidental to the movement, deep learning networks including artificial neural network (ANN) and long-short term memory (LSTM) were applied to decode movement and compared with traditional machine learning algorithms. Both ANN and LSTM were further evaluated in the time-delay decoding scheme in which temporal delays are allowed between neural signals and movements. Lastly, in the spatiotemporal decoding scheme, we trained convolutional neural network (CNN) to extract movement information from images representing the spatial arrangement of neurons, their activity, and connectomes (i.e. the relative strengths of connectivity between neurons) and combined CNN and ANN to develop a hybrid spatiotemporal network. To reveal the input features of the CNN in the hybrid network that deep learning discovered for movement decoding, we performed a sensitivity analysis and identified specific regions in the spatial domain.Main results. Deep learning networks (ANN and LSTM) outperformed traditional machine learning algorithms in the concurrent decoding scheme. The results of ANN and LSTM in the time-delay decoding scheme showed that including neural data from time points preceding movement enabled decoders to perform more robustly when the temporal relationship between the neural activity and movement dynamically changes over time. In the spatiotemporal decoding scheme, the hybrid spatiotemporal network containing the concurrent ANN decoder outperformed single-network concurrent decoders.Significance. Taken together, our study demonstrates that deep learning could become a robust and effective method for the neural decoding of behavior.
Collapse
Affiliation(s)
- Fangyu Liu
- Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, United States of America
| | - Saber Meamardoost
- Department of Chemical and Biological Engineering, University at Buffalo, Buffalo, NY 14260, United States of America
| | - Rudiyanto Gunawan
- Department of Chemical and Biological Engineering, University at Buffalo, Buffalo, NY 14260, United States of America
| | - Takaki Komiyama
- Department of Neurobiology, Center for Neural Circuits and Behavior, and Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, United States of America
| | - Claudia Mewes
- Department of Physics and Astronomy, University of Alabama, Tuscaloosa, AL 35487, United States of America
| | - Ying Zhang
- Department of Cell and Molecular Biology, University of Rhode Island, Kingston, RI 02881, United States of America
| | - EunJung Hwang
- Department of Neurobiology, Center for Neural Circuits and Behavior, and Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, United States of America.,Cell Biology and Anatomy Discipline, Center for Brain Function and Repair, Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, IL 60064, United States of America
| | - Linbing Wang
- Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, United States of America
| |
Collapse
|
4
|
Qiang N, Dong Q, Liang H, Li J, Zhang S, Zhang C, Ge B, Sun Y, Gao J, Liu T, Yue H, Zhao S. Learning brain representation using recurrent Wasserstein generative adversarial net. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 223:106979. [PMID: 35792364 DOI: 10.1016/j.cmpb.2022.106979] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 04/26/2022] [Accepted: 06/26/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE To understand brain cognition and disorders, modeling the mapping between mind and brain has been of great interest to the neuroscience community. The key is the brain representation, including functional brain networks (FBN) and their corresponding temporal features. Recently, it has been proven that deep learning models have superb representation power on functional magnetic resonance imaging (fMRI) over traditional machine learning methods. However, due to the lack of high-quality data and labels, deep learning models tend to suffer from overfitting in the training process. METHODS In this work, we applied a recurrent Wasserstein generative adversarial net (RWGAN) to learn brain representation from volumetric fMRI data. Generative adversarial net (GAN) is widely used in natural image generation and is able to capture the distribution of the input data, which enables the extraction of generalized features from fMRI and thus relieves the overfitting issue. The recurrent layers in RWGAN are designed to better model the local temporal features of the fMRI time series. The discriminator of RWGAN works as a deep feature extractor. With LASSO regression, the RWGAN model can decompose the fMRI data into temporal features and spatial features (FBNs). Furthermore, the generator of RWGAN can generate high-quality new data for fMRI augmentation. RESULTS The experimental results on seven tasks from the HCP dataset showed that the RWGAN can learn meaningful and interpretable temporal features and FBNs, compared to HCP task designs and general linear model (GLM) derived networks. Besides, the results on different training datasets showed that the RWGAN performed better on small datasets than other deep learning models. Moreover, we used the generator of RWGAN to yield fake subjects. The result showed that the fake data can also be used to learn meaningful representation compared to those learned from real data. CONCLUSIONS To our best knowledge, this work is among the earliest attempts of applying generative deep learning for modeling fMRI data. The proposed RWGAN offers a novel methodology for learning brain representation from fMRI, and it can generate high-quality fake data for the potential use of fMRI data augmentation.
Collapse
Affiliation(s)
- Ning Qiang
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China; Center for Brain and Brain-Inspired Computing Research, Department of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Qinglin Dong
- Advanced Medical Computing and Analysis, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Hongtao Liang
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Jin Li
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Shu Zhang
- Center for Brain and Brain-Inspired Computing Research, Department of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Cheng Zhang
- School of Electronics Engineering and Computer Science, Peking University, Beijing, China
| | - Bao Ge
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China; Center for Brain and Brain-Inspired Computing Research, Department of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Yifei Sun
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Jie Gao
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, Greece
| | - Huiji Yue
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China.
| | - Shijie Zhao
- School of Automation, Northwestern Polytechnical University, Xi'an, China.
| |
Collapse
|
5
|
Pang T, Zhao S, Han J, Zhang S, Guo L, Liu T. Gumbel-Softmax based Neural Architecture Search for Hierarchical Brain Networks Decomposition. Med Image Anal 2022; 82:102570. [DOI: 10.1016/j.media.2022.102570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 06/14/2022] [Accepted: 08/04/2022] [Indexed: 11/29/2022]
|
6
|
Yan J, Chen Y, Xiao Z, Zhang S, Jiang M, Wang T, Zhang T, Lv J, Becker B, Zhang R, Zhu D, Han J, Yao D, Kendrick KM, Liu T, Jiang X. Modeling spatio-temporal patterns of holistic functional brain networks via multi-head guided attention graph neural networks (Multi-Head GAGNNs). Med Image Anal 2022; 80:102518. [PMID: 35749981 DOI: 10.1016/j.media.2022.102518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 04/01/2022] [Accepted: 06/14/2022] [Indexed: 10/18/2022]
Abstract
Mounting evidence has demonstrated that complex brain function processes are realized by the interaction of holistic functional brain networks which are spatially distributed across specific brain regions in a temporally dynamic fashion. Therefore, modeling spatio-temporal patterns of holistic functional brain networks plays an important role in understanding brain function. Compared to traditional modeling methods such as principal component analysis, independent component analysis, and sparse coding, superior performance has been achieved by recent deep learning methodologies. However, there are still two limitations of existing deep learning approaches for functional brain network modeling. They either (1) merely modeled a single targeted network and ignored holistic ones at one time, or (2) underutilized both spatial and temporal features of fMRI during network modeling, and the spatial/temporal accuracy was thus not warranted. To address these limitations, we proposed a novel Multi-Head Guided Attention Graph Neural Network (Multi-Head GAGNN) to simultaneously model both spatial and temporal patterns of holistic functional brain networks. Specifically, a spatial Multi-Head Attention Graph U-Net was first adopted to model the spatial patterns of multiple brain networks, and a temporal Multi-Head Guided Attention Network was then introduced to model the corresponding temporal patterns under the guidance of modeled spatial patterns. Based on seven task fMRI datasets from the public Human Connectome Project and resting state fMRI datasets from the public Autism Brain Imaging Data Exchange I of 1448 subjects, the proposed Multi-Head GAGNN showed superior ability and generalizability in modeling both spatial and temporal patterns of holistic functional brain networks in individual brains compared to other state-of-the-art (SOTA) models. Furthermore, the modeled spatio-temporal patterns of functional brain networks via the proposed Multi-Head GAGNN can better predict the individual cognitive behavioral measures compared to the other SOTA models. This study provided a novel and powerful tool for brain function modeling as well as for understanding the brain-cognitive behavior associations.
Collapse
Affiliation(s)
- Jiadong Yan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, #2006, Xiyuan Avenue, Chengdu, Sichuan 611731, China
| | - Yuzhong Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, #2006, Xiyuan Avenue, Chengdu, Sichuan 611731, China
| | - Zhenxiang Xiao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, #2006, Xiyuan Avenue, Chengdu, Sichuan 611731, China
| | - Shu Zhang
- Center for Brain and Brain-Inspired Computing Research, School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Mingxin Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, #2006, Xiyuan Avenue, Chengdu, Sichuan 611731, China
| | - Tianqi Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, #2006, Xiyuan Avenue, Chengdu, Sichuan 611731, China
| | - Tuo Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Jinglei Lv
- School of Biomedical Engineering & Brain and Mind Centre, The University of Sydney, Camperdown, NSW, Australia
| | - Benjamin Becker
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, #2006, Xiyuan Avenue, Chengdu, Sichuan 611731, China
| | - Rong Zhang
- Neuroscience Research Institute, Key Laboratory for Neuroscience, Ministry of Education of China; Key Laboratory for Neuroscience, National Committee of Health and Family Planning of China; and Department of neurobiology, School of Basic Medical Sciences, Peking University, Beijing, China
| | - Dajiang Zhu
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019, United States
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, #2006, Xiyuan Avenue, Chengdu, Sichuan 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu 611731, China
| | - Keith M Kendrick
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, #2006, Xiyuan Avenue, Chengdu, Sichuan 611731, China
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, the University of Georgia, Athens, United States
| | - Xi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, #2006, Xiyuan Avenue, Chengdu, Sichuan 611731, China.
| |
Collapse
|
7
|
Yuan J, Ji S, Luo L, Lv J, Liu T. Control energy assessment of spatial interactions among
macro‐scale
brain networks. Hum Brain Mapp 2022; 43:2181-2203. [PMID: 35072300 PMCID: PMC8996365 DOI: 10.1002/hbm.25780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 12/04/2021] [Accepted: 01/03/2022] [Indexed: 11/19/2022] Open
Abstract
Many recent studies have revealed that spatial interactions of functional brain networks derived from fMRI data can well model functional connectomes of the human brain. However, it has been rarely explored what the energy consumption characteristics are for such spatial interactions of macro‐scale functional networks, which remains crucial for the understanding of brain organization, behavior, and dynamics. To explore this unanswered question, this article presents a novel framework for quantitative assessment of energy consumptions of macro‐scale functional brain network's spatial interactions via two main effective computational methodologies. First, we designed a novel scheme combining dictionary learning and hierarchical clustering to derive macro‐scale consistent brain network templates that can be used to define a common reference space for brain network interactions and energy assessments. Second, the control energy consumption for driving the brain networks during their spatial interactions is computed from the viewpoint of the linear network control theory. Especially, the energetically favorable brain networks were identified and their energy characteristics were comprehensively analyzed. Experimental results on the Human Connectome Project (HCP) task‐based fMRI (tfMRI) data showed that the proposed methods can reveal meaningful, diverse energy consumption patterns of macro‐scale network interactions. In particular, those networks present remarkable differences in energy consumption. The energetically least favorable brain networks are stable and consistent across HCP tasks such as motor, language, social, and working memory tasks. In general, our framework provides a new perspective to characterize human brain functional connectomes by quantitative assessment for the energy consumption of spatial interactions of macro‐scale brain networks.
Collapse
Affiliation(s)
- Jing Yuan
- College of Artificial Intelligence Nankai University Tianjin China
| | - Senquan Ji
- College of Artificial Intelligence Nankai University Tianjin China
| | - Liao Luo
- College of Artificial Intelligence Nankai University Tianjin China
| | - Jinglei Lv
- School of Biomedical Engineering The University of Sydney Sydney New South Wales Australia
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Laboratory, Department of Computer Science and Bioimaging Research Center The University of Georgia Athens Georgia USA
| |
Collapse
|
8
|
Xia Z, Zhou T, Mamoon S, Lu J. Recognition of Dementia Biomarkers With Deep Finer-DBN. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1926-1935. [PMID: 34506288 DOI: 10.1109/tnsre.2021.3111989] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The treatment of neurodegenerative diseases is expensive, and long-term treatment makes families bear a heavy burden. Accumulating evidence suggests that the high conversion rate can possibly be reduced if clinical interventions are applied at the early stage of brain diseases. Thus, a variety of deep learning methods are utilized to recognize the early stages of neurodegenerative diseases for clinical intervention and treatment. However, most existing methods have ignored the issue of sample imbalance, which often makes it difficult to train an effective model due to lack of a large number of negative samples. To address this problem, we propose a two-stage method, which is used to learn the compression and recover rules of normal subjects so that potential negative samples can be detected. The experimental results show that the proposed method can not only obtain a superb recognition result, but also give an explanation that conforms to the physiological mechanism. Most importantly, the deep learning model does not need to be retrained for each type of disease, which can be widely applied to the diagnosis of various brain diseases. Furthermore, this research could have great potential in understanding regional dysfunction of various brain diseases.
Collapse
|