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Wang R, He Q, Han C, Wang H, Shi L, Che Y. A deep learning framework for identifying Alzheimer's disease using fMRI-based brain network. Front Neurosci 2023; 17:1177424. [PMID: 37614342 PMCID: PMC10442560 DOI: 10.3389/fnins.2023.1177424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 07/24/2023] [Indexed: 08/25/2023] Open
Abstract
Background The convolutional neural network (CNN) is a mainstream deep learning (DL) algorithm, and it has gained great fame in solving problems from clinical examination and diagnosis, such as Alzheimer's disease (AD). AD is a degenerative disease difficult to clinical diagnosis due to its unclear underlying pathological mechanism. Previous studies have primarily focused on investigating structural abnormalities in the brain's functional networks related to the AD or proposing different deep learning approaches for AD classification. Objective The aim of this study is to leverage the advantages of combining brain topological features extracted from functional network exploration and deep features extracted by the CNN. We establish a novel fMRI-based classification framework that utilizes Resting-state functional magnetic resonance imaging (rs-fMRI) with the phase synchronization index (PSI) and 2D-CNN to detect abnormal brain functional connectivity in AD. Methods First, PSI was applied to construct the brain network by region of interest (ROI) signals obtained from data preprocessing stage, and eight topological features were extracted. Subsequently, the 2D-CNN was applied to the PSI matrix to explore the local and global patterns of the network connectivity by extracting eight deep features from the 2D-CNN convolutional layer. Results Finally, classification analysis was carried out on the combined PSI and 2D-CNN methods to recognize AD by using support vector machine (SVM) with 5-fold cross-validation strategy. It was found that the classification accuracy of combined method achieved 98.869%. Conclusion These findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional connections, and characterize brain functional abnormalities, which could effectively detect AD anomalies by the extracted features that may provide new insights into exploring the underlying pathogenesis of AD.
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Affiliation(s)
- Ruofan Wang
- School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, China
| | - Qiguang He
- School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, China
| | - Chunxiao Han
- Tianjin Key Laboratory of Information Sensing and Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, China
| | - Haodong Wang
- School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, China
| | - Lianshuan Shi
- School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, China
| | - Yanqiu Che
- Tianjin Key Laboratory of Information Sensing and Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, China
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Chen Q, Xu Y, Christiaen E, Wu GR, De Witte S, Vanhove C, Saunders J, Peremans K, Baeken C. Structural connectome alterations in anxious dogs: a DTI-based study. Sci Rep 2023; 13:9946. [PMID: 37337053 DOI: 10.1038/s41598-023-37121-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 06/15/2023] [Indexed: 06/21/2023] Open
Abstract
Anxiety and fear are dysfunctional behaviors commonly observed in domesticated dogs. Although dogs and humans share psychopathological similarities, little is known about how dysfunctional fear behaviors are represented in brain networks in dogs diagnosed with anxiety disorders. A combination of diffusion tensor imaging (DTI) and graph theory was used to investigate the underlying structural connections of dysfunctional anxiety in anxious dogs and compared with healthy dogs with normal behavior. The degree of anxiety was assessed using the Canine Behavioral Assessment & Research Questionnaire (C-BARQ), a widely used, validated questionnaire for abnormal behaviors in dogs. Anxious dogs showed significantly decreased clustering coefficient ([Formula: see text]), decreased global efficiency ([Formula: see text]), and increased small-worldness (σ) when compared with healthy dogs. The nodal parameters that differed between the anxious dogs and healthy dogs were mainly located in the posterior part of the brain, including the occipital lobe, posterior cingulate gyrus, hippocampus, mesencephalon, and cerebellum. Furthermore, the nodal degree ([Formula: see text]) of the left cerebellum was significantly negatively correlated with "excitability" in the C-BARQ of anxious dogs. These findings could contribute to the understanding of a disrupted brain structural connectome underlying the pathological mechanisms of anxiety-related disorders in dogs.
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Affiliation(s)
- Qinyuan Chen
- Ghent Experimental Psychiatry (GHEP) Lab, Department of Head and Skin, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium.
| | - Yangfeng Xu
- Ghent Experimental Psychiatry (GHEP) Lab, Department of Head and Skin, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
- Department of Morphology, Imaging, Orthopedics, Rehabilitation and Nutrition, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
| | - Emma Christiaen
- Medical Image and Signal Processing (MEDISIP), Department of Electronics and Information Systems, Faculty of Engineering and Architecture, Ghent University, Ghent, Belgium
| | - Guo-Rong Wu
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, China
- School of Psychology, Jiangxi Normal University, Nanchang, China
| | - Sara De Witte
- Ghent Experimental Psychiatry (GHEP) Lab, Department of Head and Skin, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
- Department of Neurology and Bru-BRAIN, University Hospital (UZ Brussel), Brussels, Belgium
- Neuroprotection & Neuromodulation Research Group (NEUR), Center for Neurosciences (C4N), Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Christian Vanhove
- Medical Image and Signal Processing (MEDISIP), Department of Electronics and Information Systems, Faculty of Engineering and Architecture, Ghent University, Ghent, Belgium
| | - Jimmy Saunders
- Department of Morphology, Imaging, Orthopedics, Rehabilitation and Nutrition, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
| | - Kathelijne Peremans
- Department of Morphology, Imaging, Orthopedics, Rehabilitation and Nutrition, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
| | - Chris Baeken
- Ghent Experimental Psychiatry (GHEP) Lab, Department of Head and Skin, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
- Vrije Universiteit Brussel (VUB), Department of Psychiatry, University Hospital (UZ Brussel), Brussels, Belgium
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
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Srivishagan S, Kumaralingam L, Thanikasalam K, Pinidiyaarachchi UAJ, Ratnarajah N. Discriminative patterns of white matter changes in Alzheimer's. Psychiatry Res Neuroimaging 2023; 328:111576. [PMID: 36495726 DOI: 10.1016/j.pscychresns.2022.111576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 11/12/2022] [Accepted: 11/22/2022] [Indexed: 12/02/2022]
Abstract
Changes in structural connectivity of the Alzheimer's brain have not been widely studied utilizing cutting-edge methodologies. This study develops an efficient structural connectome-based convolutional neural network (CNN) to classify the AD and uses explanations of CNNs' choices in classification to pinpoint the discriminative changes in white matter connectivity in AD. A CNN architecture has been developed to classify normal control (NC) and AD subjects from the weighted structural connectome. Then, the CNN classification decision is visually analyzed using gradient-based localization techniques to identify the discriminative changes in white matter connectivity in Alzheimer's. The cortical regions involved in the identified discriminative structural connectivity changes in AD are highly covered in the temporal/subcortical regions. A specific pattern is identified in the discriminative changes in structural connectivity of AD, where the white matter changes are revealed within the temporal/subcortical regions and from the temporal/subcortical regions to the frontal and parietal regions in both left and right hemispheres. The proposed approach has the potential to comprehensively analyze the discriminative structural connectivity differences in AD, change the way of detecting biomarkers, and help clinicians better understand the structural changes in AD and provide them with more confidence in automated diagnostic systems.
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Affiliation(s)
- Subaramya Srivishagan
- Department of Physical Science, Faculty of Applied Science, University of Vavuniya, Vavuniya, Sri Lanka; PGIS, University of Peradeniya, Peradeniya, Sri Lanka
| | - Logiraj Kumaralingam
- Department of Computer Science, Faculty of Science, University of Jaffna, Jaffna, Sri Lanka
| | - Kokul Thanikasalam
- Department of Computer Science, Faculty of Science, University of Jaffna, Jaffna, Sri Lanka
| | - U A J Pinidiyaarachchi
- Department of Statistics and Computer Science, Faculty of Science, University of Peradeniya, Peradeniya, Sri Lanka
| | - Nagulan Ratnarajah
- Department of Physical Science, Faculty of Applied Science, University of Vavuniya, Vavuniya, Sri Lanka.
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Terstege DJ, Durante IM, Epp JR. Brain-wide neuronal activation and functional connectivity are modulated by prior exposure to repetitive learning episodes. Front Behav Neurosci 2022; 16:907707. [PMID: 36160680 PMCID: PMC9501867 DOI: 10.3389/fnbeh.2022.907707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 08/23/2022] [Indexed: 11/26/2022] Open
Abstract
Memory storage and retrieval are shaped by past experiences. Prior learning and memory episodes have numerous impacts on brain structure from micro to macroscale. Previous experience with specific forms of learning increases the efficiency of future learning. It is less clear whether such practice effects on one type of memory might also have transferable effects to other forms of memory. Different forms of learning and memory rely on different brain-wide networks but there are many points of overlap in these networks. Enhanced structural or functional connectivity caused by one type of learning may be transferable to another type of learning due to overlap in underlying memory networks. Here, we investigated the impact of prior chronic spatial training on the task-specific functional connectivity related to subsequent contextual fear memory recall in mice. Our results show that mice exposed to prior spatial training exhibited decreased brain-wide activation compared to control mice during the retrieval of a context fear memory. With respect to functional connectivity, we observed changes in several network measures, notably an increase in global efficiency. Interestingly, we also observed an increase in network resilience based on simulated targeted node deletion. Overall, this study suggests that chronic learning has transferable effects on the functional connectivity networks of other types of learning and memory. The generalized enhancements in network efficiency and resilience suggest that learning itself may protect brain networks against deterioration.
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