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Huo Y, Jing R, Li P, Chen P, Si J, Liu G, Liu Y. Delineating the Heterogeneity of Alzheimer's Disease and Mild Cognitive Impairment Using Normative Models of Dynamic Brain Functional Networks. Biol Psychiatry 2024:S0006-3223(24)01365-9. [PMID: 38857821 DOI: 10.1016/j.biopsych.2024.05.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 05/15/2024] [Accepted: 05/30/2024] [Indexed: 06/12/2024]
Abstract
BACKGROUND Alzheimer's disease (AD), which has been identified as the most common type of dementia, presents considerable heterogeneity in its clinical manifestations. Early intervention at the stage of mild cognitive impairment (MCI) holds potential in AD prevention. However, characterizing the heterogeneity of neurobiological abnormalities and identifying MCI subtypes pose significant challenges. METHODS We constructed sex-specific normative age models of dynamic brain functional networks and mapped the deviations of the brain characteristics for individuals from multiple datasets, including 295 patients with AD, 441 patients with MCI, and 1160 normal control participants. Then, based on these individual deviation patterns, subtypes for both AD and MCI were identified using the clustering method, and their similarities and differences were comprehensively assessed. RESULTS Individuals with AD and MCI were clustered into 2 subtypes, and these subtypes exhibited significant differences in their intrinsic brain functional phenotypes and spatial atrophy patterns, as well as in disease progression and cognitive decline trajectories. The subtypes with positive deviations in AD and MCI shared similar deviation patterns, as did those with negative deviations. There was a potential transformation of MCI with negative deviation patterns into AD, and participants with MCI had a more severe cognitive decline rate. CONCLUSIONS In this study, we quantified neurophysiological heterogeneity by analyzing deviation patterns from the dynamic functional connectome normative model and identified disease subtypes of AD and MCI using a comprehensive resting-state functional magnetic resonance imaging multicenter dataset. The findings provide new insights for developing early prevention and personalized treatment strategies for AD.
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Affiliation(s)
- Yanxi Huo
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, China
| | - Rixing Jing
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, China.
| | - Peng Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Chinese Academy of Medical Sciences Research Unit, Peking University, Beijing, China
| | - Pindong Chen
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Juanning Si
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, China
| | - Guozhong Liu
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, China
| | - Yong Liu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
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2
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Qu Y, Wang P, Yao H, Wang D, Song C, Yang H, Zhang Z, Chen P, Kang X, Du K, Fan L, Zhou B, Han T, Yu C, Zhang X, Zuo N, Jiang T, Zhou Y, Liu B, Han Y, Lu J, Liu Y. Reproducible Abnormalities and Diagnostic Generalizability of White Matter in Alzheimer's Disease. Neurosci Bull 2023; 39:1533-1543. [PMID: 37014553 PMCID: PMC10533766 DOI: 10.1007/s12264-023-01041-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 11/29/2022] [Indexed: 04/05/2023] Open
Abstract
Alzheimer's disease (AD) is associated with the impairment of white matter (WM) tracts. The current study aimed to verify the utility of WM as the neuroimaging marker of AD with multisite diffusion tensor imaging datasets [321 patients with AD, 265 patients with mild cognitive impairment (MCI), 279 normal controls (NC)], a unified pipeline, and independent site cross-validation. Automated fiber quantification was used to extract diffusion profiles along tracts. Random-effects meta-analyses showed a reproducible degeneration pattern in which fractional anisotropy significantly decreased in the AD and MCI groups compared with NC. Machine learning models using tract-based features showed good generalizability among independent site cross-validation. The diffusion metrics of the altered regions and the AD probability predicted by the models were highly correlated with cognitive ability in the AD and MCI groups. We highlighted the reproducibility and generalizability of the degeneration pattern of WM tracts in AD.
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Affiliation(s)
- Yida Qu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, 300222, China
| | - Hongxiang Yao
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, 300222, China
| | - Dawei Wang
- Department of Radiology, Department of Epidemiology and Health Statistics, School of Public Health, Qilu Hospital of Shandong University, Ji'nan, 250063, China
| | - Chengyuan Song
- Department of Neurology, Qilu Hospital of Shandong University, Ji'nan, 250063, China
| | - Hongwei Yang
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Zengqiang Zhang
- Branch of Chinese, PLA General Hospital, Sanya, 572022, China
| | - Pindong Chen
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiaopeng Kang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Kai Du
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Lingzhong Fan
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Bo Zhou
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100089, China
| | - Tong Han
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin, 300222, China
| | - Chunshui Yu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Xi Zhang
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100089, China
| | - Nianming Zuo
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yuying Zhou
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, 300222, China
| | - Bing Liu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Lab of Cognition Neuroscience & Learning, Beijing Normal University, Beijing, 100091, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
- Beijing Institute of Geriatrics, Beijing, 100053, China
- National Clinical Research Center for Geriatric Disorders, Beijing, 100053, China
- Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, 100053, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China.
| | - Yong Liu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
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3
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Aberathne I, Kulasiri D, Samarasinghe S. Detection of Alzheimer's disease onset using MRI and PET neuroimaging: longitudinal data analysis and machine learning. Neural Regen Res 2023; 18:2134-2140. [PMID: 37056120 PMCID: PMC10328296 DOI: 10.4103/1673-5374.367840] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 12/08/2022] [Accepted: 01/12/2023] [Indexed: 02/17/2023] Open
Abstract
The scientists are dedicated to studying the detection of Alzheimer's disease onset to find a cure, or at the very least, medication that can slow the progression of the disease. This article explores the effectiveness of longitudinal data analysis, artificial intelligence, and machine learning approaches based on magnetic resonance imaging and positron emission tomography neuroimaging modalities for progression estimation and the detection of Alzheimer's disease onset. The significance of feature extraction in highly complex neuroimaging data, identification of vulnerable brain regions, and the determination of the threshold values for plaques, tangles, and neurodegeneration of these regions will extensively be evaluated. Developing automated methods to improve the aforementioned research areas would enable specialists to determine the progression of the disease and find the link between the biomarkers and more accurate detection of Alzheimer's disease onset.
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Affiliation(s)
- Iroshan Aberathne
- Centre for Advanced Computational Solutions (C-fACS), Lincoln University, Christchurch, New Zealand
| | - Don Kulasiri
- Centre for Advanced Computational Solutions (C-fACS), Lincoln University, Christchurch, New Zealand
| | - Sandhya Samarasinghe
- Centre for Advanced Computational Solutions (C-fACS), Lincoln University, Christchurch, New Zealand
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4
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Wen J, Li Y, Fang M, Zhu L, Feng DD, Li P. Fine-Grained and Multiple Classification for Alzheimer's Disease With Wavelet Convolution Unit Network. IEEE Trans Biomed Eng 2023; 70:2592-2603. [PMID: 37030751 DOI: 10.1109/tbme.2023.3256042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2023]
Abstract
In this article, we propose a novel wavelet convolution unit for the image-oriented neural network to integrate wavelet analysis with a vanilla convolution operator to extract deep abstract features more efficiently. On one hand, in order to acquire non-local receptive fields and avoid information loss, we define a new convolution operation by composing a traditional convolution function and approximate and detailed representations after single-scale wavelet decomposition of source images. On the other hand, multi-scale wavelet decomposition is introduced to obtain more comprehensive multi-scale feature information. Then, we fuse all these cross-scale features to improve the problem of inaccurate localization of singular points. Given the novel wavelet convolution unit, we further design a network based on it for fine-grained Alzheimer's disease classifications (i.e., Alzheimer's disease, Normal controls, early mild cognitive impairment, late mild cognitive impairment). Up to now, only a few methods have studied one or several fine-grained classifications, and even fewer methods can achieve both fine-grained and multi-class classifications. We adopt the novel network and diffuse tensor images to achieve fine-grained classifications, which achieved state-of-the-art accuracy for all eight kinds of fine-grained classifications, up to 97.30%, 95.78%, 95.00%, 94.00%, 97.89%, 95.71%, 95.07%, 93.79%. In order to build a reference standard for Alzheimer's disease classifications, we actually implemented all twelve coarse-grained and fine-grained classifications. The results show that the proposed method achieves solidly high accuracy for them. Its classification ability greatly exceeds any kind of existing Alzheimer's disease classification method.
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5
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Muller JJ, Wang R, Milddleton D, Alizadeh M, Kang KC, Hryczyk R, Zabrecky G, Hriso C, Navarreto E, Wintering N, Bazzan AJ, Wu C, Monti DA, Jiao X, Wu Q, Newberg AB, Mohamed FB. Machine learning-based classification of chronic traumatic brain injury using hybrid diffusion imaging. Front Neurosci 2023; 17:1182509. [PMID: 37694125 PMCID: PMC10484001 DOI: 10.3389/fnins.2023.1182509] [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: 03/08/2023] [Accepted: 05/30/2023] [Indexed: 09/12/2023] Open
Abstract
Background and purpose Traumatic brain injury (TBI) can cause progressive neuropathology that leads to chronic impairments, creating a need for biomarkers to detect and monitor this condition to improve outcomes. This study aimed to analyze the ability of data-driven analysis of diffusion tensor imaging (DTI) and neurite orientation dispersion imaging (NODDI) to develop biomarkers to infer symptom severity and determine whether they outperform conventional T1-weighted imaging. Materials and methods A machine learning-based model was developed using a dataset of hybrid diffusion imaging of patients with chronic traumatic brain injury. We first extracted the useful features from the hybrid diffusion imaging (HYDI) data and then used supervised learning algorithms to classify the outcome of TBI. We developed three models based on DTI, NODDI, and T1-weighted imaging, and we compared the accuracy results across different models. Results Compared with the conventional T1-weighted imaging-based classification with an accuracy of 51.7-56.8%, our machine learning-based models achieved significantly better results with DTI-based models at 58.7-73.0% accuracy and NODDI with an accuracy of 64.0-72.3%. Conclusion The machine learning-based feature selection and classification algorithm based on hybrid diffusion features significantly outperform conventional T1-weighted imaging. The results suggest that advanced algorithms can be developed for inferring symptoms of chronic brain injury using feature selection and diffusion-weighted imaging.
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Affiliation(s)
- Jennifer J. Muller
- College of Engineering, Villanova University, Villanova, PA, United States
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Ruixuan Wang
- College of Engineering, Villanova University, Villanova, PA, United States
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Devon Milddleton
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Mahdi Alizadeh
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Ki Chang Kang
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Ryan Hryczyk
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States
| | - George Zabrecky
- Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Chloe Hriso
- Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Emily Navarreto
- Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Nancy Wintering
- Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Anthony J. Bazzan
- Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Chengyuan Wu
- Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, Philadelphia, PA, United States
| | - Daniel A. Monti
- Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Xun Jiao
- College of Engineering, Villanova University, Villanova, PA, United States
| | - Qianhong Wu
- College of Engineering, Villanova University, Villanova, PA, United States
| | - Andrew B. Newberg
- Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Feroze B. Mohamed
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States
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6
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Jing R, Chen P, Wei Y, Si J, Zhou Y, Wang D, Song C, Yang H, Zhang Z, Yao H, Kang X, Fan L, Han T, Qin W, Zhou B, Jiang T, Lu J, Han Y, Zhang X, Liu B, Yu C, Wang P, Liu Y. Altered large-scale dynamic connectivity patterns in Alzheimer's disease and mild cognitive impairment patients: A machine learning study. Hum Brain Mapp 2023; 44:3467-3480. [PMID: 36988434 PMCID: PMC10203807 DOI: 10.1002/hbm.26291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 02/27/2023] [Accepted: 03/15/2023] [Indexed: 03/30/2023] Open
Abstract
Alzheimer's disease (AD) is a common neurodegeneration disease associated with substantial disruptions in the brain network. However, most studies investigated static resting-state functional connections, while the alteration of dynamic functional connectivity in AD remains largely unknown. This study used group independent component analysis and the sliding-window method to estimate the subject-specific dynamic connectivity states in 1704 individuals from three data sets. Informative inherent states were identified by the multivariate pattern classification method, and classifiers were built to distinguish ADs from normal controls (NCs) and to classify mild cognitive impairment (MCI) patients with informative inherent states similar to ADs or not. In addition, MCI subgroups with heterogeneous functional states were examined in the context of different cognition decline trajectories. Five informative states were identified by feature selection, mainly involving functional connectivity belonging to the default mode network and working memory network. The classifiers discriminating AD and NC achieved the mean area under the receiver operating characteristic curve of 0.87 with leave-one-site-out cross-validation. Alterations in connectivity strength, fluctuation, and inter-synchronization were found in AD and MCIs. Moreover, individuals with MCI were clustered into two subgroups, which had different degrees of atrophy and different trajectories of cognition decline progression. The present study uncovered the alteration of dynamic functional connectivity in AD and highlighted that the dynamic states could be powerful features to discriminate patients from NCs. Furthermore, it demonstrated that these states help to identify MCIs with faster cognition decline and might contribute to the early prevention of AD.
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Affiliation(s)
- Rixing Jing
- School of Instrument Science and Opto‐Electronics EngineeringBeijing Information Science and Technology UniversityBeijingChina
| | - Pindong Chen
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
| | - Yongbin Wei
- School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijingChina
| | - Juanning Si
- School of Instrument Science and Opto‐Electronics EngineeringBeijing Information Science and Technology UniversityBeijingChina
| | - Yuying Zhou
- Department of NeurologyTianjin Huanhu Hospital, Tianjin UniversityTianjinChina
| | - Dawei Wang
- Department of RadiologyQilu Hospital of Shandong UniversityJi'nanChina
| | - Chengyuan Song
- Department of NeurologyQilu Hospital of Shandong UniversityJi'nanChina
| | - Hongwei Yang
- Department of RadiologyXuanwu Hospital of Capital Medical UniversityBeijingChina
| | | | - Hongxiang Yao
- Department of Radiology, the Second Medical CentreNational Clinical Research Centre for Geriatric Diseases, Chinese PLA General HospitalBeijingChina
| | - Xiaopeng Kang
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
| | - Lingzhong Fan
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
| | - Tong Han
- Department of RadiologyTianjin Huanhu HospitalTianjinChina
| | - Wen Qin
- Department of RadiologyTianjin Medical University General HospitalTianjinChina
| | - Bo Zhou
- Department of Neurologythe Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General HospitalBeijingChina
| | - Tianzi Jiang
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
| | - Jie Lu
- Department of RadiologyXuanwu Hospital of Capital Medical UniversityBeijingChina
| | - Ying Han
- Department of NeurologyXuanwu Hospital of Capital Medical UniversityBeijingChina
- Beijing Institute of GeriatricsBeijingChina
- National Clinical Research Center for Geriatric DisordersBeijingChina
| | - Xi Zhang
- Department of Neurologythe Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General HospitalBeijingChina
| | - Bing Liu
- State Key Laboratory of Cognition Neuroscience & LearningBeijing Normal UniversityBeijingChina
| | - Chunshui Yu
- Department of RadiologyTianjin Medical University General HospitalTianjinChina
| | - Pan Wang
- Department of NeurologyTianjin Huanhu Hospital, Tianjin UniversityTianjinChina
| | - Yong Liu
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijingChina
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7
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Zong F, You Z, Zhou L, Deng X. Language function of the superior longitudinal fasciculus in patients with arteriovenous malformation as evidenced by automatic fiber quantification. FRONTIERS IN RADIOLOGY 2023; 3:1121879. [PMID: 37492384 PMCID: PMC10365120 DOI: 10.3389/fradi.2023.1121879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 05/03/2023] [Indexed: 07/27/2023]
Abstract
The superior longitudinal fasciculus (SLF) is a major fiber tract involved in language processing and has been used to investigate language impairments and plasticity in many neurological diseases. The SLF is divided into four main branches that connect with different cortex regions, with two branches (SLF II, SLF III) being directly related to language. However, most white matter analyses consider the SLF as a single bundle, which may underestimate the relationship between these fiber bundles and language function. In this study, we investigated the differences between branches of the SLF in patients with arteriovenous malformation (AVM), which is a unique model to investigate language reorganization. We analyzed diffusion tensor imaging data of AVM patients and healthy controls to generate whole-brain fiber tractography, and then segmented the SLF into SLF II and III based on their distinctive waypoint regions. The SLF, SLF II, and III were further quantified, and four diffusion parameters of three branches were compared between the AVMs and controls. No significant diffusivity differences of the whole SLF were observed between two groups, however, the right SLF II and III in AVMs showed significant reorganization or impairment patterns as compared to the controls. Results demonstrating the need to subtracting SLF branches when studying structure-function relationship in neurological diseases that have SLF damage.
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Affiliation(s)
- Fangrong Zong
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Zhaoyi You
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Leqing Zhou
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Xiaofeng Deng
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
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8
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Swarnalatha R. A Greedy Optimized Intelligent Framework for Early Detection of Alzheimer's Disease Using EEG Signal. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023; 2023:4808841. [PMID: 36873383 PMCID: PMC9977523 DOI: 10.1155/2023/4808841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/29/2022] [Accepted: 10/10/2022] [Indexed: 02/24/2023]
Abstract
Recent researchers have been drawn to the analysis of electroencephalogram (EEG) signals in order to confirm the disease and severity range by viewing the EEG signal which has complicated the dataset. The conventional models such as machine learning, classifiers, and other mathematical models achieved the lowest classification score. The current study proposes to implement a novel deep feature with the best solution for EEG signal analysis and severity specification. A greedy sandpiper-based recurrent neural system (SbRNS) model for predicting Alzheimer's disease (AD) severity has been proposed. The filtered data are used as input for the feature analysis and the severity range is divided into three classes: low, medium, and high. The designed approach was then implemented in the matrix laboratory (MATLAB) system, and the effectiveness score was calculated using key metrics such as precision, recall, specificity, accuracy, and misclassification score. The validation results show that the proposed scheme achieved the best classification outcome.
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Affiliation(s)
- R. Swarnalatha
- Department of Electrical & Electronics Engineering, Birla Institute of Technology & Science, Pilani, Dubai Campus, Dubai, UAE
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9
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Deng L, Wang Y. Fully Connected Multi-Kernel Convolutional Neural Network Based on Alzheimer's Disease Diagnosis. J Alzheimers Dis 2023; 92:209-228. [PMID: 36710670 DOI: 10.3233/jad-220519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
BACKGROUND There is a shortage of clinicians with sufficient expertise in the diagnosis of Alzheimer's disease (AD), and cerebrospinal fluid biometric collection and positron emission tomography diagnosis are invasive. Therefore, it is of potential significance to obtain high-precision automatic diagnosis results from diffusion tensor imaging (DTI) through deep learning, and simultaneously output feature probability maps to provide clinical auxiliary diagnosis. OBJECTIVE We proposed a factorization machine combined neural network (FMCNN) model combining a multi-function convolutional neural network (MCNN) with a fully convolutional network (FCN), while accurately diagnosing AD and mild cognitive impairment (MCI); corresponding fiber bundle visualization results are generated to describe their status. METHODS First, the DTI data is preprocessed to eliminate the influence of external factors. The fiber bundles of the corpus callosum (CC), cingulum (CG), uncinate fasciculus (UNC), and white matter (WM) were then tracked based on deterministic fiber tracking. Then the streamlines are input into CNN, MCNN, and FMCNN as one-dimensional features for classification, and the models are evaluated by performance evaluation indicators. Finally, the fiber risk probability map is output through FMCNN. RESULTS After comparing the model performance indicators of CNN, MCNN, and FMCNN, it was found that FMCNN showed the best performance in the indicators of accuracy, specificity, sensitivity, and area under the curve. By inputting the fiber bundles of the 10 regions of interest (UNC_L, UNC_R, UNC, CC, CG, CG+UNC, CG+CC, CC+UNC, CG+CC+UNC, and WM into CNN, MCNN, and FMCNN, respectively), WM shows the highest accuracy in CNN, MCNN, and FMCNN, which are 88.41%, 92.07%, and 96.95%, respectively. CONCLUSION The FMCNN proposed here can accurately diagnose AD and MCI, and the generated fiber probability map can represent the risk status of AD and MCI.
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Affiliation(s)
- Lan Deng
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, P.R. China
| | - Yuanjun Wang
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, P.R. China
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10
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Chen P, Yao H, Tijms BM, Wang P, Wang D, Song C, Yang H, Zhang Z, Zhao K, Qu Y, Kang X, Du K, Fan L, Han T, Yu C, Zhang X, Jiang T, Zhou Y, Lu J, Han Y, Liu B, Zhou B, Liu Y. Four Distinct Subtypes of Alzheimer's Disease Based on Resting-State Connectivity Biomarkers. Biol Psychiatry 2022; 93:759-769. [PMID: 36137824 DOI: 10.1016/j.biopsych.2022.06.019] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 05/19/2022] [Accepted: 06/13/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) is a neurodegenerative disorder with significant heterogeneity. Different AD phenotypes may be associated with specific brain network changes. Uncovering disease heterogeneity by using functional networks could provide insights into precise diagnoses. METHODS We investigated the subtypes of AD using nonnegative matrix factorization clustering on the previously identified 216 resting-state functional connectivities that differed between AD and normal control subjects. We conducted the analysis using a discovery dataset (n = 809) and a validated dataset (n = 291). Next, we grouped individuals with mild cognitive impairment according to the model obtained in the AD groups. Finally, the clinical measures and brain structural characteristics were compared among the subtypes to assess their relationship with differences in the functional network. RESULTS Individuals with AD were clustered into 4 subtypes reproducibly, which included those with 1) diffuse and mild functional connectivity disruption (subtype 1), 2) predominantly decreased connectivity in the default mode network accompanied by an increase in the prefrontal circuit (subtype 2), 3) predominantly decreased connectivity in the anterior cingulate cortex accompanied by an increase in prefrontal cortex connectivity (subtype 3), and 4) predominantly decreased connectivity in the basal ganglia accompanied by an increase in prefrontal cortex connectivity (subtype 4). In addition to these differences in functional connectivity, differences between the AD subtypes were found in cognition, structural measures, and cognitive decline patterns. CONCLUSIONS These comprehensive results offer new insights that may advance precision medicine for AD and facilitate strategies for future clinical trials.
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Affiliation(s)
- Pindong Chen
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Hongxiang Yao
- Department of Radiology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Betty M Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC - Location VUmc, The Netherlands
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Ji'nan, China
| | - Chengyuan Song
- Department of Neurology, Qilu Hospital of Shandong University, Ji'nan, China
| | - Hongwei Yang
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | | | - Kun Zhao
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing, China
| | - Yida Qu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaopeng Kang
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Kai Du
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Lingzhong Fan
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Tong Han
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, China
| | - Chunshui Yu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Xi Zhang
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Tianzi Jiang
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Yuying Zhou
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China; Beijing Institute of Geriatrics, Beijing, China; National Clinical Research Center for Geriatric Disorders, Beijing, China
| | - Bing Liu
- State Key Laboratory of Cognition Neuroscience & Learning, Beijing Normal University, Beijing, China
| | - Bo Zhou
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.
| | - Yong Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
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Qu Y, Wang P, Liu B, Kang X, Chen P, Du K, Liu Y. Altered Connection and Diagnosis Utility of White Matter in Alzheimer's Disease: A Multi-site Automated Fiber Quantification Study. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2923-2927. [PMID: 34891857 DOI: 10.1109/embc46164.2021.9630117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Alzheimer's disease (AD) is a typical neurodegenerative disease that is associated with cognitive decline, memory loss, and functional disconnection. Diffusion tensor imaging (DTI) has been widely used to investigate the integrity and degeneration of white matter in AD. In this study, with one of the world's largest DTI biobanks (865 individuals), we aim to explore the diagnosis utility and stability of tractbased features (extracted by automated fiber quantification (AFQ) pipeline) in AD. First, we studied the clinical association of tract-based features by detecting AD-associated alterations of diffusion properties along fiber bundles. Then, a binary classification experiment between AD and normal controls was performed using tract-based diffusion properties as features and support vector machine (SVM) as a classifier with an independent site cross-validation strategy. The average accuracy of 77.90% (the highest was 88.89%) showed that white matter properties as biomarkers had a relatively stable role in the clinical diagnosis of AD.Clinical Relevance- White matter characteristics are valid and robust biomarkers of AD, which have high accuracy and generalizability in the AD diagnosis in a large multi-site dataset.
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12
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Ding Y, Zhao K, Che T, Du K, Sun H, Liu S, Zheng Y, Li S, Liu B, Liu Y. Quantitative Radiomic Features as New Biomarkers for Alzheimer's Disease: An Amyloid PET Study. Cereb Cortex 2021; 31:3950-3961. [PMID: 33884402 DOI: 10.1093/cercor/bhab061] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 01/29/2021] [Accepted: 02/22/2021] [Indexed: 12/20/2022] Open
Abstract
Growing evidence indicates that amyloid-beta (Aβ) accumulation is one of the most common neurobiological biomarkers in Alzheimer's disease (AD). The primary aim of this study was to explore whether the radiomic features of Aβ positron emission tomography (PET) images are used as predictors and provide a neurobiological foundation for AD. The radiomics features of Aβ PET imaging of each brain region of the Brainnetome Atlas were computed for classification and prediction using a support vector machine model. The results showed that the area under the receiver operating characteristic curve (AUC) was 0.93 for distinguishing AD (N = 291) from normal control (NC; N = 334). Additionally, the AUC was 0.83 for the prediction of mild cognitive impairment (MCI) converting (N = 88) (vs. no conversion, N = 100) to AD. In the MCI and AD groups, the systemic analysis demonstrated that the classification outputs were significantly associated with clinical measures (apolipoprotein E genotype, polygenic risk scores, polygenic hazard scores, cerebrospinal fluid Aβ, and Tau, cognitive ability score, the conversion time for progressive MCI subjects and cognitive changes). These findings provide evidence that the radiomic features of Aβ PET images can serve as new biomarkers for clinical applications in AD/MCI, further providing evidence for predicting whether MCI subjects will convert to AD.
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Affiliation(s)
- Yanhui Ding
- School of Information Science and Engineering, Shandong Normal University, Ji'nan 250014, China
| | - Kun Zhao
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China.,Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Tongtong Che
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Kai Du
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100049, China
| | - Hongzan Sun
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang 110004, China
| | - Shu Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100049, China
| | - Yuanjie Zheng
- School of Information Science and Engineering, Shandong Normal University, Ji'nan 250014, China
| | - Shuyu Li
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Bing Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100049, China
| | - Yong Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100049, China.,Pazhou Lab, Guangzhou 510330, China.,School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
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