1
|
Alarjani M, Almarri B. Multivariate pattern analysis of medical imaging-based Alzheimer's disease. Front Med (Lausanne) 2024; 11:1412592. [PMID: 39099597 PMCID: PMC11294205 DOI: 10.3389/fmed.2024.1412592] [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: 04/05/2024] [Accepted: 06/06/2024] [Indexed: 08/06/2024] Open
Abstract
Alzheimer's disease (AD) is a devastating brain disorder that steadily worsens over time. It is marked by a relentless decline in memory and cognitive abilities. As the disease progresses, it leads to a significant loss of mental function. Early detection of AD is essential to starting treatments that can mitigate the progression of this disease and enhance patients' quality of life. This study aims to observe AD's brain functional connectivity pattern to extract essential patterns through multivariate pattern analysis (MVPA) and analyze activity patterns across multiple brain voxels. The optimized feature extraction techniques are used to obtain the important features for performing the training on the models using several hybrid machine learning classifiers for performing binary classification and multi-class classification. The proposed approach using hybrid machine learning classification has been applied to two public datasets named the Open Access Series of Imaging Studies (OASIS) and the AD Neuroimaging Initiative (ADNI). The results are evaluated using performance metrics, and comparisons have been made to differentiate between different stages of AD using visualization tools.
Collapse
Affiliation(s)
| | - Badar Almarri
- Department of Computer Science, College of Computer Sciences and Information Technology, King Faisal University, Al-Hofuf, Saudi Arabia
| |
Collapse
|
2
|
Huang Y, Li Y, Yuan Y, Zhang X, Yan W, Li T, Niu Y, Xu M, Yan T, Li X, Li D, Xiang J, Wang B, Yan T. Beta-informativeness-diffusion multilayer graph embedding for brain network analysis. Front Neurosci 2024; 18:1303741. [PMID: 38525375 PMCID: PMC10957763 DOI: 10.3389/fnins.2024.1303741] [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: 09/28/2023] [Accepted: 02/07/2024] [Indexed: 03/26/2024] Open
Abstract
Brain network analysis provides essential insights into the diagnosis of brain disease. Integrating multiple neuroimaging modalities has been demonstrated to be more effective than using a single modality for brain network analysis. However, a majority of existing brain network analysis methods based on multiple modalities often overlook both complementary information and unique characteristics from various modalities. To tackle this issue, we propose the Beta-Informativeness-Diffusion Multilayer Graph Embedding (BID-MGE) method. The proposed method seamlessly integrates structural connectivity (SC) and functional connectivity (FC) to learn more comprehensive information for diagnosing neuropsychiatric disorders. Specifically, a novel beta distribution mapping function (beta mapping) is utilized to increase vital information and weaken insignificant connections. The refined information helps the diffusion process concentrate on crucial brain regions to capture more discriminative features. To maximize the preservation of the unique characteristics of each modality, we design an optimal scale multilayer brain network, the inter-layer connections of which depend on node informativeness. Then, a multilayer informativeness diffusion is proposed to capture complementary information and unique characteristics from various modalities and generate node representations by incorporating the features of each node with those of their connected nodes. Finally, the node representations are reconfigured using principal component analysis (PCA), and cosine distances are calculated with reference to multiple templates for statistical analysis and classification. We implement the proposed method for brain network analysis of neuropsychiatric disorders. The results indicate that our method effectively identifies crucial brain regions associated with diseases, providing valuable insights into the pathology of the disease, and surpasses other advanced methods in classification performance.
Collapse
Affiliation(s)
- Yin Huang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Ying Li
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Yuting Yuan
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Xingyu Zhang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Wenjie Yan
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Ting Li
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Yan Niu
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Mengzhou Xu
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Ting Yan
- Translational Medicine Research Center, Shanxi Medical University, Taiyuan, China
| | - Xiaowen Li
- Computer Information Engineering Institute, Shanxi Technology and Business College, Taiyuan, China
| | - Dandan Li
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Jie Xiang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Bin Wang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Tianyi Yan
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| |
Collapse
|
3
|
Lama RK, Kwon GR. Resting-State Functional Connectivity Difference in Alzheimer's Disease and Mild Cognitive Impairment Using Threshold-Free Cluster Enhancement. Diagnostics (Basel) 2023; 13:3074. [PMID: 37835817 PMCID: PMC10572464 DOI: 10.3390/diagnostics13193074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 09/17/2023] [Accepted: 09/22/2023] [Indexed: 10/15/2023] Open
Abstract
The disruption of functional connectivity is one of the early events that occurs in the brains of Alzheimer's disease (AD) patients. This paper reports a study on the clustering structure of functional connectivity in eight important brain networks in healthy, AD, and prodromal stage subjects. We used the threshold-free cluster enhancement (TFCE) method to explore the connectivity from resting-state functional MR images (rs-fMRIs). We conducted the study on a total of 32 AD, 32 HC, and 31 MCI subjects. We modeled the brain as a graph-based network to study these impairments, and pairwise Pearson's correlation-based functional connectivity was used to construct the brain network. The study found that connections in the sensory motor network (SMN), dorsal attention network (DAN), salience network (SAN), default mode network (DMN), and cerebral network were severely affected in AD and MCI. The disruption in these networks may serve as potential biomarkers for distinguishing AD and MCI from HC. The study suggests that alterations in functional connectivity in these networks may contribute to cognitive deficits observed in AD and MCI. Additionally, a negative correlation was observed between the global clinical dementia rating (CDR) score and the Z-score of functional connectivity within identified clusters in AD subjects. These findings provide compelling evidence suggesting that the neurodegenerative disruption of functional magnetic resonance imaging (fMRI) connectivity is extensively distributed across multiple networks in individuals diagnosed with AD.
Collapse
Affiliation(s)
| | - Goo-Rak Kwon
- Department of Information and Communication Engineering, Chosun University, 309 Pilmundaero, Gwangju 61452, Republic of Korea;
| |
Collapse
|
4
|
Teng J, Mi C, Shi J, Li N. Brain disease research based on functional magnetic resonance imaging data and machine learning: a review. Front Neurosci 2023; 17:1227491. [PMID: 37662098 PMCID: PMC10469689 DOI: 10.3389/fnins.2023.1227491] [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: 05/23/2023] [Accepted: 07/13/2023] [Indexed: 09/05/2023] Open
Abstract
Brain diseases, including neurodegenerative diseases and neuropsychiatric diseases, have long plagued the lives of the affected populations and caused a huge burden on public health. Functional magnetic resonance imaging (fMRI) is an excellent neuroimaging technology for measuring brain activity, which provides new insight for clinicians to help diagnose brain diseases. In recent years, machine learning methods have displayed superior performance in diagnosing brain diseases compared to conventional methods, attracting great attention from researchers. This paper reviews the representative research of machine learning methods in brain disease diagnosis based on fMRI data in the recent three years, focusing on the most frequent four active brain disease studies, including Alzheimer's disease/mild cognitive impairment, autism spectrum disorders, schizophrenia, and Parkinson's disease. We summarize these 55 articles from multiple perspectives, including the effect of the size of subjects, extracted features, feature selection methods, classification models, validation methods, and corresponding accuracies. Finally, we analyze these articles and introduce future research directions to provide neuroimaging scientists and researchers in the interdisciplinary fields of computing and medicine with new ideas for AI-aided brain disease diagnosis.
Collapse
Affiliation(s)
- Jing Teng
- School of Control and Computer Engineering, North China Electric Power University, Beijing, China
| | - Chunlin Mi
- School of Control and Computer Engineering, North China Electric Power University, Beijing, China
| | - Jian Shi
- Department of Hematology and Critical Care Medicine, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Na Li
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, China
| |
Collapse
|
5
|
Cao Y, Kuai H, Liang P, Pan JS, Yan J, Zhong N. BNLoop-GAN: a multi-loop generative adversarial model on brain network learning to classify Alzheimer's disease. Front Neurosci 2023; 17:1202382. [PMID: 37424996 PMCID: PMC10326383 DOI: 10.3389/fnins.2023.1202382] [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: 04/08/2023] [Accepted: 05/09/2023] [Indexed: 07/11/2023] Open
Abstract
Recent advancements in AI, big data analytics, and magnetic resonance imaging (MRI) have revolutionized the study of brain diseases such as Alzheimer's Disease (AD). However, most AI models used for neuroimaging classification tasks have limitations in their learning strategies, that is batch training without the incremental learning capability. To address such limitations, the systematic Brain Informatics methodology is reconsidered to realize evidence combination and fusion computing with multi-modal neuroimaging data through continuous learning. Specifically, we introduce the BNLoop-GAN (Loop-based Generative Adversarial Network for Brain Network) model, utilizing multiple techniques such as conditional generation, patch-based discrimination, and Wasserstein gradient penalty to learn the implicit distribution of brain networks. Moreover, a multiple-loop-learning algorithm is developed to combine evidence with better sample contribution ranking during training processes. The effectiveness of our approach is demonstrated through a case study on the classification of individuals with AD and healthy control groups using various experimental design strategies and multi-modal brain networks. The BNLoop-GAN model with multi-modal brain networks and multiple-loop-learning can improve classification performance.
Collapse
Affiliation(s)
- Yu Cao
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
- Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, China
| | - Hongzhi Kuai
- Faculty of Engineering, Maebashi Institute of Technology, Maebashi, Gunma, Japan
| | - Peipeng Liang
- School of Psychology and Beijing Key Laboratory of Learning and Cognition, Capital Normal University, Beijing, China
| | - Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Jianzhuo Yan
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
- Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, China
| | - Ning Zhong
- Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, China
- Faculty of Engineering, Maebashi Institute of Technology, Maebashi, Gunma, Japan
- School of Psychology and Beijing Key Laboratory of Learning and Cognition, Capital Normal University, Beijing, China
| |
Collapse
|
6
|
Wang B, Guo M, Pan T, Li Z, Li Y, Xiang J, Cui X, Niu Y, Yang J, Wu J, Liu M, Li D. Altered higher-order coupling between brain structure and function with embedded vector representations of connectomes in schizophrenia. Cereb Cortex 2022; 33:5447-5456. [PMID: 36482789 DOI: 10.1093/cercor/bhac432] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 10/05/2022] [Accepted: 10/07/2022] [Indexed: 12/13/2022] Open
Abstract
Abstract
It has been shown that the functional dependency of the brain exists in both direct and indirect regional relationships. Therefore, it is necessary to map higher-order coupling in brain structure and function to understand brain dynamic. However, how to quantify connections between not directly regions remains unknown to schizophrenia. The word2vec is a common algorithm through create embeddings of words to solve these problems. We apply the node2vec embedding representation to characterize features on each node, their pairwise relationship can give rise to correspondence relationships between brain regions. Then we adopt pearson correlation to quantify the higher-order coupling between structure and function in normal controls and schizophrenia. In addition, we construct direct and indirect connections to quantify the coupling between their respective functional connections. The results showed that higher-order coupling is significantly higher in schizophrenia. Importantly, the anomalous cause of coupling mainly focus on indirect structural connections. The indirect structural connections play an essential role in functional connectivity–structural connectivity (SC–FC) coupling. The similarity between embedded representations capture more subtle network underlying information, our research provides new perspectives for understanding SC–FC coupling. A strong indication that the structural backbone of the brain has an intimate influence on the resting-state functional.
Collapse
Affiliation(s)
- Bin Wang
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Min Guo
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Tingting Pan
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Zhifeng Li
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Ying Li
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Xiaohong Cui
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Yan Niu
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Jiajia Yang
- Graduate School of Interdisciplinary Science and Engineering in Health Systems, 3-1-1 Tsushimanaka, kita-ku, Okayama-shi, Okayama, 700-8530, Japan
| | - Jinglong Wu
- Graduate School of Interdisciplinary Science and Engineering in Health Systems, 3-1-1 Tsushimanaka, kita-ku, Okayama-shi, Okayama, 700-8530, Japan
| | - Miaomiao Liu
- School of Psychology, Shenzhen University, No. 3688, Nanhai Avenue, Nanshan District, Shenzhen, 518061, China
| | - Dandan Li
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| |
Collapse
|
7
|
Fathian A, Jamali Y, Raoufy MR. The trend of disruption in the functional brain network topology of Alzheimer's disease. Sci Rep 2022; 12:14998. [PMID: 36056059 PMCID: PMC9440254 DOI: 10.1038/s41598-022-18987-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 08/23/2022] [Indexed: 12/19/2022] Open
Abstract
Alzheimer's disease (AD) is a progressive disorder associated with cognitive dysfunction that alters the brain's functional connectivity. Assessing these alterations has become a topic of increasing interest. However, a few studies have examined different stages of AD from a complex network perspective that cover different topological scales. This study used resting state fMRI data to analyze the trend of functional connectivity alterations from a cognitively normal (CN) state through early and late mild cognitive impairment (EMCI and LMCI) and to Alzheimer's disease. The analyses had been done at the local (hubs and activated links and areas), meso (clustering, assortativity, and rich-club), and global (small-world, small-worldness, and efficiency) topological scales. The results showed that the trends of changes in the topological architecture of the functional brain network were not entirely proportional to the AD progression. There were network characteristics that have changed non-linearly regarding the disease progression, especially at the earliest stage of the disease, i.e., EMCI. Further, it has been indicated that the diseased groups engaged somatomotor, frontoparietal, and default mode modules compared to the CN group. The diseased groups also shifted the functional network towards more random architecture. In the end, the methods introduced in this paper enable us to gain an extensive understanding of the pathological changes of the AD process.
Collapse
Affiliation(s)
- Alireza Fathian
- Biomathematics Laboratory, Department of Applied Mathematics, School of Mathematical Science, Tarbiat Modares University, Tehran, Iran
| | - Yousef Jamali
- Biomathematics Laboratory, Department of Applied Mathematics, School of Mathematical Science, Tarbiat Modares University, Tehran, Iran.
- Applied Systems Biology, Leibniz-Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute, Jena, Germany.
| | - Mohammad Reza Raoufy
- Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| |
Collapse
|
8
|
Classification and Interpretability of Mild Cognitive Impairment Based on Resting-State Functional Magnetic Resonance and Ensemble Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:2535954. [PMID: 36035823 PMCID: PMC9417789 DOI: 10.1155/2022/2535954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 06/12/2022] [Accepted: 07/06/2022] [Indexed: 11/22/2022]
Abstract
The combination and integration of multimodal imaging and clinical markers have introduced numerous classifiers to improve diagnostic accuracy in detecting and predicting AD; however, many studies cannot ensure the homogeneity of data sets and consistency of results. In our study, the XGBoost algorithm was used to classify mild cognitive impairment (MCI) and normal control (NC) populations through five rs-fMRI analysis datasets. Shapley Additive exPlanations (SHAP) is used to analyze the interpretability of the model. The highest accuracy for diagnosing MCI was 65.14% (using the mPerAF dataset). The characteristics of the left insula, right middle frontal gyrus, and right cuneus correlated positively with the output value using DC datasets. The characteristics of left cerebellum 6, right inferior frontal gyrus, opercular part, and vermis 6 correlated positively with the output value using fALFF datasets. The characteristics of the right middle temporal gyrus, left middle temporal gyrus, left temporal pole, and middle temporal gyrus correlated positively with the output value using mPerAF datasets. The characteristics of the right middle temporal gyrus, left middle temporal gyrus, and left hippocampus correlated positively with the output value using PerAF datasets. The characteristics of left cerebellum 9, vermis 9, and right precentral gyrus, right amygdala, and left middle occipital gyrus correlated positively with the output value using Wavelet-ALFF datasets. We found that the XGBoost algorithm constructed from rs-fMRI data is effective for the diagnosis and classification of MCI. The accuracy rates obtained by different rs-fMRI data analysis methods are similar, but the important features are different and involve multiple brain regions, which suggests that MCI may have a negative impact on brain function.
Collapse
|
9
|
Khojaste-Sarakhsi M, Haghighi SS, Ghomi SF, Marchiori E. Deep learning for Alzheimer's disease diagnosis: A survey. Artif Intell Med 2022; 130:102332. [DOI: 10.1016/j.artmed.2022.102332] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 04/29/2022] [Accepted: 05/30/2022] [Indexed: 11/28/2022]
|
10
|
Classification of Alzheimer’s Disease Based on Core-Large Scale Brain Network Using Multilayer Extreme Learning Machine. MATHEMATICS 2022. [DOI: 10.3390/math10121967] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Various studies suggest that the network deficit in default network mode (DMN) is prevalent in Alzheimer’s disease (AD) and mild cognitive impairment (MCI). Besides DMN, some studies reveal that network alteration occurs in salience network motor networks and large scale network. In this study we performed classification of AD and MCI from healthy control considering the network alterations in large scale network and DMN. Thus, we constructed the brain network from functional magnetic resonance (fMR) images. Pearson’s correlation-based functional connectivity was used to construct the brain network. Graph features of the brain network were converted to feature vectors using Node2vec graph-embedding technique. Two classifiers, single layered extreme learning and multilayered extreme learning machine, were used for the classification together with feature selection approaches. We performed the classification test on the brain network of different sizes including the large scale brain network, the whole brain network and the combined brain network. Experimental results showed that the least absolute shrinkage and selection operator (LASSO) feature selection method generates better classification accuracy on large network size, and that feature selection with adaptive structure learning (FSAL) feature selection technique generates better classification accuracy on small network size.
Collapse
|
11
|
Khatri U, Kwon GR. Alzheimer's Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield and Amygdala Volume of Structural MRI. Front Aging Neurosci 2022; 14:818871. [PMID: 35707703 PMCID: PMC9190953 DOI: 10.3389/fnagi.2022.818871] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 03/01/2022] [Indexed: 11/13/2022] Open
Abstract
Accurate diagnosis of the initial phase of Alzheimer's disease (AD) is essential and crucial. The objective of this research was to employ efficient biomarkers for the diagnostic analysis and classification of AD based on combining structural MRI (sMRI) and resting-state functional MRI (rs-fMRI). So far, several anatomical MRI imaging markers for AD diagnosis have been identified. The use of cortical and subcortical volumes, the hippocampus, and amygdala volume, as well as genetic patterns, has proven to be beneficial in distinguishing patients with AD from the healthy population. The fMRI time series data have the potential for specific numerical information as well as dynamic temporal information. Voxel and graphical analyses have gained popularity for analyzing neurodegenerative diseases, such as Alzheimer's and its prodromal phase, mild cognitive impairment (MCI). So far, these approaches have been utilized separately for the diagnosis of AD. In recent studies, the classification of cases of MCI into those that are not converted for a certain period as stable MCI (MCIs) and those that converted to AD as MCIc has been less commonly reported with inconsistent results. In this study, we verified and validated the potency of a proposed diagnostic framework to identify AD and differentiate MCIs from MCIc by utilizing the efficient biomarkers obtained from sMRI, along with functional brain networks of the frequency range .01-.027 at the resting state and the voxel-based features. The latter mainly included default mode networks (amplitude of low-frequency fluctuation [ALFF], fractional ALFF [ALFF], and regional homogeneity [ReHo]), degree centrality (DC), and salience networks (SN). Pearson's correlation coefficient for measuring fMRI functional networks has proven to be an efficient means for disease diagnosis. We applied the graph theory to calculate nodal features (nodal degree [ND], nodal path length [NL], and between centrality [BC]) as a graphical feature and analyzed the connectivity link between different brain regions. We extracted three-dimensional (3D) patterns to calculate regional coherence and then implement a univariate statistical t-test to access a 3D mask that preserves voxels showing significant changes. Similarly, from sMRI, we calculated the hippocampal subfield and amygdala nuclei volume using Freesurfer (version 6). Finally, we implemented and compared the different feature selection algorithms to integrate the structural features, brain networks, and voxel features to optimize the diagnostic identifications of AD using support vector machine (SVM) classifiers. We also compared the performance of SVM with Random Forest (RF) classifiers. The obtained results demonstrated the potency of our framework, wherein a combination of the hippocampal subfield, the amygdala volume, and brain networks with multiple measures of rs-fMRI could significantly enhance the accuracy of other approaches in diagnosing AD. The accuracy obtained by the proposed method was reported for binary classification. More importantly, the classification results of the less commonly reported MCIs vs. MCIc improved significantly. However, this research involved only the AD Neuroimaging Initiative (ADNI) cohort to focus on the diagnosis of AD advancement by integrating sMRI and fMRI. Hence, the study's primary disadvantage is its small sample size. In this case, the dataset we utilized did not fully reflect the whole population. As a result, we cannot guarantee that our findings will be applicable to other populations.
Collapse
Affiliation(s)
| | - Goo-Rak Kwon
- Department of Information and Communication Engineering, Chosun University, Gwangju, South Korea
| |
Collapse
|
12
|
Lu J, Zeng W, Zhang L, Shi Y. A Novel Key Features Screening Method Based on Extreme Learning Machine for Alzheimer’s Disease Study. Front Aging Neurosci 2022; 14:888575. [PMID: 35693342 PMCID: PMC9177228 DOI: 10.3389/fnagi.2022.888575] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 04/25/2022] [Indexed: 12/31/2022] Open
Abstract
The Extreme Learning Machine (ELM) is a simple and efficient Single Hidden Layer Feedforward Neural Network(SLFN) algorithm. In recent years, it has been gradually used in the study of Alzheimer’s disease (AD). When using ELM to diagnose AD based on high-dimensional features, there are often some features that have no positive impact on the diagnosis, while others have a significant impact on the diagnosis. In this paper, a novel Key Features Screening Method based on Extreme Learning Machine (KFS-ELM) is proposed. It can screen for key features that are relevant to the classification (diagnosis). It can also assign weights to key features based on their importance. We designed an experiment to screen for key features of AD. A total of 920 key functional connections screened from 4005 functional connections. Their weights were also obtained. The results of the experiment showed that: (1) Using all (4,005) features to diagnose AD, the accuracy is 95.33%. Using 920 key features to diagnose AD, the accuracy is 99.20%. The 3,085 (4,005 - 920) features that were screened out had a negative effect on the diagnosis of AD. This indicates the KFS-ELM is effective in screening key features. (2) The higher the weight of the key features and the smaller their number, the greater their impact on AD diagnosis. This indicates that the KFS-ELM is rational in assigning weights to the key features for their importance. Therefore, KFS-ELM can be used as a tool for studying features and also for improving classification accuracy.
Collapse
Affiliation(s)
- Jia Lu
- Laboratory of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, China
| | - Weiming Zeng
- Laboratory of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, China
- *Correspondence: Weiming Zeng,
| | - Lu Zhang
- Basic Experiment and Training Center, Shanghai Maritime University, Shanghai, China
| | - Yuhu Shi
- College of Information Engineering Shanghai Maritime University, Shanghai, China
| |
Collapse
|