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Xu Q, Yang J, Cheng F, Ning Z, Xi C, Sun Z. Changes in Multiparametric Magnetic Resonance Imaging and Plasma Amyloid-Beta Protein in Subjective Cognitive Decline. Brain Sci 2023; 13:1624. [PMID: 38137072 PMCID: PMC10742209 DOI: 10.3390/brainsci13121624] [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: 10/18/2023] [Revised: 11/16/2023] [Accepted: 11/21/2023] [Indexed: 12/24/2023] Open
Abstract
The association between plasma amyloid-beta protein (Aβ) and subjective cognitive decline (SCD) remains controversial. We aimed to explore the correlation between neuroimaging findings, plasma Aβ, and neuropsychological scales using data from 53 SCD patients and 46 age- and sex-matched healthy controls (HCs). Magnetic resonance imaging (MRI) was used to obtain neuroimaging data for a whole-brain voxel-based morphometry analysis and cortical functional network topological features. The SCD group had slightly lower Montreal Cognitive Assessment (MoCA) scores than the HC group. The Aβ42 levels were significantly higher in the SCD group than in the HC group (p < 0.05). The SCD patients demonstrated reduced volumes in the left hippocampus, right rectal gyrus (REC.R), and right precentral gyrus (PreCG.R); an increased percentage fluctuation in the left thalamus (PerAF); and lower average small-world coefficient (aSigma) and average global efficiency (aEg) values. Correlation analyses with Aβ and neuropsychological scales revealed significant positive correlations between the volumes of the HIP.L, REC.R, PreCG.R, and MoCA scores. The HIP.L volume and Aβ42 were negatively correlated, as were the REC.R volume and Aβ42/40. PerAF and aSigma were negatively and positively correlated with the MoCA scores, respectively. The aEg was positively correlated with Aβ42/40. SCD patients may exhibit alterations in plasma biomarkers and multi-parameter MRI that resemble those observed in Alzheimer's disease, offering a theoretical foundation for early clinical intervention in SCD.
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Affiliation(s)
- Qiaoqiao Xu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; (Q.X.); (J.Y.)
- Department of Neurology, The Third Affiliated Hospital of Anhui Medical University (Hefei City First People’s Hospital), Hefei 230061, China; (F.C.); (Z.N.)
| | - Jiajia Yang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; (Q.X.); (J.Y.)
| | - Fang Cheng
- Department of Neurology, The Third Affiliated Hospital of Anhui Medical University (Hefei City First People’s Hospital), Hefei 230061, China; (F.C.); (Z.N.)
| | - Zhiwen Ning
- Department of Neurology, The Third Affiliated Hospital of Anhui Medical University (Hefei City First People’s Hospital), Hefei 230061, China; (F.C.); (Z.N.)
| | - Chunhua Xi
- Department of Neurology, The Third Affiliated Hospital of Anhui Medical University (Hefei City First People’s Hospital), Hefei 230061, China; (F.C.); (Z.N.)
| | - Zhongwu Sun
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; (Q.X.); (J.Y.)
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Zhao M, Hao Z, Li M, Xi H, Hu S, Wen J, Gao Y, Antwi CO, Jia X, Yu Y, Ren J. Functional changes of default mode network and structural alterations of gray matter in patients with irritable bowel syndrome: a meta-analysis of whole-brain studies. Front Neurosci 2023; 17:1236069. [PMID: 37942144 PMCID: PMC10627928 DOI: 10.3389/fnins.2023.1236069] [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: 06/07/2023] [Accepted: 10/09/2023] [Indexed: 11/10/2023] Open
Abstract
Background Irritable bowel syndrome (IBS) is a brain-gut disorder with high global prevalence, resulting from abnormalities in brain connectivity of the default mode network and aberrant changes in gray matter (GM). However, the findings of previous studies about IBS were divergent. Therefore, we conducted a meta-analysis to identify common functional and structural alterations in IBS patients. Methods Altogether, we identified 12 studies involving 194 IBS patients and 230 healthy controls (HCs) from six databases using whole-brain resting state functional connectivity (rs-FC) and voxel-based morphometry. Anisotropic effect-size signed differential mapping (AES-SDM) was used to identify abnormal functional and structural changes as well as the overlap brain regions between dysconnectivity and GM alterations. Results Findings indicated that, compared with HCs, IBS patients showed abnormal rs-FC in left inferior parietal gyrus, left lingual gyrus, right angular gyrus, right precuneus, right amygdala, right median cingulate cortex, and left hippocampus. Altered GM was detected in the fusiform gyrus, left triangular inferior frontal gyrus (IFG), right superior marginal gyrus, left anterior cingulate gyrus, left rectus, left orbital IFG, right triangular IFG, right putamen, left superior parietal gyrus and right precuneus. Besides, multimodal meta-analysis identified left middle frontal gyrus, left orbital IFG, and right putamen as the overlapped regions. Conclusion Our results confirm that IBS patients have abnormal alterations in rs-FC and GM, and reveal brain regions with both functional and structural alterations. These results may contribute to understanding the underlying pathophysiology of IBS. Systematic review registration https://www.crd.york.ac.uk/prospero, identifier CRD42022351342.
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Affiliation(s)
- Mengqi Zhao
- School of Psychology, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent, Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Zeqi Hao
- School of Psychology, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent, Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Mengting Li
- School of Psychology, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent, Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Hongyu Xi
- School of Western Languages, Heilongjiang University, Harbin, China
| | - Su Hu
- School of Psychology, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent, Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Jianjie Wen
- School of Psychology, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent, Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Yanyan Gao
- School of Psychology, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent, Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Collins Opoku Antwi
- School of Psychology, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent, Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Xize Jia
- School of Psychology, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent, Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Yang Yu
- Department of Psychiatry, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jun Ren
- School of Psychology, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent, Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
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Chen XG, Yang X, Li C, Lin X, Zhang W. Non-coding RNA identification with pseudo RNA sequences and feature representation learning. Comput Biol Med 2023; 165:107355. [PMID: 37639767 DOI: 10.1016/j.compbiomed.2023.107355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 07/16/2023] [Accepted: 08/12/2023] [Indexed: 08/31/2023]
Abstract
Distinguishing non-coding RNAs (ncRNAs) from coding RNAs is very important in bioinformatics. Although many methods have been proposed for solving this task, it remains highly challenging to further improve the accuracy of ncRNA identification. In this paper, we propose a coding potential predictor using feature representation learning based on pseudo RNA sequences named CPPFLPS. In this method, we use the pseudo RNA sequences generated by simulating RNA sequence mutations as new samples for data augmentation, and six string operations simulating RNA sequence mutations are considered: base replacement, base insertion, base deletion, subsequence reversion, subsequence repetition and subsequence deletion. In the feature representation learning framework, different types of pseudo RNA sequences are added to the training set to form new training sets that can be used to train baseline classifiers, thus obtaining baseline models. The resulting labels of these baseline models are used as feature vectors to represent RNA sequences, and the resulting feature vectors acquired after feature selection are used to train a predictive model for distinguishing ncRNAs from coding RNAs. Our method achieves better performance compared with that of existing state-of-the-art methods. The implementation of the proposed method is available at https://github.com/chenxgscuec/CPPFLPS.
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Affiliation(s)
- Xian-Gan Chen
- School of Biomedical Engineering, South-Central Minzu University, Wuhan, 430074, China; Hubei Key Laboratory of Medical Information Analysis and Tumor Diagnosis & Treatment, South-Central Minzu University, Wuhan, 430074, China; Key Laboratory of Cognitive Science(South-Central Minzu University), State Ethnic Affairs Commission, Wuhan, 430074, China.
| | - Xiaofei Yang
- School of Biomedical Engineering, South-Central Minzu University, Wuhan, 430074, China; Hubei Key Laboratory of Medical Information Analysis and Tumor Diagnosis & Treatment, South-Central Minzu University, Wuhan, 430074, China; Key Laboratory of Cognitive Science(South-Central Minzu University), State Ethnic Affairs Commission, Wuhan, 430074, China.
| | - Chenhong Li
- School of Biomedical Engineering, South-Central Minzu University, Wuhan, 430074, China; Hubei Key Laboratory of Medical Information Analysis and Tumor Diagnosis & Treatment, South-Central Minzu University, Wuhan, 430074, China; Key Laboratory of Cognitive Science(South-Central Minzu University), State Ethnic Affairs Commission, Wuhan, 430074, China.
| | - Xianguang Lin
- School of Biomedical Engineering, South-Central Minzu University, Wuhan, 430074, China; Hubei Key Laboratory of Medical Information Analysis and Tumor Diagnosis & Treatment, South-Central Minzu University, Wuhan, 430074, China; Key Laboratory of Cognitive Science(South-Central Minzu University), State Ethnic Affairs Commission, Wuhan, 430074, China.
| | - Wen Zhang
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China.
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Liu Y, Chakraborty N, Qin ZS, Kundu S. Integrative Bayesian tensor regression for imaging genetics applications. Front Neurosci 2023; 17:1212218. [PMID: 37680967 PMCID: PMC10481528 DOI: 10.3389/fnins.2023.1212218] [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: 04/25/2023] [Accepted: 07/17/2023] [Indexed: 09/09/2023] Open
Abstract
Identifying biomarkers for Alzheimer's disease with a goal of early detection is a fundamental problem in clinical research. Both medical imaging and genetics have contributed informative biomarkers in literature. To further improve the performance, recently, there is an increasing interest in developing analytic approaches that combine data across modalities such as imaging and genetics. However, there are limited methods in literature that are able to systematically combine high-dimensional voxel-level imaging and genetic data for accurate prediction of clinical outcomes of interest. Existing prediction models that integrate imaging and genetic features often use region level imaging summaries, and they typically do not consider the spatial configurations of the voxels in the image or incorporate the dependence between genes that may compromise prediction ability. We propose a novel integrative Bayesian scalar-on-image regression model for predicting cognitive outcomes based on high-dimensional spatially distributed voxel-level imaging data, along with correlated transcriptomic features. We account for the spatial dependencies in the imaging voxels via a tensor approach that also enables massive dimension reduction to address the curse of dimensionality, and models the dependencies between the transcriptomic features via a Graph-Laplacian prior. We implement this approach via an efficient Markov chain Monte Carlo (MCMC) computation strategy. We apply the proposed method to the analysis of longitudinal ADNI data for predicting cognitive scores at different visits by integrating voxel-level cortical thickness measurements derived from T1w-MRI scans and transcriptomics data. We illustrate that the proposed imaging transcriptomics approach has significant improvements in prediction compared to prediction using a subset of features from only one modality (imaging or genetics), as well as when using imaging and transcriptomics features but ignoring the inherent dependencies between the features. Our analysis is one of the first to conclusively demonstrate the advantages of prediction based on combining voxel-level cortical thickness measurements along with transcriptomics features, while accounting for inherent structural information.
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Affiliation(s)
- Yajie Liu
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Nilanjana Chakraborty
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Zhaohui S. Qin
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, United States
| | - Suprateek Kundu
- Department of Biostatistics, Division of Basic Science Research, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
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Yao Z, Wang H, Yan W, Wang Z, Zhang W, Wang Z, Zhang G. Artificial intelligence-based diagnosis of Alzheimer's disease with brain MRI images. Eur J Radiol 2023; 165:110934. [PMID: 37354773 DOI: 10.1016/j.ejrad.2023.110934] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 05/21/2023] [Accepted: 06/15/2023] [Indexed: 06/26/2023]
Abstract
Alzheimer's disease, a primary neurodegenerative condition, predominantly impacts the elderly and pre-elderly population. This progressive neurological disorder is characterized by an array of symptoms including memory loss, cognitive decline, and various physiological and psychological disturbances, significantly compromising the quality of life of patients and their caregivers. Recent advancements in Magnetic Resonance Imaging (MRI) technology have catalyzed research in AI-enhanced diagnostics for Alzheimer's disease, fostering optimism for early detection and timely interventions. This progress has paved the way for the development of sophisticated algorithms and models adept at analyzing complex brain imaging data, thereby augmenting diagnostic accuracy and efficiency. This advancement fuels optimism regarding the transformative potential of AI-driven diagnostics in revolutionizing Alzheimer's disease management, with the prospect of facilitating more effective treatment strategies and improved patient outcomes. The objective of this review is to provide a comprehensive overview of recent developments in deep learning methodologies applied to brain MRI images for the classification of various stages of Alzheimer's disease, with a particular emphasis on early diagnosis. Furthermore, this review underscores the limitations of current research, discussing potential challenges and future research directions in this dynamic field.
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Affiliation(s)
- Zhaomin Yao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China; Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning 110016, China
| | - Hongyu Wang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China; Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning 110016, China
| | - Wencheng Yan
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China
| | - Zheling Wang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China
| | - Wenwen Zhang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China; Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning 110016, China
| | - Zhiguo Wang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China; Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning 110016, China.
| | - Guoxu Zhang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China; Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning 110016, China.
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6
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Hendriks WJAJ, van Cruchten RTP, Pulido R. Hereditable variants of classical protein tyrosine phosphatase genes: Will they prove innocent or guilty? Front Cell Dev Biol 2023; 10:1051311. [PMID: 36755664 PMCID: PMC9900141 DOI: 10.3389/fcell.2022.1051311] [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: 09/22/2022] [Accepted: 12/28/2022] [Indexed: 01/24/2023] Open
Abstract
Protein tyrosine phosphatases, together with protein tyrosine kinases, control many molecular signaling steps that control life at cellular and organismal levels. Impairing alterations in the genes encoding the involved proteins is expected to profoundly affect the quality of life-if compatible with life at all. Here, we review the current knowledge on the effects of germline variants that have been reported for genes encoding a subset of the protein tyrosine phosphatase superfamily; that of the thirty seven classical members. The conclusion must be that the newest genome research tools produced an avalanche of data that suggest 'guilt by association' for individual genes to specific disorders. Future research should face the challenge to investigate these accusations thoroughly and convincingly, to reach a mature genotype-phenotype map for this intriguing protein family.
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Affiliation(s)
- Wiljan J. A. J. Hendriks
- Department of Cell Biology, Radboud University Medical Centre, Nijmegen, The Netherlands,*Correspondence: Wiljan J. A. J. Hendriks,
| | | | - Rafael Pulido
- Biomarkers in Cancer Unit, Biocruces Bizkaia Health Research Institute, Barakaldo, Spain,Ikerbasque, Basque Foundation for Science, Bilbao, Spain
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7
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Detection of Association Features Based on Gene Eigenvalues and MRI Imaging Using Genetic Weighted Random Forest. Genes (Basel) 2022; 13:genes13122344. [PMID: 36553611 PMCID: PMC9777775 DOI: 10.3390/genes13122344] [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: 10/31/2022] [Revised: 12/07/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022] Open
Abstract
In the studies of Alzheimer's disease (AD), jointly analyzing imaging data and genetic data provides an effective method to explore the potential biomarkers of AD. AD can be separated into healthy controls (HC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI) and AD. In the meantime, identifying the important biomarkers of AD progression, and analyzing these biomarkers in AD provide valuable insights into understanding the mechanism of AD. In this paper, we present a novel data fusion method and a genetic weighted random forest method to mine important features. Specifically, we amplify the difference among AD, LMCI, EMCI and HC by introducing eigenvalues calculated from the gene p-value matrix for feature fusion. Furthermore, we construct the genetic weighted random forest using the resulting fused features. Genetic evolution is used to increase the diversity among decision trees and the decision trees generated are weighted by weights. After training, the genetic weighted random forest is analyzed further to detect the significant fused features. The validation experiments highlight the performance and generalization of our proposed model. We analyze the biological significance of the results and identify some significant genes (CSMD1, CDH13, PTPRD, MACROD2 and WWOX). Furthermore, the calcium signaling pathway, arrhythmogenic right ventricular cardiomyopathy and the glutamatergic synapse pathway were identified. The investigational findings demonstrate that our proposed model presents an accurate and efficient approach to identifying significant biomarkers in AD.
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Liu W, Cao L, Luo H, Wang Y. Research on Pathogenic Hippocampal Voxel Detection in Alzheimer's Disease Using Clustering Genetic Random Forest. Front Psychiatry 2022; 13:861258. [PMID: 35463515 PMCID: PMC9022175 DOI: 10.3389/fpsyt.2022.861258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 02/22/2022] [Indexed: 11/16/2022] Open
Abstract
Alzheimer's disease (AD) is an age-related neurological disease, which is closely associated with hippocampus, and subdividing the hippocampus into voxels can capture subtle signals that are easily missed by region of interest (ROI) methods. Therefore, studying interpretable associations between voxels can better understand the effect of voxel set on the hippocampus and AD. In this study, by analyzing the hippocampal voxel data, we propose a novel method based on clustering genetic random forest to identify the important voxels. Specifically, we divide the left and right hippocampus into voxels to constitute the initial feature set. Moreover, the random forest is constructed using the randomly selected samples and features. The genetic evolution is used to amplify the difference in decision trees and the clustering evolution is applied to generate offspring in genetic evolution. The important voxels are the features that reach the peak classification. The results demonstrate that our method has good classification and stability. Particularly, through biological analysis of the obtained voxel set, we find that they play an important role in AD by affecting the function of the hippocampus. These discoveries demonstrate the contribution of the voxel set to AD.
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Affiliation(s)
- Wenjie Liu
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
| | - Luolong Cao
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China
| | - Haoran Luo
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China
| | - Ying Wang
- School of Computer Science and Engineering, Changshu Institute of Technology, Suzhou, China
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