1
|
Liu X, Li W, Miao S, Liu F, Han K, Bezabih TT. HAMMF: Hierarchical attention-based multi-task and multi-modal fusion model for computer-aided diagnosis of Alzheimer's disease. Comput Biol Med 2024; 176:108564. [PMID: 38744010 DOI: 10.1016/j.compbiomed.2024.108564] [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: 01/09/2024] [Revised: 04/15/2024] [Accepted: 05/05/2024] [Indexed: 05/16/2024]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative condition, and early intervention can help slow its progression. However, integrating multi-dimensional information and deep convolutional networks increases the model parameters, affecting diagnosis accuracy and efficiency and hindering clinical diagnostic model deployment. Multi-modal neuroimaging can offer more precise diagnostic results, while multi-task modeling of classification and regression tasks can enhance the performance and stability of AD diagnosis. This study proposes a Hierarchical Attention-based Multi-task Multi-modal Fusion model (HAMMF) that leverages multi-modal neuroimaging data to concurrently learn AD classification tasks, cognitive score regression, and age regression tasks using attention-based techniques. Firstly, we preprocess MRI and PET image data to obtain two modal data, each containing distinct information. Next, we incorporate a novel Contextual Hierarchical Attention Module (CHAM) to aggregate multi-modal features. This module employs channel and spatial attention to extract fine-grained pathological features from unimodal image data across various dimensions. Using these attention mechanisms, the Transformer can effectively capture correlated features of multi-modal inputs. Lastly, we adopt multi-task learning in our model to investigate the influence of different variables on diagnosis, with a primary classification task and a secondary regression task for optimal multi-task prediction performance. Our experiments utilized MRI and PET images from 720 subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The results show that our proposed model achieves an overall accuracy of 93.15% for AD/NC recognition, and the visualization results demonstrate its strong pathological feature recognition performance.
Collapse
Affiliation(s)
- Xiao Liu
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Weimin Li
- School of Computer Engineering and Science, Shanghai University, Shanghai, China.
| | - Shang Miao
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Fangyu Liu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; University of Chinese Academy of Sciences, Beijing, China; BGI-Shenzhen, Shenzhen, China
| | - Ke Han
- Medical and Health Center, Liaocheng People's Hospital, LiaoCheng, China
| | - Tsigabu T Bezabih
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| |
Collapse
|
2
|
Contreras-Rodriguez O, Blasco G, Biarnés C, Puig J, Arnoriaga-Rodríguez M, Coll-Martinez C, Gich J, Ramió-Torrentà L, Motger-Albertí A, Pérez-Brocal V, Moya A, Radua J, Manuel Fernández-Real J. Unraveling the gut-brain connection: The association of microbiota-linked structural brain biomarkers with behavior and mental health. Psychiatry Clin Neurosci 2024; 78:339-346. [PMID: 38421082 DOI: 10.1111/pcn.13655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 01/12/2024] [Accepted: 01/26/2024] [Indexed: 03/02/2024]
Abstract
AIM The gut microbiota can influence human behavior. However, due to the massive multiple-testing problem, research into the relationship between microbiome ecosystems and the human brain faces drawbacks. This problem arises when attempting to correlate thousands of gut bacteria with thousands of brain voxels. METHODS We performed brain magnetic resonance imaging (MRI) scans on 133 participants and applied machine-learning algorithms (Ridge regressions) combined with permutation tests. Using this approach, we were able to correlate specific gut bacterial families with brain MRI signals, circumventing the difficulties of massive multiple testing while considering sex, age, and body mass index as confounding factors. RESULTS The relative abundance (RA) of the Selenomonadaceae, Clostridiaceae, and Veillonellaceae families in the gut was associated with altered cerebellar, visual, and frontal T2-mapping and diffusion tensor imaging measures. Conversely, decreased relative abundance of the Eubacteriaceae family was also linked to T2-mapping values in the cerebellum. Significantly, the brain regions associated with the gut microbiome were also correlated with depressive symptoms and attentional deficits. CONCLUSIONS Our analytical strategy offers a promising approach for identifying potential brain biomarkers influenced by gut microbiota. By gathering a deeper understanding of the microbiota-brain connection, we can gain insights into the underlying mechanisms and potentially develop targeted interventions to mitigate the detrimental effects of dysbiosis on brain function and mental health.
Collapse
Affiliation(s)
- Oren Contreras-Rodriguez
- Department of Radiology-Medical Imaging (IDI), Girona Biomedical Research Institute (IdIBGi), Dr. Josep Trueta University Hospital, Girona, Spain
- Department of Psychiatry and Legal Medicine, Faculty of Medicine, Universitat Autònoma de Barcelona, Bellaterra, Spain
- Health Institute Carlos III (ISCIII), Madrid, Spain
- CIBERSAM, Madrid, Spain
| | - Gerard Blasco
- Department of Radiology-Medical Imaging (IDI), Girona Biomedical Research Institute (IdIBGi), Dr. Josep Trueta University Hospital, Girona, Spain
| | - Carles Biarnés
- Department of Radiology-Medical Imaging (IDI), Girona Biomedical Research Institute (IdIBGi), Dr. Josep Trueta University Hospital, Girona, Spain
| | - Josep Puig
- Radiology Department CDI, Hospital Clinic of Barcelona, Barcelona, Spain
| | - Maria Arnoriaga-Rodríguez
- Department of Diabetes, Endocrinology, and Nutrition (UDEN), Girona Biomedical Research Institute (IdIBGi), Dr. Josep Trueta University Hospital, Girona, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CB06/03/0010), Girona, Spain
| | - Clàudia Coll-Martinez
- Neuroimmunology and Multiple Sclerosis Unit, Department of Neurology, Dr. Josep Trueta University Hospital, Girona, Spain
| | - Jordi Gich
- Neuroimmunology and Multiple Sclerosis Unit, Department of Neurology, Dr. Josep Trueta University Hospital, Girona, Spain
| | - Lluís Ramió-Torrentà
- Neuroimmunology and Multiple Sclerosis Unit, Department of Neurology, Dr. Josep Trueta University Hospital, Girona, Spain
| | - Anna Motger-Albertí
- Department of Diabetes, Endocrinology, and Nutrition (UDEN), Girona Biomedical Research Institute (IdIBGi), Dr. Josep Trueta University Hospital, Girona, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CB06/03/0010), Girona, Spain
- Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain
| | - Vicente Pérez-Brocal
- Department of Genomics and Health, Foundation for the Promotion of Health and Biomedical Research of Valencia Region (FISABIO-Public Health), València, Spain
- CIBEResp, Madrid, Spain
| | - Andrés Moya
- Department of Genomics and Health, Foundation for the Promotion of Health and Biomedical Research of Valencia Region (FISABIO-Public Health), València, Spain
- CIBEResp, Madrid, Spain
- Institute for Integrative Systems Biology (I2SysBio), The Spanish National Research Council (CSIC-UVEG), The University of Valencia, València, Spain
| | - Joaquim Radua
- Health Institute Carlos III (ISCIII), Madrid, Spain
- CIBERSAM, Madrid, Spain
- Imaging of Mood- and Anxiety-Related Disorders (IMARD) Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Department of Medicine, Faculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
| | - José Manuel Fernández-Real
- Health Institute Carlos III (ISCIII), Madrid, Spain
- Department of Diabetes, Endocrinology, and Nutrition (UDEN), Girona Biomedical Research Institute (IdIBGi), Dr. Josep Trueta University Hospital, Girona, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CB06/03/0010), Girona, Spain
- Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain
| |
Collapse
|
3
|
Jang I, Li B, Rashid B, Jacoby J, Huang SY, Dickerson BC, Salat DH. Brain structural indicators of β-amyloid neuropathology. Neurobiol Aging 2024; 136:157-170. [PMID: 38382159 PMCID: PMC10938906 DOI: 10.1016/j.neurobiolaging.2024.01.005] [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: 07/19/2023] [Revised: 01/10/2024] [Accepted: 01/13/2024] [Indexed: 02/23/2024]
Abstract
Recent efforts demonstrated the efficacy of identifying early-stage neuropathology of Alzheimer's disease (AD) through lumbar puncture cerebrospinal fluid assessment and positron emission tomography (PET) radiotracer imaging. These methods are effective yet are invasive, expensive, and not widely accessible. We extend and improve the multiscale structural mapping (MSSM) procedure to develop structural indicators of β-amyloid neuropathology in preclinical AD, by capturing both macrostructural and microstructural properties throughout the cerebral cortex using a structural MRI. We find that the MSSM signal is regionally altered in clear positive and negative cases of preclinical amyloid pathology (N = 220) when cortical thickness alone or hippocampal volume is not. It exhibits widespread effects of amyloid positivity across the posterior temporal, parietal, and medial prefrontal cortex, surprisingly consistent with the typical pattern of amyloid deposition. The MSSM signal is significantly correlated with amyloid PET in almost half of the cortex, much of which overlaps with regions where beta-amyloid accumulates, suggesting it could provide a regional brain 'map' that is not available from systemic markers such as plasma markers.
Collapse
Affiliation(s)
- Ikbeom Jang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA; Division of Computer Engineering, Hankuk University of Foreign Studies, Yongin, South Korea.
| | - Binyin Li
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Barnaly Rashid
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - John Jacoby
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Bradford C Dickerson
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - David H Salat
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA; Neuroimaging Research for Veterans Center, VA Boston Healthcare System, Boston, MA, USA
| |
Collapse
|
4
|
Agrawal S, Agrawal RK, Kumaran SS, Rana B, Srivastava AK. Integration of graph network with kernel SVM and logistic regression for identification of biomarkers in SCA12 and its diagnosis. Cereb Cortex 2024; 34:bhae132. [PMID: 38679476 DOI: 10.1093/cercor/bhae132] [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: 12/24/2023] [Revised: 03/02/2024] [Accepted: 03/15/2024] [Indexed: 05/01/2024] Open
Abstract
Spinocerebellar ataxia type 12 is a hereditary and neurodegenerative illness commonly found in India. However, there is no established noninvasive automatic diagnostic system for its diagnosis and identification of imaging biomarkers. This work proposes a novel four-phase machine learning-based diagnostic framework to find spinocerebellar ataxia type 12 disease-specific atrophic-brain regions and distinguish spinocerebellar ataxia type 12 from healthy using a real structural magnetic resonance imaging dataset. Firstly, each brain region is represented in terms of statistics of coefficients obtained using 3D-discrete wavelet transform. Secondly, a set of relevant regions are selected using a graph network-based method. Thirdly, a kernel support vector machine is used to capture nonlinear relationships among the voxels of a brain region. Finally, the linear relationship among the brain regions is captured to build a decision model to distinguish spinocerebellar ataxia type 12 from healthy by using the regularized logistic regression method. A classification accuracy of 95% and a harmonic mean of precision and recall, i.e. F1-score of 94.92%, is achieved. The proposed framework provides relevant regions responsible for the atrophy. The importance of each region is captured using Shapley Additive exPlanations values. We also performed a statistical analysis to find volumetric changes in spinocerebellar ataxia type 12 group compared to healthy. The promising result of the proposed framework shows that clinicians can use it for early and timely diagnosis of spinocerebellar ataxia type 12.
Collapse
Affiliation(s)
- Snigdha Agrawal
- School of Computer and Systems Sciences, Jawaharlal Nehru University, New Mehrauli Road, New Delhi-110067, India
| | - Ramesh Kumar Agrawal
- School of Computer and Systems Sciences, Jawaharlal Nehru University, New Mehrauli Road, New Delhi-110067, India
| | - S Senthil Kumaran
- Department of NMR, All India Institute of Medical Sciences, Ansari Nagar, New Delhi-110029, India
| | - Bharti Rana
- Department of Computer Science, University of Delhi, Delhi-110007, India
| | - Achal Kumar Srivastava
- Department of Neurology, All India Institute of Medical Sciences, Ansari Nagar, New Delhi-110029, India
| |
Collapse
|
5
|
Wang Z, Zhan Q, Tong B, Yang S, Hou B, Huang H, Saykin AJ, Thompson PM, Davatzikos C, Shen L. Distance-weighted Sinkhorn loss for Alzheimer's disease classification. iScience 2024; 27:109212. [PMID: 38433927 PMCID: PMC10906516 DOI: 10.1016/j.isci.2024.109212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 01/27/2024] [Accepted: 02/07/2024] [Indexed: 03/05/2024] Open
Abstract
Traditional loss functions such as cross-entropy loss often quantify the penalty for each mis-classified training sample without adequately considering its distance from the ground truth class distribution in the feature space. Intuitively, the larger this distance is, the higher the penalty should be. With this observation, we propose a penalty called distance-weighted Sinkhorn (DWS) loss. For each mis-classified training sample (with predicted label A and true label B), its contribution to the DWS loss positively correlates to the distance the training sample needs to travel to reach the ground truth distribution of all the A samples. We apply the DWS framework with a neural network to classify different stages of Alzheimer's disease. Our empirical results demonstrate that the DWS framework outperforms the traditional neural network loss functions and is comparable or better to traditional machine learning methods, highlighting its potential in biomedical informatics and data science.
Collapse
Affiliation(s)
- Zexuan Wang
- University of Pennsylvania, B301 Richards Building, 3700 Hamilton Walk, Philadelphia, PA 19104, USA
| | - Qipeng Zhan
- University of Pennsylvania, B301 Richards Building, 3700 Hamilton Walk, Philadelphia, PA 19104, USA
| | - Boning Tong
- University of Pennsylvania, B301 Richards Building, 3700 Hamilton Walk, Philadelphia, PA 19104, USA
| | - Shu Yang
- University of Pennsylvania, B301 Richards Building, 3700 Hamilton Walk, Philadelphia, PA 19104, USA
| | - Bojian Hou
- University of Pennsylvania, B301 Richards Building, 3700 Hamilton Walk, Philadelphia, PA 19104, USA
| | - Heng Huang
- University of Maryland, College Park, 8125 Paint Branch Drive, College Park, MD 20742, USA
| | - Andrew J. Saykin
- Indiana University, 355 West 16th Street, Indianapolis, IN 46202, USA
| | - Paul M. Thompson
- University of Southern California, 4676 Admiralty Way, Marina Del Rey, CA 90292, USA
| | - Christos Davatzikos
- University of Pennsylvania, B301 Richards Building, 3700 Hamilton Walk, Philadelphia, PA 19104, USA
| | - Li Shen
- University of Pennsylvania, B301 Richards Building, 3700 Hamilton Walk, Philadelphia, PA 19104, USA
| |
Collapse
|
6
|
Bhattarai P, Taha A, Soni B, Thakuri DS, Ritter E, Chand GB. Predicting cognitive dysfunction and regional hubs using Braak staging amyloid-beta biomarkers and machine learning. Brain Inform 2023; 10:33. [PMID: 38043122 PMCID: PMC10694120 DOI: 10.1186/s40708-023-00213-8] [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: 06/12/2023] [Accepted: 11/21/2023] [Indexed: 12/05/2023] Open
Abstract
Mild cognitive impairment (MCI) is a transitional stage between normal aging and early Alzheimer's disease (AD). The presence of extracellular amyloid-beta (Aβ) in Braak regions suggests a connection with cognitive dysfunction in MCI/AD. Investigating the multivariate predictive relationships between regional Aβ biomarkers and cognitive function can aid in the early detection and prevention of AD. We introduced machine learning approaches to estimate cognitive dysfunction from regional Aβ biomarkers and identify the Aβ-related dominant brain regions involved with cognitive impairment. We employed Aβ biomarkers and cognitive measurements from the same individuals to train support vector regression (SVR) and artificial neural network (ANN) models and predict cognitive performance solely based on Aβ biomarkers on the test set. To identify Aβ-related dominant brain regions involved in cognitive prediction, we built the local interpretable model-agnostic explanations (LIME) model. We found elevated Aβ in MCI compared to controls and a stronger correlation between Aβ and cognition, particularly in Braak stages III-IV and V-VII (p < 0.05) biomarkers. Both SVR and ANN, especially ANN, showed strong predictive relationships between regional Aβ biomarkers and cognitive impairment (p < 0.05). LIME integrated with ANN showed that the parahippocampal gyrus, inferior temporal gyrus, and hippocampus were the most decisive Braak regions for predicting cognitive decline. Consistent with previous findings, this new approach suggests relationships between Aβ biomarkers and cognitive impairment. The proposed analytical framework can estimate cognitive impairment from Braak staging Aβ biomarkers and delineate the dominant brain regions collectively involved in AD pathophysiology.
Collapse
Affiliation(s)
- Puskar Bhattarai
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Ahmed Taha
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Bhavin Soni
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Deepa S Thakuri
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- University of Missouri School of Medicine, Columbia, MO, USA
| | - Erin Ritter
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Biomedical Engineering, Washington University McKelvey School of Engineering, St. Louis, MO, USA
| | - Ganesh B Chand
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
- Imaging Core, Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA.
- Institute of Clinical and Translational Sciences, Washington University School of Medicine, St. Louis, MO, USA.
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA.
| |
Collapse
|
7
|
Lei B, Zhu Y, Liang E, Yang P, Chen S, Hu H, Xie H, Wei Z, Hao F, Song X, Wang T, Xiao X, Wang S, Han H. Federated Domain Adaptation via Transformer for Multi-Site Alzheimer's Disease Diagnosis. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:3651-3664. [PMID: 37527297 DOI: 10.1109/tmi.2023.3300725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/03/2023]
Abstract
In multi-site studies of Alzheimer's disease (AD), the difference of data in multi-site datasets leads to the degraded performance of models in the target sites. The traditional domain adaptation method requires sharing data from both source and target domains, which will lead to data privacy issue. To solve it, federated learning is adopted as it can allow models to be trained with multi-site data in a privacy-protected manner. In this paper, we propose a multi-site federated domain adaptation framework via Transformer (FedDAvT), which not only protects data privacy, but also eliminates data heterogeneity. The Transformer network is used as the backbone network to extract the correlation between the multi-template region of interest features, which can capture the brain abundant information. The self-attention maps in the source and target domains are aligned by applying mean squared error for subdomain adaptation. Finally, we evaluate our method on the multi-site databases based on three AD datasets. The experimental results show that the proposed FedDAvT is quite effective, achieving accuracy rates of 88.75%, 69.51%, and 69.88% on the AD vs. NC, MCI vs. NC, and AD vs. MCI two-way classification tasks, respectively.
Collapse
|
8
|
Zhang L, Wu J, Wang L, Wang L, Steffens DC, Qiu S, Potter GG, Liu M. Brain Anatomy-Guided MRI Analysis for Assessing Clinical Progression of Cognitive Impairment with Structural MRI. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2023; 14227:109-119. [PMID: 38390033 PMCID: PMC10883230 DOI: 10.1007/978-3-031-43993-3_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
Brain structural MRI has been widely used for assessing future progression of cognitive impairment (CI) based on learning-based methods. Previous studies generally suffer from the limited number of labeled training data, while there exists a huge amount of MRIs in large-scale public databases. Even without task-specific label information, brain anatomical structures provided by these MRIs can be used to boost learning performance intuitively. Unfortunately, existing research seldom takes advantage of such brain anatomy prior. To this end, this paper proposes a brain anatomy-guided representation (BAR) learning framework for assessing the clinical progression of cognitive impairment with T1-weighted MRIs. The BAR consists of a pretext model and a downstream model, with a shared brain anatomy-guided encoder for MRI feature extraction. The pretext model also contains a decoder for brain tissue segmentation, while the downstream model relies on a predictor for classification. We first train the pretext model through a brain tissue segmentation task on 9,544 auxiliary T1-weighted MRIs, yielding a generalizable encoder. The downstream model with the learned encoder is further fine-tuned on target MRIs for prediction tasks. We validate the proposed BAR on two CI-related studies with a total of 391 subjects with T1-weighted MRIs. Experimental results suggest that the BAR outperforms several state-of-the-art (SOTA) methods. The source code and pre-trained models are available at https://github.com/goodaycoder/BAR.
Collapse
Affiliation(s)
- Lintao Zhang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jinjian Wu
- The First School of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Lihong Wang
- Department of Psychiatry, University of Connecticut School of Medicine, University of Connecticut, Farmington, CT, USA
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - David C Steffens
- Department of Psychiatry, University of Connecticut School of Medicine, University of Connecticut, Farmington, CT, USA
| | - Shijun Qiu
- The First School of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Guy G Potter
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA
| | - Mingxia Liu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| |
Collapse
|
9
|
Morsy SE, Zayed N, Yassine IA. Hierarchical based classification method based on fusion of Gaussian map descriptors for Alzheimer diagnosis using T 1-weighted magnetic resonance imaging. Sci Rep 2023; 13:13734. [PMID: 37612307 PMCID: PMC10447428 DOI: 10.1038/s41598-023-40635-2] [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: 01/16/2023] [Accepted: 08/14/2023] [Indexed: 08/25/2023] Open
Abstract
Alzheimer's disease (AD) is considered one of the most spouting elderly diseases. In 2015, AD is reported the US's sixth cause of death. Substantially, non-invasive imaging is widely employed to provide biomarkers supporting AD screening, diagnosis, and progression. In this study, Gaussian descriptors-based features are proposed to be efficient new biomarkers using Magnetic Resonance Imaging (MRI) T1-weighted images to differentiate between Alzheimer's disease (AD), Mild Cognitive Impairment (MCI), and Normal controls (NC). Several Gaussian map-based features are extracted such as Gaussian shape operator, Gaussian curvature, and mean curvature. The aforementioned features are then introduced to the Support Vector Machine (SVM). They were, first, calculated separately for the Hippocampus and Amygdala. Followed by the fusion of the features. Moreover, Fusion of the regions before feature extraction was also employed. Alzheimer's disease Neuroimaging Initiative (ADNI) dataset, formed of 45, 55, and 65 cases for AD, MCI, and NC respectively, is appointed in this study. The shape operator feature outperformed the other features, with 74.6%, and 98.9% accuracy in the case of normal vs. abnormal, and AD vs. MCI classification respectively.
Collapse
Affiliation(s)
- Shereen E Morsy
- Systems and Biomedical Engineering, Cairo University, Cairo, Egypt
| | - Nourhan Zayed
- Computer and Systems Department, Electronics Research Institute, Cairo, Egypt.
- Mechanical Engineering Department, The British University in Egypt, Cairo, Egypt.
| | - Inas A Yassine
- Systems and Biomedical Engineering, Cairo University, Cairo, Egypt
| |
Collapse
|
10
|
Weinstein SM, Davatzikos C, Doshi J, Linn KA, Shinohara RT. Penalized decomposition using residuals (PeDecURe) for feature extraction in the presence of nuisance variables. Biostatistics 2023; 24:653-668. [PMID: 35950944 PMCID: PMC10345990 DOI: 10.1093/biostatistics/kxac031] [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: 01/25/2022] [Revised: 07/12/2022] [Accepted: 07/16/2022] [Indexed: 11/13/2022] Open
Abstract
Neuroimaging data are an increasingly important part of etiological studies of neurological and psychiatric disorders. However, mitigating the influence of nuisance variables, including confounders, remains a challenge in image analysis. In studies of Alzheimer's disease, for example, an imbalance in disease rates by age and sex may make it difficult to distinguish between structural patterns in the brain (as measured by neuroimaging scans) attributable to disease progression and those characteristic of typical human aging or sex differences. Concerningly, when not properly accounted for, nuisance variables pose threats to the generalizability and interpretability of findings from these studies. Motivated by this critical issue, in this work, we examine the impact of nuisance variables on feature extraction methods and propose Penalized Decomposition Using Residuals (PeDecURe), a new method for obtaining nuisance variable-adjusted features. PeDecURe estimates primary directions of variation which maximize covariance between partially residualized imaging features and a variable of interest (e.g., Alzheimer's diagnosis) while simultaneously mitigating the influence of nuisance variation through a penalty on the covariance between partially residualized imaging features and those variables. Using features derived using PeDecURe's first direction of variation, we train a highly accurate and generalizable predictive model, as evidenced by its robustness in testing samples with different underlying nuisance variable distributions. We compare PeDecURe to commonly used decomposition methods (principal component analysis (PCA) and partial least squares) as well as a confounder-adjusted variation of PCA. We find that features derived from PeDecURe offer greater accuracy and generalizability and lower correlations with nuisance variables compared with the other methods. While PeDecURe is primarily motivated by challenges that arise in the analysis of neuroimaging data, it is broadly applicable to data sets with highly correlated features, where novel methods to handle nuisance variables are warranted.
Collapse
Affiliation(s)
- Sarah M Weinstein
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Penn Statistics in Imaging and Visualization Center, 108/109B, Blockley Hall, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Christos Davatzikos
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, 3700 Hamilton Walk, Richards Building 7th Floor, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jimit Doshi
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, 3700 Hamilton Walk, Richards Building 7th Floor, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kristin A Linn
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Penn Statistics in Imaging and Visualization Center, 2nd Floor, Blockley Hall, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, USA and Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, 3700 Hamilton Walk, Richards Building 7th Floor, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Penn Statistics in Imaging and Visualization Center, 2nd Floor, Blockley Hall, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, USA and Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, 3700 Hamilton Walk, Richards Building 7th Floor, University of Pennsylvania, Philadelphia, PA 19104, USA
| | | |
Collapse
|
11
|
IDA-Net: Inheritable Deformable Attention Network of structural MRI for Alzheimer’s Disease Diagnosis. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2023]
|
12
|
Shenker JJ, Steele CJ, Zatorre RJ, Penhune VB. Using cortico-cerebellar structural patterns to classify early- and late-trained musicians. Hum Brain Mapp 2023. [PMID: 37326147 PMCID: PMC10365229 DOI: 10.1002/hbm.26395] [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: 11/29/2022] [Revised: 04/19/2023] [Accepted: 05/25/2023] [Indexed: 06/17/2023] Open
Abstract
A body of current evidence suggests that there is a sensitive period for musical training: people who begin training before the age of seven show better performance on tests of musical skill, and also show differences in brain structure-especially in motor cortical and cerebellar regions-compared with those who start later. We used support vector machine models-a subtype of supervised machine learning-to investigate distributed patterns of structural differences between early-trained (ET) and late-trained (LT) musicians and to better understand the age boundaries of the sensitive period for early musicianship. After selecting regions of interest from the cerebellum and cortical sensorimotor regions, we applied recursive feature elimination with cross-validation to produce a model which optimally and accurately classified ET and LT musicians. This model identified a combination of 17 regions, including 9 cerebellar and 8 sensorimotor regions, and maintained a high accuracy and sensitivity (true positives, i.e., ET musicians) without sacrificing specificity (true negatives, i.e., LT musicians). Critically, this model-which defined ET musicians as those who began their training before the age of 7-outperformed all other models in which age of start was earlier or later (between ages 5-10). Our model's ability to accurately classify ET and LT musicians provides additional evidence that musical training before age 7 affects cortico-cerebellar structure in adulthood, and is consistent with the hypothesis that connected brain regions interact during development to reciprocally influence brain and behavioral maturation.
Collapse
Affiliation(s)
- Joseph J Shenker
- Department of Psychology, Concordia University, Montreal, Quebec, Canada
- BRAMS: International Laboratory for Brain, Music, and Sound Research, Montreal, Quebec, Canada
| | - Christopher J Steele
- Department of Psychology, Concordia University, Montreal, Quebec, Canada
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Robert J Zatorre
- BRAMS: International Laboratory for Brain, Music, and Sound Research, Montreal, Quebec, Canada
- Cognitive Neuroscience Unit, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Virginia B Penhune
- Department of Psychology, Concordia University, Montreal, Quebec, Canada
- BRAMS: International Laboratory for Brain, Music, and Sound Research, Montreal, Quebec, Canada
| |
Collapse
|
13
|
Surianarayanan C, Lawrence JJ, Chelliah PR, Prakash E, Hewage C. Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders-A Scoping Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:3062. [PMID: 36991773 PMCID: PMC10053494 DOI: 10.3390/s23063062] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/09/2023] [Accepted: 03/09/2023] [Indexed: 06/19/2023]
Abstract
Artificial intelligence (AI) is a field of computer science that deals with the simulation of human intelligence using machines so that such machines gain problem-solving and decision-making capabilities similar to that of the human brain. Neuroscience is the scientific study of the struczture and cognitive functions of the brain. Neuroscience and AI are mutually interrelated. These two fields help each other in their advancements. The theory of neuroscience has brought many distinct improvisations into the AI field. The biological neural network has led to the realization of complex deep neural network architectures that are used to develop versatile applications, such as text processing, speech recognition, object detection, etc. Additionally, neuroscience helps to validate the existing AI-based models. Reinforcement learning in humans and animals has inspired computer scientists to develop algorithms for reinforcement learning in artificial systems, which enables those systems to learn complex strategies without explicit instruction. Such learning helps in building complex applications, like robot-based surgery, autonomous vehicles, gaming applications, etc. In turn, with its ability to intelligently analyze complex data and extract hidden patterns, AI fits as a perfect choice for analyzing neuroscience data that are very complex. Large-scale AI-based simulations help neuroscientists test their hypotheses. Through an interface with the brain, an AI-based system can extract the brain signals and commands that are generated according to the signals. These commands are fed into devices, such as a robotic arm, which helps in the movement of paralyzed muscles or other human parts. AI has several use cases in analyzing neuroimaging data and reducing the workload of radiologists. The study of neuroscience helps in the early detection and diagnosis of neurological disorders. In the same way, AI can effectively be applied to the prediction and detection of neurological disorders. Thus, in this paper, a scoping review has been carried out on the mutual relationship between AI and neuroscience, emphasizing the convergence between AI and neuroscience in order to detect and predict various neurological disorders.
Collapse
Affiliation(s)
| | | | | | - Edmond Prakash
- Research Center for Creative Arts, University for the Creative Arts (UCA), Farnham GU9 7DS, UK
| | - Chaminda Hewage
- Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UK
| |
Collapse
|
14
|
Hu J, Wang Y, Guo D, Qu Z, Sui C, He G, Wang S, Chen X, Wang C, Liu X. Diagnostic performance of magnetic resonance imaging-based machine learning in Alzheimer's disease detection: a meta-analysis. Neuroradiology 2023; 65:513-527. [PMID: 36477499 DOI: 10.1007/s00234-022-03098-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 11/28/2022] [Indexed: 12/12/2022]
Abstract
PURPOSE Advanced machine learning (ML) algorithms can assist rapid medical image recognition and realize automatic, efficient, noninvasive, and convenient diagnosis. We aim to further evaluate the diagnostic performance of ML to distinguish patients with probable Alzheimer's disease (AD) from normal older adults based on structural magnetic resonance imaging (MRI). METHODS The Medline, Embase, and Cochrane Library databases were searched for relevant literature published up until July 2021. We used the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool and Checklist for Artificial Intelligence in Medical Imaging (CLAIM) to evaluate all included studies' quality and potential bias. Random-effects models were used to calculate pooled sensitivity and specificity, and the Deeks' test was used to assess publication bias. RESULTS We included 24 models based on different brain features extracted by ML algorithms in 19 papers. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the summary receiver operating characteristic curve for ML in detecting AD were 0.85 (95%CI 0.81-0.89), 0.88 (95%CI 0.84-0.91), 7.15 (95%CI 5.40-9.47), 0.17 (95%CI 0.12-0.22), 43.34 (95%CI 26.89-69.84), and 0.93 (95%CI 0.91-0.95). CONCLUSION ML using structural MRI data performed well in diagnosing probable AD patients and normal elderly. However, more high-quality, large-scale prospective studies are needed to further enhance the reliability and generalizability of ML for clinical applications before it can be introduced into clinical practice.
Collapse
Affiliation(s)
- Jiayi Hu
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, 130021, Jilin, China
| | - Yashan Wang
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, 130021, Jilin, China
| | - Dingjie Guo
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, 130021, Jilin, China
| | - Zihan Qu
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, 130021, Jilin, China
| | - Chuanying Sui
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, 130021, Jilin, China
| | - Guangliang He
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, 130021, Jilin, China
| | - Song Wang
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, 130021, Jilin, China
| | - Xiaofei Chen
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, 130021, Jilin, China
| | - Chunpeng Wang
- School of Mathematics and Statistics, Northeast Normal University, Changchun, Jilin, China.
| | - Xin Liu
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, 130021, Jilin, China.
| |
Collapse
|
15
|
Moinian S, Vegh V, Reutens D. Towards automated in vivo parcellation of the human cerebral cortex using supervised classification of magnetic resonance fingerprinting residuals. Cereb Cortex 2023; 33:1550-1565. [PMID: 35483706 PMCID: PMC9977388 DOI: 10.1093/cercor/bhac155] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 03/29/2022] [Accepted: 03/30/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Accurate parcellation of the cerebral cortex in an individual is a guide to its underlying organization. The most promising in vivo quantitative magnetic resonance (MR)-based microstructural cortical mapping methods are yet to achieve a level of parcellation accuracy comparable to quantitative histology. METHODS We scanned 6 participants using a 3D echo-planar imaging MR fingerprinting (EPI-MRF) sequence on a 7T Siemens scanner. After projecting MRF signals to the individual-specific inflated model of the cortical surface, normalized autocorrelations of MRF residuals of vertices of 8 microstructurally distinct areas (BA1, BA2, BA4a, BA6, BA44, BA45, BA17, and BA18) from 3 cortical regions were used as feature vector inputs into linear support vector machine (SVM), radial basis function SVM (RBF-SVM), random forest, and k-nearest neighbors supervised classification algorithms. The algorithms' prediction performance was compared using: (i) features from each vertex or (ii) features from neighboring vertices. RESULTS The neighborhood-based RBF-SVM classifier achieved the highest prediction score of 0.85 for classification of MRF residuals in the central region from a held-out participant. CONCLUSIONS We developed an automated method of cortical parcellation using a combination of MR fingerprinting residual analysis and machine learning classification. Our findings provide the basis for employing unsupervised learning algorithms for whole-cortex structural parcellation in individuals.
Collapse
Affiliation(s)
- Shahrzad Moinian
- Centre for Advanced Imaging, The University of Queensland, Building 57, Research Road, St Lucia, QLD 4072, Australia.,ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Building 57, Research Road, St Lucia, QLD 4072, Australia
| | - Viktor Vegh
- Centre for Advanced Imaging, The University of Queensland, Building 57, Research Road, St Lucia, QLD 4072, Australia.,ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Building 57, Research Road, St Lucia, QLD 4072, Australia
| | - David Reutens
- Centre for Advanced Imaging, The University of Queensland, Building 57, Research Road, St Lucia, QLD 4072, Australia.,ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Building 57, Research Road, St Lucia, QLD 4072, Australia
| |
Collapse
|
16
|
Bansal D, Khanna K, Chhikara R, Dua RK, Malhotra R. BoF-SVM-based data intelligence model for detecting dementia. INTELLIGENT DECISION TECHNOLOGIES 2023. [DOI: 10.3233/idt-220256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
Abstract
Dementia is a brain condition that impairs the cognitive abilities of an individual. Mild cognitive impairment is a mediator phase of healthy and dementia controls. The motivation of this study is to predict dementia using magnetic resonance imaging data, which is significant for the diagnosis of normal control and dementia patients. The proposed model leverages effective methods like Discrete Wavelet Transform, Bag of Features, and Support Vector Machine. The four wavelets haar, Daubechies, symlets, and coiflets are used for image compression. The results of the proposed data intelligence model are promising in terms of accuracy which is 92.32% which is better than the recently proposed models. Also, the proposed data intelligence model is compared with the models which may use curvelet transform, and shearlet transform and with the methods which have gone without using DWT transforms. The comparisons have also been made with the models that have used other prevalent techniques like Principal Component Analysis, Fisher Discriminant Ratio, and Gray Level Co-occurrence Matrix. The outcomes support the usage of each technique in the suggested data intelligence paradigm.
Collapse
Affiliation(s)
- Deepika Bansal
- Department of Computer Science and Engineering, The NorthCap University, Gurugram, India
| | - Kavita Khanna
- Delhi Skill and Entrepreneurship University, New Delhi, India
| | - Rita Chhikara
- Department of Computer Science and Engineering, The NorthCap University, Gurugram, India
| | | | - Rajeev Malhotra
- Department of Neurosurgery, Max Super Speciality Hospital, New Delhi, India
| |
Collapse
|
17
|
Garg N, Choudhry MS, Bodade RM. A review on Alzheimer's disease classification from normal controls and mild cognitive impairment using structural MR images. J Neurosci Methods 2023; 384:109745. [PMID: 36395961 DOI: 10.1016/j.jneumeth.2022.109745] [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: 02/04/2022] [Revised: 10/04/2022] [Accepted: 11/11/2022] [Indexed: 11/16/2022]
Abstract
Alzheimer's disease (AD) is an irreversible neurodegenerative brain disorder that degrades the memory and cognitive ability in elderly people. The main reason for memory loss and reduction in cognitive ability is the structural changes in the brain that occur due to neuronal loss. These structural changes are most conspicuous in the hippocampus, cortex, and grey matter and can be assessed by using neuroimaging techniques viz. Positron Emission Tomography (PET), structural Magnetic Resonance Imaging (MRI) and functional MRI (fMRI), etc. Out of these neuroimaging techniques, structural MRI has evolved as the best technique as it indicates the best soft tissue contrast and high spatial resolution which is important for AD detection. Currently, the focus of researchers is on predicting the conversion of Mild Cognitive Impairment (MCI) into AD. MCI represents the transition state between expected cognitive changes with normal aging and Alzheimer's disease. Not every MCI patient progresses into Alzheimer's disease. MCI can develop into stable MCI (sMCI, patients are called non-converters) or into progressive MCI (pMCI, patients are diagnosed as MCI converters). This paper discusses the prognosis of MCI to AD conversion and presents a review of structural MRI-based studies for AD detection. AD detection framework includes feature extraction, feature selection, and classification process. This paper reviews the studies for AD detection based on different feature extraction methods and machine learning algorithms for classification. The performance of various feature extraction methods has been compared and it has been observed that the wavelet transform-based feature extraction method would give promising results for AD classification. The present study indicates that researchers are successful in classifying AD from Normal Controls (NrmC) but, it still requires a lot of work to be done for MCI/ NrmC and MCI/AD, which would help in detecting AD at its early stage.
Collapse
Affiliation(s)
- Neha Garg
- Delhi Technological University, Department of Electronics and Communication, Delhi 110042, India.
| | - Mahipal Singh Choudhry
- Delhi Technological University, Department of Electronics and Communication, Delhi 110042, India.
| | - Rajesh M Bodade
- Military College of Telecommunication Engineering (MCTE), Mhow, Indore 453441, Madhya Pradesh, India.
| |
Collapse
|
18
|
Xiao Y, Wang J, Huang K, Gao L, Yao S. Progressive structural and covariance connectivity abnormalities in patients with Alzheimer's disease. Front Aging Neurosci 2023; 14:1064667. [PMID: 36688148 PMCID: PMC9853893 DOI: 10.3389/fnagi.2022.1064667] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 12/13/2022] [Indexed: 01/07/2023] Open
Abstract
Background Alzheimer's disease (AD) is one of most prevalent neurodegenerative diseases worldwide and characterized by cognitive decline and brain structure atrophy. While studies have reported substantial grey matter atrophy related to progression of AD, it remains unclear about brain regions with progressive grey matter atrophy, covariance connectivity, and the associations with cognitive decline in AD patients. Objective This study aims to investigate the grey matter atrophy, structural covariance connectivity abnormalities, and the correlations between grey matter atrophy and cognitive decline during AD progression. Materials We analyzed neuroimaging data of healthy controls (HC, n = 45) and AD patients (n = 40) at baseline (AD-T1) and one-year follow-up (AD-T2) obtained from the Alzheimer's Disease Neuroimaging Initiative. We investigated AD-related progressive changes of grey matter volume, covariance connectivity, and the clinical relevance to further understand the pathological progression of AD. Results The results showed clear patterns of grey matter atrophy in inferior frontal gyrus, prefrontal cortex, lateral temporal gyrus, posterior cingulate cortex, insula, hippocampus, caudate, and thalamus in AD patients. There was significant atrophy in bilateral superior temporal gyrus (STG) and left caudate in AD patients over a one-year period, and the grey matter volume decrease in right STG and left caudate was correlated with cognitive decline. Additionally, we found reduced structural covariance connectivity between right STG and left caudate in AD patients. Using AD-related grey matter atrophy as features, there was high discrimination accuracy of AD patients from HC, and AD patients at different time points.
Collapse
Affiliation(s)
- Yaqiong Xiao
- Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China
| | - Jiaojian Wang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China
| | - Kaiyu Huang
- Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China
| | - Lei Gao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Shun Yao
- Department of Neurosurgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| |
Collapse
|
19
|
Lampe L, Huppertz HJ, Anderl-Straub S, Albrecht F, Ballarini T, Bisenius S, Mueller K, Niehaus S, Fassbender K, Fliessbach K, Jahn H, Kornhuber J, Lauer M, Prudlo J, Schneider A, Synofzik M, Kassubek J, Danek A, Villringer A, Diehl-Schmid J, Otto M, Schroeter ML. Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging. Neuroimage Clin 2023; 37:103320. [PMID: 36623349 PMCID: PMC9850041 DOI: 10.1016/j.nicl.2023.103320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 11/23/2022] [Accepted: 01/04/2023] [Indexed: 01/07/2023]
Abstract
INTRODUCTION Dementia syndromes can be difficult to diagnose. We aimed at building a classifier for multiple dementia syndromes using magnetic resonance imaging (MRI). METHODS Atlas-based volumetry was performed on T1-weighted MRI data of 426 patients and 51 controls from the multi-centric German Research Consortium of Frontotemporal Lobar Degeneration including patients with behavioral variant frontotemporal dementia, Alzheimer's disease, the three subtypes of primary progressive aphasia, i.e., semantic, logopenic and nonfluent-agrammatic variant, and the atypical parkinsonian syndromes progressive supranuclear palsy and corticobasal syndrome. Support vector machine classification was used to classify each patient group against controls (binary classification) and all seven diagnostic groups against each other in a multi-syndrome classifier (multiclass classification). RESULTS The binary classification models reached high prediction accuracies between 71 and 95% with a chance level of 50%. Feature importance reflected disease-specific atrophy patterns. The multi-syndrome model reached accuracies of more than three times higher than chance level but was far from 100%. Multi-syndrome model performance was not homogenous across dementia syndromes, with better performance in syndromes characterized by regionally specific atrophy patterns. Whereas diseases generally could be classified vs controls more correctly with increasing severity and duration, differentiation between diseases was optimal in disease-specific windows of severity and duration. DISCUSSION Results suggest that automated methods applied to MR imaging data can support physicians in diagnosis of dementia syndromes. It is particularly relevant for orphan diseases beside frequent syndromes such as Alzheimer's disease.
Collapse
Affiliation(s)
- Leonie Lampe
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Clinic for Cognitive Neurology, University Clinic Leipzig, Germany
| | | | | | - Franziska Albrecht
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Tommaso Ballarini
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Sandrine Bisenius
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Karsten Mueller
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Sebastian Niehaus
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | | | - Klaus Fliessbach
- Clinic for Neurodegenerative Diseases and Geriatric Psychiatry, University of Bonn, and German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Holger Jahn
- Clinic for Psychiatry and Psychotherapy, University Hospital Hamburg-Eppendorf, Germany
| | - Johannes Kornhuber
- Department of Psychiatry and Psychotherapy, Friedrich-Alexander-University of Erlangen-Nuremberg, Erlangen, Germany
| | - Martin Lauer
- Department of Psychiatry and Psychotherapy, University Wuerzburg, Germany
| | - Johannes Prudlo
- Department of Neurology, University of Rostock, and DZNE, Rostock, Germany
| | - Anja Schneider
- Clinic for Neurodegenerative Diseases and Geriatric Psychiatry, University of Bonn, and German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Department of Psychiatry and Psychotherapy, University of Goettingen, Germany
| | - Matthis Synofzik
- Department of Neurodegenerative Diseases, Centre for Neurology & Hertie-lnstitute for Clinical Brain Research, University of Tuebingen, Germany & DZNE, Tuebingen, Germany
| | - Jan Kassubek
- Department of Neurology, University of Ulm, Germany
| | - Adrian Danek
- Department of Neurology, Ludwig-Maximilians-Universität Munich, München, Germany
| | - Arno Villringer
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Clinic for Cognitive Neurology, University Clinic Leipzig, Germany
| | - Janine Diehl-Schmid
- Department of Psychiatry and Psychotherapy, Technical University of Munich, Germany
| | - Markus Otto
- Department of Neurology, University of Ulm, Germany; Department of Neurology, University of Halle, Germany
| | - Matthias L Schroeter
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Clinic for Cognitive Neurology, University Clinic Leipzig, Germany.
| | | |
Collapse
|
20
|
Chatterjee S, Byun YC. Voting Ensemble Approach for Enhancing Alzheimer's Disease Classification. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22197661. [PMID: 36236757 PMCID: PMC9571155 DOI: 10.3390/s22197661] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 10/01/2022] [Accepted: 10/05/2022] [Indexed: 05/28/2023]
Abstract
Alzheimer's disease is dementia that impairs one's thinking, behavior, and memory. It starts as a moderate condition affecting areas of the brain that make it challenging to retain recently learned information, causes mood swings, and causes confusion regarding occasions, times, and locations. The most prevalent type of dementia, called Alzheimer's disease (AD), causes memory-related problems in patients. A precise medical diagnosis that correctly classifies AD patients results in better treatment. Currently, the most commonly used classification techniques extract features from longitudinal MRI data before creating a single classifier that performs classification. However, it is difficult to train a reliable classifier to achieve acceptable classification performance due to limited sample size and noise in longitudinal MRI data. Instead of creating a single classifier, we propose an ensemble voting method that generates multiple individual classifier predictions and then combines them to develop a more accurate and reliable classifier. The ensemble voting classifier model performs better in the Open Access Series of Imaging Studies (OASIS) dataset for older adults than existing methods in important assessment criteria such as accuracy, sensitivity, specificity, and AUC. For the binary classification of with dementia and no dementia, an accuracy of 96.4% and an AUC of 97.2% is attained.
Collapse
Affiliation(s)
- Subhajit Chatterjee
- Department of Computer Engineering, Jeju National University, Jeju 63243, Korea
| | - Yung-Cheol Byun
- Department of Computer Engineering, Major of Electronic Engineering, Institute of Information Science & Technology, Jeju National University, Jeju 63243, Korea
| |
Collapse
|
21
|
Song YH, Yi JY, Noh Y, Jang H, Seo SW, Na DL, Seong JK. On the reliability of deep learning-based classification for Alzheimer's disease: Multi-cohorts, multi-vendors, multi-protocols, and head-to-head validation. Front Neurosci 2022; 16:851871. [PMID: 36161156 PMCID: PMC9490270 DOI: 10.3389/fnins.2022.851871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 07/27/2022] [Indexed: 01/18/2023] Open
Abstract
Structural changes in the brain due to Alzheimer's disease dementia (ADD) can be observed through brain T1-weighted magnetic resonance imaging (MRI) images. Many ADD diagnostic studies using brain MRI images have been conducted with machine-learning and deep-learning models. Although reliability is a key in clinical application and applicability of low-resolution MRI (LRMRI) is a key to broad clinical application, both are not sufficiently studied in the deep-learning area. In this study, we developed a 2-dimensional convolutional neural network-based classification model by adopting several methods, such as using instance normalization layer, Mixup, and sharpness aware minimization. To train the model, MRI images from 2,765 cognitively normal individuals and 1,192 patients with ADD from the Samsung medical center cohort were exploited. To assess the reliability of our classification model, we designed external validation in multiple scenarios: (1) multi-cohort validation using four additional cohort datasets including more than 30 different centers in multiple countries, (2) multi-vendor validation using three different MRI vendor subgroups, (3) LRMRI image validation, and finally, (4) head-to-head validation using ten pairs of MRI images from ten individual subjects scanned in two different centers. For multi-cohort validation, we used the MRI images from 739 subjects from the Alzheimer's Disease Neuroimaging Initiative cohort, 125 subjects from the Dementia Platform of Korea cohort, 234 subjects from the Premier cohort, and 139 subjects from the Gachon University Gil Medical Center. We further assessed classification performance across different vendors and protocols for each dataset. We achieved a mean AUC and classification accuracy of 0.9868 and 0.9482 in 5-fold cross-validation. In external validation, we obtained a comparable AUC of 0.9396 and classification accuracy of 0.8757 to other cross-validation studies in the ADNI cohorts. Furthermore, we observed the possibility of broad clinical application through LRMRI image validation by achieving a mean AUC and classification accuracy of 0.9404 and 0.8765 at cross-validation and AUC and classification accuracy of 0.8749 and 0.8281 at the ADNI cohort external validation.
Collapse
Affiliation(s)
- Yeong-Hun Song
- Department of Artificial Intelligence, Korea University, Seoul, South Korea
| | - Jun-Young Yi
- Department of Artificial Intelligence, Korea University, Seoul, South Korea
| | - Young Noh
- Department of Neurology, Gil Medical Center, Gachon University College of Medicine, Incheon, South Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea
| | - Duk L. Na
- Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea
| | - Joon-Kyung Seong
- Department of Artificial Intelligence, Korea University, Seoul, South Korea
- School of Biomedical Engineering, Korea University, Seoul, South Korea
- Interdisciplinary Program in Precision Public Health, College of Health Science, Korea University, Seoul, South Korea
| |
Collapse
|
22
|
Bai F, Puk KM, Liu J, Zhou H, Tao P, Zhou W, Wang S. Sparse group selection and analysis of function-related residue for protein-state recognition. J Comput Chem 2022; 43:1342-1354. [PMID: 35656889 PMCID: PMC9248267 DOI: 10.1002/jcc.26937] [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: 11/05/2021] [Revised: 03/23/2022] [Accepted: 05/08/2022] [Indexed: 11/08/2022]
Abstract
Machine learning methods have helped to advance wide range of scientific and technological field in recent years, including computational chemistry. As the chemical systems could become complex with high dimension, feature selection could be critical but challenging to develop reliable machine learning based prediction models, especially for proteins as bio-macromolecules. In this study, we applied sparse group lasso (SGL) method as a general feature selection method to develop classification model for an allosteric protein in different functional states. This results into a much improved model with comparable accuracy (Acc) and only 28 selected features comparing to 289 selected features from a previous study. The Acc achieves 91.50% with 1936 selected feature, which is far higher than that of baseline methods. In addition, grouping protein amino acids into secondary structures provides additional interpretability of the selected features. The selected features are verified as associated with key allosteric residues through comparison with both experimental and computational works about the model protein, and demonstrate the effectiveness and necessity of applying rigorous feature selection and evaluation methods on complex chemical systems.
Collapse
Affiliation(s)
- Fangyun Bai
- Department of Management Science and Engineering, Tongji University. Fangyun Bai and Kin Ming Puk contributed equally to this work
| | | | - Jin Liu
- Department of Pharmaceutical Sciences, University of North Texas System College of Pharmacy, University of North Texas Health Science Center
| | - Hongyu Zhou
- Department of Chemistry, Center for Scientific Computation, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University
| | - Peng Tao
- Department of Chemistry, Center for Scientific Computation, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University
| | - Wenyong Zhou
- Department of Management Science and Engineering, Tongji University
| | - Shouyi Wang
- Corresponding author: Shouyi Wang, Department of Industrial, Manufacturing and Systems Engineering, University of Texas at Arlington.
| |
Collapse
|
23
|
Zhang Y, Zhang H, Adeli E, Chen X, Liu M, Shen D. Multiview Feature Learning With Multiatlas-Based Functional Connectivity Networks for MCI Diagnosis. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:6822-6833. [PMID: 33306476 DOI: 10.1109/tcyb.2020.3016953] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Functional connectivity (FC) networks built from resting-state functional magnetic resonance imaging (rs-fMRI) has shown promising results for the diagnosis of Alzheimer's disease and its prodromal stage, that is, mild cognitive impairment (MCI). FC is usually estimated as a temporal correlation of regional mean rs-fMRI signals between any pair of brain regions, and these regions are traditionally parcellated with a particular brain atlas. Most existing studies have adopted a predefined brain atlas for all subjects. However, the constructed FC networks inevitably ignore the potentially important subject-specific information, particularly, the subject-specific brain parcellation. Similar to the drawback of the "single view" (versus the "multiview" learning) in medical image-based classification, FC networks constructed based on a single atlas may not be sufficient to reveal the underlying complicated differences between normal controls and disease-affected patients due to the potential bias from that particular atlas. In this study, we propose a multiview feature learning method with multiatlas-based FC networks to improve MCI diagnosis. Specifically, a three-step transformation is implemented to generate multiple individually specified atlases from the standard automated anatomical labeling template, from which a set of atlas exemplars is selected. Multiple FC networks are constructed based on these preselected atlas exemplars, providing multiple views of the FC network-based feature representations for each subject. We then devise a multitask learning algorithm for joint feature selection from the constructed multiple FC networks. The selected features are jointly fed into a support vector machine classifier for multiatlas-based MCI diagnosis. Extensive experimental comparisons are carried out between the proposed method and other competing approaches, including the traditional single-atlas-based method. The results indicate that our method significantly improves the MCI classification, demonstrating its promise in the brain connectome-based individualized diagnosis of brain diseases.
Collapse
|
24
|
Bai T, Du M, Zhang L, Ren L, Ruan L, Yang Y, Qian G, Meng Z, Zhao L, Deen MJ. A novel Alzheimer’s disease detection approach using GAN-based brain slice image enhancement. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.04.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
25
|
Chiu CC, Wu CM, Chien TN, Kao LJ, Qiu JT. Predicting the Mortality of ICU Patients by Topic Model with Machine-Learning Techniques. Healthcare (Basel) 2022; 10:healthcare10061087. [PMID: 35742138 PMCID: PMC9222812 DOI: 10.3390/healthcare10061087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/07/2022] [Accepted: 06/08/2022] [Indexed: 11/16/2022] Open
Abstract
Predicting clinical patients’ vital signs is a leading critical issue in intensive care units (ICUs) related studies. Early prediction of the mortality of ICU patients can reduce the overall mortality and cost of complication treatment. Some studies have predicted mortality based on electronic health record (EHR) data by using machine learning models. However, the semi-structured data (i.e., patients’ diagnosis data and inspection reports) is rarely used in these models. This study utilized data from the Medical Information Mart for Intensive Care III. We used a Latent Dirichlet Allocation (LDA) model to classify text in the semi-structured data of some particular topics and established and compared the classification and regression trees (CART), logistic regression (LR), multivariate adaptive regression splines (MARS), random forest (RF), and gradient boosting (GB). A total of 46,520 ICU Patients were included, with 11.5% mortality in the Medical Information Mart for Intensive Care III group. Our results revealed that the semi-structured data (diagnosis data and inspection reports) of ICU patients contain useful information that can assist clinical doctors in making critical clinical decisions. In addition, in our comparison of five machine learning models (CART, LR, MARS, RF, and GB), the GB model showed the best performance with the highest area under the receiver operating characteristic curve (AUROC) (0.9280), specificity (93.16%), and sensitivity (83.25%). The RF, LR, and MARS models showed better performance (AUROC are 0.9096, 0.8987, and 0.8935, respectively) than the CART (0.8511). The GB model showed better performance than other machine learning models (CART, LR, MARS, and RF) in predicting the mortality of patients in the intensive care unit. The analysis results could be used to develop a clinically useful decision support system.
Collapse
Affiliation(s)
- Chih-Chou Chiu
- Department of Business Management, National Taipei University of Technology, Taipei 106, Taiwan; (C.-C.C.); (C.-M.W.); (L.-J.K.)
| | - Chung-Min Wu
- Department of Business Management, National Taipei University of Technology, Taipei 106, Taiwan; (C.-C.C.); (C.-M.W.); (L.-J.K.)
| | - Te-Nien Chien
- College of Management, National Taipei University of Technology, Taipei 106, Taiwan
- Correspondence: ; Tel.: +886-2-2771-2171 (ext. 3403)
| | - Ling-Jing Kao
- Department of Business Management, National Taipei University of Technology, Taipei 106, Taiwan; (C.-C.C.); (C.-M.W.); (L.-J.K.)
| | - Jiantai Timothy Qiu
- Department of Obstetrics and Gynecology, Taipei Medical University Hospital, Taipei 110, Taiwan;
- College of Medicine, Taipei Medical University, Taipei 110, Taiwan
| |
Collapse
|
26
|
Yang B, Bao W, Chen B, Song D. Single_cell_GRN: gene regulatory network identification based on supervised learning method and Single-cell RNA-seq data. BioData Min 2022; 15:13. [PMID: 35690842 PMCID: PMC9188720 DOI: 10.1186/s13040-022-00297-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 05/22/2022] [Indexed: 11/30/2022] Open
Abstract
Single-cell RNA-seq overcomes the shortcomings of conventional transcriptome sequencing technology and could provide a powerful tool for distinguishing the transcriptome characteristics of various cell types in biological tissues, and comprehensively revealing the heterogeneity of gene expression between cells. Many Intelligent Computing methods have been presented to infer gene regulatory network (GRN) with single-cell RNA-seq data. In this paper, we investigate the performances of seven classifiers including support vector machine (SVM), random forest (RF), Naive Bayesian (NB), GBDT, logical regression (LR), decision tree (DT) and K-Nearest Neighbor (KNN) for solving the binary classification problems of GRN inference with single-cell RNA-seq data (Single_cell_GRN). In SVM, three different kernel functions (linear, polynomial and radial basis function) are utilized, respectively. Three real single-cell RNA-seq datasets from mouse and human are utilized. The experiment results prove that in most cases supervised learning methods (SVM, RF, NB, GBDT, LR, DT and KNN) perform better than unsupervised learning method (GENIE3) in terms of AUC. SVM, RF and KNN have the better performances than other four classifiers. In SVM, linear and polynomial kernels are more fit to model single-cell RNA-seq data.
Collapse
Affiliation(s)
- Bin Yang
- School of Information Science and Engineering, Zaozhuang University, Zaozhuang, 277160, China
| | - Wenzheng Bao
- School of Information and Electrical Engineering, Xuzhou University of Technology, Xuzhou, 221018, China.
| | - Baitong Chen
- Xuzhou First People's Hospital, Xuzhou, 221000, China
| | - Dan Song
- School of Information Science and Engineering, Zaozhuang University, Zaozhuang, 277160, China.
| |
Collapse
|
27
|
Liu Y, Tang K, Cai W, Chen A, Zhou G, Li L, Liu R. MPC-STANet: Alzheimer's Disease Recognition Method Based on Multiple Phantom Convolution and Spatial Transformation Attention Mechanism. Front Aging Neurosci 2022; 14:918462. [PMID: 35754963 PMCID: PMC9226438 DOI: 10.3389/fnagi.2022.918462] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 05/04/2022] [Indexed: 11/13/2022] Open
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disease with insidious and irreversible onset. The recognition of the disease stage of AD and the administration of effective interventional treatment are important to slow down and control the progression of the disease. However, due to the unbalanced distribution of the acquired data volume, the problem that the features change inconspicuously in different disease stages of AD, and the scattered and narrow areas of the feature areas (hippocampal region, medial temporal lobe, etc.), the effective recognition of AD remains a critical unmet need. Therefore, we first employ class-balancing operation using data expansion and Synthetic Minority Oversampling Technique (SMOTE) to avoid the AD MRI dataset being affected by classification imbalance in the training. Subsequently, a recognition network based on Multi-Phantom Convolution (MPC) and Space Conversion Attention Mechanism (MPC-STANet) with ResNet50 as the backbone network is proposed for the recognition of the disease stages of AD. In this study, we propose a Multi-Phantom Convolution in the way of convolution according to the channel direction and integrate it with the average pooling layer into two basic blocks of ResNet50: Conv Block and Identity Block to propose the Multi-Phantom Residual Block (MPRB) including Multi-Conv Block and Multi-Identity Block to better recognize the scattered and tiny disease features of Alzheimer's disease. Meanwhile, the weight coefficients are extracted from both vertical and horizontal directions using the Space Conversion Attention Mechanism (SCAM) to better recognize subtle structural changes in the AD MRI images. The experimental results show that our proposed method achieves an average recognition accuracy of 96.25%, F1 score of 95%, and mAP of 93%, and the number of parameters is only 1.69 M more than ResNet50.
Collapse
Affiliation(s)
- Yujian Liu
- College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, China
| | - Kun Tang
- College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, China
| | - Weiwei Cai
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China
- AiTech Artificial Intelligence Research Institute, Changsha, China
| | - Aibin Chen
- College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, China
| | - Guoxiong Zhou
- College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, China
| | - Liujun Li
- Department of Civil, Architectural and Environmental Engineering, Missouri University of Science and Technology, Rolla, MO, United States
| | - Runmin Liu
- Department of Civil, Architectural and Environmental Engineering, Missouri University of Science and Technology, Rolla, MO, United States
- College of Sports Engineering and Information Technology, Wuhan Sports University, Wuhan, China
| |
Collapse
|
28
|
Maximum mutual information for feature extraction from graph-structured data: Application to Alzheimer’s disease classification. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03528-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
|
29
|
Frizzell TO, Glashutter M, Liu CC, Zeng A, Pan D, Hajra SG, D’Arcy RC, Song X. Artificial intelligence in brain MRI analysis of Alzheimer's disease over the past 12 years: A systematic review. Ageing Res Rev 2022; 77:101614. [PMID: 35358720 DOI: 10.1016/j.arr.2022.101614] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 03/02/2022] [Accepted: 03/24/2022] [Indexed: 12/17/2022]
Abstract
INTRODUCTION Multiple structural brain changes in Alzheimer's disease (AD) and mild cognitive impairment (MCI) have been revealed on magnetic resonance imaging (MRI). There is a fast-growing effort in applying artificial intelligence (AI) to analyze these data. Here, we review and evaluate the AI studies in brain MRI analysis with synthesis. METHODS A systematic review of the literature, spanning the years from 2009 to 2020, was completed using the PubMed database. AI studies using MRI imaging to investigate normal aging, mild cognitive impairment, and AD-dementia were retrieved for review. Bias assessment was completed using the PROBAST criteria. RESULTS 97 relevant studies were included in the review. The studies were typically focused on the classification of AD, MCI, and normal aging (71% of the reported studies) and the prediction of MCI conversion to AD (25%). The best performance was achieved by using the deep learning-based convolution neural network algorithms (weighted average accuracy 89%), in contrast to 76-86% using Logistic Regression, Support Vector Machines, and other AI methods. DISCUSSION The synthesized evidence is paramount to developing sophisticated AI approaches to reliably capture and quantify multiple subtle MRI changes in the whole brain that exemplify the complexity and heterogeneity of AD and brain aging.
Collapse
|
30
|
Feng J, Zhang SW, Chen L. Extracting ROI-Based Contourlet Subband Energy Feature From the sMRI Image for Alzheimer's Disease Classification. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1627-1639. [PMID: 33434134 DOI: 10.1109/tcbb.2021.3051177] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Structural magnetic resonance imaging (sMRI)-based Alzheimer's disease (AD) classification and its prodromal stage-mild cognitive impairment (MCI) classification have attracted many attentions and been widely investigated in recent years. Owing to the high dimensionality, representation of the sMRI image becomes a difficult issue in AD classification. Furthermore, regions of interest (ROI) reflected in the sMRI image are not characterized properly by spatial analysis techniques, which has been a main cause of weakening the discriminating ability of the extracted spatial feature. In this study, we propose a ROI-based contourlet subband energy (ROICSE) feature to represent the sMRI image in the frequency domain for AD classification. Specifically, a preprocessed sMRI image is first segmented into 90 ROIs by a constructed brain mask. Instead of extracting features from the 90 ROIs in the spatial domain, the contourlet transform is performed on each of these ROIs to obtain their energy subbands. And then for an ROI, a subband energy (SE) feature vector is constructed to capture its energy distribution and contour information. Afterwards, SE feature vectors of the 90 ROIs are concatenated to form a ROICSE feature of the sMRI image. Finally, support vector machine (SVM) classifier is used to classify 880 subjects from ADNI and OASIS databases. Experimental results show that the ROICSE approach outperforms six other state-of-the-art methods, demonstrating that energy and contour information of the ROI are important to capture differences between the sMRI images of AD and HC subjects. Meanwhile, brain regions related to AD can also be found using the ROICSE feature, indicating that the ROICSE feature can be a promising assistant imaging marker for the AD diagnosis via the sMRI image. Code and Sample IDs of this paper can be downloaded at https://github.com/NWPU-903PR/ROICSE.git.
Collapse
|
31
|
Enhanced Fuzzy Elephant Herding Optimization-Based OTSU Segmentation and Deep Learning for Alzheimer’s Disease Diagnosis. MATHEMATICS 2022. [DOI: 10.3390/math10081259] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Several neurological illnesses and diseased sites have been studied, along with the anatomical framework of the brain, using structural MRI (sMRI). It is critical to diagnose Alzheimer’s disease (AD) patients in a timely manner to implement preventative treatments. The segmentation of brain anatomy and categorization of AD have received increased attention since they can deliver good findings spanning a vast range of information. The first research gap considered in this work is the real-time efficiency of OTSU segmentation, which is not high, despite its simplicity and good accuracy. A second issue is that feature extraction could be automated by implementing deep learning techniques. To improve picture segmentation’s real-timeliness, enhanced fuzzy elephant herding optimization (EFEHO) was used for OTSU segmentation, and named EFEHO-OTSU. The main contribution of this work is twofold. One is utilizing EFEHO in the recommended technique to seek the optimal segmentation threshold for the OTSU method. Second, dual attention multi-instance deep learning network (DA-MIDL) is recommended for the timely diagnosis of AD and its prodromal phase, mild cognitive impairment (MCI). Tests show that this technique converges faster and takes less time than the classic OTSU approach without reducing segmentation performance. This study develops a valuable tool for quick picture segmentation with good real-time efficiency. Compared to numerous conventional techniques, the suggested study attains improved categorization performance regarding accuracy and transferability.
Collapse
|
32
|
Jang I, Li B, Riphagen JM, Dickerson BC, Salat DH. Multiscale structural mapping of Alzheimer's disease neurodegeneration. Neuroimage Clin 2022; 33:102948. [PMID: 35121307 PMCID: PMC8814667 DOI: 10.1016/j.nicl.2022.102948] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 12/09/2021] [Accepted: 01/19/2022] [Indexed: 01/25/2023]
Abstract
A multiscale structural mapping (MSSM) procedure is proposed for the quantification of neurodegeneration in Alzheimer's disease using a single structural brain image. The MSSM procedure captures both macrostructural properties and indirect index of tissue microstructure throughout the cerebral cortex. The MSSM procedure provides enhanced ability for the detection of degeneration in Alzheimer’s disease and mild cognitive impairment compared to traditional measures such as cortical thickness and hippocampal volume and therefore may provide a sensitive measure of Alzheimer’s disease neurodegeneration.
The recently described biological framework of Alzheimer’s disease (AD) emphasizes three types of pathology to characterize this disorder, referred to as the ‘amyloid/tau/neurodegeneration’ (A-T-N) status. The ‘neurodegenerative’ component is typically defined by atrophy measures derived from structural magnetic resonance imaging (MRI) such as hippocampal volume. Neurodegeneration measures from imaging are associated with disease symptoms and prognosis. Thus, sensitive image-based quantification of neurodegeneration in AD has an important role in a range of clinical and research operations. Although hippocampal volume is a sensitive metric of neurodegeneration, this measure is impacted by several clinical conditions other than AD and therefore lacks specificity. In contrast, selective regional cortical atrophy, known as the ‘cortical signature of AD’ provides greater specificity to AD pathology. Although atrophy is apparent even in the preclinical stages of the disease, it is possible that increased sensitivity to degeneration could be achieved by including tissue microstructural properties in the neurodegeneration measure. However, to facilitate clinical feasibility, such information should be obtainable from a single, short, noninvasive imaging protocol. We propose a multiscale MRI procedure that advances prior work through the quantification of features at both macrostructural (morphometry) and microstructural (tissue properties obtained from multiple layers of cortex and subcortical white matter) scales from a single structural brain image (referred to as ‘multi-scale structural mapping’; MSSM). Vertex-wise partial least squares (PLS) regression was used to compress these multi-scale structural features. When contrasting patients with AD to cognitively intact matched older adults, the MSSM procedure showed substantially broader regional group differences including areas that were not statistically significant when using cortical thickness alone. Further, with multiple machine learning algorithms and ensemble procedures, we found that MSSM provides accurate detection of individuals with AD dementia (AUROC = 0.962, AUPRC = 0.976) and individuals with mild cognitive impairment (MCI) that subsequently progressed to AD dementia (AUROC = 0.908, AUPRC = 0.910). The findings demonstrate the critical advancement of neurodegeneration quantification provided through multiscale mapping. Future work will determine the sensitivity of this technique for accurately detecting individuals with earlier impairment and biomarker positivity in the absence of impairment.
Collapse
Affiliation(s)
- Ikbeom Jang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Binyin Li
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Joost M Riphagen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Faculty of Health, Medicine and Life Sciences, School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, Maastricht, the Netherlands
| | - Bradford C Dickerson
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - David H Salat
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Neuroimaging Research for Veterans Center, VA Boston Healthcare System, Boston, MA, USA
| |
Collapse
|
33
|
Agostinho D, Caramelo F, Moreira AP, Santana I, Abrunhosa A, Castelo-Branco M. Combined Structural MR and Diffusion Tensor Imaging Classify the Presence of Alzheimer's Disease With the Same Performance as MR Combined With Amyloid Positron Emission Tomography: A Data Integration Approach. Front Neurosci 2022; 15:638175. [PMID: 35069090 PMCID: PMC8766722 DOI: 10.3389/fnins.2021.638175] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Accepted: 12/13/2021] [Indexed: 11/13/2022] Open
Abstract
Background: In recent years, classification frameworks using imaging data have shown that multimodal classification methods perform favorably over the use of a single imaging modality for the diagnosis of Alzheimer's Disease. The currently used clinical approach often emphasizes the use of qualitative MRI and/or PET data for clinical diagnosis. Based on the hypothesis that classification of isolated imaging modalities is not predictive of their respective value in combined approaches, we investigate whether the combination of T1 Weighted MRI and diffusion tensor imaging (DTI) can yield an equivalent performance as the combination of quantitative structural MRI (sMRI) with amyloid-PET. Methods: We parcellated the brain into regions of interest (ROI) following different anatomical labeling atlases. For each region of interest different metrics were extracted from the different imaging modalities (sMRI, PiB-PET, and DTI) to be used as features. Thereafter, the feature sets were reduced using an embedded-based feature selection method. The final reduced sets were then used as input in support vector machine (SVM) classifiers. Three different base classifiers were created, one for each imaging modality, and validated using internal (n = 41) and external data from the ADNI initiative (n = 330 for sMRI, n = 148 for DTI and n = 55 for PiB-PET) sources. Finally, the classifiers were ensembled using a weighted method in order to evaluate the performance of different combinations. Results: For the base classifiers the following performance levels were found: sMRI-based classifier (accuracy, 92%; specificity, 97% and sensitivity, 87%), PiB-PET (accuracy, 91%; specificity, 89%; and sensitivity, 92%) and the lowest performance was attained with DTI (accuracy, 80%; specificity, 76%; and sensitivity, 82%). From the multimodal approaches, when integrating two modalities, the following results were observed: sMRI+PiB-PET (accuracy, 98%; specificity, 98%; and sensitivity, 99%), sMRI+DTI (accuracy, 97%; specificity, 99%; and sensitivity, 94%) and PiB-PET+DTI (accuracy, 91%; specificity, 90%; and sensitivity, 93%). Finally, the combination of all imaging modalities yielded an accuracy of 98%, specificity of 97% and sensitivity of 99%. Conclusion: Although DTI in isolation shows relatively poor performance, when combined with structural MR, it showed a surprising classification performance which was comparable to MR combined with amyloid PET. These results are consistent with the notion that white matter changes are also important in Alzheimer's Disease.
Collapse
Affiliation(s)
- Daniel Agostinho
- Faculty of Medicine, Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - Francisco Caramelo
- Faculty of Medicine, Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - Ana Paula Moreira
- Faculty of Medicine, Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - Isabel Santana
- Department of Neurology, Faculty of Medicine, Coimbra University Hospital (CHUC), University of Coimbra, Coimbra, Portugal
| | - Antero Abrunhosa
- Faculty of Medicine, Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - Miguel Castelo-Branco
- Faculty of Medicine, Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| |
Collapse
|
34
|
Mohamed I. Prediction of Chronic Obstructive Pulmonary Disease Stages Using Machine Learning Algorithms. INTERNATIONAL JOURNAL OF DECISION SUPPORT SYSTEM TECHNOLOGY 2022. [DOI: 10.4018/ijdsst.286693] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Identifying chronic obstructive pulmonary disease (COPD) severity stages is of great importance to control the related mortality rates and reduce the associated costs. This study aims to build prediction models for COPD stages and, to compare the relative performance of five machine learning algorithms to determine the optimal prediction algorithm. This research is based on data collected from a private hospital in Egypt for the two calendar years 2018 and 2019. Five machine learning algorithms were used for the comparison. The F1 score, specificity, sensitivity, accuracy, positive predictive value and negative predictive value were the performance measures used for algorithms comparison. Analysis included 211 patients’ records. Our results show that the best performing algorithm in most of the disease stages is the PNN with the optimal prediction accuracy and hence it can be considered as a powerful prediction tool used by decision makers in predicting severity stages of COPD.
Collapse
|
35
|
Ashraf GM, Chatzichronis S, Alexiou A, Kyriakopoulos N, Alghamdi BSA, Tayeb HO, Alghamdi JS, Khan W, Jalal MB, Atta HM. BrainFD: Measuring the Intracranial Brain Volume With Fractal Dimension. Front Aging Neurosci 2021; 13:765185. [PMID: 34899274 PMCID: PMC8662626 DOI: 10.3389/fnagi.2021.765185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 10/22/2021] [Indexed: 11/16/2022] Open
Abstract
A few methods and tools are available for the quantitative measurement of the brain volume targeting mainly brain volume loss. However, several factors, such as the clinical conditions, the time of the day, the type of MRI machine, the brain volume artifacts, the pseudoatrophy, and the variations among the protocols, produce extreme variations leading to misdiagnosis of brain atrophy. While brain white matter loss is a characteristic lesion during neurodegeneration, the main objective of this study was to create a computational tool for high precision measuring structural brain changes using the fractal dimension (FD) definition. The validation of the BrainFD software is based on T1-weighted MRI images from the Open Access Series of Imaging Studies (OASIS)-3 brain database, where each participant has multiple MRI scan sessions. The software is based on the Python and JAVA programming languages with the main functionality of the FD calculation using the box-counting algorithm, for different subjects on the same brain regions, with high accuracy and resolution, offering the ability to compare brain data regions from different subjects and on multiple sessions, creating different imaging profiles based on the Clinical Dementia Rating (CDR) scores of the participants. Two experiments were executed. The first was a cross-sectional study where the data were separated into two CDR classes. In the second experiment, a model on multiple heterogeneous data was trained, and the FD calculation for each participant of the OASIS-3 database through multiple sessions was evaluated. The results suggest that the FD variation efficiently describes the structural complexity of the brain and the related cognitive decline. Additionally, the FD efficiently discriminates the two classes achieving 100% accuracy. It is shown that this classification outperforms the currently existing methods in terms of accuracy and the size of the dataset. Therefore, the FD calculation for identifying intracranial brain volume loss could be applied as a potential low-cost personalized imaging biomarker. Furthermore, the possibilities measuring different brain areas and subregions could give robust evidence of the slightest variations to imaging data obtained from repetitive measurements to Physicians and Radiologists.
Collapse
Affiliation(s)
- Ghulam Md Ashraf
- Pre-Clinical Research Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia.,Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Stylianos Chatzichronis
- Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece.,Department of Science and Engineering, Novel Global Community Educational Foundation, Hebersham, NSW, Australia
| | - Athanasios Alexiou
- Department of Science and Engineering, Novel Global Community Educational Foundation, Hebersham, NSW, Australia.,AFNP Med Austria, Vienna, Austria
| | | | - Badrah Saeed Ali Alghamdi
- Pre-Clinical Research Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia.,Department of Physiology, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia.,The Neuroscience Research Unit, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Haythum Osama Tayeb
- The Neuroscience Research Unit, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia.,Division of Neurology, Department of Internal Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Jamaan Salem Alghamdi
- Department of Diagnostic Radiology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Waseem Khan
- Department of Radiology, King Abdulaziz University Hospital, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Manal Ben Jalal
- Department of Radiology, King Abdulaziz University Hospital, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Hazem Mahmoud Atta
- Department of Clinical Biochemistry, Faculty of Medicine, King Abdulaziz University, Rabigh, Saudi Arabia
| |
Collapse
|
36
|
Zhang F, Petersen M, Johnson L, Hall J, O’Bryant SE. Accelerating Hyperparameter Tuning in Machine Learning for Alzheimer's Disease With High Performance Computing. Front Artif Intell 2021; 4:798962. [PMID: 34957393 PMCID: PMC8692864 DOI: 10.3389/frai.2021.798962] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 11/15/2021] [Indexed: 11/27/2022] Open
Abstract
Driven by massive datasets that comprise biomarkers from both blood and magnetic resonance imaging (MRI), the need for advanced learning algorithms and accelerator architectures, such as GPUs and FPGAs has increased. Machine learning (ML) methods have delivered remarkable prediction for the early diagnosis of Alzheimer's disease (AD). Although ML has improved accuracy of AD prediction, the requirement for the complexity of algorithms in ML increases, for example, hyperparameters tuning, which in turn, increases its computational complexity. Thus, accelerating high performance ML for AD is an important research challenge facing these fields. This work reports a multicore high performance support vector machine (SVM) hyperparameter tuning workflow with 100 times repeated 5-fold cross-validation for speeding up ML for AD. For demonstration and evaluation purposes, the high performance hyperparameter tuning model was applied to public MRI data for AD and included demographic factors such as age, sex and education. Results showed that computational efficiency increased by 96%, which helped to shed light on future diagnostic AD biomarker applications. The high performance hyperparameter tuning model can also be applied to other ML algorithms such as random forest, logistic regression, xgboost, etc.
Collapse
Affiliation(s)
- Fan Zhang
- Institute for Translational Research, University of North Texas Health Science Center, Fort Worth, TX, United States
- Department of Family Medicine, University of North Texas Health Science Center, Fort Worth, TX, United States
| | - Melissa Petersen
- Institute for Translational Research, University of North Texas Health Science Center, Fort Worth, TX, United States
- Department of Family Medicine, University of North Texas Health Science Center, Fort Worth, TX, United States
| | - Leigh Johnson
- Institute for Translational Research, University of North Texas Health Science Center, Fort Worth, TX, United States
- Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, Fort Worth, TX, United States
| | - James Hall
- Institute for Translational Research, University of North Texas Health Science Center, Fort Worth, TX, United States
- Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, Fort Worth, TX, United States
| | - Sid E. O’Bryant
- Institute for Translational Research, University of North Texas Health Science Center, Fort Worth, TX, United States
- Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, Fort Worth, TX, United States
| |
Collapse
|
37
|
Fujibayashi D, Sakaguchi H, Ardakani I, Okuno A. Nonlinear registration as an effective preprocessing technique for Deep learning based classification of disease. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3245-3250. [PMID: 34891933 DOI: 10.1109/embc46164.2021.9631044] [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
A number of machine learning (ML), and particularly in recent years, deep learning (DL) approaches have been proposed for automatic classification of Alzheimer's disease (AD) using brain structural magnetic resonance imaging (MRI) data. However, the data available are limited in the case of this specific disease. Training a DL model with a large number of feature parameters on a small dataset of MRI scans will likely lead to overfitting. Overfitting reduces the generality and efficiency of the model. In this study, we show that a traditional nonlinear transformation from native space to template space, as a preprocessing stage, is effective in reducing overfitting through the reduction of spatial variations in the input data. To evaluate this effectiveness, we compare two different pre-processing approaches for DL-based AD classification task: (1) affine registration and (2) nonlinear diffeomorphic anatomical registration using exponentiated Lie algebra (DARTEL). The results show that the accuracy of the nonlinear registration based approach is much higher than the affine registration based approach. Furthermore, from the classification results obtained with noisy images, DARTEL is less susceptible to noise than affine registration. In summary, our experimental results suggest that nonlinear transformation is a preferable preprocessing step for training DL-based AD classification models on limited size datasets.
Collapse
|
38
|
Uddin S, Imam T, Hossain ME, Gide E, Sianaki OA, Moni MA, Mohammed AA, Vandana V. Intelligent type 2 diabetes risk prediction from administrative claim data. Inform Health Soc Care 2021; 47:243-257. [PMID: 34672859 DOI: 10.1080/17538157.2021.1988957] [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: 10/20/2022]
Abstract
Type 2 diabetes is a chronic, costly disease and is a serious global population health problem. Yet, the disease is well manageable and preventable if there is an early warning. This study aims to apply supervised machine learning algorithms for developing predictive models for type 2 diabetes using administrative claim data. Following guidelines from the Elixhauser Comorbidity Index, 31 variables were considered. Five supervised machine learning algorithms were used for developing type 2 diabetes prediction models. Principal component analysis was applied to rank variables' importance in predictive models. Random forest (RF) showed the highest accuracy (85.06%) among the algorithms, closely followed by the k-nearest neighbor (84.48%). The analysis further revealed RF as a high performing algorithm irrespective of data imbalance. As revealed by the principal component analysis, patient age is the most important predictor for type 2 diabetes, followed by a comorbid condition (i.e., solid tumor without metastasis). This study's finding of RF as the best performing classifier is consistent with the promise of tree-based algorithms for public data in other works. Thus, the outcome can guide in designing automated surveillance of patients at risk of forming diabetes from administrative claim information and will be useful to health regulators and insurers.
Collapse
Affiliation(s)
- Shahadat Uddin
- Complex Systems Research Group, Faculty of Engineering, The University of Sydney, Darlington, NSW, Australia
| | - Tasadduq Imam
- School of Business and Law, CQUniversity, Melbourne, VIC, Australia
| | - Md Ekramul Hossain
- Complex Systems Research Group, Faculty of Engineering, The University of Sydney, Darlington, NSW, Australia
| | - Ergun Gide
- School of Engineering and Technology, CQUniversity, Sydney, NSW, Australia
| | - Omid Ameri Sianaki
- College of Engineering and Science, Victoria University, Sydney, NSW, Australia.,Victoria University Business School, Melbourne, Victoria, Australia
| | - Mohammad Ali Moni
- School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD, Australia
| | | | - Vandana Vandana
- College of Engineering and Science, Victoria University, Sydney, NSW, Australia
| |
Collapse
|
39
|
Wong R, Luo Y, Mok VCT, Shi L. Advances in computerized MRI‐based biomarkers in Alzheimer’s disease. BRAIN SCIENCE ADVANCES 2021. [DOI: 10.26599/bsa.2021.9050005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
The use of neuroimaging examinations is crucial in Alzheimer’s disease (AD), in both research and clinical settings. Over the years, magnetic resonance imaging (MRI)–based computer‐aided diagnosis has been shown to be helpful for early screening and predicting cognitive decline. Meanwhile, an increasing number of studies have adopted machine learning for the classification of AD, with promising results. In this review article, we focus on computerized MRI‐based biomarkers of AD by reviewing representative studies that used computerized techniques to identify AD patients and predict cognitive progression. We categorized these studies based on the following applications: (1) identifying AD from normal control; (2) identifying AD from other dementia types, including vascular dementia, dementia with Lewy bodies, and frontotemporal dementia; and (3) predicting conversion from NC to mild cognitive impairment (MCI) and from MCI to AD. This systematic review could act as a state‐of‐the‐art overview of this emerging field as well as a basis for designing future studies.
Collapse
Affiliation(s)
- Raymond Wong
- BrainNow Research Institute, Shenzhen 518081, Guangdong, China
| | - Yishan Luo
- BrainNow Research Institute, Shenzhen 518081, Guangdong, China
| | - Vincent Chung-tong Mok
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong 999077, China
| | - Lin Shi
- BrainNow Research Institute, Shenzhen 518081, Guangdong, China
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong 999077, China
| |
Collapse
|
40
|
Chen T, Xiao F, Yu Z, Yuan M, Xu H, Lu L. Detection and Grading of Gliomas Using a Novel Two-Phase Machine Learning Method Based on MRI Images. Front Neurosci 2021; 15:650629. [PMID: 34054411 PMCID: PMC8160229 DOI: 10.3389/fnins.2021.650629] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 04/16/2021] [Indexed: 12/21/2022] Open
Abstract
The early detection and grading of gliomas is important for treatment decision and assessment of prognosis. Over the last decade numerous automated computer analysis tools have been proposed, which can potentially lead to more reliable and reproducible brain tumor diagnostic procedures. In this paper, we used the gradient-based features extracted from structural magnetic resonance imaging (sMRI) images to depict the subtle changes within brains of patients with gliomas. Based on the gradient features, we proposed a novel two-phase classification framework for detection and grading of gliomas. In the first phase, the probability of each local feature being related to different types (e.g., diseased or healthy for detection, benign or malignant for grading) was calculated. Then the high-level feature representing the whole MRI image was generated by concatenating the membership probability of each local feature. In the second phase, the supervised classification algorithm was used to train a classifier based on the high-level features and patient labels of the training subjects. We applied this framework on the brain imaging data collected from Zhongnan Hospital of Wuhan University for glioma detection, and the public TCIA datasets including glioblastomas (WHO IV) and low-grade gliomas (WHO II and III) data for glioma grading. The experimental results showed that the gradient-based classification framework could be a promising tool for automatic diagnosis of brain tumors.
Collapse
Affiliation(s)
- Tao Chen
- School of Information Technology, Shangqiu Normal University, Shangqiu, China
| | - Feng Xiao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Zunpeng Yu
- School of Information Management, Wuhan University, Wuhan, China
| | - Mengxue Yuan
- School of Information Management, Wuhan University, Wuhan, China
| | - Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Long Lu
- School of Information Management, Wuhan University, Wuhan, China.,Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| |
Collapse
|
41
|
Morin A, Samper-Gonzalez J, Bertrand A, Ströer S, Dormont D, Mendes A, Coupé P, Ahdidan J, Lévy M, Samri D, Hampel H, Dubois B, Teichmann M, Epelbaum S, Colliot O. Accuracy of MRI Classification Algorithms in a Tertiary Memory Center Clinical Routine Cohort. J Alzheimers Dis 2021; 74:1157-1166. [PMID: 32144978 DOI: 10.3233/jad-190594] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
BACKGROUND Automated volumetry software (AVS) has recently become widely available to neuroradiologists. MRI volumetry with AVS may support the diagnosis of dementias by identifying regional atrophy. Moreover, automatic classifiers using machine learning techniques have recently emerged as promising approaches to assist diagnosis. However, the performance of both AVS and automatic classifiers have been evaluated mostly in the artificial setting of research datasets. OBJECTIVE Our aim was to evaluate the performance of two AVS and an automatic classifier in the clinical routine condition of a memory clinic. METHODS We studied 239 patients with cognitive troubles from a single memory center cohort. Using clinical routine T1-weighted MRI, we evaluated the classification performance of: 1) univariate volumetry using two AVS (volBrain and Neuroreader™); 2) Support Vector Machine (SVM) automatic classifier, using either the AVS volumes (SVM-AVS), or whole gray matter (SVM-WGM); 3) reading by two neuroradiologists. The performance measure was the balanced diagnostic accuracy. The reference standard was consensus diagnosis by three neurologists using clinical, biological (cerebrospinal fluid) and imaging data and following international criteria. RESULTS Univariate AVS volumetry provided only moderate accuracies (46% to 71% with hippocampal volume). The accuracy improved when using SVM-AVS classifier (52% to 85%), becoming close to that of SVM-WGM (52 to 90%). Visual classification by neuroradiologists ranged between SVM-AVS and SVM-WGM. CONCLUSION In the routine practice of a memory clinic, the use of volumetric measures provided by AVS yields only moderate accuracy. Automatic classifiers can improve accuracy and could be a useful tool to assist diagnosis.
Collapse
Affiliation(s)
- Alexandre Morin
- Department of Neurology, AP-HP, Hôpital de la Pitié-Salpêtrière, Unité de Neuro-Psychiatrie Comportementale (UNPC), Paris, France.,Sorbonne Universités, UPMC Univ Paris 06, Inserm, CNRS, ICM, Paris, France.,Inria, Aramis-Project Team, Paris, France
| | - Jorge Samper-Gonzalez
- Sorbonne Universités, UPMC Univ Paris 06, Inserm, CNRS, ICM, Paris, France.,Inria, Aramis-Project Team, Paris, France
| | - Anne Bertrand
- Sorbonne Universités, UPMC Univ Paris 06, Inserm, CNRS, ICM, Paris, France.,Inria, Aramis-Project Team, Paris, France.,Department of Neuroradiology, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Sébastian Ströer
- Sorbonne Universités, UPMC Univ Paris 06, Inserm, CNRS, ICM, Paris, France.,Department of Neuroradiology, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Didier Dormont
- Sorbonne Universités, UPMC Univ Paris 06, Inserm, CNRS, ICM, Paris, France.,Inria, Aramis-Project Team, Paris, France.,Department of Neuroradiology, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Aline Mendes
- Department of Neurology, AP-HP, Hôpital de la Pitié-Salpêtrière, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), Paris, France
| | - Pierrick Coupé
- Laboratoire Bordelais de Recherche en Informatique, Unit Mixte de Recherche CNRS (UMR 5800), PICTURA Research Group, Bordeaux, France
| | | | - Marcel Lévy
- Department of Neurology, AP-HP, Hôpital de la Pitié-Salpêtrière, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), Paris, France
| | - Dalila Samri
- Department of Neurology, AP-HP, Hôpital de la Pitié-Salpêtrière, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), Paris, France
| | - Harald Hampel
- Department of Neurology, AP-HP, Hôpital de la Pitié-Salpêtrière, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), Paris, France.,AXA Research Fund and UPMC Chair, Paris, France; Sorbonne Universities, Pierre et Marie Curie University, Paris, France.,ICM, ICM-INSERM 1127, FrontLab, Paris, France
| | - Bruno Dubois
- Department of Neurology, AP-HP, Hôpital de la Pitié-Salpêtrière, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), Paris, France.,ICM, ICM-INSERM 1127, FrontLab, Paris, France
| | - Marc Teichmann
- Department of Neurology, AP-HP, Hôpital de la Pitié-Salpêtrière, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), Paris, France.,ICM, ICM-INSERM 1127, FrontLab, Paris, France
| | - Stéphane Epelbaum
- Sorbonne Universités, UPMC Univ Paris 06, Inserm, CNRS, ICM, Paris, France.,Inria, Aramis-Project Team, Paris, France.,Department of Neurology, AP-HP, Hôpital de la Pitié-Salpêtrière, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), Paris, France
| | - Olivier Colliot
- Sorbonne Universités, UPMC Univ Paris 06, Inserm, CNRS, ICM, Paris, France.,Inria, Aramis-Project Team, Paris, France.,Department of Neuroradiology, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France.,Department of Neurology, AP-HP, Hôpital de la Pitié-Salpêtrière, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), Paris, France
| |
Collapse
|
42
|
Zhang J, Dong Q, Shi J, Li Q, Stonnington CM, Gutman BA, Chen K, Reiman EM, Caselli RJ, Thompson PM, Ye J, Wang Y. Predicting future cognitive decline with hyperbolic stochastic coding. Med Image Anal 2021; 70:102009. [PMID: 33711742 PMCID: PMC8049149 DOI: 10.1016/j.media.2021.102009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2019] [Revised: 08/10/2020] [Accepted: 02/16/2021] [Indexed: 01/18/2023]
Abstract
Hyperbolic geometry has been successfully applied in modeling brain cortical and subcortical surfaces with general topological structures. However, such approaches, similar to other surface-based brain morphology analysis methods, usually generate high dimensional features. It limits their statistical power in cognitive decline prediction research, especially in datasets with limited subject numbers. To address the above limitation, we propose a novel framework termed as hyperbolic stochastic coding (HSC). We first compute diffeomorphic maps between general topological surfaces by mapping them to a canonical hyperbolic parameter space with consistent boundary conditions and extracts critical shape features. Secondly, in the hyperbolic parameter space, we introduce a farthest point sampling with breadth-first search method to obtain ring-shaped patches. Thirdly, stochastic coordinate coding and max-pooling algorithms are adopted for feature dimension reduction. We further validate the proposed system by comparing its classification accuracy with some other methods on two brain imaging datasets for Alzheimer's disease (AD) progression studies. Our preliminary experimental results show that our algorithm achieves superior results on various classification tasks. Our work may enrich surface-based brain imaging research tools and potentially result in a diagnostic and prognostic indicator to be useful in individualized treatment strategies.
Collapse
Affiliation(s)
- Jie Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85287 USA
| | - Qunxi Dong
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85287 USA
| | - Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85287 USA
| | - Qingyang Li
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85287 USA
| | | | - Boris A Gutman
- Armour College of Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ, USA
| | | | | | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Jieping Ye
- Department of Computational Medicine and Bioinformatics & Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85287 USA.
| |
Collapse
|
43
|
Wei Y, Huang N, Liu Y, Zhang X, Wang S, Tang X. Hippocampal and Amygdalar Morphological Abnormalities in Alzheimer's Disease Based on Three Chinese MRI Datasets. Curr Alzheimer Res 2021; 17:1221-1231. [PMID: 33602087 DOI: 10.2174/1567205018666210218150223] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 10/12/2020] [Accepted: 12/22/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Early detection of Alzheimer's disease (AD) and its early stage, the mild cognitive impairment (MCI), has important scientific, clinical and social significance. Magnetic resonance imaging (MRI) based statistical shape analysis provides an opportunity to detect regional structural abnormalities of brain structures caused by AD and MCI. OBJECTIVE In this work, we aimed to employ a well-established statistical shape analysis pipeline, in the framework of large deformation diffeomorphic metric mapping, to identify and quantify the regional shape abnormalities of the bilateral hippocampus and amygdala at different prodromal stages of AD, using three Chinese MRI datasets collected from different domestic hospitals. METHODS We analyzed the region-specific shape abnormalities at different stages of the neuropathology of AD by comparing the localized shape characteristics of the bilateral hippocampi and amygdalas between healthy controls and two disease groups (MCI and AD). In addition to group comparison analyses, we also investigated the association between the shape characteristics and the Mini Mental State Examination (MMSE) of each structure of interest in the disease group (MCI and AD combined) as well as the discriminative power of different morphometric biomarkers. RESULTS We found the strongest disease pathology (regional atrophy) at the subiculum and CA1 subregions of the hippocampus and the basolateral, basomedial as well as centromedial subregions of the amygdala. Furthermore, the shape characteristics of the hippocampal and amygdalar subregions exhibiting the strongest AD related atrophy were found to have the most significant positive associations with the MMSE. Employing the shape deformation marker of the hippocampus or the amygdala for automated MCI or AD detection yielded a significant accuracy boost over the corresponding volume measurement. CONCLUSION Our results suggested that the amygdalar and hippocampal morphometrics, especially those of shape morphometrics, can be used as auxiliary indicators for monitoring the disease status of an AD patient.
Collapse
Affiliation(s)
- Yuanyuan Wei
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Nianwei Huang
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Yong Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Xi Zhang
- Department of Neurology, Nanlou Division, Chinese PLA General Hospital; National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Silun Wang
- YIWEI Medical Technology Co., Ltd, Shenzhen, Guangdong, China
| | - Xiaoying Tang
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
| |
Collapse
|
44
|
Chávez-Fumagalli MA, Shrivastava P, Aguilar-Pineda JA, Nieto-Montesinos R, Del-Carpio GD, Peralta-Mestas A, Caracela-Zeballos C, Valdez-Lazo G, Fernandez-Macedo V, Pino-Figueroa A, Vera-Lopez KJ, Lino Cardenas CL. Diagnosis of Alzheimer's Disease in Developed and Developing Countries: Systematic Review and Meta-Analysis of Diagnostic Test Accuracy. J Alzheimers Dis Rep 2021; 5:15-30. [PMID: 33681713 PMCID: PMC7902992 DOI: 10.3233/adr-200263] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/02/2020] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND The present systematic review and meta-analysis of diagnostic test accuracy summarizes the last three decades in advances on diagnosis of Alzheimer's disease (AD) in developed and developing countries. OBJECTIVE To determine the accuracy of biomarkers in diagnostic tools in AD, for example, cerebrospinal fluid, positron emission tomography (PET), and magnetic resonance imaging (MRI), etc. METHODS The authors searched PubMed for published studies from 1990 to April 2020 on AD diagnostic biomarkers. 84 published studies were pooled and analyzed in this meta-analysis and diagnostic accuracy was compared by summary receiver operating characteristic statistics. RESULTS Overall, 84 studies met the criteria and were included in a meta-analysis. For EEG, the sensitivity ranged from 67 to 98%, with a median of 80%, 95% CI [75, 91], tau-PET diagnosis sensitivity ranged from 76 to 97%, with a median of 94%, 95% CI [76, 97]; and MRI sensitivity ranged from 41 to 99%, with a median of 84%, 95% CI [81, 87]. Our results showed that tau-PET diagnosis had higher performance as compared to other diagnostic methods in this meta-analysis. CONCLUSION Our findings showed an important discrepancy in diagnostic data for AD between developed and developing countries, which can impact global prevalence estimation and management of AD. Also, our analysis found a better performance for the tau-PET diagnostic over other methods to diagnose AD patients, but the expense of tau-PET scan seems to be the limiting factor in the diagnosis of AD in developing countries such as those found in Asia, Africa, and Latin America.
Collapse
Affiliation(s)
- Miguel A. Chávez-Fumagalli
- Laboratory of Genomics and Neurovascular Diseases, Vicerrectorado de investigación, Universidad Católica de Santa Maria, Arequipa, Peru
| | - Pallavi Shrivastava
- Laboratory of Genomics and Neurovascular Diseases, Vicerrectorado de investigación, Universidad Católica de Santa Maria, Arequipa, Peru
| | - Jorge A. Aguilar-Pineda
- Laboratory of Genomics and Neurovascular Diseases, Vicerrectorado de investigación, Universidad Católica de Santa Maria, Arequipa, Peru
| | - Rita Nieto-Montesinos
- Laboratory of Genomics and Neurovascular Diseases, Vicerrectorado de investigación, Universidad Católica de Santa Maria, Arequipa, Peru
| | - Gonzalo Davila Del-Carpio
- Laboratory of Genomics and Neurovascular Diseases, Vicerrectorado de investigación, Universidad Católica de Santa Maria, Arequipa, Peru
| | - Antero Peralta-Mestas
- Division of Neurology, Psychiatry and Radiology of the National Hospital ESSALUD-HNCASE, Arequipa, Peru
| | - Claudia Caracela-Zeballos
- Division of Neurology, Psychiatry and Radiology of the National Hospital ESSALUD-HNCASE, Arequipa, Peru
| | - Guillermo Valdez-Lazo
- Division of Neurology, Psychiatry and Radiology of the National Hospital ESSALUD-HNCASE, Arequipa, Peru
| | - Victor Fernandez-Macedo
- Division of Neurology, Psychiatry and Radiology of the National Hospital ESSALUD-HNCASE, Arequipa, Peru
| | - Alejandro Pino-Figueroa
- Department of Pharmaceutical Sciences, Massachusetts College of Pharmacy and Health Sciences, Boston, MA, USA
| | - Karin J. Vera-Lopez
- Laboratory of Genomics and Neurovascular Diseases, Vicerrectorado de investigación, Universidad Católica de Santa Maria, Arequipa, Peru
| | - Christian L. Lino Cardenas
- Laboratory of Genomics and Neurovascular Diseases, Vicerrectorado de investigación, Universidad Católica de Santa Maria, Arequipa, Peru
- Cardiovascular Research Center, Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
| |
Collapse
|
45
|
Buvaneswari PR, Gayathri R. Deep Learning-Based Segmentation in Classification of Alzheimer’s Disease. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021. [DOI: 10.1007/s13369-020-05193-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
|
46
|
Detection and analysis of Alzheimer’s disease using various machine learning algorithms. ACTA ACUST UNITED AC 2021. [DOI: 10.1016/j.matpr.2020.07.645] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
|
47
|
Prasuhn J, Heldmann M, Münte TF, Brüggemann N. A machine learning-based classification approach on Parkinson's disease diffusion tensor imaging datasets. Neurol Res Pract 2020; 2:46. [PMID: 33324945 PMCID: PMC7654034 DOI: 10.1186/s42466-020-00092-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 10/26/2020] [Indexed: 12/18/2022] Open
Abstract
Introduction The presence of motor signs and symptoms in Parkinson’s disease (PD) is the result of a long-lasting prodromal phase with an advancing neurodegenerative process. The identification of PD patients in an early phase is, however, crucial for developing disease-modifying drugs. The objective of our study is to investigate whether Diffusion Tensor Imaging (DTI) of the Substantia nigra (SN) analyzed by machine learning algorithms (ML) can be used to identify PD patients. Methods Our study proposes the use of computer-aided algorithms and a highly reproducible approach (in contrast to manually SN segmentation) to increase the reliability and accuracy of DTI metrics used for classification. Results The results of our study do not confirm the feasibility of the DTI approach, neither on a whole-brain level, ROI-labelled analyses, nor when focusing on the SN only. Conclusions Our study did not provide any evidence to support the hypothesis that DTI-based analysis, in particular of the SN, could be used to identify PD patients correctly.
Collapse
Affiliation(s)
- Jannik Prasuhn
- Department of Neurology, Institute of Neurogenetics, University of Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany.,Department of Neurology, University Medical Center Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany
| | - Marcus Heldmann
- Department of Neurology, University Medical Center Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany.,Institute of Psychology II, University of Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany
| | - Thomas F Münte
- Department of Neurology, University Medical Center Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany
| | - Norbert Brüggemann
- Department of Neurology, Institute of Neurogenetics, University of Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany.,Department of Neurology, University Medical Center Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany
| |
Collapse
|
48
|
Naik B, Mehta A, Shah M. Denouements of machine learning and multimodal diagnostic classification of Alzheimer's disease. Vis Comput Ind Biomed Art 2020; 3:26. [PMID: 33151420 PMCID: PMC7642580 DOI: 10.1186/s42492-020-00062-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 10/16/2020] [Indexed: 12/19/2022] Open
Abstract
Alzheimer's disease (AD) is the most common type of dementia. The exact cause and treatment of the disease are still unknown. Different neuroimaging modalities, such as magnetic resonance imaging (MRI), positron emission tomography, and single-photon emission computed tomography, have played a significant role in the study of AD. However, the effective diagnosis of AD, as well as mild cognitive impairment (MCI), has recently drawn large attention. Various technological advancements, such as robots, global positioning system technology, sensors, and machine learning (ML) algorithms, have helped improve the diagnostic process of AD. This study aimed to determine the influence of implementing different ML classifiers in MRI and analyze the use of support vector machines with various multimodal scans for classifying patients with AD/MCI and healthy controls. Conclusions have been drawn in terms of employing different classifier techniques and presenting the optimal multimodal paradigm for the classification of AD.
Collapse
Affiliation(s)
- Binny Naik
- Department of Computer Engineering, Indus University, Ahmedabad, Gujarat, 382115, India
| | - Ashir Mehta
- Department of Computer Engineering, Indus University, Ahmedabad, Gujarat, 382115, India
| | - Manan Shah
- Department of Chemical Engineering, School of Technology, Pandit Deendayal Petroleum University, Gandhinagar, Gujarat, 382007, India.
| |
Collapse
|
49
|
Folego G, Weiler M, Casseb RF, Pires R, Rocha A. Alzheimer's Disease Detection Through Whole-Brain 3D-CNN MRI. Front Bioeng Biotechnol 2020; 8:534592. [PMID: 33195111 PMCID: PMC7661929 DOI: 10.3389/fbioe.2020.534592] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Accepted: 09/18/2020] [Indexed: 12/25/2022] Open
Abstract
The projected burden of dementia by Alzheimer's disease (AD) represents a looming healthcare crisis as the population of most countries grows older. Although there is currently no cure, it is possible to treat symptoms of dementia. Early diagnosis is paramount to the development and success of interventions, and neuroimaging represents one of the most promising areas for early detection of AD. We aimed to deploy advanced deep learning methods to determine whether they can extract useful AD biomarkers from structural magnetic resonance imaging (sMRI) and classify brain images into AD, mild cognitive impairment (MCI), and cognitively normal (CN) groups. We tailored and trained Convolutional Neural Networks (CNNs) on sMRIs of the brain from datasets available in online databases. Our proposed method, ADNet, was evaluated on the CADDementia challenge and outperformed several approaches in the prior art. The method's configuration with machine-learning domain adaptation, ADNet-DA, reached 52.3% accuracy. Contributions of our study include devising a deep learning system that is entirely automatic and comparatively fast, presenting competitive results without using any patient's domain-specific knowledge about the disease. We were able to implement an end-to-end CNN system to classify subjects into AD, MCI, or CN groups, reflecting the identification of distinctive elements in brain images. In this context, our system represents a promising tool in finding biomarkers to help with the diagnosis of AD and, eventually, many other diseases.
Collapse
Affiliation(s)
- Guilherme Folego
- Institute of Computing, University of Campinas, Campinas, Brazil.,CPQD, Campinas, Brazil
| | - Marina Weiler
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Intramural Research Program (NIA/NIH/IRP), Baltimore, MD, United States
| | - Raphael F Casseb
- Seaman Family MR Research Center, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Ramon Pires
- Institute of Computing, University of Campinas, Campinas, Brazil
| | - Anderson Rocha
- Institute of Computing, University of Campinas, Campinas, Brazil
| |
Collapse
|
50
|
Classification of Alzheimer's Disease and Mild Cognitive Impairment Based on Cortical and Subcortical Features from MRI T1 Brain Images Utilizing Four Different Types of Datasets. JOURNAL OF HEALTHCARE ENGINEERING 2020; 2020:3743171. [PMID: 32952988 PMCID: PMC7482016 DOI: 10.1155/2020/3743171] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2019] [Revised: 07/09/2020] [Accepted: 07/14/2020] [Indexed: 01/18/2023]
Abstract
Alzheimer's disease (AD) is one of the most common neurodegenerative illnesses (dementia) among the elderly. Recently, researchers have developed a new method for the instinctive analysis of AD based on machine learning and its subfield, deep learning. Recent state-of-the-art techniques consider multimodal diagnosis, which has been shown to achieve high accuracy compared to a unimodal prognosis. Furthermore, many studies have used structural magnetic resonance imaging (MRI) to measure brain volumes and the volume of subregions, as well as to search for diffuse changes in white/gray matter in the brain. In this study, T1-weighted structural MRI was used for the early classification of AD. MRI results in high-intensity visible features, making preprocessing and segmentation easy. To use this image modality, we acquired four types of datasets from each dataset's server. In this work, we downloaded 326 subjects from the National Research Center for Dementia homepage, 123 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) homepage, 121 subjects from the Alzheimer's Disease Repository Without Borders homepage, and 131 subjects from the National Alzheimer's Coordinating Center homepage. In our experiment, we used the multiatlas label propagation with expectation–maximization-based refinement segmentation method. We segmented the images into 138 anatomical morphometry images (in which 40 features belonged to subcortical volumes and the remaining 98 features belonged to cortical thickness). The entire dataset was split into a 70 : 30 (training and testing) ratio before classifying the data. A principal component analysis was used for dimensionality reduction. Then, the support vector machine radial basis function classifier was used for classification between two groups—AD versus health control (HC) and early mild cognitive impairment (MCI) (EMCI) versus late MCI (LMCI). The proposed method performed very well for all four types of dataset. For instance, for the AD versus HC group, the classifier achieved an area under curve (AUC) of more than 89% for each dataset. For the EMCI versus LMCI group, the classifier achieved an AUC of more than 80% for every dataset. Moreover, we also calculated Cohen kappa and Jaccard index statistical values for all datasets to evaluate the classification reliability. Finally, we compared our results with those of recently published state-of-the-art methods.
Collapse
|