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Boren SB, Savitz SI, Gonzales N, Hasan K, Becerril-Gaitan A, Maroufy V, Li Y, Grotta J, Steven EA, Chen CJ, Sitton CW, Aronowski J, Haque ME. Longitudinal Morphometric Changes in the Corticospinal Tract Shape After Hemorrhagic Stroke. Transl Stroke Res 2023:10.1007/s12975-023-01168-y. [PMID: 37308620 DOI: 10.1007/s12975-023-01168-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 06/06/2023] [Accepted: 06/07/2023] [Indexed: 06/14/2023]
Abstract
Deep intracerebral hemorrhage (ICH) exerts a direct force on corticospinal tracts (CST) causing shape deformation. Using serial MRI, Generalized Procrustes Analysis (GPA), and Principal Components Analysis (PCA), we temporally evaluated the change in CST shape. Thirty-five deep ICH patients with ipsilesional-CST deformation were serially imaged on a 3T-MRI with a median imaging time of day-2 and 84 of onset. Anatomical and diffusion tensor images (DTI) were acquired. Using DTI color-coded maps, 15 landmarks were drawn on each CST and the centroids were computed in 3 dimensions. The contralesional-CST landmarks were used as a reference. The GPA outlined the shape coordinates and we superimposed the ipsilesional-CST shape at the two-time points. A multivariate PCA was applied to identify eigenvectors associated with the highest percentile of change. The first three principal components representing CST deformation along the left-right (PC1), anterior-posterior (PC2), and superior-inferior (PC3) respectively were responsible for 57.9% of shape variance. The PC1 (36.1%, p < 0.0001) and PC3 (9.58%, p < 0.01) showed a significant deformation between the two-time points. Compared to the contralesional-CST, the ipsilesional PC scores were significantly (p < 0.0001) different only at the first-timepoint. A significant positive association between the ipsilesional-CST deformation and hematoma volume was observed. We present a novel method to quantify CST deformation caused by ICH. Deformation most often occurs in left-right axis (PC1) and superior-inferior (PC3) directions. As compared to the reference, the significant temporal difference at the first time point suggests CST restoration over time.
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Affiliation(s)
- Seth B Boren
- Institute for Stroke and Cerebrovascular Diseases and Department of Neurology, McGovern Medical School, The University of Texas Health Science Center, 6431 Fannin Street, Houston, TX, 77030, USA
| | - Sean I Savitz
- Institute for Stroke and Cerebrovascular Diseases and Department of Neurology, McGovern Medical School, The University of Texas Health Science Center, 6431 Fannin Street, Houston, TX, 77030, USA
| | - Nicole Gonzales
- Institute for Stroke and Cerebrovascular Diseases and Department of Neurology, McGovern Medical School, The University of Texas Health Science Center, 6431 Fannin Street, Houston, TX, 77030, USA
- Department of Neurology, Neurohospitalist and Stroke Section, University of Colorado School of Medicine, Aurora, USA
| | - Khader Hasan
- Department of Interventional Diagnostic Radiology, McGovern Medical School, The University of Texas Health Science Center, Houston, USA
| | - Andrea Becerril-Gaitan
- Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center, Houston, USA
| | - Vahed Maroufy
- Department of Biostatistics and Data Science, School of Public Health, McGovern Medical School, The University of Texas Health Science Center, Houston, USA
| | - Yuan Li
- Department of Biostatistics and Data Science, School of Public Health, McGovern Medical School, The University of Texas Health Science Center, Houston, USA
| | - James Grotta
- Stroke Research and Mobile Stroke Unit, Department of Neurology, Memorial Hermann Hospital, Houston, USA
| | - Emily A Steven
- Institute for Stroke and Cerebrovascular Diseases and Department of Neurology, McGovern Medical School, The University of Texas Health Science Center, 6431 Fannin Street, Houston, TX, 77030, USA
| | - Ching-Jen Chen
- Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center, Houston, USA
| | - Clark W Sitton
- Department of Interventional Diagnostic Radiology, McGovern Medical School, The University of Texas Health Science Center, Houston, USA
| | - Jaroslaw Aronowski
- Institute for Stroke and Cerebrovascular Diseases and Department of Neurology, McGovern Medical School, The University of Texas Health Science Center, 6431 Fannin Street, Houston, TX, 77030, USA
| | - Muhammad E Haque
- Institute for Stroke and Cerebrovascular Diseases and Department of Neurology, McGovern Medical School, The University of Texas Health Science Center, 6431 Fannin Street, Houston, TX, 77030, USA.
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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.
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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
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Maikusa N, Fukami T, Matsuda H. Longitudinal analysis of brain structure using existence probability. Brain Behav 2020; 10:e01869. [PMID: 33034427 PMCID: PMC7749599 DOI: 10.1002/brb3.1869] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 09/04/2020] [Accepted: 09/08/2020] [Indexed: 11/28/2022] Open
Abstract
INTRODUCTION We propose a method to evaluate quantitatively the longitudinal structural changes in brain atrophy to provide early detection of Alzheimer's disease (AD) and mild cognitive impairment (MCI). METHODS We used existence probabilities obtained by segmenting magnetic resonance (MR) images at two different time points into four regions: gray matter, white matter, cerebrospinal fluid, and background. This method was applied to T1-weighted MR images of 110 participants with normal cognition (NL), 165 with MCI, and 82 with AD, obtained from the Japanese Alzheimer's Disease Neuroimaging Initiative database. RESULTS We obtained the coefficients of probability change (CPC) for each dataset. We found high area under the receiver operating characteristic curve (ROC) values (up to 0.908 of the difference of ROCs) for some CPC regions that are considered indicators of atrophy. Additionally, we attempted to establish a machine-learning algorithm to classify participants as NL or AD. The maximum accuracy was 92.1% for NL-AD classification and 81.2% for NL-MCI classification using CPC values between images acquired at first and sixth months, respectively. CONCLUSION These results showed that the proposed method is effective for the early detection of AD and MCI.
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Affiliation(s)
- Norihide Maikusa
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Tadanori Fukami
- Department of Informatics, Faculty of Engineering, Yamagata University, Yamagata, Japan
| | - Hiroshi Matsuda
- Department of Radiology, National Center of Neurology and Psychiatry, Tokyo, Japan
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Zheng W, Cui B, Sun Z, Li X, Han X, Yang Y, Li K, Hu L, Wang Z. Application of Generalized Split Linearized Bregman Iteration algorithm for Alzheimer's disease prediction. Aging (Albany NY) 2020; 12:6206-6224. [PMID: 32248185 PMCID: PMC7185109 DOI: 10.18632/aging.103017] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 02/25/2020] [Indexed: 12/16/2022]
Abstract
In this paper, we applied a novel method for the detection of Alzheimer's disease (AD) based on a structural magnetic resonance imaging (sMRI) dataset. Specifically, the method involved a new classification algorithm of machine learning, named Generalized Split Linearized Bregman Iteration (GSplit LBI). It combines logistic regression and structural sparsity regularizations. In the study, 57 AD patients and 47 normal controls (NCs) were enrolled. We first extracted the entire brain gray matter volume values of all subjects and then used GSplit LBI to build a predictive classification model with a 10-fold full cross-validation method. The model accuracy achieved 90.44%. To further verify which voxels in the dataset have greater impact on the prediction results, we ranked the weight parameters and obtained the top 6% of the model parameters. To verify the generalization of model prediction and the stability of feature selection, we performed a cross-test on the Alzheimer's Disease Neuroimaging Initiative (ADNI) and a Chinese dataset and achieved good performances on different cohorts. Conclusively, based on the sMRI dataset, our algorithm not only had good performance in a local cohort with high accuracy but also had good generalization of model prediction and stability of feature selection in different cohorts.
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Affiliation(s)
- Weimin Zheng
- Department of Radiology, Aerospace Center Hospital, Beijing 100049, China
| | - Bin Cui
- Department of Radiology, Aerospace Center Hospital, Beijing 100049, China
| | - Zeyu Sun
- Deepwise AI lab, Beijing 100080, China
| | - Xiuli Li
- Deepwise AI lab, Beijing 100080, China
| | - Xu Han
- Department of Radiology, Aerospace Center Hospital, Beijing 100049, China
| | - Yu Yang
- Beijing Huading Jialiang Technology Co, Beijing 100000, China
| | - Kuncheng Li
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China
| | - Lingjing Hu
- Yanjing Medical College, Capital Medical University, Beijing 101300, China
| | - Zhiqun Wang
- Department of Radiology, Aerospace Center Hospital, Beijing 100049, China
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Lombardi G, Crescioli G, Cavedo E, Lucenteforte E, Casazza G, Bellatorre A, Lista C, Costantino G, Frisoni G, Virgili G, Filippini G. Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment. Cochrane Database Syst Rev 2020; 3:CD009628. [PMID: 32119112 PMCID: PMC7059964 DOI: 10.1002/14651858.cd009628.pub2] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
BACKGROUND Mild cognitive impairment (MCI) due to Alzheimer's disease is the symptomatic predementia phase of Alzheimer's disease dementia, characterised by cognitive and functional impairment not severe enough to fulfil the criteria for dementia. In clinical samples, people with amnestic MCI are at high risk of developing Alzheimer's disease dementia, with annual rates of progression from MCI to Alzheimer's disease estimated at approximately 10% to 15% compared with the base incidence rates of Alzheimer's disease dementia of 1% to 2% per year. OBJECTIVES To assess the diagnostic accuracy of structural magnetic resonance imaging (MRI) for the early diagnosis of dementia due to Alzheimer's disease in people with MCI versus the clinical follow-up diagnosis of Alzheimer's disease dementia as a reference standard (delayed verification). To investigate sources of heterogeneity in accuracy, such as the use of qualitative visual assessment or quantitative volumetric measurements, including manual or automatic (MRI) techniques, or the length of follow-up, and age of participants. MRI was evaluated as an add-on test in addition to clinical diagnosis of MCI to improve early diagnosis of dementia due to Alzheimer's disease in people with MCI. SEARCH METHODS On 29 January 2019 we searched Cochrane Dementia and Cognitive Improvement's Specialised Register and the databases, MEDLINE, Embase, BIOSIS Previews, Science Citation Index, PsycINFO, and LILACS. We also searched the reference lists of all eligible studies identified by the electronic searches. SELECTION CRITERIA We considered cohort studies of any size that included prospectively recruited people of any age with a diagnosis of MCI. We included studies that compared the diagnostic test accuracy of baseline structural MRI versus the clinical follow-up diagnosis of Alzheimer's disease dementia (delayed verification). We did not exclude studies on the basis of length of follow-up. We included studies that used either qualitative visual assessment or quantitative volumetric measurements of MRI to detect atrophy in the whole brain or in specific brain regions, such as the hippocampus, medial temporal lobe, lateral ventricles, entorhinal cortex, medial temporal gyrus, lateral temporal lobe, amygdala, and cortical grey matter. DATA COLLECTION AND ANALYSIS Four teams of two review authors each independently reviewed titles and abstracts of articles identified by the search strategy. Two teams of two review authors each independently assessed the selected full-text articles for eligibility, extracted data and solved disagreements by consensus. Two review authors independently assessed the quality of studies using the QUADAS-2 tool. We used the hierarchical summary receiver operating characteristic (HSROC) model to fit summary ROC curves and to obtain overall measures of relative accuracy in subgroup analyses. We also used these models to obtain pooled estimates of sensitivity and specificity when sufficient data sets were available. MAIN RESULTS We included 33 studies, published from 1999 to 2019, with 3935 participants of whom 1341 (34%) progressed to Alzheimer's disease dementia and 2594 (66%) did not. Of the participants who did not progress to Alzheimer's disease dementia, 2561 (99%) remained stable MCI and 33 (1%) progressed to other types of dementia. The median proportion of women was 53% and the mean age of participants ranged from 63 to 87 years (median 73 years). The mean length of clinical follow-up ranged from 1 to 7.6 years (median 2 years). Most studies were of poor methodological quality due to risk of bias for participant selection or the index test, or both. Most of the included studies reported data on the volume of the total hippocampus (pooled mean sensitivity 0.73 (95% confidence interval (CI) 0.64 to 0.80); pooled mean specificity 0.71 (95% CI 0.65 to 0.77); 22 studies, 2209 participants). This evidence was of low certainty due to risk of bias and inconsistency. Seven studies reported data on the atrophy of the medial temporal lobe (mean sensitivity 0.64 (95% CI 0.53 to 0.73); mean specificity 0.65 (95% CI 0.51 to 0.76); 1077 participants) and five studies on the volume of the lateral ventricles (mean sensitivity 0.57 (95% CI 0.49 to 0.65); mean specificity 0.64 (95% CI 0.59 to 0.70); 1077 participants). This evidence was of moderate certainty due to risk of bias. Four studies with 529 participants analysed the volume of the total entorhinal cortex and four studies with 424 participants analysed the volume of the whole brain. We did not estimate pooled sensitivity and specificity for the volume of these two regions because available data were sparse and heterogeneous. We could not statistically evaluate the volumes of the lateral temporal lobe, amygdala, medial temporal gyrus, or cortical grey matter assessed in small individual studies. We found no evidence of a difference between studies in the accuracy of the total hippocampal volume with regards to duration of follow-up or age of participants, but the manual MRI technique was superior to automatic techniques in mixed (mostly indirect) comparisons. We did not assess the relative accuracy of the volumes of different brain regions measured by MRI because only indirect comparisons were available, studies were heterogeneous, and the overall accuracy of all regions was moderate. AUTHORS' CONCLUSIONS The volume of hippocampus or medial temporal lobe, the most studied brain regions, showed low sensitivity and specificity and did not qualify structural MRI as a stand-alone add-on test for an early diagnosis of dementia due to Alzheimer's disease in people with MCI. This is consistent with international guidelines, which recommend imaging to exclude non-degenerative or surgical causes of cognitive impairment and not to diagnose dementia due to Alzheimer's disease. In view of the low quality of most of the included studies, the findings of this review should be interpreted with caution. Future research should not focus on a single biomarker, but rather on combinations of biomarkers to improve an early diagnosis of Alzheimer's disease dementia.
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Affiliation(s)
- Gemma Lombardi
- University of FlorenceDepartment of Neurosciences, Psychology, Drug Research and Child Health (NEUROFARBA)Largo Brambilla, 3FlorenceItaly50134
| | - Giada Crescioli
- University of FlorenceDepartment of Neurosciences, Psychology, Drug Research and Child Health (NEUROFARBA)Largo Brambilla, 3FlorenceItaly50134
| | - Enrica Cavedo
- Pitie‐Salpetriere Hospital, Sorbonne UniversityAlzheimer Precision Medicine (APM), AP‐HP47 boulevard de l'HopitalParisFrance75013
| | - Ersilia Lucenteforte
- University of PisaDepartment of Clinical and Experimental MedicineVia Savi 10PisaItaly56126
| | - Giovanni Casazza
- Università degli Studi di MilanoDipartimento di Scienze Biomediche e Cliniche "L. Sacco"via GB Grassi 74MilanItaly20157
| | | | - Chiara Lista
- Fondazione I.R.C.C.S. Istituto Neurologico Carlo BestaNeuroepidemiology UnitVia Celoria, 11MilanoItaly20133
| | - Giorgio Costantino
- Ospedale Maggiore Policlinico, Università degli Studi di MilanoUOC Pronto Soccorso e Medicina D'Urgenza, Fondazione IRCCS Ca' GrandaMilanItaly
| | | | - Gianni Virgili
- University of FlorenceDepartment of Neurosciences, Psychology, Drug Research and Child Health (NEUROFARBA)Largo Brambilla, 3FlorenceItaly50134
| | - Graziella Filippini
- Carlo Besta Foundation and Neurological InstituteScientific Director’s Officevia Celoria, 11MilanItaly20133
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Tanveer M, Richhariya B, Khan RU, Rashid AH, Khanna P, Prasad M, Lin CT. Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING, COMMUNICATIONS, AND APPLICATIONS 2020; 16:1-35. [DOI: 10.1145/3344998] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 07/01/2019] [Indexed: 08/30/2023]
Abstract
Alzheimer’s disease is an incurable neurodegenerative disease primarily affecting the elderly population. Efficient automated techniques are needed for early diagnosis of Alzheimer’s. Many novel approaches are proposed by researchers for classification of Alzheimer’s disease. However, to develop more efficient learning techniques, better understanding of the work done on Alzheimer’s is needed. Here, we provide a review on 165 papers from 2005 to 2019, using various feature extraction and machine learning techniques. The machine learning techniques are surveyed under three main categories: support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) and ensemble methods. We present a detailed review on these three approaches for Alzheimer’s with possible future directions.
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Affiliation(s)
- M. Tanveer
- Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, India
| | - B. Richhariya
- Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, India
| | - R. U. Khan
- Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, India
| | - A. H. Rashid
- Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore 8 School of Computer Science and Engineering, National Institute of Science and Technology, Berhampur, Odisha, India
| | - P. Khanna
- PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India
| | - M. Prasad
- Centre for Artificial Intelligence, School of Computer Science, FEIT, University of Technology Sydney, Sydney, Australia
| | - C. T. Lin
- Centre for Artificial Intelligence, School of Computer Science, FEIT, University of Technology Sydney, Sydney, Australia
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PereznGonzalez J, Azamar C, PinanRamirez O. Automatic Classification of Alzheimer's Disease based on CPD Brain Point Cloud Registration for Feature Extraction. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:812-815. [PMID: 31946019 DOI: 10.1109/embc.2019.8856372] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
In this paper, a new scheme of feature extraction based on the registration of two point clouds that model the brain surface area and a cerebral baseline, is presented. The aim is to design an automatic Random Forest classifier based on brain-shape pattern extraction, in order to discriminate among Control subjects, patients with Mild Cognitive Impairment, and patients with Alzheimer's disease using their MRI data. The features used were rotation and displacement parameters obtained from the rigid-register transformation matrix between each subject's point cloud model and the brain baseline cloud. Additional features consisted of distance metrics performed over the aligned clouds and the baseline; those metrics attempt to estimate the cerebral cortex deformations. The training set utilized 60 MRI grouped into subsets of 20 studies a class; while the test consisted of 30 not seen data (10 per class). The proposed method achieved up to 87.2% accuracy in the final validation stage. These results suggest that our proposed scheme could be useful to discriminate among several pathologies that affect brain gray matter morphology. Moreover, the proposed features might be used as brain deformation quantifiers.
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Rallabandi VS, Tulpule K, Gattu M. Automatic classification of cognitively normal, mild cognitive impairment and Alzheimer's disease using structural MRI analysis. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2020.100305] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
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Computational Approaches Applied in the Field of Neuroscience. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2020; 1194:193-201. [DOI: 10.1007/978-3-030-32622-7_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Pena D, Barman A, Suescun J, Jiang X, Schiess MC, Giancardo L. Quantifying Neurodegenerative Progression With DeepSymNet, an End-to-End Data-Driven Approach. Front Neurosci 2019; 13:1053. [PMID: 31636533 PMCID: PMC6788344 DOI: 10.3389/fnins.2019.01053] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Accepted: 09/19/2019] [Indexed: 01/22/2023] Open
Abstract
Alzheimer's disease (AD) is the most common neurodegenerative disorder worldwide and is one of the leading sources of morbidity and mortality in the aging population. There is a long preclinical period followed by mild cognitive impairment (MCI). Clinical diagnosis and the rate of decline is variable. Progression monitoring remains a challenge in AD, and it is imperative to create better tools to quantify this progression. Brain magnetic resonance imaging (MRI) is commonly used for patient assessment. However, current approaches for analysis require strong a priori assumptions about regions of interest used and complex preprocessing pipelines including computationally expensive non-linear registrations and iterative surface deformations. These preprocessing steps are composed of many stacked processing layers. Any error or bias in an upstream layer will be propagated throughout the pipeline. Failures or biases in the non-linear subject registration and the subjective choice of atlases of specific regions are common in medical neuroimaging analysis and may hinder the translation of many approaches to the clinical practice. Here we propose a data-driven method based on an extension of a deep learning architecture, DeepSymNet, that identifies longitudinal changes without relying on prior brain regions of interest, an atlas, or non-linear registration steps. Our approach is trained end-to-end and learns how a patient's brain structure dynamically changes between two-time points directly from the raw voxels. We compare our approach with Freesurfer longitudinal pipelines and voxel-based methods using the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our model can identify AD progression with comparable results to existing Freesurfer longitudinal pipelines without the need of predefined regions of interest, non-rigid registration algorithms, or iterative surface deformation at a fraction of the processing time. When compared to other voxel-based methods which share some of the same benefits, our model showed a statistically significant performance improvement. Additionally, we show that our model can differentiate between healthy subjects and patients with MCI. The model's decision was investigated using the epsilon layer-wise propagation algorithm. We found that the predictions were driven by the pallidum, putamen, and the superior temporal gyrus. Our novel longitudinal based, deep learning approach has the potential to diagnose patients earlier and enable new computational tools to monitor neurodegeneration in clinical practice.
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Affiliation(s)
- Danilo Pena
- School of Biomedical Informatics, University of Texas Health Science Center at Houston (UTHealth), Houston, TX, United States
- Center for Precision Health, UTHealth, Houston, TX, United States
| | - Arko Barman
- School of Biomedical Informatics, University of Texas Health Science Center at Houston (UTHealth), Houston, TX, United States
- Center for Precision Health, UTHealth, Houston, TX, United States
| | - Jessika Suescun
- Department of Neurology, McGovern Medical School, UTHealth, Houston, TX, United States
| | - Xiaoqian Jiang
- School of Biomedical Informatics, University of Texas Health Science Center at Houston (UTHealth), Houston, TX, United States
| | - Mya C. Schiess
- Department of Neurology, McGovern Medical School, UTHealth, Houston, TX, United States
| | - Luca Giancardo
- School of Biomedical Informatics, University of Texas Health Science Center at Houston (UTHealth), Houston, TX, United States
- Center for Precision Health, UTHealth Diagnostic and Interventional Imaging, McGovern Medical School, UTHealth Institute for Stroke and Cerebrovascular Diseases, UTHealth, Houston, TX, United States
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Performance of machine learning methods applied to structural MRI and ADAS cognitive scores in diagnosing Alzheimer’s disease. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.08.009] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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Kruthika K, Rajeswari, Maheshappa H. Multistage classifier-based approach for Alzheimer's disease prediction and retrieval. INFORMATICS IN MEDICINE UNLOCKED 2019. [DOI: 10.1016/j.imu.2018.12.003] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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Kautzky A, Seiger R, Hahn A, Fischer P, Krampla W, Kasper S, Kovacs GG, Lanzenberger R. Prediction of Autopsy Verified Neuropathological Change of Alzheimer's Disease Using Machine Learning and MRI. Front Aging Neurosci 2018; 10:406. [PMID: 30618713 PMCID: PMC6295575 DOI: 10.3389/fnagi.2018.00406] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Accepted: 11/26/2018] [Indexed: 12/29/2022] Open
Abstract
Background: Alzheimer’s disease (AD) is the most common form of dementia. While neuropathological changes pathognomonic for AD have been defined, early detection of AD prior to cognitive impairment in the clinical setting is still lacking. Pioneer studies applying machine learning to magnetic-resonance imaging (MRI) data to predict mild cognitive impairment (MCI) or AD have yielded high accuracies, however, an algorithm predicting neuropathological change is still lacking. The objective of this study was to compute a prediction model supporting a more distinct diagnostic criterium for AD compared to clinical presentation, allowing identification of hallmark changes even before symptoms occur. Methods: Autopsy verified neuropathological changes attributed to AD, as described by a combined score for Aβ-peptides, neurofibrillary tangles and neuritic plaques issued by the National Institute on Aging – Alzheimer’s Association (NIAA), the ABC score for AD, were predicted from structural MRI data with RandomForest (RF). MRI scans were performed at least 2 years prior to death. All subjects derive from the prospective Vienna Trans-Danube Aging (VITA) study that targeted all 1750 inhabitants of the age of 75 in the starting year of 2000 in two districts of Vienna and included irregular follow-ups until death, irrespective of clinical symptoms or diagnoses. For 68 subjects MRI as well as neuropathological data were available and 49 subjects (mean age at death: 82.8 ± 2.9, 29 female) with sufficient MRI data quality were enrolled for further statistical analysis using nested cross-validation (CV). The decoding data of the inner loop was used for variable selection and parameter optimization with a fivefold CV design, the new data of the outer loop was used for model validation with optimal settings in a fivefold CV design. The whole procedure was performed ten times and average accuracies with standard deviations were reported. Results: The most informative ROIs included caudal and rostral anterior cingulate gyrus, entorhinal, fusiform and insular cortex and the subcortical ROIs anterior corpus callosum and the left vessel, a ROI comprising lacunar alterations in inferior putamen and pallidum. The resulting prediction models achieved an average accuracy for a three leveled NIAA AD score of 0.62 within the decoding sets and of 0.61 for validation sets. Higher accuracies of 0.77 for both sets, respectively, were achieved when predicting presence or absence of neuropathological change. Conclusion: Computer-aided prediction of neuropathological change according to the categorical NIAA score in AD, that currently can only be assessed post-mortem, may facilitate a more distinct and definite categorization of AD dementia. Reliable detection of neuropathological hallmarks of AD would enable risk stratification at an earlier level than prediction of MCI or clinical AD symptoms and advance precision medicine in neuropsychiatry.
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Affiliation(s)
- Alexander Kautzky
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Rene Seiger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Peter Fischer
- Department of Psychiatry, Danube Hospital, Medical Research Society Vienna D.C., Vienna, Austria
| | | | - Siegfried Kasper
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Gabor G Kovacs
- Institute of Neurology, Medical University of Vienna, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
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Osadebey ME, Pedersen M, Arnold DL, Wendel-Mitoraj KE, Alzheimer's Disease Neuroimaging Initiative FT. Standardized quality metric system for structural brain magnetic resonance images in multi-center neuroimaging study. BMC Med Imaging 2018; 18:31. [PMID: 30223797 PMCID: PMC6142697 DOI: 10.1186/s12880-018-0266-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Accepted: 07/31/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Multi-site neuroimaging offer several benefits and poses tough challenges in the drug development process. Although MRI protocol and clinical guidelines developed to address these challenges recommend the use of good quality images, reliable assessment of image quality is hampered by the several shortcomings of existing techniques. METHODS Given a test image two feature images are extracted. They are grayscale and contrast feature images. Four binary images are generated by setting four different global thresholds on the feature images. Image quality is predicted by measuring the structural similarity between appropriate pairs of binary images. The lower and upper limits of the quality index are 0 and 1. Quality prediction is based on four quality attributes; luminance contrast, texture, texture contrast and lightness. RESULTS Performance evaluation on test data from three multi-site clinical trials show good objective quality evaluation across MRI sequences, levels of distortion and quality attributes. Correlation with subjective evaluation by human observers is ≥ 0.6. CONCLUSION The results are promising for the evaluation of MRI protocols, specifically the standardization of quality index, designed to overcome the challenges encountered in multi-site clinical trials.
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Affiliation(s)
- Michael E Osadebey
- NeuroRx Research Inc, Montreal, 3575 Parc Avenue, Suite # 5322, Montreal, Quebec, H2X 3P9, Canada
| | - Marius Pedersen
- Department of Computer Science, Norwegian University of Science and Technology, Teknologivegen 22, Gjøvik, N-2815, Norway.
| | - Douglas L Arnold
- Montreal Neurological Institute and Hospital, McGill University, 3801 University St, Montreal, Quebec, H3A 2B4, Canada
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Lawrence E, Vegvari C, Ower A, Hadjichrysanthou C, De Wolf F, Anderson RM. A Systematic Review of Longitudinal Studies Which Measure Alzheimer's Disease Biomarkers. J Alzheimers Dis 2018; 59:1359-1379. [PMID: 28759968 PMCID: PMC5611893 DOI: 10.3233/jad-170261] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Alzheimer’s disease (AD) is a progressive and fatal neurodegenerative disease, with no effective treatment or cure. A gold standard therapy would be treatment to slow or halt disease progression; however, knowledge of causation in the early stages of AD is very limited. In order to determine effective endpoints for possible therapies, a number of quantitative surrogate markers of disease progression have been suggested, including biochemical and imaging biomarkers. The dynamics of these various surrogate markers over time, particularly in relation to disease development, are, however, not well characterized. We reviewed the literature for studies that measured cerebrospinal fluid or plasma amyloid-β and tau, or took magnetic resonance image or fluorodeoxyglucose/Pittsburgh compound B-positron electron tomography scans, in longitudinal cohort studies. We summarized the properties of the major cohort studies in various countries, commonly used diagnosis methods and study designs. We have concluded that additional studies with repeat measures over time in a representative population cohort are needed to address the gap in knowledge of AD progression. Based on our analysis, we suggest directions in which research could move in order to advance our understanding of this complex disease, including repeat biomarker measurements, standardization and increased sample sizes.
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Affiliation(s)
- Emma Lawrence
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | - Carolin Vegvari
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | - Alison Ower
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | | | - Frank De Wolf
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.,Janssen Prevention Center, Leiden, The Netherlands
| | - Roy M Anderson
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
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Rathore S, Habes M, Iftikhar MA, Shacklett A, Davatzikos C. A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages. Neuroimage 2017; 155:530-548. [PMID: 28414186 PMCID: PMC5511557 DOI: 10.1016/j.neuroimage.2017.03.057] [Citation(s) in RCA: 298] [Impact Index Per Article: 42.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2016] [Revised: 03/25/2017] [Accepted: 03/28/2017] [Indexed: 01/18/2023] Open
Abstract
Neuroimaging has made it possible to measure pathological brain changes associated with Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been increasingly integrated into imaging signatures of AD by means of classification frameworks, offering promising tools for individualized diagnosis and prognosis. We reviewed neuroimaging-based studies for AD and mild cognitive impairment classification, selected after online database searches in Google Scholar and PubMed (January, 1985-June, 2016). We categorized these studies based on the following neuroimaging modalities (and sub-categorized based on features extracted as a post-processing step from these modalities): i) structural magnetic resonance imaging [MRI] (tissue density, cortical surface, and hippocampal measurements), ii) functional MRI (functional coherence of different brain regions, and the strength of the functional connectivity), iii) diffusion tensor imaging (patterns along the white matter fibers), iv) fluorodeoxyglucose positron emission tomography (FDG-PET) (metabolic rate of cerebral glucose), and v) amyloid-PET (amyloid burden). The studies reviewed indicate that the classification frameworks formulated on the basis of these features show promise for individualized diagnosis and prediction of clinical progression. Finally, we provided a detailed account of AD classification challenges and addressed some future research directions.
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Affiliation(s)
- Saima Rathore
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, USA
| | - Mohamad Habes
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, USA
| | - Muhammad Aksam Iftikhar
- Department of Computer Science, Comsats Institute of Information technology, Lahore, Pakistan
| | - Amanda Shacklett
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, USA.
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Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC, Harvey D, Jack CR, Jagust W, Morris JC, Petersen RC, Saykin AJ, Shaw LM, Toga AW, Trojanowski JQ. Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials. Alzheimers Dement 2017; 13:e1-e85. [PMID: 28342697 DOI: 10.1016/j.jalz.2016.11.007] [Citation(s) in RCA: 170] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Revised: 11/21/2016] [Accepted: 11/28/2016] [Indexed: 01/31/2023]
Abstract
INTRODUCTION The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS We used standard searches to find publications using ADNI data. RESULTS (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.
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Affiliation(s)
- Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, CA, USA.
| | - Dallas P Veitch
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| | - Paul S Aisen
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | - Laurel A Beckett
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | - Nigel J Cairns
- Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Robert C Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | | | - William Jagust
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - John C Morris
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | | | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arthur W Toga
- Laboratory of Neuroimaging, Institute of Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Alzheimer's Disease Core Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Udall Parkinson's Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Prediction and classification of Alzheimer disease based on quantification of MRI deformation. PLoS One 2017; 12:e0173372. [PMID: 28264071 PMCID: PMC5338815 DOI: 10.1371/journal.pone.0173372] [Citation(s) in RCA: 70] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Accepted: 02/20/2017] [Indexed: 12/17/2022] Open
Abstract
Detecting early morphological changes in the brain and making early diagnosis are important for Alzheimer's disease (AD). High resolution magnetic resonance imaging can be used to help diagnosis and prediction of the disease. In this paper, we proposed a machine learning method to discriminate patients with AD or mild cognitive impairment (MCI) from healthy elderly and to predict the AD conversion in MCI patients by computing and analyzing the regional morphological differences of brain between groups. Distance between each pair of subjects was quantified from a symmetric diffeomorphic registration, followed by an embedding algorithm and a learning approach for classification. The proposed method obtained accuracy of 96.5% in differentiating mild AD from healthy elderly with the whole-brain gray matter or temporal lobe as region of interest (ROI), 91.74% in differentiating progressive MCI from healthy elderly and 88.99% in classifying progressive MCI versus stable MCI with amygdala or hippocampus as ROI. This deformation-based method has made full use of the pair-wise macroscopic shape difference between groups and consequently increased the power for discrimination.
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Ebadi A, Dalboni da Rocha JL, Nagaraju DB, Tovar-Moll F, Bramati I, Coutinho G, Sitaram R, Rashidi P. Ensemble Classification of Alzheimer's Disease and Mild Cognitive Impairment Based on Complex Graph Measures from Diffusion Tensor Images. Front Neurosci 2017; 11:56. [PMID: 28293162 PMCID: PMC5329061 DOI: 10.3389/fnins.2017.00056] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2016] [Accepted: 01/26/2017] [Indexed: 11/13/2022] Open
Abstract
The human brain is a complex network of interacting regions. The gray matter regions of brain are interconnected by white matter tracts, together forming one integrative complex network. In this article, we report our investigation about the potential of applying brain connectivity patterns as an aid in diagnosing Alzheimer's disease and Mild Cognitive Impairment (MCI). We performed pattern analysis of graph theoretical measures derived from Diffusion Tensor Imaging (DTI) data representing structural brain networks of 45 subjects, consisting of 15 patients of Alzheimer's disease (AD), 15 patients of MCI, and 15 healthy subjects (CT). We considered pair-wise class combinations of subjects, defining three separate classification tasks, i.e., AD-CT, AD-MCI, and CT-MCI, and used an ensemble classification module to perform the classification tasks. Our ensemble framework with feature selection shows a promising performance with classification accuracy of 83.3% for AD vs. MCI, 80% for AD vs. CT, and 70% for MCI vs. CT. Moreover, our findings suggest that AD can be related to graph measures abnormalities at Brodmann areas in the sensorimotor cortex and piriform cortex. In this way, node redundancy coefficient and load centrality in the primary motor cortex were recognized as good indicators of AD in contrast to MCI. In general, load centrality, betweenness centrality, and closeness centrality were found to be the most relevant network measures, as they were the top identified features at different nodes. The present study can be regarded as a "proof of concept" about a procedure for the classification of MRI markers between AD dementia, MCI, and normal old individuals, due to the small and not well-defined groups of AD and MCI patients. Future studies with larger samples of subjects and more sophisticated patient exclusion criteria are necessary toward the development of a more precise technique for clinical diagnosis.
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Affiliation(s)
- Ashkan Ebadi
- Department of Biomedical Engineering, University of FloridaGainesville, FL, USA
| | - Josué L. Dalboni da Rocha
- Brain and Language Lab, Department of Clinical Neuroscience, University of GenevaGeneva, Switzerland
| | - Dushyanth B. Nagaraju
- Department of Computer and Information Science and Engineering, University of FloridaGainesville, FL, USA
| | - Fernanda Tovar-Moll
- D'Or Institute for Research and Education (IDOR)Rio de Janeiro, Brazil
- Institute for Biomedical Sciences, Federal University of Rio de JaneiroRio de Janeiro, Brazil
| | - Ivanei Bramati
- D'Or Institute for Research and Education (IDOR)Rio de Janeiro, Brazil
| | - Gabriel Coutinho
- D'Or Institute for Research and Education (IDOR)Rio de Janeiro, Brazil
- Institute for Biomedical Sciences, Federal University of Rio de JaneiroRio de Janeiro, Brazil
| | - Ranganatha Sitaram
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, and Department of Psychiatry and Section of Neuroscience, Pontificia Universidad Católica de ChileSantiago, Chile
- Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de ChileSantiago, Chile
| | - Parisa Rashidi
- Department of Biomedical Engineering, University of FloridaGainesville, FL, USA
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Arbabshirani MR, Plis S, Sui J, Calhoun VD. Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. Neuroimage 2017; 145:137-165. [PMID: 27012503 PMCID: PMC5031516 DOI: 10.1016/j.neuroimage.2016.02.079] [Citation(s) in RCA: 513] [Impact Index Per Article: 73.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2015] [Revised: 02/03/2016] [Accepted: 02/25/2016] [Indexed: 01/18/2023] Open
Abstract
Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hundreds of studies have been carried out for accurate classification of patients with heterogeneous mental and neurodegenerative disorders such as schizophrenia and Alzheimer's disease. More than 500 studies have been published during the past quarter century on single subject prediction focused on a multiple brain disorders. In the first part of this study, we provide a survey of more than 200 reports in this field with a focus on schizophrenia, mild cognitive impairment (MCI), Alzheimer's disease (AD), depressive disorders, autism spectrum disease (ASD) and attention-deficit hyperactivity disorder (ADHD). Detailed information about those studies such as sample size, type and number of extracted features and reported accuracy are summarized and discussed. To our knowledge, this is by far the most comprehensive review of neuroimaging-based single subject prediction of brain disorders. In the second part, we present our opinion on major pitfalls of those studies from a machine learning point of view. Common biases are discussed and suggestions are provided. Moreover, emerging trends such as decentralized data sharing, multimodal brain imaging, differential diagnosis, disease subtype classification and deep learning are also discussed. Based on this survey, there is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders. However, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper. Emerging big data technologies and advanced data-intensive machine learning methodologies such as deep learning have coincided with an increasing need for accurate, robust and generalizable single subject prediction of brain disorders during an exciting time. In this report, we survey the past and offer some opinions regarding the road ahead.
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Affiliation(s)
- Mohammad R Arbabshirani
- The Mind Research Network, Albuquerque, NM 87106, USA; Geisinger Health System, Danville, PA 17822, USA
| | - Sergey Plis
- The Mind Research Network, Albuquerque, NM 87106, USA
| | - Jing Sui
- The Mind Research Network, Albuquerque, NM 87106, USA; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM 87106, USA; Department of ECE, University of New Mexico, Albuquerque, NM, USA
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Sørensen L, Igel C, Pai A, Balas I, Anker C, Lillholm M, Nielsen M. Differential diagnosis of mild cognitive impairment and Alzheimer's disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry. NEUROIMAGE-CLINICAL 2016; 13:470-482. [PMID: 28119818 PMCID: PMC5237821 DOI: 10.1016/j.nicl.2016.11.025] [Citation(s) in RCA: 87] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2016] [Revised: 10/21/2016] [Accepted: 11/26/2016] [Indexed: 01/01/2023]
Abstract
This paper presents a brain T1-weighted structural magnetic resonance imaging (MRI) biomarker that combines several individual MRI biomarkers (cortical thickness measurements, volumetric measurements, hippocampal shape, and hippocampal texture). The method was developed, trained, and evaluated using two publicly available reference datasets: a standardized dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the imaging arm of the Australian Imaging Biomarkers and Lifestyle flagship study of ageing (AIBL). In addition, the method was evaluated by participation in the Computer-Aided Diagnosis of Dementia (CADDementia) challenge. Cross-validation using ADNI and AIBL data resulted in a multi-class classification accuracy of 62.7% for the discrimination of healthy normal controls (NC), subjects with mild cognitive impairment (MCI), and patients with Alzheimer's disease (AD). This performance generalized to the CADDementia challenge where the method, trained using the ADNI and AIBL data, achieved a classification accuracy 63.0%. The obtained classification accuracy resulted in a first place in the challenge, and the method was significantly better (McNemar's test) than the bottom 24 methods out of the total of 29 methods contributed by 15 different teams in the challenge. The method was further investigated with learning curve and feature selection experiments using ADNI and AIBL data. The learning curve experiments suggested that neither more training data nor a more complex classifier would have improved the obtained results. The feature selection experiment showed that both common and uncommon individual MRI biomarkers contributed to the performance; hippocampal volume, ventricular volume, hippocampal texture, and parietal lobe thickness were the most important. This study highlights the need for both subtle, localized measurements and global measurements in order to discriminate NC, MCI, and AD simultaneously based on a single structural MRI scan. It is likely that additional non-structural MRI features are needed to further improve the obtained performance, especially to improve the discrimination between NC and MCI. The algorithm that won the CADDementia challenge is described and analyzed. Evaluation on data from ADNI, AIBL and the CADDementia challenge. Hippocampal texture is shown to be an important feature in the algorithm. Structural MRI intensity variations may include so far unused information. It is conjectured that additional features are needed in order to improve diagnostic performance.
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Affiliation(s)
- Lauge Sørensen
- Department of Computer Science, University of Copenhagen, Copenhagen Ø DK-2100, Denmark; Biomediq A/S, Copenhagen Ø DK-2100, Denmark
| | - Christian Igel
- Department of Computer Science, University of Copenhagen, Copenhagen Ø DK-2100, Denmark
| | - Akshay Pai
- Department of Computer Science, University of Copenhagen, Copenhagen Ø DK-2100, Denmark; Biomediq A/S, Copenhagen Ø DK-2100, Denmark
| | - Ioana Balas
- Department of Computer Science, University of Copenhagen, Copenhagen Ø DK-2100, Denmark
| | | | - Martin Lillholm
- Department of Computer Science, University of Copenhagen, Copenhagen Ø DK-2100, Denmark; Biomediq A/S, Copenhagen Ø DK-2100, Denmark
| | - Mads Nielsen
- Department of Computer Science, University of Copenhagen, Copenhagen Ø DK-2100, Denmark; Biomediq A/S, Copenhagen Ø DK-2100, Denmark
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Tetz V, Tetz G. Effect of deoxyribonuclease I treatment for dementia in end-stage Alzheimer's disease: a case report. J Med Case Rep 2016; 10:131. [PMID: 27234814 PMCID: PMC4884412 DOI: 10.1186/s13256-016-0931-6] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2015] [Accepted: 05/05/2016] [Indexed: 12/21/2022] Open
Abstract
Background Alzheimer's disease is the most common cause of dementia and is characterized by a progressive loss of brain tissue leading to amyloid-β accumulation and severe decline in cognitive function. The cause of Alzheimer’s disease is poorly understood, and available treatments are limited in their efficacy, particularly for patients with more severe symptoms. Case presentation We report the case of a 77-year-old Caucasian man with severe dementia and behavioral disturbance secondary to Alzheimer’s disease treated with memantine who began adjunct treatment with deoxyribonuclease I. Prior to initiation of deoxyribonuclease I treatment, our patient appeared to be in a stuporous state, with a Mini-Mental State Examination score of 3 and a Functional Assessment Staging Test score of 7. After obtaining informed consent from family members, we started administration of 120 mg of deoxyribonuclease I per day (1500 KU/mg) for treatment of severe cognitive impairment. Conclusions Our patient began to demonstrate rapid, considerable improvement in cognitive function 2 days following initiation of deoxyribonuclease I treatment. After 2 months of continued treatment, Mini-Mental State Examination and Functional Assessment Staging Test scores were 18 and 4, respectively.
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Affiliation(s)
- Victor Tetz
- Human Microbiology Institute, Inc., 303 5th avenue, Suite 2012, New York, NY, 10016, USA
| | - George Tetz
- CLS Therapeutics, PO BOX 634, Bordeaux Court, Les Echelons, St Peter Port, Guernsey, Channel Islands, GY1 3DR, UK.
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