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Na HK, Shin JH, Kim SW, Seo S, Kim WR, Kang JM, Lee SY, Cho J, Byun J, Okamura N, Seong JK, Noh Y. Diverging Relationships among Amyloid, Tau, and Brain Atrophy in Early-Onset and Late-Onset Alzheimer's Disease. Yonsei Med J 2024; 65:434-447. [PMID: 39048319 PMCID: PMC11284308 DOI: 10.3349/ymj.2023.0308] [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: 07/27/2023] [Revised: 01/20/2024] [Accepted: 01/23/2024] [Indexed: 07/27/2024] Open
Abstract
PURPOSE Alzheimer's disease (AD) dementia may not be a single disease entity. Early-onset AD (EOAD) and late-onset AD (LOAD) have been united under the same eponym of AD until now, but disentangling the heterogeneity according to the age of sonset has been a major tenet in the field of AD research. MATERIALS AND METHODS Ninety-nine patients with AD (EOAD, n=54; LOAD, n=45) and 66 cognitively normal controls completed both [18F]THK5351 and [18F]flutemetamol (FLUTE) positron emission tomography scans along with structural magnetic resonance imaging and detailed neuropsychological tests. RESULTS EOAD patients had higher THK retention in the precuneus, parietal, and frontal lobe, while LOAD patients had higher THK retention in the medial temporal lobe. Intravoxel correlation analyses revealed that EOAD presented narrower territory of local FLUTE-THK correlation, while LOAD presented broader territory of correlation extending to overall parieto-occipito-temporal regions. EOAD patients had broader brain areas which showed significant negative correlations between cortical thickness and THK retention, whereas in LOAD, only limited brain areas showed significant correlation with THK retention. In EOAD, most of the cognitive test results were correlated with THK retention. However, a few cognitive test results were correlated with THK retention in LOAD. CONCLUSION LOAD seemed to show gradual increase in tau and amyloid, and those two pathologies have association to each other. On the other hand, in EOAD, tau and amyloid may develop more abruptly and independently. These findings suggest LOAD and EOAD may have different courses of pathomechanism.
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Affiliation(s)
- Han Kyu Na
- Department of Neurology, Yonsei University College of Medicine, Seoul, Korea
| | - Jeong-Hyeon Shin
- Bio Medical Research Center, Bio Medical & Health Division, Korea Testing Laboratory, Daegu, Korea
| | - Sung-Woo Kim
- School of Biomedical Engineering, Korea University, Seoul, Korea
| | - Seongho Seo
- Neuroscience Research Institute, Gachon University, Incheon, Korea
- Department of Electronic Engineering, Pai Chai University, Daejeon, Korea
| | - Woo-Ram Kim
- Neuroscience Research Institute, Gachon University, Incheon, Korea
| | - Jae Myeong Kang
- Department of Psychiatry, Gachon University Gil Medical Center, Incheon, Korea
| | - Sang-Yoon Lee
- Department of Neuroscience, College of Medicine, Gachon University, Incheon, Korea
| | - Jaelim Cho
- Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Korea
| | - Justin Byun
- Department of Rehabilitation Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Nobuyuki Okamura
- Division of Pharmacology, Faculty of Medicine, Tohoku Medical and Pharmaceutical University, Sendai, Japan
| | - Joon-Kyung Seong
- School of Biomedical Engineering, Korea University, Seoul, Korea
- Department of Artificial Intelligence, Korea University, Seoul, Korea.
| | - Young Noh
- Neuroscience Research Institute, Gachon University, Incheon, Korea
- Department of Neurology, Gil Medical Center, Gachon University College of Medicine, Incheon, Korea.
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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.
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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
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Irfan M, Shahrestani S, Elkhodr M. Machine learning in neurological disorders: A multivariate LSTM and AdaBoost approach to Alzheimer's disease time series analysis. HEALTH CARE SCIENCE 2024; 3:41-52. [PMID: 38939169 PMCID: PMC11080865 DOI: 10.1002/hcs2.84] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 12/08/2023] [Accepted: 12/12/2023] [Indexed: 06/29/2024]
Abstract
Introduction Alzheimer's disease (AD) is a progressive brain disorder that impairs cognitive functions, behavior, and memory. Early detection is crucial as it can slow down the progression of AD. However, early diagnosis and monitoring of AD's advancement pose significant challenges due to the necessity for complex cognitive assessments and medical tests. Methods This study introduces a data acquisition technique and a preprocessing pipeline, combined with multivariate long short-term memory (M-LSTM) and AdaBoost models. These models utilize biomarkers from cognitive assessments and neuroimaging scans to detect the progression of AD in patients, using The AD Prediction of Longitudinal Evolution challenge cohort from the Alzheimer's Disease Neuroimaging Initiative database. Results The methodology proposed in this study significantly improved performance metrics. The testing accuracy reached 80% with the AdaBoost model, while the M-LSTM model achieved an accuracy of 82%. This represents a 20% increase in accuracy compared to a recent similar study. Discussion The findings indicate that the multivariate model, specifically the M-LSTM, is more effective in identifying the progression of AD compared to the AdaBoost model and methodologies used in recent research.
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Affiliation(s)
- Muhammad Irfan
- School of Computer, Data and Mathematical SciencesWestern Sydney UniversitySydneyAustralia
| | - Seyed Shahrestani
- School of Computer, Data and Mathematical SciencesWestern Sydney UniversitySydneyAustralia
| | - Mahmoud Elkhodr
- School of Engineering and TechnologyCentral Queensland UniversitySydneyAustralia
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Crystal O, Maralani PJ, Black S, Fischer C, Moody AR, Khademi A. Detecting conversion from mild cognitive impairment to Alzheimer's disease using FLAIR MRI biomarkers. Neuroimage Clin 2023; 40:103533. [PMID: 37952286 PMCID: PMC10666029 DOI: 10.1016/j.nicl.2023.103533] [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: 04/11/2023] [Revised: 10/05/2023] [Accepted: 10/26/2023] [Indexed: 11/14/2023]
Abstract
Mild cognitive impairment (MCI) is the prodromal phase of Alzheimer's disease (AD) and while it presents as an imperative intervention window, it is difficult to detect which subjects convert to AD (cMCI) and which ones remain stable (sMCI). The objective of this work was to investigate fluid-attenuated inversion recovery (FLAIR) MRI biomarkers and their ability to differentiate between sMCI and cMCI subjects in cross-sectional and longitudinal data. Three types of biomarkers were investigated: volume, intensity and texture. Volume biomarkers included total brain volume, cerebrospinal fluid volume (CSF), lateral ventricular volume, white matter lesion volume, subarachnoid CSF, and grey matter (GM) and white matter (WM), all normalized to intracranial volume. The mean intensity, kurtosis, and skewness of the GM and WM made up the intensity features. Texture features quantified homogeneity and microstructural tissue changes of GM and WM regions. Composite indices were also considered, which are biomarkers that represent an aggregate sum (z-score normalization and summation) of all biomarkers. The FLAIR MRI biomarkers successfully identified high-risk subjects as significant differences (p < 0.05) were found between the means of the sMCI and cMCI groups and the rate of change over time for several individual biomarkers as well as the composite indices for both cross-sectional and longitudinal analyses. Classification accuracy and feature importance analysis showed volume biomarkers to be most predictive, however, best performance was obtained when complimenting the volume biomarkers with the intensity and texture features. Using all the biomarkers, accuracy of 86.2 % and 69.2 % was achieved for normal control-AD and sMCI-cMCI classification respectively. Survival analysis demonstrated that the majority of the biomarkers showed a noticeable impact on the AD conversion probability 4 years prior to conversion. Composite indices were the top performers for all analyses including feature importance, classification, and survival analysis. This demonstrated their ability to summarize various dimensions of disease into single-valued metrics. Significant correlation (p < 0.05) with phosphorylated-tau and amyloid-beta CSF biomarkers was found with all the FLAIR biomarkers. The proposed biomarker system is easily attained as FLAIR is routinely acquired, models are not computationally intensive and the results are explainable, thus making this pipeline easily integrated into clinical workflow.
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Affiliation(s)
- Owen Crystal
- Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada; Keenan Research Center, St. Michael's Hospital, Toronto, ON, Canada.
| | - Pejman J Maralani
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Sandra Black
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON, Canada; Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; L.C. Campbell Cognitive Neurology Research Unit, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Department of Neurology, University of Toronto, Toronto, ON, Canada
| | - Corinne Fischer
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, St. Michael's Hospital, Toronto, ON, Canada; Keenan Research Center, St. Michael's Hospital, Toronto, ON, Canada
| | - Alan R Moody
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - April Khademi
- Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada; Department of Medical Imaging, University of Toronto, Toronto, ON, Canada; Keenan Research Center, St. Michael's Hospital, Toronto, ON, Canada; Institute of Biomedical Engineering, Science and Technology (iBEST), Toronto, ON, Canada October 5, 2023; Vector Institute for Artificial Intelligence, Toronto, ON, Canada
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Yang MH, Kim EH, Choi ES, Ko H. Comparison of Normative Percentiles of Brain Volume Obtained from NeuroQuant ® vs. DeepBrain ® in the Korean Population: Correlation with Cranial Shape. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2023; 84:1080-1090. [PMID: 37869130 PMCID: PMC10585089 DOI: 10.3348/jksr.2023.0006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 03/13/2023] [Accepted: 04/15/2023] [Indexed: 10/24/2023]
Abstract
Purpose This study aimed to compare the volume and normative percentiles of brain volumetry in the Korean population using quantitative brain volumetric MRI analysis tools NeuroQuant® (NQ) and DeepBrain® (DB), and to evaluate whether the differences in the normative percentiles of brain volumetry between the two tools is related to cranial shape. Materials and Methods In this retrospective study, we analyzed the brain volume reports obtained from NQ and DB in 163 participants without gross structural brain abnormalities. We measured three-dimensional diameters to evaluate the cranial shape on T1-weighted images. Statistical analyses were performed using intra-class correlation coefficients and linear correlations. Results The mean normative percentiles of the thalamus (90.8 vs. 63.3 percentile), putamen (90.0 vs. 60.0 percentile), and parietal lobe (80.1 vs. 74.1 percentile) were larger in the NQ group than in the DB group, whereas that of the occipital lobe (18.4 vs. 68.5 percentile) was smaller in the NQ group than in the DB group. We found a significant correlation between the mean normative percentiles obtained from the NQ and cranial shape: the mean normative percentile of the occipital lobe increased with the anteroposterior diameter and decreased with the craniocaudal diameter. Conclusion The mean normative percentiles obtained from NQ and DB differed significantly for many brain regions, and these differences may be related to cranial shape.
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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]
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Park S, Hong CH, Lee DG, Park K, Shin H. Prospective classification of Alzheimer's disease conversion from mild cognitive impairment. Neural Netw 2023; 164:335-344. [PMID: 37163849 DOI: 10.1016/j.neunet.2023.04.018] [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: 11/03/2022] [Revised: 02/26/2023] [Accepted: 04/12/2023] [Indexed: 05/12/2023]
Abstract
Alzheimer's disease (AD) is emerging as a serious problem with the rapid aging of the population, but due to the unclear cause of the disease and the absence of therapy, appropriate preventive measures are the next best thing. For this reason, it is important to early detect whether the disease converts from mild cognitive impairment (MCI) which is a prodromal phase of AD. With the advance in brain imaging techniques, various machine learning algorithms have become able to predict the conversion from MCI to AD by learning brain atrophy patterns. However, at the time of diagnosis, it is difficult to distinguish between the conversion group and the non-conversion group of subjects because the difference between groups is small, but the within-group variability is large in brain images. After a certain period of time, the subjects of conversion group show significant brain atrophy, whereas subjects of non-conversion group show only subtle changes due to the normal aging effect. This difference on brain atrophy makes the brain images more discriminative for learning. Motivated by this, we propose a method to perform classification by projecting brain images into the future, namely prospective classification. The experiments on the Alzheimer's Disease Neuroimaging Initiative dataset show that the prospective classification outperforms ordinary classification. Moreover, the features of prospective classification indicate the brain regions that significantly influence the conversion from MCI to AD.
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Affiliation(s)
- Sunghong Park
- Department of Psychiatry, Ajou University School of Medicine, Suwon, 16499, Republic of Korea
| | - Chang Hyung Hong
- Department of Psychiatry, Ajou University School of Medicine, Suwon, 16499, Republic of Korea
| | - Dong-Gi Lee
- Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Kanghee Park
- Korea Institute of Science and Technology Information, Seoul, 02456, Republic of Korea
| | - Hyunjung Shin
- Department of Artificial Intelligence, Ajou University, Suwon, 16499, Republic of Korea; Department of Industrial Engineering, Ajou University, Suwon, 16499, Republic of Korea.
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8
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Hollenbenders Y, Pobiruchin M, Reichenbach A. Two Routes to Alzheimer's Disease Based on Differential Structural Changes in Key Brain Regions. J Alzheimers Dis 2023; 92:1399-1412. [PMID: 36911937 DOI: 10.3233/jad-221061] [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: 03/09/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) is a neurodegenerative disorder with homogenous disease patterns. Neuropathological changes precede symptoms by up to two decades making neuroimaging biomarkers a prime candidate for early diagnosis, prognosis, and patient stratification. OBJECTIVE The goal of the study was to discern intermediate AD stages and their precursors based on neuroanatomical features for stratifying patients on their progression through different stages. METHODS Data include grey matter features from 14 brain regions extracted from longitudinal structural MRI and cognitive data obtained from 1,017 healthy controls and AD patients of ADNI. AD progression was modeled with a Hidden Markov Model, whose hidden states signify disease stages derived from the neuroanatomical data. To tie the progression in brain atrophy to a behavioral marker, we analyzed the ADAS-cog sub-scores in the stages. RESULTS The optimal model consists of eight states with differentiable neuroanatomical features, forming two routes crossing once at a very early point and merging at the final state. The cortical route is characterized by early and sustained atrophy in cortical regions. The limbic route is characterized by early decrease in limbic regions. Cognitive differences between the two routes are most noticeable in the memory domain with subjects from the limbic route experiencing stronger memory impairments. CONCLUSION Our findings corroborate that more than one pattern of grey matter deterioration with several discernable stages can be identified in the progression of AD. These neuroanatomical subtypes are behaviorally meaningful and provide a door into early diagnosis of AD and prognosis of the disease's progression.
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Affiliation(s)
- Yasmin Hollenbenders
- Medical Faculty Heidelberg, Heidelberg University, Germany.,Faculty of Computer Science, Heilbronn University of Applied Sciences, Germany.,Center for Machine Learning, Heilbronn University of Applied Sciences, Germany
| | - Monika Pobiruchin
- Faculty of Computer Science, Heilbronn University of Applied Sciences, Germany.,GECKO Institute for Medicine, Informatics and Economics, Heilbronn University of Applied Sciences, Germany
| | - Alexandra Reichenbach
- Medical Faculty Heidelberg, Heidelberg University, Germany.,Faculty of Computer Science, Heilbronn University of Applied Sciences, Germany.,Center for Machine Learning, Heilbronn University of Applied Sciences, Germany
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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.
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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.
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Jeong SH, Lee EC, Chung SJ, Lee HS, Jung JH, Sohn YH, Seong JK, Lee PH. Local striatal volume and motor reserve in drug-naïve Parkinson's disease. NPJ Parkinsons Dis 2022; 8:168. [PMID: 36470876 PMCID: PMC9722895 DOI: 10.1038/s41531-022-00429-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 11/11/2022] [Indexed: 12/12/2022] Open
Abstract
Motor reserve (MR) may explain why individuals with similar pathological changes show marked differences in motor deficits in Parkinson's disease (PD). In this study, we investigated whether estimated individual MR was linked to local striatal volume (LSV) in PD. We analyzed data obtained from 333 patients with drug naïve PD who underwent dopamine transporter scans and high-resolution 3-tesla T1-weighted structural magnetic resonance images. Using a residual model, we estimated individual MRs on the basis of initial UPDRS-III score and striatal dopamine depletion. We performed a correlation analysis between MR estimates and LSV. Furthermore, we assessed the effect of LSV, which is correlated with MR estimates, on the longitudinal increase in the levodopa-equivalent dose (LED) during the 4-year follow-up period using a linear mixed model. After controlling for intracranial volume, there was a significant positive correlation between LSV and MR estimates in the bilateral caudate, anterior putamen, and ventro-posterior putamen. The linear mixed model showed that the large local volume of anterior and ventro-posterior putamen was associated with the low requirement of LED initially and accelerated LED increment thereafter. The present study demonstrated that LSV is crucial to MR in early-stage PD, suggesting LSV as a neural correlate of MR in PD.
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Affiliation(s)
- Seong Ho Jeong
- grid.15444.300000 0004 0470 5454Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea ,grid.411627.70000 0004 0647 4151Department of Neurology, Inje University Sanggye Paik Hospital, Seoul, South Korea
| | - Eun-Chong Lee
- grid.222754.40000 0001 0840 2678School of Biomedical Engineering, Korea University, Seoul, South Korea
| | - Seok Jong Chung
- grid.15444.300000 0004 0470 5454Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea ,grid.413046.40000 0004 0439 4086Department of Neurology, Yongin Severance Hospital, Yonsei University Health System, Yongin, South Korea
| | - Hye Sun Lee
- grid.15444.300000 0004 0470 5454Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, South Korea
| | - Jin Ho Jung
- grid.411625.50000 0004 0647 1102Department of Neurology, Inje University Busan Paik Hospital, Seoul, South Korea
| | - Young H. Sohn
- grid.15444.300000 0004 0470 5454Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
| | - Joon-Kyung Seong
- grid.222754.40000 0001 0840 2678School of Biomedical Engineering, Korea University, Seoul, South Korea ,grid.222754.40000 0001 0840 2678Department of Artificial Intelligence, Korea University, Seoul, South Korea
| | - Phil Hyu Lee
- grid.15444.300000 0004 0470 5454Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea ,grid.15444.300000 0004 0470 5454Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, South Korea
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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.
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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
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Yoo HS, Lee EC, Chung SJ, Ye BS, Sohn YH, Seong JK, Lee PH. Contracted thalamic shape is associated with early development of levodopa-induced dyskinesia in Parkinson's disease. Sci Rep 2022; 12:12631. [PMID: 35879381 PMCID: PMC9314442 DOI: 10.1038/s41598-022-16747-6] [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: 02/25/2022] [Accepted: 07/14/2022] [Indexed: 01/18/2023] Open
Abstract
Levodopa-induced dyskinesia (LID), a long-term motor complication in Parkinson’s disease (PD), is attributable to both presynaptic and postsynaptic mechanisms. However, no studies have evaluated the baseline structural changes associated with LID at a subcortical level in PD. A total of 116 right-handed PD patients were recruited and based on the LID latency of 5 years, we classified patients into those vulnerable to LID (PD-vLID, n = 49) and those resistant to LID (PD-rLID, n = 67). After adjusting for covariates including dopamine transporter (DAT) availability of the posterior putamen, we compared the subcortical shape between the groups and investigated its association with the onset of LID. The PD-vLID group had lower DAT availability in the posterior putamen, higher parkinsonian motor deficits, and faster increment in levodopa equivalent dose than the PD-rLID group. The PD-vLID group had significant inward deformation in the right thalamus compared to the PD-rLID group. Inward deformation in the thalamus was associated with an earlier onset of LID at baseline. This study suggests that independent of presynaptic dopamine depletion, the thalamus is a major neural substrate for LID and that a contracted thalamic shape at baseline is closely associated with an early development of LID.
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Affiliation(s)
- Han Soo Yoo
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Eun-Chong Lee
- School of Biomedical Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, 02841, South Korea
| | - Seok Jong Chung
- Department of Neurology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, South Korea
| | - Byoung Seok Ye
- Department of Neurology, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Young H Sohn
- Department of Neurology, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Joon-Kyung Seong
- School of Biomedical Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, 02841, South Korea. .,Department of Artificial Intelligence, Korea University, Seoul, South Korea. .,Interdisciplinary Program in Precision Public Health, Korea University, Seoul, South Korea.
| | - Phil Hyu Lee
- Department of Neurology, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea. .,Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, South Korea.
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Cognitive Sequelae and Hippocampal Dysfunction in Chronic Kidney Disease following 5/6 Nephrectomy. Brain Sci 2022; 12:brainsci12070905. [PMID: 35884712 PMCID: PMC9321175 DOI: 10.3390/brainsci12070905] [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: 06/08/2022] [Revised: 06/30/2022] [Accepted: 07/06/2022] [Indexed: 01/18/2023] Open
Abstract
Neurological disorders are prevalent in patients with chronic kidney disease (CKD). Vascular factors and uremic toxins are involved with cognitive impairment in CKD. In addition, vascular dementia-induced alterations in the structure and function of the hippocampus can lead to deficits in hippocampal synaptic plasticity and cognitive function. However, regardless of this clinical evidence, the pathophysiology of cognitive impairment in patients with CKD is not fully understood. We used male Sprague Dawley rats and performed 5/6 nephrectomy to observe the changes in behavior, field excitatory postsynaptic potential, and immunostaining of the hippocampus following CKD progression. We measured the hippocampus volume on magnetic resonance imaging scans in the controls (n = 34) and end-stage renal disease (ESRD) hemodialysis patients (n = 42). In four cognition-related behavior assays, including novel object recognition, Y-maze, Barnes maze, and classical contextual fear conditioning, we identified deficits in spatial working memory, learning and memory, and contextual memory, as well as the ability to distinguish familiar and new objects, in the rats with CKD. Immunohistochemical staining of Na+/H+ exchanger1 was increased in the hippocampus of the CKD rat models. We performed double immunofluorescent staining for aquaporin-4 and glial fibrillary acidic protein and then verified the high coexpression in the hippocampus of the CKD rat model. Furthermore, results from recoding of the field excitatory postsynaptic potential (fEPSP) in the hippocampus showed the reduced amplitude and slope of fEPSP in the CKD rats. ESRD patients with cognitive impairment showed a significant decrease in the hippocampus volume compared with ESRD patients without cognitive impairment or the controls. Our findings suggest that uremia resulting from decreased kidney function may cause the destruction of the blood–brain barrier and hippocampus-related cognitive impairment in CKD.
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Kim S, Kim SW, Noh Y, Lee PH, Na DL, Seo SW, Seong JK. Harmonization of Multicenter Cortical Thickness Data by Linear Mixed Effect Model. Front Aging Neurosci 2022; 14:869387. [PMID: 35783130 PMCID: PMC9247505 DOI: 10.3389/fnagi.2022.869387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 05/16/2022] [Indexed: 01/18/2023] Open
Abstract
ObjectiveAnalyzing neuroimages being useful method in the field of neuroscience and neurology and solving the incompatibilities across protocols and vendors have become a major problem. We referred to this incompatibility as “center effects,” and in this study, we attempted to correct such center effects of cortical feature obtained from multicenter magnetic resonance images (MRIs).MethodsFor MRI of a total of 4,321 multicenter subjects, the harmonized w-score was calculated by correcting biological covariates such as age, sex, years of education, and intercranial volume (ICV) as fixed effects and center information as a random effect. Afterward, we performed classification tasks using principal component analysis (PCA) and linear discriminant analysis (LDA) to check whether the center effect was successfully corrected from the harmonized w-score.ResultsFirst, an experiment was conducted to predict the dataset origin of a random subject sampled from two different datasets, and it was confirmed that the prediction accuracy of linear mixed effect (LME) model-based w-score was significantly closer to the baseline than that of raw cortical thickness. As a second experiment, we classified the data of the normal and patient groups of each dataset, and LME model-based w-score, which is biological-feature-corrected values, showed higher classification accuracy than the raw cortical thickness data. Afterward, to verify the compatibility of the dataset used for LME model training and the dataset that is not, intraobject comparison and w-score RMSE calculation process were performed.ConclusionThrough comparison between the LME model-based w-score and existing methods and several classification tasks, we showed that the LME model-based w-score sufficiently corrects the center effects while preserving the disease effects from the dataset. We also showed that the preserved disease effects have a match with well-known disease atrophy patterns such as Alzheimer’s disease or Parkinson’s disease. Finally, through intrasubject comparison, we found that the difference between centers decreases in the LME model-based w-score compared with the raw cortical thickness and thus showed that our model well-harmonizes the data that are not used for the model training.
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Affiliation(s)
- SeungWook Kim
- Department of Bio-Convergence Engineering, Korea University, Seoul, South Korea
| | - Sung-Woo Kim
- Department of Bio-Convergence Engineering, Korea University, Seoul, South Korea
| | - Young Noh
- Department of Neurology, Gil Medical Center, Gachon University College of Medicine, Incheon, South Korea
| | - Phil Hyu Lee
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
| | - Duk L. Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, South Korea
- Samsung Alzheimer Research Center, Center for Clinical Epidemiology, Samsung Medical Center, Seoul, South Korea
- Department of Health Sciences and Technology, Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, South Korea
- *Correspondence: Sang Won Seo,
| | - Joon-Kyung Seong
- School of Biomedical Engineering, Korea University, Seoul, South Korea
- Department of Artificial Intelligence, Korea University, Seoul, South Korea
- Interdisciplinary Program in Precision Public Health, Korea University, Seoul, South Korea
- Joon-Kyung Seong,
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15
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Choi JD, Moon Y, Kim HJ, Yim Y, Lee S, Moon WJ. Choroid Plexus Volume and Permeability at Brain MRI within the Alzheimer Disease Clinical Spectrum. Radiology 2022; 304:635-645. [PMID: 35579521 DOI: 10.1148/radiol.212400] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Background Mounting evidence suggests that the choroid plexus (CP) plays an important role in the pathophysiology of Alzheimer disease (AD), but its imaging profile in cognitive impairment remains unclear. Purpose To evaluate CP volume, permeability, and susceptibility by using MRI in patients at various stages of cognitive impairment. Materials and Methods This retrospective study evaluated patients with cognitive symptoms who underwent 3.0-T MRI of the brain, including dynamic contrast-enhanced (DCE) imaging and quantitative susceptibility mapping (QSM), between January 2013 and May 2020. CP volume was automatically segmented using three-dimensional T1-weighted sequences; the volume transfer constant (ie, Ktrans) and fractional plasma volume (ie, Vp) were determined using DCE MRI, and susceptibility was assessed using QSM. The effects of CP volume, expressed as the ratio to intracranial volume, on cognition were evaluated using multivariable linear regression adjusted for age, sex, education, apolipoprotein E ε4 allele status, and volumetric measures. Results A total of 532 patients with cognitive symptoms (mean age, 72 years ± 9 [SD]; 388 women) were included: 78 with subjective cognitive impairment (SCI), 158 with early mild cognitive impairment (MCI), 149 with late MCI, and 147 with AD. Among these, 132 patients underwent DCE MRI and QSM. CP volume was greater in patients at more severe stages (ratio of intracranial volume × 103: 0.9 ± 0.3 for SCI, 1.0 ± 0.3 for early MCI, 1.1 ± 0.3 for late MCI, and 1.3 ± 0.4 for AD; P < .001). Lower Ktrans (r = -0.19; P = .03) and Vp (r = -0.20; P = .02) were negatively associated with CP volume; susceptibility was not (r = 0.15; P = .10). CP volume was negatively associated with memory (B = -0.67; standard error of the mean [SEM], 0.21; P = .01), executive function (B = -0.90; SEM, 0.31; P = .01), and global cognition (B = -0.82; SEM, 0.32; P = .01). Conclusion Among patients with cognitive symptoms, larger choroid plexus volume was associated with severity of cognitive impairment in the Alzheimer disease spectrum. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Chiang in this issue.
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Affiliation(s)
- Jong Duck Choi
- From the Departments of Radiology (J.D.C., W.J.M.) and Neurology (Y.M.), Konkuk University Medical Center, Konkuk University School of Medicine, 120-1 Neungdong-ro, Hwayang-dong, Gwangjin-gu, Seoul 05030, Korea; Research Institute of Medical Science, Konkuk University School of Medicine, Seoul, Korea (Y.M., W.J.M.); Department of Neurology, Hanyang University Hospital, Hanyang University School of Medicine, Seoul, Korea (H.J.K.); Department of Radiology, Chung-Ang University Hospital, Chung-Ang University School of Medicine, Seoul, Korea (Y.Y.); and Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea (S.L.)
| | - Yeonsil Moon
- From the Departments of Radiology (J.D.C., W.J.M.) and Neurology (Y.M.), Konkuk University Medical Center, Konkuk University School of Medicine, 120-1 Neungdong-ro, Hwayang-dong, Gwangjin-gu, Seoul 05030, Korea; Research Institute of Medical Science, Konkuk University School of Medicine, Seoul, Korea (Y.M., W.J.M.); Department of Neurology, Hanyang University Hospital, Hanyang University School of Medicine, Seoul, Korea (H.J.K.); Department of Radiology, Chung-Ang University Hospital, Chung-Ang University School of Medicine, Seoul, Korea (Y.Y.); and Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea (S.L.)
| | - Hee-Jin Kim
- From the Departments of Radiology (J.D.C., W.J.M.) and Neurology (Y.M.), Konkuk University Medical Center, Konkuk University School of Medicine, 120-1 Neungdong-ro, Hwayang-dong, Gwangjin-gu, Seoul 05030, Korea; Research Institute of Medical Science, Konkuk University School of Medicine, Seoul, Korea (Y.M., W.J.M.); Department of Neurology, Hanyang University Hospital, Hanyang University School of Medicine, Seoul, Korea (H.J.K.); Department of Radiology, Chung-Ang University Hospital, Chung-Ang University School of Medicine, Seoul, Korea (Y.Y.); and Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea (S.L.)
| | - Younghee Yim
- From the Departments of Radiology (J.D.C., W.J.M.) and Neurology (Y.M.), Konkuk University Medical Center, Konkuk University School of Medicine, 120-1 Neungdong-ro, Hwayang-dong, Gwangjin-gu, Seoul 05030, Korea; Research Institute of Medical Science, Konkuk University School of Medicine, Seoul, Korea (Y.M., W.J.M.); Department of Neurology, Hanyang University Hospital, Hanyang University School of Medicine, Seoul, Korea (H.J.K.); Department of Radiology, Chung-Ang University Hospital, Chung-Ang University School of Medicine, Seoul, Korea (Y.Y.); and Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea (S.L.)
| | - Subin Lee
- From the Departments of Radiology (J.D.C., W.J.M.) and Neurology (Y.M.), Konkuk University Medical Center, Konkuk University School of Medicine, 120-1 Neungdong-ro, Hwayang-dong, Gwangjin-gu, Seoul 05030, Korea; Research Institute of Medical Science, Konkuk University School of Medicine, Seoul, Korea (Y.M., W.J.M.); Department of Neurology, Hanyang University Hospital, Hanyang University School of Medicine, Seoul, Korea (H.J.K.); Department of Radiology, Chung-Ang University Hospital, Chung-Ang University School of Medicine, Seoul, Korea (Y.Y.); and Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea (S.L.)
| | - Won-Jin Moon
- From the Departments of Radiology (J.D.C., W.J.M.) and Neurology (Y.M.), Konkuk University Medical Center, Konkuk University School of Medicine, 120-1 Neungdong-ro, Hwayang-dong, Gwangjin-gu, Seoul 05030, Korea; Research Institute of Medical Science, Konkuk University School of Medicine, Seoul, Korea (Y.M., W.J.M.); Department of Neurology, Hanyang University Hospital, Hanyang University School of Medicine, Seoul, Korea (H.J.K.); Department of Radiology, Chung-Ang University Hospital, Chung-Ang University School of Medicine, Seoul, Korea (Y.Y.); and Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea (S.L.)
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16
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El-Sappagh S, Saleh H, Ali F, Amer E, Abuhmed T. Two-stage deep learning model for Alzheimer’s disease detection and prediction of the mild cognitive impairment time. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07263-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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17
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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.
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18
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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.
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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
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Seo Y, Jang H, Lee H. Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease. Life (Basel) 2022; 12:life12020275. [PMID: 35207561 PMCID: PMC8879055 DOI: 10.3390/life12020275] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 02/05/2022] [Accepted: 02/09/2022] [Indexed: 01/18/2023] Open
Abstract
Clinical trials for Alzheimer’s disease (AD) face multiple challenges, such as the high screen failure rate and the even allocation of heterogeneous participants. Artificial intelligence (AI), which has become a potent tool of modern science with the expansion in the volume, variety, and velocity of biological data, offers promising potential to address these issues in AD clinical trials. In this review, we introduce the current status of AD clinical trials and the topic of machine learning. Then, a comprehensive review is focused on the potential applications of AI in the steps of AD clinical trials, including the prediction of protein and MRI AD biomarkers in the prescreening process during eligibility assessment and the likelihood stratification of AD subjects into rapid and slow progressors in randomization. Finally, this review provides challenges, developments, and the future outlook on the integration of AI into AD clinical trials.
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Affiliation(s)
| | | | - Hyejoo Lee
- Correspondence: ; Tel.: +82-2-3410-1233; Fax: +82-2-3410-0052
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20
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Seok JM, Cho W, Son DH, Shin JH, Cho EB, Kim ST, Kim BJ, Seong JK, Min JH. Association of subcortical structural shapes with fatigue in neuromyelitis optica spectrum disorder. Sci Rep 2022; 12:1579. [PMID: 35091634 PMCID: PMC8799731 DOI: 10.1038/s41598-022-05531-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 01/03/2022] [Indexed: 01/18/2023] Open
Abstract
Although fatigue is a major symptom in patients with neuromyelitis optica spectrum disorder (NMOSD), the underlying mechanism remains unclear. We explored the relationship between subcortical structures and fatigue severity to identify neural substrates of fatigue in NMOSD. Clinical characteristics with brain magnetic resonance imaging were evaluated in forty patients with NMOSD. Fatigue was assessed using the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-fatigue) questionnaire (a higher score indicates less fatigue). We assessed the correlation between subcortical structures and fatigue severity using surface-based shape analysis. Most of the enrolled patients showed fatigue (72.5%; mean FACIT-fatigue score, 34.8 ± 10.8). The FACIT-fatigue score was negatively correlated with Expanded Disability Status Scale and Beck Depression Inventory scores (r = - 0.382, p = 0.016; r = - 0.578, p < 0.001). We observed that the right thalamus was the only extracted region for various threshold experiments. Further, patients with lower FACIT-fatigue scores (more fatigue) had decreased local shape volume in the right thalamus. Fatigue is common in patients with NMOSD, and atrophy in the right thalamus is strongly correlated with fatigue severity. The local shape volume of the right thalamus might serve as a biomarker of fatigue in NMOSD.
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Affiliation(s)
- Jin Myoung Seok
- Department of Neurology, Soonchunhyang University Hospital Cheonan, Soonchunhyang University College of Medicine, Cheonan, South Korea
| | - Wanzee Cho
- Department of Artificial Intelligence, Korea University, Seoul, South Korea
| | - Doo-Hwan Son
- School of Biomedical Engineering, Korea University, Seoul, South Korea
| | - Jong Hwa Shin
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Eun Bin Cho
- Department of Neurology, Gyeongsang Institute of Health Science, Gyeongsang National University School of Medicine, Jinju, South Korea
- Department of Neurology, Gyeongsang National University Changwon Hospital, Changwon, South Korea
| | - Sung Tae Kim
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Byoung Joon Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea
- Neuroscience Center, Samsung Medical Center, 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, Korea University, Seoul, South Korea.
| | - Ju-Hong Min
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea.
- Neuroscience Center, Samsung Medical Center, Seoul, South Korea.
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, 135-710, South Korea.
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Yuan S, Li H, Wu J, Sun X. Classification of Mild Cognitive Impairment With Multimodal Data Using Both Labeled and Unlabeled Samples. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2281-2290. [PMID: 33471765 DOI: 10.1109/tcbb.2021.3053061] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Mild Cognitive Impairment (MCI) is a preclinical stage of Alzheimer's Disease (AD) and is clinical heterogeneity. The classification of MCI is crucial for the early diagnosis and treatment of AD. In this study, we investigated the potential of using both labeled and unlabeled samples from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort to classify MCI through the multimodal co-training method. We utilized both structural magnetic resonance imaging (sMRI) data and genotype data of 364 MCI samples including 228 labeled and 136 unlabeled MCI samples from the ADNI-1 cohort. First, the selected quantitative trait (QT) features from sMRI data and SNP features from genotype data were used to build two initial classifiers on 228 labeled MCI samples. Then, the co-training method was implemented to obtain new labeled samples from 136 unlabeled MCI samples. Finally, the random forest algorithm was used to obtain a combined classifier to classify MCI patients in the independent ADNI-2 dataset. The experimental results showed that our proposed framework obtains an accuracy of 85.50 percent and an AUC of 0.825 for MCI classification, respectively, which showed that the combined utilization of sMRI and SNP data through the co-training method could significantly improve the performances of MCI classification.
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Grueso S, Viejo-Sobera R. Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review. Alzheimers Res Ther 2021; 13:162. [PMID: 34583745 PMCID: PMC8480074 DOI: 10.1186/s13195-021-00900-w] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 09/12/2021] [Indexed: 01/18/2023]
Abstract
BACKGROUND An increase in lifespan in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer's disease being the most prevalent. Advances in medical imaging and computational power enable new methods for the early detection of neurocognitive disorders with the goal of preventing or reducing cognitive decline. Computer-aided image analysis and early detection of changes in cognition is a promising approach for patients with mild cognitive impairment, sometimes a prodromal stage of Alzheimer's disease dementia. METHODS We conducted a systematic review following PRISMA guidelines of studies where machine learning was applied to neuroimaging data in order to predict whether patients with mild cognitive impairment might develop Alzheimer's disease dementia or remain stable. After removing duplicates, we screened 452 studies and selected 116 for qualitative analysis. RESULTS Most studies used magnetic resonance image (MRI) and positron emission tomography (PET) data but also magnetoencephalography. The datasets were mainly extracted from the Alzheimer's disease neuroimaging initiative (ADNI) database with some exceptions. Regarding the algorithms used, the most common was support vector machine with a mean accuracy of 75.4%, but convolutional neural networks achieved a higher mean accuracy of 78.5%. Studies combining MRI and PET achieved overall better classification accuracy than studies that only used one neuroimaging technique. In general, the more complex models such as those based on deep learning, combined with multimodal and multidimensional data (neuroimaging, clinical, cognitive, genetic, and behavioral) achieved the best performance. CONCLUSIONS Although the performance of the different methods still has room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals.
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Affiliation(s)
- Sergio Grueso
- Cognitive NeuroLab, Faculty of Health Sciences, Universitat Oberta de Catalunya (UOC), Rambla del Poblenou 156, 08018, Barcelona, Spain.
| | - Raquel Viejo-Sobera
- Cognitive NeuroLab, Faculty of Health Sciences, Universitat Oberta de Catalunya (UOC), Rambla del Poblenou 156, 08018, Barcelona, Spain
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Cheng B, Zhu B, Pu S. Multi-auxiliary domain transfer learning for diagnosis of MCI conversion. Neurol Sci 2021; 43:1721-1739. [PMID: 34510292 DOI: 10.1007/s10072-021-05568-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 08/14/2021] [Indexed: 01/18/2023]
Abstract
In the early stage of Alzheimer's disease (AD), mild cognitive impairment (MCI) has a higher risk of progression to AD, so the prediction of whether an MCI subject will progress to AD (known as progressive MCI, PMCI) or not (known as stable MCI, SMCI) within a certain period is particularly important in practice. It is known that such a task could benefit from jointly learning-related auxiliary tasks such as differentiating AD from PMCI or PMCI from normal control (NC) in order to take full advantage of their shared commonality. However, few existing methods along this line fully consider the correlations between the target and auxiliary tasks according to the clinical practice of AD pathology for diagnosis. To deal with this problem, in this paper, treating each task domain as a different one, we borrow the idea from transfer learning and propose a novel multi-auxiliary domain transfer learning (MaDTL) method, which explicitly utilizes the correlations between the target domain (task) and multi-auxiliary domains (tasks) according to the clinical practice. Specifically, the proposed MaDTL method incorporates two key modules. The first one is a multi-auxiliary domain transfer-based feature selection (MaDTFS) model, which can select a discriminative feature subset shared by the target domain and the multi-auxiliary domains. In the MaDTFS model, to combine more training data from multi-auxiliary domains and simultaneously suppress the negative effects resulting from the irrelevant parts of multi-auxiliary domains, we proposed a sparse group correlation Lasso that includes a proposed group correlation Lasso penalty (i.e., [Formula: see text]) and a proposed correlation Lasso penalty (i.e., [Formula: see text]). The second module in MaDTL is a multi-auxiliary domain transfer-based classification (MaDTC) model that improves the voting with linear weighting-based ensemble learning. This model extends the constraints of the linear weighting method so that it can simultaneously combine training data from multi-auxiliary domains and achieve a robust classifier by minimizing negative effects from the irrelevant part of multi-auxiliary domains. Experimental results on 409 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database with the baseline magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) data validate the effectiveness of the proposed method by significantly improving the classification accuracy to 80.37% for the identification of MCI-to-AD conversion, outperforming the state-of-the-art methods.
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Affiliation(s)
- Bo Cheng
- Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education, Chongqing Three Gorges University, Chongqing, 404100, China.
- College of Computer Science and Engineering, Chongqing Three Gorges University, Chongqing, 404100, China.
| | - Bingli Zhu
- College of Computer Science and Engineering, Chongqing Three Gorges University, Chongqing, 404100, China
| | - Shuchang Pu
- Department of Logistics Management, Chongqing Three Gorges University, Chongqing, 404100, China
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Li Y, Fang Y, Wang J, Zhang H, Hu B. Biomarker Extraction Based on Subspace Learning for the Prediction of Mild Cognitive Impairment Conversion. BIOMED RESEARCH INTERNATIONAL 2021; 2021:5531940. [PMID: 34513992 PMCID: PMC8429015 DOI: 10.1155/2021/5531940] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 08/13/2021] [Indexed: 01/18/2023]
Abstract
Accurate recognition of progressive mild cognitive impairment (MCI) is helpful to reduce the risk of developing Alzheimer's disease (AD). However, it is still challenging to extract effective biomarkers from multivariate brain structural magnetic resonance imaging (MRI) features to accurately differentiate the progressive MCI from stable MCI. We develop novel biomarkers by combining subspace learning methods with the information of AD as well as normal control (NC) subjects for the prediction of MCI conversion using multivariate structural MRI data. Specifically, we first learn two projection matrices to map multivariate structural MRI data into a common label subspace for AD and NC subjects, where the original data structure and the one-to-one correspondence between multiple variables are kept as much as possible. Afterwards, the multivariate structural MRI features of MCI subjects are mapped into a common subspace according to the projection matrices. We then perform the self-weighted operation and weighted fusion on the features in common subspace to extract the novel biomarkers for MCI subjects. The proposed biomarkers are tested on Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Experimental results indicate that our proposed biomarkers outperform the competing biomarkers on the discrimination between progressive MCI and stable MCI. And the improvement from the proposed biomarkers is not limited to a particular classifier. Moreover, the results also confirm that the information of AD and NC subjects is conducive to predicting conversion from MCI to AD. In conclusion, we find a good representation of brain features from high-dimensional MRI data, which exhibits promising performance for predicting conversion from MCI to AD.
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Affiliation(s)
- Ying Li
- Key Laboratory of TCM Data Cloud Service in Universities of Shandong, Shandong Management University, Jinan 250357, China
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
| | - Yixian Fang
- School of Mathematics and Statistics, Qilu University of Technology, Jinan 250353, China
| | | | - Huaxiang Zhang
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
| | - Bin Hu
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
- Key Laboratory of Wearable Computing of Gansu Province, Lanzhou University, Lanzhou 730000, China
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Zhu W, Sun L, Huang J, Han L, Zhang D. Dual Attention Multi-Instance Deep Learning for Alzheimer's Disease Diagnosis With Structural MRI. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:2354-2366. [PMID: 33939609 DOI: 10.1109/tmi.2021.3077079] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Structural magnetic resonance imaging (sMRI) is widely used for the brain neurological disease diagnosis, which could reflect the variations of brain. However, due to the local brain atrophy, only a few regions in sMRI scans have obvious structural changes, which are highly correlative with pathological features. Hence, the key challenge of sMRI-based brain disease diagnosis is to enhance the identification of discriminative features. To address this issue, we propose a dual attention multi-instance deep learning network (DA-MIDL) for the early diagnosis of Alzheimer's disease (AD) and its prodromal stage mild cognitive impairment (MCI). Specifically, DA-MIDL consists of three primary components: 1) the Patch-Nets with spatial attention blocks for extracting discriminative features within each sMRI patch whilst enhancing the features of abnormally changed micro-structures in the cerebrum, 2) an attention multi-instance learning (MIL) pooling operation for balancing the relative contribution of each patch and yield a global different weighted representation for the whole brain structure, and 3) an attention-aware global classifier for further learning the integral features and making the AD-related classification decisions. Our proposed DA-MIDL model is evaluated on the baseline sMRI scans of 1689 subjects from two independent datasets (i.e., ADNI and AIBL). The experimental results show that our DA-MIDL model can identify discriminative pathological locations and achieve better classification performance in terms of accuracy and generalizability, compared with several state-of-the-art methods.
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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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Guan H, Liu Y, Yang E, Yap PT, Shen D, Liu M. Multi-site MRI harmonization via attention-guided deep domain adaptation for brain disorder identification. Med Image Anal 2021; 71:102076. [PMID: 33930828 PMCID: PMC8184627 DOI: 10.1016/j.media.2021.102076] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 12/21/2020] [Accepted: 04/03/2021] [Indexed: 01/18/2023]
Abstract
Structural magnetic resonance imaging (MRI) has shown great clinical and practical values in computer-aided brain disorder identification. Multi-site MRI data increase sample size and statistical power, but are susceptible to inter-site heterogeneity caused by different scanners, scanning protocols, and subject cohorts. Multi-site MRI harmonization (MMH) helps alleviate the inter-site difference for subsequent analysis. Some MMH methods performed at imaging level or feature extraction level are concise but lack robustness and flexibility to some extent. Even though several machine/deep learning-based methods have been proposed for MMH, some of them require a portion of labeled data in the to-be-analyzed target domain or ignore the potential contributions of different brain regions to the identification of brain disorders. In this work, we propose an attention-guided deep domain adaptation (AD2A) framework for MMH and apply it to automated brain disorder identification with multi-site MRIs. The proposed framework does not need any category label information of target data, and can also automatically identify discriminative regions in whole-brain MR images. Specifically, the proposed AD2A is composed of three key modules: (1) an MRI feature encoding module to extract representations of input MRIs, (2) an attention discovery module to automatically locate discriminative dementia-related regions in each whole-brain MRI scan, and (3) a domain transfer module trained with adversarial learning for knowledge transfer between the source and target domains. Experiments have been performed on 2572 subjects from four benchmark datasets with T1-weighted structural MRIs, with results demonstrating the effectiveness of the proposed method in both tasks of brain disorder identification and disease progression prediction.
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Affiliation(s)
- Hao Guan
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yunbi Liu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Erkun Yang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Mingxia Liu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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Jové M, Mota-Martorell N, Torres P, Ayala V, Portero-Otin M, Ferrer I, Pamplona R. The Causal Role of Lipoxidative Damage in Mitochondrial Bioenergetic Dysfunction Linked to Alzheimer's Disease Pathology. Life (Basel) 2021; 11:life11050388. [PMID: 33923074 PMCID: PMC8147054 DOI: 10.3390/life11050388] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 04/19/2021] [Accepted: 04/21/2021] [Indexed: 01/18/2023] Open
Abstract
Current shreds of evidence point to the entorhinal cortex (EC) as the origin of the Alzheimer’s disease (AD) pathology in the cerebrum. Compared with other cortical areas, the neurons from this brain region possess an inherent selective vulnerability derived from particular oxidative stress conditions that favor increased mitochondrial molecular damage with early bioenergetic involvement. This alteration of energy metabolism is the starting point for subsequent changes in a multitude of cell mechanisms, leading to neuronal dysfunction and, ultimately, cell death. These events are induced by changes that come with age, creating the substrate for the alteration of several neuronal pathways that will evolve toward neurodegeneration and, consequently, the development of AD pathology. In this context, the present review will focus on description of the biological mechanisms that confer vulnerability specifically to neurons of the entorhinal cortex, the changes induced by the aging process in this brain region, and the alterations at the mitochondrial level as the earliest mechanism for the development of AD pathology. Current findings allow us to propose the existence of an altered allostatic mechanism at the entorhinal cortex whose core is made up of mitochondrial oxidative stress, lipid metabolism, and energy production, and which, in a positive loop, evolves to neurodegeneration, laying the basis for the onset and progression of AD pathology.
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Affiliation(s)
- Mariona Jové
- Department of Experimental Medicine, Lleida Biomedical Research Institute (IRBLleida), Lleida University (UdL), 25198 Lleida, Spain; (M.J.); (N.M.-M.); (P.T.); (V.A.); (M.P.-O.)
| | - Natàlia Mota-Martorell
- Department of Experimental Medicine, Lleida Biomedical Research Institute (IRBLleida), Lleida University (UdL), 25198 Lleida, Spain; (M.J.); (N.M.-M.); (P.T.); (V.A.); (M.P.-O.)
| | - Pascual Torres
- Department of Experimental Medicine, Lleida Biomedical Research Institute (IRBLleida), Lleida University (UdL), 25198 Lleida, Spain; (M.J.); (N.M.-M.); (P.T.); (V.A.); (M.P.-O.)
| | - Victoria Ayala
- Department of Experimental Medicine, Lleida Biomedical Research Institute (IRBLleida), Lleida University (UdL), 25198 Lleida, Spain; (M.J.); (N.M.-M.); (P.T.); (V.A.); (M.P.-O.)
| | - Manuel Portero-Otin
- Department of Experimental Medicine, Lleida Biomedical Research Institute (IRBLleida), Lleida University (UdL), 25198 Lleida, Spain; (M.J.); (N.M.-M.); (P.T.); (V.A.); (M.P.-O.)
| | - Isidro Ferrer
- Department of Pathology and Experimental Therapeutics, University of Barcelona, Bellvitge University Hospital/Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, 08907 Barcelona, Spain
- Center for Biomedical Research on Neurodegenerative Diseases (CIBERNED), ISCIII, 28220 Madrid, Spain
- Correspondence: (I.F.); (R.P.)
| | - Reinald Pamplona
- Department of Experimental Medicine, Lleida Biomedical Research Institute (IRBLleida), Lleida University (UdL), 25198 Lleida, Spain; (M.J.); (N.M.-M.); (P.T.); (V.A.); (M.P.-O.)
- Correspondence: (I.F.); (R.P.)
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Zhao X, Ang CKE, Acharya UR, Cheong KH. Application of Artificial Intelligence techniques for the detection of Alzheimer’s disease using structural MRI images. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.02.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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30
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Xiao R, Cui X, Qiao H, Zheng X, Zhang Y, Zhang C, Liu X. Early diagnosis model of Alzheimer’s disease based on sparse logistic regression with the generalized elastic net. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102362] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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31
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Zhang J, Zheng B, Gao A, Feng X, Liang D, Long X. A 3D densely connected convolution neural network with connection-wise attention mechanism for Alzheimer's disease classification. Magn Reson Imaging 2021; 78:119-126. [PMID: 33588019 DOI: 10.1016/j.mri.2021.02.001] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 01/18/2021] [Accepted: 02/03/2021] [Indexed: 01/18/2023]
Abstract
PURPOSE Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. In recent years, machine learning methods have been widely used on analysis of neuroimage for quantitative evaluation and computer-aided diagnosis of AD or prediction on the conversion from mild cognitive impairment (MCI) to AD. In this study, we aimed to develop a new deep learning method to detect or predict AD in an efficient way. MATERIALS AND METHODS We proposed a densely connected convolution neural network with connection-wise attention mechanism to learn the multi-level features of brain MR images for AD classification. We used the densely connected neural network to extract multi-scale features from pre-processed images, and connection-wise attention mechanism was applied to combine connections among features from different layers to hierarchically transform the MR images into more compact high-level features. Furthermore, we extended the convolution operation to 3D to capture the spatial information of MRI. The features extracted from each 3D convolution layer were integrated with features from all preceding layers with different attention, and were finally used for classification. Our method was evaluated on the baseline MRI of 968 subjects from ADNI database to discriminate (1) AD versus healthy subjects, (2) MCI converters versus healthy subjects, and (3) MCI converters versus non-converters. RESULTS The proposed method achieved 97.35% accuracy for distinguishing AD patients from healthy control, 87.82% for MCI converters against healthy control, and 78.79% for MCI converters against non-converters. Compared with some neural networks and methods reported in recent studies, the classification performance of our proposed algorithm was among the top ranks and improved in discriminating MCI subjects who were in high risks of conversion to AD. CONCLUSIONS Deep learning techniques provide a powerful tool to explore minute but intricate characteristics in MR images which may facilitate early diagnosis and prediction of AD.
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Affiliation(s)
- Jie Zhang
- Reasearch Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China; Computer Science and Engineering, Chongqing University of Technology, China
| | - Bowen Zheng
- Reasearch Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China
| | - Ang Gao
- Reasearch Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China
| | - Xin Feng
- Computer Science and Engineering, Chongqing University of Technology, China; Big Data and Machine Learning Lab, China
| | - Dong Liang
- Reasearch Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China; Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, China
| | - Xiaojing Long
- Reasearch Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China; Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, China.
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Abuhmed T, El-Sappagh S, Alonso JM. Robust hybrid deep learning models for Alzheimer’s progression detection. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2020.106688] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Skolariki K, Terrera GM, Danso SO. Predictive models for mild cognitive impairment to Alzheimer's disease conversion. Neural Regen Res 2021; 16:1766-1767. [PMID: 33510068 PMCID: PMC8328792 DOI: 10.4103/1673-5374.306071] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Affiliation(s)
- Konstantina Skolariki
- Bioinformatics and Human Electrophysiolog Laboratory, Ionian University, Corfu, Greece
| | - Graciella Muniz Terrera
- Centre for Dementia Prevention; Division of Neuroimaging, Centre for Clinical Brain Sciences and Edinburgh Imaging, University of Edinburgh, Scotland, UK
| | - Samuel O Danso
- Division of Neuroimaging, Centre for Clinical Brain Sciences and Edinburgh Imaging, University of Edinburgh, Scotland, UK
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Lee J, Lee JY, Oh SW, Chung MS, Park JE, Moon Y, Jeon HJ, Moon WJ. Evaluation of Reproducibility of Brain Volumetry between Commercial Software, Inbrain and Established Research Purpose Method, FreeSurfer. J Clin Neurol 2021; 17:307-316. [PMID: 33835753 PMCID: PMC8053534 DOI: 10.3988/jcn.2021.17.2.307] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 02/02/2021] [Accepted: 02/02/2021] [Indexed: 01/18/2023] Open
Abstract
Background and Purpose We aimed to determine the intermethod reproducibility between the commercial software Inbrain (MIDAS IT) and the established research-purpose method FreeSurfer, as well as the effect of MRI resolution and the pathological condition of subjects on their intermethod reproducibility. Methods This study included 45 healthy volunteers and 85 patients with mild cognitive impairment (MCI). In 43 of the 85 patients with MCI, three-dimensional, T1-weighted MRI data were obtained at an in-plane resolution of 1.2 mm. The data of the remaining 42 patients with MCI and the healthy volunteers were obtained at an in-plane resolution of 1.0 mm. The within-subject coefficient of variation (CoV), intraclass correlation coefficient (ICC), and effect size were calculated, and means were compared using paired t-tests. The parameters obtained at 1.0-mm and 1.2-mm resolutions in patients with MCI were compared to evaluate the effect of the in-plane resolution on the intermethod reproducibility. The parameters obtained at a 1.0-mm in-plane resolution in patients with MCI and healthy volunteers were used to analyze the effect of subject condition on intermethod reproducibility. Results Overall the two methods showed excellent reproducibility across all regions of the brain (CoV=0.5–3.9, ICC=0.93 to >0.99). In the subgroup of healthy volunteers, the intermethod reliability was only good in some regions (frontal, temporal, cingulate, and insular). The intermethod reproducibility was better in the 1.0-mm group than the 1.2-mm group in all regions other than the nucleus accumbens. Conclusions Inbrain and FreeSurfer showed good-to-excellent intermethod reproducibility for volumetric measurements. Nevertheless, some noticeable differences were found based on subject condition, image resolution, and brain region.
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Affiliation(s)
- Jungbin Lee
- Department of Radiology, Soonchunghyang University Bucheon Hospital, Bucheon, Korea
| | - Ji Young Lee
- Department of Radiology, Hanyang University Medical Center, Seoul, Korea
| | - Se Won Oh
- Department of Radiology, Soonchunhyang University Cheonan Hospital, Cheonan, Korea
| | - Mi Sun Chung
- Department of Radiology, Chung-Ang University Hospital, Seoul, Korea
| | - Ji Eun Park
- Department of Radiology, Asan Medical Center, Seoul, Korea
| | - Yeonsil Moon
- Department of Neurology, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea
| | - Hong Jun Jeon
- Department of Psychiatry, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea
| | - Won Jin Moon
- Department of Radiology, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea.
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Yee E, Ma D, Popuri K, Wang L, Beg MF. Construction of MRI-Based Alzheimer's Disease Score Based on Efficient 3D Convolutional Neural Network: Comprehensive Validation on 7,902 Images from a Multi-Center Dataset. J Alzheimers Dis 2021; 79:47-58. [PMID: 33252079 PMCID: PMC9159475 DOI: 10.3233/jad-200830] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
BACKGROUND In recent years, many convolutional neural networks (CNN) have been proposed for the classification of Alzheimer's disease. Due to memory constraints, many of the proposed CNNs work at a 2D slice-level or 3D patch-level. OBJECTIVE Here, we propose a subject-level 3D CNN that can extract the neurodegenerative patterns of the whole brain MRI and converted into a probabilistic Dementia score. METHODS We propose an efficient and lightweight subject-level 3D CNN featuring dilated convolutions. We trained our network on the ADNI data on stable Dementia of the Alzheimer's type (sDAT) from stable normal controls (sNC). To comprehensively evaluate the generalizability of our proposed network, we performed four independent tests which includes testing on images from other ADNI individuals at various stages of the dementia, images acquired from other sites (AIBL), images acquired using different protocols (OASIS), and longitudinal images acquired over a short period of time (MIRIAD). RESULTS We achieved a 5-fold cross-validated balanced accuracy of 88%in differentiating sDAT from sNC, and an overall specificity of 79.5%and sensitivity 79.7%on the entire set of 7,902 independent test images. CONCLUSION Independent testing is essential for estimating the generalization ability of the network to unseen data, but is often lacking in studies using CNN for DAT classification. This makes it difficult to compare the performances achieved using different architectures. Our comprehensive evaluation highlighting the competitive performance of our network and potential promise for generalization.
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Affiliation(s)
- Evangeline Yee
- School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Da Ma
- School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Karteek Popuri
- School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Lei Wang
- Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Mirza Faisal Beg
- School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada
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Ozzoude M, Ramirez J, Raamana PR, Holmes MF, Walker K, Scott CJM, Gao F, Goubran M, Kwan D, Tartaglia MC, Beaton D, Saposnik G, Hassan A, Lawrence-Dewar J, Dowlatshahi D, Strother SC, Symons S, Bartha R, Swartz RH, Black SE. Cortical Thickness Estimation in Individuals With Cerebral Small Vessel Disease, Focal Atrophy, and Chronic Stroke Lesions. Front Neurosci 2020; 14:598868. [PMID: 33381009 PMCID: PMC7768006 DOI: 10.3389/fnins.2020.598868] [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: 08/25/2020] [Accepted: 11/24/2020] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Regional changes to cortical thickness in individuals with neurodegenerative and cerebrovascular diseases (CVD) can be estimated using specialized neuroimaging software. However, the presence of cerebral small vessel disease, focal atrophy, and cortico-subcortical stroke lesions, pose significant challenges that increase the likelihood of misclassification errors and segmentation failures. PURPOSE The main goal of this study was to examine a correction procedure developed for enhancing FreeSurfer's (FS's) cortical thickness estimation tool, particularly when applied to the most challenging MRI obtained from participants with chronic stroke and CVD, with varying degrees of neurovascular lesions and brain atrophy. METHODS In 155 CVD participants enrolled in the Ontario Neurodegenerative Disease Research Initiative (ONDRI), FS outputs were compared between a fully automated, unmodified procedure and a corrected procedure that accounted for potential sources of error due to atrophy and neurovascular lesions. Quality control (QC) measures were obtained from both procedures. Association between cortical thickness and global cognitive status as assessed by the Montreal Cognitive Assessment (MoCA) score was also investigated from both procedures. RESULTS Corrected procedures increased "Acceptable" QC ratings from 18 to 76% for the cortical ribbon and from 38 to 92% for tissue segmentation. Corrected procedures reduced "Fail" ratings from 11 to 0% for the cortical ribbon and 62 to 8% for tissue segmentation. FS-based segmentation of T1-weighted white matter hypointensities were significantly greater in the corrected procedure (5.8 mL vs. 15.9 mL, p < 0.001). The unmodified procedure yielded no significant associations with global cognitive status, whereas the corrected procedure yielded positive associations between MoCA total score and clusters of cortical thickness in the left superior parietal (p = 0.018) and left insula (p = 0.04) regions. Further analyses with the corrected cortical thickness results and MoCA subscores showed a positive association between left superior parietal cortical thickness and Attention (p < 0.001). CONCLUSION These findings suggest that correction procedures which account for brain atrophy and neurovascular lesions can significantly improve FS's segmentation results and reduce failure rates, thus maximizing power by preventing the loss of our important study participants. Future work will examine relationships between cortical thickness, cerebral small vessel disease, and cognitive dysfunction due to neurodegenerative disease in the ONDRI study.
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Affiliation(s)
- Miracle Ozzoude
- LC Campbell Cognitive Neurology Research, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Joel Ramirez
- LC Campbell Cognitive Neurology Research, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | | | - Melissa F. Holmes
- LC Campbell Cognitive Neurology Research, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Kirstin Walker
- LC Campbell Cognitive Neurology Research, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Christopher J. M. Scott
- LC Campbell Cognitive Neurology Research, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Fuqiang Gao
- LC Campbell Cognitive Neurology Research, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Maged Goubran
- LC Campbell Cognitive Neurology Research, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Donna Kwan
- Centre for Neuroscience Studies, Queens University, Kingston, ON, Canada
| | - Maria C. Tartaglia
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, Canada
- Division of Neurology, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Derek Beaton
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - Gustavo Saposnik
- Stroke Outcomes and Decision Neuroscience Research Unit, Division of Neurology, St. Michael’s Hospital, University of Toronto, Toronto, ON, Canada
| | - Ayman Hassan
- Thunder Bay Regional Health Research Institute, Thunder Bay, ON, Canada
| | | | - Dariush Dowlatshahi
- Department of Medicine (Neurology), Ottawa Hospital Research Institute, University of Ottawa, Ottawa, ON, Canada
| | - Stephen C. Strother
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Sean Symons
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Robert Bartha
- Centre for Functional and Metabolic Mapping, Department of Medical Biophysics, Robarts Research Institute, University of Western Ontario, London, ON, Canada
| | - Richard H. Swartz
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Sandra E. Black
- LC Campbell Cognitive Neurology Research, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
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Lee JY, Oh SW, Chung MS, Park JE, Moon Y, Jeon HJ, Moon WJ. Clinically Available Software for Automatic Brain Volumetry: Comparisons of Volume Measurements and Validation of Intermethod Reliability. Korean J Radiol 2020; 22:405-414. [PMID: 33236539 PMCID: PMC7909859 DOI: 10.3348/kjr.2020.0518] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Revised: 06/08/2020] [Accepted: 06/17/2020] [Indexed: 01/18/2023] Open
Abstract
OBJECTIVE To compare two clinically available MR volumetry software, NeuroQuant® (NQ) and Inbrain® (IB), and examine the inter-method reliabilities and differences between them. MATERIALS AND METHODS This study included 172 subjects (age range, 55-88 years; mean age, 71.2 years), comprising 45 normal healthy subjects, 85 patients with mild cognitive impairment, and 42 patients with Alzheimer's disease. Magnetic resonance imaging scans were analyzed with IB and NQ. Mean differences were compared with the paired t test. Inter-method reliability was evaluated with Pearson's correlation coefficients and intraclass correlation coefficients (ICCs). Effect sizes were also obtained to document the standardized mean differences. RESULTS The paired t test showed significant volume differences in most regions except for the amygdala between the two methods. Nevertheless, inter-method measurements between IB and NQ showed good to excellent reliability (0.72 < r < 0.96, 0.83 < ICC < 0.98) except for the pallidum, which showed poor reliability (left: r = 0.03, ICC = 0.06; right: r = -0.05, ICC = -0.09). For the measurements of effect size, volume differences were large in most regions (0.05 < r < 6.15). The effect size was the largest in the pallidum and smallest in the cerebellum. CONCLUSION Comparisons between IB and NQ showed significantly different volume measurements with large effect sizes. However, they showed good to excellent inter-method reliability in volumetric measurements for all brain regions, with the exception of the pallidum. Clinicians using these commercial software should take into consideration that different volume measurements could be obtained depending on the software used.
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Affiliation(s)
- Ji Young Lee
- Department of Radiology, Hanyang University Medical Center, Seoul, Korea
| | - Se Won Oh
- Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Mi Sun Chung
- Department of Radiology, Chung-Ang University Hospital, Seoul, Korea
| | - Ji Eun Park
- Department of Radiology, Asan Medical Center, Seoul, Korea
| | - Yeonsil Moon
- Department of Neurology, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea
| | - Hong Jun Jeon
- Department of Psychiatry, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea
| | - Won Jin Moon
- Department of Radiology, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea.
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Cho J, Noh Y, Kim SY, Sohn J, Noh J, Kim W, Cho SK, Seo H, Seo G, Lee SK, Seo S, Koh SB, Oh SS, Kim HJ, Seo SW, Shin DS, Kim N, Kim HH, Lee JI, Kim C. Long-Term Ambient Air Pollution Exposures and Brain Imaging Markers in Korean Adults: The Environmental Pollution-Induced Neurological EFfects (EPINEF) Study. ENVIRONMENTAL HEALTH PERSPECTIVES 2020; 128:117006. [PMID: 33215932 PMCID: PMC7678746 DOI: 10.1289/ehp7133] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
BACKGROUND Only a limited number of neuroimaging studies have explored the effects of ambient air pollution in adults. The prior studies have investigated only cortical volume, and they have reported mixed findings, particularly for gray matter. Furthermore, the association between nitrogen dioxide (NO2) and neuroimaging markers has been little studied in adults. OBJECTIVES We investigated the association between long-term exposure to air pollutants (NO2, particulate matter (PM) with aerodynamic diameters of ≤10μm (PM10) and ≤2.5μm (PM2.5), and neuroimaging markers. METHODS The study included 427 men and 530 women dwelling in four cities in the Republic of Korea. Long-term concentrations of PM10, NO2, and PM2.5 at residential addresses were estimated. Neuroimaging markers (cortical thickness and subcortical volume) were obtained from brain magnetic resonance images. A generalized linear model was used, adjusting for potential confounders. RESULTS A 10-μg/m3 increase in PM10 was associated with reduced thicknesses in the frontal [-0.02mm (95% CI: -0.03, -0.01)] and temporal lobes [-0.06mm (95% CI: -0.07, -0.04)]. A 10-μg/m3 increase in PM2.5 was associated with a thinner temporal cortex [-0.18mm (95% CI: -0.27, -0.08)]. A 10-ppb increase in NO2 was associated with reduced thicknesses in the global [-0.01mm (95% CI: -0.01, 0.00)], frontal [-0.02mm (95% CI: -0.03, -0.01)], parietal [-0.02mm (95% CI: -0.03, -0.01)], temporal [-0.04mm (95% CI: -0.05, -0.03)], and insular lobes [-0.01mm (95% CI: -0.02, 0.00)]. The air pollutants were also associated with increased thicknesses in the occipital and cingulate lobes. Subcortical structures associated with the air pollutants included the thalamus, caudate, pallidum, hippocampus, amygdala, and nucleus accumbens. DISCUSSION The findings suggest that long-term exposure to high ambient air pollution may lead to cortical thinning and reduced subcortical volume in adults. https://doi.org/10.1289/EHP7133.
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Affiliation(s)
- Jaelim Cho
- School of Medicine, University of Auckland, Auckland, New Zealand
- Institute of Human Complexity and Systems Science, Yonsei University, Incheon, Republic of Korea
- Institute for Environmental Research, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Young Noh
- Department of Neurology, Gachon University Gil Medical Center, Incheon, Republic of Korea
| | - Sun Young Kim
- Department of Cancer Control and Population Health, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Republic of Korea
| | - Jungwoo Sohn
- Department of Preventive Medicine, Jeonbuk National University Medical School, Jeonju, Republic of Korea
| | - Juhwan Noh
- Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Woojin Kim
- Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Seong-Kyung Cho
- Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hwasun Seo
- Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Gayoung Seo
- Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Seung-Koo Lee
- Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Seongho Seo
- Department of Neuroscience, Gachon University College of Medicine, Incheon, Republic of Korea
- Department of Electronic Engineering, Pai Chai University, Daejeon, Republic of Korea
| | - Sang-Baek Koh
- Department of Occupational and Environmental Medicine, Wonju Severance Christian Hospital, Wonju College of Medicine, Yonsei University, Wonju, Republic of Korea
| | - Sung Soo Oh
- Department of Occupational and Environmental Medicine, Wonju Severance Christian Hospital, Wonju College of Medicine, Yonsei University, Wonju, Republic of Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Dae-Seock Shin
- MIDAS Information Technology Co., Ltd., Seongnam, Republic of Korea
| | - Nakyoung Kim
- MIDAS Information Technology Co., Ltd., Seongnam, Republic of Korea
| | - Ho Hyun Kim
- Department of Integrated Environmental Systems, Pyeongtaek University, Pyeongtaek, Republic of Korea
| | - Jung Il Lee
- Korea Testing & Research Institute, Gwacheon, Republic of Korea
| | - Changsoo Kim
- Institute of Human Complexity and Systems Science, Yonsei University, Incheon, Republic of Korea
- Institute for Environmental Research, Yonsei University College of Medicine, Seoul, Republic of Korea
- Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
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A dynamic decision model for diagnosis of dementia, Alzheimer's disease and Mild Cognitive Impairment. Comput Biol Med 2020; 126:104010. [PMID: 33007623 DOI: 10.1016/j.compbiomed.2020.104010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 09/15/2020] [Accepted: 09/16/2020] [Indexed: 01/18/2023]
Abstract
CDSS (Clinical Decision Support System) is a domain within digital health that aims at supporting clinicians by suggesting the most probable diagnosis based on knowledge obtained from patient data. Usually, decision models used by current CDSS are static, i.e., they are not updated when new data are included, which could allow them to acquire new knowledge and enhance system accuracy. This paper proposes a dynamic decision model that automatically updates itself from classifier models using supervised machine learning algorithms. Our supervised learning process ranks several decision models using classifier performance measures, considering available patient data, filled by the health center, or local clinical guidelines. The decision model with the best performance is then selected to be used in our CDSS, which is designed for the diagnosis of D (Dementia), AD (Alzheimer's Disease), and MCI (Mild Cognitive Impairment). Patient datasets from CAD (Center for Alzheimer's Disease), at the Institute of Psychiatry of UFRJ (Federal University of Rio de Janeiro), and CRASI (Center of Reference in Attention to Health of the Elderly), at Antonio Pedro Hospital of UFF (Fluminense Federal University), are used. The main conclusion is that the proposed dynamic decision model, which offers the ability to be continuously refined with more recent diagnostic criteria or even personalized according to the local domain or clinical guidelines, provides an efficient alternative for diagnosis of Dementia, AD, and MCI.
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Multivariate Data Analysis and Machine Learning for Prediction of MCI-to-AD Conversion. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2020; 1194:81-103. [PMID: 32468526 DOI: 10.1007/978-3-030-32622-7_8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
There has always been a need for discovering efficient and dependable Alzheimer's disease (AD) diagnostic biomarkers. Like the majority of diseases, the earlier the diagnosis, the most effective the treatment. (Semi)-automated structural magnetic resonance imaging (MRI) processing approaches are very popular in AD research. Mild cognitive impairment (MCI) is considered to be a stage between normal cognitive ageing and dementia. MCI can often be the prodromal stage of AD. Around 10-15% of MCI patients convert to AD per year. In this study, we used three supervised machine learning (ML) techniques to differentiate MCI converters (MCIc) from MCI non-converters (MCInc) and predict their conversion rates from baseline MRI data (cortical thickness (CTH) and hippocampal volume (HCV)). A total of 803 participants from the ADNI cohort were included in this study (188 AD, 107 MCIc, 257 MCInc and 156 healthy controls (HC)). We studied the classification abilities of three different WEKA classifiers (support vector machine (SVM), decision trees (J48) and Naive Bayes (NB)). We built six different classification models, three models based on CTH and three based on HCV (CTH-SVM, CTH-J48, CTH-NB, HCV-SVM, HCV-J48 and HCV-NB). For the classification experiments, we obtained up to 71% sensitivity and up to 56% specificity. The prediction of conversion showed accuracy for up to 84%. The value of certain multivariate models derived from the classification experiments has exhibited robust and effective results in MCIc identification. However, there was a limitation in this study since we could not compare the CTH with the HCV models seeing as the data used originated from different subjects. As future direction, we propose the creation of a model that would combine various features with data originating from the same subjects, thus being a far more reliable and accurate prognostic tool.
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Chin J, Kim DE, Lee H, Yun J, Lee BH, Park J, Yeom J, Shin DS, Na DL. A Validation Study of the Inbrain CST: a Tablet Computer-based Cognitive Screening Test for Elderly People with Cognitive Impairment. J Korean Med Sci 2020; 35:e292. [PMID: 32864906 PMCID: PMC7458852 DOI: 10.3346/jkms.2020.35.e292] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 07/07/2020] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Computerized versions of cognitive screening test could have advantages over pencil-and-paper versions by eliminating rater-dependent factors and saving the time required to score the tests and report the results. We developed a computerized cognitive screening test (Inbrain Cognitive Screening Test [Inbrain CST]) that takes about 30 minutes to administer on a touchscreen computer and is composed of neuropsychological tests already shown to be sensitive in detecting early cognitive decline in Alzheimer's disease (AD). The aims of this study were to 1) introduce normative data for Inbrain CST, 2) verify its reliability and validity, 3) assess clinical usefulness, and 4) identify neuroanatomical correlates of Inbrain CST. METHODS The Inbrain CST runs on the Microsoft Windows 10 operating system and comprises 7 subtests that encompass 5 cognitive domains: attention, language, visuospatial, memory, and executive functions. First, we recruited 480 cognitively normal elderly people (age 50-90) from communities nationwide to establish normative data for Inbrain CST. Second, we enrolled 97 patients from our dementia clinic (26 with subjective cognitive decline [SCD], 42 with amnestic mild cognitive impairment [aMCI], and 29 with dementia due to AD) and investigated sensitivity and specificity of Inbrain CST for discriminating cognitively impaired patients from those with SCD using receiver operating characteristic (ROC) curve analyses. Third, we compared the Inbrain CST scores with those from another neuropsychological test battery to obtain concurrent validity and assessed test-retest reliability. Finally, magnetic resonance imaging (MRI)-based cortical thickness analyses were performed to provide anatomical substrates for performances on the Inbrain CST. RESULTS First, in the normative sample, the total score on the Inbrain CST was significantly affected by age, years of education, and gender. Second, Inbrain CST scores among the three patient groups decreased in the order of SCD, aMCI, and AD dementia, and the ROC curve analysis revealed that Inbrain CST had good discriminative power for differentiating cognitively impaired patients from those with SCD. Third, the Inbrain CST subtests had high concurrent validity and test-retest reliability. Finally, in the cortical thickness analysis, each cognitive domain score and the total score of Inbrain CST showed distinct patterns of anatomical correlates that fit into the previously known brain-behavior relationship. CONCLUSION Inbrain CST had good validity, reliability, and clinical usefulness in detecting cognitive impairment in the elderly. Furthermore, it showed neuroanatomical validity through MRI cortical thinning patterns. These results suggest that Inbrain CST is a useful cognitive screening tool with efficiency and validity to detect mild impairments in cognition in clinical settings.
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Affiliation(s)
- Juhee Chin
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Da Eun Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Hyejoo Lee
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Juoh Yun
- MIDAS Information Technology Co., Ltd., Seongnam, Korea
| | - Byung Hwa Lee
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jongha Park
- MIDAS Information Technology Co., Ltd., Seongnam, Korea
| | - Jiho Yeom
- MIDAS Information Technology Co., Ltd., Seongnam, Korea
| | | | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
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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.
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Kang W, Shin JH, Han KM, Kim A, Kang Y, Kang J, Tae WS, Paik JW, Lee HW, Seong JK, Ham BJ. Local shape volume alterations in subcortical structures of suicide attempters with major depressive disorder. Hum Brain Mapp 2020; 41:4925-4934. [PMID: 32804434 PMCID: PMC7643352 DOI: 10.1002/hbm.25168] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 07/14/2020] [Accepted: 08/01/2020] [Indexed: 01/18/2023] Open
Abstract
Suicide is among the most important global health concerns; accordingly, an increasing number of studies have shown the risks for suicide attempt(s) in terms of brain morphometric features and their clinical correlates. However, brain studies addressing suicidal vulnerability have been more focused on demonstrating impairments in cortical structures than in the subcortical structures. Using local shape volumes (LSV) analysis, we investigated subcortical structures with their clinical correlates in depressed patients who attempted suicide. Then we compared them with depressed patients without a suicidal history and age- and sex-matched healthy controls (HCs; i.e., 47 suicide attempters with depression, 47 non-suicide attempters with depression, and 109 HCs). Significant volumetric differences were found between suicidal and nonsuicidal depressed patients in several vertices: 16 in the left amygdala; 201 in the left hippocampus; 1,057 in the left putamen; and 140 in the left pallidum; 1 in the right pallidum; and 6 in the bilateral thalamus. These findings indicated subcortical alterations in LSV in components of the limbic-cortical-striatal-pallidal-thalamic circuits. Moreover, our results demonstrated that the basal ganglia was correlated with perceived stress levels, and the thalamus was correlated with suicidal ideation. We suggest that suicidality in major depressive disorder may involve subcortical volume alterations.
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Affiliation(s)
- Wooyoung Kang
- Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Republic of Korea
| | - Jeong-Hyeon Shin
- Medical & Health Device Division, Korea Testing Laboratory, Seoul, Republic of Korea
| | - Kyu-Man Han
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Aram Kim
- Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Republic of Korea
| | - Youbin Kang
- Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Republic of Korea
| | - June Kang
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Woo-Suk Tae
- Brain Convergence Research Center, Korea University Anam Hospital, Seoul, Republic of Korea
| | - Jong-Woo Paik
- Department of Neuropsychiatry, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Hae-Woo Lee
- Department of Psychiatry, Seoul Medical Center, Seoul, Republic of Korea
| | - Joon-Kyung Seong
- School of Biomedical Engineering, Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea
| | - Byung-Joo Ham
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
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Feng J, Zhang SW, Chen L. Identification of Alzheimer's disease based on wavelet transformation energy feature of the structural MRI image and NN classifier. Artif Intell Med 2020; 108:101940. [DOI: 10.1016/j.artmed.2020.101940] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 07/01/2020] [Accepted: 08/07/2020] [Indexed: 02/07/2023]
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Abstract
This scientific commentary refers to ‘Network localization of clinical, cognitive, and neuropsychiatric symptoms in Alzheimer’s disease’, by Tetreault etal. (doi:10.1093/brain/awaa058).
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Affiliation(s)
- Michele Veldsman
- Department of Experimental Psychology, University of Oxford, Oxford, UK
- The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia
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Lian C, Liu M, Zhang J, Shen D. Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer's Disease Diagnosis Using Structural MRI. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2020; 42:880-893. [PMID: 30582529 PMCID: PMC6588512 DOI: 10.1109/tpami.2018.2889096] [Citation(s) in RCA: 169] [Impact Index Per Article: 42.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Structural magnetic resonance imaging (sMRI) has been widely used for computer-aided diagnosis of neurodegenerative disorders, e.g., Alzheimer's disease (AD), due to its sensitivity to morphological changes caused by brain atrophy. Recently, a few deep learning methods (e.g., convolutional neural networks, CNNs) have been proposed to learn task-oriented features from sMRI for AD diagnosis, and achieved superior performance than the conventional learning-based methods using hand-crafted features. However, these existing CNN-based methods still require the pre-determination of informative locations in sMRI. That is, the stage of discriminative atrophy localization is isolated to the latter stages of feature extraction and classifier construction. In this paper, we propose a hierarchical fully convolutional network (H-FCN) to automatically identify discriminative local patches and regions in the whole brain sMRI, upon which multi-scale feature representations are then jointly learned and fused to construct hierarchical classification models for AD diagnosis. Our proposed H-FCN method was evaluated on a large cohort of subjects from two independent datasets (i.e., ADNI-1 and ADNI-2), demonstrating good performance on joint discriminative atrophy localization and brain disease diagnosis.
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Affiliation(s)
- Chunfeng Lian
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Mingxia Liu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jun Zhang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, South Korea
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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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Normative brain volume reports may improve differential diagnosis of dementing neurodegenerative diseases in clinical practice. Eur Radiol 2020; 30:2821-2829. [PMID: 32002640 DOI: 10.1007/s00330-019-06602-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 10/23/2019] [Accepted: 11/27/2019] [Indexed: 01/31/2023]
Abstract
OBJECTIVES Normative brain volume reports (NBVRs) are becoming more and more available for the workup of dementia patients in clinical routine. However, it is yet unknown how this information can be used in the radiological decision-making process. The present study investigates the diagnostic value of NBVRs for detection and differential diagnosis of distinct regional brain atrophy in several dementing neurodegenerative disorders. METHODS NBVRs were obtained for 81 consecutive patients with distinct dementing neurodegenerative diseases and 13 healthy controls (HC). Forty Alzheimer's disease (AD; 18 with dementia, 22 with mild cognitive impairment (MCI), 11 posterior cortical atrophy (PCA)), 20 frontotemporal dementia (FTD), and ten semantic dementia (SD) cases were analyzed, and reports were tested qualitatively for the representation of atrophy patterns. Gold standard diagnoses were based on the patients' clinical course, FDG-PET imaging, and/or cerebrospinal fluid (CSF) biomarkers following established diagnostic criteria. Diagnostic accuracy of pattern representations was calculated. RESULTS NBVRs improved the correct identification of patients vs. healthy controls based on structural MRI for rater 1 (p < 0.001) whereas the amount of correct classifications was rather unchanged for rater 2. Correct differential diagnosis of dementing neurodegenerative disorders was significantly improved for both rater 1 (p = 0.001) and rater 2 (p = 0.022). Furthermore, interrater reliability was improved from moderate to excellent for both detection and differential diagnosis of neurodegenerative diseases (κ = 0.556/0.894 and κ = 0.403/0.850, respectively). CONCLUSION NBVRs deliver valuable and observer-independent information, which can improve differential diagnosis of neurodegenerative diseases. KEY POINTS • Normative brain volume reports increase detection of neurodegenerative atrophy patterns compared to visual reading alone. • Differential diagnosis of regionally distinct atrophy patterns is improved. • Agreement between radiologists is significantly improved from moderate to excellent when using normative brain volume reports.
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