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Moon SW, Zhao L, Matloff W, Hobel S, Berger R, Kwon D, Kim J, Toga AW, Dinov ID. Brain structure and allelic associations in Alzheimer's disease. CNS Neurosci Ther 2023; 29:1034-1048. [PMID: 36575854 PMCID: PMC10018103 DOI: 10.1111/cns.14073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 12/06/2022] [Accepted: 12/11/2022] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Alzheimer's disease (AD), the most prevalent form of dementia, affects 6.5 million Americans and over 50 million people globally. Clinical, genetic, and phenotypic studies of dementia provide some insights of the observed progressive neurodegenerative processes, however, the mechanisms underlying AD onset remain enigmatic. AIMS This paper examines late-onset dementia-related cognitive impairment utilizing neuroimaging-genetics biomarker associations. MATERIALS AND METHODS The participants, ages 65-85, included 266 healthy controls (HC), 572 volunteers with mild cognitive impairment (MCI), and 188 Alzheimer's disease (AD) patients. Genotype dosage data for AD-associated single nucleotide polymorphisms (SNPs) were extracted from the imputed ADNI genetics archive using sample-major additive coding. Such 29 SNPs were selected, representing a subset of independent SNPs reported to be highly associated with AD in a recent AD meta-GWAS study by Jansen and colleagues. RESULTS We identified the significant correlations between the 29 genomic markers (GMs) and the 200 neuroimaging markers (NIMs). The odds ratios and relative risks for AD and MCI (relative to HC) were predicted using multinomial linear models. DISCUSSION In the HC and MCI cohorts, mainly cortical thickness measures were associated with GMs, whereas the AD cohort exhibited different GM-NIM relations. Network patterns within the HC and AD groups were distinct in cortical thickness, volume, and proportion of White to Gray Matter (pct), but not in the MCI cohort. Multinomial linear models of clinical diagnosis showed precisely the specific NIMs and GMs that were most impactful in discriminating between AD and HC, and between MCI and HC. CONCLUSION This study suggests that advanced analytics provide mechanisms for exploring the interrelations between morphometric indicators and GMs. The findings may facilitate further clinical investigations of phenotypic associations that support deep systematic understanding of AD pathogenesis.
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Affiliation(s)
- Seok Woo Moon
- Department of Neuropsychiatry, Research Institute of Medical ScienceKonkuk University School of MedicineSeoulKorea
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USCCaliforniaLos AngelesUSA
| | - Lu Zhao
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USCCaliforniaLos AngelesUSA
| | - William Matloff
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USCCaliforniaLos AngelesUSA
| | - Sam Hobel
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USCCaliforniaLos AngelesUSA
| | - Ryan Berger
- Microbiology & ImmunologyUniversity of MichiganAnn ArborMichiganUSA
| | - Daehong Kwon
- Department of Biomedical Science and EngineeringKonkuk UniversitySeoulKorea
| | - Jaebum Kim
- Department of Biomedical Science and EngineeringKonkuk UniversitySeoulKorea
| | - Arthur W. Toga
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USCCaliforniaLos AngelesUSA
| | - Ivo D. Dinov
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USCCaliforniaLos AngelesUSA
- Department of Health Behavior and Biological Sciences, Statistics Online Computational Resource (SOCR), Michigan Institute for Data Science (MIDAS)University of MichiganAnn ArborMichiganUSA
- Department of StatisticsUniversity of CaliforniaLos AngelesCaliforniaUSA
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Moon SW. Neuroimaging Genetics and Network Analysis in Alzheimer's Disease. Curr Alzheimer Res 2023; 20:526-538. [PMID: 37957920 DOI: 10.2174/0115672050265188231107072215] [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: 06/20/2023] [Revised: 07/22/2023] [Accepted: 08/13/2023] [Indexed: 11/15/2023]
Abstract
The issue of the genetics in brain imaging phenotypes serves as a crucial link between two distinct scientific fields: neuroimaging genetics (NG). The articles included here provide solid proof that this NG link has considerable synergy. There is a suitable collection of articles that offer a wide range of viewpoints on how genetic variations affect brain structure and function. They serve as illustrations of several study approaches used in contemporary genetics and neuroscience. Genome-wide association studies and candidate-gene association are two examples of genetic techniques. Cortical gray matter structural/volumetric measures from magnetic resonance imaging (MRI) are sources of information on brain phenotypes. Together, they show how various scientific disciplines have benefited from significant technological advances, such as the single-nucleotide polymorphism array in genetics and the development of increasingly higher-resolution MRI imaging. Moreover, we discuss NG's contribution to expanding our knowledge about the heterogeneity within Alzheimer's disease as well as the benefits of different network analyses.
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Affiliation(s)
- Seok Woo Moon
- Department of Psychiatry, Institute of Medical Science, Konkuk University School of Medicine, Chungju, Republic of Korea
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Zhou J, Li K, Luo X, Zeng Q, Jiaerken Y, Wang S, Xu X, Liu X, Li Z, Zhang T, Fu Y, Zhao S, Huang P, Zhang M. Distinct impaired patterns of intrinsic functional network centrality in patients with early- and late-onset Alzheimer's disease. Brain Imaging Behav 2021; 15:2661-2670. [PMID: 33844192 DOI: 10.1007/s11682-021-00470-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Accepted: 03/04/2021] [Indexed: 10/21/2022]
Abstract
Early-onset Alzheimer's disease (EOAD) involves multiple cognitive domains and shows more rapid progression than late-onset Alzheimer's disease (LOAD). However, the difference in pathogenesis between EOAD and LOAD is still unclear. Accordingly, we applied intrinsic network analysis to explore the potential neuropathological mechanism underlying distinct clinical phenotypes. According to the cut-off age of 65, we included 20 EOAD patients, 20 LOAD patients, and 36 age-matched controls (19 young and 17 old controls). We employed resting-state functional MRI and network centrality analysis to explore the local (degree centrality (DC)) and global (eigenvector centrality (EC)) functional integrity. Two-sample t-test analysis was performed, with gray matter volume, age, gender, and education as covariates. Furthermore, we performed a correlation analysis between network metrics and cognition. Compared to young controls, EOAD patients exhibited lower DC in the middle temporal gyrus (MTG), parahippocampal gyrus (PHG), superior temporal gyrus (STG), and lower EC in the MTG, PHG, and postcentral gyrus. In contrast, LOAD patients exhibited lower DC in the STG and anterior cingulum gyrus and higher DC in the middle frontal gyrus compared to old controls. No significant difference in EC was observed in LOAD patients. Furthermore, both DC and EC correlated with cognitive performance. Our study demonstrated divergent functional network impairments in EOAD and LOAD patients. EOAD patients showed more complex network damage involving both local and global centrality properties, while LOAD patients mainly featured local functional connectivity changes. Such centrality impairments are related to poor cognition, especially regarding memory performance.
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Affiliation(s)
- Jiong Zhou
- Department of Neurology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Kaicheng Li
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, No.88 Jie-fang Road, Shang-cheng District, Hangzhou, 310009, China
| | - Xiao Luo
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, No.88 Jie-fang Road, Shang-cheng District, Hangzhou, 310009, China
| | - Qingze Zeng
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, No.88 Jie-fang Road, Shang-cheng District, Hangzhou, 310009, China
| | - Yerfan Jiaerken
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, No.88 Jie-fang Road, Shang-cheng District, Hangzhou, 310009, China
| | - Shuyue Wang
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, No.88 Jie-fang Road, Shang-cheng District, Hangzhou, 310009, China
| | - Xiaopei Xu
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, No.88 Jie-fang Road, Shang-cheng District, Hangzhou, 310009, China
| | - Xiaocao Liu
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, No.88 Jie-fang Road, Shang-cheng District, Hangzhou, 310009, China
| | - Zheyu Li
- Department of Neurology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Tianyi Zhang
- Department of Neurology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Yanv Fu
- Department of Neurology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Shuai Zhao
- Department of Neurology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, No.88 Jie-fang Road, Shang-cheng District, Hangzhou, 310009, China.
| | - Minming Zhang
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, No.88 Jie-fang Road, Shang-cheng District, Hangzhou, 310009, China.
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Tang M, Gao C, Goutman SA, Kalinin A, Mukherjee B, Guan Y, Dinov ID. Model-Based and Model-Free Techniques for Amyotrophic Lateral Sclerosis Diagnostic Prediction and Patient Clustering. Neuroinformatics 2019; 17:407-421. [PMID: 30460455 PMCID: PMC6527505 DOI: 10.1007/s12021-018-9406-9] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Amyotrophic lateral sclerosis (ALS) is a complex progressive neurodegenerative disorder with an estimated prevalence of about 5 per 100,000 people in the United States. In this study, the ALS disease progression is measured by the change of Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS) score over time. The study aims to provide clinical decision support for timely forecasting of the ALS trajectory as well as accurate and reproducible computable phenotypic clustering of participants. Patient data are extracted from DREAM-Phil Bowen ALS Prediction Prize4Life Challenge data, most of which are from the Pooled Resource Open-Access ALS Clinical Trials Database (PRO-ACT) archive. We employed model-based and model-free machine-learning methods to predict the change of the ALSFRS score over time. Using training and testing data we quantified and compared the performance of different techniques. We also used unsupervised machine learning methods to cluster the patients into separate computable phenotypes and interpret the derived subcohorts. Direct prediction of univariate clinical outcomes based on model-based (linear models) or model-free (machine learning based techniques - random forest and Bayesian adaptive regression trees) was only moderately successful. The correlation coefficients between clinically observed changes in ALSFRS scores relative to the model-based/model-free predicted counterparts were 0.427 (random forest) and 0.545(BART). The reliability of these results were assessed using internal statistical cross validation and well as external data validation. Unsupervised clustering generated very reliable and consistent partitions of the patient cohort into four computable phenotypic subgroups. These clusters were explicated by identifying specific salient clinical features included in the PRO-ACT archive that discriminate between the derived subcohorts. There are differences between alternative analytical methods in forecasting specific clinical phenotypes. Although predicting univariate clinical outcomes may be challenging, our results suggest that modern data science strategies are useful in clustering patients and generating evidence-based ALS hypotheses about complex interactions of multivariate factors. Predicting univariate clinical outcomes using the PRO-ACT data yields only marginal accuracy (about 70%). However, unsupervised clustering of participants into sub-groups generates stable, reliable and consistent (exceeding 95%) computable phenotypes whose explication requires interpretation of multivariate sets of features. HIGHLIGHTS: • Used a large ALS data archive of 8,000 patients consisting of 3 million records, including 200 clinical features tracked over 12 months. • Employed model-based and model-free methods to predict ALSFRS changes over time, cluster patients into cohorts, and derive computable phenotypes. • Research findings include stable, reliable, and consistent (95%) patient stratification into computable phenotypes. However, clinical explication of the results requires interpretation of multivariate information. Graphical Abstract ᅟ.
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Affiliation(s)
- Ming Tang
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Chao Gao
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Stephen A Goutman
- Department of Neurology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Alexandr Kalinin
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Bhramar Mukherjee
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Ivo D Dinov
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI, 48109, USA.
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA.
- Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI, 48109, USA.
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Kalinin AA, Allyn-Feuer A, Ade A, Fon GV, Meixner W, Dilworth D, Husain SS, de Wet JR, Higgins GA, Zheng G, Creekmore A, Wiley JW, Verdone JE, Veltri RW, Pienta KJ, Coffey DS, Athey BD, Dinov ID. 3D Shape Modeling for Cell Nuclear Morphological Analysis and Classification. Sci Rep 2018; 8:13658. [PMID: 30209281 PMCID: PMC6135819 DOI: 10.1038/s41598-018-31924-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Accepted: 08/29/2018] [Indexed: 02/08/2023] Open
Abstract
Quantitative analysis of morphological changes in a cell nucleus is important for the understanding of nuclear architecture and its relationship with pathological conditions such as cancer. However, dimensionality of imaging data, together with a great variability of nuclear shapes, presents challenges for 3D morphological analysis. Thus, there is a compelling need for robust 3D nuclear morphometric techniques to carry out population-wide analysis. We propose a new approach that combines modeling, analysis, and interpretation of morphometric characteristics of cell nuclei and nucleoli in 3D. We used robust surface reconstruction that allows accurate approximation of 3D object boundary. Then, we computed geometric morphological measures characterizing the form of cell nuclei and nucleoli. Using these features, we compared over 450 nuclei with about 1,000 nucleoli of epithelial and mesenchymal prostate cancer cells, as well as 1,000 nuclei with over 2,000 nucleoli from serum-starved and proliferating fibroblast cells. Classification of sets of 9 and 15 cells achieved accuracy of 95.4% and 98%, respectively, for prostate cancer cells, and 95% and 98% for fibroblast cells. To our knowledge, this is the first attempt to combine these methods for 3D nuclear shape modeling and morphometry into a highly parallel pipeline workflow for morphometric analysis of thousands of nuclei and nucleoli in 3D.
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Affiliation(s)
- Alexandr A Kalinin
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.,Statistics Online Computational Resource (SOCR), Department of Health Behavior and Biological Sciences, University of Michigan School of Nursing, Ann Arbor, MI, USA
| | - Ari Allyn-Feuer
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Alex Ade
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Gordon-Victor Fon
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Walter Meixner
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - David Dilworth
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Syed S Husain
- Statistics Online Computational Resource (SOCR), Department of Health Behavior and Biological Sciences, University of Michigan School of Nursing, Ann Arbor, MI, USA
| | - Jeffrey R de Wet
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Gerald A Higgins
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Gen Zheng
- Division of Gastroenterology, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Amy Creekmore
- Division of Gastroenterology, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - John W Wiley
- Division of Gastroenterology, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - James E Verdone
- Department of Urology, James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Robert W Veltri
- Department of Urology, James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kenneth J Pienta
- Department of Urology, James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Donald S Coffey
- Department of Urology, James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Brian D Athey
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA. .,Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI, USA.
| | - Ivo D Dinov
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA. .,Statistics Online Computational Resource (SOCR), Department of Health Behavior and Biological Sciences, University of Michigan School of Nursing, Ann Arbor, MI, USA. .,Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI, USA.
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Marino S, Xu J, Zhao Y, Zhou N, Zhou Y, Dinov ID. Controlled feature selection and compressive big data analytics: Applications to biomedical and health studies. PLoS One 2018; 13:e0202674. [PMID: 30161148 PMCID: PMC6116997 DOI: 10.1371/journal.pone.0202674] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Accepted: 08/07/2018] [Indexed: 12/30/2022] Open
Abstract
The theoretical foundations of Big Data Science are not fully developed, yet. This study proposes a new scalable framework for Big Data representation, high-throughput analytics (variable selection and noise reduction), and model-free inference. Specifically, we explore the core principles of distribution-free and model-agnostic methods for scientific inference based on Big Data sets. Compressive Big Data analytics (CBDA) iteratively generates random (sub)samples from a big and complex dataset. This subsampling with replacement is conducted on the feature and case levels and results in samples that are not necessarily consistent or congruent across iterations. The approach relies on an ensemble predictor where established model-based or model-free inference techniques are iteratively applied to preprocessed and harmonized samples. Repeating the subsampling and prediction steps many times, yields derived likelihoods, probabilities, or parameter estimates, which can be used to assess the algorithm reliability and accuracy of findings via bootstrapping methods, or to extract important features via controlled variable selection. CBDA provides a scalable algorithm for addressing some of the challenges associated with handling complex, incongruent, incomplete and multi-source data and analytics challenges. Albeit not fully developed yet, a CBDA mathematical framework will enable the study of the ergodic properties and the asymptotics of the specific statistical inference approaches via CBDA. We implemented the high-throughput CBDA method using pure R as well as via the graphical pipeline environment. To validate the technique, we used several simulated datasets as well as a real neuroimaging-genetics of Alzheimer's disease case-study. The CBDA approach may be customized to provide generic representation of complex multimodal datasets and to provide stable scientific inference for large, incomplete, and multisource datasets.
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Affiliation(s)
- Simeone Marino
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Jiachen Xu
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Yi Zhao
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Nina Zhou
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Yiwang Zhou
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Ivo D. Dinov
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
- Michigan Institute for Data Science, University of Michigan, Ann Arbor, Michigan, United States of America
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Moon SW, Lee B, Choi YC. Changes in the Hippocampal Volume and Shape in Early-Onset Mild Cognitive Impairment. Psychiatry Investig 2018; 15:531-537. [PMID: 29695149 PMCID: PMC5976007 DOI: 10.30773/pi.2018.02.12] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Accepted: 02/12/2018] [Indexed: 01/11/2023] Open
Abstract
OBJECTIVE The aim of this study was to examine the change in the hippocampal volume and shape in early-onset mild cognitive impairment (EO-MCI) associated with the APOE ε4 carrier state. METHODS This study had 50 subjects aged 55-63 years, all of whom were diagnosed with MCI at baseline via the Korean version of the Consortium to Establish a Registry for Alzheimer's Disease Assessment Packet. The EO-MCI patients were divided into the MCI continued (MCIcont) and Alzheimer's disease (AD) converted (ADconv) groups 2 years later. The hippocampal volume and shape were measured for all the subjects. The local shape analysis (LSA) was used to conduct based on the 2-year-interval magnetic resonance imaging scans. RESULTS There was a significant correlation between APOE ε4 allele and hippocampal volume atrophy. Over two years, the volume reduction in the left hippocampus was found to be faster than that in the right hippocampus, especially in the APOE ε4 carriers. LSA showed that the 2 subfields were significantly affected in the left hippocampus. CONCLUSION These results suggest that the possession of APOE ε4 allele may lead to greater predilection for left hippocampal atrophy in EO-MCI, and some specific subfields of the hippocampus may be more prominently involved.
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Affiliation(s)
- Seok Woo Moon
- Department of Psychiatry, Konkuk University School of Medicine, Chungju, Republic of Korea
| | - Boram Lee
- Department of Psychiatry, Graduate School of Konkuk University, Seoul, Republic of Korea
| | - Young Chil Choi
- Department of Radiology, Konkuk University School of Medicine, Chungju, Republic of Korea
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Leandrou S, Petroudi S, Kyriacou PA, Reyes-Aldasoro CC, Pattichis CS. Quantitative MRI Brain Studies in Mild Cognitive Impairment and Alzheimer's Disease: A Methodological Review. IEEE Rev Biomed Eng 2018; 11:97-111. [PMID: 29994606 DOI: 10.1109/rbme.2018.2796598] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Classifying and predicting Alzheimer's disease (AD) in individuals with memory disorders through clinical and psychometric assessment is challenging, especially in mild cognitive impairment (MCI) subjects. Quantitative structural magnetic resonance imaging acquisition methods in combination with computer-aided diagnosis are currently being used for the assessment of AD. These acquisitions methods include voxel-based morphometry, volumetric measurements in specific regions of interest (ROIs), cortical thickness measurements, shape analysis, and texture analysis. This review evaluates the aforementioned methods in the classification of cases into one of the following three groups: normal controls, MCI, and AD subjects. Furthermore, the performance of the methods is assessed on the prediction of conversion from MCI to AD. In parallel, it is also assessed which ROIs are preferred in both classification and prognosis through the different states of the disease. Structural changes in the early stages of the disease are more pronounced in the medial temporal lobe, especially in the entorhinal cortex, whereas with disease progression, both entorhinal cortex and hippocampus offer similar discriminative power. However, for the conversion from MCI subjects to AD, entorhinal cortex provides better predictive accuracies rather than other structures, such as the hippocampus.
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Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC, Harvey D, Jack CR, Jagust W, Morris JC, Petersen RC, Saykin AJ, Shaw LM, Toga AW, Trojanowski JQ. Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials. Alzheimers Dement 2017; 13:e1-e85. [PMID: 28342697 DOI: 10.1016/j.jalz.2016.11.007] [Citation(s) in RCA: 170] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Revised: 11/21/2016] [Accepted: 11/28/2016] [Indexed: 01/31/2023]
Abstract
INTRODUCTION The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS We used standard searches to find publications using ADNI data. RESULTS (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.
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Affiliation(s)
- Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, CA, USA.
| | - Dallas P Veitch
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| | - Paul S Aisen
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | - Laurel A Beckett
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | - Nigel J Cairns
- Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Robert C Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | | | - William Jagust
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - John C Morris
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | | | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arthur W Toga
- Laboratory of Neuroimaging, Institute of Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Alzheimer's Disease Core Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Udall Parkinson's Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Park KH, Noh Y, Choi EJ, Kim H, Chun S, Son YD. Functional Connectivity of the Hippocampus in Early- and vs. Late-Onset Alzheimer's Disease. J Clin Neurol 2017; 13:387-393. [PMID: 29057631 PMCID: PMC5653627 DOI: 10.3988/jcn.2017.13.4.387] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2016] [Revised: 06/17/2017] [Accepted: 06/20/2017] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND AND PURPOSE Early-onset Alzheimer's disease (EOAD) and late-onset Alzheimer's disease (LOAD) have different clinical and neuroimaging characteristics, but memory decline is usually present in both types. However, there have been few functional studies focused on the hippocampus in Alzheimer's disease. We therefore investigated the functional connectivity between the hippocampus and other brain regions using resting-state fMRI and compared the findings between EOAD and LOAD. METHODS We recruited 13 patients with EOAD and 19 patients with LOAD at the early disease stage. Twenty-one young controls and ten old controls were also recruited. Each participant completed a standardized neuropsychological battery of tests and underwent T1-weighted structural MRI. fMRI data were acquired during the resting state using 3-T MRI. The functional connectivity to the hippocampus was calculated based on automated anatomical labeling templates. RESULTS The functional connectivity from the hippocampus to other brain regions differed between patients with EOAD and LOAD. The LOAD patients showed decreased hippocampal connectivity to cortical regions, such as to the middle temporal cortex, orbitofrontal cortex, postcentral cortex, supramarginal cortex, and rolandic operculum. In contrast, EOAD patients showed smaller functional changes of the cortical regions connected to the hippocampus, such as the middle frontal cortex. CONCLUSIONS EOAD and LOAD patients exhibited different hippocampal connectivity. The memory decline in EOAD may be due to brain areas other than the hippocampus.
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Affiliation(s)
- Kee Hyung Park
- Department of Neurology, Gachon University Gil Medical Center, Incheon, Korea.
| | - Young Noh
- Department of Neurology, Gachon University Gil Medical Center, Incheon, Korea.,Neuroscience Research Institute, Gachon University, Incheon, Korea
| | - Eun Jung Choi
- Neuroscience Research Institute, Gachon University, Incheon, Korea
| | - Hyungsik Kim
- Department of Family Medicine, Gachon University Gil Medical Center, Incheon, Korea
| | - Sohyun Chun
- Department of Radiology, Gachon University Gil Medical Center, Incheon, Korea
| | - Young Don Son
- Neuroscience Research Institute, Gachon University, Incheon, Korea.,Department of Biomedical Engineering, College of Health Science, Gachon University, Incheon, Korea.
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