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Beckett LA, Saito N, Donohue MC, Harvey DJ. Contributions of the ADNI Biostatistics Core. Alzheimers Dement 2024. [PMID: 39140601 DOI: 10.1002/alz.14159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 06/18/2024] [Accepted: 06/20/2024] [Indexed: 08/15/2024]
Abstract
The goal of the Biostatistics Core of the Alzheimer's Disease Neuroimaging Initiative (ADNI) has been to ensure that sound study designs and statistical methods are used to meet the overall goals of ADNI. We have supported the creation of a well-validated and well-curated longitudinal database of clinical and biomarker information on ADNI participants and helped to make this accessible and usable for researchers. We have developed a statistical methodology for characterizing the trajectories of clinical and biomarker change for ADNI participants across the spectrum from cognitively normal to dementia, including multivariate patterns and evidence for heterogeneity in cognitive aging. We have applied these methods and adapted them to improve clinical trial design. ADNI-4 will offer us a chance to help extend these efforts to a more diverse cohort with an even richer panel of biomarker data to support better knowledge of and treatment for Alzheimer's disease and related dementias. HIGHLIGHTS: The Alzheimer's Disease Neuroimaging Initiative (ADNI) Biostatistics Core provides study design and analytic support to ADNI investigators. Core members develop and apply novel statistical methodology to work with ADNI data and support clinical trial design. The Core contributes to the standardization, validation, and harmonization of biomarker data. The Core serves as a resource to the wider research community to address questions related to the data and study as a whole.
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Affiliation(s)
- Laurel A Beckett
- Department of Public Health Sciences, University of California, Davis, California, USA
| | - Naomi Saito
- Department of Public Health Sciences, University of California, Davis, California, USA
| | - Michael C Donohue
- Department of Neurology, University of Southern California, Los Angeles, California, USA
| | - Danielle J Harvey
- Department of Public Health Sciences, University of California, Davis, California, USA
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2
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Zheng W, Liu H, Li Z, Li K, Wang Y, Hu B, Dong Q, Wang Z. Classification of Alzheimer's disease based on hippocampal multivariate morphometry statistics. CNS Neurosci Ther 2023; 29:2457-2468. [PMID: 37002795 PMCID: PMC10401169 DOI: 10.1111/cns.14189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 03/07/2023] [Accepted: 03/13/2023] [Indexed: 08/05/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a neurodegenerative disease characterized by progressive cognitive decline, and mild cognitive impairment (MCI) is associated with a high risk of developing AD. Hippocampal morphometry analysis is believed to be the most robust magnetic resonance imaging (MRI) markers for AD and MCI. Multivariate morphometry statistics (MMS), a quantitative method of surface deformations analysis, is confirmed to have strong statistical power for evaluating hippocampus. AIMS We aimed to test whether surface deformation features in hippocampus can be employed for early classification of AD, MCI, and healthy controls (HC). METHODS We first explored the differences in hippocampus surface deformation among these three groups by using MMS analysis. Additionally, the hippocampal MMS features of selective patches and support vector machine (SVM) were used for the binary classification and triple classification. RESULTS By the results, we identified significant hippocampal deformation among the three groups, especially in hippocampal CA1. In addition, the binary classification of AD/HC, MCI/HC, AD/MCI showed good performances, and area under curve (AUC) of triple-classification model achieved 0.85. Finally, positive correlations were found between the hippocampus MMS features and cognitive performances. CONCLUSIONS The study revealed significant hippocampal deformation among AD, MCI, and HC. Additionally, we confirmed that hippocampal MMS can be used as a sensitive imaging biomarker for the early diagnosis of AD at the individual level.
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Affiliation(s)
- Weimin Zheng
- Department of Radiology, Aerospace Center Hospital, Beijing, China
| | - Honghong Liu
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Zhigang Li
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Kuncheng Li
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, Arizona, USA
| | - Bin Hu
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Qunxi Dong
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Zhiqun Wang
- Department of Radiology, Aerospace Center Hospital, Beijing, China
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3
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Maikusa N, Shigemoto Y, Chiba E, Kimura Y, Matsuda H, Sato N. Harmonized Z-Scores Calculated from a Large-Scale Normal MRI Database to Evaluate Brain Atrophy in Neurodegenerative Disorders. J Pers Med 2022; 12:jpm12101555. [PMID: 36294692 PMCID: PMC9605567 DOI: 10.3390/jpm12101555] [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: 08/22/2022] [Revised: 09/16/2022] [Accepted: 09/16/2022] [Indexed: 12/05/2022] Open
Abstract
Alzheimer’s disease (AD), the most common type of dementia in elderly individuals, slowly and progressively diminishes the cognitive function. Mild cognitive impairment (MCI) is also a significant risk factor for the onset of AD. Magnetic resonance imaging (MRI) is widely used for the detection and understanding of the natural progression of AD and other neurodegenerative disorders. For proper assessment of these diseases, a reliable database of images from cognitively healthy participants is important. However, differences in magnetic field strength or the sex and age of participants between a normal database and an evaluation data set can affect the accuracy of the detection and evaluation of neurodegenerative disorders. We developed a brain segmentation procedure, based on 30 Japanese brain atlases, and suggest a harmonized Z-score to correct the differences in field strength and sex and age from a large data set (1235 cognitively healthy participants), including 1.5 T and 3 T T1-weighted brain images. We evaluated our harmonized Z-score for AD discriminative power and classification accuracy between stable MCI and progressive MCI. Our procedure can perform brain segmentation in approximately 30 min. The harmonized Z-score of the hippocampus achieved high accuracy (AUC = 0.96) for AD detection and moderate accuracy (AUC = 0.70) to classify stable or progressive MCI. These results show that our method can detect AD with high accuracy and high generalization capability. Moreover, it may discriminate between stable and progressive MCI. Our study has some limitations: the age groups in the 1.5 T data set and 3 T data set are significantly different. In this study, we focused on AD, which is primarily a disease of elderly patients. For other diseases in different age groups, the harmonized Z-score needs to be recalculated using different data sets.
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Affiliation(s)
- Norihide Maikusa
- Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo, Tokyo 113-8654, Japan
- Department of Radiology, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
- Correspondence: ; Tel.: +81-42-341-2711
| | - Yoko Shigemoto
- Department of Radiology, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Emiko Chiba
- Department of Radiology, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Yukio Kimura
- Department of Radiology, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Hiroshi Matsuda
- Department of Radiology, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Noriko Sato
- Department of Radiology, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
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4
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Li Y, Qiu Z, Fan X, Liu X, Chang EIC, Xu Y. Integrated 3d flow-based multi-atlas brain structure segmentation. PLoS One 2022; 17:e0270339. [PMID: 35969596 PMCID: PMC9377636 DOI: 10.1371/journal.pone.0270339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 06/09/2022] [Indexed: 11/18/2022] Open
Abstract
MRI brain structure segmentation plays an important role in neuroimaging studies. Existing methods either spend much CPU time, require considerable annotated data, or fail in segmenting volumes with large deformation. In this paper, we develop a novel multi-atlas-based algorithm for 3D MRI brain structure segmentation. It consists of three modules: registration, atlas selection and label fusion. Both registration and label fusion leverage an integrated flow based on grayscale and SIFT features. We introduce an effective and efficient strategy for atlas selection by employing the accompanying energy generated in the registration step. A 3D sequential belief propagation method and a 3D coarse-to-fine flow matching approach are developed in both registration and label fusion modules. The proposed method is evaluated on five public datasets. The results show that it has the best performance in almost all the settings compared to competitive methods such as ANTs, Elastix, Learning to Rank and Joint Label Fusion. Moreover, our registration method is more than 7 times as efficient as that of ANTs SyN, while our label transfer method is 18 times faster than Joint Label Fusion in CPU time. The results on the ADNI dataset demonstrate that our method is applicable to image pairs that require a significant transformation in registration. The performance on a composite dataset suggests that our method succeeds in a cross-modality manner. The results of this study show that the integrated 3D flow-based method is effective and efficient for brain structure segmentation. It also demonstrates the power of SIFT features, multi-atlas segmentation and classical machine learning algorithms for a medical image analysis task. The experimental results on public datasets show the proposed method's potential for general applicability in various brain structures and settings.
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Affiliation(s)
- Yeshu Li
- School of Computer Science and Engineering, Beihang University, Beijing, China
| | - Ziming Qiu
- Electrical and Computer Engineering, Tandon School of Engineering, New York University, Brooklyn, NY, United States of America
| | - Xingyu Fan
- Bioengineering College, Chongqing University, Chongqing, China
| | - Xianglong Liu
- School of Computer Science and Engineering, Beihang University, Beijing, China
| | | | - Yan Xu
- School of Biological Science and Medical Engineering, State Key Laboratory of Software Development Environment, Key Laboratory of Biomechanics, Mechanobiology of Ministry of Education and Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
- Microsoft Research, Beijing, China
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5
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A predictive model using the mesoscopic architecture of the living brain to detect Alzheimer's disease. COMMUNICATIONS MEDICINE 2022; 2:70. [PMID: 35759330 PMCID: PMC9209493 DOI: 10.1038/s43856-022-00133-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 05/24/2022] [Indexed: 01/12/2023] Open
Abstract
Background Alzheimer's disease, the most common cause of dementia, causes a progressive and irreversible deterioration of cognition that can sometimes be difficult to diagnose, leading to suboptimal patient care. Methods We developed a predictive model that computes multi-regional statistical morpho-functional mesoscopic traits from T1-weighted MRI scans, with or without cognitive scores. For each patient, a biomarker called "Alzheimer's Predictive Vector" (ApV) was derived using a two-stage least absolute shrinkage and selection operator (LASSO). Results The ApV reliably discriminates between people with (ADrp) and without (nADrp) Alzheimer's related pathologies (98% and 81% accuracy between ADrp - including the early form, mild cognitive impairment - and nADrp in internal and external hold-out test sets, respectively), without any a priori assumptions or need for neuroradiology reads. The new test is superior to standard hippocampal atrophy (26% accuracy) and cerebrospinal fluid beta amyloid measure (62% accuracy). A multiparametric analysis compared DTI-MRI derived fractional anisotropy, whose readout of neuronal loss agrees with ADrp phenotype, and SNPrs2075650 is significantly altered in patients with ADrp-like phenotype. Conclusions This new data analytic method demonstrates potential for increasing accuracy of Alzheimer diagnosis.
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6
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Chen K, Guo X, Pan R, Xiong C, Harvey DJ, Chen Y, Yao L, Su Y, Reiman EM. Limitations of clinical trial sample size estimate by subtraction of two measurements. Stat Med 2022; 41:1137-1147. [PMID: 34725853 PMCID: PMC8916961 DOI: 10.1002/sim.9244] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 09/16/2021] [Accepted: 10/13/2021] [Indexed: 11/10/2022]
Abstract
In planning randomized clinical trials (RCTs) for diseases such as Alzheimer's disease (AD), researchers frequently rely on the use of existing data obtained from only two time points to estimate sample size via the subtraction of baseline from follow-up measurements in each subject. However, the inadequacy of this method has not been reported. The aim of this study is to discuss the limitation of sample size estimation based on the subtraction of available data from only two time points for RCTs. Mathematical equations are derived to demonstrate the condition under which the obtained data pairs with variable time intervals could be used to adequately estimate sample size. The MRI-based hippocampal volume measurements from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Monte Carlo simulations (MCS) were used to illustrate the existing bias and variability of estimates. MCS results support the theoretically derived condition under which the subtraction approach may work. MCS also show the systematically under- or over-estimated sample sizes by up to 32.27 % bias. Not used properly, such subtraction approach outputs the same sample size regardless of trial durations partly due to the way measurement errors are handled. Estimating sample size by subtracting two measurements should be treated with caution. Such estimates can be biased, the magnitude of which depends on the planned RCT duration. To estimate sample sizes, we recommend using more than two measurements and more comprehensive approaches such as linear mixed effect models.
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Affiliation(s)
- Kewei Chen
- Banner Alzheimer’s Institute, Phoenix, Arizona, USA
- Department of Mathematics and Statistics, Arizona State University, Tempe, Arizona, USA
- Department of Neurology, University of Arizona, Phoenix, Arizona, USA
| | - Xiaojuan Guo
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Rong Pan
- Department of Mathematics and Statistics, Arizona State University, Tempe, Arizona, USA
| | - Chengjie Xiong
- Knight Alzheimer’s Disease Research Center, St. Louis, Missouri, USA
- Division of Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | | | - Yinghua Chen
- Banner Alzheimer’s Institute, Phoenix, Arizona, USA
- Arizona Alzheimer’s Consortium, Phoenix, Arizona, USA
| | - Li Yao
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
| | - Yi Su
- Banner Alzheimer’s Institute, Phoenix, Arizona, USA
| | - Eric M. Reiman
- Banner Alzheimer’s Institute, Phoenix, Arizona, USA
- Division of Neurogenomics, Translational Genomics Research Institute, Phoenix, Arizona, USA
- Department of Psychiatry, University of Arizona, Tucson, Arizona, USA
- Arizona Alzheimer’s Consortium, Phoenix, Arizona, USA
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7
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Pople CB, Meng Y, Li DZ, Bigioni L, Davidson B, Vecchio LM, Hamani C, Rabin JS, Lipsman N. Neuromodulation in the Treatment of Alzheimer's Disease: Current and Emerging Approaches. J Alzheimers Dis 2021; 78:1299-1313. [PMID: 33164935 DOI: 10.3233/jad-200913] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Neuromodulation as a treatment strategy for psychiatric and neurological diseases has grown in popularity in recent years, with the approval of repetitive transcranial magnetic stimulation (rTMS) for the treatment of depression being one such example. These approaches offer new hope in the treatment of diseases that have proven largely intractable to traditional pharmacological approaches. For this reason, neuromodulation is increasingly being explored for the treatment of Alzheimer's disease. However, such approaches have variable, and, in many cases, very limited evidence for safety and efficacy, with most human evidence obtained in small clinical trials. Here we review work in animal models and humans with Alzheimer's disease exploring emerging neuromodulation modalities. Approaches reviewed include deep brain stimulation, transcranial magnetic stimulation, transcranial electrical stimulation, ultrasound stimulation, photobiomodulation, and visual or auditory stimulation. In doing so, we clarify the current evidence for these approaches in treating Alzheimer's disease and identify specific areas where additional work is needed to facilitate their clinical translation.
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Affiliation(s)
- Christopher B Pople
- Harquail Centre for Neuromodulation, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Ying Meng
- Harquail Centre for Neuromodulation, Sunnybrook Research Institute, Toronto, ON, Canada.,Division of Neurosurgery, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Daniel Z Li
- Harquail Centre for Neuromodulation, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Luca Bigioni
- Harquail Centre for Neuromodulation, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Benjamin Davidson
- Harquail Centre for Neuromodulation, Sunnybrook Research Institute, Toronto, ON, Canada.,Division of Neurosurgery, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Laura M Vecchio
- Biological Sciences Platform, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Clement Hamani
- Harquail Centre for Neuromodulation, Sunnybrook Research Institute, Toronto, ON, Canada.,Division of Neurosurgery, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Jennifer S Rabin
- Harquail Centre for Neuromodulation, Sunnybrook Research Institute, Toronto, ON, Canada.,Department of Medicine (Neurology), University of Toronto, Toronto, Ontario, Canada.,Rehabilitation Sciences Institute, University of Toronto, Toronto ON, Canada
| | - Nir Lipsman
- Harquail Centre for Neuromodulation, Sunnybrook Research Institute, Toronto, ON, Canada.,Division of Neurosurgery, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
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8
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Zhu Y, Kim M, Zhu X, Kaufer D, Wu G. Long range early diagnosis of Alzheimer's disease using longitudinal MR imaging data. Med Image Anal 2021; 67:101825. [PMID: 33137699 PMCID: PMC10613455 DOI: 10.1016/j.media.2020.101825] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Revised: 08/25/2020] [Accepted: 08/25/2020] [Indexed: 01/16/2023]
Abstract
The enormous social and economic cost of Alzheimer's disease (AD) has driven a number of neuroimaging investigations for early detection and diagnosis. Towards this end, various computational approaches have been applied to longitudinal imaging data in subjects with Mild Cognitive Impairment (MCI), as serial brain imaging could increase sensitivity for detecting changes from baseline, and potentially serve as a diagnostic biomarker for AD. However, current state-of-the-art brain imaging diagnostic methods have limited utility in clinical practice due to the lack of robust predictive power. To address this limitation, we propose a flexible spatial-temporal solution to predict the risk of MCI conversion to AD prior to the onset of clinical symptoms by sequentially recognizing abnormal structural changes from longitudinal magnetic resonance (MR) image sequences. Firstly, our model is trained to sequentially recognize different length partial MR image sequences from different stages of AD. Secondly, our method is leveraged by the inexorably progressive nature of AD. To that end, a Temporally Structured Support Vector Machine (TS-SVM) model is proposed to constrain the partial MR image sequence's detection score to increase monotonically with AD progression. Furthermore, in order to select the best morphological features for enabling classifiers, we propose a joint feature selection and classification framework. We demonstrate that our early diagnosis method using only two follow-up MR scans is able to predict conversion to AD 12 months ahead of an AD clinical diagnosis with 81.75% accuracy.
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Affiliation(s)
- Yingying Zhu
- Department of Computer Science, University of Texas at Arlington, TX, USA.
| | - Minjeong Kim
- Department of Computer Science, University of North Carolina at Greensboro, NC, USA
| | - Xiaofeng Zhu
- Department of Computer Science, University of Electronic Science and Technology of China, Chengdu, China
| | - Daniel Kaufer
- Department of Neurology, University of North Carolina at Chapel Hill, USA
| | - Guorong Wu
- Department of Psychiatry, University of North Carolina at Chapel Hill, NC, USA.
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9
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Wang G, Dong Q, Wu J, Su Y, Chen K, Su Q, Zhang X, Hao J, Yao T, Liu L, Zhang C, Caselli RJ, Reiman EM, Wang Y. Developing univariate neurodegeneration biomarkers with low-rank and sparse subspace decomposition. Med Image Anal 2021; 67:101877. [PMID: 33166772 PMCID: PMC7725891 DOI: 10.1016/j.media.2020.101877] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Revised: 08/24/2020] [Accepted: 10/13/2020] [Indexed: 01/01/2023]
Abstract
Cognitive decline due to Alzheimer's disease (AD) is closely associated with brain structure alterations captured by structural magnetic resonance imaging (sMRI). It supports the validity to develop sMRI-based univariate neurodegeneration biomarkers (UNB). However, existing UNB work either fails to model large group variances or does not capture AD dementia (ADD) induced changes. We propose a novel low-rank and sparse subspace decomposition method capable of stably quantifying the morphological changes induced by ADD. Specifically, we propose a numerically efficient rank minimization mechanism to extract group common structure and impose regularization constraints to encode the original 3D morphometry connectivity. Further, we generate regions-of-interest (ROI) with group difference study between common subspaces of Aβ+AD and Aβ-cognitively unimpaired (CU) groups. A univariate morphometry index (UMI) is constructed from these ROIs by summarizing individual morphological characteristics weighted by normalized difference between Aβ+AD and Aβ-CU groups. We use hippocampal surface radial distance feature to compute the UMIs and validate our work in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. With hippocampal UMIs, the estimated minimum sample sizes needed to detect a 25% reduction in the mean annual change with 80% power and two-tailed P=0.05are 116, 279 and 387 for the longitudinal Aβ+AD, Aβ+mild cognitive impairment (MCI) and Aβ+CU groups, respectively. Additionally, for MCI patients, UMIs well correlate with hazard ratio of conversion to AD (4.3, 95% CI = 2.3-8.2) within 18 months. Our experimental results outperform traditional hippocampal volume measures and suggest the application of UMI as a potential UNB.
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Affiliation(s)
- Gang Wang
- Ulsan Ship and Ocean College, Ludong University, Yantai, China.
| | - Qunxi Dong
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, P.O. Box 878809 Tempe, AZ 85287, USA
| | - Jianfeng Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, P.O. Box 878809 Tempe, AZ 85287, USA
| | - Yi Su
- Banner Alzheimer's Institute and Banner Good Samaritan Pet Center, Phoenix, AZ, USA
| | - Kewei Chen
- Banner Alzheimer's Institute and Banner Good Samaritan Pet Center, Phoenix, AZ, USA
| | - Qingtang Su
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Xiaofeng Zhang
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Jinguang Hao
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Tao Yao
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Li Liu
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Caiming Zhang
- Shandong Province Key Lab of Digital Media Technology, Shandong University of Finance and Economics, Jinan, China
| | | | - Eric M Reiman
- Banner Alzheimer's Institute and Banner Good Samaritan Pet Center, Phoenix, AZ, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, P.O. Box 878809 Tempe, AZ 85287, USA.
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10
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Maikusa N, Fukami T, Matsuda H. Longitudinal analysis of brain structure using existence probability. Brain Behav 2020; 10:e01869. [PMID: 33034427 PMCID: PMC7749599 DOI: 10.1002/brb3.1869] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 09/04/2020] [Accepted: 09/08/2020] [Indexed: 11/28/2022] Open
Abstract
INTRODUCTION We propose a method to evaluate quantitatively the longitudinal structural changes in brain atrophy to provide early detection of Alzheimer's disease (AD) and mild cognitive impairment (MCI). METHODS We used existence probabilities obtained by segmenting magnetic resonance (MR) images at two different time points into four regions: gray matter, white matter, cerebrospinal fluid, and background. This method was applied to T1-weighted MR images of 110 participants with normal cognition (NL), 165 with MCI, and 82 with AD, obtained from the Japanese Alzheimer's Disease Neuroimaging Initiative database. RESULTS We obtained the coefficients of probability change (CPC) for each dataset. We found high area under the receiver operating characteristic curve (ROC) values (up to 0.908 of the difference of ROCs) for some CPC regions that are considered indicators of atrophy. Additionally, we attempted to establish a machine-learning algorithm to classify participants as NL or AD. The maximum accuracy was 92.1% for NL-AD classification and 81.2% for NL-MCI classification using CPC values between images acquired at first and sixth months, respectively. CONCLUSION These results showed that the proposed method is effective for the early detection of AD and MCI.
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Affiliation(s)
- Norihide Maikusa
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Tadanori Fukami
- Department of Informatics, Faculty of Engineering, Yamagata University, Yamagata, Japan
| | - Hiroshi Matsuda
- Department of Radiology, National Center of Neurology and Psychiatry, Tokyo, Japan
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11
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Zhang Y, Hao Y, Li L, Xia K, Wu G. A Novel Computational Proxy for Characterizing Cognitive Reserve in Alzheimer's Disease. J Alzheimers Dis 2020; 78:1217-1228. [PMID: 33252088 DOI: 10.3233/jad-201011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
BACKGROUND Although the abnormal depositions of amyloid plaques and neurofibrillary tangles are the hallmark of Alzheimer's disease (AD), converging evidence shows that the individual's neurodegeneration trajectory is regulated by the brain's capability to maintain normal cognition. OBJECTIVE The concept of cognitive reserve has been introduced into the field of neuroscience, acting as a moderating factor for explaining the paradoxical relationship between the burden of AD pathology and the clinical outcome. It is of high demand to quantify the degree of conceptual cognitive reserve on an individual basis. METHODS We propose a novel statistical model to quantify an individual's cognitive reserve against neuropathological burdens, where the predictors include demographic data (such as age and gender), socioeconomic factors (such as education and occupation), cerebrospinal fluid biomarkers, and AD-related polygenetic risk score. We conceptualize cognitive reserve as a joint product of AD pathology and socioeconomic factors where their interaction manifests a significant role in counteracting the progression of AD in our statistical model. RESULTS We apply our statistical models to re-investigate the moderated neurodegeneration trajectory by considering cognitive reserve, where we have discovered that 1) high education individuals have significantly higher reserve against the neuropathology than the low education group; however, 2) the cognitive decline in the high education group is significantly faster than low education individuals after the level of pathological burden increases beyond the tipping point. CONCLUSION We propose a computational proxy of cognitive reserve that can be used in clinical routine to assess the progression of AD.
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Affiliation(s)
- Ying Zhang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yajing Hao
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Lang Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kai Xia
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Guorong Wu
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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12
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Bruchhage MMK, Correia S, Malloy P, Salloway S, Deoni S. Machine Learning Classification Identifies Cerebellar Contributions to Early and Moderate Cognitive Decline in Alzheimer's Disease. Front Aging Neurosci 2020; 12:524024. [PMID: 33240072 PMCID: PMC7669549 DOI: 10.3389/fnagi.2020.524024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2020] [Accepted: 09/28/2020] [Indexed: 12/17/2022] Open
Abstract
Alzheimer's disease (AD) is one of the most common forms of dementia, marked by progressively degrading cognitive function. Although cerebellar changes occur throughout AD progression, its involvement and predictive contribution in its earliest stages, as well as gray or white matter components involved, remains unclear. We used MRI machine learning-based classification to assess the contribution of two tissue components [volume fraction myelin (VFM), and gray matter (GM) volume] within the whole brain, the neocortex, the whole cerebellum as well as its anterior and posterior parts and their predictive contribution to the first two stages of AD and typically aging controls. While classification accuracy increased with AD stages, VFM was the best predictor for all early stages of dementia when compared with typically aging controls. However, we document overall higher cerebellar prediction accuracy when compared to the whole brain with distinct structural signatures of higher anterior cerebellar contribution to mild cognitive impairment (MCI) and higher posterior cerebellar contribution to mild/moderate stages of AD for each tissue property. Based on these different cerebellar profiles and their unique contribution to early disease stages, we propose a refined model of cerebellar contribution to early AD development.
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Affiliation(s)
- Muriel M. K. Bruchhage
- Advanced Baby Imaging Lab, Hasbro Children’s Hospital, Rhode Island Hospital, Providence, RI, United States
- Department of Pediatrics, Warren Alpert Medical School at Brown University, Providence, RI, United States
| | - Stephen Correia
- Butler Hospital Memory and Aging Program, Providence, RI, United States
- Department of Human Behavior and Psychiatry, Warren Alpert Medical School at Brown University, Providence, RI, United States
| | - Paul Malloy
- Butler Hospital Memory and Aging Program, Providence, RI, United States
- Department of Human Behavior and Psychiatry, Warren Alpert Medical School at Brown University, Providence, RI, United States
| | - Stephen Salloway
- Butler Hospital Memory and Aging Program, Providence, RI, United States
- Department of Human Behavior and Psychiatry, Warren Alpert Medical School at Brown University, Providence, RI, United States
- Department of Neurology, Warren Alpert Medical School at Brown University, Providence, RI, United States
| | - Sean Deoni
- Advanced Baby Imaging Lab, Hasbro Children’s Hospital, Rhode Island Hospital, Providence, RI, United States
- Department of Pediatrics, Warren Alpert Medical School at Brown University, Providence, RI, United States
- Maternal, Newborn and Child Health Discovery & Tools, Bill & Melinda Gates Foundation, Seattle, WA, United States
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13
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Dong Q, Zhang W, Stonnington CM, Wu J, Gutman BA, Chen K, Su Y, Baxter LC, Thompson PM, Reiman EM, Caselli RJ, Wang Y. Applying surface-based morphometry to study ventricular abnormalities of cognitively unimpaired subjects prior to clinically significant memory decline. NEUROIMAGE-CLINICAL 2020; 27:102338. [PMID: 32683323 PMCID: PMC7371915 DOI: 10.1016/j.nicl.2020.102338] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 06/15/2020] [Accepted: 07/02/2020] [Indexed: 12/31/2022]
Abstract
A completely automated surface-based ventricular morphometry system. Generate a whole connected 3D ventricular shape model. Test-retest the system in two independent CU subject cohorts. Subregional ventricular abnormalities prior to clinically memory decline.
Ventricular volume (VV) is a widely used structural magnetic resonance imaging (MRI) biomarker in Alzheimer’s disease (AD) research. Abnormal enlargements of VV can be detected before clinically significant memory decline. However, VV does not pinpoint the details of subregional ventricular expansions. Here we introduce a ventricular morphometry analysis system (VMAS) that generates a whole connected 3D ventricular shape model and encodes a great deal of ventricular surface deformation information that is inaccessible by VV. VMAS contains an automated segmentation approach and surface-based multivariate morphometry statistics. We applied VMAS to two independent datasets of cognitively unimpaired (CU) groups. To our knowledge, it is the first work to detect ventricular abnormalities that distinguish normal aging subjects from those who imminently progress to clinically significant memory decline. Significant bilateral ventricular morphometric differences were first shown in 38 members of the Arizona APOE cohort, which included 18 CU participants subsequently progressing to the clinically significant memory decline within 2 years after baseline visits (progressors), and 20 matched CU participants with at least 4 years of post-baseline cognitive stability (non-progressors). VMAS also detected significant differences in bilateral ventricular morphometry in 44 Alzheimer’s Disease Neuroimaging Initiative (ADNI) subjects (18 CU progressors vs. 26 CU non-progressors) with the same inclusion criterion. Experimental results demonstrated that the ventricular anterior horn regions were affected bilaterally in CU progressors, and more so on the left. VMAS may track disease progression at subregional levels and measure the effects of pharmacological intervention at a preclinical stage.
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Affiliation(s)
- Qunxi Dong
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Wen Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | | | - Jianfeng Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Boris A Gutman
- Armour College of Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ, USA
| | - Yi Su
- Banner Alzheimer's Institute, Phoenix, AZ, USA
| | - Leslie C Baxter
- Human Brain Imaging Laboratory, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | | | | | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
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14
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Tuokkola T, Karrasch M, Koikkalainen J, Parkkola R, Lötjönen J, Löyttyniemi E, Hurme S, Rinne JO. Association between Deep Gray Matter Changes and Neurocognitive Function in Mild Cognitive Impairment and Alzheimer's Disease: A Tensor-Based Morphometric MRI Study. Dement Geriatr Cogn Disord 2020; 48:68-78. [PMID: 31514198 DOI: 10.1159/000502476] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Accepted: 08/04/2019] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Atrophy of the deep gray matter (DGM) has been associated with a risk of conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) and the degree of cognitive impairment. However, specific knowledge of the associations between degenerative DGM changes and neurocognitive functions remains limited. OBJECTIVE To examine degenerative DGM changes and evaluate their association with neurocognitive functions. METHOD We examined DGM volume changes with tensor-based morphometry (TBM) and analyzed the relationships between DGM changes and neurocognitive functions in control (n = 58), MCI (n = 38), and AD (n = 58) groups with multiple linear regression analyses. RESULTS In all DGM areas, the AD group had the largest changes in TBM volume. The differences in TBM volume changes were larger between the control group and the AD group than between the other pairs of groups. In the AD group, volume changes of the right thalamus were significantly associated with episodic memory, learning, and semantic processing. Significant or trend-level associations were identified between bilateral caudate nucleus changes and episodic memory as well as semantic processing. In the control and MCI groups, very few significant associations emerged. CONCLUSIONS Atrophy of the DGM structures, especially the thalamus and caudate nucleus, is related to cognitive impairment in AD. DGM atrophy is associated with tests reflecting both subcortical and cortical cognitive functions.
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Affiliation(s)
- Terhi Tuokkola
- Turku PET Centre, Turku University Hospital, Finland, and University of Turku, Turku, Finland,
| | - Mira Karrasch
- Department of Psychology, Abo Akademi University, Turku, Finland
| | | | - Riitta Parkkola
- Department of Radiology, University Hospital of Turku, Finland, and University of Turku, Turku, Finland
| | | | | | - Saija Hurme
- Department of Biostatistics, University of Turku, Turku, Finland
| | - Juha Olavi Rinne
- Turku PET Centre, Turku University Hospital, Finland, and University of Turku, Turku, Finland.,Division of Clinical Neurosciences, Turku University Hospital, Turku, Finland
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15
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Kong D, Fan Y, Hao J, Zhang X, Su Q, Yao T, Zhang C, Xiao L, Wang G. Cortical thickness computation by solving tetrahedron-based harmonic field. Comput Biol Med 2020; 120:103727. [PMID: 32250856 DOI: 10.1016/j.compbiomed.2020.103727] [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: 09/06/2019] [Revised: 03/11/2020] [Accepted: 03/21/2020] [Indexed: 11/30/2022]
Abstract
Cortical thickness computation in magnetic resonance imaging (MRI) is an important method to study the brain morphological changes induced by neurodegenerative diseases. This paper presents an algorithm of thickness measurement based on a volumetric Laplacian operator (VLO), which is able to capture accurately the geometric information of brain images. The proposed algorithm is a novel three-step method: 1) The rule of parity and the shrinkage strategy are combined to detect and fix the intersection error regions between the cortical surface meshes separated by FreeSurfer software and the tetrahedral mesh is constructed which reflects the original morphological features of the cerebral cortex, 2) VLO and finite element method are combined to compute the temperature distribution in the cerebral cortex under the Dirichlet boundary conditions, and 3) the thermal gradient line is determined based on the constructed local isothermal surfaces and linear geometric interpolation results. Combined with half-face data storage structure, the cortical thickness can be computed accurately and effectively from the length of each gradient line. With the obtained thickness, we set experiments to study the group differences among groups of Alzheimer's disease (AD, N = 110), mild cognitive impairment (MCI, N = 101) and healthy control people (CTL, N = 128) by statistical analysis. The results show that the q-value associated with the group differences is 0.0458 between AD and CTL, 0.0371 between MCI and CTL, and 0.0044 between AD and MCI. Practical tests demonstrate that the algorithm of thickness measurement has high efficiency and is generic to be applied to various biological structures that have internal and external surfaces.
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Affiliation(s)
- Deping Kong
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Yonghui Fan
- School of Computing, Informatics, And Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Jinguang Hao
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Xiaofeng Zhang
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Qingtang Su
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Tao Yao
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Caiming Zhang
- Shandong Co-Innovation Center of Future Intelligent Computing, Yantai, China
| | - Liang Xiao
- School of Computer Science and Engineering, Nanjing University of Science & Technology, Nanjing, China
| | - Gang Wang
- School of Information and Electrical Engineering, Ludong University, Yantai, China; School of Computing, Informatics, And Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
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16
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Cole JH, Jolly A, de Simoni S, Bourke N, Patel MC, Scott G, Sharp DJ. Spatial patterns of progressive brain volume loss after moderate-severe traumatic brain injury. Brain 2019; 141:822-836. [PMID: 29309542 PMCID: PMC5837530 DOI: 10.1093/brain/awx354] [Citation(s) in RCA: 99] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2017] [Accepted: 11/08/2017] [Indexed: 12/14/2022] Open
Abstract
Traumatic brain injury leads to significant loss of brain volume, which continues into the chronic stage. This can be sensitively measured using volumetric analysis of MRI. Here we: (i) investigated longitudinal patterns of brain atrophy; (ii) tested whether atrophy is greatest in sulcal cortical regions; and (iii) showed how atrophy could be used to power intervention trials aimed at slowing neurodegeneration. In 61 patients with moderate-severe traumatic brain injury (mean age = 41.55 years ± 12.77) and 32 healthy controls (mean age = 34.22 years ± 10.29), cross-sectional and longitudinal (1-year follow-up) brain structure was assessed using voxel-based morphometry on T1-weighted scans. Longitudinal brain volume changes were characterized using a novel neuroimaging analysis pipeline that generates a Jacobian determinant metric, reflecting spatial warping between baseline and follow-up scans. Jacobian determinant values were summarized regionally and compared with clinical and neuropsychological measures. Patients with traumatic brain injury showed lower grey and white matter volume in multiple brain regions compared to controls at baseline. Atrophy over 1 year was pronounced following traumatic brain injury. Patients with traumatic brain injury lost a mean (± standard deviation) of 1.55% ± 2.19 of grey matter volume per year, 1.49% ± 2.20 of white matter volume or 1.51% ± 1.60 of whole brain volume. Healthy controls lost 0.55% ± 1.13 of grey matter volume and gained 0.26% ± 1.11 of white matter volume; equating to a 0.22% ± 0.83 reduction in whole brain volume. Atrophy was greatest in white matter, where the majority (84%) of regions were affected. This effect was independent of and substantially greater than that of ageing. Increased atrophy was also seen in cortical sulci compared to gyri. There was no relationship between atrophy and time since injury or age at baseline. Atrophy rates were related to memory performance at the end of the follow-up period, as well as to changes in memory performance, prior to multiple comparison correction. In conclusion, traumatic brain injury results in progressive loss of brain tissue volume, which continues for many years post-injury. Atrophy is most prominent in the white matter, but is also more pronounced in cortical sulci compared to gyri. These findings suggest the Jacobian determinant provides a method of quantifying brain atrophy following a traumatic brain injury and is informative in determining the long-term neurodegenerative effects after injury. Power calculations indicate that Jacobian determinant images are an efficient surrogate marker in clinical trials of neuroprotective therapeutics.
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Affiliation(s)
- James H Cole
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Imperial College London, Division of Brain Sciences, Hammersmith Hospital, London, UK
| | - Amy Jolly
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Imperial College London, Division of Brain Sciences, Hammersmith Hospital, London, UK
| | - Sara de Simoni
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Imperial College London, Division of Brain Sciences, Hammersmith Hospital, London, UK
| | - Niall Bourke
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Imperial College London, Division of Brain Sciences, Hammersmith Hospital, London, UK
| | - Maneesh C Patel
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Imperial College London, Division of Brain Sciences, Hammersmith Hospital, London, UK
| | - Gregory Scott
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Imperial College London, Division of Brain Sciences, Hammersmith Hospital, London, UK
| | - David J Sharp
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Imperial College London, Division of Brain Sciences, Hammersmith Hospital, London, UK
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17
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Dong Q, Zhang W, Wu J, Li B, Schron EH, McMahon T, Shi J, Gutman BA, Chen K, Baxter LC, Thompson PM, Reiman EM, Caselli RJ, Wang Y. Applying surface-based hippocampal morphometry to study APOE-E4 allele dose effects in cognitively unimpaired subjects. NEUROIMAGE-CLINICAL 2019; 22:101744. [PMID: 30852398 PMCID: PMC6411498 DOI: 10.1016/j.nicl.2019.101744] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 02/02/2019] [Accepted: 03/02/2019] [Indexed: 11/30/2022]
Abstract
Apolipoprotein E (APOE) e4 is the major genetic risk factor for late-onset Alzheimer's disease (AD). The dose-dependent impact of this allele on hippocampal volumes has been documented, but its influence on general hippocampal morphology in cognitively unimpaired individuals is still elusive. Capitalizing on the study of a large number of cognitively unimpaired late middle aged and older adults with two, one and no APOE-e4 alleles, the current study aims to characterize the ability of our automated surface-based hippocampal morphometry algorithm to distinguish between these three levels of genetic risk for AD and demonstrate its superiority to a commonly used hippocampal volume measurement. We examined the APOE-e4 dose effect on cross-sectional hippocampal morphology analysis in a magnetic resonance imaging (MRI) database of 117 cognitively unimpaired subjects aged between 50 and 85 years (mean = 57.4, SD = 6.3), including 36 heterozygotes (e3/e4), 37 homozygotes (e4/e4) and 44 non-carriers (e3/e3). The proposed automated framework includes hippocampal surface segmentation and reconstruction, higher-order hippocampal surface correspondence computation, and hippocampal surface deformation analysis with multivariate statistics. In our experiments, the surface-based method identified APOE-e4 dose effects on the left hippocampal morphology. Compared to the widely-used hippocampal volume measure, our hippocampal morphometry statistics showed greater statistical power by distinguishing cognitively unimpaired subjects with two, one, and no APOE-e4 alleles. Our findings mirrored previous studies showing that APOE-e4 has a dose effect on the acceleration of brain structure deformities. The results indicated that the proposed surface-based hippocampal morphometry measure is a potential preclinical AD imaging biomarker for cognitively unimpaired individuals. Applied surface-based hippocampal morphometry on cognitively unimpaired subjects. Our study identified APOE-e4 dose effects on cognitively unimpaired subjects. Surface-based hippocampal morphometry outperformed the hippocampal volume measure. Surface-based hippocampal morphometry may be a potential preclinical AD biomarker.
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Affiliation(s)
- Qunxi Dong
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Wen Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Jianfeng Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Bolun Li
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | | | - Travis McMahon
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Boris A Gutman
- Armour College of Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ, USA
| | - Leslie C Baxter
- Human Brain Imaging Laboratory, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | | | | | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
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18
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Tuokkola T, Koikkalainen J, Parkkola R, Karrasch M, Lötjönen J, Rinne JO. Longitudinal changes in the brain in mild cognitive impairment: a magnetic resonance imaging study using the visual rating method and tensor-based morphometry. Acta Radiol 2018; 59:973-979. [PMID: 28952780 DOI: 10.1177/0284185117734418] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Background Brain atrophy is associated with mild cognitive impairment (MCI), and by using volumetric and visual analyzing methods, it is possible to differentiate between individuals with progressive MCI (MCIp) and stable MCI (MCIs). Automated analysis methods detect degenerative changes in the brain earlier and more reliably than visual methods. Purpose To detect and evaluate structural brain changes between and within the MCIs, MCIp, and control groups during a two-year follow-up period. Material and Methods Brain magnetic resonance imaging (MRI) scans of 11 participants with MCIs, 18 participants with MCIp, and 84 controls were analyzed by the visual rating method (VRM) and tensor-based morphometry (TBM). Results At baseline, both VRM and TBM differentiated the whole MCI group (combined MCIs and MCIp) and the MCIp group from the control group, but they did not differentiate the MCIs group from the control group. At follow-up, both methods differentiated the MCIp group from the control group, but minor differences between the MCIs and control groups were only seen by TBM. Neuropsychological tests did not find differences between the MCIs and control groups at follow-up. Neither method revealed relevant signs of brain atrophy progression within or between MCI subgroups during the follow-up time. Conclusion Both methods are equally good in the evaluation of structural brain changes in MCI if the groups are sufficiently large and the disease progresses to AD. Only TBM disclosed minor atrophic changes in the MCIs group compared to controls at follow-up. The results need to be confirmed with a large patient group and longer follow-up time.
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Affiliation(s)
- Terhi Tuokkola
- Turku PET Centre, Turku University Hospital, Turku, Finland
| | - Juha Koikkalainen
- University of Eastern Finland, Faculty of Health Sciences, Kuopio, Finland
| | - Riitta Parkkola
- Department of Radiology, University Hospital of Turku and Turku University Hospital, Turku, Finland
| | - Mira Karrasch
- Department of Psychology, Abo Akademi University, Turku, Finland
| | - Jyrki Lötjönen
- Aalto University, Department of Neuroscience and Biomedical Engineering, Helsinki, Finland VTT Technical Research Centre of Finland, Tampere, Finland
| | - Juha O Rinne
- Turku PET Centre, Turku University Hospital, Turku, Finland
- Finland Division of Clinical Neurosciences, Turku University Hospital, Turku, Finland
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19
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Tu Y, Wen C, Zhang W, Wu J, Zhang J, Chen K, Caselli RJ, Reiman EM, Reiman EM, Wang Y. Isometry Invariant Shape Descriptors for Abnormality Detection on Brain Surfaces Affected by Alzheimer's Disease. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:427-4631. [PMID: 30440425 PMCID: PMC6241283 DOI: 10.1109/embc.2018.8513129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Alzheimer's disease (AD), a progressive brain disorder, is the most common neurodegenerative disease in older adults. There is a need for brain structural magnetic resonance imaging (MRI) biomarkers to help assess AD progression and intervention effects. Prior research showed that surface based brain imaging features hold great promise as efficient AD biomarkers. However, the complex geometry of cortical surfaces poses a major challenge to defining such a feature that is sensitive in qualification, robust in analysis, and intuitive in visualization. Here we propose a novel isometry invariant shape descriptor for brain morphometry analysis. First, we calculate a global area-preserving mapping from cortical surface to the unit sphere. Based on the mapping, the Beltrami coefficient shape descriptor is calculated. An analysis of average shape descriptors reveals that our detected features are consistent with some previous AD studies where medial temporal lobe volume was identified as an important AD imaging biomarker. We further apply a novel patch-based spherical sparse coding scheme for feature dimension reduction. Later, a support vector machine (SVM) classifier is applied to discriminate 135 amyloid-beta positive persons with the clinical diagnosis of Mild Cognitive Impairment (MCI) from 248 amyloid-beta-negative normal control subjects. The 5-folder cross-validation accuracy is about 81.82\% on the dataset, outperforming some traditional, Freesurfer based, brain surface features. The results show that our shape descriptor is effective in distinguishing dementia due to AD from age-matched normal aging individuals. Our isometry invariant shape descriptors may provide a unique and intuitive way to inspect cortical surface and its morphometry changes.
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20
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Lawrence E, Vegvari C, Ower A, Hadjichrysanthou C, De Wolf F, Anderson RM. A Systematic Review of Longitudinal Studies Which Measure Alzheimer's Disease Biomarkers. J Alzheimers Dis 2018; 59:1359-1379. [PMID: 28759968 PMCID: PMC5611893 DOI: 10.3233/jad-170261] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Alzheimer’s disease (AD) is a progressive and fatal neurodegenerative disease, with no effective treatment or cure. A gold standard therapy would be treatment to slow or halt disease progression; however, knowledge of causation in the early stages of AD is very limited. In order to determine effective endpoints for possible therapies, a number of quantitative surrogate markers of disease progression have been suggested, including biochemical and imaging biomarkers. The dynamics of these various surrogate markers over time, particularly in relation to disease development, are, however, not well characterized. We reviewed the literature for studies that measured cerebrospinal fluid or plasma amyloid-β and tau, or took magnetic resonance image or fluorodeoxyglucose/Pittsburgh compound B-positron electron tomography scans, in longitudinal cohort studies. We summarized the properties of the major cohort studies in various countries, commonly used diagnosis methods and study designs. We have concluded that additional studies with repeat measures over time in a representative population cohort are needed to address the gap in knowledge of AD progression. Based on our analysis, we suggest directions in which research could move in order to advance our understanding of this complex disease, including repeat biomarker measurements, standardization and increased sample sizes.
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Affiliation(s)
- Emma Lawrence
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | - Carolin Vegvari
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | - Alison Ower
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | | | - Frank De Wolf
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.,Janssen Prevention Center, Leiden, The Netherlands
| | - Roy M Anderson
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
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21
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Fujishima M, Kawaguchi A, Maikusa N, Kuwano R, Iwatsubo T, Matsuda H. Sample Size Estimation for Alzheimer's Disease Trials from Japanese ADNI Serial Magnetic Resonance Imaging. J Alzheimers Dis 2018; 56:75-88. [PMID: 27911297 PMCID: PMC5240548 DOI: 10.3233/jad-160621] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background: Little is known about the sample sizes required for clinical trials of Alzheimer’s disease (AD)-modifying treatments using atrophy measures from serial brain magnetic resonance imaging (MRI) in the Japanese population. Objective: The primary objective of the present study was to estimate how large a sample size would be needed for future clinical trials for AD-modifying treatments in Japan using atrophy measures of the brain as a surrogate biomarker. Methods: Sample sizes were estimated from the rates of change of the whole brain and hippocampus by the k-means normalized boundary shift integral (KN-BSI) and cognitive measures using the data of 537 Japanese Alzheimer’s Neuroimaging Initiative (J-ADNI) participants with a linear mixed-effects model. We also examined the potential use of ApoE status as a trial enrichment strategy. Results: The hippocampal atrophy rate required smaller sample sizes than cognitive measures of AD and mild cognitive impairment (MCI). Inclusion of ApoE status reduced sample sizes for AD and MCI patients in the atrophy measures. Conclusion: These results show the potential use of longitudinal hippocampal atrophy measurement using automated image analysis as a progression biomarker and ApoE status as a trial enrichment strategy in a clinical trial of AD-modifying treatment in Japanese people.
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Affiliation(s)
- Motonobu Fujishima
- Integrative Brain Imaging Center (IBIC), National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan.,Department of Diagnostic Radiology, Kojinkai Josai Clinic, Maebashi, Gunma, Japan
| | - Atsushi Kawaguchi
- Center for Comprehensive Community Medicine, Faculty of Medicine, Saga University, Saga, Japan
| | - Norihide Maikusa
- Integrative Brain Imaging Center (IBIC), National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
| | - Ryozo Kuwano
- Brain Research Institute, Niigata University, Niigata, Japan
| | - Takeshi Iwatsubo
- Department of Neuropathology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Hiroshi Matsuda
- Integrative Brain Imaging Center (IBIC), National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
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22
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Mak E, Su L, Williams GB, Firbank MJ, Lawson RA, Yarnall AJ, Duncan GW, Mollenhauer B, Owen AM, Khoo TK, Brooks DJ, Rowe JB, Barker RA, Burn DJ, O'Brien JT. Longitudinal whole-brain atrophy and ventricular enlargement in nondemented Parkinson's disease. Neurobiol Aging 2017; 55:78-90. [PMID: 28431288 PMCID: PMC5454799 DOI: 10.1016/j.neurobiolaging.2017.03.012] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2016] [Revised: 03/05/2017] [Accepted: 03/08/2017] [Indexed: 11/28/2022]
Abstract
We investigated whole-brain atrophy and ventricular enlargement over 18 months in nondemented Parkinson's disease (PD) and examined their associations with clinical measures and baseline CSF markers. PD subjects (n = 100) were classified at baseline into those with mild cognitive impairment (MCI; PD-MCI, n = 36) and no cognitive impairment (PD-NC, n = 64). Percentage of whole-brain volume change (PBVC) and ventricular expansion over 18 months were assessed with FSL-SIENA and ventricular enlargement (VIENA) respectively. PD-MCI showed increased global atrophy (-1.1% ± 0.8%) and ventricular enlargement (6.9 % ± 5.2%) compared with both PD-NC (PBVC: -0.4 ± 0.5, p < 0.01; VIENA: 2.1% ± 4.3%, p < 0.01) and healthy controls. In a subset of 35 PD subjects, CSF levels of tau, and Aβ42/Aβ40 ratio were correlated with PBVC and ventricular enlargement respectively. The sample size required to demonstrate a 20% reduction in PBVC and VIENA was approximately 1/15th of that required to detect equivalent changes in cognitive decline. These findings suggest that longitudinal MRI measurements have potential to serve as surrogate markers to complement clinical assessments for future disease-modifying trials in PD.
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Affiliation(s)
- Elijah Mak
- Department of Psychiatry, University of Cambridge, Cambridgeshire, UK
| | - Li Su
- Department of Psychiatry, University of Cambridge, Cambridgeshire, UK
| | - Guy B Williams
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridgeshire, UK
| | - Michael J Firbank
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK
| | - Rachael A Lawson
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK
| | - Alison J Yarnall
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK
| | - Gordon W Duncan
- Medicine of the Elderly, Western General Hospital, Edinburgh, UK
| | - Brit Mollenhauer
- Paracelsus-Elena-Klinik, Kassel, Germany; University Medical Center Goettingen, Institute of Neuropathology, Goettingen, Germany
| | - Adrian M Owen
- Brain and Mind Institute, University of Western Ontario, London, Canada; Department of Psychology, University of Western Ontario, London, Canada
| | - Tien K Khoo
- Menzies Health Institute, Queensland and School of Medicine, Griffith University, Gold Coast, Australia
| | - David J Brooks
- Division of Neuroscience, Imperial College London, London, UK; Institute of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - James B Rowe
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Medical Research Council, Cognition and Brain Sciences Unit, Cambridge, UK; Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
| | - Roger A Barker
- John van Geest Centre for Brain Repair, University of Cambridge, Cambridge, UK
| | - David J Burn
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK
| | - John T O'Brien
- Department of Psychiatry, University of Cambridge, Cambridgeshire, UK.
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23
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Patel SK, Restrepo C, Werden E, Churilov L, Ekinci EI, Srivastava PM, Ramchand J, Wai B, Chambers B, O’Callaghan CJ, Darby D, Hachinski V, Cumming T, Donnan G, Burrell LM, Brodtmann A. Does left ventricular hypertrophy affect cognition and brain structural integrity in type 2 diabetes? Study design and rationale of the Diabetes and Dementia (D2) study. BMC Endocr Disord 2017; 17:24. [PMID: 28388897 PMCID: PMC5384138 DOI: 10.1186/s12902-017-0173-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2017] [Accepted: 03/31/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Cognitive impairment is common in type 2 diabetes mellitus, and there is a strong association between type 2 diabetes and Alzheimer's disease. However, we do not know which type 2 diabetes patients will dement or which biomarkers predict cognitive decline. Left ventricular hypertrophy (LVH) is potentially such a marker. LVH is highly prevalent in type 2 diabetes and is a strong, independent predictor of cardiovascular events. To date, no studies have investigated the association between LVH and cognitive decline in type 2 diabetes. The Diabetes and Dementia (D2) study is designed to establish whether patients with type 2 diabetes and LVH have increased rates of brain atrophy and cognitive decline. METHODS The D2 study is a single centre, observational, longitudinal case control study that will follow 168 adult patients aged >50 years with type 2 diabetes: 50% with LVH (case) and 50% without LVH (control). It will assess change in cardiovascular risk, brain imaging and neuropsychological testing between two time-points, baseline (0 months) and 24 months. The primary outcome is brain volume change at 24 months. The co-primary outcome is the presence of cognitive decline at 24 months. The secondary outcome is change in left ventricular mass associated with brain atrophy and cognitive decline at 24 months. DISCUSSION The D2 study will test the hypothesis that patients with type 2 diabetes and LVH will exhibit greater brain atrophy than those without LVH. An understanding of whether LVH contributes to cognitive decline, and in which patients, will allow us to identify patients at particular risk. TRIAL REGISTRATION Australian New Zealand Clinical Trials Registry ( ACTRN12616000546459 ), date registered, 28/04/2016.
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Affiliation(s)
- Sheila K. Patel
- The Florey Institute of Neuroscience and Mental Health, Melbourne Brain Centre, Austin Health, 245 Burgundy Street, Heidelberg, VIC 3084 Australia
- Department of Medicine, University of Melbourne, Austin Health, Level 7, Lance Townsend Building, 145 Studley Road, Heidelberg, VIC 3084 Australia
| | - Carolina Restrepo
- The Florey Institute of Neuroscience and Mental Health, Melbourne Brain Centre, Austin Health, 245 Burgundy Street, Heidelberg, VIC 3084 Australia
| | - Emilio Werden
- The Florey Institute of Neuroscience and Mental Health, Melbourne Brain Centre, Austin Health, 245 Burgundy Street, Heidelberg, VIC 3084 Australia
| | - Leonid Churilov
- The Florey Institute of Neuroscience and Mental Health, Melbourne Brain Centre, Austin Health, 245 Burgundy Street, Heidelberg, VIC 3084 Australia
| | - Elif I. Ekinci
- Department of Medicine, University of Melbourne, Austin Health, Level 7, Lance Townsend Building, 145 Studley Road, Heidelberg, VIC 3084 Australia
- Austin Health Endocrine Centre, Heidelberg, VIC Australia
| | - Piyush M. Srivastava
- Department of Medicine, University of Melbourne, Austin Health, Level 7, Lance Townsend Building, 145 Studley Road, Heidelberg, VIC 3084 Australia
- Department of Cardiology, Austin Health, Heidelberg, VIC Australia
| | - Jay Ramchand
- Department of Medicine, University of Melbourne, Austin Health, Level 7, Lance Townsend Building, 145 Studley Road, Heidelberg, VIC 3084 Australia
- Department of Cardiology, Austin Health, Heidelberg, VIC Australia
| | - Bryan Wai
- Department of Medicine, University of Melbourne, Austin Health, Level 7, Lance Townsend Building, 145 Studley Road, Heidelberg, VIC 3084 Australia
- Department of Cardiology, Austin Health, Heidelberg, VIC Australia
| | - Brian Chambers
- Department of Medicine, University of Melbourne, Austin Health, Level 7, Lance Townsend Building, 145 Studley Road, Heidelberg, VIC 3084 Australia
- Department of Neurology, Austin Health, Heidelberg, VIC Australia
| | - Christopher J. O’Callaghan
- Department of Medicine, University of Melbourne, Austin Health, Level 7, Lance Townsend Building, 145 Studley Road, Heidelberg, VIC 3084 Australia
- Department of Clinical Pharmacology, Austin Health, Heidelberg, VIC Australia
| | - David Darby
- The Florey Institute of Neuroscience and Mental Health, Melbourne Brain Centre, Austin Health, 245 Burgundy Street, Heidelberg, VIC 3084 Australia
| | - Vladimir Hachinski
- Department of Clinical Neurological Sciences, London Health Sciences Centre, University of Western Ontario, London, Canada
| | - Toby Cumming
- The Florey Institute of Neuroscience and Mental Health, Melbourne Brain Centre, Austin Health, 245 Burgundy Street, Heidelberg, VIC 3084 Australia
| | - Geoff Donnan
- The Florey Institute of Neuroscience and Mental Health, Melbourne Brain Centre, Austin Health, 245 Burgundy Street, Heidelberg, VIC 3084 Australia
| | - Louise M. Burrell
- Department of Medicine, University of Melbourne, Austin Health, Level 7, Lance Townsend Building, 145 Studley Road, Heidelberg, VIC 3084 Australia
- Department of Cardiology, Austin Health, Heidelberg, VIC Australia
| | - Amy Brodtmann
- The Florey Institute of Neuroscience and Mental Health, Melbourne Brain Centre, Austin Health, 245 Burgundy Street, Heidelberg, VIC 3084 Australia
- Department of Medicine, University of Melbourne, Austin Health, Level 7, Lance Townsend Building, 145 Studley Road, Heidelberg, VIC 3084 Australia
- Department of Neurology, Austin Health, Heidelberg, VIC Australia
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24
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Zhu Y, Zhu X, Kim M, Shen D, Wu G. Early Diagnosis of Alzheimer's Disease by Joint Feature Selection and Classification on Temporally Structured Support Vector Machine. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2016; 9900:264-272. [PMID: 28149964 PMCID: PMC5278780 DOI: 10.1007/978-3-319-46720-7_31] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
The diagnosis of Alzheimer's disease (AD) from neuroimaging data at the pre-clinical stage has been intensively investigated because of the immense social and economic cost. In the past decade, computational approaches on longitudinal image sequences have been actively investigated with special attention to Mild Cognitive Impairment (MCI), which is an intermediate stage between normal control (NC) and AD. However, current state-of-the-art diagnosis methods have limited power in clinical practice, due to the excessive requirements such as equal and immoderate number of scans in longitudinal imaging data. More critically, very few methods are specifically designed for the early alarm of AD uptake. To address these limitations, we propose a flexible spatial-temporal solution for early detection of AD by recognizing abnormal structure changes from longitudinal MR image sequence. Specifically, our method is leveraged by the non-reversible nature of AD progression. We employ temporally structured SVM to accurately alarm AD at early stage by enforcing the monotony on classification result to avoid unrealistic and inconsistent diagnosis result along time. Furthermore, in order to select best features which can well collaborate with the classifier, we present as joint feature selection and classification framework. The evaluation on more than 150 longitudinal subjects from ADNI dataset shows that our method is able to alarm the conversion of AD 12 months prior to the clinical diagnosis with at least 82.5 % accuracy. It is worth noting that our proposed method works on widely used MR images and does not have restriction on the number of scans in the longitudinal sequence, which is very attractive to real clinical practice.
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Affiliation(s)
- Yingying Zhu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Xiaofeng Zhu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Minjeong Kim
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Guorong Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA
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25
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Aksman LM, Lythgoe DJ, Williams SCR, Jokisch M, Mönninghoff C, Streffer J, Jöckel KH, Weimar C, Marquand AF. Making use of longitudinal information in pattern recognition. Hum Brain Mapp 2016; 37:4385-4404. [PMID: 27451934 PMCID: PMC5111621 DOI: 10.1002/hbm.23317] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2016] [Revised: 06/20/2016] [Accepted: 07/05/2016] [Indexed: 12/31/2022] Open
Abstract
Longitudinal designs are widely used in medical studies as a means of observing within-subject changes over time in groups of subjects, thereby aiming to improve sensitivity for detecting disease effects. Paralleling an increased use of such studies in neuroimaging has been the adoption of pattern recognition algorithms for making individualized predictions of disease. However, at present few pattern recognition methods exist to make full use of neuroimaging data that have been collected longitudinally, with most methods relying instead on cross-sectional style analysis. This article presents a principal component analysis-based feature construction method that uses longitudinal high-dimensional data to improve predictive performance of pattern recognition algorithms. The method can be applied to data from a wide range of longitudinal study designs and permits an arbitrary number of time-points per subject. We apply the method to two longitudinal datasets, one containing subjects with mild cognitive impairment along with healthy controls, the other with early dementia subjects and healthy controls. Across both datasets, we show improvements in predictive accuracy relative to cross-sectional classifiers for discriminating disease subjects from healthy controls on the basis of whole-brain structural magnetic resonance image-based voxels. In addition, we can transfer longitudinal information from one set of subjects to make disease predictions in another set of subjects. The proposed method is simple and, as a feature construction method, flexible with respect to the choice of classifier and image registration algorithm. Hum Brain Mapp 37:4385-4404, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Leon M Aksman
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - David J Lythgoe
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Steven C R Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Martha Jokisch
- Department of Neurology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Christoph Mönninghoff
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Johannes Streffer
- Janssen-Pharmaceutical Companies of Johnson & Johnson, Janssen Research and Development, Beerse, Belgium
| | - Karl-Heinz Jöckel
- Institute for Medical Informatics, Biometry and Epidemiology, University Hospital of Essen, University Duisburg-Essen, Germany
| | - Christian Weimar
- Department of Neurology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Andre F Marquand
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.,Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
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26
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Zetterberg H, Skillbäck T, Mattsson N, Trojanowski JQ, Portelius E, Shaw LM, Weiner MW, Blennow K. Association of Cerebrospinal Fluid Neurofilament Light Concentration With Alzheimer Disease Progression. JAMA Neurol 2016; 73:60-7. [PMID: 26524180 DOI: 10.1001/jamaneurol.2015.3037] [Citation(s) in RCA: 340] [Impact Index Per Article: 42.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
IMPORTANCE The extent to which large-caliber axonal degeneration contributes to Alzheimer disease (AD) progression is unknown. Cerebrospinal fluid (CSF) neurofilament light (NFL) concentration is a general marker of damage to large-caliber myelinated axons. OBJECTIVE To test whether CSF NFL concentration is associated with cognitive decline and imaging evidence of neurodegeneration and white matter change in AD. DESIGN, SETTING, AND PARTICIPANTS A commercially available immunoassay was used to analyze CSF NFL concentration in a cohort of patients with AD (n = 95) or mild cognitive impairment (MCI) (n = 192) and in cognitively normal individuals (n = 110) from the Alzheimer's Disease Neuroimaging Initiative. The study dates were January 2005 to December 2007. The NFL analysis was performed in November 2014. MAIN OUTCOMES AND MEASURES Correlation was investigated among baseline CSF NFL concentration and longitudinal cognitive impairment, white matter change, and regional brain atrophy within each diagnostic group. RESULTS Cerebrospinal fluid NFL concentration (median [interquartile range]) was higher in the AD dementia group (1479 [1134-1842] pg/mL), stable MCI group (no progression to AD during follow-up; 1182 [923-1687] pg/mL), and progressive MCI group (MCI with progression to AD dementia during follow-up; 1336 [1061-1693] pg/mL) compared with control participants (1047 [809-1265] pg/mL) (P < .001 for all) and in the AD dementia group compared with the stable MCI group (P = .01). In the MCI group, a higher CSF NFL concentration was associated with faster brain atrophy over time as measured by changes in whole-brain volume (β = -4177, P = .003), ventricular volume (β = 1835, P < .001), and hippocampus volume (β = -54.22, P < .001); faster disease progression as reflected by decreased Mini-Mental State Examination scores (β = -1.077, P < .001) and increased Alzheimer Disease Assessment Scale cognitive subscale scores (β = 2.30, P < .001); and faster white matter intensity change (β = 598.7, P < .001). CONCLUSIONS AND RELEVANCE Cerebrospinal fluid NFL concentration is increased by the early clinical stage of AD and is associated with cognitive deterioration and structural brain changes over time. This finding corroborates the contention that degeneration of large-caliber axons is an important feature of AD neurodegeneration.
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Affiliation(s)
- Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden2Department of Molecular Neuroscience, University College London Institute of Neurology, London, Engla
| | - Tobias Skillbäck
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
| | - Niklas Mattsson
- Department of Veterans Affairs Medical Center, University of California, San Francisco4Center for Imaging of Neurodegenerative Diseases, University of California, San Francisco5Department of Radiology and Biomedical Imaging, University of California, San
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Disease Research, Institute on Aging, University of Pennsylvania School of Medicine, Philadelphia
| | - Erik Portelius
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Disease Research, Institute on Aging, University of Pennsylvania School of Medicine, Philadelphia
| | - Michael W Weiner
- Department of Veterans Affairs Medical Center, University of California, San Francisco
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
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27
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Schmidt-Richberg A, Ledig C, Guerrero R, Molina-Abril H, Frangi A, Rueckert D. Learning Biomarker Models for Progression Estimation of Alzheimer's Disease. PLoS One 2016; 11:e0153040. [PMID: 27096739 PMCID: PMC4838309 DOI: 10.1371/journal.pone.0153040] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2015] [Accepted: 03/22/2016] [Indexed: 11/26/2022] Open
Abstract
Being able to estimate a patient's progress in the course of Alzheimer's disease and predicting future progression based on a number of observed biomarker values is of great interest for patients, clinicians and researchers alike. In this work, an approach for disease progress estimation is presented. Based on a set of subjects that convert to a more severe disease stage during the study, models that describe typical trajectories of biomarker values in the course of disease are learned using quantile regression. A novel probabilistic method is then derived to estimate the current disease progress as well as the rate of progression of an individual by fitting acquired biomarkers to the models. A particular strength of the method is its ability to naturally handle missing data. This means, it is applicable even if individual biomarker measurements are missing for a subject without requiring a retraining of the model. The functionality of the presented method is demonstrated using synthetic and--employing cognitive scores and image-based biomarkers--real data from the ADNI study. Further, three possible applications for progress estimation are demonstrated to underline the versatility of the approach: classification, construction of a spatio-temporal disease progression atlas and prediction of future disease progression.
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Affiliation(s)
| | - Christian Ledig
- Biomedical Image Analysis Group, Imperial College London, London, United Kingdom
| | - Ricardo Guerrero
- Biomedical Image Analysis Group, Imperial College London, London, United Kingdom
| | - Helena Molina-Abril
- Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), University of Sheffield, Sheffield, United Kingdom
| | - Alejandro Frangi
- Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), University of Sheffield, Sheffield, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Imperial College London, London, United Kingdom
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28
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Influence of APOE Genotype on Hippocampal Atrophy over Time - An N=1925 Surface-Based ADNI Study. PLoS One 2016; 11:e0152901. [PMID: 27065111 PMCID: PMC4827849 DOI: 10.1371/journal.pone.0152901] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2015] [Accepted: 03/21/2016] [Indexed: 11/25/2022] Open
Abstract
The apolipoprotein E (APOE) e4 genotype is a powerful risk factor for late-onset Alzheimer’s disease (AD). In the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort, we previously reported significant baseline structural differences in APOE e4 carriers relative to non-carriers, involving the left hippocampus more than the right—a difference more pronounced in e4 homozygotes than heterozygotes. We now examine the longitudinal effects of APOE genotype on hippocampal morphometry at 6-, 12- and 24-months, in the ADNI cohort. We employed a new automated surface registration system based on conformal geometry and tensor-based morphometry. Among different hippocampal surfaces, we computed high-order correspondences, using a novel inverse-consistent surface-based fluid registration method and multivariate statistics consisting of multivariate tensor-based morphometry (mTBM) and radial distance. At each time point, using Hotelling’s T2 test, we found significant morphological deformation in APOE e4 carriers relative to non-carriers in the full cohort as well as in the non-demented (pooled MCI and control) subjects at each follow-up interval. In the complete ADNI cohort, we found greater atrophy of the left hippocampus than the right, and this asymmetry was more pronounced in e4 homozygotes than heterozygotes. These findings, combined with our earlier investigations, demonstrate an e4 dose effect on accelerated hippocampal atrophy, and support the enrichment of prevention trial cohorts with e4 carriers.
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29
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Moon SW, Dinov ID, Hobel S, Zamanyan A, Choi YC, Shi R, Thompson PM, Toga AW. Structural Brain Changes in Early-Onset Alzheimer's Disease Subjects Using the LONI Pipeline Environment. J Neuroimaging 2015; 25:728-37. [PMID: 25940587 PMCID: PMC4537660 DOI: 10.1111/jon.12252] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2014] [Revised: 03/20/2015] [Accepted: 03/22/2015] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND AND PURPOSE This study investigates 36 subjects aged 55-65 from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to expand our knowledge of early-onset (EO) Alzheimer's Disease (EO-AD) using neuroimaging biomarkers. METHODS Nine of the subjects had EO-AD, and 27 had EO mild cognitive impairment (EO-MCI). The structural ADNI data were parcellated using BrainParser, and the 15 most discriminating neuroimaging markers between the two cohorts were extracted using the Global Shape Analysis (GSA) Pipeline workflow. Then the Local Shape Analysis (LSA) Pipeline workflow was used to conduct local (per-vertex) post-hoc statistical analyses of the shape differences based on the participants' diagnoses (EO-MCI+EO-AD). Tensor-based Morphometry (TBM) and multivariate regression models were used to identify the significance of the structural brain differences based on the participants' diagnoses. RESULTS The significant between-group regional differences using GSA were found in 15 neuroimaging markers. The results of the LSA analysis workflow were based on the subject diagnosis, age, years of education, apolipoprotein E (ε4), Mini-Mental State Examination, visiting times, and logical memory as regressors. All the variables had significant effects on the regional shape measures. Some of these effects survived the false discovery rate (FDR) correction. Similarly, the TBM analysis showed significant effects on the Jacobian displacement vector fields, but these effects were reduced after FDR correction. CONCLUSIONS These results may explain some of the differences between EO-AD and EO-MCI, and some of the characteristics of the EO cognitive impairment subjects.
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Affiliation(s)
- Seok Woo Moon
- Department of Psychiatry, Konkuk University School of Medicine, Seoul 143-701, Korea
| | - Ivo D. Dinov
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
- University of Michigan, School of Nursing, Ann Arbor, MI 48109, USA
| | - Sam Hobel
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
| | - Alen Zamanyan
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
| | - Young Chil Choi
- Department of Radiology, Konkuk University School of Medicine, Seoul 143-701, Korea
| | - Ran Shi
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
| | - Paul M. Thompson
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
| | - Arthur W. Toga
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
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30
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Zhan L, Liu Y, Wang Y, Zhou J, Jahanshad N, Ye J, Thompson PM. Boosting brain connectome classification accuracy in Alzheimer's disease using higher-order singular value decomposition. Front Neurosci 2015; 9:257. [PMID: 26257601 PMCID: PMC4513242 DOI: 10.3389/fnins.2015.00257] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2015] [Accepted: 07/10/2015] [Indexed: 11/14/2022] Open
Abstract
Alzheimer's disease (AD) is a progressive brain disease. Accurate detection of AD and its prodromal stage, mild cognitive impairment (MCI), are crucial. There is also a growing interest in identifying brain imaging biomarkers that help to automatically differentiate stages of Alzheimer's disease. Here, we focused on brain structural networks computed from diffusion MRI and proposed a new feature extraction and classification framework based on higher order singular value decomposition and sparse logistic regression. In tests on publicly available data from the Alzheimer's Disease Neuroimaging Initiative, our proposed framework showed promise in detecting brain network differences that help in classifying different stages of Alzheimer's disease.
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Affiliation(s)
- Liang Zhan
- Imaging Genetics Center, Keck School of Medicine, University of Southern California Marina del Rey, CA, USA
| | - Yashu Liu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University Tempe, AZ, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University Tempe, AZ, USA
| | - Jiayu Zhou
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University Tempe, AZ, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Keck School of Medicine, University of Southern California Marina del Rey, CA, USA
| | - Jieping Ye
- Department of Computational Medicine and Bioinformatics, University of Michigan Ann Arbor, MI, USA ; Department of Electrical Engineering and Computer Science, University of Michigan Ann Arbor, MI, USA
| | - Paul M Thompson
- Imaging Genetics Center, Keck School of Medicine, University of Southern California Marina del Rey, CA, USA
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31
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Whitwell JL, Duffy JR, Strand EA, Machulda MM, Tosakulwong N, Weigand SD, Senjem ML, Spychalla AJ, Gunter JL, Petersen RC, Jack CR, Josephs KA. Sample size calculations for clinical trials targeting tauopathies: a new potential disease target. J Neurol 2015; 262:2064-72. [PMID: 26076744 DOI: 10.1007/s00415-015-7821-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2014] [Revised: 06/08/2015] [Accepted: 06/09/2015] [Indexed: 12/12/2022]
Abstract
Disease-modifying therapies are being developed to target tau pathology, and should, therefore, be tested in primary tauopathies. We propose that progressive apraxia of speech should be considered one such target group. In this study, we investigate potential neuroimaging and clinical outcome measures for progressive apraxia of speech and determine sample size estimates for clinical trials. We prospectively recruited 24 patients with progressive apraxia of speech who underwent two serial MRI with an interval of approximately 2 years. Detailed speech and language assessments included the Apraxia of Speech Rating Scale and Motor Speech Disorders severity scale. Rates of ventricular expansion and rates of whole brain, striatal and midbrain atrophy were calculated. Atrophy rates across 38 cortical regions were also calculated and the regions that best differentiated patients from controls were selected. Sample size estimates required to power placebo-controlled treatment trials were calculated. The smallest sample size estimates were obtained with rates of atrophy of the precentral gyrus and supplementary motor area, with both measures requiring less than 50 subjects per arm to detect a 25% treatment effect with 80% power. These measures outperformed the other regional and global MRI measures and the clinical scales. Regional rates of cortical atrophy, therefore, provide the best outcome measures in progressive apraxia of speech. The small sample size estimates demonstrate feasibility for including progressive apraxia of speech in future clinical treatment trials targeting tau.
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Affiliation(s)
| | - Joseph R Duffy
- Division of Speech Pathology, Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Edythe A Strand
- Division of Speech Pathology, Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Mary M Machulda
- Division of Neuropsychology, Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Nirubol Tosakulwong
- Division of Biostatistics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Stephen D Weigand
- Division of Biostatistics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Matthew L Senjem
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA.,Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | | | - Jeffrey L Gunter
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA.,Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | - Ronald C Petersen
- Division of Behavioral Neurology, Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Keith A Josephs
- Division of Behavioral Neurology, Department of Neurology, Mayo Clinic, Rochester, MN, USA
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Wang G, Zhang X, Su Q, Shi J, Caselli RJ, Wang Y. A novel cortical thickness estimation method based on volumetric Laplace-Beltrami operator and heat kernel. Med Image Anal 2015; 22:1-20. [PMID: 25700360 PMCID: PMC4405465 DOI: 10.1016/j.media.2015.01.005] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2013] [Revised: 01/22/2015] [Accepted: 01/23/2015] [Indexed: 12/31/2022]
Abstract
Cortical thickness estimation in magnetic resonance imaging (MRI) is an important technique for research on brain development and neurodegenerative diseases. This paper presents a heat kernel based cortical thickness estimation algorithm, which is driven by the graph spectrum and the heat kernel theory, to capture the gray matter geometry information from the in vivo brain magnetic resonance (MR) images. First, we construct a tetrahedral mesh that matches the MR images and reflects the inherent geometric characteristics. Second, the harmonic field is computed by the volumetric Laplace-Beltrami operator and the direction of the steamline is obtained by tracing the maximum heat transfer probability based on the heat kernel diffusion. Thereby we can calculate the cortical thickness information between the point on the pial and white matter surfaces. The new method relies on intrinsic brain geometry structure and the computation is robust and accurate. To validate our algorithm, we apply it to study the thickness differences associated with Alzheimer's disease (AD) and mild cognitive impairment (MCI) on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Our preliminary experimental results on 151 subjects (51 AD, 45 MCI, 55 controls) show that the new algorithm may successfully detect statistically significant difference among patients of AD, MCI and healthy control subjects. Our computational framework is efficient and very general. It has the potential to be used for thickness estimation on any biological structures with clearly defined inner and outer surfaces.
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Affiliation(s)
- Gang Wang
- School of Information and Electrical Engineering, Ludong University, Yantai, China; School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Xiaofeng Zhang
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Qingtang Su
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Richard J Caselli
- Department of Neurology, Mayo Clinic Arizona, Scottsdale, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA.
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Kehoe EG, Farrell D, Metzler-Baddeley C, Lawlor BA, Kenny RA, Lyons D, McNulty JP, Mullins PG, Coyle D, Bokde AL. Fornix White Matter is Correlated with Resting-State Functional Connectivity of the Thalamus and Hippocampus in Healthy Aging but Not in Mild Cognitive Impairment - A Preliminary Study. Front Aging Neurosci 2015; 7:10. [PMID: 25698967 PMCID: PMC4318417 DOI: 10.3389/fnagi.2015.00010] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2014] [Accepted: 01/22/2015] [Indexed: 01/31/2023] Open
Abstract
In this study, we wished to examine the relationship between the structural connectivity of the fornix, a white matter (WM) tract in the limbic system, which is affected in amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease, and the resting-state functional connectivity (FC) of two key related subcortical structures, the thalamus, and hippocampus. Twenty-two older healthy controls (HC) and 18 older adults with aMCI underwent multi-modal MRI scanning. The fornix was reconstructed using constrained-spherical deconvolution-based tractography. The FC between the thalamus and hippocampus was calculated using a region-of-interest approach from which the mean time series were exacted and correlated. Diffusion tensor imaging measures of the WM microstructure of the fornix were correlated against the Fisher Z correlation values from the FC analysis. There was no difference between the groups in the fornix WM measures, nor in the resting-state FC of the thalamus and hippocampus. We did however find that the relationship between functional and structural connectivity differed significantly between the groups. In the HCs, there was a significant positive association between linear diffusion (CL) in the fornix and the FC of the thalamus and hippocampus, however, there was no relationship between these measures in the aMCI group. These preliminary findings suggest that in aMCI, the relationship between the functional and structural connectivity of regions of the limbic system may be significantly altered compared to healthy ageing. The combined use of diffusion weighted imaging and functional MRI may advance our understanding of neural network changes in aMCI, and elucidate subtle changes in the relationship between structural and functional brain networks.
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Affiliation(s)
- Elizabeth G Kehoe
- Trinity College Institute of Neuroscience and Cognitive Systems Group, Discipline of Psychiatry, School of Medicine, Trinity College Dublin , Dublin , Ireland
| | - Dervla Farrell
- Trinity College Institute of Neuroscience and Cognitive Systems Group, Discipline of Psychiatry, School of Medicine, Trinity College Dublin , Dublin , Ireland
| | - Claudia Metzler-Baddeley
- Cardiff University Brain Research Imaging Centre (CUBRIC), Neuroscience and Mental Health Research Institute (NMHRI), School of Psychology, Cardiff University , Cardiff , UK
| | - Brian A Lawlor
- Department of Psychiatry, Jonathan Swift Clinic, St. James Hospital, Trinity College Institute of Neuroscience, Trinity College Dublin , Dublin , Ireland
| | - Rose Anne Kenny
- Mercer's Institute for Successful Ageing, St. James Hospital, Trinity College Institute of Neuroscience, Trinity College Dublin , Dublin , Ireland
| | | | - Jonathan P McNulty
- School of Medicine and Medical Science, University College Dublin , Dublin , Ireland
| | | | - Damien Coyle
- Intelligent Systems Research Centre, University of Ulster , Derry , UK
| | - Arun L Bokde
- Trinity College Institute of Neuroscience and Cognitive Systems Group, Discipline of Psychiatry, School of Medicine, Trinity College Dublin , Dublin , Ireland
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Nir TM, Villalon-Reina JE, Prasad G, Jahanshad N, Joshi SH, Toga AW, Bernstein MA, Jack CR, Weiner MW, Thompson PM. Diffusion weighted imaging-based maximum density path analysis and classification of Alzheimer's disease. Neurobiol Aging 2015; 36 Suppl 1:S132-40. [PMID: 25444597 PMCID: PMC4283487 DOI: 10.1016/j.neurobiolaging.2014.05.037] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2013] [Revised: 05/13/2014] [Accepted: 05/13/2014] [Indexed: 10/24/2022]
Abstract
Characterizing brain changes in Alzheimer's disease (AD) is important for patient prognosis and for assessing brain deterioration in clinical trials. In this diffusion weighted imaging study, we used a new fiber-tract modeling method to investigate white matter integrity in 50 elderly controls (CTL), 113 people with mild cognitive impairment, and 37 AD patients. After clustering tractography using a region-of-interest atlas, we used a shortest path graph search through each bundle's fiber density map to derive maximum density paths (MDPs), which we registered across subjects. We calculated the fractional anisotropy (FA) and mean diffusivity (MD) along all MDPs and found significant MD and FA differences between AD patients and CTL subjects, as well as MD differences between CTL and late mild cognitive impairment subjects. MD and FA were also associated with widely used clinical scores. As an MDP is a compact low-dimensional representation of white matter organization, we tested the utility of diffusion tensor imaging measures along these MDPs as features for support vector machine based classification of AD.
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Affiliation(s)
- Talia M Nir
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA
| | - Julio E Villalon-Reina
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA
| | - Gautam Prasad
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA
| | - Shantanu H Joshi
- Department of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | - Arthur W Toga
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA
| | - Matt A Bernstein
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Michael W Weiner
- Department of Radiology and Biomedical Imaging, UCSF School of Medicine, San Francisco, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA; Department of Neurology, University of Southern California, Los Angeles, CA, USA; Department of Psychiatry, University of Southern California, Los Angeles, CA, USA; Department of Radiology, University of Southern California, Los Angeles, CA, USA; Department of Engineering, University of Southern California, Los Angeles, CA, USA; Department of Pediatrics, University of Southern California, Los Angeles, CA, USA; Department of Ophthalmology, University of Southern California, Los Angeles, CA, USA.
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35
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Shi J, Stonnington CM, Thompson PM, Chen K, Gutman B, Reschke C, Baxter LC, Reiman EM, Caselli RJ, Wang Y. Studying ventricular abnormalities in mild cognitive impairment with hyperbolic Ricci flow and tensor-based morphometry. Neuroimage 2015; 104:1-20. [PMID: 25285374 PMCID: PMC4252650 DOI: 10.1016/j.neuroimage.2014.09.062] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2014] [Revised: 09/20/2014] [Accepted: 09/29/2014] [Indexed: 11/29/2022] Open
Abstract
Mild Cognitive Impairment (MCI) is a transitional stage between normal aging and dementia and people with MCI are at high risk of progression to dementia. MCI is attracting increasing attention, as it offers an opportunity to target the disease process during an early symptomatic stage. Structural magnetic resonance imaging (MRI) measures have been the mainstay of Alzheimer's disease (AD) imaging research, however, ventricular morphometry analysis remains challenging because of its complicated topological structure. Here we describe a novel ventricular morphometry system based on the hyperbolic Ricci flow method and tensor-based morphometry (TBM) statistics. Unlike prior ventricular surface parameterization methods, hyperbolic conformal parameterization is angle-preserving and does not have any singularities. Our system generates a one-to-one diffeomorphic mapping between ventricular surfaces with consistent boundary matching conditions. The TBM statistics encode a great deal of surface deformation information that could be inaccessible or overlooked by other methods. We applied our system to the baseline MRI scans of a set of MCI subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI: 71 MCI converters vs. 62 MCI stable). Although the combined ventricular area and volume features did not differ between the two groups, our fine-grained surface analysis revealed significant differences in the ventricular regions close to the temporal lobe and posterior cingulate, structures that are affected early in AD. Significant correlations were also detected between ventricular morphometry, neuropsychological measures, and a previously described imaging index based on fluorodeoxyglucose positron emission tomography (FDG-PET) scans. This novel ventricular morphometry method may offer a new and more sensitive approach to study preclinical and early symptomatic stage AD.
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Affiliation(s)
- Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | | | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Kewei Chen
- Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA
| | - Boris Gutman
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Cole Reschke
- Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA
| | - Leslie C Baxter
- Human Brain Imaging Laboratory, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Eric M Reiman
- Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA
| | | | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
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36
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Kehoe EG, McNulty JP, Mullins PG, Bokde ALW. Advances in MRI biomarkers for the diagnosis of Alzheimer's disease. Biomark Med 2014; 8:1151-69. [DOI: 10.2217/bmm.14.42] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
With the prevalence of Alzheimer's disease (AD) predicted to increase substantially over the coming decades, the development of effective biomarkers for the early detection of the disease is paramount. In this short review, the main neuroimaging techniques which have shown potential as biomarkers for AD are introduced, with a focus on MRI. Structural MRI measures of the hippocampus and medial temporal lobe are still the most clinically validated biomarkers for AD, but newer techniques such as functional MRI and diffusion tensor imaging offer great scope in tracking changes in the brain, particularly in functional and structural connectivity, which may precede gray matter atrophy. These new advances in neuroimaging methods require further development and crucially, standardization; however, before they are used as biomarkers to aid in the diagnosis of AD.
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Affiliation(s)
- Elizabeth G Kehoe
- The Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
- Cognitive Systems Group, Discipline of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Jonathan P McNulty
- School of Medicine & Medical Science, University College Dublin, Dublin, Ireland
| | | | - Arun L W Bokde
- The Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
- Cognitive Systems Group, Discipline of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland
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Abstract
Since the launch in 2003 of the Alzheimer's Disease Neuroimaging Initiative (ADNI) in the USA, ever growing, similarly oriented consortia have been organized and assembled around the world. The various accomplishments of ADNI have contributed substantially to a better understanding of the underlying physiopathology of aging and Alzheimer's disease (AD). These accomplishments are basically predicated in the trinity of multimodality, standardization and sharing. This multimodality approach can now better identify those subjects with AD-specific traits that are more likely to present cognitive decline in the near future and that might represent the best candidates for smaller but more efficient therapeutic trials - trials that, through gained and shared knowledge, can be more focused on a specific target or a specific stage of the disease process. In summary, data generated from ADNI have helped elucidate some of the pathophysiological mechanisms underpinning aging and AD pathology, while contributing to the international effort in setting the groundwork for biomarker discovery and establishing standards for early diagnosis of AD.
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Affiliation(s)
- Victor L Villemagne
- Department of Nuclear Medicine and Centre for PET, Austin Health, 145 Studley Road, Heidelberg 3084, VIC, Australia
- The Florey Institute for Neurosciences and Mental Health, The University of Melbourne, 30 Royal Parade, Melbourne 3010, VIC, Australia
- Department of Medicine, The University of Melbourne, Grattan Street, Melbourne 3010, VIC, Australia
| | - Seong Yoon Kim
- Asan Medical Center, University of Ulsan Medical College, 88 Olympic-Ro 43-Gil, Songpa-Gu, Seoul, Korea
| | - Christopher C Rowe
- Department of Nuclear Medicine and Centre for PET, Austin Health, 145 Studley Road, Heidelberg 3084, VIC, Australia
- Department of Medicine, The University of Melbourne, Grattan Street, Melbourne 3010, VIC, Australia
| | - Takeshi Iwatsubo
- Department of Neuropathology, School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku 113-0033, Tokyo, Japan
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Shi J, Leporé N, Gutman BA, Thompson PM, Baxter LC, Caselli RJ, Wang Y. Genetic influence of apolipoprotein E4 genotype on hippocampal morphometry: An N = 725 surface-based Alzheimer's disease neuroimaging initiative study. Hum Brain Mapp 2014; 35:3903-18. [PMID: 24453132 PMCID: PMC4269525 DOI: 10.1002/hbm.22447] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2013] [Revised: 11/23/2013] [Accepted: 11/26/2013] [Indexed: 01/12/2023] Open
Abstract
The apolipoprotein E (APOE) e4 allele is the most prevalent genetic risk factor for Alzheimer's disease (AD). Hippocampal volumes are generally smaller in AD patients carrying the e4 allele compared to e4 noncarriers. Here we examined the effect of APOE e4 on hippocampal morphometry in a large imaging database-the Alzheimer's Disease Neuroimaging Initiative (ADNI). We automatically segmented and constructed hippocampal surfaces from the baseline MR images of 725 subjects with known APOE genotype information including 167 with AD, 354 with mild cognitive impairment (MCI), and 204 normal controls. High-order correspondences between hippocampal surfaces were enforced across subjects with a novel inverse consistent surface fluid registration method. Multivariate statistics consisting of multivariate tensor-based morphometry (mTBM) and radial distance were computed for surface deformation analysis. Using Hotelling's T(2) test, we found significant morphological deformation in APOE e4 carriers relative to noncarriers in the entire cohort as well as in the nondemented (pooled MCI and control) subjects, affecting the left hippocampus more than the right, and this effect was more pronounced in e4 homozygotes than heterozygotes. Our findings are consistent with previous studies that showed e4 carriers exhibit accelerated hippocampal atrophy; we extend these findings to a novel measure of hippocampal morphometry. Hippocampal morphometry has significant potential as an imaging biomarker of early stage AD.
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Affiliation(s)
- Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State UniversityTempeArizona
| | - Natasha Leporé
- Department of RadiologyChildren's Hospital Los AngelesLos AngelesCalifornia
| | - Boris A. Gutman
- Imaging Genetics CenterInstitute for Neuroimaging and InformaticsUniversity of Southern CaliforniaLos AngelesCalifornia
| | - Paul M. Thompson
- Department of NeurologyImaging Genetics CenterLaboratory of Neuro ImagingUCLA School of MedicineLos AngelesCalifornia
- Department of Psychiatry and Biobehavioral SciencesSemel Institute, UCLA School of MedicineLos AngelesCalifornia
| | - Leslie C. Baxter
- Human Brain Imaging Laboratory, Barrow Neurological InstitutePhoenixArizona
| | | | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State UniversityTempeArizona
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Brodtmann A, Werden E, Pardoe H, Li Q, Jackson G, Donnan G, Cowie T, Bradshaw J, Darby D, Cumming T. Charting Cognitive and Volumetric Trajectories after Stroke: Protocol for the Cognition and Neocortical Volume after Stroke (CANVAS) Study. Int J Stroke 2014; 9:824-8. [DOI: 10.1111/ijs.12301] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2013] [Accepted: 05/07/2014] [Indexed: 11/30/2022]
Abstract
Rationale Globally, stroke and dementia are leading causes of disability and mortality. More than one third of stroke patients will develop dementia, but mechanisms are unclear. Aims The study aims to establish whether brain volume change is associated with poststroke dementia, and to elucidate potential causal mechanisms, including genetic markers, amyloid deposition and vascular risk factors. An understanding of whether – and in whom – stroke is neurodegenerative is critical for the strategic use of potential disease-modifying therapies. Hypotheses That stroke patients will exhibit greater brain volume loss than comparable cohorts of stroke-free controls; and that those who develop dementia will exhibit greater brain volume loss than those who do not. Design Advanced brain imaging techniques are used to longitudinally measure brain volume and cortical thickness in 135 stroke patients. Concurrent neuropsychological testing will correlate clinical profile with these measures. Primary outcomes Primary imaging end-point is brain volume change between three-months and three-years poststroke; primary clinical outcome is the presence of dementia at three-years. Secondary outcomes We will examine the correlations with the following variables: dementia subtype; physical activity levels; behavioral dysfunction as measured by patient and caregiver-reported scales; structural and functional brain connectivity disruption; apolipoprotein E; and specific neuropsychological test scores. Discussion Magnetic resonance imaging markers of structural brain aging and performance on neuropsychological tests are powerful predictors of dementia. We need to understand the trajectory of regional brain volume change and cognitive decline in patients after stroke. This will allow future risk stratification for prognostic counseling, service planning, and early therapeutic intervention.
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Affiliation(s)
- Amy Brodtmann
- The Florey Institute for Neuroscience and Mental Health, University of Melbourne, Melbourne, Vic., Australia
- Austin Health, Heidelberg, Melbourne, Vic., Australia
- Eastern Clinical Research Unit, Monash University, Box Hill Hospital, Melbourne, Vic., Australia
| | - Emilio Werden
- The Florey Institute for Neuroscience and Mental Health, University of Melbourne, Melbourne, Vic., Australia
| | - Heath Pardoe
- The Florey Institute for Neuroscience and Mental Health, University of Melbourne, Melbourne, Vic., Australia
- Comprehensive Epilepsy Center, Department of Neurology, New York University Langone Medical Center, New York, NY, USA
| | - Qi Li
- The Florey Institute for Neuroscience and Mental Health, University of Melbourne, Melbourne, Vic., Australia
| | - Graeme Jackson
- The Florey Institute for Neuroscience and Mental Health, University of Melbourne, Melbourne, Vic., Australia
- Austin Health, Heidelberg, Melbourne, Vic., Australia
| | - Geoffrey Donnan
- The Florey Institute for Neuroscience and Mental Health, University of Melbourne, Melbourne, Vic., Australia
| | - Tiffany Cowie
- The Centre for Translational Pathology, University of Melbourne, Melbourne, Vic., Australia
| | | | - David Darby
- The Florey Institute for Neuroscience and Mental Health, University of Melbourne, Melbourne, Vic., Australia
- Melbourne Brain Centre, Royal Melbourne Hospital, Melbourne, Vic., Australia
| | - Toby Cumming
- The Florey Institute for Neuroscience and Mental Health, University of Melbourne, Melbourne, Vic., Australia
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40
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Adluru N, Destiche DJ, Lu SYF, Doran ST, Birdsill AC, Melah KE, Okonkwo OC, Alexander AL, Dowling NM, Johnson SC, Sager MA, Bendlin BB. White matter microstructure in late middle-age: Effects of apolipoprotein E4 and parental family history of Alzheimer's disease. NEUROIMAGE-CLINICAL 2014; 4:730-42. [PMID: 24936424 PMCID: PMC4053649 DOI: 10.1016/j.nicl.2014.04.008] [Citation(s) in RCA: 59] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2013] [Revised: 04/16/2014] [Accepted: 04/17/2014] [Indexed: 01/05/2023]
Abstract
INTRODUCTION Little is still known about the effects of risk factors for Alzheimer's disease (AD) on white matter microstructure in cognitively healthy adults. The purpose of this cross-sectional study was to assess the effect of two well-known risk factors for AD, parental family history and APOE4 genotype. METHODS This study included 343 participants from the Wisconsin Registry for Alzheimer's Prevention, who underwent diffusion tensor imaging (DTI). A region of interest analysis was performed on fractional anisotropy maps, in addition to mean, radial, and axial diffusivity maps, aligned to a common template space using a diffeomorphic, tensor-based registration method. The analysis focused on brain regions known to be affected in AD including the corpus callosum, superior longitudinal fasciculus, fornix, cingulum, and uncinate fasciculus. Analyses assessed the impact of APOE4, parental family history of AD, age, and sex on white matter microstructure in late middle-aged participants (aged 47-76 years). RESULTS Both APOE4 and parental family history were associated with microstructural white matter differences. Participants with parental family history of AD had higher FA in the genu of the corpus callosum and the superior longitudinal fasciculus. We observed an interaction between family history and APOE4, where participants who were family history positive but APOE4 negative had lower axial diffusivity in the uncinate fasciculus, and participants who were both family history positive and APOE4 positive had higher axial diffusivity in this region. We also observed an interaction between APOE4 and age, whereby older participants (=65 years of age) who were APOE4 carriers, had higher MD in the superior longitudinal fasciculus and in the portion of the cingulum bundle running adjacent to the cingulate cortex, compared to non-carriers. Older participants who were APOE4 carriers also showed higher radial diffusivity in the genu compared to non-carriers. Across all participants, age had an effect on FA, MD, and axial and radial diffusivities. Sex differences were observed in FA and radial diffusivity. CONCLUSION APOE4 genotype, parental family history of AD, age, and sex are all associated with microstructural white matter differences in late middle-aged adults. In participants at risk for AD, alterations in diffusion characteristics-both expected and unexpected-may represent cellular changes occurring at the earliest disease stages, but further work is needed. Higher mean, radial, and axial diffusivities were observed in participants who are more likely to be experiencing later stage preclinical pathology, including participants who were both older and carried APOE4, or who were positive for both APOE4 and parental family history of AD.
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Affiliation(s)
- Nagesh Adluru
- Waisman Laboratory for Brain Imaging and Behavior, Madison, WI, USA
| | | | | | - Samuel T Doran
- Waisman Laboratory for Brain Imaging and Behavior, Madison, WI, USA
| | - Alex C Birdsill
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Department of Medicine, 600 Highland Avenue, Madison, WI 53792, USA
| | - Kelsey E Melah
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Department of Medicine, 600 Highland Avenue, Madison, WI 53792, USA
| | - Ozioma C Okonkwo
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Department of Medicine, 600 Highland Avenue, Madison, WI 53792, USA
| | - Andrew L Alexander
- Waisman Laboratory for Brain Imaging and Behavior, Madison, WI, USA ; Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, 1111 Highland Ave, Madison, WI 53705, USA ; University of Wisconsin School of Medicine and Public Health, Department of Psychiatry, 6001 Research Park Blvd, Madison, WI 53719, USA
| | - N Maritza Dowling
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Department of Medicine, 600 Highland Avenue, Madison, WI 53792, USA ; Department of Biostatistics and Medical Informatics, 600 Highland Avenue, Madison, WI 53792, USA
| | - Sterling C Johnson
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Department of Medicine, 600 Highland Avenue, Madison, WI 53792, USA ; Geriatric Research, Education and Clinical Center (GRECC), William S. Middleton Memorial Veteran's Hospital, 2500 Overlook Terrace, Madison, WI 53705, USA ; Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, 7818 Big Sky Drive, Madison, WI 53719, USA
| | - Mark A Sager
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Department of Medicine, 600 Highland Avenue, Madison, WI 53792, USA ; Geriatric Research, Education and Clinical Center (GRECC), William S. Middleton Memorial Veteran's Hospital, 2500 Overlook Terrace, Madison, WI 53705, USA ; Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, 7818 Big Sky Drive, Madison, WI 53719, USA
| | - Barbara B Bendlin
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Department of Medicine, 600 Highland Avenue, Madison, WI 53792, USA
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Zhang J, Wang J, Wang X, Feng D. The adaptive FEM elastic model for medical image registration. Phys Med Biol 2013; 59:97-118. [PMID: 24334618 DOI: 10.1088/0031-9155/59/1/97] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
This paper proposes an adaptive mesh refinement strategy for the finite element method (FEM) based elastic registration model. The signature matrix for mesh refinement takes into account the regional intensity variance and the local deformation displacement. The regional intensity variance reflects detailed information for improving registration accuracy and the deformation displacement fine-tunes the mesh refinement for a more efficient algorithm. The gradient flows of two different similarity metrics, the sum of the squared difference and the spatially encoded mutual information for the mono-modal and multi-modal registrations, are used to derive external forces to drive the model to the equilibrium state. We compared our approach to three other models: (1) the conventional multi-resolution FEM registration algorithm; (2) the FEM elastic method that uses variation information for mesh refinement; and (3) the robust block matching based registration. Comparisons among different methods in a dataset with 20 CT image pairs upon artificial deformation demonstrate that our registration method achieved significant improvement in accuracies. Experimental results in another dataset of 40 real medical image pairs for both mono-modal and multi-modal registrations also show that our model outperforms the other three models in its accuracy.
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Affiliation(s)
- Jingya Zhang
- School of Electronic and Information Engineering, Soochow University, Suzhou 215006, People's Republic of China. Dept Phys, Changshu Inst Technol, Changshu 215500, People's Republic of China
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42
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Lutkenhoff ES, McArthur DL, Hua X, Thompson PM, Vespa PM, Monti MM. Thalamic atrophy in antero-medial and dorsal nuclei correlates with six-month outcome after severe brain injury. NEUROIMAGE-CLINICAL 2013; 3:396-404. [PMID: 24273723 PMCID: PMC3815017 DOI: 10.1016/j.nicl.2013.09.010] [Citation(s) in RCA: 50] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2013] [Revised: 09/26/2013] [Accepted: 09/27/2013] [Indexed: 11/25/2022]
Abstract
The primary and secondary damage to neural tissue inflicted by traumatic brain injury is a leading cause of death and disability. The secondary processes, in particular, are of great clinical interest because of their potential susceptibility to intervention. We address the dynamics of tissue degeneration in cortico-subcortical circuits after severe brain injury by assessing volume change in individual thalamic nuclei over the first six-months post-injury in a sample of 25 moderate to severe traumatic brain injury patients. Using tensor-based morphometry, we observed significant localized thalamic atrophy over the six-month period in antero-dorsal limbic nuclei as well as in medio-dorsal association nuclei. Importantly, the degree of atrophy in these nuclei was predictive, even after controlling for full-brain volume change, of behavioral outcome at six-months post-injury. Furthermore, employing a data-driven decision tree model, we found that physiological measures, namely the extent of atrophy in the anterior thalamic nucleus, were the most predictive variables of whether patients had regained consciousness by six-months, followed by behavioral measures. Overall, these findings suggest that the secondary non-mechanical degenerative processes triggered by severe brain injury are still ongoing after the first week post-trauma and target specifically antero-medial and dorsal thalamic nuclei. This result therefore offers a potential window of intervention, and a specific target region, in agreement with the view that specific cortico-thalamo-cortical circuits are crucial to the maintenance of large-scale network neural activity and thereby the restoration of cognitive function after severe brain injury. Performed acute and chronic structural MRI in 25 severe TBI patients Tensor brain morphometry (TBM) shows localized thalamic acute-to-chronic atrophy. Anterior, medio- and lateral-dorsal nuclei are the most significant. Atrophy in these nuclei predicts 6-month outcome scores (GOSe).
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Affiliation(s)
- Evan S Lutkenhoff
- Department of Psychology, University of California Los Angeles, Los Angeles, CA 90095, USA
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43
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Nir TM, Jahanshad N, Villalon-Reina JE, Toga AW, Jack CR, Weiner MW, Thompson PM. Effectiveness of regional DTI measures in distinguishing Alzheimer's disease, MCI, and normal aging. Neuroimage Clin 2013; 3:180-95. [PMID: 24179862 PMCID: PMC3792746 DOI: 10.1016/j.nicl.2013.07.006] [Citation(s) in RCA: 223] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2013] [Revised: 07/03/2013] [Accepted: 07/21/2013] [Indexed: 01/08/2023]
Abstract
The Alzheimer's Disease Neuroimaging Initiative (ADNI) recently added diffusion tensor imaging (DTI), among several other new imaging modalities, in an effort to identify sensitive biomarkers of Alzheimer's disease (AD). While anatomical MRI is the main structural neuroimaging method used in most AD studies and clinical trials, DTI is sensitive to microscopic white matter (WM) changes not detectable with standard MRI, offering additional markers of neurodegeneration. Prior DTI studies of AD report lower fractional anisotropy (FA), and increased mean, axial, and radial diffusivity (MD, AxD, RD) throughout WM. Here we assessed which DTI measures may best identify differences among AD, mild cognitive impairment (MCI), and cognitively healthy elderly control (NC) groups, in region of interest (ROI) and voxel-based analyses of 155 ADNI participants (mean age: 73.5 ± 7.4; 90 M/65 F; 44 NC, 88 MCI, 23 AD). Both VBA and ROI analyses revealed widespread group differences in FA and all diffusivity measures. DTI maps were strongly correlated with widely-used clinical ratings (MMSE, CDR-sob, and ADAS-cog). When effect sizes were ranked, FA analyses were least sensitive for picking up group differences. Diffusivity measures could detect more subtle MCI differences, where FA could not. ROIs showing strongest group differentiation (lowest p-values) included tracts that pass through the temporal lobe, and posterior brain regions. The left hippocampal component of the cingulum showed consistently high effect sizes for distinguishing groups, across all diffusivity and anisotropy measures, and in correlations with cognitive scores.
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Affiliation(s)
- Talia M. Nir
- Imaging Genetics Center, Laboratory of Neuro Imaging,
Department of Neurology, UCLA School of Medicine, Los Angeles, CA,
USA
| | - Neda Jahanshad
- Imaging Genetics Center, Laboratory of Neuro Imaging,
Department of Neurology, UCLA School of Medicine, Los Angeles, CA,
USA
| | - Julio E. Villalon-Reina
- Imaging Genetics Center, Laboratory of Neuro Imaging,
Department of Neurology, UCLA School of Medicine, Los Angeles, CA,
USA
| | - Arthur W. Toga
- Imaging Genetics Center, Laboratory of Neuro Imaging,
Department of Neurology, UCLA School of Medicine, Los Angeles, CA,
USA
| | - Clifford R. Jack
- Department of Radiology, Mayo Clinic and Foundation,
Rochester, MN, USA
| | - Michael W. Weiner
- Department of Radiology and Biomedical Imaging, UCSF School
of Medicine, San Francisco, CA, USA
| | - Paul M. Thompson
- Imaging Genetics Center, Laboratory of Neuro Imaging,
Department of Neurology, UCLA School of Medicine, Los Angeles, CA,
USA
- Deptartment of Psychiatry, Semel Institute, UCLA School of
Medicine, Los Angeles, CA, USA
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44
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Braskie MN, Toga AW, Thompson PM. Recent advances in imaging Alzheimer's disease. J Alzheimers Dis 2013; 33 Suppl 1:S313-27. [PMID: 22672880 DOI: 10.3233/jad-2012-129016] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Advances in brain imaging technology in the past five years have contributed greatly to the understanding of Alzheimer's disease (AD). Here, we review recent research related to amyloid imaging, new methods for magnetic resonance imaging analyses, and statistical methods. We also review research that evaluates AD risk factors and brain imaging, in the context of AD prediction and progression. We selected a variety of illustrative studies, describing how they advanced the field and are leading AD research in promising new directions.
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Affiliation(s)
- Meredith N Braskie
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA 90095-7334, USA
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45
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Kim J, Avants B, Whyte J, Gee JC. Methodological considerations in longitudinal morphometry of traumatic brain injury. Front Hum Neurosci 2013; 7:52. [PMID: 23549059 PMCID: PMC3581852 DOI: 10.3389/fnhum.2013.00052] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2012] [Accepted: 02/07/2013] [Indexed: 11/17/2022] Open
Abstract
Traumatic brain injury (TBI) has recently been reconceptualized as a chronic, evolving disease process. This new view necessitates quantitative assessment of post-injury changes in brain structure that may allow more accurate monitoring and prediction of recovery. In particular, TBI is known to trigger neurodegenerative processes and therefore quantifying progression of diffuse atrophy over time is currently of utmost interest. However, there are various methodological issues inherent to longitudinal morphometry in TBI. In this paper, we first overview several of these methodological challenges: lesion evolution, neurosurgical procedures, power, bias, and non-linearity. We then introduce a sensitive, reliable, and unbiased longitudinal multivariate analysis protocol that combines dimensionality reduction and region of interest approaches. This analysis pipeline is demonstrated using a small dataset consisting of four chronic TBI survivors.
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Affiliation(s)
- Junghoon Kim
- Moss Rehabilitation Research Institute Elkins Park, PA, USA
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46
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Duara R, Loewenstein DA, Shen Q, Barker W, Greig MT, Varon D, Murray ME, Dickson DW. Regional patterns of atrophy on MRI in Alzheimer’s disease: Neuropsychological features and progression rates in the ADNI cohort. ACTA ACUST UNITED AC 2013. [DOI: 10.4236/aad.2013.24019] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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47
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Hua X, Hibar DP, Ching CRK, Boyle CP, Rajagopalan P, Gutman BA, Leow AD, Toga AW, Jack CR, Harvey D, Weiner MW, Thompson PM. Unbiased tensor-based morphometry: improved robustness and sample size estimates for Alzheimer's disease clinical trials. Neuroimage 2012; 66:648-61. [PMID: 23153970 DOI: 10.1016/j.neuroimage.2012.10.086] [Citation(s) in RCA: 88] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2012] [Revised: 10/29/2012] [Accepted: 10/30/2012] [Indexed: 01/11/2023] Open
Abstract
Various neuroimaging measures are being evaluated for tracking Alzheimer's disease (AD) progression in therapeutic trials, including measures of structural brain change based on repeated scanning of patients with magnetic resonance imaging (MRI). Methods to compute brain change must be robust to scan quality. Biases may arise if any scans are thrown out, as this can lead to the true changes being overestimated or underestimated. Here we analyzed the full MRI dataset from the first phase of Alzheimer's Disease Neuroimaging Initiative (ADNI-1) from the first phase of Alzheimer's Disease Neuroimaging Initiative (ADNI-1) and assessed several sources of bias that can arise when tracking brain changes with structural brain imaging methods, as part of a pipeline for tensor-based morphometry (TBM). In all healthy subjects who completed MRI scanning at screening, 6, 12, and 24months, brain atrophy was essentially linear with no detectable bias in longitudinal measures. In power analyses for clinical trials based on these change measures, only 39AD patients and 95 mild cognitive impairment (MCI) subjects were needed for a 24-month trial to detect a 25% reduction in the average rate of change using a two-sided test (α=0.05, power=80%). Further sample size reductions were achieved by stratifying the data into Apolipoprotein E (ApoE) ε4 carriers versus non-carriers. We show how selective data exclusion affects sample size estimates, motivating an objective comparison of different analysis techniques based on statistical power and robustness. TBM is an unbiased, robust, high-throughput imaging surrogate marker for large, multi-site neuroimaging studies and clinical trials of AD and MCI.
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Affiliation(s)
- Xue Hua
- Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA 90095-1769, USA
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48
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Zeng W, Shi R, Wang Y, Yau ST, Gu X. Teichmüller Shape Descriptor and Its Application to Alzheimer’s Disease Study. Int J Comput Vis 2012. [DOI: 10.1007/s11263-012-0586-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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49
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Duyn JH, Koretsky AP. Novel frontiers in ultra-structural and molecular MRI of the brain. Curr Opin Neurol 2011; 24:386-93. [PMID: 21734576 DOI: 10.1097/wco.0b013e328348972a] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
PURPOSE OF REVIEW Recent developments in the MRI of the brain continue to expand its use in basic and clinical neuroscience. This review highlights some areas of recent progress. RECENT FINDINGS Higher magnetic field strengths and improved signal detectors have allowed improved visualization of the various properties of the brain, facilitating the anatomical definition of function-specific areas and their connections. For example, by sensitizing the MRI signal to the magnetic susceptibility of tissue, it is starting to become possible to reveal the laminar structure of the cortex and identify millimeter-scale fiber bundles. Using exogenous contrast agents, and innovative ways to manipulate contrast, it is becoming possible to highlight specific fiber tracts and cell populations. These techniques are bringing us closer to understanding the evolutionary blueprint of the brain, improving the detection and characterization of disease, and help to guide treatment. SUMMARY Recent MRI techniques are leading to more detailed and more specific contrast in the study of the brain.
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Affiliation(s)
- Jeff H Duyn
- Laboratory of Functional and Molecular Imaging, National Institutes of Health, Bethesda, Maryland 20892-1060, USA.
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50
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Chambon C, Wegener N, Gravius A, Danysz W. Behavioural and cellular effects of exogenous amyloid-β peptides in rodents. Behav Brain Res 2011; 225:623-41. [PMID: 21884730 DOI: 10.1016/j.bbr.2011.08.024] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2011] [Revised: 08/10/2011] [Accepted: 08/16/2011] [Indexed: 12/29/2022]
Abstract
A better understanding of Alzheimer's disease (AD) and the development of disease modifying therapies are some of the biggest challenges of the 21st century. One of the core features of AD are amyloid plaques composed of amyloid-beta (Aβ) peptides. The first hypothesis proposed that cognitive deficits are linked to plaque-development and transgenic mice have been generated to study this link, thereby providing a good model to develop new therapeutic approaches. Since later it was recognised that in AD patients the cognitive deficit is rather correlated to soluble amyloid levels, consequently, a new hypothesis appeared associating the earliest amyloid toxicity to these soluble species. The purpose of this review is to give a summary of behavioural and cellular data obtained after soluble Aβ peptide administration into rodents' brain, thereby showing that this model is a valid tool to investigate AD pathology when no plaques are present. Additionally, this method offers an excellent, efficient model to test compounds which could act at such early stages of the disease.
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Affiliation(s)
- Caroline Chambon
- In Vivo Pharmacology, Merz Pharmaceuticals GmbH, Eckenheimer Landstrasse 100, D-60318 Frankfurt am Main, Germany.
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