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Quek Y, Fung YL, Bourgeat P, Vogrin SJ, Collins SJ, Bowden SC. Combining neuropsychological assessment and structural neuroimaging to identify early Alzheimer's disease in a memory clinic cohort. Brain Behav 2024; 14:e3505. [PMID: 38688879 PMCID: PMC11061200 DOI: 10.1002/brb3.3505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 04/03/2024] [Accepted: 04/08/2024] [Indexed: 05/02/2024] Open
Abstract
INTRODUCTION The current study examined the contributions of comprehensive neuropsychological assessment and volumetric assessment of selected mesial temporal subregions on structural magnetic resonance imaging (MRI) to identify patients with amnestic mild cognitive impairment (aMCI) and mild probable Alzheimer's disease (AD) dementia in a memory clinic cohort. METHODS Comprehensive neuropsychological assessment and automated entorhinal, transentorhinal, and hippocampal volume measurements were conducted in 40 healthy controls, 38 patients with subjective memory symptoms, 16 patients with aMCI, 16 patients with mild probable AD dementia. Multinomial logistic regression was used to compare the neuropsychological and MRI measures. RESULTS Combining the neuropsychological and MRI measures improved group membership prediction over the MRI measures alone but did not improve group membership prediction over the neuropsychological measures alone. CONCLUSION Comprehensive neuropsychological assessment was an important tool to evaluate cognitive impairment. The mesial temporal volumetric MRI measures contributed no diagnostic value over and above the determinations made through neuropsychological assessment.
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Affiliation(s)
- Yi‐En Quek
- Melbourne School of Psychological SciencesThe University of MelbourneParkvilleVictoriaAustralia
| | - Yi Leng Fung
- Melbourne School of Psychological SciencesThe University of MelbourneParkvilleVictoriaAustralia
| | - Pierrick Bourgeat
- The Australian e‐Health Research CentreCSIRO Health and BiosecurityHerstonQueenslandAustralia
| | - Simon J. Vogrin
- Department of Clinical NeurosciencesSt. Vincent's Hospital MelbourneFitzroyVictoriaAustralia
| | - Steven J. Collins
- Department of Clinical NeurosciencesSt. Vincent's Hospital MelbourneFitzroyVictoriaAustralia
- Department of MedicineThe Royal Melbourne HospitalThe University of MelbourneParkvilleVictoriaAustralia
| | - Stephen C. Bowden
- Melbourne School of Psychological SciencesThe University of MelbourneParkvilleVictoriaAustralia
- Department of Clinical NeurosciencesSt. Vincent's Hospital MelbourneFitzroyVictoriaAustralia
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Pradeepkiran JA, Baig J, Islam MA, Kshirsagar S, Reddy PH. Amyloid-β and Phosphorylated Tau are the Key Biomarkers and Predictors of Alzheimer's Disease. Aging Dis 2024:AD.2024.0286. [PMID: 38739937 DOI: 10.14336/ad.2024.0286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 04/24/2024] [Indexed: 05/16/2024] Open
Abstract
Alzheimer's disease (AD) is a age-related neurodegenerative disease and is a major public health concern both in Texas, US and Worldwide. This neurodegenerative disease is mainly characterized by amyloid-beta (Aβ) and phosphorylated Tau (p-Tau) accumulation in the brains of patients with AD and increasing evidence suggests that these are key biomarkers in AD. Both Aβ and p-tau can be detected through various imaging techniques (such as positron emission tomography, PET) and cerebrospinal fluid (CSF) analysis. The presence of these biomarkers in individuals, who are asymptomatic or have mild cognitive impairment can indicate an increased risk of developing AD in the future. Furthermore, the combination of Aβ and p-tau biomarkers is often used for more accurate diagnosis and prediction of AD progression. Along with AD being a neurodegenerative disease, it is associated with other chronic conditions such as cardiovascular disease, obesity, depression, and diabetes because studies have shown that these comorbid conditions make people more vulnerable to AD. In the first part of this review, we discuss that biofluid-based biomarkers such as Aβ, p-Tau in cerebrospinal fluid (CSF) and Aβ & p-Tau in plasma could be used as an alternative sensitive technique to diagnose AD. In the second part, we discuss the underlying molecular mechanisms of chronic conditions linked with AD and how they affect the patients in clinical care.
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Affiliation(s)
| | - Javaria Baig
- Internal Medicine Department, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
| | - Md Ariful Islam
- Internal Medicine Department, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
| | - Sudhir Kshirsagar
- Internal Medicine Department, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
| | - P Hemachandra Reddy
- Internal Medicine Department, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
- Pharmacology & Neuroscience Department, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
- Neurology Department, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
- Speech, Language and Hearing Sciences Departments, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
- Public Health Department, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
- Nutritional Sciences Department, College of Human Sciences, Texas Tech University, Lubbock, TX 79409, USA
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Stouffer KM, Trouvé A, Younes L, Kunst M, Ng L, Zeng H, Anant M, Fan J, Kim Y, Chen X, Rue M, Miller MI. Cross-modality mapping using image varifolds to align tissue-scale atlases to molecular-scale measures with application to 2D brain sections. Nat Commun 2024; 15:3530. [PMID: 38664422 PMCID: PMC11045777 DOI: 10.1038/s41467-024-47883-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 04/10/2024] [Indexed: 04/28/2024] Open
Abstract
This paper explicates a solution to building correspondences between molecular-scale transcriptomics and tissue-scale atlases. This problem arises in atlas construction and cross-specimen/technology alignment where specimens per emerging technology remain sparse and conventional image representations cannot efficiently model the high dimensions from subcellular detection of thousands of genes. We address these challenges by representing spatial transcriptomics data as generalized functions encoding position and high-dimensional feature (gene, cell type) identity. We map onto low-dimensional atlas ontologies by modeling regions as homogeneous random fields with unknown transcriptomic feature distribution. We solve simultaneously for the minimizing geodesic diffeomorphism of coordinates through LDDMM and for these latent feature densities. We map tissue-scale mouse brain atlases to gene-based and cell-based transcriptomics data from MERFISH and BARseq technologies and to histopathology and cross-species atlases to illustrate integration of diverse molecular and cellular datasets into a single coordinate system as a means of comparison and further atlas construction.
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Affiliation(s)
- Kaitlin M Stouffer
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA.
- Centre Borelli, ENS Paris-Saclay, Gif-sur-yvette, France.
| | - Alain Trouvé
- Centre Borelli, ENS Paris-Saclay, Gif-sur-yvette, France
| | - Laurent Younes
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
| | | | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Manjari Anant
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jean Fan
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Yongsoo Kim
- Department of Neural and Behavioral Sciences, Penn State University, College of Medicine, State College, PA, USA
| | - Xiaoyin Chen
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Mara Rue
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Michael I Miller
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA.
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Wang X, Salminen LE, Petkus AJ, Driscoll I, Millstein J, Beavers DP, Espeland MA, Erus G, Braskie MN, Thompson PM, Gatz M, Chui HC, Resnick SM, Kaufman JD, Rapp SR, Shumaker S, Brown M, Younan D, Chen JC. Association between late-life air pollution exposure and medial temporal lobe atrophy in older women. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.28.23298708. [PMID: 38077091 PMCID: PMC10705610 DOI: 10.1101/2023.11.28.23298708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Background Ambient air pollution exposures increase risk for Alzheimer's disease (AD) and related dementias, possibly due to structural changes in the medial temporal lobe (MTL). However, existing MRI studies examining exposure effects on the MTL were cross-sectional and focused on the hippocampus, yielding mixed results. Method To determine whether air pollution exposures were associated with MTL atrophy over time, we conducted a longitudinal study including 653 cognitively unimpaired community-dwelling older women from the Women's Health Initiative Memory Study with two MRI brain scans (MRI-1: 2005-6; MRI-2: 2009-10; Mage at MRI-1=77.3±3.5years). Using regionalized universal kriging models, exposures at residential locations were estimated as 3-year annual averages of fine particulate matter (PM2.5) and nitrogen dioxide (NO2) prior to MRI-1. Bilateral gray matter volumes of the hippocampus, amygdala, parahippocampal gyrus (PHG), and entorhinal cortex (ERC) were summed to operationalize the MTL. We used linear regressions to estimate exposure effects on 5-year volume changes in the MTL and its subregions, adjusting for intracranial volume, sociodemographic, lifestyle, and clinical characteristics. Results On average, MTL volume decreased by 0.53±1.00cm3 over 5 years. For each interquartile increase of PM2.5 (3.26μg/m3) and NO2 (6.77ppb), adjusted MTL volume had greater shrinkage by 0.32cm3 (95%CI=[-0.43, -0.21]) and 0.12cm3 (95%CI=[-0.22, -0.01]), respectively. The exposure effects did not differ by APOE ε4 genotype, sociodemographic, and cardiovascular risk factors, and remained among women with low-level PM2.5 exposure. Greater PHG atrophy was associated with higher PM2.5 (b=-0.24, 95%CI=[-0.29, -0.19]) and NO2 exposures (b=-0.09, 95%CI=[-0.14, -0.04]). Higher exposure to PM2.5 but not NO2 was also associated with greater ERC atrophy. Exposures were not associated with amygdala or hippocampal atrophy. Conclusion In summary, higher late-life PM2.5 and NO2 exposures were associated with greater MTL atrophy over time in cognitively unimpaired older women. The PHG and ERC - the MTL cortical subregions where AD neuropathologies likely begin, may be preferentially vulnerable to air pollution neurotoxicity.
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Affiliation(s)
- Xinhui Wang
- Department of Neurology, University of Southern California, Los Angeles, California
| | - Lauren E Salminen
- Department of Neurology, University of Southern California, Los Angeles, California
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Andrew J Petkus
- Department of Neurology, University of Southern California, Los Angeles, California
| | - Ira Driscoll
- Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin
| | - Joshua Millstein
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California
| | - Daniel P Beavers
- Departments of Statistical Sciences, Wake Forest University, Winston-Salem, North Carolina
| | - Mark A Espeland
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina
- Department of Biostatistics and Data Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pennsylvania
| | - Meredith N Braskie
- Department of Neurology, University of Southern California, Los Angeles, California
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Paul M Thompson
- Department of Neurology, University of Southern California, Los Angeles, California
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Margaret Gatz
- Center for Economic and Social Research, University of Southern California, Los Angeles, California
| | - Helena C Chui
- Department of Neurology, University of Southern California, Los Angeles, California
| | - Susan M Resnick
- The Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland
| | - Joel D Kaufman
- Departments of Environmental & Occupational Health Sciences, Medicine (General Internal Medicine), and Epidemiology, University of Washington, Seattle, Washington
| | - Stephen R Rapp
- Departments of Psychiatry and Behavioral Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina
- Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Sally Shumaker
- Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Mark Brown
- Department of Biostatistics and Data Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Diana Younan
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California
| | - Jiu-Chiuan Chen
- Department of Neurology, University of Southern California, Los Angeles, California
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California
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Choi US, Park JY, Lee JJ, Choi KY, Won S, Lee KH. Predicting mild cognitive impairments from cognitively normal brains using a novel brain age estimation model based on structural magnetic resonance imaging. Cereb Cortex 2023; 33:10858-10866. [PMID: 37718166 DOI: 10.1093/cercor/bhad331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 08/20/2023] [Accepted: 08/22/2023] [Indexed: 09/19/2023] Open
Abstract
Brain age prediction is a practical method used to quantify brain aging and detect neurodegenerative diseases such as Alzheimer's disease (AD). However, very few studies have considered brain age prediction as a biomarker for the conversion of cognitively normal (CN) to mild cognitive impairment (MCI). In this study, we developed a novel brain age prediction model using brain volume and cortical thickness features. We calculated an acceleration of brain age (ABA) derived from the suggested model to estimate different diagnostic groups (CN, MCI, and AD) and to classify CN to MCI and MCI to AD conversion groups. We observed a strong association between ABA and the 3 diagnostic groups. Additionally, the classification models for CN to MCI conversion and MCI to AD conversion exhibited acceptable and robust performances, with area under the curve values of 0.66 and 0.76, respectively. We believe that our proposed model provides a reliable estimate of brain age for elderly individuals and can identify those at risk of progressing from CN to MCI. This model has great potential to reveal a diagnosis associated with a change in cognitive decline.
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Affiliation(s)
- Uk-Su Choi
- Gwangju Alzheimer's and Related Dementia Cohort Research Center, Chosun University, Gwangju 61452, Republic of Korea
- Medical Device Development Center, Daegu-Gyeongbuk Medical Innovation Foundation, Daegu 41061, Republic of Korea
| | - Jun Young Park
- Gwangju Alzheimer's and Related Dementia Cohort Research Center, Chosun University, Gwangju 61452, Republic of Korea
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul 08826, Republic of Korea
- Neurozen Inc., Seoul 06168, Republic of Korea
| | - Jang Jae Lee
- Gwangju Alzheimer's and Related Dementia Cohort Research Center, Chosun University, Gwangju 61452, Republic of Korea
| | - Kyu Yeong Choi
- Gwangju Alzheimer's and Related Dementia Cohort Research Center, Chosun University, Gwangju 61452, Republic of Korea
| | - Sungho Won
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul 08826, Republic of Korea
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Republic of Korea
- Institute of Health and Environment, Seoul National University, Seoul 08826, Republic of Korea
| | - Kun Ho Lee
- Gwangju Alzheimer's and Related Dementia Cohort Research Center, Chosun University, Gwangju 61452, Republic of Korea
- Department of Biomedical Sciences, Chosun University, Gwangju 61452, Republic of Korea
- Korea Brain Research Institute, Daegu 41061, Republic of Korea
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Osiecka Z, Fausto BA, Gills JL, Sinha N, Malin SK, Gluck MA. Obesity reduces hippocampal structure and function in older African Americans with the APOE-ε4 Alzheimer's disease risk allele. Front Aging Neurosci 2023; 15:1239727. [PMID: 37731955 PMCID: PMC10507275 DOI: 10.3389/fnagi.2023.1239727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 08/15/2023] [Indexed: 09/22/2023] Open
Abstract
Introduction Excess body weight and Alzheimer's disease (AD) disproportionately affect older African Americans. While mid-life obesity increases risk for AD, few data exist on the relationship between late-life obesity and AD, or how obesity-based and genetic risk for AD interact. Although the APOE-ε4 allele confers a strong genetic risk for AD, it is unclear if late-life obesity poses a greater risk for APOE-ε4 carriers compared to non-carriers. Here we assessed: (1) the influence of body mass index (BMI) (normal; overweight; class 1 obese; ≥ class 2 obese) on cognitive and structural MRI measures of AD risk; and (2) the interaction between BMI and APOE-ε4 in older African Americans. Methods Seventy cognitively normal older African American participants (Mage = 69.50 years; MBMI = 31.01 kg/m2; 39% APOE-ε4 allele carriers; 86% female) completed anthropometric measurements, physical assessments, saliva collection for APOE-ε4 genotyping, cognitive testing, health and lifestyle questionnaires, and structural neuroimaging [volume/surface area (SA) for medial temporal lobe subregions and hippocampal subfields]. Covariates included age, sex, education, literacy, depressive symptomology, and estimated aerobic fitness. Results Using ANCOVAs, we observed that individuals who were overweight demonstrated better hippocampal cognitive function (generalization of learning: a sensitive marker of preclinical AD) than individuals with normal BMI, p = 0.016, ηp2 = 0.18. However, individuals in the obese categories who were APOE-ε4 non-carriers had larger hippocampal subfield cornu Ammonis region 1 (CA1) volumes, while those who were APOE-ε4 carriers had smaller CA1 volumes, p = 0.003, ηp2 = 0.23. Discussion Thus, being overweight by BMI standards may preserve hippocampal function, but obesity reduces hippocampal structure and function in older African Americans with the APOE-ε4 Alzheimer's disease risk allele.
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Affiliation(s)
- Zuzanna Osiecka
- Aging and Brain Health Alliance, Center for Molecular and Behavioral Neuroscience, Rutgers University–Newark, Newark, NJ, United States
| | - Bernadette A. Fausto
- Aging and Brain Health Alliance, Center for Molecular and Behavioral Neuroscience, Rutgers University–Newark, Newark, NJ, United States
| | - Joshua L. Gills
- Aging and Brain Health Alliance, Center for Molecular and Behavioral Neuroscience, Rutgers University–Newark, Newark, NJ, United States
| | - Neha Sinha
- Aging and Brain Health Alliance, Center for Molecular and Behavioral Neuroscience, Rutgers University–Newark, Newark, NJ, United States
| | - Steven K. Malin
- Department of Kinesiology and Health, School of Arts and Sciences, Rutgers University, New Brunswick, NJ, United States
| | - Mark A. Gluck
- Aging and Brain Health Alliance, Center for Molecular and Behavioral Neuroscience, Rutgers University–Newark, Newark, NJ, United States
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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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Song J. Amygdala activity and amygdala-hippocampus connectivity: Metabolic diseases, dementia, and neuropsychiatric issues. Biomed Pharmacother 2023; 162:114647. [PMID: 37011482 DOI: 10.1016/j.biopha.2023.114647] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 03/30/2023] [Accepted: 03/31/2023] [Indexed: 04/04/2023] Open
Abstract
With rapid aging of the population worldwide, the number of people with dementia is dramatically increasing. Some studies have emphasized that metabolic syndrome, which includes obesity and diabetes, leads to increased risks of dementia and cognitive decline. Factors such as insulin resistance, hyperglycemia, high blood pressure, dyslipidemia, and central obesity in metabolic syndrome are associated with synaptic failure, neuroinflammation, and imbalanced neurotransmitter levels, leading to the progression of dementia. Due to the positive correlation between diabetes and dementia, some studies have called it "type 3 diabetes". Recently, the number of patients with cognitive decline due to metabolic imbalances has considerably increased. In addition, recent studies have reported that neuropsychiatric issues such as anxiety, depressive behavior, and impaired attention are common factors in patients with metabolic disease and those with dementia. In the central nervous system (CNS), the amygdala is a central region that regulates emotional memory, mood disorders, anxiety, attention, and cognitive function. The connectivity of the amygdala with other brain regions, such as the hippocampus, and the activity of the amygdala contribute to diverse neuropathological and neuropsychiatric issues. Thus, this review summarizes the significant consequences of the critical roles of amygdala connectivity in both metabolic syndromes and dementia. Further studies on amygdala function in metabolic imbalance-related dementia are needed to treat neuropsychiatric problems in patients with this type of dementia.
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Affiliation(s)
- Juhyun Song
- Department of Anatomy, Chonnam National University Medical School, Hwasun 58128, Jeollanam-do, Republic of Korea.
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9
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Henzen NA, Reinhardt J, Blatow M, Kressig RW, Krumm S. Excellent Interrater Reliability for Manual Segmentation of the Medial Perirhinal Cortex. Brain Sci 2023; 13:850. [PMID: 37371329 DOI: 10.3390/brainsci13060850] [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: 04/07/2023] [Revised: 05/15/2023] [Accepted: 05/18/2023] [Indexed: 06/29/2023] Open
Abstract
Objective: Evaluation of interrater reliability for manual segmentation of brain structures that are affected first by neurofibrillary tau pathology in Alzheimer's disease. Method: Medial perirhinal cortex, lateral perirhinal cortex, and entorhinal cortex were manually segmented by two raters on structural magnetic resonance images of 44 adults (20 men; mean age = 69.2 ± 10.4 years). Intraclass correlation coefficients (ICC) of cortical thickness and volumes were calculated. Results: Very high ICC values of manual segmentation for the cortical thickness of all regions (0.953-0.986) and consistently lower ICC values for volume estimates of the medial and lateral perirhinal cortex (0.705-0.874). Conclusions: The applied manual segmentation protocol allows different raters to achieve remarkably similar cortical thickness estimates for regions of the parahippocampal gyrus. In addition, the results suggest a preference for cortical thickness over volume as a reliable measure of atrophy, especially for regions affected by collateral sulcus variability (i.e., medial and lateral perirhinal cortex). The results provide a basis for future automated segmentation and collection of normative data.
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Affiliation(s)
- Nicolas A Henzen
- University Department of Geriatric Medicine FELIX PLATTER, 4055 Basel, Switzerland
- Faculty of Psychology, University of Basel, 4001 Basel, Switzerland
| | - Julia Reinhardt
- Division of Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Basel, University of Basel, 4031 Basel, Switzerland
- Department of Cardiology and Cardiovascular Research Institute Basel (CRIB), University Hospital Basel, University of Basel, 4031 Basel, Switzerland
- Department of Orthopedic Surgery and Traumatology, University Hospital of Basel, University of Basel, 4031 Basel, Switzerland
| | - Maria Blatow
- Section of Neuroradiology, Department of Radiology and Nuclear Medicine, Neurocenter, Cantonal Hospital Lucerne, University of Lucerne, 6000 Lucerne, Switzerland
| | - Reto W Kressig
- University Department of Geriatric Medicine FELIX PLATTER, 4055 Basel, Switzerland
- Faculty of Medicine, University of Basel, 4056 Basel, Switzerland
| | - Sabine Krumm
- University Department of Geriatric Medicine FELIX PLATTER, 4055 Basel, Switzerland
- Faculty of Medicine, University of Basel, 4056 Basel, Switzerland
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Chaari N, Akdağ HC, Rekik I. Comparative survey of multigraph integration methods for holistic brain connectivity mapping. Med Image Anal 2023; 85:102741. [PMID: 36638747 DOI: 10.1016/j.media.2023.102741] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 12/27/2022] [Accepted: 01/03/2023] [Indexed: 01/09/2023]
Abstract
One of the greatest scientific challenges in network neuroscience is to create a representative map of a population of heterogeneous brain networks, which acts as a connectional fingerprint. The connectional brain template (CBT), also named network atlas, presents a powerful tool for capturing the most representative and discriminative traits of a given population while preserving its topological patterns. The idea of a CBT is to integrate a population of heterogeneous brain connectivity networks, derived from different neuroimaging modalities or brain views (e.g., structural and functional), into a unified holistic representation. Here we review current state-of-the-art methods designed to estimate well-centered and representative CBT for populations of single-view and multi-view brain networks. We start by reviewing each CBT learning method, then we introduce the evaluation measures to compare CBT representativeness of populations generated by single-view and multigraph integration methods, separately, based on the following criteria: Centeredness, biomarker-reproducibility, node-level similarity, global-level similarity, and distance-based similarity. We demonstrate that the deep graph normalizer (DGN) method significantly outperforms other multi-graph and all single-view integration methods for estimating CBTs using a variety of healthy and disordered datasets in terms of centeredness, reproducibility (i.e., graph-derived biomarkers reproducibility that disentangle the typical from the atypical connectivity variability), and preserving the topological traits at both local and global graph-levels.
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Affiliation(s)
- Nada Chaari
- BASIRA lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey; Faculty of Management, Istanbul Technical University, Istanbul, Turkey
| | | | - Islem Rekik
- BASIRA lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey; Computing, Imperial-X Translation and Innovation Hub, Imperial College London, London, UK.
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Stouffer KM, Chen C, Kulason S, Xu E, Witter MP, Ceritoglu C, Albert MS, Mori S, Troncoso J, Tward DJ, Miller MI. Early amygdala and ERC atrophy linked to 3D reconstruction of rostral neurofibrillary tau tangle pathology in Alzheimer's disease. Neuroimage Clin 2023; 38:103374. [PMID: 36934675 PMCID: PMC10034129 DOI: 10.1016/j.nicl.2023.103374] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 03/07/2023] [Accepted: 03/08/2023] [Indexed: 03/17/2023]
Abstract
Previous research has emphasized the unique impact of Alzheimer's Disease (AD) pathology on the medial temporal lobe (MTL), a reflection that tau pathology is particularly striking in the entorhinal and transentorhinal cortex (ERC, TEC) early in the course of disease. However, other brain regions are affected by AD pathology during its early phases. Here, we use longitudinal diffeomorphometry to measure the atrophy rate from MRI of the amygdala compared with that in the ERC and TEC in cognitively unimpaired (CU) controls, CU individuals who progressed to mild cognitive impairment (MCI), and individuals with MCI who progressed to dementia of the AD type (DAT), using a dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our results show significantly higher atrophy rates of the amygdala in both groups of 'converters' (CU→MCI, MCI→DAT) compared to controls, with rates of volume loss comparable to rates of thickness loss in the ERC and TEC. We localize atrophy within the amygdala within each of these groups using fixed effects modeling. Controlling for the familywise error rate highlights the medial regions of the amygdala as those with significantly higher atrophy in both groups of converters than in controls. Using our recently developed method, referred to as Projective LDDMM, we map measures of neurofibrillary tau tangles (NFTs) from digital pathology to MRI atlases and reconstruct dense 3D spatial distributions of NFT density within regions of the MTL. The distribution of NFTs is consistent with the spatial distribution of MR measured atrophy rates, revealing high densities (and atrophy) in the amygdala (particularly medial), ERC, and rostral third of the MTL. The similarity of the location of NFTs in AD and shape changes in a well-defined clinical population suggests that amygdalar atrophy rate, as measured through MRI may be a viable biomarker for AD.
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Affiliation(s)
- Kaitlin M Stouffer
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore 21218, MD, USA.
| | - Claire Chen
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore 21218, MD, USA
| | - Sue Kulason
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore 21218, MD, USA
| | - Eileen Xu
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore 21218, MD, USA
| | - Menno P Witter
- Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, 7491 Trondheim, Norway
| | - Can Ceritoglu
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore 21218, MD, USA
| | - Marilyn S Albert
- Departments of Neurology, Johns Hopkins School of Medicine, 733 N Broadway, Baltimore 21205, MD, USA
| | - Susumu Mori
- Department of Radiology, Johns Hopkins School of Medicine, 733 N Broadway, Baltimore 21205, MD, USA
| | - Juan Troncoso
- Department of Pathology, Johns Hopkins School of Medicine, 733 N Broadway, Baltimore 21205, MD, USA
| | - Daniel J Tward
- Departments of Computational Medicine and Neurology, University of California, Los Angeles, UCLA Brain Mapping Center, 660 Charles E. Young Drive South, Los Angeles 90095, CA, USA
| | - Michael I Miller
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore 21218, MD, USA
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12
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Jarholm JA, Bjørnerud A, Dalaker TO, Akhavi MS, Kirsebom BE, Pålhaugen L, Nordengen K, Grøntvedt GR, Nakling A, Kalheim LF, Almdahl IS, Tecelão S, Fladby T, Selnes P. Medial Temporal Lobe Atrophy in Predementia Alzheimer's Disease: A Longitudinal Multi-Site Study Comparing Staging and A/T/N in a Clinical Research Cohort. J Alzheimers Dis 2023; 94:259-279. [PMID: 37248900 PMCID: PMC10657682 DOI: 10.3233/jad-221274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/22/2023] [Indexed: 05/31/2023]
Abstract
BACKGROUND Atrophy of the medial temporal lobe (MTL) is a biological characteristic of Alzheimer's disease (AD) and can be measured by segmentation of magnetic resonance images (MRI). OBJECTIVE To assess the clinical utility of automated volumetry in a cognitively well-defined and biomarker-classified multi-center longitudinal predementia cohort. METHODS We used Automatic Segmentation of Hippocampal Subfields (ASHS) to determine MTL morphometry from MRI. We harmonized scanner effects using the recently developed longitudinal ComBat. Subjects were classified according to the A/T/N system, and as normal controls (NC), subjective cognitive decline (SCD), or mild cognitive impairment (MCI). Positive or negative values of A, T, and N were determined by cerebrospinal fluid measurements of the Aβ42/40 ratio, phosphorylated and total tau. From 406 included subjects, longitudinal data was available for 206 subjects by stage, and 212 subjects by A/T/N. RESULTS Compared to A-/T-/N- at baseline, the entorhinal cortex, anterior and posterior hippocampus were smaller in A+/T+orN+. Compared to NC A- at baseline, these subregions were also smaller in MCI A+. Longitudinally, SCD A+ and MCI A+, and A+/T-/N- and A+/T+orN+, had significantly greater atrophy compared to controls in both anterior and posterior hippocampus. In the entorhinal and parahippocampal cortices, longitudinal atrophy was observed only in MCI A+ compared to NC A-, and in A+/T-/N- and A+/T+orN+ compared to A-/T-/N-. CONCLUSION We found MTL neurodegeneration largely consistent with existing models, suggesting that harmonized MRI volumetry may be used under conditions that are common in clinical multi-center cohorts.
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Affiliation(s)
- Jonas Alexander Jarholm
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Atle Bjørnerud
- Department of Physics, University of Oslo, Oslo, Norway
- Unit for Computational Radiology and Artificial Intelligence, Oslo University hospital, Oslo, Norway
- Department of Psychology, Faculty for Social Sciences, University of Oslo, Oslo, Norway
| | - Turi Olene Dalaker
- Department of Radiology, Stavanger Medical Imaging Laboratory, Stavanger University Hospital, Stavanger, Norway
| | - Mehdi Sadat Akhavi
- Department of Technology and Innovation, The Intervention Center, Oslo University Hospital, Oslo, Norway
| | - Bjørn Eivind Kirsebom
- Department of Neurology, University Hospital of North Norway, Tromso, Norway
- Department of Psychology, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromso, Norway
| | - Lene Pålhaugen
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Kaja Nordengen
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Gøril Rolfseng Grøntvedt
- Department of Neurology and Clinical Neurophysiology, University Hospital of Trondheim, Trondheim, Norway
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Arne Nakling
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Lisa F. Kalheim
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ina S. Almdahl
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Sandra Tecelão
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
| | - Tormod Fladby
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Per Selnes
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
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13
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Dong H, Guo L, Yang H, Zhu W, Liu F, Xie Y, Zhang Y, Xue K, Li Q, Liang M, Zhang N, Qin W. Association between gray matter atrophy, cerebral hypoperfusion, and cognitive impairment in Alzheimer's disease. Front Aging Neurosci 2023; 15:1129051. [PMID: 37091519 PMCID: PMC10117777 DOI: 10.3389/fnagi.2023.1129051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 03/15/2023] [Indexed: 04/25/2023] Open
Abstract
Background Alzheimer's disease (AD) is one of the most severe neurodegenerative diseases leading to dementia in the elderly. Cerebral atrophy and hypoperfusion are two important pathophysiological characteristics. However, it is still unknown about the area-specific causal pathways between regional gray matter atrophy, cerebral hypoperfusion, and cognitive impairment in AD patients. Method Forty-two qualified AD patients and 49 healthy controls (HC) were recruited in this study. First, we explored voxel-wise inter-group differences in gray matter volume (GMV) and arterial spin labeling (ASL) -derived cerebral blood flow (CBF). Then we explored the voxel-wise associations between GMV and Mini-Mental State Examination (MMSE) score, GMV and CBF, and CBF and MMSE to identify brain targets contributing to cognitive impairment in AD patients. Finally, a mediation analysis was applied to test the causal pathways among atrophied GMV, hypoperfusion, and cognitive impairment in AD. Results Voxel-wise permutation test identified that the left middle temporal gyrus (MTG) had both decreased GMV and CBF in the AD. Moreover, the GMV of this region was positively correlated with MMSE and its CBF, and CBF of this region was also positively correlated with MMSE in AD (p < 0.05, corrected). Finally, mediation analysis revealed that gray matter atrophy of left MTG drives cognitive impairment of AD via the mediation of CBF (proportion of mediation = 55.82%, β = 0.242, 95% confidence interval by bias-corrected and accelerated bootstrap: 0.082 to 0.530). Conclusion Our findings indicated suggested that left MTG is an important hub linking gray matter atrophy, hypoperfusion, and cognitive impairment for AD, and might be a potential treatment target for AD.
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Affiliation(s)
- Haoyang Dong
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Lining Guo
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Hailei Yang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Wenshuang Zhu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Fang Liu
- Department of Neurology, Tianjin Medical University General Hospital, Tianjin, China
| | - Yingying Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yu Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Kaizhong Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Qiang Li
- Technical College for the Deaf, Tianjin University of Technology, Tianjin, China
| | - Meng Liang
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Nan Zhang
- Department of Neurology, Tianjin Medical University General Hospital, Tianjin, China
- *Correspondence: Nan Zhang,
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
- Wen Qin,
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14
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Guo X, Chen K, Chen Y, Xiong C, Su Y, Yao L, Reiman EM. A Computational Monte Carlo Simulation Strategy to Determine the Temporal Ordering of Abnormal Age Onset Among Biomarkers of Alzheimer's Disease. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2613-2622. [PMID: 34428151 PMCID: PMC9588284 DOI: 10.1109/tcbb.2021.3106939] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
To quantitatively determining the temporal ordering of abnormal age onsets (AAO) among various biomarkers for Alzheimer's disease (AD), we introduced a computational Monte-Carlo simulation (CMCS) to statistically examine such ordering of an AAO pair or over all AAOs. The CMCS 1) simulates longitudinal data, estimates AAO for each iteration, and finally assesses the type-I error of an AAO pair or all AAO ordering. Using hippocampus volume (VHC), cerebral glucose hypometabolic convergence index (HCI), plasma neurofilament light (NfL), mini-mental state exam (MMSE), the auditory verbal learning test-long term memory (AVLT-LTM), short term memory (AVLT-STM) and clinical-dementia rating sum of box scale (CDR-SOB) from 382 mild cognitive impairment converters and non-converters, the CMCS estimated type-I error for the earlier AAO of VHC, AVLT_STM and AVLT_LTM each than MMSE was significant (p<0.002). The type-I error for the overall AAO temporal ordering of VHC ≤ AVLT_STM ≤ AVLT_LTM < HCI ≤ MMSE ≤ CDR-SOB ≤ NfL was p = 0.012. These findings showed that our CMCS is capable of providing statistical inferences for quantifying AAO ordering which has important implications in advancing our understanding of AD.
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15
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Platero C. Categorical predictive and disease progression modeling in the early stage of Alzheimer's disease. J Neurosci Methods 2022; 374:109581. [PMID: 35346695 DOI: 10.1016/j.jneumeth.2022.109581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 03/02/2022] [Accepted: 03/21/2022] [Indexed: 10/18/2022]
Abstract
BACKGROUND A preclinical stage of Alzheimer's disease (AD) precedes the symptomatic phases of mild cognitive impairment (MCI) and dementia, which constitutes a window of opportunities for preventive therapies or delaying dementia onset. NEW METHOD We propose to use categorical predictive models based on survival analysis with longitudinal data which are capable of determining subsets of markers to classify cognitively unimpaired (CU) subjects who progress into MCI/dementia or not. Subsequently, the proposed combination of markers was used to construct disease progression models (DPMs), which reveal long-term pathological trajectories from short-term clinical data. The proposed methodology was applied to a population recruited by the ADNI. RESULTS A very small subset of standard MRI-based data, CSF markers and cognitive measures was used to predict CU-to-MCI/dementia progression. The longitudinal data of these selected markers were used to construct DPMs using the algorithms of growth models by alternating conditional expectation (GRACE) and the latent time joint mixed effects model (LTJMM). The results show that the natural history of the proposed cognitive decline classifies the subjects well according to the clinical groups and shows a moderate correlation between the conversion times and their estimates by the algorithms. COMPARISON WITH EXISTING METHODS Unlike the training of the DPM algorithms without preselection of the markers, here, it is proposed to construct and evaluate the DPMs using the subsets of markers defined by the categorical predictive models. CONCLUSIONS The estimates of the natural history of the proposed cognitive decline from GRACE were more robust than those using LTJMM. The transition from normal to cognitive decline is mostly associated with an increase in temporal atrophy, worsening of clinical scores and pTAU/Aβ. Furthermore, pTAU/Aβ, Everyday Cognition score and the normalized volume of the entorhinal cortex show alterations of more than 20% fifteen years before the onset of cognitive decline.
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Affiliation(s)
- Carlos Platero
- Health Science Technology Group, Technical University of Madrid, Ronda de Valencia 3, 28012 Madrid, Spain
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16
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Golriz Khatami S, Salimi Y, Hofmann-Apitius M, Oxtoby NP, Birkenbihl C. Comparison and aggregation of event sequences across ten cohorts to describe the consensus biomarker evolution in Alzheimer's disease. Alzheimers Res Ther 2022; 14:55. [PMID: 35443691 PMCID: PMC9020023 DOI: 10.1186/s13195-022-01001-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 04/06/2022] [Indexed: 11/10/2022]
Abstract
BACKGROUND Previous models of Alzheimer's disease (AD) progression were primarily hypothetical or based on data originating from single cohort studies. However, cohort datasets are subject to specific inclusion and exclusion criteria that influence the signals observed in their collected data. Furthermore, each study measures only a subset of AD-relevant variables. To gain a comprehensive understanding of AD progression, the heterogeneity and robustness of estimated progression patterns must be understood, and complementary information contained in cohort datasets be leveraged. METHODS We compared ten event-based models that we fit to ten independent AD cohort datasets. Additionally, we designed and applied a novel rank aggregation algorithm that combines partially overlapping, individual event sequences into a meta-sequence containing the complementary information from each cohort. RESULTS We observed overall consistency across the ten event-based model sequences (average pairwise Kendall's tau correlation coefficient of 0.69 ± 0.28), despite variance in the positioning of mainly imaging variables. The changes described in the aggregated meta-sequence are broadly consistent with the current understanding of AD progression, starting with cerebrospinal fluid amyloid beta, followed by tauopathy, memory impairment, FDG-PET, and ultimately brain deterioration and impairment of visual memory. CONCLUSION Overall, the event-based models demonstrated similar and robust disease cascades across independent AD cohorts. Aggregation of data-driven results can combine complementary strengths and information of patient-level datasets. Accordingly, the derived meta-sequence draws a more complete picture of AD pathology compared to models relying on single cohorts.
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Affiliation(s)
- Sepehr Golriz Khatami
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt Augustin, Germany.
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115, Bonn, Germany.
| | - Yasamin Salimi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115, Bonn, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115, Bonn, Germany
| | - Neil P Oxtoby
- Centre for Medical Image Computing and Department of Computer Science, University College London, Gower St, London, WC1E 6BT, UK
| | - Colin Birkenbihl
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115, Bonn, Germany
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17
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Wei X, Du X, Xie Y, Suo X, He X, Ding H, Zhang Y, Ji Y, Chai C, Liang M, Yu C, Liu Y, Qin W. Mapping cerebral atrophic trajectory from amnestic mild cognitive impairment to Alzheimer's disease. Cereb Cortex 2022; 33:1310-1327. [PMID: 35368064 PMCID: PMC9930625 DOI: 10.1093/cercor/bhac137] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 02/13/2022] [Accepted: 03/13/2022] [Indexed: 11/14/2022] Open
Abstract
Alzheimer's disease (AD) patients suffer progressive cerebral atrophy before dementia onset. However, the region-specific atrophic processes and the influences of age and apolipoprotein E (APOE) on atrophic trajectory are still unclear. By mapping the region-specific nonlinear atrophic trajectory of whole cerebrum from amnestic mild cognitive impairment (aMCI) to AD based on longitudinal structural magnetic resonance imaging data from Alzheimer's disease Neuroimaging Initiative (ADNI) database, we unraveled a quadratic accelerated atrophic trajectory of 68 cerebral regions from aMCI to AD, especially in the superior temporal pole, caudate, and hippocampus. Besides, interaction analyses demonstrated that APOE ε4 carriers had faster atrophic rates than noncarriers in 8 regions, including the caudate, hippocampus, insula, etc.; younger patients progressed faster than older patients in 32 regions, especially for the superior temporal pole, hippocampus, and superior temporal gyrus; and 15 regions demonstrated complex interaction among age, APOE, and disease progression, including the caudate, hippocampus, etc. (P < 0.05/68, Bonferroni correction). Finally, Cox proportional hazards regression model based on the identified region-specific biomarkers could effectively predict the time to AD conversion within 10 years. In summary, cerebral atrophic trajectory mapping could help a comprehensive understanding of AD development and offer potential biomarkers for predicting AD conversion.
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Affiliation(s)
| | | | | | | | - Xiaoxi He
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Hao Ding
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China,School of Medical Imaging, Tianjin Medical University, Tianjin 300070, China
| | - Yu Zhang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Yi Ji
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Chao Chai
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Meng Liang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China,School of Medical Imaging, Tianjin Medical University, Tianjin 300070, China
| | - Chunshui Yu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China,School of Medical Imaging, Tianjin Medical University, Tianjin 300070, China
| | - Yong Liu
- Corresponding author: Wen Qin, Department of Radiology, and Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Anshan Road No 154, Heping District, Tianjin 300052, China. ; Yong Liu, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
| | - Wen Qin
- Corresponding author: Wen Qin, Department of Radiology, and Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Anshan Road No 154, Heping District, Tianjin 300052, China. ; Yong Liu, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
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18
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Wang G, Zhou W, Kong D, Qu Z, Ba M, Hao J, Yao T, Dong Q, Su Y, Reiman EM, Caselli RJ, Chen K, Wang Y. Studying APOE ɛ4 Allele Dose Effects with a Univariate Morphometry Biomarker. J Alzheimers Dis 2022; 85:1233-1250. [PMID: 34924383 PMCID: PMC10498787 DOI: 10.3233/jad-215149] [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] [Indexed: 01/06/2023]
Abstract
BACKGROUND A univariate neurodegeneration biomarker (UNB) based on MRI with strong statistical discrimination power would be highly desirable for studying hippocampal surface morphological changes associated with APOE ɛ4 genetic risk for AD in the cognitively unimpaired (CU) population. However, existing UNB work either fails to model large group variances or does not capture AD induced changes. OBJECTIVE We proposed a subspace decomposition method capable of exploiting a UNB to represent the hippocampal morphological changes related to the APOE ɛ4 dose effects among the longitudinal APOE ɛ4 homozygotes (HM, N = 30), heterozygotes (HT, N = 49) and non-carriers (NC, N = 61). METHODS Rank minimization mechanism combined with sparse constraint considering the local continuity of the hippocampal atrophy regions is used to extract group common structures. Based on the group common structures of amyloid-β (Aβ) positive AD patients and Aβ negative CU subjects, we identified the regions-of-interest (ROI), which reflect significant morphometry changes caused by the AD development. Then univariate morphometry index (UMI) is constructed from these ROIs. RESULTS The proposed UMI demonstrates a more substantial statistical discrimination power to distinguish the longitudinal groups with different APOE ɛ4 genotypes than the hippocampal volume measurements. And different APOE ɛ4 allele load affects the shrinkage rate of the hippocampus, i.e., HM genotype will cause the largest atrophy rate, followed by HT, and the smallest is NC. CONCLUSION The UMIs may capture the APOE ɛ4 risk allele-induced brain morphometry abnormalities and reveal the dose effects of APOE ɛ4 on the hippocampal morphology in cognitively normal individuals.
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Affiliation(s)
- Gang Wang
- School of Ulsan Ship and Ocean College, Ludong University, Yantai, China
| | - Wenju Zhou
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China
| | - Deping Kong
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Zongshuai Qu
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Maowen Ba
- Department of Neurology, The Affiliated Yantai Yuhuangding Hospital of Qingdao 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
| | - Qunxi Dong
- Institute of Engineering Medicine, Beijing Institute of Technology, Beijing, China
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
| | - Yi Su
- Banner Alzheimer’s Institute, 100 Washtenaw Avenue, Phoenix, AZ, USA
| | - Eric M Reiman
- Banner Alzheimer’s Institute, 100 Washtenaw Avenue, Phoenix, AZ, USA
| | | | - Kewei Chen
- Banner Alzheimer’s Institute, 100 Washtenaw Avenue, Phoenix, AZ, USA
| | - Yalin Wang
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
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19
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Zhou Y, Si X, Chen Y, Chao Y, Lin CP, Li S, Zhang X, Ming D, Li Q. Hippocampus- and Thalamus-Related Fiber-Specific White Matter Reductions in Mild Cognitive Impairment. Cereb Cortex 2021; 32:3159-3174. [PMID: 34891164 DOI: 10.1093/cercor/bhab407] [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] [Received: 06/08/2021] [Revised: 09/04/2021] [Accepted: 10/20/2021] [Indexed: 11/13/2022] Open
Abstract
Early diagnosis of mild cognitive impairment (MCI) fascinates screening high-risk Alzheimer's disease (AD). White matter is found to degenerate earlier than gray matter and functional connectivity during MCI. Although studies reveal white matter degenerates in the limbic system for MCI, how other white matter degenerates during MCI remains unclear. In our method, regions of interest with a high level of resting-state functional connectivity with hippocampus were selected as seeds to track fibers based on diffusion tensor imaging (DTI). In this way, hippocampus-temporal and thalamus-related fibers were selected, and each fiber's DTI parameters were extracted. Then, statistical analysis, machine learning classification, and Pearson's correlations with behavior scores were performed between MCI and normal control (NC) groups. Results show that: 1) the mean diffusivity of hippocampus-temporal and thalamus-related fibers are significantly higher in MCI and could be used to classify 2 groups effectively. 2) Compared with normal fibers, the degenerated fibers detected by the DTI indexes, especially for hippocampus-temporal fibers, have shown significantly higher correlations with cognitive scores. 3) Compared with the hippocampus-temporal fibers, thalamus-related fibers have shown significantly higher correlations with depression scores within MCI. Our results provide novel biomarkers for the early diagnoses of AD.
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Affiliation(s)
- Yu Zhou
- School of Microelectronics, Tianjin University, Tianjin 300072, China
| | - Xiaopeng Si
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, China.,Institute of Applied Psychology, Tianjin University, Tianjin 300350, China
| | - Yuanyuan Chen
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, China
| | - Yiping Chao
- Graduate Institute of Biomedical Engineering, Chang Gung University, Taoyuan 33302, Taiwan.,Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan 33302, Taiwan
| | - Ching-Po Lin
- Institute of Neuroscience Hsinchu City, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan
| | - Sicheng Li
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, China
| | - Xingjian Zhang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, China
| | - Qiang Li
- School of Microelectronics, Tianjin University, Tianjin 300072, China
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20
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An N, Fu Y, Shi J, Guo HN, Yang ZW, Li YC, Li S, Wang Y, Yao ZJ, Hu B. Synergistic Effects of APOE and CLU May Increase the Risk of Alzheimer's Disease: Acceleration of Atrophy in the Volumes and Shapes of the Hippocampus and Amygdala. J Alzheimers Dis 2021; 80:1311-1327. [PMID: 33682707 DOI: 10.3233/jad-201162] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND The volume loss of the hippocampus and amygdala in non-demented individuals has been reported to increase the risk of developing Alzheimer's disease (AD). Many neuroimaging genetics studies mainly focused on the individual effects of APOE and CLU on neuroimaging to understand their neural mechanisms, whereas their synergistic effects have been rarely studied. OBJECTIVE To assess whether APOE and CLU have synergetic effects, we investigated the epistatic interaction and combined effects of the two genetic variants on morphological degeneration of hippocampus and amygdala in the non-demented elderly at baseline and 2-year follow-up. METHODS Besides the widely-used volume indicator, the surface-based morphometry method was also adopted in this study to evaluate shape alterations. RESULTS Our results showed a synergistic effect of homozygosity for the CLU risk allele C in rs11136000 and APOEɛ4 on the hippocampal and amygdalar volumes during a 2-year follow-up. Moreover, the combined effects of APOEɛ4 and CLU C were stronger than either of the individual effects in the atrophy progress of the amygdala. CONCLUSION These findings indicate that brain morphological changes are caused by more than one gene variant, which may help us to better understand the complex endogenous mechanism of AD.
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Affiliation(s)
- Na An
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Yu Fu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Jie Shi
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Han-Ning Guo
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Zheng-Wu Yang
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Yong-Chao Li
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Shan Li
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Yin Wang
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Zhi-Jun Yao
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Bin Hu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China.,Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.,Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
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21
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Risk of early-onset dementia among persons with tinnitus: a retrospective case-control study. Sci Rep 2021; 11:13399. [PMID: 34183724 PMCID: PMC8238939 DOI: 10.1038/s41598-021-92802-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 06/16/2021] [Indexed: 12/13/2022] Open
Abstract
Higher rates of poor cognitive performance are known to prevail among persons with tinnitus in all age groups. However, no study has explored the association between tinnitus and early-onset dementia. We hypothesize that tinnitus may precede or occur concurrently with subclinical or early onset dementia in adults younger than 65 years of age. This case–control study used data from the Taiwan National Health Insurance Research Database, identifying 1308 patients with early-onset dementia (dementia diagnosed before 65 years of age) and 1308 matched controls. We used multivariable logistic regressions to estimate odds ratios (ORs) for prior tinnitus among patients with dementia versus controls. Among total 2616 sample participants, the prevalence of prior tinnitus was 18%, 21.5% among cases and 14.5% among controls (p < 0.001). Multivariable logistic regression showed and adjusted OR for prior tinnitus of 1.6 for cases versus controls (95% CI: 1.3 ~ 2.0). After adjusting for sociodemographic characteristics and medical co-morbidities, patients with early-onset dementia had a 67% higher likelihood of having prior tinnitus (OR = 1.628; 95% CI = 1.321–2.006). Our findings showed that pre-existing tinnitus was associated with a 68% increased risk of developing early-onset dementia among young and middle-aged adults. The results call for greater awareness of tinnitus as a potential harbinger of future dementia in this population.
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22
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Liu J, Li M, Luo Y, Yang S, Li W, Bi Y. Alzheimer's disease detection using depthwise separable convolutional neural networks. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 203:106032. [PMID: 33713959 DOI: 10.1016/j.cmpb.2021.106032] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Accepted: 02/25/2021] [Indexed: 05/02/2023]
Abstract
To diagnose Alzheimer's disease (AD), neuroimaging methods such as magnetic resonance imaging have been employed. Recent progress in computer vision with deep learning (DL) has further inspired research focused on machine learning algorithms. However, a few limitations of these algorithms, such as the requirement for large number of training images and the necessity for powerful computers, still hinder the extensive usage of AD diagnosis based on machine learning. In addition, large number of training parameters and heavy computation make the DL systems difficult in integrating with mobile embedded devices, for example the mobile phones. For AD detection using DL, most of the current research solely focused on improving the classification performance, while few studies have been done to obtain a more compact model with less complexity and relatively high recognition accuracy. In order to solve this problem and improve the efficiency of the DL algorithm, a deep separable convolutional neural network model is proposed for AD classification in this paper. The depthwise separable convolution (DSC) is used in this work to replace the conventional convolution. Compared to the traditional neural networks, the parameters and computing cost of the proposed neural network are found greatly reduced. The parameters and computational costs of the proposed neural network are found to be significantly reduced compared with conventional neural networks. With its low power consumption, the proposed model is particularly suitable for embedding mobile devices. Experimental findings show that the DSC algorithm, based on the OASIS magnetic resonance imaging dataset, is very successful for AD detection. Moreover, transfer learning is employed in this work to improve model performance. Two trained models with complex networks, namely AlexNet and GoogLeNet, are used for transfer learning, with average classification rates of 91.40%, 93.02% and a less power consumption.
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Affiliation(s)
- Junxiu Liu
- School of Electronic Engineering, Guangxi Normal University, Guilin, 541004, China
| | - Mingxing Li
- School of Electronic Engineering, Guangxi Normal University, Guilin, 541004, China
| | - Yuling Luo
- School of Electronic Engineering, Guangxi Normal University, Guilin, 541004, China.
| | - Su Yang
- School of Computing and Engineering, University of West London, London, United Kingdom
| | - Wei Li
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Yifei Bi
- College of Foreign Languages, University of Shanghai for Science and Technology, Shanghai, China
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23
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Jové M, Mota-Martorell N, Torres P, Ayala V, Portero-Otin M, Ferrer I, Pamplona R. The Causal Role of Lipoxidative Damage in Mitochondrial Bioenergetic Dysfunction Linked to Alzheimer's Disease Pathology. Life (Basel) 2021; 11:life11050388. [PMID: 33923074 PMCID: PMC8147054 DOI: 10.3390/life11050388] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 04/19/2021] [Accepted: 04/21/2021] [Indexed: 01/18/2023] Open
Abstract
Current shreds of evidence point to the entorhinal cortex (EC) as the origin of the Alzheimer’s disease (AD) pathology in the cerebrum. Compared with other cortical areas, the neurons from this brain region possess an inherent selective vulnerability derived from particular oxidative stress conditions that favor increased mitochondrial molecular damage with early bioenergetic involvement. This alteration of energy metabolism is the starting point for subsequent changes in a multitude of cell mechanisms, leading to neuronal dysfunction and, ultimately, cell death. These events are induced by changes that come with age, creating the substrate for the alteration of several neuronal pathways that will evolve toward neurodegeneration and, consequently, the development of AD pathology. In this context, the present review will focus on description of the biological mechanisms that confer vulnerability specifically to neurons of the entorhinal cortex, the changes induced by the aging process in this brain region, and the alterations at the mitochondrial level as the earliest mechanism for the development of AD pathology. Current findings allow us to propose the existence of an altered allostatic mechanism at the entorhinal cortex whose core is made up of mitochondrial oxidative stress, lipid metabolism, and energy production, and which, in a positive loop, evolves to neurodegeneration, laying the basis for the onset and progression of AD pathology.
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Affiliation(s)
- Mariona Jové
- Department of Experimental Medicine, Lleida Biomedical Research Institute (IRBLleida), Lleida University (UdL), 25198 Lleida, Spain; (M.J.); (N.M.-M.); (P.T.); (V.A.); (M.P.-O.)
| | - Natàlia Mota-Martorell
- Department of Experimental Medicine, Lleida Biomedical Research Institute (IRBLleida), Lleida University (UdL), 25198 Lleida, Spain; (M.J.); (N.M.-M.); (P.T.); (V.A.); (M.P.-O.)
| | - Pascual Torres
- Department of Experimental Medicine, Lleida Biomedical Research Institute (IRBLleida), Lleida University (UdL), 25198 Lleida, Spain; (M.J.); (N.M.-M.); (P.T.); (V.A.); (M.P.-O.)
| | - Victoria Ayala
- Department of Experimental Medicine, Lleida Biomedical Research Institute (IRBLleida), Lleida University (UdL), 25198 Lleida, Spain; (M.J.); (N.M.-M.); (P.T.); (V.A.); (M.P.-O.)
| | - Manuel Portero-Otin
- Department of Experimental Medicine, Lleida Biomedical Research Institute (IRBLleida), Lleida University (UdL), 25198 Lleida, Spain; (M.J.); (N.M.-M.); (P.T.); (V.A.); (M.P.-O.)
| | - Isidro Ferrer
- Department of Pathology and Experimental Therapeutics, University of Barcelona, Bellvitge University Hospital/Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, 08907 Barcelona, Spain
- Center for Biomedical Research on Neurodegenerative Diseases (CIBERNED), ISCIII, 28220 Madrid, Spain
- Correspondence: (I.F.); (R.P.)
| | - Reinald Pamplona
- Department of Experimental Medicine, Lleida Biomedical Research Institute (IRBLleida), Lleida University (UdL), 25198 Lleida, Spain; (M.J.); (N.M.-M.); (P.T.); (V.A.); (M.P.-O.)
- Correspondence: (I.F.); (R.P.)
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24
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Wei Y, Huang N, Liu Y, Zhang X, Wang S, Tang X. Hippocampal and Amygdalar Morphological Abnormalities in Alzheimer's Disease Based on Three Chinese MRI Datasets. Curr Alzheimer Res 2021; 17:1221-1231. [PMID: 33602087 DOI: 10.2174/1567205018666210218150223] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 10/12/2020] [Accepted: 12/22/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Early detection of Alzheimer's disease (AD) and its early stage, the mild cognitive impairment (MCI), has important scientific, clinical and social significance. Magnetic resonance imaging (MRI) based statistical shape analysis provides an opportunity to detect regional structural abnormalities of brain structures caused by AD and MCI. OBJECTIVE In this work, we aimed to employ a well-established statistical shape analysis pipeline, in the framework of large deformation diffeomorphic metric mapping, to identify and quantify the regional shape abnormalities of the bilateral hippocampus and amygdala at different prodromal stages of AD, using three Chinese MRI datasets collected from different domestic hospitals. METHODS We analyzed the region-specific shape abnormalities at different stages of the neuropathology of AD by comparing the localized shape characteristics of the bilateral hippocampi and amygdalas between healthy controls and two disease groups (MCI and AD). In addition to group comparison analyses, we also investigated the association between the shape characteristics and the Mini Mental State Examination (MMSE) of each structure of interest in the disease group (MCI and AD combined) as well as the discriminative power of different morphometric biomarkers. RESULTS We found the strongest disease pathology (regional atrophy) at the subiculum and CA1 subregions of the hippocampus and the basolateral, basomedial as well as centromedial subregions of the amygdala. Furthermore, the shape characteristics of the hippocampal and amygdalar subregions exhibiting the strongest AD related atrophy were found to have the most significant positive associations with the MMSE. Employing the shape deformation marker of the hippocampus or the amygdala for automated MCI or AD detection yielded a significant accuracy boost over the corresponding volume measurement. CONCLUSION Our results suggested that the amygdalar and hippocampal morphometrics, especially those of shape morphometrics, can be used as auxiliary indicators for monitoring the disease status of an AD patient.
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Affiliation(s)
- Yuanyuan Wei
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Nianwei Huang
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Yong Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Xi Zhang
- Department of Neurology, Nanlou Division, Chinese PLA General Hospital; National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Silun Wang
- YIWEI Medical Technology Co., Ltd, Shenzhen, Guangdong, China
| | - Xiaoying Tang
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
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25
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26
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Conley AC, Albert KM, Boyd BD, Kim SG, Shokouhi S, McDonald BC, Saykin AJ, Dumas JA, Newhouse PA. Cognitive complaints are associated with smaller right medial temporal gray-matter volume in younger postmenopausal women. Menopause 2020; 27:1220-1227. [PMID: 33110037 PMCID: PMC9153070 DOI: 10.1097/gme.0000000000001613] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE Menopause is associated with increasing cognitive complaints and older women are at increased risk of developing Alzheimer disease compared to men. However, there is difficulty in early markers of risk using objective performance measures. We investigated the impact of subjective cognitive complaints on the cortical structure in a sample of younger postmenopausal women. METHODS Data for this cross-sectional study were drawn from the baseline visit of a longer double-blind study examining estrogen-cholinergic interactions in normal postmenopausal women. Structural Magnetic Resonance Imaging was acquired on 44 women, aged 50-60 years and gray-matter volume was defined by voxel-based morphometry. Subjective measures of cognitive complaints and postmenopausal symptoms were obtained as well as tests of verbal episodic and working memory performance. RESULTS Increased levels of cognitive complaints were associated with lower gray-matter volume in the right medial temporal lobe (r = -0.445, P < 0.002, R = 0.2). Increased depressive symptoms and somatic complaints were also related to increased cognitive complaints and smaller medial temporal volumes but did not mediate the effect of cognitive complaints. In contrast, there was no association between performance on the memory tasks and subjective cognitive ratings, or medial temporal lobe volume. CONCLUSIONS The findings of the present study indicate that the level of reported cognitive complaints in postmenopausal women may be associated with reduced gray-matter volume which may be associated with cortical changes that may increase risk of future cognitive decline. : Video Summary:http://links.lww.com/MENO/A626.
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Affiliation(s)
- Alexander C. Conley
- Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN
| | - Kimberly M. Albert
- Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN
| | - Brian D. Boyd
- Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN
| | - Shin-Gyeom Kim
- Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN
- Department of Neuropsychiatry, Soonchunhyang University, Bucheon Hospital, Republic of Korea
| | - Sepideh Shokouhi
- Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN
| | - Brenna C. McDonald
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN
| | - Andrew J. Saykin
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN
| | - Julie A. Dumas
- Clinical Neuroscience Research Unit, Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT
| | - Paul A. Newhouse
- Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN
- Geriatric Research, Education, and Clinical Center, Veterans Affairs Tennessee Valley Health System, Nashville, TN
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27
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Martí-Juan G, Sanroma-Guell G, Cacciaglia R, Falcon C, Operto G, Molinuevo JL, González Ballester MÁ, Gispert JD, Piella G. Nonlinear interaction between APOE ε4 allele load and age in the hippocampal surface of cognitively intact individuals. Hum Brain Mapp 2020; 42:47-64. [PMID: 33017488 PMCID: PMC7721244 DOI: 10.1002/hbm.25202] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 07/16/2020] [Accepted: 08/11/2020] [Indexed: 01/27/2023] Open
Abstract
The ε4 allele of the gene Apolipoprotein E is the major genetic risk factor for Alzheimer's Disease. APOE ε4 has been associated with changes in brain structure in cognitively impaired and unimpaired subjects, including atrophy of the hippocampus, which is one of the brain structures that is early affected by AD. In this work we analyzed the impact of APOE ε4 gene dose and its association with age, on hippocampal shape assessed with multivariate surface analysis, in a ε4‐enriched cohort of n = 479 cognitively healthy individuals. Furthermore, we sought to replicate our findings on an independent dataset of n = 969 individuals covering the entire AD spectrum. We segmented the hippocampus of the subjects with a multi‐atlas‐based approach, obtaining high‐dimensional meshes that can be analyzed in a multivariate way. We analyzed the effects of different factors including APOE, sex, and age (in both cohorts) as well as clinical diagnosis on the local 3D hippocampal surface changes. We found specific regions on the hippocampal surface where the effect is modulated by significant APOE ε4 linear and quadratic interactions with age. We compared between APOE and diagnosis effects from both cohorts, finding similarities between APOE ε4 and AD effects on specific regions, and suggesting that age may modulate the effect of APOE ε4 and AD in a similar way.
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Affiliation(s)
- Gerard Martí-Juan
- BCN MedTech, Departament de Tecnologies de la Informació i les Comunicacions, Universitat Pompeu Fabra, Barcelona, Spain
| | | | - Raffaele Cacciaglia
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain.,Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Carles Falcon
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain.,Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBERBBN), Madrid, Spain
| | - Grégory Operto
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain.,Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain.,Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain.,Universitat Pompeu Fabra, Barcelona, Spain
| | - Miguel Ángel González Ballester
- BCN MedTech, Departament de Tecnologies de la Informació i les Comunicacions, Universitat Pompeu Fabra, Barcelona, Spain.,ICREA, Barcelona, Spain
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain.,Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBERBBN), Madrid, Spain.,Universitat Pompeu Fabra, Barcelona, Spain
| | - Gemma Piella
- BCN MedTech, Departament de Tecnologies de la Informació i les Comunicacions, Universitat Pompeu Fabra, Barcelona, Spain
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28
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Lim YY, Baker JE, Bruns L, Mills A, Fowler C, Fripp J, Rainey-Smith SR, Ames D, Masters CL, Maruff P. Association of deficits in short-term learning and Aβ and hippocampal volume in cognitively normal adults. Neurology 2020; 95:e2577-e2585. [PMID: 32887774 DOI: 10.1212/wnl.0000000000010728] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Accepted: 06/04/2020] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To determine the extent to which deficits in learning over 6 days are associated with β-amyloid-positive (Aβ+) and hippocampal volume in cognitively normal (CN) adults. METHODS Eighty CN older adults who had undergone PET neuroimaging to determine Aβ status (n = 42 Aβ- and 38 Aβ+), MRI to determine hippocampal and ventricular volume, and repeated assessment of memory were recruited from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study. Participants completed the Online Repeatable Cognitive Assessment-Language Learning Test (ORCA-LLT), which required they learn associations between 50 Chinese characters and their English language equivalents over 6 days. ORCA-LLT assessments were supervised on the first day and were completed remotely online for all remaining days. RESULTS Learning curves in the Aβ+ CN participants were significantly worse than those in matched Aβ- CN participants, with the magnitude of this difference very large (d [95% confidence interval (CI)] 2.22 [1.64-2.75], p < 0.001), and greater than differences between these groups for memory decline since their enrollment in AIBL (d [95% CI] 0.52 [0.07-0.96], p = 0.021), or memory impairment at their most recent visit. In Aβ+ CN adults, slower rates of learning were associated with smaller hippocampal and larger ventricular volumes. CONCLUSIONS These results suggest that in CN participants, Aβ+ is associated more strongly with a deficit in learning than any aspect of memory dysfunction. Slower rates of learning in Aβ+ CN participants were associated with hippocampal volume loss. Considered together, these data suggest that the primary cognitive consequence of Aβ+ is a failure to benefit from experience when exposed to novel stimuli, even over very short periods.
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Affiliation(s)
- Yen Ying Lim
- From Florey Institute of Neuroscience and Mental Health (Y.Y.L., J.E.B., A.M., C.F., C.L.M., P.M.), Parkville; Turner Institute for Brain and Mental Health (Y.Y.L., A.M.), School of Psychological Sciences, Monash University, Clayton; School of Computing and Information Systems (L.B.), The University of Melbourne, Parkville, Victoria; CSIRO Health and Biosecurity (J.F.), Australian e-Health Research Centre, Brisbane; Centre of Excellence for Alzheimer's Disease Research and Care (S.R.R.-S.), School of Medical Sciences, Edith Cowan University; Sir James McCusker Alzheimer's Disease Research Unit (Hollywood Private Hospital) (S.R.R.-S.), Perth; National Ageing Research Institute (D.A.), Parkville, Victoria; Academic Unit for Psychiatry of Old Age, Department of Psychiatry (D.A.), The University of Melbourne, St. George's Hospital, Kew; and Cogstate Ltd. (P.M.), Melbourne, Victoria, Australia.
| | - Jenalle E Baker
- From Florey Institute of Neuroscience and Mental Health (Y.Y.L., J.E.B., A.M., C.F., C.L.M., P.M.), Parkville; Turner Institute for Brain and Mental Health (Y.Y.L., A.M.), School of Psychological Sciences, Monash University, Clayton; School of Computing and Information Systems (L.B.), The University of Melbourne, Parkville, Victoria; CSIRO Health and Biosecurity (J.F.), Australian e-Health Research Centre, Brisbane; Centre of Excellence for Alzheimer's Disease Research and Care (S.R.R.-S.), School of Medical Sciences, Edith Cowan University; Sir James McCusker Alzheimer's Disease Research Unit (Hollywood Private Hospital) (S.R.R.-S.), Perth; National Ageing Research Institute (D.A.), Parkville, Victoria; Academic Unit for Psychiatry of Old Age, Department of Psychiatry (D.A.), The University of Melbourne, St. George's Hospital, Kew; and Cogstate Ltd. (P.M.), Melbourne, Victoria, Australia
| | - Loren Bruns
- From Florey Institute of Neuroscience and Mental Health (Y.Y.L., J.E.B., A.M., C.F., C.L.M., P.M.), Parkville; Turner Institute for Brain and Mental Health (Y.Y.L., A.M.), School of Psychological Sciences, Monash University, Clayton; School of Computing and Information Systems (L.B.), The University of Melbourne, Parkville, Victoria; CSIRO Health and Biosecurity (J.F.), Australian e-Health Research Centre, Brisbane; Centre of Excellence for Alzheimer's Disease Research and Care (S.R.R.-S.), School of Medical Sciences, Edith Cowan University; Sir James McCusker Alzheimer's Disease Research Unit (Hollywood Private Hospital) (S.R.R.-S.), Perth; National Ageing Research Institute (D.A.), Parkville, Victoria; Academic Unit for Psychiatry of Old Age, Department of Psychiatry (D.A.), The University of Melbourne, St. George's Hospital, Kew; and Cogstate Ltd. (P.M.), Melbourne, Victoria, Australia
| | - Andrea Mills
- From Florey Institute of Neuroscience and Mental Health (Y.Y.L., J.E.B., A.M., C.F., C.L.M., P.M.), Parkville; Turner Institute for Brain and Mental Health (Y.Y.L., A.M.), School of Psychological Sciences, Monash University, Clayton; School of Computing and Information Systems (L.B.), The University of Melbourne, Parkville, Victoria; CSIRO Health and Biosecurity (J.F.), Australian e-Health Research Centre, Brisbane; Centre of Excellence for Alzheimer's Disease Research and Care (S.R.R.-S.), School of Medical Sciences, Edith Cowan University; Sir James McCusker Alzheimer's Disease Research Unit (Hollywood Private Hospital) (S.R.R.-S.), Perth; National Ageing Research Institute (D.A.), Parkville, Victoria; Academic Unit for Psychiatry of Old Age, Department of Psychiatry (D.A.), The University of Melbourne, St. George's Hospital, Kew; and Cogstate Ltd. (P.M.), Melbourne, Victoria, Australia
| | - Christopher Fowler
- From Florey Institute of Neuroscience and Mental Health (Y.Y.L., J.E.B., A.M., C.F., C.L.M., P.M.), Parkville; Turner Institute for Brain and Mental Health (Y.Y.L., A.M.), School of Psychological Sciences, Monash University, Clayton; School of Computing and Information Systems (L.B.), The University of Melbourne, Parkville, Victoria; CSIRO Health and Biosecurity (J.F.), Australian e-Health Research Centre, Brisbane; Centre of Excellence for Alzheimer's Disease Research and Care (S.R.R.-S.), School of Medical Sciences, Edith Cowan University; Sir James McCusker Alzheimer's Disease Research Unit (Hollywood Private Hospital) (S.R.R.-S.), Perth; National Ageing Research Institute (D.A.), Parkville, Victoria; Academic Unit for Psychiatry of Old Age, Department of Psychiatry (D.A.), The University of Melbourne, St. George's Hospital, Kew; and Cogstate Ltd. (P.M.), Melbourne, Victoria, Australia
| | - Jurgen Fripp
- From Florey Institute of Neuroscience and Mental Health (Y.Y.L., J.E.B., A.M., C.F., C.L.M., P.M.), Parkville; Turner Institute for Brain and Mental Health (Y.Y.L., A.M.), School of Psychological Sciences, Monash University, Clayton; School of Computing and Information Systems (L.B.), The University of Melbourne, Parkville, Victoria; CSIRO Health and Biosecurity (J.F.), Australian e-Health Research Centre, Brisbane; Centre of Excellence for Alzheimer's Disease Research and Care (S.R.R.-S.), School of Medical Sciences, Edith Cowan University; Sir James McCusker Alzheimer's Disease Research Unit (Hollywood Private Hospital) (S.R.R.-S.), Perth; National Ageing Research Institute (D.A.), Parkville, Victoria; Academic Unit for Psychiatry of Old Age, Department of Psychiatry (D.A.), The University of Melbourne, St. George's Hospital, Kew; and Cogstate Ltd. (P.M.), Melbourne, Victoria, Australia
| | - Stephanie R Rainey-Smith
- From Florey Institute of Neuroscience and Mental Health (Y.Y.L., J.E.B., A.M., C.F., C.L.M., P.M.), Parkville; Turner Institute for Brain and Mental Health (Y.Y.L., A.M.), School of Psychological Sciences, Monash University, Clayton; School of Computing and Information Systems (L.B.), The University of Melbourne, Parkville, Victoria; CSIRO Health and Biosecurity (J.F.), Australian e-Health Research Centre, Brisbane; Centre of Excellence for Alzheimer's Disease Research and Care (S.R.R.-S.), School of Medical Sciences, Edith Cowan University; Sir James McCusker Alzheimer's Disease Research Unit (Hollywood Private Hospital) (S.R.R.-S.), Perth; National Ageing Research Institute (D.A.), Parkville, Victoria; Academic Unit for Psychiatry of Old Age, Department of Psychiatry (D.A.), The University of Melbourne, St. George's Hospital, Kew; and Cogstate Ltd. (P.M.), Melbourne, Victoria, Australia
| | - David Ames
- From Florey Institute of Neuroscience and Mental Health (Y.Y.L., J.E.B., A.M., C.F., C.L.M., P.M.), Parkville; Turner Institute for Brain and Mental Health (Y.Y.L., A.M.), School of Psychological Sciences, Monash University, Clayton; School of Computing and Information Systems (L.B.), The University of Melbourne, Parkville, Victoria; CSIRO Health and Biosecurity (J.F.), Australian e-Health Research Centre, Brisbane; Centre of Excellence for Alzheimer's Disease Research and Care (S.R.R.-S.), School of Medical Sciences, Edith Cowan University; Sir James McCusker Alzheimer's Disease Research Unit (Hollywood Private Hospital) (S.R.R.-S.), Perth; National Ageing Research Institute (D.A.), Parkville, Victoria; Academic Unit for Psychiatry of Old Age, Department of Psychiatry (D.A.), The University of Melbourne, St. George's Hospital, Kew; and Cogstate Ltd. (P.M.), Melbourne, Victoria, Australia
| | - Colin L Masters
- From Florey Institute of Neuroscience and Mental Health (Y.Y.L., J.E.B., A.M., C.F., C.L.M., P.M.), Parkville; Turner Institute for Brain and Mental Health (Y.Y.L., A.M.), School of Psychological Sciences, Monash University, Clayton; School of Computing and Information Systems (L.B.), The University of Melbourne, Parkville, Victoria; CSIRO Health and Biosecurity (J.F.), Australian e-Health Research Centre, Brisbane; Centre of Excellence for Alzheimer's Disease Research and Care (S.R.R.-S.), School of Medical Sciences, Edith Cowan University; Sir James McCusker Alzheimer's Disease Research Unit (Hollywood Private Hospital) (S.R.R.-S.), Perth; National Ageing Research Institute (D.A.), Parkville, Victoria; Academic Unit for Psychiatry of Old Age, Department of Psychiatry (D.A.), The University of Melbourne, St. George's Hospital, Kew; and Cogstate Ltd. (P.M.), Melbourne, Victoria, Australia
| | - Paul Maruff
- From Florey Institute of Neuroscience and Mental Health (Y.Y.L., J.E.B., A.M., C.F., C.L.M., P.M.), Parkville; Turner Institute for Brain and Mental Health (Y.Y.L., A.M.), School of Psychological Sciences, Monash University, Clayton; School of Computing and Information Systems (L.B.), The University of Melbourne, Parkville, Victoria; CSIRO Health and Biosecurity (J.F.), Australian e-Health Research Centre, Brisbane; Centre of Excellence for Alzheimer's Disease Research and Care (S.R.R.-S.), School of Medical Sciences, Edith Cowan University; Sir James McCusker Alzheimer's Disease Research Unit (Hollywood Private Hospital) (S.R.R.-S.), Perth; National Ageing Research Institute (D.A.), Parkville, Victoria; Academic Unit for Psychiatry of Old Age, Department of Psychiatry (D.A.), The University of Melbourne, St. George's Hospital, Kew; and Cogstate Ltd. (P.M.), Melbourne, Victoria, Australia
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Kulason S, Xu E, Tward DJ, Bakker A, Albert M, Younes L, Miller MI. Entorhinal and Transentorhinal Atrophy in Preclinical Alzheimer's Disease. Front Neurosci 2020; 14:804. [PMID: 32973425 PMCID: PMC7472871 DOI: 10.3389/fnins.2020.00804] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 07/09/2020] [Indexed: 12/20/2022] Open
Abstract
This study examines the atrophy patterns in the entorhinal and transentorhinal cortices of subjects that converted from normal cognition to mild cognitive impairment. The regions were manually segmented from 3T MRI, then corrected for variability in boundary definition over time using an automated approach called longitudinal diffeomorphometry. Cortical thickness was calculated by deforming the gray matter-white matter boundary surface to the pial surface using an approach called normal geodesic flow. The surface was parcellated based on four atlases using large deformation diffeomorphic metric mapping. Average cortical thickness was calculated for (1) manually-defined entorhinal cortex, and (2) manually-defined transentorhinal cortex. Group-wise difference analysis was applied to determine where atrophy occurred, and change point analysis was applied to determine when atrophy started to occur. The results showed that by the time a diagnosis of mild cognitive impairment is made, the transentorhinal cortex and entorhinal cortex was up to 0.6 mm thinner than a control with normal cognition. A change point in atrophy rate was detected in the transentorhinal cortex 9–14 years prior to a diagnosis of mild cognitive impairment, and in the entorhinal cortex 8–11 years prior. The findings are consistent with autopsy findings that demonstrate neuronal changes in the transentorhinal cortex before the entorhinal cortex.
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Affiliation(s)
- Sue Kulason
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, United States.,Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Eileen Xu
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, United States
| | - Daniel J Tward
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Neurology, Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, Los Angeles, CA, United States
| | - Arnold Bakker
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, United States.,Department of Neurology, Johns Hopkins University, Baltimore, MD, United States
| | - Marilyn Albert
- Department of Neurology, Johns Hopkins University, Baltimore, MD, United States
| | - Laurent Younes
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, United States.,Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States.,Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, United States
| | - Michael I Miller
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, United States.,Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.,Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, United States
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30
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Jacob A, Tward DJ, Resnick S, Smith PF, Lopez C, Rebello E, Wei EX, Ratnanather JT, Agrawal Y. Vestibular function and cortical and sub-cortical alterations in an aging population. Heliyon 2020; 6:e04728. [PMID: 32904672 PMCID: PMC7457317 DOI: 10.1016/j.heliyon.2020.e04728] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Revised: 10/10/2019] [Accepted: 08/12/2020] [Indexed: 01/26/2023] Open
Abstract
While it is well known that the vestibular system is responsible for maintaining balance, posture and coordination, there is increasing evidence that it also plays an important role in cognition. Moreover, a growing number of epidemiological studies are demonstrating a link between vestibular dysfunction and cognitive deficits in older adults; however, the exact pathways through which vestibular loss may affect cognition are unknown. In this cross-sectional study, we sought to identify relationships between vestibular function and variation in morphometry in brain structures from structural neuroimaging. We used a subset of 80 participants from the Baltimore Longitudinal Study of Aging, who had both brain MRI and vestibular physiological data acquired during the same visit. Vestibular function was evaluated through the cervical vestibular-evoked myogenic potential (cVEMP). The brain structures of interest that we analyzed were the hippocampus, amygdala, thalamus, caudate nucleus, putamen, insula, entorhinal cortex (ERC), trans-entorhinal cortex (TEC) and perirhinal cortex, as these structures comprise or are connected with the putative "vestibular cortex." We modeled the volume and shape of these structures as a function of the presence/absence of cVEMP and the cVEMP amplitude, adjusting for age and sex. We observed reduced overall volumes of the hippocampus and the ERC associated with poorer vestibular function. In addition, we also found significant relationships between the shape of the hippocampus (p = 0.0008), amygdala (p = 0.01), thalamus (p = 0.008), caudate nucleus (p = 0.002), putamen (p = 0.02), and ERC-TEC complex (p = 0.008) and vestibular function. These findings provide novel insight into the multiple pathways through which vestibular loss may impact brain structures that are critically involved in spatial memory, navigation and orientation.
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Affiliation(s)
- Athira Jacob
- Center for Imaging Science and Institute for Computational Medicine,
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD,
USA
| | - Daniel J. Tward
- Center for Imaging Science and Institute for Computational Medicine,
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD,
USA
| | - Susan Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging,
Baltimore, MD, USA
| | - Paul F. Smith
- Department Pharmacology and Toxicology, School of Medical Sciences, The
Brain Health Research Centre, University of Otago, New Zealand
| | - Christophe Lopez
- Aix Marseille Universite, Centre National de la Recherche Scientifique,
Marseille, France
| | - Elliott Rebello
- Department of Otolaryngology–Head and Neck Surgery, Johns Hopkins
University School of Medicine, Baltimore, MD, USA
| | - Eric X. Wei
- Department of Otolaryngology–Head and Neck Surgery, Johns Hopkins
University School of Medicine, Baltimore, MD, USA
| | - J. Tilak Ratnanather
- Center for Imaging Science and Institute for Computational Medicine,
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD,
USA
| | - Yuri Agrawal
- Department of Otolaryngology–Head and Neck Surgery, Johns Hopkins
University School of Medicine, Baltimore, MD, USA
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31
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Fryzlewicz P. Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection. J Korean Stat Soc 2020. [DOI: 10.1007/s42952-020-00060-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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32
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Tward D, Brown T, Kageyama Y, Patel J, Hou Z, Mori S, Albert M, Troncoso J, Miller M. Diffeomorphic Registration With Intensity Transformation and Missing Data: Application to 3D Digital Pathology of Alzheimer's Disease. Front Neurosci 2020; 14:52. [PMID: 32116503 PMCID: PMC7027169 DOI: 10.3389/fnins.2020.00052] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Accepted: 01/14/2020] [Indexed: 12/15/2022] Open
Abstract
This paper examines the problem of diffeomorphic image registration in the presence of differing image intensity profiles and sparsely sampled, missing, or damaged tissue. Our motivation comes from the problem of aligning 3D brain MRI with 100-micron isotropic resolution to histology sections at 1 × 1 × 1,000-micron resolution with multiple varying stains. We pose registration as a penalized Bayesian estimation, exploiting statistical models of image formation where the target images are modeled as sparse and noisy observations of the atlas. In this injective setting, there is no assumption of symmetry between atlas and target. Cross-modality image matching is achieved by jointly estimating polynomial transformations of the atlas intensity. Missing data is accommodated via a multiple atlas selection procedure where several atlas images may be of homogeneous intensity and correspond to "background" or "artifact." The two concepts are combined within an Expectation-Maximization algorithm, where atlas selection posteriors and deformation parameters are updated iteratively and polynomial coefficients are computed in closed form. We validate our method with simulated images, examples from neuropathology, and a standard benchmarking dataset. Finally, we apply it to reconstructing digital pathology and MRI in standard atlas coordinates. By using a standard convolutional neural network to detect tau tangles in histology slices, this registration method enabled us to quantify the 3D density distribution of tauopathy throughout the medial temporal lobe of an Alzheimer's disease postmortem specimen.
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Affiliation(s)
- Daniel Tward
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, United States
| | - Timothy Brown
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, United States
| | - Yusuke Kageyama
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Jaymin Patel
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Zhipeng Hou
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Susumu Mori
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Marilyn Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Juan Troncoso
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Michael Miller
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, United States
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Montandon ML, Herrmann FR, Garibotto V, Rodriguez C, Haller S, Giannakopoulos P. Determinants of mesial temporal lobe volume loss in older individuals with preserved cognition: a longitudinal PET amyloid study. Neurobiol Aging 2019; 87:108-114. [PMID: 32057528 DOI: 10.1016/j.neurobiolaging.2019.12.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Revised: 12/03/2019] [Accepted: 12/05/2019] [Indexed: 12/11/2022]
Abstract
Mesial temporal lobe (MTL) is prominently affected in normal aging and associated with neurodegeneration in AD. Whether or not MTL atrophy is dependent on increasing amyloid load before the emergence of cognitive deficits is still disputed. We performed a 4.5-year longitudinal study in 75 older community dwellers (48 women, mean age: 79.3 years) including magnetic resonance imaging at baseline and follow-up, positron emission tomography amyloid during follow-up, neuropsychological assessment at 18 and 55 months, and APOE genotyping. Linear regression models were used to identify predictors of the MTL volume loss. Amyloid load was negatively associated with bilateral MTL volume at baseline explaining almost 10.5% of its variability. In multivariate models including time of follow-up and demographic variables (older age, male gender), this percentage exceeded 35%. The APOE4 allele independently contributed another 6%. Cognitive changes had a modest but still significant negative association with MTL volume loss. Our data support a multifactorial model including amyloid deposition, older age, male gender, APOE4 allele, and slight decline of cognitive abilities as independent predictors of MTL volume loss in brain aging.
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Affiliation(s)
- Marie-Louise Montandon
- Department of Rehabilitation and Geriatrics, Geneva University Hospitals and University of Geneva, Thônex, Switzerland; Department of Psychiatry, University of Geneva, Thônex, Switzerland
| | - François R Herrmann
- Department of Rehabilitation and Geriatrics, Geneva University Hospitals and University of Geneva, Thônex, Switzerland.
| | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, Diagnostic Department, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Cristelle Rodriguez
- Department of Psychiatry, University of Geneva, Thônex, Switzerland; Medical Direction, University of Geneva Hospitals, Geneva, Switzerland
| | - Sven Haller
- CIRD - Centre d'Imagerie Rive Droite, Geneva, Switzerland; Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden; Department of Neuroradiology, Faculty of Medicine of the University of Geneva, Geneva, Switzerland
| | - Panteleimon Giannakopoulos
- Department of Psychiatry, University of Geneva, Thônex, Switzerland; Medical Direction, University of Geneva Hospitals, Geneva, Switzerland
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Bilgel M, Jedynak BM. Predicting time to dementia using a quantitative template of disease progression. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2019; 11:205-215. [PMID: 30859120 PMCID: PMC6396328 DOI: 10.1016/j.dadm.2019.01.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
INTRODUCTION Characterization of longitudinal trajectories of biomarkers implicated in sporadic Alzheimer's disease (AD) in decades before clinical diagnosis is important for disease prevention and monitoring. METHODS We used a multivariate Bayesian model to temporally align 1369 Alzheimer's disease Neuroimaging Initiative participants based on the similarity of their longitudinal biomarker measures and estimated a quantitative template of the temporal evolution of cerebrospinal fluid Aβ 1 - 42 , p- ta u 181 p , and t-tau and hippocampal volume, brain glucose metabolism, and cognitive measurements. We computed biomarker trajectories as a function of time to AD dementia and predicted AD dementia onset age in a disjoint sample. RESULTS Quantitative template showed early changes in verbal memory, cerebrospinal fluid Aβ1-42 and p-tau181p, and hippocampal volume. Mean error in predicted AD dementia onset age was < 1.5 years. DISCUSSION Our method provides a quantitative approach for characterizing the natural history of AD starting at preclinical stages despite the lack of individual-level longitudinal data spanning the entire disease timeline.
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Affiliation(s)
- Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Bruno M. Jedynak
- Dept. of Mathematics and Statistics, Portland State University, Portland, OR, USA
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35
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Wang L, Heywood A, Stocks J, Bae J, Ma D, Popuri K, Toga AW, Kantarci K, Younes L, Mackenzie IR, Zhang F, Beg MF, Rosen H. Grant Report on PREDICT-ADFTD: Multimodal Imaging Prediction of AD/FTD and Differential Diagnosis. JOURNAL OF PSYCHIATRY AND BRAIN SCIENCE 2019; 4:e190017. [PMID: 31754634 PMCID: PMC6868780 DOI: 10.20900/jpbs.20190017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
We report on the ongoing project "PREDICT-ADFTD: Multimodal Imaging Prediction of AD/FTD and Differential Diagnosis" describing completed and future work supported by this grant. This project is a multi-site, multi-study collaboration effort with research spanning seven sites across the US and Canada. The overall goal of the project is to study neurodegeneration within Alzheimer's Disease, Frontotemporal Dementia, and related neurodegenerative disorders, using a variety of brain imaging and computational techniques to develop methods for the early and accurate prediction of disease and its course. The overarching goal of the project is to develop the earliest and most accurate biomarker that can differentiate clinical diagnoses to inform clinical trials and patient care. In its third year, this project has already completed several projects to achieve this goal, focusing on (1) structural MRI (2) machine learning and (3) FDG-PET and multimodal imaging. Studies utilizing structural MRI have identified key features of underlying pathology by studying hippocampal deformation that is unique to clinical diagnosis and also post-mortem confirmed neuropathology. Several machine learning experiments have shown high classification accuracy in the prediction of disease based on Convolutional Neural Networks utilizing MRI images as input. In addition, we have also achieved high accuracy in predicting conversion to DAT up to five years in the future. Further, we evaluated multimodal models that combine structural and FDG-PET imaging, in order to compare the predictive power of multimodal to unimodal models. Studies utilizing FDG-PET have shown significant predictive ability in the prediction and progression of disease.
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Affiliation(s)
- Lei Wang
- Northwestern University Feinberg School of Medicine, Chicago, 60611 IL, USA
| | - Ashley Heywood
- Northwestern University Feinberg School of Medicine, Chicago, 60611 IL, USA
| | - Jane Stocks
- Northwestern University Feinberg School of Medicine, Chicago, 60611 IL, USA
| | - Jinhyeong Bae
- Northwestern University Feinberg School of Medicine, Chicago, 60611 IL, USA
| | - Da Ma
- School of Engineering Science, Simon Fraser University, Burnaby, V6A1S6 BC, Canada
| | - Karteek Popuri
- School of Engineering Science, Simon Fraser University, Burnaby, V6A1S6 BC, Canada
| | - Arthur W. Toga
- Keck School of Medicine of University of Southern California, Los Angeles, 90033 CA, USA
| | - Kejal Kantarci
- Departments of Neurology and Radiology, Mayo Clinic, Rochester, 55905 MN, USA
| | - Laurent Younes
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, 21218 MD, USA
| | - Ian R. Mackenzie
- Department of Pathology and Lab Medicine, University of British Columbia, Vancouver, B6T1Z4 BC, Canada
| | - Fengqing Zhang
- Department of Psychology, Drexel University, Philadelphia, 19104 PA, USA
| | - Mirza Faisal Beg
- School of Engineering Science, Simon Fraser University, Burnaby, V6A1S6 BC, Canada
| | - Howard Rosen
- Department of Neurology, University of California, San Francisco, 94143 CA, USA
| | - Alzheimer’s Disease Neuroimaging Initiative
- Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu/). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNIAcknowledgement_List.pdf
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Chen Q, Boeve BF, Tosakulwong N, Lesnick T, Brushaber D, Dheel C, Fields J, Forsberg L, Gavrilova R, Gearhart D, Haley D, Gunter JL, Graff‐Radford J, Jones D, Knopman D, Graff‐Radford N, Kraft R, Lapid M, Rademakers R, Wszolek ZK, Rosen H, Boxer AL, Kantarci K. Brain MR Spectroscopy Changes Precede Frontotemporal Lobar Degeneration Phenoconversion in Mapt Mutation Carriers. J Neuroimaging 2019; 29:624-629. [PMID: 31173437 PMCID: PMC6731148 DOI: 10.1111/jon.12642] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 04/16/2019] [Accepted: 05/22/2019] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND AND PURPOSE The objective of this study was to longitudinally investigate the trajectory of change in 1 H MRS measurements in asymptomatic MAPT mutation carriers who became symptomatic during follow-up, and to determine the time at which the neurochemical alterations accelerated during disease progression. METHODS We identified eight MAPT mutations carriers who transitioned from asymptomatic to symptomatic disease during follow-up. All participants were longitudinally followed with an average of 7.75 years (range 4-11 years) and underwent two or more single voxel 1 H MRS examinations from the posterior cingulate voxel, with a total of 60 examinations. The rate of longitudinal change for each metabolite was estimated using linear mixed models. A flex point model was used to estimate the flex time point of the change in slope. RESULTS The decrease in the NAA/mI ratio accelerated 2.09 years prior to symptom onset, and continued to decline. A similar trajectory was observed in the presumed glial marker mI/Cr ratio accelerating 1.86 years prior to symptom onset. CONCLUSIONS Our findings support the potential use of longitudinal 1 H MRS for monitoring the neurodegenerative progression in MAPT mutation carriers starting from the asymptomatic stage.
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Affiliation(s)
- Qin Chen
- Department of RadiologyMayo Clinic
- Department of NeurologyMayo Clinic
| | - Bradley F. Boeve
- Department of Health Sciences ResearchMayo Clinic
- Research ServicesMayo ClinicRochesterMinnesota
| | | | | | - Danielle Brushaber
- Department of Psychology and PsychiatryMayo Clinic
- Research ServicesMayo ClinicRochesterMinnesota
| | - Christina Dheel
- Department of Health Sciences ResearchMayo Clinic
- Research ServicesMayo ClinicRochesterMinnesota
| | - Julie Fields
- Department of Clinical Genomic and NeurologyMayo Clinic
| | - Leah Forsberg
- Department of Health Sciences ResearchMayo Clinic
- Research ServicesMayo ClinicRochesterMinnesota
| | | | - Debra Gearhart
- Department of Health Sciences ResearchMayo Clinic
- Research ServicesMayo ClinicRochesterMinnesota
| | - Dana Haley
- Department of NeuroscienceMayo ClinicJacksonvilleFlorida
| | | | - Jonathan Graff‐Radford
- Department of Health Sciences ResearchMayo Clinic
- Research ServicesMayo ClinicRochesterMinnesota
| | | | - David Knopman
- Department of Health Sciences ResearchMayo Clinic
- Research ServicesMayo ClinicRochesterMinnesota
| | | | - Ruth Kraft
- Department of Health Sciences ResearchMayo Clinic
- Research ServicesMayo ClinicRochesterMinnesota
| | - Maria Lapid
- Department of Clinical Genomic and NeurologyMayo Clinic
| | - Rosa Rademakers
- Research ServicesMayo ClinicRochesterMinnesota
- Memory and Aging CenterUniversity of California San FranciscoSan Francisco
| | | | - Howie Rosen
- Department of NeurologyWest China Hospital of Sichuan UniversityChengduSichuanChina
| | - Adam L. Boxer
- Department of NeurologyWest China Hospital of Sichuan UniversityChengduSichuanChina
| | - Kejal Kantarci
- Department of RadiologyMayo Clinic
- Research ServicesMayo ClinicRochesterMinnesota
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Biomarker-Based Signature of Alzheimer's Disease in Pre-MCI Individuals. Brain Sci 2019; 9:brainsci9090213. [PMID: 31450744 PMCID: PMC6769621 DOI: 10.3390/brainsci9090213] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2019] [Revised: 08/10/2019] [Accepted: 08/20/2019] [Indexed: 12/11/2022] Open
Abstract
Alzheimer’s disease (AD) pathology begins decades before the onset of clinical symptoms. It is recognized as a clinicobiological entity, being detectable in vivo independently of the clinical stage by means of pathophysiological biomarkers. Accordingly, neuropathological studies that were carried out on healthy elderly subjects, with or without subjective experience of cognitive decline, reported evidence of AD pathology in a high proportion of cases. At present, mild cognitive impairment (MCI) represents the only clinically diagnosed pre-dementia stage. Several attempts have been carried out to detect AD as early as possible, when subtle cognitive alterations, still not fulfilling MCI criteria, appear. Importantly, pre-MCI individuals showing the positivity of pathophysiological AD biomarkers show a risk of progression similar to MCI patients. In view of successful treatment with disease modifying agents, in a clinical setting, a timely diagnosis is mandatory. In clinical routine, biomarkers assessment should be taken into consideration whenever a subject with subtle cognitive deficits (pre-MCI), who is aware of his/her decline, requests to know the cause of such disturbances. In this review, we report the available neuropsychological and biomarkers data that characterize the pre-MCI patients, thus proposing pre-MCI as the first clinical manifestation of AD.
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Herrmann FR, Rodriguez C, Haller S, Garibotto V, Montandon ML, Giannakopoulos P. Gray Matter Densities in Limbic Areas and APOE4 Independently Predict Cognitive Decline in Normal Brain Aging. Front Aging Neurosci 2019; 11:157. [PMID: 31316372 PMCID: PMC6609870 DOI: 10.3389/fnagi.2019.00157] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Accepted: 06/11/2019] [Indexed: 11/25/2022] Open
Abstract
Cross-sectional magnetic resonance imaging (MRI) studies reported significant associations between gray matter (GM) density changes in various limbic and neocortical areas and worst cognitive performances in elderly controls. Longitudinal studies in this field remain scarce and led to conflicting data. We report a clinico-radiological investigation of 380 cognitively preserved individuals who undergo neuropsychological assessment at baseline and after 18 months. All cases were assessed using a continuous cognitive score taking into account the global evolution of neuropsychological performances. The vast majority of Mini Mental State Examination (MMSE) 29 and 30 cases showed equal or worst performance at follow-up due to a ceiling effect. GM densities, white matter hyperintensities and arterial spin labeling (ASL) values were assessed in the hippocampus, amygdala, mesial temporal and parietal cortex at inclusion using 3 Tesla MRI Scans. Florbetapir positron emission tomography (PET) amyloid was available in a representative subsample of 64 cases. Regional amyloid uptake ratios (SUVr), mean cortical SUVr values (mcSUVr) and corresponding z-scores were calculated. Linear regression models were built to explore the association between the continuous cognitive score and imaging variables. The presence of an APOE-ε4 allele was negatively related to the continuous cognitive score. Among the areas studied, significant associations were found between GM densities in the hippocampus and amygdala but not mesial temporal and parietal areas and continuous cognitive score. Neither ASL values, Fazekas score nor mean and regional PET amyloid load was related to the cognitive score. In multivariate models, the presence of APOE-ε4 allele and GM densities in the hippocampus and amygdala were independently associated with worst cognitive evolution at follow-up. Our data support the idea that early GM damage in the hippocampus and amygdala occur long before the emergence of the very first signs of cognitive failure in brain aging.
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Affiliation(s)
- François R Herrmann
- Department of Rehabilitation and Geriatrics, Division of Geriatrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Cristelle Rodriguez
- Department of Psychiatry, University of Geneva, Geneva, Switzerland.,Medical Direction, Geneva University Hospitals, Geneva, Switzerland
| | - Sven Haller
- CIRD Centre d'Imagerie Rive Droite, Geneva, Switzerland.,Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden.,Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Valentina Garibotto
- Faculty of Medicine, University of Geneva, Geneva, Switzerland.,Division of Nuclear Medicine and Molecular Imaging, Diagnostic Department, Geneva University Hospitals, Geneva, Switzerland
| | - Marie-Louise Montandon
- Department of Rehabilitation and Geriatrics, Division of Geriatrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland.,Department of Psychiatry, University of Geneva, Geneva, Switzerland
| | - Panteleimon Giannakopoulos
- Department of Psychiatry, University of Geneva, Geneva, Switzerland.,Medical Direction, Geneva University Hospitals, Geneva, Switzerland
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Younes L, Albert M, Moghekar A, Soldan A, Pettigrew C, Miller MI. Identifying Changepoints in Biomarkers During the Preclinical Phase of Alzheimer's Disease. Front Aging Neurosci 2019; 11:74. [PMID: 31001108 PMCID: PMC6454004 DOI: 10.3389/fnagi.2019.00074] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 03/14/2019] [Indexed: 01/29/2023] Open
Abstract
Objective: Several models have been proposed for the evolution of Alzheimer's disease (AD) biomarkers. The aim of this study was to identify changepoints in a range of biomarkers during the preclinical phase of AD. Methods: We examined nine measures based on cerebrospinal fluid (CSF), magnetic resonance imaging (MRI) and cognitive testing, obtained from 306 cognitively normal individuals, a subset of whom subsequently progressed to the symptomatic phase of AD. A changepoint model was used to determine which of the measures had a significant change in slope in relation to clinical symptom onset. Results: All nine measures had significant changepoints, all of which preceded symptom onset, however, the timing of these changepoints varied considerably. A single measure, CSF t-tau, had an early changepoint (34 years prior to symptom onset). A group of measures, including the remaining CSF measures (CSF Abeta and phosphorylated tau) and all cognitive tests had changepoints 10-15 years prior to symptom onset. A second group is formed by medial temporal lobe shape composite measures, with a 6-year time difference between the right and left side (respectively nine and 3 years prior to symptom onset). Conclusion: These findings highlight the long period of time prior to symptom onset during which AD pathology is accumulating in the brain. There are several significant findings, including the early changes in cognition and the laterality of the MRI findings. Additional work is needed to clarify their significance.
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Affiliation(s)
- Laurent Younes
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, United States
| | - Marilyn Albert
- Department of Neurology, Johns Hopkins University, Baltimore, MD, United States
| | - Abhay Moghekar
- Department of Neurology, Johns Hopkins University, Baltimore, MD, United States
| | - Anja Soldan
- Department of Neurology, Johns Hopkins University, Baltimore, MD, United States
| | - Corinne Pettigrew
- Department of Neurology, Johns Hopkins University, Baltimore, MD, United States
| | - Michael I Miller
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, United States.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
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40
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Probabilistic disease progression modeling to characterize diagnostic uncertainty: Application to staging and prediction in Alzheimer's disease. Neuroimage 2019; 190:56-68. [DOI: 10.1016/j.neuroimage.2017.08.059] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Revised: 08/07/2017] [Accepted: 08/23/2017] [Indexed: 12/30/2022] Open
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Abstract
Brain imaging studies have shown that slow and progressive cerebral atrophy characterized the development of Alzheimer's Disease (AD). Despite a large number of studies dedicated to AD, key questions about the lifespan evolution of AD biomarkers remain open. When does the AD model diverge from the normal aging model? What is the lifespan trajectory of imaging biomarkers for AD? How do the trajectories of biomarkers in AD differ from normal aging? To answer these questions, we proposed an innovative way by inferring brain structure model across the entire lifespan using a massive number of MRI (N = 4329). We compared the normal model based on 2944 control subjects with the pathological model based on 3262 patients (AD + Mild cognitive Impaired subjects) older than 55 years and controls younger than 55 years. Our study provides evidences of early divergence of the AD models from the normal aging trajectory before 40 years for the hippocampus, followed by the lateral ventricles and the amygdala around 40 years. Moreover, our lifespan model reveals the evolution of these biomarkers and suggests close abnormality evolution for the hippocampus and the amygdala, whereas trajectory of ventricular enlargement appears to follow an inverted U-shape. Finally, our models indicate that medial temporal lobe atrophy and ventricular enlargement are two mid-life physiopathological events characterizing AD brain.
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42
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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: 30] [Impact Index Per Article: 6.0] [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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Cury C, Durrleman S, Cash DM, Lorenzi M, Nicholas JM, Bocchetta M, van Swieten JC, Borroni B, Galimberti D, Masellis M, Tartaglia MC, Rowe JB, Graff C, Tagliavini F, Frisoni GB, Laforce R, Finger E, de Mendonça A, Sorbi S, Ourselin S, Rohrer JD, Modat M. Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases: Initial application to the GENFI cohort. Neuroimage 2019; 188:282-290. [PMID: 30529631 PMCID: PMC6414401 DOI: 10.1016/j.neuroimage.2018.11.063] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2018] [Revised: 11/15/2018] [Accepted: 11/30/2018] [Indexed: 12/18/2022] Open
Abstract
Brain atrophy as measured from structural MR images, is one of the primary imaging biomarkers used to track neurodegenerative disease progression. In diseases such as frontotemporal dementia or Alzheimer's disease, atrophy can be observed in key brain structures years before any clinical symptoms are present. Atrophy is most commonly captured as volume change of key structures and the shape changes of these structures are typically not analysed despite being potentially more sensitive than summary volume statistics over the entire structure. In this paper we propose a spatiotemporal analysis pipeline based on Large Diffeomorphic Deformation Metric Mapping (LDDMM) to detect shape changes from volumetric MRI scans. We applied our framework to a cohort of individuals with genetic variants of frontotemporal dementia and healthy controls from the Genetic FTD Initiative (GENFI) study. Our method, take full advantage of the LDDMM framework, and relies on the creation of a population specific average spatiotemporal trajectory of a relevant brain structure of interest, the thalamus in our case. The residuals from each patient data to the average spatiotemporal trajectory are then clustered and studied to assess when presymptomatic mutation carriers differ from healthy control subjects. We found statistical differences in shape in the anterior region of the thalamus at least five years before the mutation carrier subjects develop any clinical symptoms. This region of the thalamus has been shown to be predominantly connected to the frontal lobe, consistent with the pattern of cortical atrophy seen in the disease.
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Affiliation(s)
- Claire Cury
- Department of Medical Physics and Biomedical Engineering, University College London, United Kingdom; Dementia Research Centre, UCL Queen Square Institute of Neurology, University College of London, WC1N 3BG, London, United Kingdom.
| | - Stanley Durrleman
- Inria Aramis Project-team Centre Paris-Rocquencourt, Inserm U 1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, F-75013, Paris, France
| | - David M Cash
- Department of Medical Physics and Biomedical Engineering, University College London, United Kingdom; Dementia Research Centre, UCL Queen Square Institute of Neurology, University College of London, WC1N 3BG, London, United Kingdom
| | - Marco Lorenzi
- Department of Medical Physics and Biomedical Engineering, University College London, United Kingdom; Epione Team, Inria Sophia Antipolis, Sophia Antipolis, France
| | - Jennifer M Nicholas
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College of London, WC1N 3BG, London, United Kingdom; Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Martina Bocchetta
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College of London, WC1N 3BG, London, United Kingdom
| | | | | | - Daniela Galimberti
- Dept. of Pathophysiology and Transplantation, "Dino Ferrari" Center, University of Milan, Fondazione C Granda, IRCCS Ospedale Maggiore Policlinico, Milan, Italy
| | - Mario Masellis
- Cognitive Neurology Research Unit, Sunnybrook Health Sciences Centre, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Department of Medicine, University of Toronto, Canada
| | | | | | - Caroline Graff
- Karolinska Institutet, Stockholm, Sweden; Karolinska Institutet, Department NVS, Center for Alzheimer Research, Division of Neurogeriatrics, Sweden; Department of Geriatric Medicine, Karolinska University Hospital, Stockholm, Sweden
| | | | | | | | | | | | - Sandro Sorbi
- Department of Neurosciences, Psychology, Drug Research and Child Health (NEUROFARBA), University of Florence, Florence, Italy; IRCCS Don Gnocchi, Firenze, Italy
| | - Sebastien Ourselin
- Department of Medical Physics and Biomedical Engineering, University College London, United Kingdom; Dementia Research Centre, UCL Queen Square Institute of Neurology, University College of London, WC1N 3BG, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom
| | - Jonathan D Rohrer
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College of London, WC1N 3BG, London, United Kingdom
| | - Marc Modat
- Department of Medical Physics and Biomedical Engineering, University College London, United Kingdom; Dementia Research Centre, UCL Queen Square Institute of Neurology, University College of London, WC1N 3BG, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom
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44
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Kulason S, Tward DJ, Brown T, Sicat CS, Liu CF, Ratnanather JT, Younes L, Bakker A, Gallagher M, Albert M, Miller MI. Cortical thickness atrophy in the transentorhinal cortex in mild cognitive impairment. NEUROIMAGE-CLINICAL 2018; 21:101617. [PMID: 30552075 PMCID: PMC6412863 DOI: 10.1016/j.nicl.2018.101617] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 11/19/2018] [Accepted: 11/24/2018] [Indexed: 11/24/2022]
Abstract
This study examines the atrophy rates of subjects with mild cognitive impairment (MCI) compared to controls in four regions within the medial temporal lobe: the transentorhinal cortex (TEC), entorhinal cortex (ERC), hippocampus, and amygdala. These regions were manually segmented and then corrected for undesirable longitudinal variability via Large Deformation Diffeomorphic Metric Mapping (LDDMM) based longitudinal diffeomorphometry. Diffeomorphometry techniques were used to compare thickness measurements in the TEC with the ERC. There were more significant changes in thickness atrophy rate in the TEC than medial regions of the entorhinal cortex. Volume measures were also calculated for all four regions. Classifiers were constructed using linear discriminant analysis to demonstrate that average thickness and atrophy rate of TEC together was the most discriminating measure compared to the thickness and volume measures in the areas examined, in differentiating MCI from controls. These findings are consistent with autopsy findings demonstrating that initial neuronal changes are found in TEC before spreading more medially in the ERC and to other regions in the medial temporal lobe. These findings suggest that the TEC thickness could serve as a biomarker for Alzheimer's disease in the prodromal phase of the disease. The transentorhinal cortex is significantly thinner in subjects with mild cognitive impairment than controls. Mildly cognitively impaired subjects show a significantly greater atrophy rate in the transentorhinal cortex than controls.
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Affiliation(s)
- Sue Kulason
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA.
| | - Daniel J Tward
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA; Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Timothy Brown
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Chelsea S Sicat
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Chin-Fu Liu
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - J Tilak Ratnanather
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Laurent Younes
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Arnold Bakker
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Michela Gallagher
- Department of Psychological and Brain Sciences, Johns Hopkins School of Arts and Sciences, Baltimore, MD 21218, USA
| | - Marilyn Albert
- Department of Neurology, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Michael I Miller
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA; Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD 21218, USA
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ten Kate M, Ingala S, Schwarz AJ, Fox NC, Chételat G, van Berckel BNM, Ewers M, Foley C, Gispert JD, Hill D, Irizarry MC, Lammertsma AA, Molinuevo JL, Ritchie C, Scheltens P, Schmidt ME, Visser PJ, Waldman A, Wardlaw J, Haller S, Barkhof F. Secondary prevention of Alzheimer's dementia: neuroimaging contributions. Alzheimers Res Ther 2018; 10:112. [PMID: 30376881 PMCID: PMC6208183 DOI: 10.1186/s13195-018-0438-z] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Accepted: 10/10/2018] [Indexed: 02/06/2023]
Abstract
BACKGROUND In Alzheimer's disease (AD), pathological changes may arise up to 20 years before the onset of dementia. This pre-dementia window provides a unique opportunity for secondary prevention. However, exposing non-demented subjects to putative therapies requires reliable biomarkers for subject selection, stratification, and monitoring of treatment. Neuroimaging allows the detection of early pathological changes, and longitudinal imaging can assess the effect of interventions on markers of molecular pathology and rates of neurodegeneration. This is of particular importance in pre-dementia AD trials, where clinical outcomes have a limited ability to detect treatment effects within the typical time frame of a clinical trial. We review available evidence for the use of neuroimaging in clinical trials in pre-dementia AD. We appraise currently available imaging markers for subject selection, stratification, outcome measures, and safety in the context of such populations. MAIN BODY Amyloid positron emission tomography (PET) is a validated in-vivo marker of fibrillar amyloid plaques. It is appropriate for inclusion in trials targeting the amyloid pathway, as well as to monitor treatment target engagement. Amyloid PET, however, has limited ability to stage the disease and does not perform well as a prognostic marker within the time frame of a pre-dementia AD trial. Structural magnetic resonance imaging (MRI), providing markers of neurodegeneration, can improve the identification of subjects at risk of imminent decline and hence play a role in subject inclusion. Atrophy rates (either hippocampal or whole brain), which can be reliably derived from structural MRI, are useful in tracking disease progression and have the potential to serve as outcome measures. MRI can also be used to assess comorbid vascular pathology and define homogeneous groups for inclusion or for subject stratification. Finally, MRI also plays an important role in trial safety monitoring, particularly the identification of amyloid-related imaging abnormalities (ARIA). Tau PET to measure neurofibrillary tangle burden is currently under development. Evidence to support the use of advanced MRI markers such as resting-state functional MRI, arterial spin labelling, and diffusion tensor imaging in pre-dementia AD is preliminary and requires further validation. CONCLUSION We propose a strategy for longitudinal imaging to track early signs of AD including quantitative amyloid PET and yearly multiparametric MRI.
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Affiliation(s)
- Mara ten Kate
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
- Alzheimer Center & Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, PO Box 7056, 1007 MB Amsterdam, the Netherlands
| | - Silvia Ingala
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
| | - Adam J. Schwarz
- Takeda Pharmaceuticals Comparny, Cambridge, MA USA
- Eli Lilly and Company, Indianapolis, Indiana USA
| | - Nick C. Fox
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
| | - Gaël Chételat
- Institut National de la Santé et de la Recherche Médicale, Inserm UMR-S U1237, Université de Caen-Normandie, GIP Cyceron, Caen, France
| | - Bart N. M. van Berckel
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
| | - Michael Ewers
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
| | | | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
| | | | | | - Adriaan A. Lammertsma
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
| | - Craig Ritchie
- Centre for Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Philip Scheltens
- Alzheimer Center & Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, PO Box 7056, 1007 MB Amsterdam, the Netherlands
| | | | - Pieter Jelle Visser
- Alzheimer Center & Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, PO Box 7056, 1007 MB Amsterdam, the Netherlands
| | - Adam Waldman
- Centre for Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Joanna Wardlaw
- Centre for Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Dementia Research Centre, University of Edinburgh, Edinburgh, UK
| | - Sven Haller
- Affidea Centre de Diagnostic Radiologique de Carouge, Geneva, Switzerland
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
- Insititutes of Neurology and Healthcare Engineering, University College London, London, UK
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Tang X, Ross CA, Johnson H, Paulsen JS, Younes L, Albin RL, Ratnanather JT, Miller MI. Regional subcortical shape analysis in premanifest Huntington's disease. Hum Brain Mapp 2018; 40:1419-1433. [PMID: 30376191 DOI: 10.1002/hbm.24456] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 10/18/2018] [Accepted: 10/23/2018] [Indexed: 11/11/2022] Open
Abstract
Huntington's disease (HD) involves preferential and progressive degeneration of striatum and other subcortical regions as well as regional cortical atrophy. It is caused by a CAG repeat expansion in the Huntingtin gene, and the longer the expansion the earlier the age of onset. Atrophy begins prior to manifest clinical signs and symptoms, and brain atrophy in premanifest expansion carriers can be studied. We employed a diffeomorphometric pipeline to contrast subcortical structures' morphological properties in a control group with three disease groups representing different phases of premanifest HD (far, intermediate, and near to onset) as defined by the length of the CAG expansion and the participant's age (CAG-Age-Product). A total of 1,428 magnetic resonance image scans from 694 participants from the PREDICT-HD cohort were used. We found significant region-specific atrophies in all subcortical structures studied, with the estimated abnormality onset time varying from structure to structure. Heterogeneous shape abnormalities of caudate nuclei were present in premanifest HD participants estimated furthest from onset and putaminal shape abnormalities were present in participants intermediate to onset. Thalamic, hippocampal, and amygdalar shape abnormalities were present in participants nearest to onset. We assessed whether the estimated progression of subcortical pathology in premanifest HD tracked specific pathways. This is plausible for changes in basal ganglia circuits but probably not for changes in hippocampus and amygdala. The regional shape analyses conducted in this study provide useful insights into the effects of HD pathology in subcortical structures.
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Affiliation(s)
- Xiaoying Tang
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Christopher A Ross
- Division of Neurobiology, Departments of Psychiatry, Neurology, Neuroscience and Pharmacology, and Program in Cellular and Molecular Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Hans Johnson
- Departments of Neurology and Psychiatry, The University of Iowa Carver College of Medicine, Iowa City, Iowa
| | - Jane S Paulsen
- Departments of Neurology and Psychiatry, The University of Iowa Carver College of Medicine, Iowa City, Iowa
| | - Laurent Younes
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, Maryland.,Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland.,Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Roger L Albin
- Neurology Service and GRECC, VAAAHS, Ann Arbor, Michigan.,Department of Neurology, University of Michigan Medical School, Ann Arbor, Michigan
| | - J Tilak Ratnanather
- Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland.,Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Michael I Miller
- Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland.,Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
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47
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Bi XA, Xu Q, Luo X, Sun Q, Wang Z. Analysis of Progression Toward Alzheimer's Disease Based on Evolutionary Weighted Random Support Vector Machine Cluster. Front Neurosci 2018; 12:716. [PMID: 30349454 PMCID: PMC6186825 DOI: 10.3389/fnins.2018.00716] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Accepted: 09/19/2018] [Indexed: 12/02/2022] Open
Abstract
Alzheimer’s disease (AD) could be described into following four stages: healthy control (HC), early mild cognitive impairment (EMCI), late MCI (LMCI) and AD dementia. The discriminations between different stages of AD are considerably important issues for future pre-dementia treatment. However, it is still challenging to identify LMCI from EMCI because of the subtle changes in imaging which are not noticeable. In addition, there were relatively few studies to make inferences about the brain dynamic changes in the cognitive progression from EMCI to LMCI to AD. Inspired by the above problems, we proposed an advanced approach of evolutionary weighted random support vector machine cluster (EWRSVMC). Where the predictions of numerous weighted SVM classifiers are aggregated for improving the generalization performance. We validated our method in multiple binary classifications using Alzheimer’s Disease Neuroimaging Initiative dataset. As a result, the encouraging accuracy of 90% for EMCI/LMCI and 88.89% for LMCI/AD were achieved respectively, demonstrating the excellent discriminating ability. Furthermore, disease-related brain regions underlying the AD progression could be found out on the basis of the amount of discriminative information. The findings of this study provide considerable insight into the neurophysiological mechanisms in AD development.
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Affiliation(s)
- Xia-An Bi
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Qian Xu
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Xianhao Luo
- College of Mathematics and Statistics, Hunan Normal University, Changsha, China
| | - Qi Sun
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Zhigang Wang
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
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Wu D, Faria AV, Younes L, Ross CA, Mori S, Miller MI. Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI-Application in Premanifest Huntington's Disease. J Vis Exp 2018. [PMID: 29939188 DOI: 10.3791/57256] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
Abstract
Recent advances in MRI offer a variety of useful markers to identify neurodegenerative diseases. In Huntington's disease (HD), regional brain atrophy begins many years prior to the motor onset (during the "premanifest" period), but the spatiotemporal pattern of regional atrophy across the brain has not been fully characterized. Here we demonstrate an online cloud-computing platform, "MRICloud", which provides atlas-based whole-brain segmentation of T1-weighted images at multiple granularity levels, and thereby, enables us to access the regional features of brain anatomy. We then describe a regression model that detects statistically significant inflection points, at which regional brain atrophy starts to be noticeable, i.e. the "change-point", with respect to a disease progression index. We used the CAG-age product (CAP) score to index the disease progression in HD patients. Change-point analysis of the volumetric measurements from the segmentation pipeline, therefore, provides important information of the order and pattern of structural atrophy across the brain. The paper illustrates the use of these techniques on T1-weighted MRI data of premanifest HD subjects from a large multicenter PREDICT-HD study. This design potentially has wide applications in a range of neurodegenerative diseases to investigate the dynamic changes of brain anatomy.
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Affiliation(s)
- Dan Wu
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine;
| | - Andreia V Faria
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine
| | - Laurent Younes
- Center for Imaging Science, Johns Hopkins University; Institute for Computational Medicine, Johns Hopkins University; Department of Applied Mathematics and Statistics, Johns Hopkins University
| | - Christopher A Ross
- Division of Neurobiology, Departments of Psychiatry, Neurology, Neuroscience and Pharmacology, and Program in Cellular and Molecular Medicine, Johns Hopkins University School of Medicine
| | - Susumu Mori
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Michael I Miller
- Center for Imaging Science, Johns Hopkins University; Institute for Computational Medicine, Johns Hopkins University; Department of Biomedical Engineering, Johns Hopkins University
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49
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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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50
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Kinnunen KM, Cash DM, Poole T, Frost C, Benzinger TLS, Ahsan RL, Leung KK, Cardoso MJ, Modat M, Malone IB, Morris JC, Bateman RJ, Marcus DS, Goate A, Salloway SP, Correia S, Sperling RA, Chhatwal JP, Mayeux RP, Brickman AM, Martins RN, Farlow MR, Ghetti B, Saykin AJ, Jack CR, Schofield PR, McDade E, Weiner MW, Ringman JM, Thompson PM, Masters CL, Rowe CC, Rossor MN, Ourselin S, Fox NC. Presymptomatic atrophy in autosomal dominant Alzheimer's disease: A serial magnetic resonance imaging study. Alzheimers Dement 2018; 14:43-53. [PMID: 28738187 PMCID: PMC5751893 DOI: 10.1016/j.jalz.2017.06.2268] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2017] [Revised: 06/10/2017] [Accepted: 06/12/2017] [Indexed: 01/01/2023]
Abstract
INTRODUCTION Identifying at what point atrophy rates first change in Alzheimer's disease is important for informing design of presymptomatic trials. METHODS Serial T1-weighted magnetic resonance imaging scans of 94 participants (28 noncarriers, 66 carriers) from the Dominantly Inherited Alzheimer Network were used to measure brain, ventricular, and hippocampal atrophy rates. For each structure, nonlinear mixed-effects models estimated the change-points when atrophy rates deviate from normal and the rates of change before and after this point. RESULTS Atrophy increased after the change-point, which occurred 1-1.5 years (assuming a single step change in atrophy rate) or 3-8 years (assuming gradual acceleration of atrophy) before expected symptom onset. At expected symptom onset, estimated atrophy rates were at least 3.6 times than those before the change-point. DISCUSSION Atrophy rates are pathologically increased up to seven years before "expected onset". During this period, atrophy rates may be useful for inclusion and tracking of disease progression.
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Affiliation(s)
- Kirsi M. Kinnunen
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK
| | - David M. Cash
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK,Department of Medical Physics and Bioengineering, Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK,Corresponding author. Tel.: +44 203 448 3054; Fax: +44 (0)20 3448 3104.,
| | - Teresa Poole
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK,Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
| | - Chris Frost
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK,Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
| | | | - R. Laila Ahsan
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK
| | - Kelvin K. Leung
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK
| | - M. Jorge Cardoso
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK,Department of Medical Physics and Bioengineering, Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Marc Modat
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK,Department of Medical Physics and Bioengineering, Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Ian B. Malone
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK
| | - John C. Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Randall J. Bateman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Daniel S. Marcus
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Alison Goate
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stephen P. Salloway
- Department of Neurology, Butler Hospital, Warren Alpert Medical School, Brown University, Providence, RI, USA
| | - Stephen Correia
- Department of Neurology, Butler Hospital, Warren Alpert Medical School, Brown University, Providence, RI, USA
| | - Reisa A. Sperling
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jasmeer P. Chhatwal
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Richard P. Mayeux
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Adam M. Brickman
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Ralph N. Martins
- Centre of Excellence for Alzheimer’s Disease Research and Care, School of Exercise, Biomedical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
| | - Martin R. Farlow
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Bernardino Ghetti
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences, Centre for Neuroimaging, Indiana University School of Medicine, Indianapolis, IN, USA
| | | | - Peter R. Schofield
- Neuroscience Research Australia, Randwick, NSW, Australia,School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Eric McDade
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Michael W. Weiner
- Department of Radiology, School of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - John M. Ringman
- Department of Neurology, Keck USC School of Medicine, Los Angeles, CA, USA
| | - Paul M. Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Colin L. Masters
- The Florey Institute, University of Melbourne, Parkville, VIC, Australia
| | - Christopher C. Rowe
- Department of Nuclear Medicine and Centre for PET, Austin Health, Heidelberg, VIC, Australia,Department of Medicine, University of Melbourne, Austin Health, Heidelberg, VIC, Australia
| | - Martin N. Rossor
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK
| | - Sebastien Ourselin
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK,Department of Medical Physics and Bioengineering, Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Nick C. Fox
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK
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