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Zhu C, Li H, Song Z, Jiang M, Song L, Li L, Wang X, Zheng Q. Jointly constrained group sparse connectivity representation improves early diagnosis of Alzheimer's disease on routinely acquired T1-weighted imaging-based brain network. Health Inf Sci Syst 2024; 12:19. [PMID: 38464465 PMCID: PMC10917732 DOI: 10.1007/s13755-023-00269-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 12/27/2023] [Indexed: 03/12/2024] Open
Abstract
Background Radiomics-based morphological brain networks (radMBN) constructed from routinely acquired structural MRI (sMRI) data have gained attention in Alzheimer's disease (AD). However, the radMBN suffers from limited characterization of AD because sMRI only characterizes anatomical changes and is not a direct measure of neuronal pathology or brain activity. Purpose To establish a group sparse representation of the radMBN under a joint constraint of group-level white matter fiber connectivity and individual-level sMRI regional similarity (JCGS-radMBN). Methods Two publicly available datasets were adopted, including 120 subjects from ADNI with both T1-weighted image (T1WI) and diffusion MRI (dMRI) for JCGS-radMBN construction, 818 subjects from ADNI and 200 subjects solely with T1WI from AIBL for validation in early AD diagnosis. Specifically, the JCGS-radMBN was conducted by jointly estimating non-zero connections among subjects, with the regularization term constrained by group-level white matter fiber connectivity and individual-level sMRI regional similarity. Then, a triplet graph convolutional network was adopted for early AD diagnosis. The discriminative brain connections were identified using a two-sample t-test, and the neurobiological interpretation was validated by correlating the discriminative brain connections with cognitive scores. Results The JCGS-radMBN exhibited superior classification performance over five brain network construction methods. For the typical NC vs. AD classification, the JCGS-radMBN increased by 1-30% in accuracy over the alternatives on ADNI and AIBL. The discriminative brain connections exhibited a strong connectivity to hippocampus, parahippocampal gyrus, and basal ganglia, and had significant correlation with MMSE scores. Conclusion The proposed JCGS-radMBN facilitated the AD characterization of brain network established on routinely acquired imaging modality of sMRI. Supplementary Information The online version of this article (10.1007/s13755-023-00269-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Chuanzhen Zhu
- School of Computer and Control Engineering, Yantai University, No 30, Qingquan Road, Laishan District, Yantai, 264005 Shandong China
| | - Honglun Li
- Departments of Medical Oncology and Radiology, Affiliated Yantai Yuhuangding Hospital of Qingdao University Medical College, Yantai, 264099 China
| | - Zhiwei Song
- School of Computer and Control Engineering, Yantai University, No 30, Qingquan Road, Laishan District, Yantai, 264005 Shandong China
| | - Minbo Jiang
- School of Computer and Control Engineering, Yantai University, No 30, Qingquan Road, Laishan District, Yantai, 264005 Shandong China
| | - Limei Song
- School of Medical Imaging, Weifang Medical University, Weifang, 261000 China
| | - Lin Li
- Yantaishan Hospital Affiliated to Binzhou Medical University, Yantai, 264003 China
| | - Xuan Wang
- School of Computer and Control Engineering, Yantai University, No 30, Qingquan Road, Laishan District, Yantai, 264005 Shandong China
| | - Qiang Zheng
- School of Computer and Control Engineering, Yantai University, No 30, Qingquan Road, Laishan District, Yantai, 264005 Shandong China
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Javadi M, Sharma R, Tsiamyrtzis P, Webb AG, Leiss E, Tsekos NV. Let UNet Play an Adversarial Game: Investigating the Effect of Adversarial Training in Enhancing Low-Resolution MRI. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01205-8. [PMID: 39085718 DOI: 10.1007/s10278-024-01205-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 07/08/2024] [Accepted: 07/12/2024] [Indexed: 08/02/2024]
Abstract
Adversarial training has attracted much attention in enhancing the visual realism of images, but its efficacy in clinical imaging has not yet been explored. This work investigated adversarial training in a clinical context, by training 206 networks on the OASIS-1 dataset for improving low-resolution and low signal-to-noise ratio (SNR) magnetic resonance images. Each network corresponded to a different combination of perceptual and adversarial loss weights and distinct learning rate values. For each perceptual loss weighting, we identified its corresponding adversarial loss weighting that minimized structural disparity. Each optimally weighted adversarial loss yielded an average SSIM reduction of 1.5%. We further introduced a set of new metrics to assess other clinically relevant image features: Gradient Error (GE) to measure structural disparities; Sharpness to compute edge clarity; and Edge-Contrast Error (ECE) to quantify any distortion of the pixel distribution around edges. Including adversarial loss increased structural enhancement in visual inspection, which correlated with statistically consistent GE reductions (p-value << 0.05). This also resulted in increased Sharpness; however, the level of statistical significance was dependent on the perceptual loss weighting. Additionally, adversarial loss yielded ECE reductions for smaller perceptual loss weightings, while showing non-significant increases (p-value >> 0.05) when these weightings were higher, demonstrating that the increased Sharpness does not adversely distort the pixel distribution around the edges in the image. These studies clearly suggest that adversarial training significantly improves the performance of an MRI enhancement pipeline, and highlights the need for systematic studies of hyperparameter optimization and investigation of alternative image quality metrics.
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Affiliation(s)
- Mohammad Javadi
- Medical Robotics and Imaging Lab, Department of Computer Science, University of Houston, 501, Philip G. Hoffman Hall, 4800 Calhoun Road, Houston, TX, 77204, USA
| | - Rishabh Sharma
- Medical Robotics and Imaging Lab, Department of Computer Science, University of Houston, 501, Philip G. Hoffman Hall, 4800 Calhoun Road, Houston, TX, 77204, USA
| | - Panagiotis Tsiamyrtzis
- Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy
- Department of Statistics, Athens University of Economics and Business, Athens, Greece
| | - Andrew G Webb
- C.J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Ernst Leiss
- Department of Computer Science, University of Houston, Houston, TX, USA
| | - Nikolaos V Tsekos
- Medical Robotics and Imaging Lab, Department of Computer Science, University of Houston, 501, Philip G. Hoffman Hall, 4800 Calhoun Road, Houston, TX, 77204, USA.
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Strianese D, Laezza MP, Tortora F, Fusco G, de Divitiis O, D’Aponte A, Briganti F, Elefante A. Long-Term Survival and Factors Associated with Increased Mortality in Patients with Ocular Adnexal Lymphomas. Cancers (Basel) 2024; 16:2252. [PMID: 38927956 PMCID: PMC11201500 DOI: 10.3390/cancers16122252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Revised: 06/05/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
Orbital and ocular adnexal lymphoma (OAL) affects the orbit and the surrounding structures and can arise as several subtypes with variable prognoses. We performed an observational study on the relationship between OAL subtype, diagnostic features, and prognosis to offer valuable insights into imaging techniques, such as Positron Emission Tomography with 2-deoxy-2-[fluorine-18] fluoro-D-glucose integrated with Computed Tomography (18F-FDG PET-CT), in predicting outcomes. With this aim, we retrospectively reviewed 99 patients with OALs, recording demographics, cancer subtype, location and treatment, 18FDG avidity, and bone marrow positivity. We divided patients into Group 1 (those presenting with extranodal marginal zone lymphoma-EMZL) and Group 2, including all other subtypes. The primary outcome was long-term cancer-specific survival (CSS) based on key predictors, performed through Kaplan-Meier curves and the log-rank test, with a p < 0.05 significance threshold. The mean patient age was 67 years (57-75.5). The most frequent histopathologic subtypes were EMZL lymphoma in 69 patients (69.7%), small lymphocytic lymphoma (11.1%) and diffuse-large B-cell lymphoma (10.1%). Patients of Group 1 showed a better prognosis (CSS = 80%) compared to those of Group 2 (CSS = 60%) (p = 0.01). In patients with high-grade lymphoma, the occurrence of 18FDG avidity (p = 0.003) and bone marrow positivity (p = 0.005) were related to a worse prognosis. In our group, EMZL was the most prominent subtype of OALs and exhibited the best prognosis, low 18FDG avidity, and bone marrow negativity. By observing specific patterns in radiological findings, it is possible to increase our understanding of disease progression, treatment response, and the overall prognosis in OAL patients.
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Affiliation(s)
- Diego Strianese
- Department of Neuroscience, University of Naples Federico II, 80131 Naples, Italy; (D.S.); (M.P.L.); (A.D.)
| | - Maria Paola Laezza
- Department of Neuroscience, University of Naples Federico II, 80131 Naples, Italy; (D.S.); (M.P.L.); (A.D.)
- Department of Medicine and Health Sciences, University of Molise, 86100 Campobasso, Italy
| | - Fabio Tortora
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy; (F.T.); (G.F.); (F.B.)
| | - Giancarlo Fusco
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy; (F.T.); (G.F.); (F.B.)
| | - Oreste de Divitiis
- Department of Neurosciences and Reproductive and Odontostomatological Sciences, University of Naples Federico II, 80131 Naples, Italy;
| | - Antonella D’Aponte
- Department of Neuroscience, University of Naples Federico II, 80131 Naples, Italy; (D.S.); (M.P.L.); (A.D.)
| | - Francesco Briganti
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy; (F.T.); (G.F.); (F.B.)
| | - Andrea Elefante
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy; (F.T.); (G.F.); (F.B.)
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Kang X, Wang D, Lin J, Yao H, Zhao K, Song C, Chen P, Qu Y, Yang H, Zhang Z, Zhou B, Han T, Liao Z, Chen Y, Lu J, Yu C, Wang P, Zhang X, Li M, Zhang X, Jiang T, Zhou Y, Liu B, Han Y, Liu Y. Convergent Neuroimaging and Molecular Signatures in Mild Cognitive Impairment and Alzheimer's Disease: A Data-Driven Meta-Analysis with N = 3,118. Neurosci Bull 2024:10.1007/s12264-024-01218-x. [PMID: 38824231 DOI: 10.1007/s12264-024-01218-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Accepted: 11/24/2023] [Indexed: 06/03/2024] Open
Abstract
The current study aimed to evaluate the susceptibility to regional brain atrophy and its biological mechanism in Alzheimer's disease (AD). We conducted data-driven meta-analyses to combine 3,118 structural magnetic resonance images from three datasets to obtain robust atrophy patterns. Then we introduced a set of radiogenomic analyses to investigate the biological basis of the atrophy patterns in AD. Our results showed that the hippocampus and amygdala exhibit the most severe atrophy, followed by the temporal, frontal, and occipital lobes in mild cognitive impairment (MCI) and AD. The extent of atrophy in MCI was less severe than that in AD. A series of biological processes related to the glutamate signaling pathway, cellular stress response, and synapse structure and function were investigated through gene set enrichment analysis. Our study contributes to understanding the manifestations of atrophy and a deeper understanding of the pathophysiological processes that contribute to atrophy, providing new insight for further clinical research on AD.
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Affiliation(s)
- Xiaopeng Kang
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Ji'nan, 250063, China
| | - Jiaji Lin
- Department of Neurology, the Second Affiliated Hospital of Air Force Medical University, Xi'an, 710032, China
- Department of Radiology, Chinese PLA General Hospital, Beijing, 100853, China
| | - Hongxiang Yao
- Department of Radiology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, China
| | - Kun Zhao
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100191, China
| | - Chengyuan Song
- Department of Neurology, Qilu Hospital of Shandong University, Ji'nan, 250063, China
| | - Pindong Chen
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Yida Qu
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Hongwei Yang
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Zengqiang Zhang
- Branch of Chinese, PLA General Hospital, Sanya, 572013, China
| | - Bo Zhou
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, China
| | - Tong Han
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin, 300222, China
| | - Zhengluan Liao
- Department of Psychiatry, People's Hospital of Hangzhou Medical College, Zhejiang Provincial People's Hospital, Hangzhou, 310014, China
| | - Yan Chen
- Department of Psychiatry, People's Hospital of Hangzhou Medical College, Zhejiang Provincial People's Hospital, Hangzhou, 310014, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Chunshui Yu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300070, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, 300222, China
| | - Xinqing Zhang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Ming Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650201, Yunnan, China
| | - Xi Zhang
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, China
| | - Tianzi Jiang
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Yuying Zhou
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, 300222, China
| | - Bing Liu
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- State Key Lab of Cognition Neuroscience & Learning, Beijing Normal University, Beijing, 100875, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China.
- National Clinical Research Center for Geriatric Disorders, Beijing, 100053, China.
- Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, 100053, China.
| | - Yong Liu
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100191, China.
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5
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Mak E, Reid RI, Przybelski SA, Lesnick TG, Schwarz CG, Senjem ML, Raghavan S, Vemuri P, Jack CR, Min HK, Jain MK, Miyagawa T, Forsberg LK, Fields JA, Savica R, Graff-Radford J, Jones DT, Botha H, St Louis EK, Knopman DS, Ramanan VK, Dickson DW, Graff-Radford NR, Ferman TJ, Petersen RC, Lowe VJ, Boeve BF, O'Brien JT, Kantarci K. Influences of amyloid-β and tau on white matter neurite alterations in dementia with Lewy bodies. NPJ Parkinsons Dis 2024; 10:76. [PMID: 38570511 PMCID: PMC10991290 DOI: 10.1038/s41531-024-00684-4] [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: 07/19/2023] [Accepted: 03/13/2024] [Indexed: 04/05/2024] Open
Abstract
Dementia with Lewy bodies (DLB) is a neurodegenerative condition often co-occurring with Alzheimer's disease (AD) pathology. Characterizing white matter tissue microstructure using Neurite Orientation Dispersion and Density Imaging (NODDI) may help elucidate the biological underpinnings of white matter injury in individuals with DLB. In this study, diffusion tensor imaging (DTI) and NODDI metrics were compared in 45 patients within the dementia with Lewy bodies spectrum (mild cognitive impairment with Lewy bodies (n = 13) and probable dementia with Lewy bodies (n = 32)) against 45 matched controls using conditional logistic models. We evaluated the associations of tau and amyloid-β with DTI and NODDI parameters and examined the correlations of AD-related white matter injury with Clinical Dementia Rating (CDR). Structural equation models (SEM) explored relationships among age, APOE ε4, amyloid-β, tau, and white matter injury. The DLB spectrum group exhibited widespread white matter abnormalities, including reduced fractional anisotropy, increased mean diffusivity, and decreased neurite density index. Tau was significantly associated with limbic and temporal white matter injury, which was, in turn, associated with worse CDR. SEM revealed that amyloid-β exerted indirect effects on white matter injury through tau. We observed widespread disruptions in white matter tracts in DLB that were not attributed to AD pathologies, likely due to α-synuclein-related injury. However, a fraction of the white matter injury could be attributed to AD pathology. Our findings underscore the impact of AD pathology on white matter integrity in DLB and highlight the utility of NODDI in elucidating the biological basis of white matter injury in DLB.
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Affiliation(s)
- Elijah Mak
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Robert I Reid
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
- Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | - Scott A Przybelski
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Timothy G Lesnick
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | | | - Matthew L Senjem
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
- Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | | | | | | | - Hoon Ki Min
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Manoj K Jain
- Department of Radiology, Mayo Clinic, Jacksonville, FL, USA
| | - Toji Miyagawa
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | | | - Julie A Fields
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Rodolfo Savica
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | | | - David T Jones
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Erik K St Louis
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
- Center for Sleep Medicine, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | | | | | - Dennis W Dickson
- Department of Psychiatry and Psychology, Mayo Clinic, Jacksonville, FL, USA
| | | | - Tanis J Ferman
- Department of Neurology, Mayo Clinic, Jacksonville, FL, USA
| | - Ronald C Petersen
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | - John T O'Brien
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN, USA.
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Lee P, Chou K, Lee W, Peng L, Chen L, Lin C, Liang C, Chung C. Altered cerebellar and caudate gray-matter volumes and structural covariance networks preceding dual cognitive and mobility impairments in older people. Alzheimers Dement 2024; 20:2420-2433. [PMID: 38298159 PMCID: PMC11032519 DOI: 10.1002/alz.13714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 11/17/2023] [Accepted: 12/16/2023] [Indexed: 02/02/2024]
Abstract
INTRODUCTION The neuroanatomical changes driving both cognitive and mobility impairments, an emerging preclinical dementia syndrome, are not fully understood. We examined gray-matter volumes (GMVs) and structural covariance networks (SCNs) abnormalities in community-based older people preceding the conversion to physio-cognitive decline syndrome (PCDS). METHODS Voxel-wise brain GMV and established SCNs were compared between PCDS and non-PCDS converters. RESULTS The study included 343 individuals (60.2 ± 6.9 years, 49.6% men) with intact cognitive and mobility functions. Over an average 5.6-year follow-up, 116 transitioned to PCDS. Identified regions with abnormal GMVs in PCDS converters were over cerebellum and caudate, which served as seeds for SCNs establishment. Significant differences in cerebellum-based (to right frontal pole and left middle frontal gyrus) and caudate-based SCNs (to right caudate putamen, right planum temporale, left precentral gyrus, right postcentral gyrus, and left parietal operculum) between converters and nonconverters were observed. DISCUSSION This study reveals early neuroanatomic changes, emphasizing the cerebellum's role, in dual cognitive and mobility impairments. HIGHLIGHTS Neuroanatomic precursors of dual cognitive and mobility impairments are identified. Cerebellar GMV reductions and increased right caudate GMV precede the onset of PCDS. Altered cerebellum- and caudate-based SCNs drive PCDS transformation. This research establishes a foundation for understanding PCDS as a specific dementia syndrome.
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Affiliation(s)
- Pei‐Lin Lee
- Center for Healthy Longevity and Aging SciencesNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
- Institute of NeuroscienceNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
| | - Kun‐Hsien Chou
- Institute of NeuroscienceNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
- Brain Research CenterNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
| | - Wei‐Ju Lee
- Center for Healthy Longevity and Aging SciencesNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
- Department of Family MedicineTaipei Veterans General Hospital Yuanshan BranchYi‐LanTaiwan
| | - Li‐Ning Peng
- Center for Healthy Longevity and Aging SciencesNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
- Center for Geriatric and GerontologyTaipei Veterans General HospitalTaipeiTaiwan
| | - Liang‐Kung Chen
- Center for Healthy Longevity and Aging SciencesNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
- Center for Geriatric and GerontologyTaipei Veterans General HospitalTaipeiTaiwan
- Taipei Municipal Gan‐Dau Hospital (managed by Taipei Veterans General Hospital)TaipeiTaiwan
| | - Ching‐Po Lin
- Institute of NeuroscienceNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
- Brain Research CenterNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
- Department of Education and ResearchTaipei City HospitalTaipeiTaiwan
| | - Chih‐Kuang Liang
- Center for Healthy Longevity and Aging SciencesNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
- Center for Geriatrics and GerontologyKaohsiung Veterans General HospitalKaohsiungTaiwan
- Division of NeurologyDepartment of Internal MedicineKaohsiung Veterans General HospitalKaohsiungTaiwan
| | - Chih‐Ping Chung
- Center for Healthy Longevity and Aging SciencesNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
- Department of NeurologyNeurological InstituteTaipei Veterans General HospitalTaipeiTaiwan
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7
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Wang Y, Gao R, Wei T, Johnston L, Yuan X, Zhang Y, Yu Z. Predicting long-term progression of Alzheimer's disease using a multimodal deep learning model incorporating interaction effects. J Transl Med 2024; 22:265. [PMID: 38468358 PMCID: PMC10926590 DOI: 10.1186/s12967-024-05025-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 02/24/2024] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND Identifying individuals with mild cognitive impairment (MCI) at risk of progressing to Alzheimer's disease (AD) provides a unique opportunity for early interventions. Therefore, accurate and long-term prediction of the conversion from MCI to AD is desired but, to date, remains challenging. Here, we developed an interpretable deep learning model featuring a novel design that incorporates interaction effects and multimodality to improve the prediction accuracy and horizon for MCI-to-AD progression. METHODS This multi-center, multi-cohort retrospective study collected structural magnetic resonance imaging (sMRI), clinical assessments, and genetic polymorphism data of 252 patients with MCI at baseline from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our deep learning model was cross-validated on the ADNI-1 and ADNI-2/GO cohorts and further generalized in the ongoing ADNI-3 cohort. We evaluated the model performance using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and F1 score. RESULTS On the cross-validation set, our model achieved superior results for predicting MCI conversion within 4 years (AUC, 0.962; accuracy, 92.92%; sensitivity, 88.89%; specificity, 95.33%) compared to all existing studies. In the independent test, our model exhibited consistent performance with an AUC of 0.939 and an accuracy of 92.86%. Integrating interaction effects and multimodal data into the model significantly increased prediction accuracy by 4.76% (P = 0.01) and 4.29% (P = 0.03), respectively. Furthermore, our model demonstrated robustness to inter-center and inter-scanner variability, while generating interpretable predictions by quantifying the contribution of multimodal biomarkers. CONCLUSIONS The proposed deep learning model presents a novel perspective by combining interaction effects and multimodality, leading to more accurate and longer-term predictions of AD progression, which promises to improve pre-dementia patient care.
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Affiliation(s)
- Yifan Wang
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai, 200240, China
- SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
| | - Ruitian Gao
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai, 200240, China
- SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
| | - Ting Wei
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai, 200240, China
- SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
| | - Luke Johnston
- School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China
| | - Xin Yuan
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai, 200240, China
- SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
| | - Yue Zhang
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai, 200240, China
- SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
| | - Zhangsheng Yu
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai, 200240, China.
- SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China.
- School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China.
- Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Chumin EJ, Burton CP, Silvola R, Miner EW, Persohn SC, Veronese M, Territo PR. Brain metabolic network covariance and aging in a mouse model of Alzheimer's disease. Alzheimers Dement 2024; 20:1538-1549. [PMID: 38032015 PMCID: PMC10984484 DOI: 10.1002/alz.13538] [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: 06/23/2023] [Revised: 10/09/2023] [Accepted: 10/11/2023] [Indexed: 12/01/2023]
Abstract
INTRODUCTION Alzheimer's disease (AD), the leading cause of dementia worldwide, represents a human and financial impact for which few effective drugs exist to treat the disease. Advances in molecular imaging have enabled assessment of cerebral glycolytic metabolism, and network modeling of brain region have linked to alterations in metabolic activity to AD stage. METHODS We performed 18 F-FDG positron emission tomography (PET) imaging in 4-, 6-, and 12-month-old 5XFAD and littermate controls (WT) of both sexes and analyzed region data via brain metabolic covariance analysis. RESULTS The 5XFAD model mice showed age-related changes in glucose uptake relative to WT mice. Analysis of community structure of covariance networks was different across age and sex, with a disruption of metabolic coupling in the 5XFAD model. DISCUSSION The current study replicates clinical AD findings and indicates that metabolic network covariance modeling provides a translational tool to assess disease progression in AD models.
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Affiliation(s)
- Evgeny J. Chumin
- Stark Neurosciences Research InstituteIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Psychological and Brain SciencesIndiana UniversityBloomingtonIndianaUSA
- Indiana University Network Science Institute, Indiana UniversityBloomingtonIndianaUSA
| | - Charles P. Burton
- Stark Neurosciences Research InstituteIndiana University School of MedicineIndianapolisIndianaUSA
| | - Rebecca Silvola
- Department of MedicineDivision of Clinical PharmacologyIndiana University School of MedicineIndianapolisIndianaUSA
- Eli Lilly and CompanyIndianapolisIndianaUSA
| | - Ethan W. Miner
- Stark Neurosciences Research InstituteIndiana University School of MedicineIndianapolisIndianaUSA
| | - Scott C. Persohn
- Stark Neurosciences Research InstituteIndiana University School of MedicineIndianapolisIndianaUSA
| | - Mattia Veronese
- Department of Information EngineeringUniversity of PaduaPaduaItaly
- Department of NeuroimagingKing's College LondonLondonUK
| | - Paul R. Territo
- Stark Neurosciences Research InstituteIndiana University School of MedicineIndianapolisIndianaUSA
- Department of MedicineDivision of Clinical PharmacologyIndiana University School of MedicineIndianapolisIndianaUSA
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9
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Yang K, Liu L, Wen Y. The impact of Bayesian optimization on feature selection. Sci Rep 2024; 14:3948. [PMID: 38366092 PMCID: PMC10873405 DOI: 10.1038/s41598-024-54515-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Accepted: 02/13/2024] [Indexed: 02/18/2024] Open
Abstract
Feature selection is an indispensable step for the analysis of high-dimensional molecular data. Despite its importance, consensus is lacking on how to choose the most appropriate feature selection methods, especially when the performance of the feature selection methods itself depends on hyper-parameters. Bayesian optimization has demonstrated its advantages in automatically configuring the settings of hyper-parameters for various models. However, it remains unclear whether Bayesian optimization can benefit feature selection methods. In this research, we conducted extensive simulation studies to compare the performance of various feature selection methods, with a particular focus on the impact of Bayesian optimization on those where hyper-parameters tuning is needed. We further utilized the gene expression data obtained from the Alzheimer's Disease Neuroimaging Initiative to predict various brain imaging-related phenotypes, where various feature selection methods were employed to mine the data. We found through simulation studies that feature selection methods with hyper-parameters tuned using Bayesian optimization often yield better recall rates, and the analysis of transcriptomic data further revealed that Bayesian optimization-guided feature selection can improve the accuracy of disease risk prediction models. In conclusion, Bayesian optimization can facilitate feature selection methods when hyper-parameter tuning is needed and has the potential to substantially benefit downstream tasks.
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Affiliation(s)
- Kaixin Yang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, No 56 Xinjian South Road, Yingze District, Taiyuan, Shanxi, China
| | - Long Liu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, No 56 Xinjian South Road, Yingze District, Taiyuan, Shanxi, China.
| | - Yalu Wen
- Department of Statistics, University of Auckland, 38 Princes Street, Auckland Central, Auckland, 1010, New Zealand.
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10
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Vanderlinden G, Carron C, Vandenberghe R, Vandenbulcke M, Van Laere K. In vivo PET of synaptic density as potential diagnostic marker for cognitive disorders: prospective comparison with current imaging markers for neuronal dysfunction and relation to symptomatology - study protocol. BMC Med Imaging 2024; 24:41. [PMID: 38347458 PMCID: PMC10860316 DOI: 10.1186/s12880-024-01224-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 02/05/2024] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND 18F-FDG brain PET is clinically used for differential diagnosis in cognitive dysfunction of unclear etiology and for exclusion of a neurodegenerative cause in patients with cognitive impairment in late-life psychiatric disorders. 18F-FDG PET measures regional glucose metabolism, which represents a combination of neuronal/synaptic activity but also astrocytic activity and neuroinflammation. Recently, imaging of synaptic vesicle protein 2 A (SV2A) has become available and was shown to be a proxy of synaptic density. This prospective study will investigate the use of 18F-SynVesT-1 for imaging SV2A and its discriminative power for differential diagnosis in cognitive disorders in a head-to-head comparison to 18F-FDG PET. In addition, simultaneous PET/MR allows an evaluation of contributing factors and the additional value of advanced MRI imaging to FDG/SV2A PET imaging will be investigated. In this work, the study design and protocol are depicted. METHODS In this prospective, multimodal imaging study, 110 patients with uncertain diagnosis of cognitive impairment who are referred for 18F-FDG PET brain imaging in their diagnostic work-up in a tertiary memory clinic will be recruited. In addition, 40 healthy volunteers (HV) between 18 and 85 years (M/F) will be included. All study participants will undergo simultaneous 18F-SynVesT-1 PET/MR and an extensive neuropsychological evaluation. Amyloid status will be measured by PET using 18FNAV4694, in HV above 50 years of age. Structural T1-weighted and T2-weighted fluid-attenuated inversion recovery MR images, triple-tagging arterial spin labeling (ASL) and resting-state functional MRI (rs-fMRI) will be obtained. The study has been registered on ClinicalTrials.gov (NCT05384353) and is approved by the local Research Ethics Committee. DISCUSSION The main endpoint of the study will be the comparison of the diagnostic accuracy between 18F-SynVesT-1 and 18F-FDG PET in cognitive disorders with uncertain etiology and in exclusion of a neurodegenerative cause in patients with cognitive impairment in late-life psychiatric disorders. The strength of the relationship between cognition and imaging data will be assessed, as well as the potential incremental diagnostic value of including MR volumetry, ASL perfusion and rs-fMRI.
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Affiliation(s)
- Greet Vanderlinden
- Nuclear Medicine and Molecular Imaging, Imaging and Pathology, KU Leuven, Leuven, Belgium.
| | - Charles Carron
- Nuclear Medicine and Molecular Imaging, Imaging and Pathology, KU Leuven, Leuven, Belgium
- Division of Nuclear Medicine, University Hospitals UZ Leuven, Leuven, Belgium
| | - Rik Vandenberghe
- Department of Neurology, University Hospitals UZ Leuven, Leuven, Belgium
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Mathieu Vandenbulcke
- Research Group Psychiatry, KU Leuven, Leuven, Belgium
- Department of Old-Age Psychiatry, University Hospitals UZ Leuven, Leuven, Belgium
- Leuven Brain Institute, Leuven, Belgium
| | - Koen Van Laere
- Nuclear Medicine and Molecular Imaging, Imaging and Pathology, KU Leuven, Leuven, Belgium
- Division of Nuclear Medicine, University Hospitals UZ Leuven, Leuven, Belgium
- Leuven Brain Institute, Leuven, Belgium
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11
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Zhang Q, Liu X, Gao S, Yan S, Li A, Wei Z, Han S, Hou Y, Li X, Cao D, Yue J. Multimodal magnetic resonance imaging on brain structure and function changes in vascular cognitive impairment without dementia. Front Aging Neurosci 2023; 15:1278390. [PMID: 38035274 PMCID: PMC10687453 DOI: 10.3389/fnagi.2023.1278390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 10/23/2023] [Indexed: 12/02/2023] Open
Abstract
Vascular cognitive impairment not dementia (VCIND) is one of the three subtypes of vascular cognitive impairment (VCI), with cognitive dysfunction and symptoms ranging between normal cognitive function and vascular dementia. The specific mechanisms underlying VCIND are still not fully understood, and there is a lack of specific diagnostic markers in clinical practice. With the rapid development of magnetic resonance imaging (MRI) technology, structural MRI (sMRI) and functional MRI (fMRI) have become effective methods for exploring the neurobiological mechanisms of VCIND and have made continuous progress. This article provides a comprehensive overview of the research progress in VCIND using multimodal MRI, including sMRI, diffusion tensor imaging, resting-state fMRI, and magnetic resonance spectroscopy. By integrating findings from these multiple modalities, this study presents a novel perspective on the neuropathological mechanisms underlying VCIND. It not only highlights the importance of multimodal MRI in unraveling the complex nature of VCIND but also lays the foundation for future research examining the relationship between brain structure, function, and cognitive impairment in VCIND. These new perspectives and strategies ultimately hold the potential to contribute to the development of more effective diagnostic tools and therapeutic interventions for VCIND.
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Affiliation(s)
- Qinhong Zhang
- Shenzhen Frontiers in Chinese Medicine Research Co., Ltd., Shenzhen, China
- Department of Acupuncture and Moxibustion, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Xiao Liu
- Department of Pediatrics, First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Shenglan Gao
- Graduate School of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Shiyan Yan
- School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese Medicine, Beijing, China
| | - Ang Li
- Servier (Beijing) Pharmaceutical Research and Development Co., Ltd., Beijing, China
| | - Zeyi Wei
- Graduate School of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Shengwang Han
- Third Ward of Rehabilitation Department, Second Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Yu Hou
- Department of Gynecology, Harbin Traditional Chinese Medicine Hospital, Harbin, China
| | - Xiaoling Li
- Division of CT and MRI, First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Danna Cao
- Division of CT and MRI, First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Jinhuan Yue
- Shenzhen Frontiers in Chinese Medicine Research Co., Ltd., Shenzhen, China
- Department of Acupuncture and Moxibustion, Vitality University, Hayward, CA, United States
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12
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Ren X, Dong B, Luan Y, Wu Y, Huang Y. Alterations via inter-regional connective relationships in Alzheimer's disease. Front Hum Neurosci 2023; 17:1276994. [PMID: 38021241 PMCID: PMC10672243 DOI: 10.3389/fnhum.2023.1276994] [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: 08/13/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Abstract
Disruptions in the inter-regional connective correlation within the brain are believed to contribute to memory impairment. To detect these corresponding correlation networks in Alzheimer's disease (AD), we conducted three types of inter-regional correlation analysis, including structural covariance, functional connectivity and group-level independent component analysis (group-ICA). The analyzed data were obtained from the Alzheimer's Disease Neuroimaging Initiative, comprising 52 cognitively normal (CN) participants without subjective memory concerns, 52 individuals with late mild cognitive impairment (LMCI) and 52 patients with AD. We firstly performed vertex-wise cortical thickness analysis to identify brain regions with cortical thinning in AD and LMCI patients using structural MRI data. These regions served as seeds to construct both structural covariance networks and functional connectivity networks for each subject. Additionally, group-ICA was performed on the functional data to identify intrinsic brain networks at the cohort level. Through a comparison of the structural covariance and functional connectivity networks with ICA networks, we identified several inter-regional correlation networks that consistently exhibited abnormal connectivity patterns among AD and LMCI patients. Our findings suggest that reduced inter-regional connectivity is predominantly observed within a subnetwork of the default mode network, which includes the posterior cingulate and precuneus regions, in both AD and LMCI patients. This disruption of connectivity between key nodes within the default mode network provides evidence supporting the hypothesis that impairments in brain networks may contribute to memory deficits in AD and LMCI.
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Affiliation(s)
- Xiaomei Ren
- College of Electrical Engineering, Sichuan University, Chengdu, China
| | - Bowen Dong
- College of Electrical Engineering, Sichuan University, Chengdu, China
| | - Ying Luan
- Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Ye Wu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
| | - Yunzhi Huang
- Institute for AI in Medicine, School of Artificial Intelligence (School of Future Technology), Nanjing University of Information Science and Technology, Nanjing, China
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13
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Dubois B, von Arnim CAF, Burnie N, Bozeat S, Cummings J. Biomarkers in Alzheimer's disease: role in early and differential diagnosis and recognition of atypical variants. Alzheimers Res Ther 2023; 15:175. [PMID: 37833762 PMCID: PMC10571241 DOI: 10.1186/s13195-023-01314-6] [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: 01/04/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023]
Abstract
BACKGROUND Development of in vivo biomarkers has shifted the diagnosis of Alzheimer's disease (AD) from the later dementia stages of disease towards the earlier stages and has introduced the potential for pre-symptomatic diagnosis. The International Working Group recommends that AD diagnosis is restricted in the clinical setting to people with specific AD phenotypes and supportive biomarker findings. MAIN BODY In this review, we discuss the phenotypic presentation and use of biomarkers for the early diagnosis of typical and atypical AD and describe how this can support clinical decision making, benefit patient communication, and improve the patient journey. Early diagnosis is essential to optimize the benefits of available and emerging treatments. As atypical presentations of AD often mimic other dementias, differential diagnosis can be challenging and can be facilitated using AD biomarkers. However, AD biomarkers alone are not sufficient to confidently diagnose AD or predict disease progression and should be supplementary to clinical assessment to help inform the diagnosis of AD. CONCLUSIONS Use of AD biomarkers with incorporation of atypical AD phenotypes into diagnostic criteria will allow earlier diagnosis of patients with atypical clinical presentations that otherwise would have been misdiagnosed and treated inappropriately. Early diagnosis is essential to guide informed discussion, appropriate care and support, and individualized treatment. It is hoped that disease-modifying treatments will impact the underlying AD pathology; thus, determining the patient's AD phenotype will be a critical factor in guiding the therapeutic approach and the assessment of the effects of interventions.
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Affiliation(s)
- Bruno Dubois
- Assistance Publique-Hôpitaux de Paris (AP-HP), Memory and Alzheimer's Disease Institute, Sorbonne University, Paris, France
- Brain Institute, Sorbonne University, Paris, France
| | | | - Nerida Burnie
- General Practice, South West London CCG, London, UK
- London Dementia Clinical Network, London, UK
| | | | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Pam Quirk Brain Health and Biomarker Laboratory, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas, Las Vegas, NV, USA
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14
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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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15
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Zhao Y, Wang B, Liu CF, Faria AV, Miller MI, Caffo BS, Luo X. Identifying brain hierarchical structures associated with Alzheimer's disease using a regularized regression method with tree predictors. Biometrics 2023; 79:2333-2345. [PMID: 36263865 PMCID: PMC10115907 DOI: 10.1111/biom.13775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 10/03/2022] [Indexed: 11/30/2022]
Abstract
Brain segmentation at different levels is generally represented as hierarchical trees. Brain regional atrophy at specific levels was found to be marginally associated with Alzheimer's disease outcomes. In this study, we propose an ℓ1 -type regularization for predictors that follow a hierarchical tree structure. Considering a tree as a directed acyclic graph, we interpret the model parameters from a path analysis perspective. Under this concept, the proposed penalty regulates the total effect of each predictor on the outcome. With regularity conditions, it is shown that under the proposed regularization, the estimator of the model coefficient is consistent in ℓ2 -norm and the model selection is also consistent. When applied to a brain sMRI dataset acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the proposed approach identifies brain regions where atrophy in these regions demonstrates the declination in memory. With regularization on the total effects, the findings suggest that the impact of atrophy on memory deficits is localized from small brain regions, but at various levels of brain segmentation. Data used in preparation of this paper were obtained from the ADNI database.
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Affiliation(s)
- Yi Zhao
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Bingkai Wang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Chin-Fu Liu
- Center for Imaging Science, Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Andreia V. Faria
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Michael I. Miller
- Center for Imaging Science, Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Brian S. Caffo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Xi Luo
- Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, Houston, Texas, USA
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16
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He B, Sheng C, Yu X, Zhang L, Chen F, Han Y. Alterations of gut microbiota are associated with brain structural changes in the spectrum of Alzheimer's disease: the SILCODE study in Hainan cohort. Front Aging Neurosci 2023; 15:1216509. [PMID: 37520126 PMCID: PMC10375500 DOI: 10.3389/fnagi.2023.1216509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 06/12/2023] [Indexed: 08/01/2023] Open
Abstract
Background The correlation between gut microbiota and Alzheimer's disease (AD) is increasingly being recognized by clinicians. However, knowledge about the gut-brain-cognition interaction remains largely unknown. Methods One hundred and twenty-seven participants, including 35 normal controls (NCs), 62 with subjective cognitive decline (SCD), and 30 with cognitive impairment (CI), were included in this study. The participants underwent neuropsychological assessments and fecal microbiota analysis through 16S ribosomal RNA (rRNA) Illumina Miseq sequencing technique. Structural MRI data were analyzed for cortical anatomical features, including thickness, sulcus depth, fractal dimension, and Toro's gyrification index using the SBM method. The association of altered gut microbiota among the three groups with structural MRI metrics and cognitive function was evaluated. Furthermore, co-expression network analysis was conducted to investigate the gut-brain-cognition interactions. Results The abundance of Lachnospiraceae, Lachnospiracea_incertae_sedis, Fusicatenibacter, and Anaerobutyricum decreased with cognitive ability. Rikenellaceae, Odoribacteraceae, and Alistipes were specifically enriched in the CI group. Mediterraneibacter abundance was correlated with changes in brain gray matter and cerebrospinal fluid volume (p = 0.0214, p = 0.0162) and significantly with changes in cortical structures in brain regions, such as the internal olfactory area and the parahippocampal gyrus. The three colonies enriched in the CI group were positively correlated with cognitive function and significantly associated with changes in cortical structure related to cognitive function, such as the precuneus and syrinx gyrus. Conclusion This study provided evidence that there was an inner relationship among the altered gut microbiota, brain atrophy, and cognitive decline. Targeting the gut microbiota may be a novel therapeutic strategy for early AD.
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Affiliation(s)
- Beiqi He
- School of Biomedical Engineering, Hainan University, Haikou, China
| | - Can Sheng
- Department of Neurology, The Affiliated Hospital of Jining Medical University, Jining, China
| | - Xianfeng Yu
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Liang Zhang
- School of Biomedical Engineering, Hainan University, Haikou, China
| | - Feng Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, China
| | - Ying Han
- School of Biomedical Engineering, Hainan University, Haikou, China
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
- Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China
- National Clinical Research Center for Geriatric Disorders, Beijing, China
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17
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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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18
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Negi D, Granak S, Shorter S, O'Leary VB, Rektor I, Ovsepian SV. Molecular Biomarkers of Neuronal Injury in Epilepsy Shared with Neurodegenerative Diseases. Neurotherapeutics 2023; 20:767-778. [PMID: 36884195 PMCID: PMC10275849 DOI: 10.1007/s13311-023-01355-7] [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: 02/08/2023] [Indexed: 03/09/2023] Open
Abstract
In neurodegenerative diseases, changes in neuronal proteins in the cerebrospinal fluid and blood are viewed as potential biomarkers of the primary pathology in the central nervous system (CNS). Recent reports suggest, however, that level of neuronal proteins in fluids also alters in several types of epilepsy in various age groups, including children. With increasing evidence supporting clinical and sub-clinical seizures in Alzheimer's disease, Lewy body dementia, Parkinson's disease, and in other less common neurodegenerative conditions, these findings call into question the specificity of neuronal protein response to neurodegenerative process and urge analysis of the effects of concomitant epilepsy and other comorbidities. In this article, we revisit the evidence for alterations in neuronal proteins in the blood and cerebrospinal fluid associated with epilepsy with and without neurodegenerative diseases. We discuss shared and distinctive characteristics of changes in neuronal markers, review their neurobiological mechanisms, and consider the emerging opportunities and challenges for their future research and diagnostic use.
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Affiliation(s)
- Deepika Negi
- Faculty of Engineering and Science, University of Greenwich London, Chatham Maritime, Kent, ME4 4TB, UK
| | - Simon Granak
- National Institute of Mental Health, Topolova 748, Klecany, 25067, Czech Republic
| | - Susan Shorter
- Faculty of Engineering and Science, University of Greenwich London, Chatham Maritime, Kent, ME4 4TB, UK
| | - Valerie B O'Leary
- Department of Medical Genetics, Third Faculty of Medicine, Charles University, Ruská 87, Prague, 10000, Czech Republic
| | - Ivan Rektor
- First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic
- Multimodal and Functional Neuroimaging Research Group, CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Saak V Ovsepian
- Faculty of Engineering and Science, University of Greenwich London, Chatham Maritime, Kent, ME4 4TB, UK.
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El-Sappagh S, Alonso-Moral JM, Abuhmed T, Ali F, Bugarín-Diz A. Trustworthy artificial intelligence in Alzheimer’s disease: state of the art, opportunities, and challenges. Artif Intell Rev 2023. [DOI: 10.1007/s10462-023-10415-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
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20
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Advanced Overview of Biomarkers and Techniques for Early Diagnosis of Alzheimer's Disease. Cell Mol Neurobiol 2023:10.1007/s10571-023-01330-y. [PMID: 36847930 DOI: 10.1007/s10571-023-01330-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 02/15/2023] [Indexed: 03/01/2023]
Abstract
The development of early non-invasive diagnosis methods and identification of novel biomarkers are necessary for managing Alzheimer's disease (AD) and facilitating effective prognosis and treatment. AD has multi-factorial nature and involves complex molecular mechanism, which causes neuronal degeneration. The primary challenges in early AD detection include patient heterogeneity and lack of precise diagnosis at the preclinical stage. Several cerebrospinal fluid (CSF) and blood biomarkers have been proposed to show excellent diagnosis ability by identifying tau pathology and cerebral amyloid beta (Aβ) for AD. Intense research endeavors are being made to develop ultrasensitive detection techniques and find potent biomarkers for early AD diagnosis. To mitigate AD worldwide, understanding various CSF biomarkers, blood biomarkers, and techniques that can be used for early diagnosis is imperative. This review attempts to provide information regarding AD pathophysiology, genetic and non-genetic factors associated with AD, several potential blood and CSF biomarkers, like neurofilament light, neurogranin, Aβ, and tau, along with biomarkers under development for AD detection. Besides, numerous techniques, such as neuroimaging, spectroscopic techniques, biosensors, and neuroproteomics, which are being explored to aid early AD detection, have been discussed. The insights thus gained would help in finding potential biomarkers and suitable techniques for the accurate diagnosis of early AD before cognitive dysfunction.
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21
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Gregory S, Pullen H, Ritchie CW, Shannon OM, Stevenson EJ, Muniz-Terrera G. Mediterranean diet and structural neuroimaging biomarkers of Alzheimer's and cerebrovascular disease: A systematic review. Exp Gerontol 2023; 172:112065. [PMID: 36529364 DOI: 10.1016/j.exger.2022.112065] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 11/23/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022]
Abstract
Previous studies have demonstrated an association between adherence to the Mediterranean diet (MedDiet) and better cognitive performance, lower incidence of dementia and lower Alzheimer's disease biomarker burden. The aim of this systematic review was to evaluate the evidence base for MedDiet associations with hippocampal volume and white matter hyperintensity volume (WMHV). We searched systematically for studies reporting on MedDiet and hippocampal volume or WMHV in MedLine, EMBASE, CINAHL and PsycInfo. Searches were initially carried out on 21st July 2021 with final searches run on 23rd November 2022. Risk of bias was assessed using the NIH Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. Of an initial 112 papers identified, seven papers were eligible for inclusion in the review reporting on 21,933 participants. Four studies reported on hippocampal volume, with inconclusive or no associations seen with MedDiet adherence. Two studies found a significant association between higher MedDiet adherence and lower WMHV, while two other studies found no significant associations. Overall these results highlight a gap in our knowledge about the associations between the MedDiet and AD and cerebrovascular related structural neuroimaging findings.
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Affiliation(s)
- Sarah Gregory
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
| | - Hannah Pullen
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
| | - Craig W Ritchie
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Brain Health Scotland, UK.
| | - Oliver M Shannon
- Faculty of Medical Sciences, Human Nutrition Research Centre, Newcastle University, Newcastle Upon Tyne, UK.
| | - Emma J Stevenson
- Faculty of Medical Sciences, Human Nutrition Research Centre, Newcastle University, Newcastle Upon Tyne, UK.
| | - Graciela Muniz-Terrera
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Social Medicine, Ohio University, OH, USA.
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22
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Increased Hippocampal-Inferior Temporal Gyrus White Matter Connectivity following Donepezil Treatment in Patients with Early Alzheimer's Disease: A Diffusion Tensor Probabilistic Tractography Study. J Clin Med 2023; 12:jcm12030967. [PMID: 36769615 PMCID: PMC9917574 DOI: 10.3390/jcm12030967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/17/2023] [Accepted: 01/21/2023] [Indexed: 01/31/2023] Open
Abstract
The incidence of Alzheimer's disease (AD) has been increasing each year, and a defective hippocampus has been primarily associated with an early stage of AD. However, the effect of donepezil treatment on hippocampus-related networks is unknown. Thus, in the current study, we evaluated the hippocampal white matter (WM) connectivity in patients with early-stage AD before and after donepezil treatment using probabilistic tractography, and we further determined the WM integrity and changes in brain volume. Ten patients with early-stage AD (mean age = 72.4 ± 7.9 years; seven females and three males) and nine healthy controls (HC; mean age = 70.7 ± 3.5 years; six females and three males) underwent a magnetic resonance (MR) examination. After performing the first MR examination, the patients received donepezil treatment for 6 months. The brain volumes and diffusion tensor imaging scalars of 11 regions of interest (the superior/middle/inferior frontal gyrus, the superior/middle/inferior temporal gyrus, the amygdala, the caudate nucleus, the hippocampus, the putamen, and the thalamus) were measured using MR imaging and DTI, respectively. Seed-based structural connectivity analyses were focused on the hippocampus. The patients with early AD had a lower hippocampal volume and WM connectivity with the superior frontal gyrus and higher mean diffusivity (MD) and radial diffusivity (RD) in the amygdala than HC (p < 0.05, Bonferroni-corrected). However, brain areas with a higher (or lower) brain volume and WM connectivity were not observed in the HC compared with the patients with early AD. After six months of donepezil treatment, the patients with early AD showed increased hippocampal-inferior temporal gyrus (ITG) WM connectivity (p < 0.05, Bonferroni-corrected).
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23
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Garg N, Choudhry MS, Bodade RM. A review on Alzheimer's disease classification from normal controls and mild cognitive impairment using structural MR images. J Neurosci Methods 2023; 384:109745. [PMID: 36395961 DOI: 10.1016/j.jneumeth.2022.109745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 10/04/2022] [Accepted: 11/11/2022] [Indexed: 11/16/2022]
Abstract
Alzheimer's disease (AD) is an irreversible neurodegenerative brain disorder that degrades the memory and cognitive ability in elderly people. The main reason for memory loss and reduction in cognitive ability is the structural changes in the brain that occur due to neuronal loss. These structural changes are most conspicuous in the hippocampus, cortex, and grey matter and can be assessed by using neuroimaging techniques viz. Positron Emission Tomography (PET), structural Magnetic Resonance Imaging (MRI) and functional MRI (fMRI), etc. Out of these neuroimaging techniques, structural MRI has evolved as the best technique as it indicates the best soft tissue contrast and high spatial resolution which is important for AD detection. Currently, the focus of researchers is on predicting the conversion of Mild Cognitive Impairment (MCI) into AD. MCI represents the transition state between expected cognitive changes with normal aging and Alzheimer's disease. Not every MCI patient progresses into Alzheimer's disease. MCI can develop into stable MCI (sMCI, patients are called non-converters) or into progressive MCI (pMCI, patients are diagnosed as MCI converters). This paper discusses the prognosis of MCI to AD conversion and presents a review of structural MRI-based studies for AD detection. AD detection framework includes feature extraction, feature selection, and classification process. This paper reviews the studies for AD detection based on different feature extraction methods and machine learning algorithms for classification. The performance of various feature extraction methods has been compared and it has been observed that the wavelet transform-based feature extraction method would give promising results for AD classification. The present study indicates that researchers are successful in classifying AD from Normal Controls (NrmC) but, it still requires a lot of work to be done for MCI/ NrmC and MCI/AD, which would help in detecting AD at its early stage.
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Affiliation(s)
- Neha Garg
- Delhi Technological University, Department of Electronics and Communication, Delhi 110042, India.
| | - Mahipal Singh Choudhry
- Delhi Technological University, Department of Electronics and Communication, Delhi 110042, India.
| | - Rajesh M Bodade
- Military College of Telecommunication Engineering (MCTE), Mhow, Indore 453441, Madhya Pradesh, India.
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24
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Tian Y, Cook JJ, Johnson GA. Restoring morphology of light sheet microscopy data based on magnetic resonance histology. Front Neurosci 2023; 16:1011895. [PMID: 36685227 PMCID: PMC9846533 DOI: 10.3389/fnins.2022.1011895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 12/12/2022] [Indexed: 01/06/2023] Open
Abstract
The combination of cellular-resolution whole brain light sheet microscopy (LSM) images with an annotated atlas enables quantitation of cellular features in specific brain regions. However, most existing methods register LSM data with existing canonical atlases, e.g., The Allen Brain Atlas (ABA), which have been generated from tissue that has been distorted by removal from the skull, fixation and physical handling. This limits the accuracy of the regional morphologic measurement. Here, we present a method to combine LSM data with magnetic resonance histology (MRH) of the same specimen to restore the morphology of the LSM images to the in-skull geometry. Our registration pipeline which maps 3D LSM big data (terabyte per dataset) to MRH of the same mouse brain provides registration with low displacement error in ∼10 h with limited manual input. The registration pipeline is optimized using multiple stages of transformation at multiple resolution scales. A three-step procedure including pointset initialization, automated ANTs registration with multiple optimized transformation stages, and finalized application of the transforms on high-resolution LSM data has been integrated into a simple, structured, and robust workflow. Excellent agreement has been seen between registered LSM data and reference MRH data both locally and globally. This workflow has been applied to a collection of datasets with varied combinations of MRH contrasts from diffusion tensor images and LSM with varied immunohistochemistry, providing a routine method for streamlined registration of LSM images to MRH. Lastly, the method maps a reduced set of the common coordinate framework (CCFv3) labels from the Allen Brain Atlas onto the geometrically corrected full resolution LSM data. The pipeline maintains the individual brain morphology and allows more accurate regional annotations and measurements of volumes and cell density.
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25
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Mirabnahrazam G, Ma D, Beaulac C, Lee S, Popuri K, Lee H, Cao J, Galvin JE, Wang L, Beg MF. Predicting time-to-conversion for dementia of Alzheimer's type using multi-modal deep survival analysis. Neurobiol Aging 2023; 121:139-156. [PMID: 36442416 PMCID: PMC10535369 DOI: 10.1016/j.neurobiolaging.2022.10.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 10/08/2022] [Accepted: 10/11/2022] [Indexed: 11/27/2022]
Abstract
Dementia of Alzheimer's Type (DAT) is a complex disorder influenced by numerous factors, and it is difficult to predict individual progression trajectory from normal or mildly impaired cognition to DAT. An in-depth examination of multiple modalities of data may yield an accurate estimate of time-to-conversion to DAT for preclinical subjects at various stages of disease development. We used a deep-learning model designed for survival analyses to predict subjects' time-to-conversion to DAT using the baseline data of 401 subjects with 63 features from MRI, genetic, and CDC (Cognitive tests, Demographic, and CSF) data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our study demonstrated that CDC data outperform genetic or MRI data in predicting DAT time-to-conversion for subjects with Mild Cognitive Impairment (MCI). On the other hand, genetic data provided the most predictive power for subjects with Normal Cognition (NC) at the time of the visit. Furthermore, combining MRI and genetic features improved the time-to-event prediction over using either modality alone. Finally, adding CDC to any combination of features only worked as well as using only the CDC features.
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Affiliation(s)
- Ghazal Mirabnahrazam
- School of Engineering, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Da Ma
- School of Medicine, Wake Forest University, Winston-Salem, NC, USA; School of Engineering, Simon Fraser University, Burnaby, British Columbia, Canada.
| | - Cédric Beaulac
- Department of Mathematics and Statistics, University of Victoria, Victoria, British Columbia, Canada; School of Engineering, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Sieun Lee
- Mental Health & Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK; School of Engineering, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Karteek Popuri
- Department of Computer Science, Memorial University of Newfoundland, St. John's, Newfoundland & Labrador, Canada; School of Engineering, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Hyunwoo Lee
- Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Jiguo Cao
- Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, British Columbia, Canada
| | - James E Galvin
- Comprehensive Center for Brain Health, Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Lei Wang
- Psychiatry and Behavioral Health, Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Mirza Faisal Beg
- School of Engineering, Simon Fraser University, Burnaby, British Columbia, Canada.
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Mulugeta A, Navale SS, Lumsden AL, Llewellyn DJ, Hyppönen E. Healthy Lifestyle, Genetic Risk and Brain Health: A Gene-Environment Interaction Study in the UK Biobank. Nutrients 2022; 14:nu14193907. [PMID: 36235559 PMCID: PMC9570683 DOI: 10.3390/nu14193907] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 09/15/2022] [Accepted: 09/20/2022] [Indexed: 11/16/2022] Open
Abstract
Genetic susceptibility and lifestyle affect the risk of dementia but there is little direct evidence for their associations with preclinical changes in brain structure. We investigated the association of genetic dementia risk and healthy lifestyle with brain morphometry, and whether effects from elevated genetic risk are modified by lifestyle changes. We used prospective data from up to 25,894 UK Biobank participants (median follow-up of 8.8 years), and defined healthy lifestyle according to American Heart Association criteria as BMI < 30, no smoking, healthy diet and regular physical activity). Higher genetic risk was associated with lower hippocampal volume (beta −0.16 cm3, 95% CI −0.22, −0.11) and total brain volume (−4.34 cm3, 95% CI −7.68, −1.01) in participants aged ≥60 years but not <60 years. Healthy lifestyle was associated with higher total brain, grey matter and hippocampal volumes, and lower volume of white matter hyperintensities, with no effect modification by age or genetic risk. In conclusion, adverse effects of high genetic risk on brain health were only found in older participants, while adhering to healthy lifestyle recommendations is beneficial regardless of age or genetic risk.
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Affiliation(s)
- Anwar Mulugeta
- Australian Centre for Precision Health, Unit of Clinical and Health Sciences, University of South Australia, Adelaide, SA 5001, Australia
- South Australian Health and Medical Research Institute, Adelaide, SA 5000, Australia
- Department of Pharmacology and Clinical Pharmacy, College of Health Science, Addis Ababa University, Addis Ababa P.O. Box 9086, Ethiopia
| | - Shreeya S. Navale
- Australian Centre for Precision Health, Unit of Clinical and Health Sciences, University of South Australia, Adelaide, SA 5001, Australia
| | - Amanda L. Lumsden
- Australian Centre for Precision Health, Unit of Clinical and Health Sciences, University of South Australia, Adelaide, SA 5001, Australia
- South Australian Health and Medical Research Institute, Adelaide, SA 5000, Australia
| | - David J. Llewellyn
- College of Medicine and Health, University of Exeter, Devon EX1 2LU, UK
- Alan Turing Institute, London NW1 2DB, UK
| | - Elina Hyppönen
- Australian Centre for Precision Health, Unit of Clinical and Health Sciences, University of South Australia, Adelaide, SA 5001, Australia
- South Australian Health and Medical Research Institute, Adelaide, SA 5000, Australia
- Correspondence: ; Tel.: +61-(08)-83022518
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27
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Identification and validation of a gray matter volume network in Alzheimer's disease. J Neurol Sci 2022; 440:120344. [PMID: 35908305 DOI: 10.1016/j.jns.2022.120344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Revised: 07/12/2022] [Accepted: 07/14/2022] [Indexed: 11/20/2022]
Abstract
OBJECTIVE This study aims to identify and validate a gray matter volume network in patients with Alzheimer's disease (AD). METHODS To identify a disease-related network, a principal component analysis-based algorithm, Scaled Subprofile Model, was applied to gray matter volume data derived from structural T1-weighted magnetic resonance imaging of the training sample that consisted of nine patients with AD (women, four; dementia, seven; mild cognitive impairment, two; age, 66.7 ± 8.8 [mean ± SD] years) with positive 18F-flutemetamol amyloid positron emission tomography and eight age-matched healthy controls obtained on-site. The network expression scores were calculated by topographic profile rating in the validation sample obtained via the Open Access Series of Imaging Studies and comprised 12 patients with AD dementia (women, four; age, 70.0 ± 3.7 years) and 12 age-matched healthy controls. RESULTS A significant network from the training sample, for which subject expression differed between the groups (permutation test, P = 0.006; sensitivity and specificity, 100%; area under the curve, 1), was identified. This network was represented by the principal components 1, 2, and 3 and showed a relative decrease in the inferior parietal lobule including angular gyrus, inferior temporal gyrus, premotor cortex, amygdala, hippocampus, and precuneus. It significantly differed between the groups with a sensitivity, specificity, and area under the curve of 83%, 91%, and 0.85, respectively, in the validation sample (P = 0.003). CONCLUSIONS An AD-related gray matter volume network that captured relevant regions was identified in amyloid positron emission tomography-positive patients and validated in an independent sample.
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28
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Gonuguntla V, Yang E, Guan Y, Koo B, Kim J. Brain signatures based on structural MRI: Classification for MCI, PMCI, and AD. Hum Brain Mapp 2022; 43:2845-2860. [PMID: 35289025 PMCID: PMC9120560 DOI: 10.1002/hbm.25820] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 01/23/2022] [Accepted: 02/08/2022] [Indexed: 12/05/2022] Open
Abstract
Structural MRI (sMRI) provides valuable information for understanding neurodegenerative illnesses such as Alzheimer's Disease (AD) since it detects the brain's cerebral atrophy. The development of brain networks utilizing single imaging data-sMRI is an understudied area that has the potential to provide a network neuroscientific viewpoint on the brain. In this paper, we proposed a framework for constructing a brain network utilizing sMRI data, followed by the extraction of signature networks and important regions of interest (ROIs). To construct a brain network using sMRI, nodes are defined as regions described by the brain atlas, and edge weights are determined using a distance measure called the Sorensen distance between probability distributions of gray matter tissue probability maps. The brain signatures identified are based on the changes in the networks of disease and control subjects. To validate the proposed methodology, we first identified the brain signatures and critical ROIs associated with mild cognitive impairment (MCI), progressive MCI (PMCI), and Alzheimer's disease (AD) with 60 reference subjects (15 each of control, MCI, PMCI, and AD). Then, 200 examination subjects (50 each of control, MCI, PMCI, and AD) were selected to evaluate the identified signature patterns. Results demonstrate that the proposed framework is capable of extracting brain signatures and has a number of potential applications in the disciplines of brain mapping, brain communication, and brain network-based applications.
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Affiliation(s)
| | - Ehwa Yang
- Medical Science Research InstituteSamsung Medical CenterSeoulSouth Korea
| | - Yi Guan
- Department of Anatomy and NeurobiologyBoston University School of MedicineBostonMassachusettsUSA
| | - Bang‐Bon Koo
- Department of Anatomy and NeurobiologyBoston University School of MedicineBostonMassachusettsUSA
| | - Jae‐Hun Kim
- Department of Radiology, Samsung Medical CenterSungkyunkwan University School of MedicineSeoulSouth Korea
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29
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Liu K, Li Q, Yao L, Guo X. The Coupled Representation of Hierarchical Features for Mild Cognitive Impairment and Alzheimer's Disease Classification. Front Neurosci 2022; 16:902528. [PMID: 35720713 PMCID: PMC9205193 DOI: 10.3389/fnins.2022.902528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 04/25/2022] [Indexed: 11/15/2022] Open
Abstract
Structural magnetic resonance imaging (MRI) features have played an increasingly crucial role in discriminating patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) from normal controls (NC). However, the large number of structural MRI studies only extracted low-level neuroimaging features or simply concatenated multitudinous features while ignoring the interregional covariate information. The appropriate representation and integration of multilevel features will be preferable for the precise discrimination in the progression of AD. In this study, we proposed a novel inter-coupled feature representation method and built an integration model considering the two-level (the regions of interest (ROI) level and the network level) coupled features based on structural MRI data. For the intra-coupled interactions about the network-level features, we performed the ROI-level (intra- and inter-) coupled interaction within each network by feature expansion and coupling learning. For the inter-coupled interaction of the network-level features, we measured the coupled relationships among different networks via Canonical correlation analysis. We evaluated the classification performance using coupled feature representations on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Results showed that the coupled integration model with hierarchical features achieved the optimal classification performance with an accuracy of 90.44% for AD and NC groups, with an accuracy of 87.72% for the MCI converter (MCI-c) and MCI non-converter (MCI-nc) groups. These findings suggested that our two-level coupled interaction representation of hierarchical features has been the effective means for the precise discrimination of MCI-c from MCI-nc groups and, therefore, helpful in the characterization of different AD courses.
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Affiliation(s)
- Ke Liu
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
- Engineering Research Center of Intelligent Technology and Educational Application, Beijing Normal University, Beijing, China
| | - Qing Li
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Li Yao
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
- Engineering Research Center of Intelligent Technology and Educational Application, Beijing Normal University, Beijing, China
| | - Xiaojuan Guo
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
- Engineering Research Center of Intelligent Technology and Educational Application, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
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30
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Drouka A, Mamalaki E, Karavasilis E, Scarmeas N, Yannakoulia M. Dietary and Nutrient Patterns and Brain MRI Biomarkers in Dementia-Free Adults. Nutrients 2022; 14:nu14112345. [PMID: 35684145 PMCID: PMC9183163 DOI: 10.3390/nu14112345] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 05/28/2022] [Accepted: 05/31/2022] [Indexed: 12/15/2022] Open
Abstract
Cognitive impairment is a rapidly growing public health problem. As there is no curative treatment for dementia, the proactive management of modifiable risk factors and the identification of early biomarkers indicative of the cognitive decline are of great importance. Although nutrition is one of the most extensively studied lifestyle factor in relation to cognitive health, its association with brain magnetic resonance imaging (MRI) biomarkers is not well established. In the present work, we review available studies relating dietary or nutrient patterns with brain MRI biomarkers in dementia-free adults. Greater adherence to the Mediterranean diet has been associated with the preservation of structural connectivity and less brain atrophy in adults without dementia. In addition, specific nutrient patterns, characterized by a high intake of antioxidant vitamins, polyphenols and unsaturated fatty acids, have been related to larger brain volume. Although the results are encouraging regarding the role of dietary and nutrient patterns on imaging biomarkers, more well-designed observational longitudinal studies and clinical trials are needed in order to confirm potentially causal relationships and better understand underlying mechanisms.
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Affiliation(s)
- Archontoula Drouka
- Department of Nutrition and Dietetics, Harokopio University, 176 71 Athens, Greece; (A.D.); (E.M.); (M.Y.)
| | - Eirini Mamalaki
- Department of Nutrition and Dietetics, Harokopio University, 176 71 Athens, Greece; (A.D.); (E.M.); (M.Y.)
| | | | - Nikolaos Scarmeas
- 1st Department of Neurology, Medical School, National and Kapodistrian University of Athens, Aeginition University Hospital, 115 28 Athens, Greece
- The Gertrude H. Sergievsky Center, Department of Neurology, Taub Institute for Research in Alzheimer’s, Disease and the Aging Brain, Columbia University, New York, NY 10027, USA
- Correspondence:
| | - Mary Yannakoulia
- Department of Nutrition and Dietetics, Harokopio University, 176 71 Athens, Greece; (A.D.); (E.M.); (M.Y.)
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31
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Neuroimaging Modalities in Alzheimer’s Disease: Diagnosis and Clinical Features. Int J Mol Sci 2022; 23:ijms23116079. [PMID: 35682758 PMCID: PMC9181385 DOI: 10.3390/ijms23116079] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 11/17/2022] Open
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disease causing progressive cognitive decline until eventual death. AD affects millions of individuals worldwide in the absence of effective treatment options, and its clinical causes are still uncertain. The onset of dementia symptoms indicates severe neurodegeneration has already taken place. Therefore, AD diagnosis at an early stage is essential as it results in more effective therapy to slow its progression. The current clinical diagnosis of AD relies on mental examinations and brain imaging to determine whether patients meet diagnostic criteria, and biomedical research focuses on finding associated biomarkers by using neuroimaging techniques. Multiple clinical brain imaging modalities emerged as potential techniques to study AD, showing a range of capacity in their preciseness to identify the disease. This review presents the advantages and limitations of brain imaging modalities for AD diagnosis and discusses their clinical value.
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32
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Casanova R, Hsu FC, Barnard RT, Anderson AM, Talluri R, Whitlow CT, Hughes TM, Griswold M, Hayden KM, Gottesman RF, Wagenknecht LE. Comparing data-driven and hypothesis-driven MRI-based predictors of cognitive impairment in individuals from the Atherosclerosis Risk in Communities (ARIC) study. Alzheimers Dement 2022; 18:561-571. [PMID: 34310039 PMCID: PMC8789939 DOI: 10.1002/alz.12427] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 06/15/2021] [Accepted: 06/15/2021] [Indexed: 01/10/2023]
Abstract
INTRODUCTION A data-driven index of dementia risk based on magnetic resonance imaging (MRI), the Alzheimer's Disease Pattern Similarity (AD-PS) score, was estimated for participants in the Atherosclerosis Risk in Communities (ARIC) study. METHODS AD-PS scores were generated for 839 cognitively non-impaired individuals with a mean follow-up of 4.86 years. The scores and a hypothesis-driven volumetric measure based on several brain regions susceptible to AD were compared as predictors of incident cognitive impairment in different settings. RESULTS Logistic regression analyses suggest the data-driven AD-PS scores to be more predictive of incident cognitive impairment than its counterpart. Both biomarkers were more predictive of incident cognitive impairment in participants who were White, female, and apolipoprotein E gene (APOE) ε4 carriers. Random forest analyses including predictors from different domains ranked the AD-PS scores as the most relevant MRI predictor of cognitive impairment. CONCLUSIONS Overall, the AD-PS scores were the stronger MRI-derived predictors of incident cognitive impairment in cognitively non-impaired individuals.
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Affiliation(s)
- Ramon Casanova
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem
| | - Fang-Chi Hsu
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem
| | - Ryan T. Barnard
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem
| | - Andrea M. Anderson
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem
| | - Rajesh Talluri
- University of Mississippi Medical Center, Jackson, MS, USA
| | | | - Timothy M. Hughes
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | | | - Kathleen M. Hayden
- Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem
| | | | - Lynne E. Wagenknecht
- Divison of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
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Wang B, Caffo BS, Luo X, Liu C, Faria AV, Miller MI, Zhao Y. Regularized regression on compositional trees with application to MRI analysis. J R Stat Soc Ser C Appl Stat 2022; 71:541-561. [DOI: 10.1111/rssc.12545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
- Bingkai Wang
- Department of BiostatisticsJohns Hopkins Bloomberg School of Public Health BaltimoreMarylandUSA
| | - Brian S. Caffo
- Department of BiostatisticsJohns Hopkins Bloomberg School of Public Health BaltimoreMarylandUSA
| | - Xi Luo
- Department of Biostatistics and Data ScienceThe University of Texas Health Science Center at Houston HoustonTexasUSA
| | - Chin‐Fu Liu
- Center for Imaging Science, Biomedical EngineeringJohns Hopkins University BaltimoreMarylandUSA
| | - Andreia V. Faria
- Department of RadiologyJohns Hopkins University School of Medicine BaltimoreMarylandUSA
| | - Michael I. Miller
- Center for Imaging Science, Biomedical EngineeringJohns Hopkins University BaltimoreMarylandUSA
| | - Yi Zhao
- Department of BiostatisticsIndiana University School of Medicine and for the Alzheimer's Disease Neuroimaging Initiative IndianapolisIndianaUSA
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34
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Sensing Alzheimer’s Disease Utilizing Au Electrode by Controlling Nanorestructuring. CHEMOSENSORS 2022. [DOI: 10.3390/chemosensors10030094] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
This paper reports the development of Alzheimer’s disease (AD) sensor through early detection of amyloid-beta (Aβ) (1–42) using simple nanorestructuring of Au sheet plate by oxidation-reduction cycle (ORC) via the electrochemical system. The topology of Au substrates was enhanced through the roughening and Au grains grown by a simple ORC technique in aqueous solutions containing 0.1 mol/L KCl electrolytes. The roughened substrate was then functionalized with the highly specific antibody β-amyloid Aβ (1–28) through HS-PEG-NHS modification, which enabled effective and direct detection of Aβ (1–42) peptide. The efficacy of the ORC method had been exhibited in the polished Au surface by approximately 15% larger electro-active sites compared to the polished Au without ORC. The ORC polished structure demonstrated a rapid, accurate, precise, reproducible, and highly sensitive detection of Aβ (1–42) peptide with a low detection limit of 10.4 fg/mL and a wide linear range of 10−2 to 106 pg/mL. The proposed structure had been proven to have potential as an early-stage Alzheimer’s disease (AD) detection platform with low-cost fabrication and ease of operation.
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Mirabnahrazam G, Ma D, Lee S, Popuri K, Lee H, Cao J, Wang L, Galvin JE, Beg MF. Machine Learning Based Multimodal Neuroimaging Genomics Dementia Score for Predicting Future Conversion to Alzheimer's Disease. J Alzheimers Dis 2022; 87:1345-1365. [PMID: 35466939 PMCID: PMC9195128 DOI: 10.3233/jad-220021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The increasing availability of databases containing both magnetic resonance imaging (MRI) and genetic data allows researchers to utilize multimodal data to better understand the characteristics of dementia of Alzheimer's type (DAT). OBJECTIVE The goal of this study was to develop and analyze novel biomarkers that can help predict the development and progression of DAT. METHODS We used feature selection and ensemble learning classifier to develop an image/genotype-based DAT score that represents a subject's likelihood of developing DAT in the future. Three feature types were used: MRI only, genetic only, and combined multimodal data. We used a novel data stratification method to better represent different stages of DAT. Using a pre-defined 0.5 threshold on DAT scores, we predicted whether a subject would develop DAT in the future. RESULTS Our results on Alzheimer's Disease Neuroimaging Initiative (ADNI) database showed that dementia scores using genetic data could better predict future DAT progression for currently normal control subjects (Accuracy = 0.857) compared to MRI (Accuracy = 0.143), while MRI can better characterize subjects with stable mild cognitive impairment (Accuracy = 0.614) compared to genetics (Accuracy = 0.356). Combining MRI and genetic data showed improved classification performance in the remaining stratified groups. CONCLUSION MRI and genetic data can contribute to DAT prediction in different ways. MRI data reflects anatomical changes in the brain, while genetic data can detect the risk of DAT progression prior to the symptomatic onset. Combining information from multimodal data appropriately can improve prediction performance.
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Affiliation(s)
| | - Da Ma
- School of Engineering, Simon Fraser University, Burnaby, BC, Canada
- School of Medicine, Wake Forest University, Winston-Salem, NC, USA
| | - Sieun Lee
- School of Engineering, Simon Fraser University, Burnaby, BC, Canada
- Mental Health & Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Karteek Popuri
- School of Engineering, Simon Fraser University, Burnaby, BC, Canada
| | - Hyunwoo Lee
- Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Jiguo Cao
- Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC, Canada
| | - Lei Wang
- Psychiatry and Behavioral Health, Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - James E Galvin
- Comprehensive Center for Brain Health, Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Mirza Faisal Beg
- School of Engineering, Simon Fraser University, Burnaby, BC, Canada
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Duan W, Sehrawat P, Zhou TD, Becker JT, Lopez OL, Gach HM, Dai W. Pattern of Altered Magnetization Transfer Rate in Alzheimer's Disease. J Alzheimers Dis 2022; 88:693-705. [PMID: 35694929 PMCID: PMC9382719 DOI: 10.3233/jad-220335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Biomarkers for Alzheimer's disease (AD) are crucial for early diagnosis and treatment monitoring once disease modifying therapies become available. OBJECTIVE This study aims to quantify the forward magnetization transfer rate (kfor) map from brain tissue water to macromolecular protons and use it to identify the brain regions with abnormal kfor in AD and AD progression. METHODS From the Cardiovascular Health Study (CHS) cognition study, magnetization transfer imaging (MTI) was acquired at baseline from 63 participants, including 20 normal controls (NC), 18 with mild cognitive impairment (MCI), and 25 AD subjects. Of those, 53 participants completed a follow-up MRI scan and were divided into four groups: 15 stable NC, 12 NC-to-MCI, 12 stable MCI, and 14 MCI/AD-to-AD subjects. kfor maps were compared across NC, MCI, and AD groups at baseline for the cross-sectional study and across four longitudinal groups for the longitudinal study. RESULTS We found a lower kfor in the frontal gray matter (GM), parietal GM, frontal corona radiata (CR) white matter (WM) tracts, frontal and parietal superior longitudinal fasciculus (SLF) WM tracts in AD relative to both NC and MCI. Further, we observed progressive decreases of kfor in the frontal GM, parietal GM, frontal and parietal CR WM tracts, and parietal SLF WM tracts in stable MCI. In the parietal GM, parietal CR WM tracts, and parietal SLF WM tracts, we found trend differences between MCI/AD-to-AD and stable NC. CONCLUSION Forward magnetization transfer rate is a promising biomarker for AD diagnosis and progression.
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Affiliation(s)
- Wenna Duan
- Department of Computer Science, State University of New York at Binghamton, Binghamton, NY
| | - Parshant Sehrawat
- Department of Computer Science, State University of New York at Binghamton, Binghamton, NY
| | - Tony D. Zhou
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering, Washington University in St. Louis, Saint Louis, MO
| | - James T. Becker
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA
- Department of Psychiatry and Neurology, University of Pittsburgh, Pittsburgh, PA
| | - Oscar L. Lopez
- Department of Psychiatry and Neurology, University of Pittsburgh, Pittsburgh, PA
| | - H. Michael Gach
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering, Washington University in St. Louis, Saint Louis, MO
| | - Weiying Dai
- Department of Computer Science, State University of New York at Binghamton, Binghamton, NY
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Zou H, Li K, Zeng D, Luo S. Bayesian inference and dynamic prediction of multivariate joint model with functional data: An application to Alzheimer's disease. Stat Med 2021; 40:6855-6872. [PMID: 34649301 PMCID: PMC8671252 DOI: 10.1002/sim.9214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 08/03/2021] [Accepted: 09/20/2021] [Indexed: 01/01/2023]
Abstract
Alzheimer's disease (AD) is a severe neurodegenerative disorder impairing multiple domains, for example, cognition and behavior. Assessing the risk of AD progression and initiating timely interventions at early stages are critical to improve the quality of life for AD patients. Due to the heterogeneous nature and complex mechanisms of AD, one single longitudinal outcome is insufficient to assess AD severity and disease progression. Therefore, AD studies collect multiple longitudinal outcomes, including cognitive and behavioral measurements, as well as structural brain images such as magnetic resonance imaging (MRI). How to utilize the multivariate longitudinal outcomes and MRI data to make efficient statistical inference and prediction is an open question. In this article, we propose a multivariate joint model with functional data (MJM-FD) framework that relates multiple correlated longitudinal outcomes to a survival outcome, and use the scalar-on-function regression method to include voxel-based whole-brain MRI data as functional predictors in both longitudinal and survival models. We adopt a Bayesian paradigm to make statistical inference and develop a dynamic prediction framework to predict an individual's future longitudinal outcomes and risk of a survival event. We validate the MJM-FD framework through extensive simulation studies and apply it to the motivating Alzheimer's Disease Neuroimaging Initiative (ADNI) study.
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Affiliation(s)
- Haotian Zou
- Gillings School of Global Public Health, Department of Biostatistics, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Kan Li
- Merck Research Lab, Merck & Co, North Wales, Pennsylvania
| | - Donglin Zeng
- Department of Biostatistics, Gillings School of Global Public Health, CB#7420, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Sheng Luo
- Corresponding author: Sheng Luo, Department of Biostatistics and Informatics, Duke University, 2424 Erwin Rd, Durham, NC 27705, USA ()
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38
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Neuroanatomical associations of depression, anxiety and apathy neuropsychiatric symptoms in patients with Alzheimer's disease. Acta Neurol Belg 2021; 121:1469-1480. [PMID: 32319015 DOI: 10.1007/s13760-020-01349-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 03/24/2020] [Indexed: 12/21/2022]
Abstract
Depression, anxiety and apathy are 'common neuropsychiatric symptoms (NPS) in Alzheimer's disease (AD). We aimed to find regional gray matter (GM) volume difference of these symptoms, in AD patients compared to AD control, and investigate possible associations of GM atrophy with cognitive covariant. Study subjects were retrieved from the Alzheimer's Disease Neuroimaging Initiative database. Thirty-five participants are AD control, 27 AD patients with anxiety, 19 with depression and 24 with apathy, ages ≥ 55.1 years. Recruited subjects had an assessment of their clinical and structural MRI data. GM differences and clinical data were analyzed using voxel-based morphometry and ANOVA with Scheffe post hoc test, respectively. We found significant GM volumes differences in the left insula, left parahippocampal, posterior cingulate and the bilateral putamen in the anxiety group. The results also revealed that the right parahippocampal, Brodmann area 38 and the middle frontal gyrus were significant in patients with depression. Significant results were with a p < 0.05, corrected with AlphaSim program for multiple comparisons. The left insula had a strong negative association with Clinical Dementia Rate Sum of Boxes and Alzheimer's Disease Assessment Scale-cognitive subscale-13 items in anxiety and apathy groups. The difference in GM density in the left insula and hippocampus plays a crucial role in depression, anxiety and apathy NPS and outline precise approaches to test these symptoms.
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Pursuit of precision medicine: Systems biology approaches in Alzheimer's disease mouse models. Neurobiol Dis 2021; 161:105558. [PMID: 34767943 PMCID: PMC10112395 DOI: 10.1016/j.nbd.2021.105558] [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: 05/31/2021] [Revised: 11/05/2021] [Accepted: 11/08/2021] [Indexed: 12/12/2022] Open
Abstract
Alzheimer's disease (AD) is a complex disease that is mediated by numerous factors and manifests in various forms. A systems biology approach to studying AD involves analyses of various body systems, biological scales, environmental elements, and clinical outcomes to understand the genotype to phenotype relationship that potentially drives AD development. Currently, there are many research investigations probing how modifiable and nonmodifiable factors impact AD symptom presentation. This review specifically focuses on how imaging modalities can be integrated into systems biology approaches using model mouse populations to link brain level functional and structural changes to disease onset and progression. Combining imaging and omics data promotes the classification of AD into subtypes and paves the way for precision medicine solutions to prevent and treat AD.
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Wittens MMJ, Allemeersch GJ, Sima DM, Naeyaert M, Vanderhasselt T, Vanbinst AM, Buls N, De Brucker Y, Raeymaekers H, Fransen E, Smeets D, van Hecke W, Nagels G, Bjerke M, de Mey J, Engelborghs S. Inter- and Intra-Scanner Variability of Automated Brain Volumetry on Three Magnetic Resonance Imaging Systems in Alzheimer's Disease and Controls. Front Aging Neurosci 2021; 13:746982. [PMID: 34690745 PMCID: PMC8530224 DOI: 10.3389/fnagi.2021.746982] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Accepted: 09/08/2021] [Indexed: 12/02/2022] Open
Abstract
Magnetic Resonance Imaging (MRI) has become part of the clinical routine for diagnosing neurodegenerative disorders. Since acquisitions are performed at multiple centers using multiple imaging systems, detailed analysis of brain volumetry differences between MRI systems and scan-rescan acquisitions can provide valuable information to correct for different MRI scanner effects in multi-center longitudinal studies. To this end, five healthy controls and five patients belonging to various stages of the AD continuum underwent brain MRI acquisitions on three different MRI systems (Philips Achieva dStream 1.5T, Philips Ingenia 3T, and GE Discovery MR750w 3T) with harmonized scan parameters. Each participant underwent two subsequent MRI scans per imaging system, repeated on three different MRI systems within 2 h. Brain volumes computed by icobrain dm (v5.0) were analyzed using absolute and percentual volume differences, Dice similarity (DSC) and intraclass correlation coefficients, and coefficients of variation (CV). Harmonized scans obtained with different scanners of the same manufacturer had a measurement error closer to the intra-scanner performance. The gap between intra- and inter-scanner comparisons grew when comparing scans from different manufacturers. This was observed at image level (image contrast, similarity, and geometry) and translated into a higher variability of automated brain volumetry. Mixed effects modeling revealed a significant effect of scanner type on some brain volumes, and of the scanner combination on DSC. The study concluded a good intra- and inter-scanner reproducibility, as illustrated by an average intra-scanner (inter-scanner) CV below 2% (5%) and an excellent overlap of brain structure segmentation (mean DSC > 0.88).
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Affiliation(s)
- Mandy Melissa Jane Wittens
- Reference Center for Biological Markers of Dementia, Laboratory of Neurochemistry and Behavior, University of Antwerp, Antwerp, Belgium.,Center for Neurosciences (C4N) and Department of Neurology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Brussels, Belgium
| | - Gert-Jan Allemeersch
- Department of Radiology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Brussels, Belgium
| | | | - Maarten Naeyaert
- Department of Radiology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Brussels, Belgium
| | - Tim Vanderhasselt
- Department of Radiology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Brussels, Belgium
| | - Anne-Marie Vanbinst
- Department of Radiology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Brussels, Belgium
| | - Nico Buls
- Department of Radiology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Brussels, Belgium
| | - Yannick De Brucker
- Department of Radiology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Brussels, Belgium
| | - Hubert Raeymaekers
- Department of Radiology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Brussels, Belgium
| | - Erik Fransen
- StatUa Center for Statistics, University of Antwerp, Antwerp, Belgium
| | | | | | - Guy Nagels
- Center for Neurosciences (C4N) and Department of Neurology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Brussels, Belgium
| | - Maria Bjerke
- Reference Center for Biological Markers of Dementia, Laboratory of Neurochemistry and Behavior, University of Antwerp, Antwerp, Belgium.,Center for Neurosciences (C4N) and Department of Neurology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Brussels, Belgium
| | - Johan de Mey
- Department of Radiology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Brussels, Belgium
| | - Sebastiaan Engelborghs
- Reference Center for Biological Markers of Dementia, Laboratory of Neurochemistry and Behavior, University of Antwerp, Antwerp, Belgium.,Center for Neurosciences (C4N) and Department of Neurology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Brussels, Belgium
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Silhan D, Pashkovska O, Bartos A. Hippocampo-Horn Percentage and Parietal Atrophy Score for Easy Visual Assessment of Brain Atrophy on Magnetic Resonance Imaging in Early- and Late-Onset Alzheimer's Disease. J Alzheimers Dis 2021; 84:1259-1266. [PMID: 34633317 PMCID: PMC8673546 DOI: 10.3233/jad-210372] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
BACKGROUND Magnetic resonance imaging (MRI) visual scales of brain atrophy are important for differential diagnosis of dementias in routine clinical practice. Atrophy patterns in early- and late-onset Alzheimer's disease (AD) can be different according to some studies. OBJECTIVE Our goal was to assess brain atrophy patterns in early- and late-onset AD using our recently developed simple MRI visual scales and evaluate their reliability. METHODS We used Hippocampo-horn percentage (Hip-hop) and Parietal Atrophy Score (PAS) to compare mediotemporal and parietal atrophy on brain MRI among 4 groups: 26 patients with early-onset AD, 21 younger cognitively normal persons, 32 patients with late-onset AD, and 36 older cognitively normal persons. Two raters scored all brain MRI to assess reliability of the Hip-hop and PAS. Brain MRIs were obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. RESULTS The patients with early-onset AD had significantly more pronounced mediotemporal and also parietal atrophy bilaterally compared to the controls (both p < 0.01). The patients with late-onset AD had significantly more pronounced only mediotemporal atrophy bilaterally compared to the controls (p < 0.000001), but parietal lobes were the same. Intra-rater and inter-rater reliability of both visual scales Hip-hop and PAS were almost perfect in all cases (weighted-kappa value ranged from 0.90 to 0.99). CONCLUSION While mediotemporal atrophy detected using Hip-hop is universal across the whole AD age spectrum, parietal atrophy detected using PAS is worth rating only in early-onset AD. Hip-hop and PAS are very reliable MRI visual scales.
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Affiliation(s)
- David Silhan
- Department of Neurology, Charles University, Third Faculty of Medicine, Prague, Czech Republic
| | - Olga Pashkovska
- Department of Neurology, Charles University, Third Faculty of Medicine, Prague, Czech Republic
| | - Ales Bartos
- Department of Neurology, Charles University, Third Faculty of Medicine, Prague, Czech Republic
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Park S, Kim Y. Bias-generating factors in biofluid amyloid-β measurements for Alzheimer's disease diagnosis. Biomed Eng Lett 2021; 11:287-295. [PMID: 34616582 DOI: 10.1007/s13534-021-00201-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 08/05/2021] [Accepted: 08/08/2021] [Indexed: 01/03/2023] Open
Abstract
Alzheimer's disease (AD) is the most prevalent cause of dementia worldwide, yet the dearth of readily accessible diagnostic biomarkers is a substantial hindrance towards progressing to effective preventive and therapeutic approaches. Due to a long delay between cerebral amyloid-β (Aβ) accumulation and the onset of cognitive impairments, biomarkers that reflect Aβ pathology and enable routine screening for disease progression are of urgent need for application in the clinical diagnosis of AD. According to accumulating evidences, cerebrospinal fluid (CSF) and plasma offer windows to the brain as they allow monitoring of biochemical changes in the brain. Considering the high availability and accuracy in depicting Aβ deposition in the brain, Aβ levels in CSF and plasma are regarded as promising fluid biomarkers for the diagnosis of AD patients at an early stage. However, clinical data with intra- and interindividual variations in the concentrations of CSF and plasma Aβ implicate the need to reevaluate current Aβ detection methods and establish a standardized operating procedure. Therefore, this review introduces three bias-generating factors in biofluid Aβ measurement that may hamper the accurate Aβ quantification and how such complications can be overcome for the widespread implementation of fluid Aβ detection in clinical practice.
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Affiliation(s)
- Sohui Park
- Department of Pharmacy, Department of Integrative Biotechnology and Translational Medicine, and Yonsei Institute of Pharmaceutical Sciences, College of Pharmacy, Yonsei University, Incheon, 21983 Republic of Korea
| | - YoungSoo Kim
- Department of Pharmacy, Department of Integrative Biotechnology and Translational Medicine, and Yonsei Institute of Pharmaceutical Sciences, College of Pharmacy, Yonsei University, Incheon, 21983 Republic of Korea
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El-Gamal FEZA, Elmogy M, Mahmoud A, Shalaby A, Switala AE, Ghazal M, Soliman H, Atwan A, Alghamdi NS, Barnes GN, El-Baz A. A Personalized Computer-Aided Diagnosis System for Mild Cognitive Impairment (MCI) Using Structural MRI (sMRI). SENSORS (BASEL, SWITZERLAND) 2021; 21:5416. [PMID: 34450858 PMCID: PMC8400990 DOI: 10.3390/s21165416] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 07/28/2021] [Accepted: 08/03/2021] [Indexed: 12/31/2022]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder that targets the central nervous system (CNS). Statistics show that more than five million people in America face this disease. Several factors hinder diagnosis at an early stage, in particular, the divergence of 10-15 years between the onset of the underlying neuropathological changes and patients becoming symptomatic. This study surveyed patients with mild cognitive impairment (MCI), who were at risk of conversion to AD, with a local/regional-based computer-aided diagnosis system. The described system allowed for visualization of the disorder's effect on cerebral cortical regions individually. The CAD system consists of four steps: (1) preprocess the scans and extract the cortex, (2) reconstruct the cortex and extract shape-based features, (3) fuse the extracted features, and (4) perform two levels of diagnosis: cortical region-based followed by global. The experimental results showed an encouraging performance of the proposed system when compared with related work, with a maximum accuracy of 86.30%, specificity 88.33%, and sensitivity 84.88%. Behavioral and cognitive correlations identified brain regions involved in language, executive function/cognition, and memory in MCI subjects, which regions are also involved in the neuropathology of AD.
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Affiliation(s)
- Fatma El-Zahraa A. El-Gamal
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (F.E.-Z.A.E.-G.); (A.M.); (A.S.); (A.E.S.); (A.E.-B.)
- Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt; (M.E.); (H.S.); (A.A.)
| | - Mohammed Elmogy
- Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt; (M.E.); (H.S.); (A.A.)
| | - Ali Mahmoud
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (F.E.-Z.A.E.-G.); (A.M.); (A.S.); (A.E.S.); (A.E.-B.)
| | - Ahmed Shalaby
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (F.E.-Z.A.E.-G.); (A.M.); (A.S.); (A.E.S.); (A.E.-B.)
| | - Andrew E. Switala
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (F.E.-Z.A.E.-G.); (A.M.); (A.S.); (A.E.S.); (A.E.-B.)
| | - Mohammed Ghazal
- Department of Electrical and Computer Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates;
| | - Hassan Soliman
- Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt; (M.E.); (H.S.); (A.A.)
| | - Ahmed Atwan
- Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt; (M.E.); (H.S.); (A.A.)
| | - Norah Saleh Alghamdi
- College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, Riyadh 11564, Saudi Arabia
| | - Gregory Neal Barnes
- Department of Neurology, University of Louisville, Louisville, KY 40292, USA;
| | - Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (F.E.-Z.A.E.-G.); (A.M.); (A.S.); (A.E.S.); (A.E.-B.)
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44
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Rasmussen ID, Boayue NM, Mittner M, Bystad M, Grnli OK, Vangberg TR, Csifcsák G, Aslaksen PM. High-Definition Transcranial Direct Current Stimulation Improves Delayed Memory in Alzheimer's Disease Patients: A Pilot Study Using Computational Modeling to Optimize Electrode Position. J Alzheimers Dis 2021; 83:753-769. [PMID: 34366347 DOI: 10.3233/jad-210378] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
BACKGROUND The optimal stimulation parameters when using transcranial direct current stimulation (tDCS) to improve memory performance in patients with Alzheimer's disease (AD) are lacking. In healthy individuals, inter-individual differences in brain anatomy significantly influence current distribution during tDCS, an effect that might be aggravated by variations in cortical atrophy in AD patients. OBJECTIVE To measure the effect of individualized HD-tDCS in AD patients. METHODS Nineteen AD patients were randomly assigned to receive active or sham high-definition tDCS (HD-tDCS). Computational modeling of the HD-tDCS-induced electric field in each patient's brain was analyzed based on magnetic resonance imaging (MRI) scans. The chosen montage provided the highest net anodal electric field in the left dorsolateral prefrontal cortex (DLPFC). An accelerated HD-tDCS design was conducted (2 mA for 3×20 min) on two separate days. Pre- and post-intervention cognitive tests and T1 and T2-weighted MRI and diffusion tensor imaging data at baseline were analyzed. RESULTS Different montages were optimal for individual patients. The active HD-tDCS group improved significantly in delayed memory and MMSE performance compared to the sham group. Five participants in the active group had higher scores on delayed memory post HD-tDCS, four remained stable and one declined. The active HD-tDCS group had a significant positive correlation between fractional anisotropy in the anterior thalamic radiation and delayed memory score. CONCLUSION HD-tDCS significantly improved delayed memory in AD. Our study can be regarded as a proof-of-concept attempt to increase tDCS efficacy. The present findings should be confirmed in larger samples.
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Affiliation(s)
- Ingrid Daae Rasmussen
- Department of Psychology, Research Group for Cognitive Neuroscience, Faculty of Health Sciences, UiT The Artic University of Norway, Tromsø, Norway.,Department of Geropsychiatry, University Hospital of North Norway, Norway
| | - Nya Mehnwolo Boayue
- Department of Psychology, Research Group for Cognitive Neuroscience, Faculty of Health Sciences, UiT The Artic University of Norway, Tromsø, Norway
| | - Matthias Mittner
- Department of Psychology, Research Group for Cognitive Neuroscience, Faculty of Health Sciences, UiT The Artic University of Norway, Tromsø, Norway
| | - Martin Bystad
- Department of Psychology, Research Group for Cognitive Neuroscience, Faculty of Health Sciences, UiT The Artic University of Norway, Tromsø, Norway.,Department of Geropsychiatry, University Hospital of North Norway, Norway
| | - Ole K Grnli
- Department of Geropsychiatry, University Hospital of North Norway, Norway
| | - Torgil Riise Vangberg
- Department of Clinical Medicine, University hospital of North Norway, Norway.,PET Center, University hospital of North Norway, Tromsø, Norway
| | - Gábor Csifcsák
- Department of Psychology, Research Group for Cognitive Neuroscience, Faculty of Health Sciences, UiT The Artic University of Norway, Tromsø, Norway
| | - Per M Aslaksen
- Department of Psychology, Research Group for Cognitive Neuroscience, Faculty of Health Sciences, UiT The Artic University of Norway, Tromsø, Norway.,Department of Child and Adolescent Psychiatry, University Hospital of North Norway, Tromsø, Norway
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45
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Archetti D, Young AL, Oxtoby NP, Ferreira D, Mårtensson G, Westman E, Alexander DC, Frisoni GB, Redolfi A. Inter-Cohort Validation of SuStaIn Model for Alzheimer's Disease. Front Big Data 2021; 4:661110. [PMID: 34095821 PMCID: PMC8173213 DOI: 10.3389/fdata.2021.661110] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 05/04/2021] [Indexed: 01/15/2023] Open
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disorder which spans several years from preclinical manifestations to dementia. In recent years, interest in the application of machine learning (ML) algorithms to personalized medicine has grown considerably, and a major challenge that such models face is the transferability from the research settings to clinical practice. The objective of this work was to demonstrate the transferability of the Subtype and Stage Inference (SuStaIn) model from well-characterized research data set, employed as training set, to independent less-structured and heterogeneous test sets representative of the clinical setting. The training set was composed of MRI data of 1043 subjects from the Alzheimer’s disease Neuroimaging Initiative (ADNI), and the test set was composed of data from 767 subjects from OASIS, Pharma-Cog, and ViTA clinical datasets. Both sets included subjects covering the entire spectrum of AD, and for both sets volumes of relevant brain regions were derived from T1-3D MRI scans processed with Freesurfer v5.3 cross-sectional stream. In order to assess the predictive value of the model, subpopulations of subjects with stable mild cognitive impairment (MCI) and MCIs that progressed to AD dementia (pMCI) were identified in both sets. SuStaIn identified three disease subtypes, of which the most prevalent corresponded to the typical atrophy pattern of AD. The other SuStaIn subtypes exhibited similarities with the previously defined hippocampal sparing and limbic predominant atrophy patterns of AD. Subject subtyping proved to be consistent in time for all cohorts and the staging provided by the model was correlated with cognitive performance. Classification of subjects on the basis of a combination of SuStaIn subtype and stage, mini mental state examination and amyloid-β1-42 cerebrospinal fluid concentration was proven to predict conversion from MCI to AD dementia on par with other novel statistical algorithms, with ROC curves that were not statistically different for the training and test sets and with area under curve respectively equal to 0.77 and 0.76. This study proves the transferability of a SuStaIn model for AD from research data to less-structured clinical cohorts, and indicates transferability to the clinical setting.
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Affiliation(s)
- Damiano Archetti
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Alexandra L Young
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Department of Computer Science, UCL Centre for Medical Image Computing, London, United Kingdom
| | - Neil P Oxtoby
- Department of Computer Science, UCL Centre for Medical Image Computing, London, United Kingdom
| | - Daniel Ferreira
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.,Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Gustav Mårtensson
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Eric Westman
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Daniel C Alexander
- Department of Computer Science, UCL Centre for Medical Image Computing, London, United Kingdom
| | - Giovanni B Frisoni
- Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland.,Laboratory of Alzheimer's Neuroimaging and Epidemiology - LANE, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
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46
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Reproducible Evaluation of Diffusion MRI Features for Automatic Classification of Patients with Alzheimer's Disease. Neuroinformatics 2021; 19:57-78. [PMID: 32524428 DOI: 10.1007/s12021-020-09469-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Diffusion MRI is the modality of choice to study alterations of white matter. In past years, various works have used diffusion MRI for automatic classification of Alzheimer's disease. However, classification performance obtained with different approaches is difficult to compare because of variations in components such as input data, participant selection, image preprocessing, feature extraction, feature rescaling (FR), feature selection (FS) and cross-validation (CV) procedures. Moreover, these studies are also difficult to reproduce because these different components are not readily available. In a previous work (Samper-González et al. 2018), we propose an open-source framework for the reproducible evaluation of AD classification from T1-weighted (T1w) MRI and PET data. In the present paper, we first extend this framework to diffusion MRI data. Specifically, we add: conversion of diffusion MRI ADNI data into the BIDS standard and pipelines for diffusion MRI preprocessing and feature extraction. We then apply the framework to compare different components. First, FS has a positive impact on classification results: highest balanced accuracy (BA) improved from 0.76 to 0.82 for task CN vs AD. Secondly, voxel-wise features generally gives better performance than regional features. Fractional anisotropy (FA) and mean diffusivity (MD) provided comparable results for voxel-wise features. Moreover, we observe that the poor performance obtained in tasks involving MCI were potentially caused by the small data samples, rather than by the data imbalance. Furthermore, no extensive classification difference exists for different degree of smoothing and registration methods. Besides, we demonstrate that using non-nested validation of FS leads to unreliable and over-optimistic results: 5% up to 40% relative increase in BA. Lastly, with proper FR and FS, the performance of diffusion MRI features is comparable to that of T1w MRI. All the code of the framework and the experiments are publicly available: general-purpose tools have been integrated into the Clinica software package ( www.clinica.run ) and the paper-specific code is available at: https://github.com/aramis-lab/AD-ML .
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Vo T, Mishra A, Ithapu V, Singh V, Newton MA. Dimension constraints improve hypothesis testing for large-scale, graph-associated, brain-image data. Biostatistics 2021; 23:860-874. [PMID: 33616173 PMCID: PMC9295049 DOI: 10.1093/biostatistics/kxab001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2019] [Revised: 01/06/2021] [Accepted: 01/08/2021] [Indexed: 11/15/2022] Open
Abstract
For large-scale testing with graph-associated data, we present an empirical Bayes mixture
technique to score local false-discovery rates (FDRs). Compared to procedures that ignore
the graph, the proposed Graph-based Mixture Model (GraphMM) method gains power in settings
where non-null cases form connected subgraphs, and it does so by regularizing parameter
contrasts between testing units. Simulations show that GraphMM controls the FDR in a
variety of settings, though it may lose control with excessive regularization. On magnetic
resonance imaging data from a study of brain changes associated with the onset of
Alzheimer’s disease, GraphMM produces greater yield than conventional large-scale testing
procedures.
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Affiliation(s)
- Tien Vo
- Department of Biostatistics and Medical Informatics, University of Wisconsin at Madison 610 Walnut Street, Madison, WI, USA
| | - Akshay Mishra
- Department of Biostatistics and Medical Informatics, University of Wisconsin at Madison 610 Walnut Street, Madison, WI, USA
| | - Vamsi Ithapu
- Department of Biostatistics and Medical Informatics, University of Wisconsin at Madison 610 Walnut Street, Madison, WI, USA
| | - Vikas Singh
- Department of Biostatistics and Medical Informatics, University of Wisconsin at Madison 610 Walnut Street, Madison, WI, USA
| | - Michael A Newton
- Department of Biostatistics and Medical Informatics, University of Wisconsin at Madison 610 Walnut Street, Madison, WI, USA
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48
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Raghavan SS, Iqbal S, Ayyadurai N, Gunasekaran K. Insights in the structural understanding of amyloidogenicity and mutation-led conformational dynamics of amyloid beta (Aβ) through molecular dynamics simulations and principal component analysis. J Biomol Struct Dyn 2021; 40:5577-5587. [PMID: 33438527 DOI: 10.1080/07391102.2021.1871955] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Abnormal protein aggregation in the nervous tissue leads to several neurodegenerative disorders like Alzheimer's disease (AD). In AD, accumulation of the amyloid beta (Aβ) peptide is proposed to be an early important event in pathogenesis. Significant research efforts are devoted so as to understand the Aβ misfolding and aggregation. Molecular dynamics (MD) simulations complement experiments and provide structural information at the atomic level with dynamics without facing the same experimental limitations. Artificial missense mutations are employed experimentally and computationally for providing insights into the structure-function relationships of amyloid-β in relation to the pathologies of AD. Present work describes the MD simulations for 100 ns so as to probe the structural and conformational dynamics of Aβ1-42 assemblies and its mutants. Essential dynamics analysis with respect to conformational deviation of Cα was evaluated to identify the largest residual fluctuation of Cα. Conformational stability of all Aβ mutants was analyzed by computing RMSD, deciphering the convergence is reached in the last 20 ns in all replicas. To highlight the low frequency mode of motion corresponding to the highest amplitude, atomic displacements seen in trajectory, distance pair principal component analysis (dpPCA) was performed, which adumbrated mutations strongly affect the conformational dynamics of investigated model when compared with wild type. Dynamic cross correlation matrix (DCCM) also suggests the conserved interactions of wild Aβ and imply mutations in β3-β4 loop region induce deformity and residual fluctuations as observed from simulation. Present study indicate the mutational energy landscape which induces deformation leading to fibrillation.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Sriram Srinivasa Raghavan
- Department of Crystallography and Biophysics, University of Madras, Chennai, India.,Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India.,Division of Biotechnology, Council of Scientific and Industrial Research-Central Leather Research Institute (CSIR-CLRI), Chennai, India
| | - Saleem Iqbal
- Department of Crystallography and Biophysics, University of Madras, Chennai, India.,Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, India
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49
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Analyzing the effect of APOE on Alzheimer's disease progression using an event-based model for stratified populations. Neuroimage 2020; 227:117646. [PMID: 33338617 DOI: 10.1016/j.neuroimage.2020.117646] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 11/12/2020] [Accepted: 12/10/2020] [Indexed: 02/08/2023] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia and is phenotypically heterogeneous. APOE is a triallelic gene which correlates with phenotypic heterogeneity in AD. In this work, we determined the effect of APOE alleles on the disease progression timeline of AD using a discriminative event-based model (DEBM). Since DEBM is a data-driven model, stratification into smaller disease subgroups would lead to more inaccurate models as compared to fitting the model on the entire dataset. Hence our secondary aim is to propose and evaluate novel approaches in which we split the different steps of DEBM into group-aspecific and group-specific parts, where the entire dataset is used to train the group-aspecific parts and only the data from a specific group is used to train the group-specific parts of the DEBM. We performed simulation experiments to benchmark the accuracy of the proposed approaches and to select the optimal approach. Subsequently, the chosen approach was applied to the baseline data of 417 cognitively normal, 235 mild cognitively impaired who convert to AD within 3 years, and 342 AD patients from the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset to gain new insights into the effect of APOE carriership on the disease progression timeline of AD. In the ε4 carrier group, the model predicted with high confidence that CSF Amyloidβ42 and the cognitive score of Alzheimer's Disease Assessment Scale (ADAS) are early biomarkers. Hippocampus was the earliest volumetric biomarker to become abnormal, closely followed by the CSF Phosphorylated Tau181 (PTAU) biomarker. In the homozygous ε3 carrier group, the model predicted a similar ordering among CSF biomarkers. However, the volume of the fusiform gyrus was identified as one of the earliest volumetric biomarker. While the findings in the ε4 carrier and the homozygous ε3 carrier groups fit the current understanding of progression of AD, the finding in the ε2 carrier group did not. The model predicted, with relatively low confidence, CSF Neurogranin as one of the earliest biomarkers along with cognitive score of Mini-Mental State Examination (MMSE). Amyloid β42 was found to become abnormal after PTAU. The presented models could aid understanding of the disease, and in selecting homogeneous group of presymptomatic subjects at-risk of developing symptoms for clinical trials.
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50
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Sprung J, Warner DO, Knopman DS, Petersen RC, Mielke MM, Jack CR, Martin DP, Hanson AC, Schroeder DR, Przybelski SA, Schulte PJ, Laporta ML, Weingarten TN, Vemuri P. Brain MRI after critical care admission: A longitudinal imaging study. J Crit Care 2020; 62:117-123. [PMID: 33340966 DOI: 10.1016/j.jcrc.2020.11.024] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 10/14/2020] [Accepted: 11/30/2020] [Indexed: 12/19/2022]
Abstract
PURPOSE To investigate the association between episodes of critical care hospitalizations and delirium with structural brain changes in older adults. MATERIALS AND METHODS We included Mayo Clinic Study of Aging participants ≥60 years old at the time of study enrollment (October 29, 2004, through September 11, 2017) with available brain MRI and 'amyloid' positron emission tomography (PET) scans. We tested the hypothesis that a) intensive care unit (ICU) admission is associated with greater cortical thinning and atrophy in entorhinal cortex, inferior temporal cortex, middle temporal cortex, and fusiform cortex (Alzheimer''s disease-signature regions); b) atrophy in hippocampus and corpus callosum; c) delirium accelerates these changes; and d) ICU admission is not associated with increased deposition of cortical amyloid. RESULTS ICU admission was associated with cortical thinning in temporal, frontal, and parietal cortices, and decreases in hippocampal/corpus callosum volumes, but not Alzheimer''s disease-signature regions. For hippocampal volume, and 10 of 14 cortical thickness measurements, the change following ICU admission was significantly more pronounced for those who experienced delirium. ICU admission was not associated with an increased amyloid burden. CONCLUSIONS Critical care hospitalization is associated with accelerated brain atrophy in selected brain regions, without increases in amyloid deposition, suggesting a pathogenesis based on neurodegeneration unrelated to Alzheimer''s pathway.
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Affiliation(s)
- Juraj Sprung
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic College of Medicine and Science, Rochester, MN, USA.
| | - David O Warner
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic College of Medicine and Science, Rochester, MN, USA.
| | - David S Knopman
- Department of Neurology, Mayo Clinic College of Medicine and Science, Rochester, MN, USA.
| | - Ronald C Petersen
- Department of Neurology, Mayo Clinic College of Medicine and Science, Rochester, MN, USA.
| | - Michelle M Mielke
- Department of Neurology, Mayo Clinic College of Medicine and Science, Rochester, MN, USA; Health Sciences Research, Division of Epidemiology, Mayo Clinic College of Medicine and Science, Rochester, MN, USA.
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN, USA.
| | - David P Martin
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic College of Medicine and Science, Rochester, MN, USA.
| | - Andrew C Hanson
- Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine and Science, Rochester, MN, USA.
| | - Darrell R Schroeder
- Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine and Science, Rochester, MN, USA.
| | - Scott A Przybelski
- Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine and Science, Rochester, MN, USA.
| | - Phillip J Schulte
- Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine and Science, Rochester, MN, USA.
| | - Mariana L Laporta
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic College of Medicine and Science, Rochester, MN, USA.
| | - Toby N Weingarten
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic College of Medicine and Science, Rochester, MN, USA.
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN, USA.
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