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Optimized Cortical Subdivision for Classification of Alzheimer's Disease With Cortical Thickness
[chapter]
2013
Bildverarbeitung für die Medizin 2013
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In several studies, brain atrophy measured by cortical thickness has shown to be a meaningful biomarker for Alzheimer's disease. ...
Using two-stage feature selection, optimal gyral and sulcal subregions are determined for the fina.l classification with sparse logistic regression. ...
for Alzheimer's disease and mild cognitive impairment is presented that optimizes the selection of mean cortical thickness variables from a set provided by a cortical parcellation scheme. ...
doi:10.1007/978-3-642-36480-8_8
dblp:conf/bildmed/RichterM13
fatcat:hnctn6d7ejgcla5wezhohfedmq
Callosal thickness profiles for prognosticating conversion from mild cognitive impairment to Alzheimer's disease: A classification approach
2018
Brain and Behavior
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Alzheimer's disease (AD) is the most common form of dementia. Finding biomarkers to prognosticate transition from mild cognitive impairment (MCI) to AD is important to clinical medicine. ...
Promising imaging biomarkers of AD conversion identified so far include atrophy of the cerebral cortex and subcortical gray matter nuclei. ...
| INTRODUC TI ON Alzheimer's disease (AD) is the most common form of dementia (Weiner et al., 2013) . ...
doi:10.1002/brb3.1142
pmid:30565884
pmcid:PMC6305917
fatcat:55ucrqi2rbdprm7rtyo4mhlh4u
Differential Diagnosis of Frontotemporal Dementia, Alzheimer's Disease, and Normal Aging Using a Multi-Scale Multi-Type Feature Generative Adversarial Deep Neural Network on Structural Magnetic Resonance Images
2020
Frontiers in Neuroscience
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Methods: Alzheimer's disease and Frontotemporal dementia are the first and third most common forms of dementia. ...
For disease-specific intervention and treatment, it is essential to develop a computer-aided system to improve the accuracy of their differential diagnosis. ...
This work was supported by Natural Sciences and Engineering Research Council (NSERC), Canadian Institutes of Health Research (CIHR), Michael Smith Foundation for Health Research (MSFHR), Brain Canada, ...
doi:10.3389/fnins.2020.00853
pmid:33192235
pmcid:PMC7643018
fatcat:fizizgbxyzggxgq72iyoje5b54
Thickness NETwork (ThickNet) Features for the Detection of Prodromal AD
[chapter]
2013
Lecture Notes in Computer Science
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Regional analysis of cortical thickness has been studied extensively in building imaging biomarkers for early detection of Alzheimer's disease (AD), but not its inter-regional covariation. ...
Fusing them with multiple kernel learning, we demonstrate their potential for the detection of prodromal AD. ...
Weiner and the Freesurfer team at University of California, San Francisco for the computation and quality control of Freesurfer processing for ADNI dataset. ...
doi:10.1007/978-3-319-02267-3_15
fatcat:ohb7hubeyjhwhdttounjksokfy
Differentiating between Normal Aging, Mild Cognitive Impairment, and Alzheimer's disease with FDG-PET: Effects of Normalization Region and Partial Volume Correction Method
2013
Journal of Alzheimer's Disease & Parkinsonism
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The effects of partial volume correction using either a gray matter mask or cortical thickness and subcortical volume residuals were also examined. ...
Objective: In Alzheimer's FDG PET research, the choice of reference region for normalization and use of partial volume correction are inconsistent and have not been studied in a large multi-center study ...
The optimal choice of reference region for normalization has been debated in the field, with the pons [4] , cerebellum [2, [5] [6] [7] , and primary somatosensory cortices [3] all being suggested to ...
doi:10.4172/2161-0460.1000113
fatcat:etnefdnlbvgorkngh53wcoh2nu
Impact of spatial scale and edge weight on predictive power of cortical thickness networks
[article]
2017
bioRxiv
pre-print
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Network-level analysis based on anatomical covariance (cortical thickness) has been gaining increasing popularity recently. ...
In order to obtain a clear understanding of relative performance, there is a need for systematic comparison. ...
ADNI Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense ...
doi:10.1101/170381
fatcat:udwioimtsja2baw2amwnhtbmum
Convolutional Neural Network on Semi-Regular Triangulated Meshes and its Application to Brain Image Data
[article]
2019
arXiv
pre-print
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We demonstrated the use of this vertex-based graph CNN for the classification of mild cognitive impairment (MCI) and Alzheimer's disease (AD) based on 3169 MRI scans of the Alzheimer's Disease Neuroimaging ...
We compared the performance of the vertex-based graph CNN with that of the spectral graph CNN. ...
The study was supported by Institute of Data Science at the National University of Singapore. ...
arXiv:1903.08828v3
fatcat:gv4gmz2l4bghjbzjzg4xw2d6vm
Differential Diagnosis of Frontotemporal Dementia and Alzheimer's Disease using Generative Adversarial Network
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2109.05627v1 fatcat:bt5g7enulbhlxascoawibm3jri
Frontotemporal dementia and Alzheimer's disease are two common forms of dementia and are easily misdiagnosed as each other due to their similar pattern of clinical symptoms. ...
Recent development of Deep-learning-based approaches in the field of medical image computing are delivering some of the best performance for many binary classification tasks, although its application in ...
The patch volume and local cortical thickness were extracted at each level ; 2) differential classification: a generative adversarial network was trained with the multiscale features to perform differential ...
arXiv:2109.05627v2
fatcat:vetdm24ojrcxhfq43az6ejaoae
Novel ThickNet features for the discrimination of amnestic MCI subtypes
2014
NeuroImage: Clinical
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This result may offer the possibility of early detection of Alzheimer's disease via improved discrimination of aMCI subtypes. ...
ThickNet features are extracted from the properties of a graph constructed from inter-regional covariation of cortical thickness. ...
Acknowledgments We gratefully acknowledge funding support from Alzheimer Society Canada for both P. R. Raamana ...
doi:10.1016/j.nicl.2014.09.005
pmid:25379441
pmcid:PMC4215394
fatcat:o5yfifkrtre7xc6klu4v64cjq4
Age-related effects on cortical thickness patterns of the Rhesus monkey brain
2012
Neurobiology of Aging
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the reduced occurrence of neurological diseases such as Alzheimer's disease, and the possibility of obtaining relevant behavioral data and post-mortem tissue for histological analyses. ...
Age related effects were observed in several cortical areas, in particular in the somato-sensory and motor cortices, where a robust negative correlation of cortical thickness with age was observed, similar ...
For international collaboration, author Jong-Min Lee has received support from the Korea Science and Engineering Foundation (KOSEF) NRL program grant funded by the Korea government (MEST) [grant number ...
doi:10.1016/j.neurobiolaging.2010.07.010
pmid:20801549
pmcid:PMC4521210
fatcat:4yytbyddb5bzpfhrnd5fci6pcu
Computational anatomical methods as applied to ageing and dementia
2007
British Journal of Radiology
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The cellular hallmarks of Alzheimer's disease (AD) accumulate in the living brain up to 30 years before the characteristic symptoms of dementia can be identified. ...
Here, we review three computational approaches to map brain deficits in AD: cortical thickness maps, tensor-based morphometry, and hippocampal/ventricular surface modeling. ...
Acknowledgments This work was supported by the National Institute on Aging (NIA), the National Library of Medicine, the National Institute for Biomedical Imaging and Bioengineering, the National Center ...
doi:10.1259/bjr/20005470
pmid:18445748
fatcat:b5l3zf7grnfovcf64hibfsjvrm
Conformal invariants for multiply connected surfaces: Application to landmark curve-based brain morphometry analysis
2017
Medical Image Analysis
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We also applied the new shape indices to analyze brain morphometry abnormalities associated with Alzheimer's disease (AD). ...
We studied the baseline MRI scans of a set of healthy control and AD patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI: 30 healthy control subjects vs. 30 AD patients). ...
Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense ...
doi:10.1016/j.media.2016.09.001
pmid:27639215
pmcid:PMC5099092
fatcat:qczplwszpzdppanhoom5wiwvaa
Prediction and classification of Alzheimer disease based on quantification of MRI deformation
2017
PLoS ONE
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Detecting early morphological changes in the brain and making early diagnosis are important for Alzheimer's disease (AD). ...
Distance between each pair of subjects was quantified from a symmetric diffeomorphic registration, followed by an embedding algorithm and a learning approach for classification. ...
Acknowledgments Data used in preparation of this study were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu/). ...
doi:10.1371/journal.pone.0173372
pmid:28264071
pmcid:PMC5338815
fatcat:nyj5rgd2qbe7ph4jhirgfhte3m
Structural MRI biomarkers for preclinical and mild Alzheimer's disease
2009
Human Brain Mapping
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Noninvasive MRI biomarkers for Alzheimer's disease (AD) may enable earlier clinical diagnosis and the monitoring of therapeutic effectiveness. ...
To assess potential neuroimaging biomarkers, the Alzheimer's Disease Neuroimaging Initiative is following normal controls (NC) and individuals with mild cognitive impairment (MCI) or AD. ...
The terms of this arrangement have been reviewed and approved by the University of California, San Diego in accordance with its conflict of interest policies. ...
doi:10.1002/hbm.20744
pmid:19277975
pmcid:PMC2951116
fatcat:57s3xpymjbdsbjguklgzvrm454
Mapping cortical change in Alzheimer's disease, brain development, and schizophrenia
2004
NeuroImage
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Recently, we created time-lapse movies of brain structure for a variety of diseases. ...
This paper describes algorithms that can identify patterns of brain structure and function associated with Alzheimer's disease, schizophrenia, normal aging, and abnormal brain development based on imaging ...
P.T.: R21 EB01651, R21 RR019771, P50 AG016570), by the National Institute of Mental Health, the National Institute of Drug Abuse, and the March of Dimes, (to E.R.S.: K01 MH01733, R21 DA15878, MOD 5FY03 ...
doi:10.1016/j.neuroimage.2004.07.071
pmid:15501091
fatcat:5yfumgtotnawngpmlimb5d2nom
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