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Optimized Cortical Subdivision for Classification of Alzheimer's Disease With Cortical Thickness [chapter]

Mirco Richter, Dorit Merhof
2013 Bildverarbeitung für die Medizin 2013  
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

Chris Adamson, Richard Beare, Gareth Ball, Mark Walterfang, Marc Seal, the Alzheimer's Disease Neuroimaging Initiative
2018 Brain and Behavior  
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

Da Ma, Donghuan Lu, Karteek Popuri, Lei Wang, Mirza Faisal Beg, Alzheimer's Disease Neuroimaging Initiative
2020 Frontiers in Neuroscience  
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]

Pradeep Reddy Raamana, Lei Wang, Mirza Faisal Beg
2013 Lecture Notes in Computer Science  
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

Corinna M Bauer
2013 Journal of Alzheimer's Disease & Parkinsonism  
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]

Pradeep Reddy Raamana, Stephen C. Strother
2017 bioRxiv   pre-print
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]

Caoqiang Liu, Hui Ji, Anqi Qiu
2019 arXiv   pre-print
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]

Da Ma and Donghuan Lu and Karteek Popuri and Mirza Faisal Beg
2021 arXiv   pre-print
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

Pradeep Reddy Raamana, Wei Wen, Nicole A. Kochan, Henry Brodaty, Perminder S. Sachdev, Lei Wang, Mirza Faisal Beg
2014 NeuroImage: Clinical  
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

Bang-Bon Koo, Steven P. Schettler, Donna E. Murray, Jong-Min Lee, Ronald J. Killiany, Douglas L. Rosene, Dae-Shik Kim, Itamar Ronen
2012 Neurobiology of Aging  
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  
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

Jie Shi, Wen Zhang, Miao Tang, Richard J. Caselli, Yalin Wang
2017 Medical Image Analysis  
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/ pmid:27639215 pmcid:PMC5099092 fatcat:qczplwszpzdppanhoom5wiwvaa

Prediction and classification of Alzheimer disease based on quantification of MRI deformation

Xiaojing Long, Lifang Chen, Chunxiang Jiang, Lijuan Zhang, Kewei Chen
2017 PLoS ONE  
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 (  ... 
doi:10.1371/journal.pone.0173372 pmid:28264071 pmcid:PMC5338815 fatcat:nyj5rgd2qbe7ph4jhirgfhte3m

Structural MRI biomarkers for preclinical and mild Alzheimer's disease

Christine Fennema-Notestine, Donald J. Hagler, Linda K. McEvoy, Adam S. Fleisher, Elaine H. Wu, David S. Karow, Anders M. Dale
2009 Human Brain Mapping  
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

Paul M. Thompson, Kiralee M. Hayashi, Elizabeth R. Sowell, Nitin Gogtay, Jay N. Giedd, Judith L. Rapoport, Greig I. de Zubicaray, Andrew L. Janke, Stephen E. Rose, James Semple, David M. Doddrell, Yalin Wang (+3 others)
2004 NeuroImage  
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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