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Functional principal component model for high-dimensional brain imaging

Vadim Zipunnikov, Brian Caffo, David M. Yousem, Christos Davatzikos, Brian S. Schwartz, Ciprian Crainiceanu
<span title="">2011</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sa477uo7lveh7hchpikpixop5u" style="color: black;">NeuroImage</a> </i> &nbsp;
The constructive relationship allows to deploy the numerical efficiency of SVD to fully estimate the components of FPCA, even for extremely high-dimensional functional objects, such as brain images.  ...  We establish a fundamental equivalence between singular value decomposition (SVD) and functional principal components analysis (FPCA) models.  ...  While this approach is useful for both analyzing deformation fields as an outcome (functional principal components analysis), it is also useful for regression models where morphometric deformation is a  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.neuroimage.2011.05.085">doi:10.1016/j.neuroimage.2011.05.085</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/21798354">pmid:21798354</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC3169674/">pmcid:PMC3169674</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/odxknvyxxrb7de5gljzl6w33nm">fatcat:odxknvyxxrb7de5gljzl6w33nm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20160713004203/http://biostats.bepress.com:80/cgi/viewcontent.cgi?article=1224&amp;context=jhubiostat" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/0c/40/0c400bb4d76027350acdf523f91755846fe004f6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.neuroimage.2011.05.085"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> elsevier.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3169674" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

A weighted cluster kernel PCA prediction model for multi-subject brain imaging data

Ying Guo
<span title="">2010</span> <i title="International Press of Boston"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/op2nporwsjcpne4gikw3jf6pwm" style="color: black;">Statistics and its Interface</a> </i> &nbsp;
Brain imaging data have shown great promise as a useful predictor for psychiatric conditions, cognitive functions and many other neural-related outcomes.  ...  Development of prediction models based on imaging data is challenging due to the high dimensionality of the data, noisy measurements, complex correlation structures among voxels, small sample sizes, and  ...  Giuseppe Pagnoni for providing the Zen meditation data. We also thank the associate editor and the two referees for valuable comments and suggestions. Received 2 August 2009  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4310/sii.2010.v3.n1.a9">doi:10.4310/sii.2010.v3.n1.a9</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/20657752">pmid:20657752</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC2908327/">pmcid:PMC2908327</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7zzbaaf33vdubgam4lofqv3xue">fatcat:7zzbaaf33vdubgam4lofqv3xue</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180720172156/http://www.intlpress.com/site/pub/files/_fulltext/journals/sii/2010/0003/0001/SII-2010-0003-0001-a009.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/8d/83/8d831134fb5eb400af30e576d9ab46430f69cd06.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4310/sii.2010.v3.n1.a9"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2908327" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Large-scale Granger causality analysis on resting-state functional MRI

Adora M. D'Souza, Anas Zainul Abidin, Lutz Leistritz, Axel Wismüller, Barjor Gimi, Andrzej Krol
<span title="2016-03-29">2016</span> <i title="SPIE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rflnl43pmffshgys2ogr3clxxm" style="color: black;">Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging</a> </i> &nbsp;
By integrating dimensionality reduction into predictive time series modeling, large-scale Granger Causality (lsGC) analysis method can reveal directed information flow suggestive of causal influence at  ...  We demonstrate an approach to measure the information flow between each pair of time series in resting-state functional MRI (fMRI) data of the human brain and subsequently recover its underlying network  ...  Our lsGC analysis method overcomes the problem of an underdetermined system by first incorporating a dimensionality reduction step retaining high variance principal components, followed by modelling the  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1117/12.2217264">doi:10.1117/12.2217264</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/29170585">pmid:29170585</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5697152/">pmcid:PMC5697152</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/mibam/DSouzaALW16.html">dblp:conf/mibam/DSouzaALW16</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/oe5u6twzhverbckgxkkrr6yyw4">fatcat:oe5u6twzhverbckgxkkrr6yyw4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200210060914/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC5697152&amp;blobtype=pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ac/2f/ac2f08694cb2fac57e0fb55ec20e05c4c194a1b7.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1117/12.2217264"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5697152" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Brain Tumor Classification Using Back Propagation Neural Network

N. Sumitra, Rakesh Kumar Saxena
<span title="2013-02-05">2013</span> <i title="MECS Publisher"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7hgv6dkr7vaq7mvfy3joc3nu2y" style="color: black;">International Journal of Image Graphics and Signal Processing</a> </i> &nbsp;
The conventional method for medical resonance brain images classification and tumors detection is by human inspection.  ...  Hence, this paper presents Neural Network techniques for the classification of the magnetic resonance human brain images.  ...  One of the most common forms of dimensionality reduction is principal components analysis.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5815/ijigsp.2013.02.07">doi:10.5815/ijigsp.2013.02.07</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sijf5zrhabbbxdncsuubf2tixa">fatcat:sijf5zrhabbbxdncsuubf2tixa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170810194937/http://www.mecs-press.org/ijigsp//ijigsp-v5-n2/IJIGSP-V5-N2-7.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/37/91/37913cef30879adedca772b04ee90a7ddc17ec7f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5815/ijigsp.2013.02.07"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Variable Selection for Functional Logistic Regression in fMRI Data Analysis
fMRI Veri Analizinde Fonksiyonel Lojistik Regresyon İçin Değişken Seçimi

Nedret BİLLOR, Jessica GODWIN
<span title="">2015</span> <i title="Ortadogu Reklam Tanitim Yayincilik Turizm Egitim Insaat Sanayi ve Ticaret A.S."> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vqzeahyqxzhulgvqgoxwf76s3u" style="color: black;">Turkiye Klinikleri Journal of Biostatistics</a> </i> &nbsp;
allows an experimenter to take images of a subject's brain over time.  ...  In this paper, we develop a variable selection technique, which tackles high dimensionality and correlation problems in fMRI data, based on L 1 regularization-group lasso for the functional logistic regression  ...  Since then more statistical analysis of fMRI data has been functional in nature. There is a need to attack the problem of high dimensionality of brain imaging data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5336/biostatic.2015-43642">doi:10.5336/biostatic.2015-43642</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3dwauorg7zbiha4fcxqf7h7jpi">fatcat:3dwauorg7zbiha4fcxqf7h7jpi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190428144319/https://www.turkiyeklinikleri.com/pdf/?pdf=85589a853bea55823c0541d5d99ec5a4" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/5c/2b/5c2bb16128f2d0f4d0ab279df74b3c3df1fb38b9.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5336/biostatic.2015-43642"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

A NOVEL METHOD FOR VISUALIZING FUNCTIONAL CONNECTIVITY USING PRINCIPAL COMPONENT ANALYSIS

SHAWN MIKULA, ERNST NIEBUR
<span title="">2006</span> <i title="Informa UK Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ewp6njt6q5a3vjrqpqxsga5x3u" style="color: black;">International Journal of Neuroscience</a> </i> &nbsp;
Functional connectivity is a useful measure of voxel-wise functional magnetic resonance imaging signals that allows for the identification of functionally related brain areas and distributed networks.  ...  This method does not rely on seed voxels, but rather employs a reduction of the high-dimensionality of the functional connectivity via a projection onto a three-dimensional color space using principal  ...  Kirby Center for Functional Brain Imaging. The fMRI data is whole-brain, comprised of 18 axial slices, with a TR of 2 seconds.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1080/00207450500505761">doi:10.1080/00207450500505761</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/16574580">pmid:16574580</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yxeirakgcrh7vefktrigva5tni">fatcat:yxeirakgcrh7vefktrigva5tni</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170809115019/http://connectomes.org/pdf/mikula2006.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ca/d6/cad69d3fddb07520c29b83634cac8d258da059ff.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1080/00207450500505761"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> tandfonline.com </button> </a>

Detection of obsessive compulsive disorder using resting-state functional connectivity data

Sona Khaneh Shenas, Ugur Halici, Metehan Cicek
<span title="">2013</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xwzukv5b3jal3eeyj6k4eqrvxa" style="color: black;">2013 6th International Conference on Biomedical Engineering and Informatics</a> </i> &nbsp;
In the first approach the rs-FC fMRI data were subsampled and then the dimensionality of the subsampled data was reduced using the Principal Component Analysis (PCA), Kernel Principal Component Analysis  ...  disease that might be affiliated with abnormal resting-state functional connectivity (rs-FC) in default mode network (DMN) of brain.  ...  ) and then efficiently computes the principal components in high-dimensional feature space [49] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/bmei.2013.6746921">doi:10.1109/bmei.2013.6746921</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/bmei/ShenasHC13.html">dblp:conf/bmei/ShenasHC13</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bcuxj4e25rgxrdnxvfpaixpypm">fatcat:bcuxj4e25rgxrdnxvfpaixpypm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170809073029/http://etd.lib.metu.edu.tr/upload/12616342/index.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a7/bd/a7bd3a01b509b6f36190e332fa375a772e9dfb01.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/bmei.2013.6746921"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Statistical Challenges in Modeling Big Brain Signals [article]

Zhaoxia Yu, Dustin Pluta, Tong Shen, Chuansheng Chen, Gui Xue, Hernando Ombao
<span title="2017-11-01">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Brain signal data are inherently big: massive in amount, complex in structure, and high in dimensions. These characteristics impose great challenges for statistical inference and learning.  ...  In a recent work, Ting et al. (2017) proposed a method for high dimensional by finding low-dimensional representations via principal components analysis.  ...  Some commonly used data drive methods include principal component analysis (PCA), high order singular value decomposition (the high dimension extension of PCA), and independent component analysis (ICA)  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1711.00432v1">arXiv:1711.00432v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tozy4qe5dncdfnzb53lcooieke">fatcat:tozy4qe5dncdfnzb53lcooieke</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191019005648/https://arxiv.org/pdf/1711.00432v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b6/e0/b6e020fb8c37c4cd18080ac832cbe7f94a03149b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1711.00432v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Estimating brain age from structural MRI and MEG data: Insights from dimensionality reduction techniques [article]

Alba Xifra-Porxas, Arna Ghosh, Georgios D. Mitsis, Marie-Hélène Boudrias
<span title="2019-11-29">2019</span> <i title="Cold Spring Harbor Laboratory"> biorxiv/medrxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
the high dimensionality of neuroimaging data.  ...  To this end, we examined the performance of dimensionality reduction and multivariate associative techniques, namely Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA), to tackle  ...  Dimensionality reduction techniques We used T1-weighted MR images and resting-state MEG data to develop a brain-age prediction framework that uses both structural and functional information of the brain  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/859660">doi:10.1101/859660</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lzwruuchfrfmreuppnwp62ebnu">fatcat:lzwruuchfrfmreuppnwp62ebnu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210715113318/https://gala.gre.ac.uk/id/eprint/31301/7/31301%20XIFRA-PORXASEstimating_Brain_Age_From_Structural_MRI_And_MEG_Data_%28OA%29_2021.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f3/4e/f34edb3989e7b068363aa3e1e55ff793ff9f3763.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/859660"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> biorxiv.org </button> </a>

Low-Dimensional Statistics of Anatomical Variability via Compact Representation of Image Deformations [chapter]

Miaomiao Zhang, William M. Wells, Polina Golland
<span title="">2016</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
To combat the high dimensionality of the deformation descriptors, we develop a probabilistic model of principal geodesic analysis in a bandlimited low-dimensional space that still captures the underlying  ...  Our model yields a more compact representation of group variation at substantially lower computational cost than models based on the high-dimensional state-of-the-art approaches such as tangent space PCA  ...  The high-dimensional nature of the data (e.g., a 128 3 displacement grid as a shape descriptor for a 3D brain MRI) presents significant challenges for the statistical methods when extracting relevant latent  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-46726-9_20">doi:10.1007/978-3-319-46726-9_20</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/28664199">pmid:28664199</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5484008/">pmcid:PMC5484008</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qxdoywbmtjc35kf4jqtrfv7l2e">fatcat:qxdoywbmtjc35kf4jqtrfv7l2e</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200206191149/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC5484008&amp;blobtype=pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/58/3c/583c8888308d0fb92222de23a0ab35bb9b3d5ecb.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-46726-9_20"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5484008" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Brain Age Estimation Using Multiple Regression Analysis in Brain MR Images

Saadia Binte Alam, Ryosuke Nakano, Syoji Kobashi
<span title="">2016</span> <i title="ICIC International"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/gj6lacbygvhjhbfu4ttj3j645a" style="color: black;">International Journal of Innovative Computing, Information and Control</a> </i> &nbsp;
To evaluate brain deformation, this paper proposes an estimation method for both neonatal and adult brain age using manifold learning, principal component analysis, followed by multiple regression models  ...  Physiological age estimation based on human brain MR images has been an interesting research field over the past years.  ...  Step 3: Register the brain MR image to a template MR image using non-rigid linear image registration FNIRT. 2.3.Dimensionality reduction method.2.3.1. Principal component analysis.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.24507/ijicic.12.04.1385">doi:10.24507/ijicic.12.04.1385</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2takzmkvvzhwhh4fiv35h2gpjm">fatcat:2takzmkvvzhwhh4fiv35h2gpjm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220301114343/http://www.ijicic.org/ijicic-120426.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c3/1d/c31d812a3b8f12a5978b7ef959a86b09f08e8c8c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.24507/ijicic.12.04.1385"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Statistical image analysis of longitudinal RAVENS images

Seonjoo Lee, Vadim Zipunnikov, Daniel S. Reich, Dzung L. Pham
<span title="2015-10-20">2015</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wrk3kouosrhcxiprcbguskdipu" style="color: black;">Frontiers in Neuroscience</a> </i> &nbsp;
In this paper, RAVENS images are analyzed using a longitudinal variant of voxel-based morphometry (VBM) and longitudinal functional principal component analysis (LFPCA) for high-dimensional images.  ...  Regional analysis of volumes examined in normalized space (RAVENS) are transformation images used in the study of brain morphometry.  ...  More precisely, we start by modeling the observed data with high-dimensional longitudinal functional principal component analysis (HD-LFPCA).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fnins.2015.00368">doi:10.3389/fnins.2015.00368</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/26539071">pmid:26539071</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4611144/">pmcid:PMC4611144</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vbdwonjss5gblamkgf4d3hwpsa">fatcat:vbdwonjss5gblamkgf4d3hwpsa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20141005172547/http://fdawg.org/FDAWG/ProjectPictures/Ravens_Paper.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c7/5c/c75c09a41e7dc1af084d757f266c8933776f1240.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fnins.2015.00368"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> frontiersin.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4611144" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Multilevel Functional Principal Component Analysis for High-Dimensional Data

Vadim Zipunnikov, Brian Caffo, David M. Yousem, Christos Davatzikos, Brian S. Schwartz, Ciprian Crainiceanu
<span title="">2011</span> <i title="Informa UK Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/uuo44cjltzg2flanabrks3a5fi" style="color: black;">Journal of Computational And Graphical Statistics</a> </i> &nbsp;
We propose fast and scalable statistical methods for the analysis of hundreds or thousands of high dimensional vectors observed at multiple visits.  ...  The approach can be applied to any type of study where data can be unfolded into a long vector including densely observed functions and images.  ...  Functional principal component models for high dimensional brain volumetrics. Johns Hopkins University, Working paper .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1198/jcgs.2011.10122">doi:10.1198/jcgs.2011.10122</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/25960627">pmid:25960627</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4425352/">pmcid:PMC4425352</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mhfmpsriqvg7pbkor73pkldgyy">fatcat:mhfmpsriqvg7pbkor73pkldgyy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190428110217/https://biostats.bepress.com/cgi/viewcontent.cgi?article=1219&amp;context=jhubiostat" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/bd/63/bd63c803bf46f459cb898ea3d52e2d5eb46c9acc.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1198/jcgs.2011.10122"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4425352" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Fast nonrigid image registration using statistical deformation models learned from richly-annotated data

John A. Onofrey, Lawrence H. Staib, Xenophon Papademetris
<span title="">2013</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qcepwfkflvg5toaa6fh2alj3b4" style="color: black;">2013 IEEE 10th International Symposium on Biomedical Imaging</a> </i> &nbsp;
Index Terms nonrigid registration; statistical deformation models; dimensionality reduction A variety of nonrigid registration methods and atlases exist for spatial normalization of functional areas [2  ...  Our method makes use of a statistical deformation model based upon a principal component analysis of deformations learned from a manually-segmented dataset to perform an initial registration.  ...  The model coefficients w compactly parameterize a high-dimensional FFD transformation d in Equation 1 .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/isbi.2013.6556541">doi:10.1109/isbi.2013.6556541</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/25000401">pmid:25000401</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4077025/">pmcid:PMC4077025</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/isbi/OnofreySP13.html">dblp:conf/isbi/OnofreySP13</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pgt24wp5cna4npx3qmnrw3q72q">fatcat:pgt24wp5cna4npx3qmnrw3q72q</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200206144526/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC4077025&amp;blobtype=pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/6e/77/6e77ce717de70c3d5c2695f4e74f2f677736629f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/isbi.2013.6556541"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4077025" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Multivariate discriminant analysis of multiparametric brain MRI to differentiate high grade and low grade gliomas — A computer-aided diagnosis development study

Fusun Citak Er, Zeynep Firat, Ilhami Kovanlikaya, Ugur Ture, Esin Ozturk-Isik
<span title="">2013</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/d6r2bn2k6vbaldrrfuifr2gtqm" style="color: black;">13th IEEE International Conference on BioInformatics and BioEngineering</a> </i> &nbsp;
Principal component analysis was performed prior to discriminant analysis for dimensional reduction.  ...  Quadratic discriminant analysis provided a better discrimination than linear discriminant analysis for this dataset. This study is a model for a computer aided diagnosis system for glioma grading.  ...  Principal Component Analysis (PCA) method was used to uncorrelate data for dimensional reduction.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/bibe.2013.6701649">doi:10.1109/bibe.2013.6701649</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/bibe/ErFKTO13.html">dblp:conf/bibe/ErFKTO13</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rmtr5czxdjd45bkiwazuqmebfy">fatcat:rmtr5czxdjd45bkiwazuqmebfy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170819032456/https://www.computer.org/csdl/proceedings/bibe/2013/9999/00/06701649.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/8c/bc/8cbce87c5e9a8fc411fbcb269928f6e1523e634f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/bibe.2013.6701649"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>
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