Journal ArticleDOI
Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: exploring the influence of various parameters
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TLDR
A framework for automatically and efficiently estimating the age of healthy subjects from their T(1)-weighted MRI scans using a kernel method for regression is introduced and indicated favorable performance of the RVM and identified the number of training samples as the critical factor for prediction accuracy.About:
This article is published in NeuroImage.The article was published on 2010-04-15. It has received 621 citations till now.read more
Citations
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The Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception.
Michael W. Weiner,Michael W. Weiner,Dallas P. Veitch,Paul S. Aisen,Laurel A. Beckett,Nigel J. Cairns,Robert C. Green,Danielle J Harvey,Clifford R. Jack,William J. Jagust,Enchi Liu,John C. Morris,Ronald C. Petersen,Andrew J. Saykin,Mark E. Schmidt,Leslie M. Shaw,Judith Siuciak,Holly Soares,Arthur W. Toga,John Q. Trojanowski +19 more
TL;DR: The major accomplishments of ADNI have been the development of standardized methods for clinical tests, magnetic resonance imaging, positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting, and the improvement of clinical trial efficiency.
Journal ArticleDOI
Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review
TL;DR: Support-Vector-Machine has been successfully applied in the context of disease diagnosis, transition prediction and treatment prognosis, using both structural and functional neuroimaging data, and those studies that applied it to the investigation of Alzheimer's disease, schizophrenia, major depression, bipolar disorder, presymptomatic Huntington's disease and autistic spectrum disorder are reviewed.
Journal ArticleDOI
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.
TL;DR: There is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders, however, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper.
Journal ArticleDOI
Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker.
James H. Cole,Rudra P. K. Poudel,Dimosthenis Tsagkrasoulis,Matthan W.A. Caan,Claire J. Steves,Tim D. Spector,Giovanni Montana,Giovanni Montana +7 more
TL;DR: Age predictions can be accurately generated on raw T1‐MRI data, substantially reducing computation time for novel data, bringing the process closer to giving real‐time information on brain health in clinical settings.
Journal ArticleDOI
Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease
TL;DR: This paper proposes a general methodology, namely multi-modal multi-task (M3T) learning, to jointly predict multiple variables from multi- modal data, which can achieve better performance on both regression and classification tasks than the conventional learning methods.
References
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Book
Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
Journal ArticleDOI
Pattern Recognition and Machine Learning
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
Journal ArticleDOI
An introduction to variable and feature selection
Isabelle Guyon,André Elisseeff +1 more
TL;DR: The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.
Journal ArticleDOI
The Clinical Dementia Rating (CDR): Current version and scoring rules
Journal ArticleDOI
Gene Selection for Cancer Classification using Support Vector Machines
TL;DR: In this article, a Support Vector Machine (SVM) method based on recursive feature elimination (RFE) was proposed to select a small subset of genes from broad patterns of gene expression data, recorded on DNA micro-arrays.
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