Generalizable patterns in neuroimaging: how many principal components?
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The Functional Segregation and Integration Model: Mixture Model Representations of Consistent and Variable Group-Level Connectivity in fMRIData Driven Estimation of Imputation Error—A Strategy for Imputation with a Reject OptionQuantifying functional connectivity in multi-subject fMRI data using component modelsMachine learning classifiers and fMRI: a tutorial overviewMultivariate strategies in functional magnetic resonance imagingA review of feature reduction techniques in neuroimagingPrefrontal gray matter volume mediates genetic risks for obesitySupport vector machine classification of arterial volume-weighted arterial spin tagging imagesProbabilistic independent component analysis for functional magnetic resonance imagingIndependent component analysis of functional MRI: what is signal and what is noise?Use of neuroanatomical pattern classification to identify subjects in at-risk mental states of psychosis and predict disease transitionChanges in resting state effective connectivity in the motor network following rehabilitation of upper extremity poststroke paresisA new approach to estimating the signal dimension of concatenated resting-state functional MRI data sets.Adaptation of Francisella tularensis to the mammalian environment is governed by cues which can be mimicked in vitro.Tracking Equilibrium and Nonequilibrium Shifts in Data with TREND.Evaluation and comparison of GLM- and CVA-based fMRI processing pipelines with Java-based fMRI processing pipeline evaluation system.Unsupervised spatiotemporal fMRI data analysis using support vector machines.Performance of principal component analysis and independent component analysis with respect to signal extraction from noisy positron emission tomography data - a study on computer simulated imagesA mutual information-based metric for evaluation of fMRI data-processing approaches.Dimensionality estimation for optimal detection of functional networks in BOLD fMRI data.Data-driven optimization and evaluation of 2D EPI and 3D PRESTO for BOLD fMRI at 7 Tesla: I. Focal coverage.Identifying group discriminative and age regressive sub-networks from DTI-based connectivity via a unified framework of non-negative matrix factorization and graph embedding.Unsupervised spatiotemporal analysis of fMRI data using graph-based visualizations of self-organizing maps.PCATMIP: enhancing signal intensity in diffusion-weighted magnetic resonance imagingFinding imaging patterns of structural covariance via Non-Negative Matrix Factorization.How many separable sources? Model selection in independent components analysis.Predicting functional cortical ROIs via DTI-derived fiber shape models.Individualized differential diagnosis of schizophrenia and mood disorders using neuroanatomical biomarkers.Classifying Schizophrenia Using Multimodal Multivariate Pattern Recognition Analysis: Evaluating the Impact of Individual Clinical Profiles on the Neurodiagnostic Performance.Independent component analysis for brain fMRI does not select for independencePredicting Response to Repetitive Transcranial Magnetic Stimulation in Patients With Schizophrenia Using Structural Magnetic Resonance Imaging: A Multisite Machine Learning Analysis.A decomposition model and voxel selection framework for fMRI analysis to predict neural response of visual stimuli.fMRI single trial discovery of spatio-temporal brain activity patterns.A novel joint sparse partial correlation method for estimating group functional networks.Point-process principal components analysis via geometric optimization.Functional magnetic resonance imaging in real time (FIRE): sliding-window correlation analysis and reference-vector optimization.On characterizing population commonalities and subject variations in brain networks.A novel approach to activation detection in fMRI based on empirical mode decomposition.Model Selection for Gaussian Kernel PCA DenoisingDeep Convolutional Autoencoders vs PCA in a Highly-Unbalanced Parkinson’s Disease Dataset: A DaTSCAN Study
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Generalizable patterns in neuroimaging: how many principal components?
description
1999 nî lūn-bûn
@nan
1999 թուականի Մայիսին հրատարակուած գիտական յօդուած
@hyw
1999 թվականի մայիսին հրատարակված գիտական հոդված
@hy
1999年の論文
@ja
1999年論文
@yue
1999年論文
@zh-hant
1999年論文
@zh-hk
1999年論文
@zh-mo
1999年論文
@zh-tw
1999年论文
@wuu
name
Generalizable patterns in neuroimaging: how many principal components?
@ast
Generalizable patterns in neuroimaging: how many principal components?
@da
Generalizable patterns in neuroimaging: how many principal components?
@de
Generalizable patterns in neuroimaging: how many principal components?
@en
Generalizable patterns in neuroimaging: how many principal components?
@fo
Generalizable patterns in neuroimaging: how many principal components?
@fr
Generalizable patterns in neuroimaging: how many principal components?
@is
Generalizable patterns in neuroimaging: how many principal components?
@kl
Generalizable patterns in neuroimaging: how many principal components?
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Generalizable patterns in neuroimaging: how many principal components?
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type
label
Generalizable patterns in neuroimaging: how many principal components?
@ast
Generalizable patterns in neuroimaging: how many principal components?
@da
Generalizable patterns in neuroimaging: how many principal components?
@de
Generalizable patterns in neuroimaging: how many principal components?
@en
Generalizable patterns in neuroimaging: how many principal components?
@fo
Generalizable patterns in neuroimaging: how many principal components?
@fr
Generalizable patterns in neuroimaging: how many principal components?
@is
Generalizable patterns in neuroimaging: how many principal components?
@kl
Generalizable patterns in neuroimaging: how many principal components?
@nb
Generalizable patterns in neuroimaging: how many principal components?
@nl
prefLabel
Generalizable patterns in neuroimaging: how many principal components?
@ast
Generalizable patterns in neuroimaging: how many principal components?
@da
Generalizable patterns in neuroimaging: how many principal components?
@de
Generalizable patterns in neuroimaging: how many principal components?
@en
Generalizable patterns in neuroimaging: how many principal components?
@fo
Generalizable patterns in neuroimaging: how many principal components?
@fr
Generalizable patterns in neuroimaging: how many principal components?
@is
Generalizable patterns in neuroimaging: how many principal components?
@kl
Generalizable patterns in neuroimaging: how many principal components?
@nb
Generalizable patterns in neuroimaging: how many principal components?
@nl
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Generalizable patterns in neuroimaging: how many principal components?
@en
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Generalization can be defined ...... ipal component analysis (PCA).
@en
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10.1006/NIMG.1998.0425
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1999-05-01T00:00:00Z