Comparison of two exploratory data analysis methods for fMRI: fuzzy clustering vs. principal component analysis.
about
The Functional Segregation and Integration Model: Mixture Model Representations of Consistent and Variable Group-Level Connectivity in fMRITransient brain activity disentangles fMRI resting-state dynamics in terms of spatially and temporally overlapping networks.Overcoming the effects of false positives and threshold bias in graph theoretical analyses of neuroimaging data.Statistical approaches to functional neuroimaging dataContribution of exploratory methods to the investigation of extended large-scale brain networks in functional MRI: methodologies, results, and challengesReview of methods for functional brain connectivity detection using fMRI.Unsupervised spatiotemporal fMRI data analysis using support vector machines.Search for patterns of functional specificity in the brain: a nonparametric hierarchical Bayesian model for group fMRI dataSimTB, a simulation toolbox for fMRI data under a model of spatiotemporal separabilityDetermining functional connectivity using fMRI data with diffusion-based anatomical weighting.Brain Imaging AnalysisEvaluating Functional Autocorrelation within Spatially Distributed Neural Processing Networks.Combining self-organizing mapping and supervised affinity propagation clustering approach to investigate functional brain networks involved in motor imagery and execution with fMRI measurements.Development of 2dTCA for the detection of irregular, transient BOLD activity.Bootstrap generation and evaluation of an fMRI simulation database.Comparison of fMRI analysis methods for heterogeneous BOLD responses in block design studies.fMRI single trial discovery of spatio-temporal brain activity patterns.Select and Cluster: A Method for Finding Functional Networks of Clustered Voxels in fMRI.Beyond Noise: Using Temporal ICA to Extract Meaningful Information from High-Frequency fMRI Signal Fluctuations during Rest.A Novel Feature-Map Based ICA Model for Identifying the Individual, Intra/Inter-Group Brain Networks across Multiple fMRI Datasets.Global and system-specific resting-state fMRI fluctuations are uncorrelated: principal component analysis reveals anti-correlated networks.Detection of epileptic activity in fMRI without recording the EEG.Comparing functional connectivity via thresholding correlations and singular value decompositionMonitoring acute inflammatory processes in mouse muscle by MR imaging and spectroscopy: a comparison with pathological results.Synchronization-based approach for detecting functional activation of brain.Spatial independent component analysis of functional MRI time-series: to what extent do results depend on the algorithm used?Cortical mapping of visceral pain in patients with GI disorders using functional magnetic resonance imaging.Model-free functional MRI analysis using topographic independent component analysis.Detection and characterization of single-trial fMRI bold responses: paradigm free mapping.Clustering of fMRI data: the elusive optimal number of clusters
P2860
Q26720266-412FF763-42B7-4407-9D8F-D2F960043B8CQ30659369-174716A4-DACA-42E7-A873-A8BF7BFF0AEAQ30953030-D4EBF595-69D2-4EB3-8DD0-D3DE83A1552AQ31135429-CC487BF9-67C2-4F4F-B3A1-7ABBA5CD1E22Q31156332-4E11F368-F879-44B5-A7CB-D5DB7FADE0DEQ33396114-01BA3363-0EAB-4007-A03A-E50DA76B7680Q33426591-0414CE68-215F-427F-9B83-3689157563B0Q34008121-519E5BEA-8503-4013-AFBF-25CC251BC341Q34104371-A0E9302D-290D-4706-8BED-DF1A8505A566Q34283349-E20A8CF0-BD52-4B39-AE05-204F7F660583Q34304242-D6CEB086-2BDB-465F-B273-54E3866E1479Q35017631-9F9634DF-1ACA-4D73-B26A-57E0B693ED2CQ35859683-E0AAABD4-5AC7-4D8F-96B8-B9BDAAE0FA93Q37286165-0CEEEA4F-540C-49FD-AE15-ACF1F373F44AQ37440811-B2C89038-073B-4F9C-9256-A0B8D98C4A44Q39086875-1C2F1EA8-D161-4EBC-A108-2471B8547178Q39159870-69E17743-4005-40B6-9D71-CF4C4FCB7C80Q39363832-3918A45C-FDA7-4BBD-AAE2-575ADC6E7692Q40248751-06B84594-49AD-4C70-959B-CD573BF6649CQ41699661-32C2A13F-D52F-4490-A80C-62229CCB6DAFQ42118083-2482E57A-AE59-4BFF-8AB5-EDA3816C670BQ42571003-2170815D-16BF-4657-99B3-71A082D750F8Q42847147-EF4F1C51-C1F6-4AAE-ADAA-5677385D79C6Q43968214-898CE3F2-DB14-43DB-AD9F-6AB9105368C2Q46688871-E1E4D590-B36D-46E2-8411-E265BDD9208EQ48551830-DA7C958C-678D-41AD-B90C-55E2CFC47869Q48676884-C8C39F61-44FD-42E6-8544-F4E439EF13F4Q51614499-B06182EC-9A73-46CE-93B3-D16AFF90B714Q51645627-1A60D740-B388-4F54-B9B9-4563E2D3D900Q57484733-8E803435-A93B-4FE2-AD8A-F04099284D8A
P2860
Comparison of two exploratory data analysis methods for fMRI: fuzzy clustering vs. principal component analysis.
description
2000 nî lūn-bûn
@nan
2000 թուականի Յունուարին հրատարակուած գիտական յօդուած
@hyw
2000 թվականի հունվարին հրատարակված գիտական հոդված
@hy
2000年の論文
@ja
2000年論文
@yue
2000年論文
@zh-hant
2000年論文
@zh-hk
2000年論文
@zh-mo
2000年論文
@zh-tw
2000年论文
@wuu
name
Comparison of two exploratory ...... principal component analysis.
@ast
Comparison of two exploratory ...... principal component analysis.
@en
type
label
Comparison of two exploratory ...... principal component analysis.
@ast
Comparison of two exploratory ...... principal component analysis.
@en
prefLabel
Comparison of two exploratory ...... principal component analysis.
@ast
Comparison of two exploratory ...... principal component analysis.
@en
P2093
P921
P1476
Comparison of two exploratory ...... principal component analysis.
@en
P2093
P356
10.1016/S0730-725X(99)00102-2
P577
2000-01-01T00:00:00Z