Quantitative prediction of subjective pain intensity from whole-brain fMRI data using Gaussian processes.
about
Towards a physiology-based measure of pain: patterns of human brain activity distinguish painful from non-painful thermal stimulationMultivariate pattern recognition for diagnosis and prognosis in clinical neuroimaging: state of the art, current challenges and future trendsBrain imaging of pain: state of the art.Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjectsA review of feature reduction techniques in neuroimagingBeyond patient reported pain: perfusion magnetic resonance imaging demonstrates reproducible cerebral representation of ongoing post-surgical painMultivariate neural biomarkers of emotional states are categorically distinct.Imaging pain in arthritis: advances in structural and functional neuroimaging.What makes a pattern? Matching decoding methods to data in multivariate pattern analysisMultivariate linear regression of high-dimensional fMRI data with multiple target variables.SGPP: spatial Gaussian predictive process models for neuroimaging dataBayesian multi-task learning for decoding multi-subject neuroimaging data.A tool for classifying individuals with chronic back pain: using multivariate pattern analysis with functional magnetic resonance imaging dataDiagnostic classification of specific phobia subtypes using structural MRI data: a machine-learning approach.Predictive modelling using neuroimaging data in the presence of confounds.Biomarkers, designs, and interpretations of resting-state fMRI in translational pharmacological research: A review of state-of-the-Art, challenges, and opportunities for studying brain chemistry.Pain: a distributed brain information network?Using NMR approaches to drive the search for new CNS therapeutics.Physiological Signal-Based Method for Measurement of Pain Intensity.Brain mediators of the effects of noxious heat on pain.Pattern recognition and functional neuroimaging help to discriminate healthy adolescents at risk for mood disorders from low risk adolescentsPredicting brain activity using a Bayesian spatial modelNovel machine learning methods for ERP analysis: a validation from research on infants at risk for autism.Decoding semi-constrained brain activity from FMRI using support vector machines and gaussian processesBrain Imaging AnalysisExamination of the predictive value of structural magnetic resonance scans in bipolar disorder: a pattern classification approachDecoding the perception of pain from fMRI using multivariate pattern analysis.Linking brain-wide multivoxel activation patterns to behaviour: Examples from language and math.What does brain response to neutral faces tell us about major depression? evidence from machine learning and fMRIDisorder-specific predictive classification of adolescents with attention deficit hyperactivity disorder (ADHD) relative to autism using structural magnetic resonance imaging.Dynamic change of global and local information processing in propofol-induced loss and recovery of consciousness.Predictors of treatment response in young people at ultra-high risk for psychosis who received long-chain omega-3 fatty acids.Multivariate decoding of cerebral blood flow measures in a clinical model of on-going postsurgical pain.MANIA-a pattern classification toolbox for neuroimaging data.The brain in chronic pain: clinical implications.Predicting the Naturalistic Course of Major Depressive Disorder Using Clinical and Multimodal Neuroimaging Information: A Multivariate Pattern Recognition StudyUsing structural MRI to identify individuals at genetic risk for bipolar disorders: a 2-cohort, machine learning study.Normalization of Pain-Evoked Neural Responses Using Spontaneous EEG Improves the Performance of EEG-Based Cross-Individual Pain Prediction.Diagnostic neuroimaging across diseases.Investigating the Predictive Value of Functional MRI to Appetitive and Aversive Stimuli: A Pattern Classification Approach
P2860
Q21135244-2B87E983-CF91-4946-A896-584B279B9D18Q27008516-AB86DD3D-1033-4DE6-BB17-11975E8EA52CQ28067180-3713BEED-3604-4109-99B9-14957477F40AQ28656446-1442CC68-CA30-47DE-8FE8-6EE9146BF61AQ28658984-A8172CA0-A417-42CE-BC0B-2258E0042883Q28742439-56E50069-86A3-49ED-80CE-458210CDF0F1Q30373110-3224DCB5-69F3-420B-939F-EA31F7C2E80BQ30570689-AF780396-C4B2-4888-B26D-61AD503FA849Q30578730-F09F1B42-3A3C-4F90-9497-6117EEA27E56Q30657027-116928E6-F6ED-442E-AD6D-5DB7F971F7D9Q30700256-1A3E8A99-33CE-4606-BF6F-DFF7FB6476ABQ30757760-B54B3283-DD74-4BC0-A55D-59B53910B085Q30829443-A5EF1222-80B0-4312-B13C-8B2EB657B9A5Q30837129-2DF6C82E-3E5C-4F19-A550-537D9AC95B49Q31158492-B780C5FC-319D-4F98-A04A-A82EE60BC975Q31158600-54907163-4D68-4049-92FE-BF33DEEB36F1Q33359828-AFF28B57-40FF-4CEC-A59E-580C3346B06BQ33613767-CC291F4E-100D-4BE9-BD02-912DBA9228C4Q33729404-4A9E2506-F0DA-4DA9-A7E4-501D08D2A737Q33923093-5CC67BF8-B851-4431-A359-1ABC3EED523CQ34166129-4B8F67DB-51BA-4F95-AAFB-B5BD47DBE090Q34248012-E5DF7698-04D3-450D-AD27-134A7209F99AQ34251504-E78E5E49-E443-4EED-A6A6-E57795D09E75Q34259785-0B2075CA-E588-4A57-8401-EA6108E21C3DQ34304242-F2FDC652-26A4-4F65-9A90-93AC001AEF98Q34348581-9643E12E-4B2F-4E47-A44B-60AAF5519B74Q34393585-7DCD39AC-3825-48C1-92CE-667A5BCD59A9Q34414824-4D7E35BA-63C0-40DE-B43A-7FA7B59BC957Q34656317-20FE553F-933D-48C2-AE7B-28F14583E556Q34733936-434F9186-B128-420C-896C-C4FE71237F26Q35022277-C0EC4439-69CE-4202-A1B3-96E1F00C885BQ35034786-0CD60807-FE5B-41D8-A6C7-F46548010831Q35066130-01B31B99-3E92-49D7-9181-A49A926341A7Q35133977-01908B59-7F56-4A2A-8EF8-53FE4C471A14Q35580854-BAF2C01C-61E6-467A-BA23-29825198B957Q35666041-F1FEF20A-7AA5-46E2-90A9-C9D73CD441BDQ35976147-37418B70-E299-462B-A9AF-7EA8515DEE29Q36009094-2BE03F82-90B9-4F17-869E-D9FF8C4EB25BQ36167245-A76190CC-9240-4F6A-8DDB-3DD171258D88Q36199688-9468DC15-ED7A-450F-9DCA-288DB3A16AEA
P2860
Quantitative prediction of subjective pain intensity from whole-brain fMRI data using Gaussian processes.
description
2009 nî lūn-bûn
@nan
2009 թուականի Հոկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2009 թվականի հոտեմբերին հրատարակված գիտական հոդված
@hy
2009年の論文
@ja
2009年論文
@yue
2009年論文
@zh-hant
2009年論文
@zh-hk
2009年論文
@zh-mo
2009年論文
@zh-tw
2009年论文
@wuu
name
Quantitative prediction of sub ...... data using Gaussian processes.
@ast
Quantitative prediction of sub ...... data using Gaussian processes.
@en
Quantitative prediction of sub ...... data using Gaussian processes.
@nl
type
label
Quantitative prediction of sub ...... data using Gaussian processes.
@ast
Quantitative prediction of sub ...... data using Gaussian processes.
@en
Quantitative prediction of sub ...... data using Gaussian processes.
@nl
prefLabel
Quantitative prediction of sub ...... data using Gaussian processes.
@ast
Quantitative prediction of sub ...... data using Gaussian processes.
@en
Quantitative prediction of sub ...... data using Gaussian processes.
@nl
P2093
P1433
P1476
Quantitative prediction of sub ...... data using Gaussian processes.
@en
P2093
Carlton Chu
Janaina Mourão-Miranda
Michael Brammer
Steven Coen
P304
P356
10.1016/J.NEUROIMAGE.2009.10.072
P407
P577
2009-10-29T00:00:00Z