about
How machine learning is shaping cognitive neuroimaging.Inverse retinotopy: inferring the visual content of images from brain activation patternsScikit-learn: Machine Learning in PythonWhich fMRI clustering gives good brain parcellations?Fast reproducible identification and large-scale databasing of individual functional cognitive networks.Identification of Mood-Relevant Brain Connections Using a Continuous, Subject-Driven Rumination Paradigm.Graph-based inter-subject pattern analysis of FMRI data.Data-driven HRF estimation for encoding and decoding models.Dynamical components analysis of fMRI data through kernel PCAConnectivity-based parcellation: Critique and implications.Structural analysis of fMRI data revisited: improving the sensitivity and reliability of fMRI group studies.Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example.Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines.Best practices in data analysis and sharing in neuroimaging using MRI.Feature characterization in fMRI data: the Information Bottleneck approach.Exploring the Early Organization and Maturation of Linguistic Pathways in the Human Infant BrainSchizophrenia as a network disease: disruption of emergent brain function in patients with auditory hallucinationsA disconnection account of Gerstmann syndrome: functional neuroanatomy evidence.Machine learning for neuroimaging with scikit-learnMachine learning patterns for neuroimaging-genetic studies in the cloud.Formal Models of the Network Co-occurrence Underlying Mental Operations.Deciphering cortical number coding from human brain activity patterns.An automatic valuation system in the human brain: evidence from functional neuroimaging.Decoding fMRI activity in the time domain improves classification performance.Joint prediction of multiple scores captures better individual traits from brain images.Seeing it all: Convolutional network layers map the function of the human visual system.Transport on Riemannian Manifold for Connectivity-Based Brain Decoding.Bootstrapped Permutation Test for Multiresponse Inference on Brain Behavior Associations.Robust regression for large-scale neuroimaging studies.Transmodal Learning of Functional Networks for Alzheimer's Disease Prediction.Total variation regularization for fMRI-based prediction of behavior.Multiclass Sparse Bayesian Regression for fMRI-Based PredictionDistinct alterations in Parkinson's medication-state and disease-state connectivityConnectivity-based parcellation of the cortical mantle using q-ball diffusion imaging.Learning Neural Representations of Human Cognition across Many fMRI StudiesCohort-level brain mapping: learning cognitive atoms to single out specialized regions.Detecting outliers in high-dimensional neuroimaging datasets with robust covariance estimators.Different shades of default mode disturbance in schizophrenia: Subnodal covariance estimation in structure and function.Randomized parcellation based inference.A probabilistic framework to infer brain functional connectivity from anatomical connections.
P50
Q27686937-875E6684-76E7-43A2-AA37-C07826853A7BQ28267605-990D3F0A-72E5-423F-AA58-602DC9045C38Q28365500-15FBABE1-463B-446F-8C21-D51F000ABD8EQ28656459-09A0C7E2-16D6-4AF5-AA47-6A2D10494726Q30481276-C9AF32D1-0BAB-44F8-9360-F87026801BE6Q30705169-11358E86-35C5-49E3-934A-257DBB90B9CAQ30843022-F2FF63EB-E7D7-45F4-8906-0E192B87BEFDQ30859150-692E7F92-2ECA-4362-A3AB-18AFBCCDADEFQ30881597-D0AA84D1-0DB7-4308-9418-BD13A38CD386Q30997822-728821A2-E46D-4F7D-AED6-A51CFCB74230Q31131119-011B334F-A18D-4CBD-ACD0-26E596292E1FQ31143424-36C2A68F-96AC-4066-A5DE-5988ADBC8AF6Q31149852-50D5B5EA-83AE-4CCE-9200-9F3C8AD521C6Q31165126-41584557-0ED9-4A68-B885-40D98C4E7AE6Q33209375-E09547BE-2B79-4AF0-936C-5A7D82478D53Q33268755-06450E0D-D811-47F4-8CEF-6C037727A50FQ34561987-C70EBD27-9713-40A1-B79A-1A207B2BD848Q35014501-24E50526-D5A3-410C-B961-E082B3488615Q35112233-F2C812D3-75E3-4D69-AC0D-3573D4F55BB7Q35159992-969F4300-F255-4521-A4B4-2AD63768AA33Q36054262-8B554A5F-780F-46DD-9DB2-A7BB44D3D6EEQ38378731-2AE69781-48BB-4C5A-813A-0434366E8D2EQ38447865-50BC35C4-C59D-4915-B26D-9B48AB825AF7Q38628340-BE799AB9-63AF-4047-A5BA-DECC4EA7C634Q38694093-0D480D69-1323-4D65-A8D5-08E798E5DF43Q39253244-79380649-40FE-4DF5-95EE-260EF21F3E7BQ40641152-6436F491-4D0C-479C-A0B6-9CD31317F28BQ40683232-EFD416A6-325C-4291-BD03-BF923E11A6CFQ41321338-DF1AECE9-267A-42B7-B50B-FBE049622412Q41643611-EF96B99F-8881-4B8C-9297-F34BABDE7C9EQ42012640-CB77D997-6E5D-4DA2-9C1E-84D3B5A76953Q42037641-4C8B7377-DFCD-45E0-A016-FA03C449B24AQ42159804-A155EB31-FA7B-4B93-A7BF-6349189FC139Q43107526-0D677838-5880-472C-B39C-9308B9C924DBQ44652356-647CD7E8-4F62-448F-94C0-20C05D5E76A9Q45303827-C8F2E563-60E8-45FA-B7FD-3CE27B52082AQ45356905-B35119FF-4887-4605-B1F5-5386C02B69D6Q45944145-BF6EB2B1-474A-4980-AE5A-C746B3E92B4FQ46346887-3503C984-AF20-4DB3-AD00-D3CED1B9B7ACQ46498394-22A7588A-9987-4976-8C36-F77D10EA6D8A
P50
description
hulumtues
@sq
onderzoeker
@nl
researcher
@en
հետազոտող
@hy
name
Bertrand Thirion
@ast
Bertrand Thirion
@da
Bertrand Thirion
@de
Bertrand Thirion
@en
Bertrand Thirion
@es
Bertrand Thirion
@fo
Bertrand Thirion
@fr
Bertrand Thirion
@is
Bertrand Thirion
@kl
Bertrand Thirion
@nb
type
label
Bertrand Thirion
@ast
Bertrand Thirion
@da
Bertrand Thirion
@de
Bertrand Thirion
@en
Bertrand Thirion
@es
Bertrand Thirion
@fo
Bertrand Thirion
@fr
Bertrand Thirion
@is
Bertrand Thirion
@kl
Bertrand Thirion
@nb
prefLabel
Bertrand Thirion
@ast
Bertrand Thirion
@da
Bertrand Thirion
@de
Bertrand Thirion
@en
Bertrand Thirion
@es
Bertrand Thirion
@fo
Bertrand Thirion
@fr
Bertrand Thirion
@is
Bertrand Thirion
@kl
Bertrand Thirion
@nb
P106
P108
P1960
MeKi5_AAAAAJ
P21
P2456
P31
P496
0000-0001-5018-7895