Data-driven HRF estimation for encoding and decoding models

Despite the common usage of a canonical, data-independent, hemodynamic response function (HRF), it is known that the shape of the HRF varies across brain regions and subjects. This suggests that a data-driven estimation of this function could lead to more statistical power when modeling BOLD fMRI da...

Full description

Saved in:

Bibliographic Details
Published in:	NeuroImage (Orlando, Fla.) Vol. 104; pp. 209 - 220
Main Authors:	Pedregosa, Fabian, Eickenberg, Michael, Ciuciu, Philippe, Thirion, Bertrand, Gramfort, Alexandre
Format:	Journal Article
Language:	English
Published:	United States Elsevier Inc 01.01.2015 Elsevier Limited Elsevier
Subjects:	BOLD Brain - physiology Brain Mapping - methods Computer Science Decoding Derivatives Economic models Encoding Estimates Finite impulse response (FIR) Functional MRI (fMRI) Hemodynamic response function (HRF) Humans Image Processing, Computer-Assisted Machine Learning Magnetic Resonance Imaging - methods Methods Models, Neurological Neurovascular Coupling Optimization Regression Analysis Studies Visual Perception - physiology Machine learning Hemodynamic response function (HRF) Finite impulse response (FIR) BOLD Encoding Functional MRI (fMRI) Decoding Optimization Finite Impulse Response (FIR) Hemodynamic Response Function (HRF) Machine Learning
ISSN:	1053-8119, 1095-9572, 1095-9572
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	Despite the common usage of a canonical, data-independent, hemodynamic response function (HRF), it is known that the shape of the HRF varies across brain regions and subjects. This suggests that a data-driven estimation of this function could lead to more statistical power when modeling BOLD fMRI data. However, unconstrained estimation of the HRF can yield highly unstable results when the number of free parameters is large. We develop a method for the joint estimation of activation and HRF by means of a rank constraint, forcing the estimated HRF to be equal across events or experimental conditions, yet permitting it to differ across voxels. Model estimation leads to an optimization problem that we propose to solve with an efficient quasi-Newton method, exploiting fast gradient computations. This model, called GLM with Rank-1 constraint (R1-GLM), can be extended to the setting of GLM with separate designs which has been shown to improve decoding accuracy in brain activity decoding experiments. We compare 10 different HRF modeling methods in terms of encoding and decoding scores on two different datasets. Our results show that the R1-GLM model outperforms competing methods in both encoding and decoding settings, positioning it as an attractive method both from the points of view of accuracy and computational efficiency. [Display omitted] •R1-GLM method allows to jointly estimate activation patterns and HRF.•Rank constraint reduces variance in voxelwise fitting and is solved efficiently.•R1-GLM extends to parametric models of HRF and GLM with separate designs.•R1-GLM outperforms competing methods in the fit of the BOLD response (encoding).•R1-GLM also improves decoding accuracy in brain reading settings.
AbstractList	Despite the common usage of a canonical, data-independent, hemodynamic response function (HRF), it is known that the shape of the HRF varies across brain regions and subjects. This suggests that a data-driven estimation of this function could lead to more statistical power when modeling BOLD fMRI data. However, unconstrained estimation of the HRF can yield highly unstable results when the number of free parameters is large. We develop a method for the joint estimation of activation and HRF by means of a rank constraint, forcing the estimated HRF to be equal across events or experimental conditions, yet permitting it to differ across voxels. Model estimation leads to an optimization problem that we propose to solve with an efficient quasi-Newton method, exploiting fast gradient computations. This model, called GLM with Rank-1 constraint (R1-GLM), can be extended to the setting of GLM with separate designs which has been shown to improve decoding accuracy in brain activity decoding experiments. We compare 10 different HRF modeling methods in terms of encoding and decoding scores on two different datasets. Our results show that the R1-GLM model outperforms competing methods in both encoding and decoding settings, positioning it as an attractive method both from the points of view of accuracy and computational efficiency. Despite the common usage of a canonical, data-independent, hemodynamic response function (HRF), it is known that the shape of the HRF varies across brain regions and subjects. This suggests that a data-driven estimation of this function could lead to more statistical power when modeling BOLD fMRI data. However, unconstrained estimation of the HRF can yield highly unstable results when the number of free parameters is large. We develop a method for the joint estimation of activation and HRF by means of a rank constraint, forcing the estimated HRF to be equal across events or experimental conditions, yet permitting it to differ across voxels. Model estimation leads to an optimization problem that we propose to solve with an efficient quasi-Newton method, exploiting fast gradient computations. This model, called GLM with Rank-1 constraint (R1-GLM), can be extended to the setting of GLM with separate designs which has been shown to improve decoding accuracy in brain activity decoding experiments. We compare 10 different HRF modeling methods in terms of encoding and decoding scores on two different datasets. Our results show that the R1-GLM model outperforms competing methods in both encoding and decoding settings, positioning it as an attractive method both from the points of view of accuracy and computational efficiency. [Display omitted] •R1-GLM method allows to jointly estimate activation patterns and HRF.•Rank constraint reduces variance in voxelwise fitting and is solved efficiently.•R1-GLM extends to parametric models of HRF and GLM with separate designs.•R1-GLM outperforms competing methods in the fit of the BOLD response (encoding).•R1-GLM also improves decoding accuracy in brain reading settings. Despite the common usage of a canonical, data-independent, hemodynamic response function (HRF), it is known that the shape of the HRF varies across brain regions and subjects. This suggests that a data-driven estimation of this function could lead to more statistical power when modeling BOLD fMRI data. However, unconstrained estimation of the HRF can yield highly unstable results when the number of free parameters is large. We develop a method for the joint estimation of activation and HRF using a rank constraint causing the estimated HRF to be equal across events/conditions, yet permitting it to be different across voxels. Model estimation leads to an optimization problem that we propose to solve with an efficient quasi-Newton method exploiting fast gradient computations. This model, called GLM with Rank-1 constraint (R1-GLM), can be extended to the setting of GLM with separate designs which has been shown to improve decoding accuracy in brain activity decoding experiments. We compare 10 different HRF modeling methods in terms of encoding and decoding score in two different datasets. Our results show that the R1-GLM model significantly outperforms competing methods in both encoding and decoding settings, positioning it as an attractive method both from the points of view of accuracy and computational efficiency. Despite the common usage of a canonical, data-independent, hemodynamic response function (HRF), it is known that the shape of the HRF varies across brain regions and subjects. This suggests that a data-driven estimation of this function could lead to more statistical power when modeling BOLD fMRI data. However, unconstrained estimation of the HRF can yield highly unstable results when the number of free parameters is large. We develop a method for the joint estimation of activation and HRF by means of a rank constraint, forcing the estimated HRF to be equal across events or experimental conditions, yet permitting it to differ across voxels. Model estimation leads to an optimization problem that we propose to solve with an efficient quasi-Newton method, exploiting fast gradient computations. This model, called GLM with Rank-1 constraint (R1-GLM), can be extended to the setting of GLM with separate designs which has been shown to improve decoding accuracy in brain activity decoding experiments. We compare 10 different HRF modeling methods in terms of encoding and decoding scores on two different datasets. Our results show that the R1-GLM model outperforms competing methods in both encoding and decoding settings, positioning it as an attractive method both from the points of view of accuracy and computational efficiency.Despite the common usage of a canonical, data-independent, hemodynamic response function (HRF), it is known that the shape of the HRF varies across brain regions and subjects. This suggests that a data-driven estimation of this function could lead to more statistical power when modeling BOLD fMRI data. However, unconstrained estimation of the HRF can yield highly unstable results when the number of free parameters is large. We develop a method for the joint estimation of activation and HRF by means of a rank constraint, forcing the estimated HRF to be equal across events or experimental conditions, yet permitting it to differ across voxels. Model estimation leads to an optimization problem that we propose to solve with an efficient quasi-Newton method, exploiting fast gradient computations. This model, called GLM with Rank-1 constraint (R1-GLM), can be extended to the setting of GLM with separate designs which has been shown to improve decoding accuracy in brain activity decoding experiments. We compare 10 different HRF modeling methods in terms of encoding and decoding scores on two different datasets. Our results show that the R1-GLM model outperforms competing methods in both encoding and decoding settings, positioning it as an attractive method both from the points of view of accuracy and computational efficiency.
Author	Ciuciu, Philippe Eickenberg, Michael Thirion, Bertrand Pedregosa, Fabian Gramfort, Alexandre
Author_xml	– sequence: 1 givenname: Fabian orcidid: 0000-0003-4025-3953 surname: Pedregosa fullname: Pedregosa, Fabian email: fabian.pedregosa@inria.fr organization: Parietal Team, INRIA Saclay-Île-de-France, Saclay, France – sequence: 2 givenname: Michael surname: Eickenberg fullname: Eickenberg, Michael organization: Parietal Team, INRIA Saclay-Île-de-France, Saclay, France – sequence: 3 givenname: Philippe surname: Ciuciu fullname: Ciuciu, Philippe organization: Parietal Team, INRIA Saclay-Île-de-France, Saclay, France – sequence: 4 givenname: Bertrand surname: Thirion fullname: Thirion, Bertrand organization: Parietal Team, INRIA Saclay-Île-de-France, Saclay, France – sequence: 5 givenname: Alexandre surname: Gramfort fullname: Gramfort, Alexandre organization: Institut Mines-Telecom, Telecom ParisTech, CNRS LTCI, 37-39 Rue Dareau, 75014 Paris, France
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/25304775$$D View this record in MEDLINE/PubMed https://inria.hal.science/hal-00952554$$DView record in HAL
BookMark	eNqNkV1rFDEUhoNU7If-BRnwRi9mPJlJJgmCWFvrCguC6HXI5qNmnU3aZGah_95Md6uwV3uVD548Oee85-gkxGARqjA0GHD_ft0EO6XoN-rWNi1g0oBooIdn6AyDoLWgrD2Z97SrOcbiFJ3nvAYAgQl_gU5b2gFhjJ6hD9dqVLVJfmtDtfhxU9k8Fu3oY6hcTJUNOhofbisVTGXs_rCJxg75JXru1JDtq_16gX7dfPl5taiX379-u7pc1pqKfqyd6qlQbqX1CrDWlDKOwRHV9YYo2nPqMCU9Zhh3XFPLWscpc8wwxkEo47oL9G7n_a0GeZdKeelBRuXl4nIp57vSF20pJVtS2Lc79i7F-6k0Izc-azsMKtg4ZYn7jgLmQI5CScsAC1rQNwfoOk4plKYL1VLBWwGz8PWemlYba_6V-jTtAnzcATrFnJN1UvvxcdZjUn6QGOQcr1zL__HKOV4JQpZ4i4AfCJ7-OOLp593TEpzdeptk1r6ka41PVo_SRH-M5NOBRA8-eK2GP_bhOMVfprzYGw
CitedBy_id	crossref_primary_10_1016_j_neuroimage_2019_116059 crossref_primary_10_1016_j_isci_2024_110395 crossref_primary_10_1177_0013164416667982 crossref_primary_10_3389_fncom_2017_00007 crossref_primary_10_1162_imag_a_00469 crossref_primary_10_1371_journal_pcbi_1010819 crossref_primary_10_1093_cercor_bhae292 crossref_primary_10_1016_j_neuroimage_2018_10_080 crossref_primary_10_1371_journal_pone_0277801 crossref_primary_10_3389_fnins_2015_00375 crossref_primary_10_1016_j_jpsychires_2023_11_043 crossref_primary_10_1016_j_neuroimage_2021_117814 crossref_primary_10_1038_s41592_020_0941_6 crossref_primary_10_1007_s11682_020_00304_8 crossref_primary_10_1016_j_neuroimage_2021_118418 crossref_primary_10_1007_s42113_025_00257_5 crossref_primary_10_1002_mrm_26365 crossref_primary_10_1016_j_neuroimage_2019_07_006 crossref_primary_10_1088_1361_6587_add052 crossref_primary_10_3389_fnins_2017_00543 crossref_primary_10_1016_j_jmp_2016_06_009 crossref_primary_10_1016_j_neuroimage_2025_121338 crossref_primary_10_1162_imag_a_00285 crossref_primary_10_7554_eLife_36928 crossref_primary_10_1016_j_jneumeth_2020_108778 crossref_primary_10_1038_s42003_021_02294_9 crossref_primary_10_1016_j_neuroimage_2023_120224 crossref_primary_10_1016_j_neuroimage_2017_08_018 crossref_primary_10_1109_JPROC_2015_2425807 crossref_primary_10_7554_eLife_82566 crossref_primary_10_1016_j_ecosta_2021_02_006 crossref_primary_10_1016_j_tics_2016_03_014 crossref_primary_10_1109_TMI_2014_2379914 crossref_primary_10_1016_j_pneurobio_2021_102174 crossref_primary_10_1016_j_neuroimage_2019_04_012 crossref_primary_10_1016_j_neuroimage_2020_117652 crossref_primary_10_1111_epi_13717 crossref_primary_10_1109_TMI_2016_2544251 crossref_primary_10_1016_j_neuroimage_2021_118266 crossref_primary_10_3389_fpubh_2024_1417966 crossref_primary_10_1038_s41598_021_83420_9
Cites_doi	10.1007/BF01589116 10.1126/science.1134239 10.1002/mrm.1910390109 10.1016/j.neuroimage.2012.05.057 10.1155/2013/643129 10.1016/j.neuroimage.2003.11.029 10.2307/2332226 10.1002/(SICI)1097-0193(1999)8:2/3<109::AID-HBM7>3.0.CO;2-W 10.1016/j.neuroimage.2008.05.052 10.1002/hbm.10100 10.1016/j.neuroimage.2013.07.043 10.1214/12-AOAS609 10.1038/nature06713 10.1016/j.neuroimage.2010.07.073 10.1021/ac60214a047 10.1016/j.neuroimage.2003.12.024 10.1016/j.neuroimage.2013.05.100 10.1002/(SICI)1521-4036(199905)41:2<149::AID-BIMJ149>3.0.CO;2-E 10.1016/j.neuroimage.2012.01.133 10.1109/TMI.2003.817759 10.1080/00401706.1979.10489751 10.1016/j.neuroimage.2011.08.076 10.1016/j.neuroimage.2013.02.048 10.1002/hbm.20310 10.1109/42.897811 10.1038/nrn1931 10.1016/S1053-8119(03)00049-1 10.1016/j.neuroimage.2012.08.014 10.1016/j.neuroimage.2008.01.011 10.1016/j.neuron.2008.11.004 10.1016/j.neuroimage.2014.04.052 10.1006/nimg.2001.0940 10.1109/TSP.2005.853303 10.1016/j.neuron.2009.09.006 10.1016/j.neuroimage.2007.02.020 10.1109/TMI.2010.2042064 10.1006/nimg.1998.0419 10.1016/j.neuroimage.2013.05.036 10.1214/11-AOAS476
ContentType	Journal Article
Copyright	2014 Elsevier Inc. Copyright © 2014 Elsevier Inc. All rights reserved. Copyright Elsevier Limited Jan 1, 2015 Distributed under a Creative Commons Attribution 4.0 International License
Copyright_xml	– notice: 2014 Elsevier Inc. – notice: Copyright © 2014 Elsevier Inc. All rights reserved. – notice: Copyright Elsevier Limited Jan 1, 2015 – notice: Distributed under a Creative Commons Attribution 4.0 International License
DBID	AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7TK 7X7 7XB 88E 88G 8AO 8FD 8FE 8FH 8FI 8FJ 8FK ABUWG AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M1P M2M M7P P64 PHGZM PHGZT PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS PSYQQ Q9U RC3 7X8 7QO 1XC VOOES
DOI	10.1016/j.neuroimage.2014.09.060
DatabaseName	CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Neurosciences Abstracts Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Psychology Database (Alumni) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central Natural Science Collection ProQuest One ProQuest Central Korea Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) ProQuest Biological Science Collection Health & Medical Collection (Alumni Edition) PML(ProQuest Medical Library) Psychology Database Biological Science Database Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest One Psychology ProQuest Central Basic Genetics Abstracts MEDLINE - Academic Biotechnology Research Abstracts Hyper Article en Ligne (HAL) Hyper Article en Ligne (HAL) (Open Access)
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) ProQuest One Psychology ProQuest Central Student Technology Research Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Health & Medical Research Collection Genetics Abstracts Health Research Premium Collection Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest Biological Science Collection ProQuest Central Basic ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Psychology Journals (Alumni) Biological Science Database ProQuest SciTech Collection Neurosciences Abstracts ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts ProQuest Health & Medical Complete ProQuest Medical Library ProQuest Psychology Journals ProQuest One Academic UKI Edition Engineering Research Database ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic Biotechnology Research Abstracts
DatabaseTitleList	ProQuest One Psychology Engineering Research Database MEDLINE MEDLINE - Academic
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Medicine Computer Science
EISSN	1095-9572
EndPage	220
ExternalDocumentID	oai:HAL:hal-00952554v4 3500825621 25304775 10_1016_j_neuroimage_2014_09_060 S1053811914008027
Genre	Research Support, Non-U.S. Gov't Journal Article
GroupedDBID	--- --K --M .1- .FO .~1 0R~ 123 1B1 1RT 1~. 1~5 4.4 457 4G. 5RE 5VS 7-5 71M 7X7 88E 8AO 8FE 8FH 8FI 8FJ 8P~ 9JM AABNK AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AATTM AAXKI AAXLA AAXUO AAYWO ABBQC ABCQJ ABFNM ABFRF ABIVO ABJNI ABMAC ABMZM ABUWG ACDAQ ACGFO ACGFS ACIEU ACLOT ACPRK ACRLP ACVFH ADBBV ADCNI ADEZE ADFRT AEBSH AEFWE AEIPS AEKER AENEX AEUPX AFJKZ AFKRA AFPUW AFRHN AFTJW AFXIZ AGUBO AGWIK AGYEJ AHHHB AHMBA AIEXJ AIIUN AIKHN AITUG AJRQY AJUYK AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU ANZVX AXJTR AZQEC BBNVY BENPR BHPHI BKOJK BLXMC BNPGV BPHCQ BVXVI CCPQU CS3 DM4 DU5 DWQXO EBS EFBJH EFKBS EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN FYUFA G-Q GBLVA GNUQQ GROUPED_DOAJ HCIFZ HMCUK IHE J1W KOM LG5 LK8 LX8 M1P M29 M2M M2V M41 M7P MO0 MOBAO N9A O-L O9- OAUVE OVD OZT P-8 P-9 P2P PC. PHGZM PHGZT PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PSYQQ Q38 ROL RPZ SAE SCC SDF SDG SDP SES SSH SSN SSZ T5K TEORI UKHRP UV1 YK3 Z5R ZU3 ~G- ~HD 3V. AACTN AADPK AAIAV ABLVK ABYKQ AFKWA AJBFU AJOXV AMFUW C45 HMQ LCYCR RIG SNS ZA5 29N 53G 9DU AAFWJ AAQXK AAYXX ABXDB ACRPL ADFGL ADMUD ADNMO ADVLN ADXHL AFFHD AFPKN AGHFR AGQPQ AIGII AKRLJ APXCP ASPBG AVWKF AZFZN CAG CITATION COF FEDTE FGOYB G-2 HDW HEI HMK HMO HVGLF HZ~ OK1 R2- SEW WUQ XPP ZMT AGCQF AGRNS ALIPV CGR CUY CVF ECM EIF NPM 7TK 7XB 8FD 8FK FR3 K9. P64 PKEHL PQEST PQUKI PRINS Q9U RC3 7X8 PUEGO 7QO 1XC VOOES
ID	FETCH-LOGICAL-c596t-fa659afbccb01cc557810f4a36d4a5685f1546171138c5e72f857f7d77809adf3
IEDL.DBID	M7P
ISICitedReferencesCount	45
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000345393800021&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1053-8119 1095-9572
IngestDate	Sat Oct 25 11:31:55 EDT 2025 Tue Oct 07 09:14:24 EDT 2025 Sun Sep 28 06:06:10 EDT 2025 Thu Nov 20 19:10:45 EST 2025 Mon Jul 21 05:50:43 EDT 2025 Tue Nov 18 20:36:48 EST 2025 Sat Nov 29 03:33:26 EST 2025 Fri Feb 23 02:24:27 EST 2024 Tue Oct 14 19:34:53 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Keywords	Machine learning Hemodynamic response function (HRF) Finite impulse response (FIR) BOLD Encoding Functional MRI (fMRI) Decoding Optimization Finite Impulse Response (FIR) Hemodynamic Response Function (HRF) Machine Learning
Language	English
License	Copyright © 2014 Elsevier Inc. All rights reserved. Distributed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c596t-fa659afbccb01cc557810f4a36d4a5685f1546171138c5e72f857f7d77809adf3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0003-4025-3953 0000-0001-5374-962X 0000-0001-9791-4404
OpenAccessLink	https://inria.hal.science/hal-00952554
PMID	25304775
PQID	1625982904
PQPubID	2031077
PageCount	12
ParticipantIDs	hal_primary_oai_HAL_hal_00952554v4 proquest_miscellaneous_1635018044 proquest_miscellaneous_1634270195 proquest_journals_1625982904 pubmed_primary_25304775 crossref_citationtrail_10_1016_j_neuroimage_2014_09_060 crossref_primary_10_1016_j_neuroimage_2014_09_060 elsevier_sciencedirect_doi_10_1016_j_neuroimage_2014_09_060 elsevier_clinicalkey_doi_10_1016_j_neuroimage_2014_09_060
PublicationCentury	2000
PublicationDate	2015-01-01 2015-01-00 2015-Jan-01 20150101
PublicationDateYYYYMMDD	2015-01-01
PublicationDate_xml	– month: 01 year: 2015 text: 2015-01-01 day: 01
PublicationDecade	2010
PublicationPlace	United States
PublicationPlace_xml	– name: United States – name: Amsterdam
PublicationTitle	NeuroImage (Orlando, Fla.)
PublicationTitleAlternate	Neuroimage
PublicationYear	2015
Publisher	Elsevier Inc Elsevier Limited Elsevier
Publisher_xml	– name: Elsevier Inc – name: Elsevier Limited – name: Elsevier
References	Turner, Mumford, Poldrack, Ashby (bb0230) 2012; 62 Lindquist, Wager (bb0135) 2007; 28 Badillo, Vincent, Ciuciu (bb0010) 2013; 82 Mumford, Turner, Ashby, Poldrack (bb0170) 2012; 59 Dale (bb0045) 1999; 8 Kendall (bb0120) 1938; 30 Woolrich, Behrens, Smith (bb0250) 2004; 21 Chaari, Forbes, Vincent, Ciuciu (bb0030) 2012; 15 (Pt 3) Kriegeskorte, Mur, Bandettini (bb0125) 2008; 2 Makni, Beckmann, Smith, Woolrich (bb0145) 2008; 42 Lei, Tong, Yan (bb0130) 2013; 2013 Cox, Savoy (bb0040) 2003; 19 Ciuciu, Poline, Marrelec, Idier, Pallier, Benali (bb0035) 2003; 22 Vincent, Risser, Ciuciu (bb0235) 2010; 29 Zhang, Li, Beckes, Brown, Coan (bb0255) 2012; 63 Kay, Naselaris, Prenger, Gallant (bb0115) 2008; 452 Poline, Brett (bb0205) 2012; 62 Glover (bb0075) 1999; 9 Friston, Holmes, Poline (bb0065) 1995 Doyle, Ashburner, Zelaya, Williams, Mehta, Marquand (bb0055) 2013; 81 Vu, Ravikumar, Naselaris, Kay, Gallant, Yu (bb0240) 2011; 5 Tom, Fox, Trepel, Poldrack (bb0225) 2007; 315 Boynton, Engel, Glover, Heeger (bb0020) 1996; 16 Nocedal, Wright (bb0185) 2006 Naselaris, Kay, Nishimoto, Gallant (bb0175) 2011; 56 Wang, Zhu, Fan, Giovanello, Lin (bb0245) 2013; 7 Goutte, Nielsen, Hansen (bb0085) 2000; 19 Kay, Naselaris, Gallant (bb0110) 2011 Miyawaki, Uchida, Yamashita, Sato, Morito, Tanabe, Sadato, Kamitani (bb0160) 2008; 60 Pedregosa, Grisel, Weiss, Passos, Brucher (bb0195) 2011; 12 Horn, Johnson (bb0105) 1991 Handwerker, Ollinger, D'Esposito (bb0090) 2004; 21 Golub, Heath, Wahba (bb0080) 1979; 21 Zhang, Li, Beckes, Coan (bb0260) 2013; 75 Casanova, Ryali, Serences, Yang, Kraft, Laurienti, Maldjian (bb0025) 2008; 40 Bekhti, Zilber, Pedregosa, Ciuciu, Van Wassenhove, Gramfort (bb0015) 2014 Savitzky, Golay (bb0215) 1964; 36 Friston, Ashburner, Kiebel, Nichols, Penny (bb0060) 2011 Schoenmakers, Barth, Heskes, van Gerven (bb0220) 2013; 83 Degras, Lindquist (bb0050) 2014; 98C Haynes, Rees (bb0095) 2006; 7 Liu, Nocedal (bb0140) 1989; 45 Naselaris, Prenger, Kay, Oliver, Gallant (bb0180) 2009; 63 Poldrack, Mumford, Nichols (bb0200) 2011 Henson, Price, Rugg, Turner, Friston (bb0100) 2002; 15 Marrelec, Benali, Ciuciu, Pélégrini-Issac, Poline (bb0155) 2003; 19 Röhmel, Mansmann (bb0210) 1999; 41 Friston, Josephs, Rees, Turner (bb0070) 1998; 39 Badillo, Varoquaux, Ciuciu (bb0005) 2013 Mourão Miranda, Friston, Brammer (bb0165) 2007; 36 Makni, Ciuciu, Idier, Poline (bb0150) 2005; 53 Pedregosa, Eickenberg, Thirion, Gramfort (bb0190) 2013 Badillo (10.1016/j.neuroimage.2014.09.060_bb0005) 2013 Golub (10.1016/j.neuroimage.2014.09.060_bb0080) 1979; 21 Naselaris (10.1016/j.neuroimage.2014.09.060_bb0180) 2009; 63 Mourão Miranda (10.1016/j.neuroimage.2014.09.060_bb0165) 2007; 36 Henson (10.1016/j.neuroimage.2014.09.060_bb0100) 2002; 15 Schoenmakers (10.1016/j.neuroimage.2014.09.060_bb0220) 2013; 83 Degras (10.1016/j.neuroimage.2014.09.060_bb0050) 2014; 98C Tom (10.1016/j.neuroimage.2014.09.060_bb0225) 2007; 315 Doyle (10.1016/j.neuroimage.2014.09.060_bb0055) 2013; 81 Röhmel (10.1016/j.neuroimage.2014.09.060_bb0210) 1999; 41 Marrelec (10.1016/j.neuroimage.2014.09.060_bb0155) 2003; 19 Goutte (10.1016/j.neuroimage.2014.09.060_bb0085) 2000; 19 Woolrich (10.1016/j.neuroimage.2014.09.060_bb0250) 2004; 21 Handwerker (10.1016/j.neuroimage.2014.09.060_bb0090) 2004; 21 Friston (10.1016/j.neuroimage.2014.09.060_bb0070) 1998; 39 Friston (10.1016/j.neuroimage.2014.09.060_bb0060) 2011 Badillo (10.1016/j.neuroimage.2014.09.060_bb0010) 2013; 82 Bekhti (10.1016/j.neuroimage.2014.09.060_bb0015) 2014 Makni (10.1016/j.neuroimage.2014.09.060_bb0150) 2005; 53 Naselaris (10.1016/j.neuroimage.2014.09.060_bb0175) 2011; 56 Miyawaki (10.1016/j.neuroimage.2014.09.060_bb0160) 2008; 60 Zhang (10.1016/j.neuroimage.2014.09.060_bb0255) 2012; 63 Casanova (10.1016/j.neuroimage.2014.09.060_bb0025) 2008; 40 Zhang (10.1016/j.neuroimage.2014.09.060_bb0260) 2013; 75 Kay (10.1016/j.neuroimage.2014.09.060_bb0115) 2008; 452 Pedregosa (10.1016/j.neuroimage.2014.09.060_bb0190) 2013 Boynton (10.1016/j.neuroimage.2014.09.060_bb0020) 1996; 16 Poline (10.1016/j.neuroimage.2014.09.060_bb0205) 2012; 62 Vincent (10.1016/j.neuroimage.2014.09.060_bb0235) 2010; 29 Kriegeskorte (10.1016/j.neuroimage.2014.09.060_bb0125) 2008; 2 Chaari (10.1016/j.neuroimage.2014.09.060_bb0030) 2012; 15 (Pt 3) Mumford (10.1016/j.neuroimage.2014.09.060_bb0170) 2012; 59 Vu (10.1016/j.neuroimage.2014.09.060_bb0240) 2011; 5 Liu (10.1016/j.neuroimage.2014.09.060_bb0140) 1989; 45 Glover (10.1016/j.neuroimage.2014.09.060_bb0075) 1999; 9 Kendall (10.1016/j.neuroimage.2014.09.060_bb0120) 1938; 30 Nocedal (10.1016/j.neuroimage.2014.09.060_bb0185) 2006 Friston (10.1016/j.neuroimage.2014.09.060_bb0065) 1995 Haynes (10.1016/j.neuroimage.2014.09.060_bb0095) 2006; 7 Horn (10.1016/j.neuroimage.2014.09.060_bb0105) 1991 Lindquist (10.1016/j.neuroimage.2014.09.060_bb0135) 2007; 28 Poldrack (10.1016/j.neuroimage.2014.09.060_bb0200) 2011 Turner (10.1016/j.neuroimage.2014.09.060_bb0230) 2012; 62 Cox (10.1016/j.neuroimage.2014.09.060_bb0040) 2003; 19 Pedregosa (10.1016/j.neuroimage.2014.09.060_bb0195) 2011; 12 Savitzky (10.1016/j.neuroimage.2014.09.060_bb0215) 1964; 36 Dale (10.1016/j.neuroimage.2014.09.060_bb0045) 1999; 8 Kay (10.1016/j.neuroimage.2014.09.060_bb0110) Wang (10.1016/j.neuroimage.2014.09.060_bb0245) 2013; 7 Makni (10.1016/j.neuroimage.2014.09.060_bb0145) 2008; 42 Ciuciu (10.1016/j.neuroimage.2014.09.060_bb0035) 2003; 22 Lei (10.1016/j.neuroimage.2014.09.060_bb0130) 2013; 2013
References_xml	– volume: 15 (Pt 3) start-page: 180 year: 2012 end-page: 188 ident: bb0030 article-title: Hemodynamic-informed parcellation of fMRI data in a joint detection estimation framework publication-title: International Conference on Medical Image Computing and Computer-Assisted Intervention – volume: 83 start-page: 951 year: 2013 end-page: 961 ident: bb0220 article-title: Linear reconstruction of perceived images from human brain activity publication-title: NeuroImage – volume: 29 start-page: 1059 year: 2010 end-page: 1074 ident: bb0235 article-title: Spatially adaptive mixture modeling for analysis of fMRI time series publication-title: IEEE Trans. Med. Imaging – volume: 63 start-page: 1754 year: 2012 end-page: 1765 ident: bb0255 article-title: Nonparametric inference of the hemodynamic response using multi-subject fMRI data publication-title: NeuroImage – volume: 28 start-page: 764 year: 2007 end-page: 784 ident: bb0135 article-title: Validity and power in hemodynamic response modeling: a comparison study and a new approach publication-title: Hum. Brain Mapp. – volume: 63 start-page: 902 year: 2009 end-page: 915 ident: bb0180 article-title: Bayesian reconstruction of natural images from human brain activity publication-title: Neuron – volume: 15 start-page: 83 year: 2002 end-page: 97 ident: bb0100 article-title: Detecting latency differences in event-related BOLD responses: application to words versus nonwords and initial versus repeated face presentations publication-title: NeuroImage – volume: 60 start-page: 915 year: 2008 end-page: 929 ident: bb0160 article-title: Visual image reconstruction from human brain activity using a combination of multiscale local image decoders publication-title: Neuron – volume: 40 start-page: 1606 year: 2008 end-page: 1618 ident: bb0025 article-title: The impact of temporal regularization on estimates of the BOLD hemodynamic response function: a comparative analysis publication-title: NeuroImage – volume: 56 start-page: 400 year: 2011 end-page: 410 ident: bb0175 article-title: Encoding and decoding in fMRI publication-title: NeuroImage – volume: 75 start-page: 136 year: 2013 end-page: 145 ident: bb0260 article-title: A semi-parametric model of the hemodynamic response for multi-subject fMRI data publication-title: NeuroImage – volume: 41 start-page: 149 year: 1999 end-page: 170 ident: bb0210 article-title: Unconditional non-asymptotic one-sided tests for independent binomial proportions when the interest lies in showing non-inferiority and/or superiority publication-title: Biom. J. – volume: 8 start-page: 109 year: 1999 end-page: 114 ident: bb0045 article-title: Optimal experimental design for event-related fMRI publication-title: Hum. Brain Mapp. – volume: 36 start-page: 1627 year: 1964 end-page: 1639 ident: bb0215 article-title: Smoothing and differentiation of data by simplified least squares procedures publication-title: Anal. Chem. – volume: 98C start-page: 61 year: 2014 end-page: 72 ident: bb0050 article-title: A hierarchical model for simultaneous detection and estimation in multi-subject fMRI studies publication-title: NeuroImage – volume: 16 start-page: 4207 year: 1996 end-page: 4221 ident: bb0020 article-title: Linear Systems Analysis of Functional Magnetic Resonance Imaging in Human V1 – volume: 9 start-page: 416 year: 1999 end-page: 429 ident: bb0075 article-title: Deconvolution of impulse response in event-related BOLD fMRI publication-title: NeuroImage – start-page: 3 year: 2013 end-page: 6 ident: bb0190 article-title: HRF estimation improves sensitivity of fMRI encoding and decoding models publication-title: Proceedings of the 3nd International Workshop on Pattern Recognition in NeuroImaging – volume: 39 start-page: 41 year: 1998 end-page: 52 ident: bb0070 article-title: Nonlinear event-related responses in fMRI publication-title: Magn. Reson. Med. – volume: 62 start-page: 1429 year: 2012 end-page: 1438 ident: bb0230 article-title: Spatiotemporal activity estimation for multivoxel pattern analysis with rapid event-related designs publication-title: NeuroImage – volume: 19 start-page: 261 year: 2003 end-page: 270 ident: bb0040 article-title: Functional magnetic resonance imaging (fMRI) brain reading: detecting and classifying distributed patterns of fMRI activity in human visual cortex publication-title: NeuroImage – volume: 2013 start-page: 643129 year: 2013 ident: bb0130 article-title: A mixed L2 norm regularized HRF estimation method for rapid event-related fMRI experiments publication-title: Comput. Math. Methods Med. – volume: 53 start-page: 3488 year: 2005 end-page: 3502 ident: bb0150 article-title: Joint detection-estimation of brain activity in functional MRI: a multichannel deconvolution solution publication-title: IEEE Trans. Signal Process. – volume: 21 start-page: 1639 year: 2004 end-page: 1651 ident: bb0090 article-title: Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses publication-title: NeuroImage – volume: 62 start-page: 871 year: 2012 end-page: 880 ident: bb0205 article-title: The general linear model and fMRI: does love last forever? publication-title: NeuroImage – volume: 59 start-page: 2636 year: 2012 end-page: 2643 ident: bb0170 article-title: Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses publication-title: NeuroImage – volume: 36 start-page: 88 year: 2007 end-page: 99 ident: bb0165 article-title: Dynamic discrimination analysis: a spatial–temporal SVM publication-title: NeuroImage – volume: 82 start-page: 433 year: 2013 end-page: 448 ident: bb0010 article-title: Group-level impacts of within- and between-subject hemodynamic variability in fMRI publication-title: NeuroImage – volume: 7 start-page: 523 year: 2006 end-page: 534 ident: bb0095 article-title: Decoding mental states from brain activity in humans publication-title: Nat. Rev. Neurosci. – year: 2006 ident: bb0185 article-title: Numerical Optimization, Series in Operations Research and Financial Engineering – volume: 22 start-page: 1235 year: 2003 end-page: 1251 ident: bb0035 article-title: Unsupervised robust nonparametric estimation of the hemodynamic response function for any fMRI experiment publication-title: IEEE Trans. Med. Imaging – volume: 42 start-page: 1381 year: 2008 end-page: 1396 ident: bb0145 article-title: Bayesian deconvolution of fMRI data using bilinear dynamical systems publication-title: NeuroImage – volume: 81 start-page: 347 year: 2013 end-page: 357 ident: bb0055 article-title: Multivariate decoding of brain images using ordinal regression publication-title: NeuroImage – year: 1991 ident: bb0105 article-title: Topics in Matrix Analysis – year: 2011 ident: bb0200 article-title: Handbook of Functional MRI Data Analysis – start-page: 211 year: 2013 end-page: 215 ident: bb0005 article-title: Hemodynamic estimation based on consensus clustering publication-title: 2013 International Workshop on Pattern Recognition in Neuroimaging – volume: 452 start-page: 352 year: 2008 end-page: 355 ident: bb0115 article-title: Identifying natural images from human brain activity publication-title: Nature – year: 2014 ident: bb0015 article-title: Decoding perceptual thresholds from MEG/EEG publication-title: Pattern Recoginition in Neuroimaging (PRNI) (2014). Tubingen, Germany – volume: 315 start-page: 515 year: 2007 end-page: 518 ident: bb0225 article-title: The neural basis of loss aversion in decision-making under risk publication-title: Science (New York, N.Y.) – volume: 30 start-page: 81 year: 1938 end-page: 93 ident: bb0120 article-title: A new measure of rank correlation publication-title: Biometrika – volume: 45 start-page: 503 year: 1989 end-page: 528 ident: bb0140 article-title: On the limited memory BFGS method for large scale optimization publication-title: Math. Program. – volume: 5 start-page: 1159 year: 2011 ident: bb0240 article-title: Encoding and decoding V1 fMRI responses to natural images with sparse nonparametric models publication-title: Ann. Appl. Stat. – year: 2011 ident: bb0110 article-title: fMRI of Human Visual Areas in Response to Natural Images – volume: 12 start-page: 2825 year: 2011 end-page: 2830 ident: bb0195 article-title: Scikit-learn: machine learning in python publication-title: J. Mach. Learn. Res. – volume: 7 start-page: 904 year: 2013 end-page: 935 ident: bb0245 article-title: Multiscale adaptive smoothing models for the hemodynamic response function in fMRI publication-title: Ann. Appl. Stat. – year: 2011 ident: bb0060 article-title: Statistical Parametric Mapping: The Analysis of Functional Brain Images: The Analysis of Functional Brain Images – volume: 19 start-page: 1188 year: 2000 end-page: 1201 ident: bb0085 article-title: Modeling the haemodynamic response in fMRI using smooth FIR filters publication-title: IEEE Trans. Med. Imaging – volume: 19 start-page: 1 year: 2003 end-page: 17 ident: bb0155 article-title: Robust bayesian estimation of the hemodynamic response function in event-related BOLD fMRI using basic physiological information publication-title: Hum. Brain Mapp. – volume: 21 start-page: 215 year: 1979 end-page: 223 ident: bb0080 article-title: Generalized cross-validation as a method for choosing a good ridge parameter publication-title: Technometrics – volume: 21 start-page: 1748 year: 2004 end-page: 1761 ident: bb0250 article-title: Constrained linear basis sets for HRF modelling using variational bayes publication-title: NeuroImage – volume: 2 year: 2008 ident: bb0125 article-title: Representational similarity analysis—connecting the branches of systems neuroscience publication-title: Front. Syst. Neurosci. – year: 1995 ident: bb0065 article-title: Statistical Parametric Maps in Functional Imaging: A General Linear Approach – volume: 45 start-page: 503 issue: 1–3 year: 1989 ident: 10.1016/j.neuroimage.2014.09.060_bb0140 article-title: On the limited memory BFGS method for large scale optimization publication-title: Math. Program. doi: 10.1007/BF01589116 – volume: 315 start-page: 515 issue: 5811 year: 2007 ident: 10.1016/j.neuroimage.2014.09.060_bb0225 article-title: The neural basis of loss aversion in decision-making under risk publication-title: Science (New York, N.Y.) doi: 10.1126/science.1134239 – volume: 39 start-page: 41 issue: 1 year: 1998 ident: 10.1016/j.neuroimage.2014.09.060_bb0070 article-title: Nonlinear event-related responses in fMRI publication-title: Magn. Reson. Med. doi: 10.1002/mrm.1910390109 – volume: 62 start-page: 1429 issue: 3 year: 2012 ident: 10.1016/j.neuroimage.2014.09.060_bb0230 article-title: Spatiotemporal activity estimation for multivoxel pattern analysis with rapid event-related designs publication-title: NeuroImage doi: 10.1016/j.neuroimage.2012.05.057 – volume: 2013 start-page: 643129 year: 2013 ident: 10.1016/j.neuroimage.2014.09.060_bb0130 article-title: A mixed L2 norm regularized HRF estimation method for rapid event-related fMRI experiments publication-title: Comput. Math. Methods Med. doi: 10.1155/2013/643129 – year: 2011 ident: 10.1016/j.neuroimage.2014.09.060_bb0060 – volume: 21 start-page: 1639 issue: 4 year: 2004 ident: 10.1016/j.neuroimage.2014.09.060_bb0090 article-title: Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses publication-title: NeuroImage doi: 10.1016/j.neuroimage.2003.11.029 – volume: 30 start-page: 81 issue: 1/2 year: 1938 ident: 10.1016/j.neuroimage.2014.09.060_bb0120 article-title: A new measure of rank correlation publication-title: Biometrika doi: 10.2307/2332226 – year: 2011 ident: 10.1016/j.neuroimage.2014.09.060_bb0200 – volume: 8 start-page: 109 issue: 2–3 year: 1999 ident: 10.1016/j.neuroimage.2014.09.060_bb0045 article-title: Optimal experimental design for event-related fMRI publication-title: Hum. Brain Mapp. doi: 10.1002/(SICI)1097-0193(1999)8:2/3<109::AID-HBM7>3.0.CO;2-W – volume: 42 start-page: 1381 issue: 4 year: 2008 ident: 10.1016/j.neuroimage.2014.09.060_bb0145 article-title: Bayesian deconvolution of fMRI data using bilinear dynamical systems publication-title: NeuroImage doi: 10.1016/j.neuroimage.2008.05.052 – volume: 19 start-page: 1 issue: 1 year: 2003 ident: 10.1016/j.neuroimage.2014.09.060_bb0155 article-title: Robust bayesian estimation of the hemodynamic response function in event-related BOLD fMRI using basic physiological information publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.10100 – start-page: 3 year: 2013 ident: 10.1016/j.neuroimage.2014.09.060_bb0190 article-title: HRF estimation improves sensitivity of fMRI encoding and decoding models – volume: 83 start-page: 951 year: 2013 ident: 10.1016/j.neuroimage.2014.09.060_bb0220 article-title: Linear reconstruction of perceived images from human brain activity publication-title: NeuroImage doi: 10.1016/j.neuroimage.2013.07.043 – year: 2014 ident: 10.1016/j.neuroimage.2014.09.060_bb0015 article-title: Decoding perceptual thresholds from MEG/EEG – volume: 7 start-page: 904 issue: 2 year: 2013 ident: 10.1016/j.neuroimage.2014.09.060_bb0245 article-title: Multiscale adaptive smoothing models for the hemodynamic response function in fMRI publication-title: Ann. Appl. Stat. doi: 10.1214/12-AOAS609 – volume: 452 start-page: 352 issue: 7185 year: 2008 ident: 10.1016/j.neuroimage.2014.09.060_bb0115 article-title: Identifying natural images from human brain activity publication-title: Nature doi: 10.1038/nature06713 – volume: 56 start-page: 400 issue: 2 year: 2011 ident: 10.1016/j.neuroimage.2014.09.060_bb0175 article-title: Encoding and decoding in fMRI publication-title: NeuroImage doi: 10.1016/j.neuroimage.2010.07.073 – year: 2006 ident: 10.1016/j.neuroimage.2014.09.060_bb0185 – volume: 36 start-page: 1627 issue: 8 year: 1964 ident: 10.1016/j.neuroimage.2014.09.060_bb0215 article-title: Smoothing and differentiation of data by simplified least squares procedures publication-title: Anal. Chem. doi: 10.1021/ac60214a047 – volume: 21 start-page: 1748 issue: 4 year: 2004 ident: 10.1016/j.neuroimage.2014.09.060_bb0250 article-title: Constrained linear basis sets for HRF modelling using variational bayes publication-title: NeuroImage doi: 10.1016/j.neuroimage.2003.12.024 – volume: 82 start-page: 433 year: 2013 ident: 10.1016/j.neuroimage.2014.09.060_bb0010 article-title: Group-level impacts of within- and between-subject hemodynamic variability in fMRI publication-title: NeuroImage doi: 10.1016/j.neuroimage.2013.05.100 – volume: 41 start-page: 149 issue: 2 year: 1999 ident: 10.1016/j.neuroimage.2014.09.060_bb0210 article-title: Unconditional non-asymptotic one-sided tests for independent binomial proportions when the interest lies in showing non-inferiority and/or superiority publication-title: Biom. J. doi: 10.1002/(SICI)1521-4036(199905)41:2<149::AID-BIMJ149>3.0.CO;2-E – volume: 62 start-page: 871 issue: 2 year: 2012 ident: 10.1016/j.neuroimage.2014.09.060_bb0205 article-title: The general linear model and fMRI: does love last forever? publication-title: NeuroImage doi: 10.1016/j.neuroimage.2012.01.133 – start-page: 211 year: 2013 ident: 10.1016/j.neuroimage.2014.09.060_bb0005 article-title: Hemodynamic estimation based on consensus clustering – volume: 16 start-page: 4207 issue: 13 year: 1996 ident: 10.1016/j.neuroimage.2014.09.060_bb0020 article-title: Linear Systems Analysis of Functional Magnetic Resonance Imaging in Human V1 – volume: 22 start-page: 1235 issue: 10 year: 2003 ident: 10.1016/j.neuroimage.2014.09.060_bb0035 article-title: Unsupervised robust nonparametric estimation of the hemodynamic response function for any fMRI experiment publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2003.817759 – volume: 21 start-page: 215 issue: 2 year: 1979 ident: 10.1016/j.neuroimage.2014.09.060_bb0080 article-title: Generalized cross-validation as a method for choosing a good ridge parameter publication-title: Technometrics doi: 10.1080/00401706.1979.10489751 – volume: 59 start-page: 2636 issue: 3 year: 2012 ident: 10.1016/j.neuroimage.2014.09.060_bb0170 article-title: Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses publication-title: NeuroImage doi: 10.1016/j.neuroimage.2011.08.076 – volume: 2 year: 2008 ident: 10.1016/j.neuroimage.2014.09.060_bb0125 article-title: Representational similarity analysis—connecting the branches of systems neuroscience publication-title: Front. Syst. Neurosci. – volume: 75 start-page: 136 year: 2013 ident: 10.1016/j.neuroimage.2014.09.060_bb0260 article-title: A semi-parametric model of the hemodynamic response for multi-subject fMRI data publication-title: NeuroImage doi: 10.1016/j.neuroimage.2013.02.048 – volume: 28 start-page: 764 issue: 8 year: 2007 ident: 10.1016/j.neuroimage.2014.09.060_bb0135 article-title: Validity and power in hemodynamic response modeling: a comparison study and a new approach publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.20310 – volume: 19 start-page: 1188 issue: 12 year: 2000 ident: 10.1016/j.neuroimage.2014.09.060_bb0085 article-title: Modeling the haemodynamic response in fMRI using smooth FIR filters publication-title: IEEE Trans. Med. Imaging doi: 10.1109/42.897811 – volume: 7 start-page: 523 issue: 7 year: 2006 ident: 10.1016/j.neuroimage.2014.09.060_bb0095 article-title: Decoding mental states from brain activity in humans publication-title: Nat. Rev. Neurosci. doi: 10.1038/nrn1931 – ident: 10.1016/j.neuroimage.2014.09.060_bb0110 – volume: 19 start-page: 261 issue: 2 year: 2003 ident: 10.1016/j.neuroimage.2014.09.060_bb0040 article-title: Functional magnetic resonance imaging (fMRI) brain reading: detecting and classifying distributed patterns of fMRI activity in human visual cortex publication-title: NeuroImage doi: 10.1016/S1053-8119(03)00049-1 – volume: 63 start-page: 1754 issue: 3 year: 2012 ident: 10.1016/j.neuroimage.2014.09.060_bb0255 article-title: Nonparametric inference of the hemodynamic response using multi-subject fMRI data publication-title: NeuroImage doi: 10.1016/j.neuroimage.2012.08.014 – volume: 40 start-page: 1606 issue: 4 year: 2008 ident: 10.1016/j.neuroimage.2014.09.060_bb0025 article-title: The impact of temporal regularization on estimates of the BOLD hemodynamic response function: a comparative analysis publication-title: NeuroImage doi: 10.1016/j.neuroimage.2008.01.011 – year: 1991 ident: 10.1016/j.neuroimage.2014.09.060_bb0105 – year: 1995 ident: 10.1016/j.neuroimage.2014.09.060_bb0065 – volume: 60 start-page: 915 issue: 5 year: 2008 ident: 10.1016/j.neuroimage.2014.09.060_bb0160 article-title: Visual image reconstruction from human brain activity using a combination of multiscale local image decoders publication-title: Neuron doi: 10.1016/j.neuron.2008.11.004 – volume: 98C start-page: 61 year: 2014 ident: 10.1016/j.neuroimage.2014.09.060_bb0050 article-title: A hierarchical model for simultaneous detection and estimation in multi-subject fMRI studies publication-title: NeuroImage doi: 10.1016/j.neuroimage.2014.04.052 – volume: 15 start-page: 83 issue: 1 year: 2002 ident: 10.1016/j.neuroimage.2014.09.060_bb0100 article-title: Detecting latency differences in event-related BOLD responses: application to words versus nonwords and initial versus repeated face presentations publication-title: NeuroImage doi: 10.1006/nimg.2001.0940 – volume: 53 start-page: 3488 issue: 9 year: 2005 ident: 10.1016/j.neuroimage.2014.09.060_bb0150 article-title: Joint detection-estimation of brain activity in functional MRI: a multichannel deconvolution solution publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2005.853303 – volume: 63 start-page: 902 issue: 6 year: 2009 ident: 10.1016/j.neuroimage.2014.09.060_bb0180 article-title: Bayesian reconstruction of natural images from human brain activity publication-title: Neuron doi: 10.1016/j.neuron.2009.09.006 – volume: 36 start-page: 88 issue: 1 year: 2007 ident: 10.1016/j.neuroimage.2014.09.060_bb0165 article-title: Dynamic discrimination analysis: a spatial–temporal SVM publication-title: NeuroImage doi: 10.1016/j.neuroimage.2007.02.020 – volume: 29 start-page: 1059 issue: 4 year: 2010 ident: 10.1016/j.neuroimage.2014.09.060_bb0235 article-title: Spatially adaptive mixture modeling for analysis of fMRI time series publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2010.2042064 – volume: 9 start-page: 416 issue: 4 year: 1999 ident: 10.1016/j.neuroimage.2014.09.060_bb0075 article-title: Deconvolution of impulse response in event-related BOLD fMRI publication-title: NeuroImage doi: 10.1006/nimg.1998.0419 – volume: 81 start-page: 347 year: 2013 ident: 10.1016/j.neuroimage.2014.09.060_bb0055 article-title: Multivariate decoding of brain images using ordinal regression publication-title: NeuroImage doi: 10.1016/j.neuroimage.2013.05.036 – volume: 5 start-page: 1159 issue: 2B year: 2011 ident: 10.1016/j.neuroimage.2014.09.060_bb0240 article-title: Encoding and decoding V1 fMRI responses to natural images with sparse nonparametric models publication-title: Ann. Appl. Stat. doi: 10.1214/11-AOAS476 – volume: 12 start-page: 2825 year: 2011 ident: 10.1016/j.neuroimage.2014.09.060_bb0195 article-title: Scikit-learn: machine learning in python publication-title: J. Mach. Learn. Res. – volume: 15 (Pt 3) start-page: 180 year: 2012 ident: 10.1016/j.neuroimage.2014.09.060_bb0030 article-title: Hemodynamic-informed parcellation of fMRI data in a joint detection estimation framework
SSID	ssj0009148
Score	2.3967185
Snippet	Despite the common usage of a canonical, data-independent, hemodynamic response function (HRF), it is known that the shape of the HRF varies across brain...
SourceID	hal proquest pubmed crossref elsevier
SourceType	Open Access Repository Aggregation Database Index Database Enrichment Source Publisher
StartPage	209
SubjectTerms	BOLD Brain - physiology Brain Mapping - methods Computer Science Decoding Derivatives Economic models Encoding Estimates Finite impulse response (FIR) Functional MRI (fMRI) Hemodynamic response function (HRF) Humans Image Processing, Computer-Assisted Machine Learning Magnetic Resonance Imaging - methods Methods Models, Neurological Neurovascular Coupling Optimization Regression Analysis Studies Visual Perception - physiology
Title	Data-driven HRF estimation for encoding and decoding models
URI	https://www.clinicalkey.com/#!/content/1-s2.0-S1053811914008027 https://dx.doi.org/10.1016/j.neuroimage.2014.09.060 https://www.ncbi.nlm.nih.gov/pubmed/25304775 https://www.proquest.com/docview/1625982904 https://www.proquest.com/docview/1634270195 https://www.proquest.com/docview/1635018044 https://inria.hal.science/hal-00952554
Volume	104
WOSCitedRecordID	wos000345393800021&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1095-9572 dateEnd: 20191231 omitProxy: false ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: AIEXJ dateStart: 19950301 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1095-9572 dateEnd: 20251011 omitProxy: false ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: M7P dateStart: 19980501 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1095-9572 dateEnd: 20251011 omitProxy: false ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: 7X7 dateStart: 20020801 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1095-9572 dateEnd: 20251011 omitProxy: false ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: BENPR dateStart: 19980501 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Psychology Database customDbUrl: eissn: 1095-9572 dateEnd: 20251011 omitProxy: false ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: M2M dateStart: 20020801 isFulltext: true titleUrlDefault: https://www.proquest.com/psychology providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3db9MwED_RDaG9jG_oGFVAvFrEjj9i7QGNsaoPa1VNIPXNcpxYDEG6td3-fnyJk77AVGkvkZz4Ijs-n3_xnX8H8ClNneU680Rw7gkvJCc6F5KwKnPe5QWTtqHMv1CzWb5Y6HnccFvHsMrOJjaGulw63CP_TBGoo9ePf7m-IZg1Cr2rMYXGAPaRJYE1oXvzLeku5e1ROJGRnFIdI3na-K6GL_LqT5i1GODVsp02RJX_XJ4GPzFO8n8gtFmMxk8f2o1ncBhhaHLa6s1zeFTVL-DJNDraX8LJN7uxpFyhLUwml-MEyTjaU45JgLkJ0l_iqpfYukzKKhaatDrrV_BjfP79bEJingXihJYb4q0U2vrCuSKlzokwiWnquc1kya2QufABZwWkQ2mWO1Ep5nOhvCqVylNtS5-9hr16WVdvIZGZU55JjbxkPKtYEGcBY_q0cLbSpRyC6j6vcZGEHHNh_DZdtNkvsx0YgwNjUm3CwAyB9pLXLRHHDjK6G0HTHTQNptGE1WIH2ZNeNoKRFmTsKP0xKEzfUOTwnpxeGLyHoDb8x_E7PoTjTkdMtBxrs1WQIXzoH4c5j44cW1fLW6yTcYY8-uLeOsjVmPLwnjetrvbNYQKdrUoc3d-Ad3AQuiTaDadj2Nusbqv38Njdba7WqxEM1EI113wE-1_PZ_PLUJqy6aiZhH8B04EzkQ
linkProvider	ProQuest
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB61BQEX3o-FAgHB0SJ2bCdWhVBFWW3V3RVCRerNdRxbFEG27G6L-FP8Rjxxkr1AtZceOOYxkZN8nvkSz3wD8CpNreEq80Rw7gkvJSeqEJIwl1lvi5JJ00jmj_PptDg6Uh834HdXC4NplZ1PbBx1NbP4j_wNRaKOq3783ekPgl2jcHW1a6ERYXHgfv0Mn2yLt_t74f2-Zmz44fD9iLRdBYgVSi6JN1Io40try5RaKwJkaeq5yWTFjZCF8IFVhLhOaVZY4XLmC5H7vMrzIlWm8lm47iZc4VgyiqmCbLIS-aU8lt6JjBSUqjZzKOaTNfqUJ9-Dl8CEsqiu2ghj_jUcbn7BvMx_kd4m-A1v_W-P7TbcbGl2shvnxR3YcPVduDZpEwnuwc6eWRpSzdHXJ6NPwwTFRmIVZxJofILynhjVE1NXSeXajaZt0OI-fL6UoT-ArXpWu0eQyMzmnkmFums8cyyYs8ChfVpa41QlB5B3r1PbVmQde31801023Ve9AoJGIOhU6QCEAdDe8jQKjaxhozrE6K6QNrh-HaLhGrY7vW1LtiKJWtP6ZQBoP1DUKB_tjjXuQ9IevlP5OR_AdodJ3XrGhV4BcgAv-sPBp-FCland7AzPyTjDPgHiwnNQizLl4ToP49zoh8MELibn4vHFA3gO10eHk7Ee708PnsCNcHsi_lzbhq3l_Mw9hav2fHmymD9rpnkCx5c9Qf4AnTKLkQ
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB61Kaq48H4EChgER6t-7Hq9qhAqpFGqhiiqQOptu157RRE4JUmL-Gv8Oma8a-cCVS49cPRjrLX9zcxn7-w3AK-jyGgmUxtyxmzIioyFMudZmFSpsSYvkkw3kvljMZnkJydyugG_27UwVFbZxsQmUJczQ__Id2Mi6jTrx3atL4uYDobvzn-E1EGKZlrbdhoOIkfVr5_4-bZ4ezjAd_0mSYYHnz6MQt9hIDRcZsvQ6oxLbQtjiig2hiN848gynWYl0zzLuUWGgTk-jtPc8EokNufCilKIPJK6tCledxO2BJIM1oOt9weT6fFK8jdmbiEeT8M8jqWvI3LVZY1a5dl3jBlUXua0VhuZzL8mx80vVKX5LwrcpMLh7f_5Id6BW56AB_vOY-7CRlXfg-2PvsTgPuwN9FKH5ZyyQDA6HgYkQ-LWdwZI8AMS_qR8H-i6DMrKbzQNhRYP4PO1DP0h9OpZXT2GIEuNsEkmSZGNpVWC5gmyaxsVRleyzPog2lerjJdfpy4g31RbZ_dVrUChCBQqkgpB0Ye4szx3EiRr2MgWPapdYotJQWGeXMN2r7P1NMzRqzWtXyFYu4GSevlof6xoH9F5_IJll6wPOy0-lY-ZC7UCZx9edocx2tEUlq6r2QWdk7KEOgjwK88hlcqI4XUeOT_phpNwmmYW_MnVA3gB2-gXanw4OXoKN_HuuPvrtgO95fyiegY3zOXybDF_7n0-gNPr9pA_dK2Vqw
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Data-driven+HRF+estimation+for+encoding+and+decoding+models&rft.jtitle=NeuroImage+%28Orlando%2C+Fla.%29&rft.au=Pedregosa%2C+Fabian&rft.au=Eickenberg%2C+Michael&rft.au=Ciuciu%2C+Philippe&rft.au=Thirion%2C+Bertrand&rft.date=2015-01-01&rft.eissn=1095-9572&rft.volume=104&rft.spage=209&rft_id=info:doi/10.1016%2Fj.neuroimage.2014.09.060&rft_id=info%3Apmid%2F25304775&rft.externalDocID=25304775
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1053-8119&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1053-8119&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1053-8119&client=summon