Moderated t-tests for group-level fMRI analysis
•We introduce a moderated t-statistic for performing group-level fMRI analysis.•The approach helps alleviate problems related to small sample sizes.•The approach outperforms several standard approaches.•An R-package is introduced for application of the method to fMRI data. In recent years, there has...
Gespeichert in:
| Veröffentlicht in: | NeuroImage (Orlando, Fla.) Jg. 237; S. 118141 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
United States
Elsevier Inc
15.08.2021
Elsevier Limited Elsevier |
| Schlagworte: | |
| ISSN: | 1053-8119, 1095-9572, 1095-9572 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | •We introduce a moderated t-statistic for performing group-level fMRI analysis.•The approach helps alleviate problems related to small sample sizes.•The approach outperforms several standard approaches.•An R-package is introduced for application of the method to fMRI data.
In recent years, there has been significant criticism of functional magnetic resonance imaging (fMRI) studies with small sample sizes. The argument is that such studies have low statistical power, as well as reduced likelihood for statistically significant results to be true effects. The prevalence of these studies has led to a situation where a large number of published results are not replicable and likely false. Despite this growing body of evidence, small sample fMRI studies continue to be regularly performed; likely due to the high cost of scanning. In this report we investigate the use of a moderated t-statistic for performing group-level fMRI analysis to help alleviate problems related to small sample sizes. The proposed approach, implemented in the popular R-package LIMMA (linear models for microarray data), has found wide usage in the genomics literature for dealing with similar issues. Utilizing task-based fMRI data from the Human Connectome Project (HCP), we compare the performance of the moderated t-statistic with the standard t-statistic, as well as the pseudo t-statistic commonly used in non-parametric fMRI analysis. We find that the moderated t-test significantly outperforms both alternative approaches for studies with sample sizes less than 40 subjects. Further, we find that the results were consistent both when using voxel-based and cluster-based thresholding. We also introduce an R-package, LIMMI (linear models for medical images), that provides a quick and convenient way to apply the method to fMRI data. |
|---|---|
| AbstractList | •We introduce a moderated t-statistic for performing group-level fMRI analysis.•The approach helps alleviate problems related to small sample sizes.•The approach outperforms several standard approaches.•An R-package is introduced for application of the method to fMRI data.
In recent years, there has been significant criticism of functional magnetic resonance imaging (fMRI) studies with small sample sizes. The argument is that such studies have low statistical power, as well as reduced likelihood for statistically significant results to be true effects. The prevalence of these studies has led to a situation where a large number of published results are not replicable and likely false. Despite this growing body of evidence, small sample fMRI studies continue to be regularly performed; likely due to the high cost of scanning. In this report we investigate the use of a moderated t-statistic for performing group-level fMRI analysis to help alleviate problems related to small sample sizes. The proposed approach, implemented in the popular R-package LIMMA (linear models for microarray data), has found wide usage in the genomics literature for dealing with similar issues. Utilizing task-based fMRI data from the Human Connectome Project (HCP), we compare the performance of the moderated t-statistic with the standard t-statistic, as well as the pseudo t-statistic commonly used in non-parametric fMRI analysis. We find that the moderated t-test significantly outperforms both alternative approaches for studies with sample sizes less than 40 subjects. Further, we find that the results were consistent both when using voxel-based and cluster-based thresholding. We also introduce an R-package, LIMMI (linear models for medical images), that provides a quick and convenient way to apply the method to fMRI data. In recent years, there has been significant criticism of functional magnetic resonance imaging (fMRI) studies with small sample sizes. The argument is that such studies have low statistical power, as well as reduced likelihood for statistically significant results to be true effects. The prevalence of these studies has led to a situation where a large number of published results are not replicable and likely false. Despite this growing body of evidence, small sample fMRI studies continue to be regularly performed; likely due to the high cost of scanning. In this report we investigate the use of a moderated t-statistic for performing group-level fMRI analysis to help alleviate problems related to small sample sizes. The proposed approach, implemented in the popular R-package LIMMA (linear models for microarray data), has found wide usage in the genomics literature for dealing with similar issues. Utilizing task-based fMRI data from the Human Connectome Project (HCP), we compare the performance of the moderated t-statistic with the standard t-statistic, as well as the pseudo t-statistic commonly used in non-parametric fMRI analysis. We find that the moderated t-test significantly outperforms both alternative approaches for studies with sample sizes less than 40 subjects. Further, we find that the results were consistent both when using voxel-based and cluster-based thresholding. We also introduce an R-package, LIMMI (linear models for medical images), that provides a quick and convenient way to apply the method to fMRI data. In recent years, there has been significant criticism of functional magnetic resonance imaging (fMRI) studies with small sample sizes. The argument is that such studies have low statistical power, as well as reduced likelihood for statistically significant results to be true effects. The prevalence of these studies has led to a situation where a large number of published results are not replicable and likely false. Despite this growing body of evidence, small sample fMRI studies continue to be regularly performed; likely due to the high cost of scanning. In this report we investigate the use of a moderated t-statistic for performing group-level fMRI analysis to help alleviate problems related to small sample sizes. The proposed approach, implemented in the popular R-package LIMMA (linear models for microarray data), has found wide usage in the genomics literature for dealing with similar issues. Utilizing task-based fMRI data from the Human Connectome Project (HCP), we compare the performance of the moderated t-statistic with the standard t-statistic, as well as the pseudo t-statistic commonly used in non-parametric fMRI analysis. We find that the moderated t-test significantly outperforms both alternative approaches for studies with sample sizes less than 40 subjects. Further, we find that the results were consistent both when using voxel-based and cluster-based thresholding. We also introduce an R-package, LIMMI (linear models for medical images), that provides a quick and convenient way to apply the method to fMRI data.In recent years, there has been significant criticism of functional magnetic resonance imaging (fMRI) studies with small sample sizes. The argument is that such studies have low statistical power, as well as reduced likelihood for statistically significant results to be true effects. The prevalence of these studies has led to a situation where a large number of published results are not replicable and likely false. Despite this growing body of evidence, small sample fMRI studies continue to be regularly performed; likely due to the high cost of scanning. In this report we investigate the use of a moderated t-statistic for performing group-level fMRI analysis to help alleviate problems related to small sample sizes. The proposed approach, implemented in the popular R-package LIMMA (linear models for microarray data), has found wide usage in the genomics literature for dealing with similar issues. Utilizing task-based fMRI data from the Human Connectome Project (HCP), we compare the performance of the moderated t-statistic with the standard t-statistic, as well as the pseudo t-statistic commonly used in non-parametric fMRI analysis. We find that the moderated t-test significantly outperforms both alternative approaches for studies with sample sizes less than 40 subjects. Further, we find that the results were consistent both when using voxel-based and cluster-based thresholding. We also introduce an R-package, LIMMI (linear models for medical images), that provides a quick and convenient way to apply the method to fMRI data. |
| ArticleNumber | 118141 |
| Author | Lindquist, Martin A. Wang, Guoqing Muschelli, John |
| Author_xml | – sequence: 1 givenname: Guoqing surname: Wang fullname: Wang, Guoqing – sequence: 2 givenname: John surname: Muschelli fullname: Muschelli, John – sequence: 3 givenname: Martin A. orcidid: 0000-0003-2289-0828 surname: Lindquist fullname: Lindquist, Martin A. email: mlindqui@jhsph.edu |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33962000$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNkktv1DAUhSNURB_wF1AkNmwy9SN24g2CVtCO1AoJwdpy7JvBwRMPtjPS_HscpqV0VrPy6_jz8b3nvDgZ_QhFUWK0wAjzy2ExwhS8XasVLAgieIFxi2v8ojjDSLBKsIaczHNGqxZjcVqcxzgghASu21fFKaWCk7w8Ky7vvYGgEpgyVQliimXvQ7kKftpUDrbgyv7-27JUo3K7aOPr4mWvXIQ3D-NF8ePL5-_Xt9Xd15vl9ae7SnOKUlUjiknLFOYcqFKMQd-0XDeYINpz0nYNFy3VUAvEG-gEEg1TRkGHWwSiNvSiWO65xqtBbkL-athJr6z8u-HDSqqQrHYgDTdaI9px05EadX2He44zG9MuM02XWR_2rM3UrcFoGFNQ7hn0-clof8qV38qWCCaIyID3D4Dgf0-5SHJtowbn1Ah-ipIwUlNOasKy9N2BdPBTyMWbVTUXnAneZNXb_x39s_LYlyfLOvgYA_RS26SS9bNB6yRGcg6CHORTEOQcBLkPQga0B4DHN464erW_Crm_WwtBRm1h1GBsAJ1yA-wxkI8HEO3saLVyv2B3HOIPwLToPQ |
| CitedBy_id | crossref_primary_10_7554_eLife_100123 crossref_primary_10_1093_biomtc_ujae116 crossref_primary_10_1002_hbm_26692 crossref_primary_10_1080_15622975_2025_2556848 crossref_primary_10_1038_s41598_024_67662_x crossref_primary_10_3389_fgene_2021_712306 crossref_primary_10_7554_eLife_100123_3 crossref_primary_10_1016_j_neuroimage_2022_119192 crossref_primary_10_3390_ijms25010474 crossref_primary_10_3390_cancers15123237 |
| Cites_doi | 10.1186/gb-2004-5-10-r80 10.1016/S1053-8119(18)31587-8 10.1093/nar/gkv007 10.1006/nimg.1996.0074 10.1016/j.neuroimage.2003.12.023 10.1101/295048 10.1038/nrn.2016.167 10.1016/j.neuroimage.2014.05.043 10.1002/hbm.1058 10.1016/j.neuroimage.2008.03.061 10.1001/jamapsychiatry.2016.3356 10.1093/biostatistics/kxn028 10.1038/nbt.3004 10.1016/j.neuroimage.2009.05.034 10.1038/nrn3475 10.1016/j.neuroimage.2012.03.093 10.1016/j.neuroimage.2013.05.033 10.3389/fnins.2015.00316 10.1016/j.neuroimage.2015.02.042 10.1016/j.neuroimage.2013.04.127 10.1016/j.neuroimage.2011.07.077 10.1016/j.neuroimage.2013.05.041 10.1080/01621459.1975.10479864 10.1016/j.neuroimage.2019.116468 10.1073/pnas.1602413113 10.1002/hbm.460010306 10.1016/j.neuroimage.2012.02.018 10.1007/0-387-29362-0_23 10.1016/j.neuroimage.2013.01.047 10.2202/1544-6115.1027 |
| ContentType | Journal Article |
| Copyright | 2021 The Author(s) Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved. 2021. The Author(s) |
| Copyright_xml | – notice: 2021 The Author(s) – notice: Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved. – notice: 2021. The Author(s) |
| DBID | 6I. AAFTH 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 5PM DOA |
| DOI | 10.1016/j.neuroimage.2021.118141 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access 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 (subscription) Natural Science Collection ProQuest One Community College ProQuest Central 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 PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| 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 |
| DatabaseTitleList | ProQuest One Psychology MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: BENPR name: ProQuest Central (subscription) url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1095-9572 |
| EndPage | 118141 |
| ExternalDocumentID | oai_doaj_org_article_d6dcc03b6db240bfb1f613ce13b5addb PMC8295929 33962000 10_1016_j_neuroimage_2021_118141 S1053811921004183 |
| Genre | Journal Article Research Support, N.I.H., Extramural |
| GrantInformation_xml | – fundername: NIBIB NIH HHS grantid: R01 EB016061 – fundername: NIBIB NIH HHS grantid: R01 EB026549 |
| 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 AAFWJ AAIKJ AAKOC AALRI AAOAW 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 ADVLN AEBSH AEFWE AEIPS AEKER AENEX AEUPX AFJKZ AFKRA AFPKN AFPUW AFRHN AFTJW AFXIZ AGUBO AGWIK AGYEJ AHHHB AHMBA AIEXJ AIGII AIIUN AIKHN AITUG AJRQY AJUYK AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU ANZVX APXCP AXJTR AZQEC BBNVY BENPR BHPHI BKOJK BLXMC BNPGV BPHCQ BVXVI CCPQU CS3 DM4 DU5 DWQXO EBS EFBJH EFKBS 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 OK1 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 6I. AACTN AADPK AAFTH AAIAV AAQFI ABLVK ABYKQ AFKWA AJOXV AMFUW C45 HMQ LCYCR NCXOZ SNS ZA5 29N 53G 9DU AAQXK AAYXX ABXDB ACRPL ADFGL ADMUD ADNMO ADXHL AFFHD AGHFR AGQPQ AKRLJ ASPBG AVWKF AZFZN CAG CITATION COF EFLBG EJD FEDTE FGOYB G-2 HDW HEI HMK HMO HVGLF HZ~ R2- SEW WUQ XPP ZMT ALIPV CGR CUY CVF ECM EIF NPM 3V. 7TK 7XB 8FD 8FK FR3 K9. P64 PKEHL PQEST PQUKI PRINS Q9U RC3 7X8 5PM |
| ID | FETCH-LOGICAL-c630t-4031285a166e3aa55ef786c71203f628b76983ce49067eb90975adaeb180e94d3 |
| IEDL.DBID | 7X7 |
| ISICitedReferencesCount | 9 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000671785900004&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 | Tue Oct 14 19:07:30 EDT 2025 Tue Nov 04 01:40:51 EST 2025 Sun Nov 09 10:46:21 EST 2025 Tue Oct 07 07:11:48 EDT 2025 Thu Apr 03 07:00:58 EDT 2025 Sat Nov 29 07:10:20 EST 2025 Tue Nov 18 20:36:48 EST 2025 Fri Feb 23 02:43:07 EST 2024 Tue Oct 14 19:31:00 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | fMRI Group analysis Moderated t-test LIMMA LIMMI |
| Language | English |
| License | This is an open access article under the CC BY-NC-ND license. Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c630t-4031285a166e3aa55ef786c71203f628b76983ce49067eb90975adaeb180e94d3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Credit authorship contribution statement Guoqing Wang: Conceptualization, Methodology, Software, Formal analysis, Writing - original draft. John Muschelli: Formal analysis, Software, Writing - review & editing. Martin A. Lindquist: Conceptualization, Methodology, Writing - review & editing, Supervision, Funding acquisition. |
| ORCID | 0000-0003-2289-0828 |
| OpenAccessLink | https://doaj.org/article/d6dcc03b6db240bfb1f613ce13b5addb |
| PMID | 33962000 |
| PQID | 2546965967 |
| PQPubID | 2031077 |
| PageCount | 1 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_d6dcc03b6db240bfb1f613ce13b5addb pubmedcentral_primary_oai_pubmedcentral_nih_gov_8295929 proquest_miscellaneous_2524362425 proquest_journals_2546965967 pubmed_primary_33962000 crossref_citationtrail_10_1016_j_neuroimage_2021_118141 crossref_primary_10_1016_j_neuroimage_2021_118141 elsevier_sciencedirect_doi_10_1016_j_neuroimage_2021_118141 elsevier_clinicalkey_doi_10_1016_j_neuroimage_2021_118141 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-08-15 |
| PublicationDateYYYYMMDD | 2021-08-15 |
| PublicationDate_xml | – month: 08 year: 2021 text: 2021-08-15 day: 15 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: Amsterdam |
| PublicationTitle | NeuroImage (Orlando, Fla.) |
| PublicationTitleAlternate | Neuroimage |
| PublicationYear | 2021 |
| Publisher | Elsevier Inc Elsevier Limited Elsevier |
| Publisher_xml | – name: Elsevier Inc – name: Elsevier Limited – name: Elsevier |
| References | Chen, Kang, Wang (bib0005) 2015; 9 Geuter, S., Qi, G., Welsh, R. C., Wager, T. D., Lindquist, M., 2018. Effect size and power in fMRI group analysis. bioRxiv, 1–23295048. 10.1101/295048 Mejia, Nebel, Shou, Crainiceanu, Pekar, Mostofsky, Caffo, Lindquist (bib0019) 2015; 112 Eklund, Nichols, Knutsson (bib0009) 2016; 113 Smyth (bib0031) 2004; 3 Shou, Eloyan, Nebel, Mejia, Pekar, Mostofsky, Caffo, Lindquist, Crainiceanu (bib0028) 2014; 102 Chen, G., Taylor, P. A., Cox, R. W., 2016. Is the statistic value all we should care about in neuroimaging?bioRxiv. Johnson, Kotz (bib0017) 1970; 2 R Core Team (bib0025) 2019 Holmes, Friston (bib0015) 1998; 7 Friston, Holmes, Poline, Price, Frith (bib0010) 1996; 4 Lindquist, Spicer, Asllani, Wager (bib0018) 2012; 59 Cohen (bib0006) 1988 Woolrich, Behrens, Beckmann, Jenkinson, Smith (bib0035) 2004; 21 James, Stein (bib0016) 1961; 1 Poldrack, Baker, Durnez, Gorgolewski, Matthews, Munafò, Nichols, Poline, Vul, Yarkoni (bib0024) 2017; 18 Su, Caffo, Garrett-Mayer, Bassett (bib0032) 2008; 10 Smyth (bib0030) 2005 Chen, Saad, Britton, Pine, Cox (bib0003) 2013; 73 Mumford, Nichols (bib0020) 2009; 47 Nichols, Holmes (bib0022) 2002; 15 Barch, Burgess, Harms, Petersen, Schlaggar, Corbetta, Glasser, Curtiss, Dixit, Feldt (bib0001) 2013; 80 Eklund, Andersson, Josephson, Johannesson, Knutsson (bib0008) 2012; 61 , Gentleman, Carey, Bates, Bolstad, Dettling, Dudoit, Ellis, Gautier, Ge, Gentry (bib0012) 2004; 5 Noble, Scheinost, Constable (bib0023) 2020; 209 Van Essen, Smith, Barch, Behrens, Yacoub, Ugurbil, Consortium (bib0033) 2013; 80 Reddan, Lindquist, Wager (bib0026) 2017; 74 Munafò, Noble, Browne, Brunner, Button, Ferreira, Holmans, Langbehn, Lewis, Lindquist (bib0021) 2014; 32 Smith, Nichols (bib0029) 2009; 44 10.1101/064212 Efron, Morris (bib0007) 1975; 70 Ritchie, Phipson, Wu, Hu, Law, Shi, Smyth (bib0027) 2015; 43 Friston, Worsley, Frackowiak, Mazziotta, Evans (bib0011) 1994; 1 Button, Ioannidis, Mokrysz, Nosek, Flint, Robinson, Munafò (bib0002) 2013; 14 Glasser, Sotiropoulos, Wilson, Coalson, Fischl, Andersson, Xu, Jbabdi, Webster, Polimeni (bib0014) 2013; 80 Van Essen, Ugurbil, Auerbach, Barch, Behrens, Bucholz, Chang, Chen, Corbetta, Curtiss (bib0034) 2012; 62 Lindquist (10.1016/j.neuroimage.2021.118141_bib0018) 2012; 59 Friston (10.1016/j.neuroimage.2021.118141_sbref0010) 1996; 4 Eklund (10.1016/j.neuroimage.2021.118141_sbref0008) 2012; 61 Poldrack (10.1016/j.neuroimage.2021.118141_bib0024) 2017; 18 Glasser (10.1016/j.neuroimage.2021.118141_bib0014) 2013; 80 10.1016/j.neuroimage.2021.118141_bib0013 Cohen (10.1016/j.neuroimage.2021.118141_bib0006) 1988 Munafò (10.1016/j.neuroimage.2021.118141_bib0021) 2014; 32 Smyth (10.1016/j.neuroimage.2021.118141_bib0031) 2004; 3 Mejia (10.1016/j.neuroimage.2021.118141_bib0019) 2015; 112 Van Essen (10.1016/j.neuroimage.2021.118141_bib0034) 2012; 62 Nichols (10.1016/j.neuroimage.2021.118141_bib0022) 2002; 15 Shou (10.1016/j.neuroimage.2021.118141_bib0028) 2014; 102 Smith (10.1016/j.neuroimage.2021.118141_bib0029) 2009; 44 10.1016/j.neuroimage.2021.118141_bib0004 Smyth (10.1016/j.neuroimage.2021.118141_bib0030) 2005 Woolrich (10.1016/j.neuroimage.2021.118141_bib0035) 2004; 21 Barch (10.1016/j.neuroimage.2021.118141_bib0001) 2013; 80 Holmes (10.1016/j.neuroimage.2021.118141_bib0015) 1998; 7 Eklund (10.1016/j.neuroimage.2021.118141_bib0009) 2016; 113 Johnson (10.1016/j.neuroimage.2021.118141_bib0017) 1970; 2 Su (10.1016/j.neuroimage.2021.118141_bib0032) 2008; 10 Chen (10.1016/j.neuroimage.2021.118141_bib0005) 2015; 9 Friston (10.1016/j.neuroimage.2021.118141_sbref0011) 1994; 1 R Core Team (10.1016/j.neuroimage.2021.118141_sbref0025) 2019 Gentleman (10.1016/j.neuroimage.2021.118141_bib0012) 2004; 5 James (10.1016/j.neuroimage.2021.118141_bib0016) 1961; 1 Ritchie (10.1016/j.neuroimage.2021.118141_bib0027) 2015; 43 Chen (10.1016/j.neuroimage.2021.118141_bib0003) 2013; 73 Mumford (10.1016/j.neuroimage.2021.118141_bib0020) 2009; 47 Button (10.1016/j.neuroimage.2021.118141_bib0002) 2013; 14 Efron (10.1016/j.neuroimage.2021.118141_bib0007) 1975; 70 Noble (10.1016/j.neuroimage.2021.118141_bib0023) 2020; 209 Van Essen (10.1016/j.neuroimage.2021.118141_bib0033) 2013; 80 Reddan (10.1016/j.neuroimage.2021.118141_bib0026) 2017; 74 |
| References_xml | – volume: 7 start-page: S754 year: 1998 ident: bib0015 article-title: Generalisability, random effects and population inference publication-title: NeuroImage – reference: , – volume: 209 start-page: 116468 year: 2020 ident: bib0023 article-title: Cluster failure or power failure? Evaluating sensitivity in cluster-level inference publication-title: NeuroImage – volume: 18 start-page: 115 year: 2017 ident: bib0024 article-title: Scanning the horizon: towards transparent and reproducible neuroimaging research publication-title: Nat. Rev. Neurosci. – volume: 2 year: 1970 ident: bib0017 article-title: Distributions in Statistics: Continuous Univariate Distributions – reference: Chen, G., Taylor, P. A., Cox, R. W., 2016. Is the statistic value all we should care about in neuroimaging?bioRxiv. – volume: 80 start-page: 62 year: 2013 end-page: 79 ident: bib0033 article-title: The WU-minn human connectome project: an overview publication-title: NeuroImage – volume: 74 start-page: 207 year: 2017 end-page: 208 ident: bib0026 article-title: Effect size estimation in neuroimaging publication-title: JAMA Psychiatry – year: 2019 ident: bib0025 article-title: R: A Language and Environment for Statistical Computing – volume: 5 start-page: R80 year: 2004 ident: bib0012 article-title: Bioconductor: open software development for computational biology and bioinformatics publication-title: Genome Biol. – volume: 14 start-page: 365 year: 2013 end-page: 376 ident: bib0002 article-title: Power failure: why small sample size undermines the reliability of neuroscience publication-title: Nat. Rev. Neurosci. – volume: 59 start-page: 490 year: 2012 end-page: 501 ident: bib0018 article-title: Estimating and testing variance components in a multi-level GLM publication-title: NeuroImage – volume: 80 start-page: 169 year: 2013 end-page: 189 ident: bib0001 article-title: Function in the human connectome: task-fMRI and individual differences in behavior publication-title: NeuroImage – volume: 15 start-page: 1 year: 2002 end-page: 25 ident: bib0022 article-title: Nonparametric permutation tests for functional neuroimaging: a primer with examples publication-title: Hum. Brain Mapp. – reference: Geuter, S., Qi, G., Welsh, R. C., Wager, T. D., Lindquist, M., 2018. Effect size and power in fMRI group analysis. bioRxiv, 1–23295048. 10.1101/295048 – volume: 112 start-page: 14 year: 2015 end-page: 29 ident: bib0019 article-title: Improving reliability of subject-level resting-state fMRI parcellation with shrinkage estimators publication-title: NeuroImage – volume: 102 start-page: 938 year: 2014 end-page: 944 ident: bib0028 article-title: Shrinkage prediction of seed-voxel brain connectivity using resting state fMRI publication-title: NeuroImage – volume: 62 start-page: 2222 year: 2012 end-page: 2231 ident: bib0034 article-title: The human connectome project: a data acquisition perspective publication-title: NeuroImage – volume: 70 start-page: 311 year: 1975 end-page: 319 ident: bib0007 article-title: Data analysis using stein’s estimator and its generalizations publication-title: J. Am. Stat. Assoc. – year: 1988 ident: bib0006 article-title: Statistical Power Analysis of the Behavioral Sciences – volume: 47 start-page: 1469 year: 2009 end-page: 1475 ident: bib0020 article-title: Simple group fMRI modeling and inference publication-title: NeuroImage – volume: 32 start-page: 871 year: 2014 end-page: 873 ident: bib0021 article-title: Scientific rigor and the art of motorcycle maintenance publication-title: Nat. Biotechnol. – reference: . 10.1101/064212 – volume: 1 start-page: 210 year: 1994 end-page: 220 ident: bib0011 article-title: Assessing the significance of focal activations using their spatial extent publication-title: Hum. Brain Mapp. – volume: 43 start-page: e47 year: 2015 ident: bib0027 article-title: LIMMA powers differential expression analyses for RNA-sequencing and microarray studies publication-title: Nucleic Acids Res. – volume: 10 start-page: 219 year: 2008 end-page: 227 ident: bib0032 article-title: Modified test statistics by inter-voxel variance shrinkage with an application to fMRI publication-title: Biostatistics – volume: 21 start-page: 1732 year: 2004 end-page: 1747 ident: bib0035 article-title: Multilevel linear modelling for fMRI group analysis using Bayesian inference publication-title: NeuroImage – year: 2005 ident: bib0030 article-title: Limma: linear models for microarray data publication-title: Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Statistics for Biology and Health – volume: 3 start-page: 1 year: 2004 end-page: 25 ident: bib0031 article-title: Linear models and empirical Bayes methods for assessing differential expression in microarray experiments publication-title: Stat. Appl. Genet. Mol. Biol. – volume: 44 start-page: 83 year: 2009 end-page: 98 ident: bib0029 article-title: Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference publication-title: NeuroImage – volume: 9 start-page: 316 year: 2015 ident: bib0005 article-title: An empirical Bayes normalization method for connectivity metrics in resting state fMRI publication-title: Front. Neurosc. – volume: 4 start-page: 223 year: 1996 end-page: 235 ident: bib0010 article-title: Detecting activations in pet and fMRI: levels of inference and power publication-title: NeuroImage – volume: 113 start-page: 7900 year: 2016 end-page: 7905 ident: bib0009 article-title: Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates publication-title: Proc. Natl. Acad. Sci. – volume: 61 start-page: 565 year: 2012 end-page: 578 ident: bib0008 article-title: Does parametric fMRI analysis with SPM yield valid results? An empirical study of 1484 rest datasets publication-title: NeuroImage – volume: 73 start-page: 176 year: 2013 end-page: 190 ident: bib0003 article-title: Linear mixed-effects modeling approach to fMRI group analysis publication-title: NeuroImage – volume: 80 start-page: 105 year: 2013 end-page: 124 ident: bib0014 article-title: The minimal preprocessing pipelines for the human connectome project publication-title: NeuroImage – volume: 1 start-page: 361 year: 1961 end-page: 379 ident: bib0016 article-title: Estimation with quadratic loss publication-title: Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability – volume: 5 start-page: R80 issue: 10 year: 2004 ident: 10.1016/j.neuroimage.2021.118141_bib0012 article-title: Bioconductor: open software development for computational biology and bioinformatics publication-title: Genome Biol. doi: 10.1186/gb-2004-5-10-r80 – volume: 7 start-page: S754 year: 1998 ident: 10.1016/j.neuroimage.2021.118141_bib0015 article-title: Generalisability, random effects and population inference publication-title: NeuroImage doi: 10.1016/S1053-8119(18)31587-8 – volume: 43 start-page: e47 issue: 7 year: 2015 ident: 10.1016/j.neuroimage.2021.118141_bib0027 article-title: LIMMA powers differential expression analyses for RNA-sequencing and microarray studies publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkv007 – volume: 4 start-page: 223 issue: 3 year: 1996 ident: 10.1016/j.neuroimage.2021.118141_sbref0010 article-title: Detecting activations in pet and fMRI: levels of inference and power publication-title: NeuroImage doi: 10.1006/nimg.1996.0074 – volume: 21 start-page: 1732 issue: 4 year: 2004 ident: 10.1016/j.neuroimage.2021.118141_bib0035 article-title: Multilevel linear modelling for fMRI group analysis using Bayesian inference publication-title: NeuroImage doi: 10.1016/j.neuroimage.2003.12.023 – ident: 10.1016/j.neuroimage.2021.118141_bib0013 doi: 10.1101/295048 – volume: 18 start-page: 115 issue: 2 year: 2017 ident: 10.1016/j.neuroimage.2021.118141_bib0024 article-title: Scanning the horizon: towards transparent and reproducible neuroimaging research publication-title: Nat. Rev. Neurosci. doi: 10.1038/nrn.2016.167 – volume: 102 start-page: 938 year: 2014 ident: 10.1016/j.neuroimage.2021.118141_bib0028 article-title: Shrinkage prediction of seed-voxel brain connectivity using resting state fMRI publication-title: NeuroImage doi: 10.1016/j.neuroimage.2014.05.043 – volume: 15 start-page: 1 issue: 1 year: 2002 ident: 10.1016/j.neuroimage.2021.118141_bib0022 article-title: Nonparametric permutation tests for functional neuroimaging: a primer with examples publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.1058 – volume: 44 start-page: 83 issue: 1 year: 2009 ident: 10.1016/j.neuroimage.2021.118141_bib0029 article-title: Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference publication-title: NeuroImage doi: 10.1016/j.neuroimage.2008.03.061 – year: 1988 ident: 10.1016/j.neuroimage.2021.118141_bib0006 – volume: 74 start-page: 207 issue: 3 year: 2017 ident: 10.1016/j.neuroimage.2021.118141_bib0026 article-title: Effect size estimation in neuroimaging publication-title: JAMA Psychiatry doi: 10.1001/jamapsychiatry.2016.3356 – volume: 10 start-page: 219 issue: 2 year: 2008 ident: 10.1016/j.neuroimage.2021.118141_bib0032 article-title: Modified test statistics by inter-voxel variance shrinkage with an application to fMRI publication-title: Biostatistics doi: 10.1093/biostatistics/kxn028 – volume: 32 start-page: 871 issue: 9 year: 2014 ident: 10.1016/j.neuroimage.2021.118141_bib0021 article-title: Scientific rigor and the art of motorcycle maintenance publication-title: Nat. Biotechnol. doi: 10.1038/nbt.3004 – volume: 47 start-page: 1469 issue: 4 year: 2009 ident: 10.1016/j.neuroimage.2021.118141_bib0020 article-title: Simple group fMRI modeling and inference publication-title: NeuroImage doi: 10.1016/j.neuroimage.2009.05.034 – volume: 14 start-page: 365 issue: 5 year: 2013 ident: 10.1016/j.neuroimage.2021.118141_bib0002 article-title: Power failure: why small sample size undermines the reliability of neuroscience publication-title: Nat. Rev. Neurosci. doi: 10.1038/nrn3475 – volume: 61 start-page: 565 issue: 3 year: 2012 ident: 10.1016/j.neuroimage.2021.118141_sbref0008 article-title: Does parametric fMRI analysis with SPM yield valid results? An empirical study of 1484 rest datasets publication-title: NeuroImage doi: 10.1016/j.neuroimage.2012.03.093 – volume: 80 start-page: 169 year: 2013 ident: 10.1016/j.neuroimage.2021.118141_bib0001 article-title: Function in the human connectome: task-fMRI and individual differences in behavior publication-title: NeuroImage doi: 10.1016/j.neuroimage.2013.05.033 – ident: 10.1016/j.neuroimage.2021.118141_bib0004 – volume: 9 start-page: 316 year: 2015 ident: 10.1016/j.neuroimage.2021.118141_bib0005 article-title: An empirical Bayes normalization method for connectivity metrics in resting state fMRI publication-title: Front. Neurosc. doi: 10.3389/fnins.2015.00316 – volume: 112 start-page: 14 year: 2015 ident: 10.1016/j.neuroimage.2021.118141_bib0019 article-title: Improving reliability of subject-level resting-state fMRI parcellation with shrinkage estimators publication-title: NeuroImage doi: 10.1016/j.neuroimage.2015.02.042 – volume: 1 start-page: 361 year: 1961 ident: 10.1016/j.neuroimage.2021.118141_bib0016 article-title: Estimation with quadratic loss – volume: 2 year: 1970 ident: 10.1016/j.neuroimage.2021.118141_bib0017 – volume: 80 start-page: 105 year: 2013 ident: 10.1016/j.neuroimage.2021.118141_bib0014 article-title: The minimal preprocessing pipelines for the human connectome project publication-title: NeuroImage doi: 10.1016/j.neuroimage.2013.04.127 – volume: 59 start-page: 490 issue: 1 year: 2012 ident: 10.1016/j.neuroimage.2021.118141_bib0018 article-title: Estimating and testing variance components in a multi-level GLM publication-title: NeuroImage doi: 10.1016/j.neuroimage.2011.07.077 – volume: 80 start-page: 62 year: 2013 ident: 10.1016/j.neuroimage.2021.118141_bib0033 article-title: The WU-minn human connectome project: an overview publication-title: NeuroImage doi: 10.1016/j.neuroimage.2013.05.041 – volume: 70 start-page: 311 issue: 350 year: 1975 ident: 10.1016/j.neuroimage.2021.118141_bib0007 article-title: Data analysis using stein’s estimator and its generalizations publication-title: J. Am. Stat. Assoc. doi: 10.1080/01621459.1975.10479864 – volume: 209 start-page: 116468 year: 2020 ident: 10.1016/j.neuroimage.2021.118141_bib0023 article-title: Cluster failure or power failure? Evaluating sensitivity in cluster-level inference publication-title: NeuroImage doi: 10.1016/j.neuroimage.2019.116468 – volume: 113 start-page: 7900 issue: 28 year: 2016 ident: 10.1016/j.neuroimage.2021.118141_bib0009 article-title: Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates publication-title: Proc. Natl. Acad. Sci. doi: 10.1073/pnas.1602413113 – volume: 1 start-page: 210 issue: 3 year: 1994 ident: 10.1016/j.neuroimage.2021.118141_sbref0011 article-title: Assessing the significance of focal activations using their spatial extent publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.460010306 – volume: 62 start-page: 2222 issue: 4 year: 2012 ident: 10.1016/j.neuroimage.2021.118141_bib0034 article-title: The human connectome project: a data acquisition perspective publication-title: NeuroImage doi: 10.1016/j.neuroimage.2012.02.018 – year: 2005 ident: 10.1016/j.neuroimage.2021.118141_bib0030 article-title: Limma: linear models for microarray data doi: 10.1007/0-387-29362-0_23 – volume: 73 start-page: 176 year: 2013 ident: 10.1016/j.neuroimage.2021.118141_bib0003 article-title: Linear mixed-effects modeling approach to fMRI group analysis publication-title: NeuroImage doi: 10.1016/j.neuroimage.2013.01.047 – volume: 3 start-page: 1 issue: 1 year: 2004 ident: 10.1016/j.neuroimage.2021.118141_bib0031 article-title: Linear models and empirical Bayes methods for assessing differential expression in microarray experiments publication-title: Stat. Appl. Genet. Mol. Biol. doi: 10.2202/1544-6115.1027 – year: 2019 ident: 10.1016/j.neuroimage.2021.118141_sbref0025 |
| SSID | ssj0009148 |
| Score | 2.433922 |
| Snippet | •We introduce a moderated t-statistic for performing group-level fMRI analysis.•The approach helps alleviate problems related to small sample sizes.•The... In recent years, there has been significant criticism of functional magnetic resonance imaging (fMRI) studies with small sample sizes. The argument is that... |
| SourceID | doaj pubmedcentral proquest pubmed crossref elsevier |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 118141 |
| SubjectTerms | Adult Connectome Data Interpretation, Statistical Estimates fMRI Functional magnetic resonance imaging Functional Neuroimaging - methods Genomics Group analysis Humans Hypotheses LIMMA LIMMI Linear Models Magnetic Resonance Imaging - methods Models, Statistical Moderated t-test Performance evaluation Psychomotor Performance Statistical analysis Student's t-test |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3BTtwwEB1VqKp6QaUUmhaqIHFNsePEjtUTRUVwAFVVK3Gz7MQRW9FQsYHvZyZ2AqGH7oFrEq-S5_HM83rmDcC-YL6g47iM1bzNCq9Y5pzIM--ks6KxTNjQbEKdn1cXF_r7o1ZflBMW5IEDcAeNbOqaCUd9jwrmWsdbjEC158KVuDYdeV-m9LiZGuV2keXHvJ2QzTWoQy7-4BrFPWHOP1O9ZcFnwWjQ7J_FpH8559PUyUex6PgNrEcSmR6Gl9-AF757C6_O4jH5JhwMHc6QRTZpnyGX7JcpctN0qODIrihNKG3PfpymNiqSvINfx99-Hp1ksTNCVkvBetz0CYwrpeVSemFtWfpWVbJWPGeilXnllNQVglRoDEbeaaYVgmXRL1fM66IRW7DWXXf-PaSK-wY5kFS476LNi5PodyiEVarwrRYJqBEiU0fZcOpecWXG_LDf5gFcQ-CaAG4CfBr5N0hnrDDmK83C9DyJXw8X0CRMNAnzP5NIQI9zaMb6UvSI-EOLFV7gyzQ2cpDALVYcvTOajIm-YGmo4wDJNkqVwN50G1cxHc3Yzl_f0jN5IahUp0xgO1jYhIEQWlJBFc7EzPZmIM3vdIvLQSm8ynWJ_PfDc6D6EV7Tl9L_6bzcgbX-5tbvwsv6rl8sbz4Ny-8eAf82_g priority: 102 providerName: Directory of Open Access Journals |
| Title | Moderated t-tests for group-level fMRI analysis |
| URI | https://www.clinicalkey.com/#!/content/1-s2.0-S1053811921004183 https://dx.doi.org/10.1016/j.neuroimage.2021.118141 https://www.ncbi.nlm.nih.gov/pubmed/33962000 https://www.proquest.com/docview/2546965967 https://www.proquest.com/docview/2524362425 https://pubmed.ncbi.nlm.nih.gov/PMC8295929 https://doaj.org/article/d6dcc03b6db240bfb1f613ce13b5addb |
| Volume | 237 |
| WOSCitedRecordID | wos000671785900004&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: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1095-9572 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: DOA dateStart: 20200101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1095-9572 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: AIEXJ dateStart: 20200101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1095-9572 dateEnd: 20251014 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: 20251014 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 (subscription) customDbUrl: eissn: 1095-9572 dateEnd: 20251014 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: 20251014 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/eLvHCXMwpV1Lj9MwELZgF6G98H4ElipIXA12nNixOCAW7QoOraoVSL1ZseNA0ZLutll-PzOOk1KQUCUuPjRxFXvGM5_tmW8IeSWYz_E6jjLHG5p7xai1IqPeSluJumKi6otNqNmsXCz0PB64bWJY5WATg6GuVw7PyN8gbzuS30n17vKKYtUovF2NJTRukkMsm416rhZqS7rL8z4VrhC05FzHSJ4-vivwRS5_wKqFXWLGX2MGZs533FNg8d_xUn-j0D-DKX_zTmd3_3dc98idiEvT970i3Sc3fPuA3J7Gm_eHhIWiaQBM67SjKeDTbpMC3k1DVgi9wNCjtJmef0qryHLyiHw5O_384SON1Raok4J1sJEU4KuKikvpRVUVhW9UKZ3iGRONzEqrpC6F87kGB-etZloVVV2BrS-Z13ktHpODdtX6pyRV3NeAq6SCvRxuiKwEW4ZusVS5b7RIiBom2bhIRY4VMS7MEHP23WzFY1A8phdPQvjY87Kn49ijzwnKcXwfCbXDD6v1VxPXp6ll7RwTFstr5cw2ljcAdJznwsIoa5sQPWiBGXJWwcrCHy33-IC3Y9-Ia3q8smfv40FxTLQvG7PVmoS8HB-DZcDrnqr1q2t8J8sFpv8UCXnS6-g4B0JoiUlaIIkd7d2ZpN0n7fJbYB8vM10Apn727896To5wDHj6zotjctCtr_0Lcsv97Jab9SQs09CWE3J4cjqbn0_CaQi002yKrZr_Ah24SmQ |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Zb9QwEB6VgoAX7iNQIEjwGLDjxI6FEOKqumqzQqhIfXPjxIFFJVt2UxB_it_ITK4lIKF96QOviR35GH_zTTwHwCPBXETXcQHLeRlETrHAWhEGzkqbiSJjImuLTajpNDk40O824GcfC0NulT0mNkBdzHP6R_6U8rZT8jupXhx_DahqFN2u9iU0WrHYdT--o8m2fD55g_v7OAy33-6_3gm6qgJBLgWr0WASiMlxxqV0Isvi2JUqkbniIROlDBOrpE5E7iKNQO6sZlrFWZEhpiXM6agQ-N0zcDZCTUgVE9IwXSX55VEbeheLIOFcd55DrT9Zk59y9gVRAq3SkD-hiM-Ij9RhUzVgpBX_Zr1_Om_-pg23L_9v63gFLnW823_ZHpSrsOGqa3A-7TwLrgNrisIh8S78OvCRf9dLH_m830S9BEfkWuWX6fuJn3VZXG7Ah1MZ703YrOaVuw2-4q5A3igV2qpk8FmJWE1qP1GRK7XwQPWbavIu1TpV_DgyvU_dZ7MSB0PiYFpx8IAPPY_bdCNr9HlFcjO0p4ThzYP54qPp8McUsshzJiyVD4uYLS0vkcjljguLsyysB7qXOtPH5KIWwQ_N1hjAs6Fvx9taPrZm761eUE2Hn0uzklIPHg6vEfnoOiur3PyE2oSRoPCm2INb7ZkY1kAILSkIDXdidFpGizR-U80-NdnVk1DHaDPc-fewHsCFnf10z-xNprt34SLNh24aeLwFm_XixN2Dc_m3erZc3G8gwofD0z5LvwCKIp-T |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1bb9MwFD4aHZp44X4pDAgSPIbZcWLHQggxtopqrKomkPbmxYkDRSMdbQfir_HrOCdxUgoS6sseeE3syJdzPn8nPheAp4K5mK7jQpbzMoydYqG1IgqdlTYTRcZE1hSbUKNRenysxxvws42FIbfKFhNroC6mOf0j36G87ZT8Tqqd0rtFjPcGr86-hlRBim5a23IajYgcuB_f0Xybvxzu4V4_i6LB_vs3b0NfYSDMpWALNJ4E4nOScSmdyLIkcaVKZa54xEQpo9QqqVORu1gjqDurmVZJVmSIbylzOi4EfvcSbCqBRk8PNnf3R-OjZcpfHjeBeIkIU8619yNqvMvqbJWTL4gZaKNG_DnFf8Z85XCsawisnJF_c-A_XTl_OxsH1_7nVb0OVz0jD143KnQDNlx1E7YOvc_BLWB1uTik5EWwCANk5ot5gEw_qONhwlNyugrKw6NhkPn8Lrfhw4WM9w70qmnl7kGguCuQUUqFViyZglYiihMhSFXsSi36oNoNNrlPwk61QE5N62332SxFw5BomEY0-sC7nmdNIpI1-uySDHXtKZV4_WA6-2g8MplCFnnOhKXCYjGzpeUlUrzccWFxloXtg24l0LTRuni-4IcmawzgRdfXM7qGqa3Ze7sVWuORdW6WEtuHJ91rxES66MoqNz2nNlEsKPAp6cPdRj-6NRBCSwpPw51Y0ZyVRVp9U00-1XnX00gnaE3c__ewHsMWqpB5NxwdPIArNB26guDJNvQWs3P3EC7n3xaT-eyRx4sATi5amX4B43Cptg |
| 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=Moderated+t-tests+for+group-level+fMRI+analysis&rft.jtitle=NeuroImage+%28Orlando%2C+Fla.%29&rft.au=Wang%2C+Guoqing&rft.au=Muschelli%2C+John&rft.au=Lindquist%2C+Martin+A.&rft.date=2021-08-15&rft.pub=Elsevier+Inc&rft.issn=1053-8119&rft.volume=237&rft_id=info:doi/10.1016%2Fj.neuroimage.2021.118141&rft.externalDocID=S1053811921004183 |
| 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 |