{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T06:55:38Z","timestamp":1789196138206,"version":"build-2803163510"},"reference-count":56,"publisher":"National Academy of Sciences","issue":"32","license":[{"start":{"date-parts":[[2022,8,4]],"date-time":"2022-08-04T00:00:00Z","timestamp":1659571200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000025","name":"HHS | NIH | National Institute of Mental Health","doi-asserted-by":"publisher","award":["K00MH122372"],"award-info":[{"award-number":["K00MH122372"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000070","name":"HHS | NIH | National Institute of Biomedical Imaging and Bioengineering","doi-asserted-by":"publisher","award":["R01EB027119"],"award-info":[{"award-number":["R01EB027119"]}],"id":[{"id":"10.13039\/100000070","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.pnas.org"],"crossmark-restriction":true},"short-container-title":["Proc. Natl. Acad. Sci. U.S.A."],"published-print":{"date-parts":[[2022,8,9]]},"abstract":"<jats:p>Inference in neuroimaging typically occurs at the level of focal brain areas or circuits. Yet, increasingly, well-powered studies paint a much richer picture of broad-scale effects distributed throughout the brain, suggesting that many focal reports may only reflect the tip of the iceberg of underlying effects. How focal versus broad-scale perspectives influence the inferences we make has not yet been comprehensively evaluated using real data. Here, we compare sensitivity and specificity across procedures representing multiple levels of inference using an empirical benchmarking procedure that resamples task-based connectomes from the Human Connectome Project dataset (\u223c1,000 subjects, 7 tasks, 3 resampling group sizes, 7 inferential procedures). Only broad-scale (network and whole brain) procedures obtained the traditional 80% statistical power level to detect an average effect, reflecting &gt;20% more statistical power than focal (edge and cluster) procedures. Power also increased substantially for false discovery rate\u2013 compared with familywise error rate\u2013controlling procedures. The downsides are fairly limited; the loss in specificity for broad-scale and FDR procedures was relatively modest compared to the gains in power. Furthermore, the broad-scale methods we introduce are simple, fast, and easy to use, providing a straightforward starting point for researchers. This also points to the promise of more sophisticated broad-scale methods for not only functional connectivity but also related fields, including task-based activation. Altogether, this work demonstrates that shifting the scale of inference and choosing FDR control are both immediately attainable and can help remedy the issues with statistical power plaguing typical studies in the field.<\/jats:p>","DOI":"10.1073\/pnas.2203020119","type":"journal-article","created":{"date-parts":[[2022,8,4]],"date-time":"2022-08-04T13:26:53Z","timestamp":1659619613000},"update-policy":"https:\/\/doi.org\/10.1073\/pnas.cm10313","source":"Crossref","is-referenced-by-count":58,"title":["Improving power in functional magnetic resonance imaging by moving beyond cluster-level inference"],"prefix":"10.1073","volume":"119","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4804-5553","authenticated-orcid":false,"given":"Stephanie","family":"Noble","sequence":"first","affiliation":[{"name":"Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06519"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4312-8974","authenticated-orcid":false,"given":"Amanda F.","family":"Mejia","sequence":"additional","affiliation":[{"name":"Department of Statistics, Indiana University Bloomington, Bloomington, IN 47408"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Zalesky","sequence":"additional","affiliation":[{"name":"Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, VIC 3010, Australia"},{"name":"Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6301-1167","authenticated-orcid":false,"given":"Dustin","family":"Scheinost","sequence":"additional","affiliation":[{"name":"Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06519"},{"name":"Department of Biomedical Engineering, Yale School of Medicine, New Haven, CT 06520"},{"name":"Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520"},{"name":"Department of Statistics and Data Science, Yale University, New Haven, CT 06511"},{"name":"Child Study Center, Yale School of Medicine, New Haven, CT 06519"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"341","published-online":{"date-parts":[[2022,8,4]]},"reference":[{"key":"e_1_3_4_1_2","doi-asserted-by":"publisher","DOI":"10.1006\/nimg.1996.0074"},{"key":"e_1_3_4_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2010.06.041"},{"key":"e_1_3_4_3_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0184923"},{"key":"e_1_3_4_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2019.116468"},{"key":"e_1_3_4_5_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1121049109"},{"key":"e_1_3_4_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59728-3_44"},{"key":"e_1_3_4_7_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41593-020-00719-y"},{"key":"e_1_3_4_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2014.11.059"},{"key":"e_1_3_4_9_2","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-0193(1998)6:3<160::AID-HBM5>3.0.CO;2-1"},{"key":"e_1_3_4_10_2","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.20919"},{"key":"e_1_3_4_11_2","first-page":"458","article-title":"The constrained network-based statistic: A new level of inference for neuroimaging","volume":"12267","author":"Noble S.","year":"2020","unstructured":"S. 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