{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T06:44:12Z","timestamp":1768805052175,"version":"3.49.0"},"reference-count":60,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2019,7,6]],"date-time":"2019-07-06T00:00:00Z","timestamp":1562371200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Italian Ministry of Health","award":["PE- 2013-02355372"],"award-info":[{"award-number":["PE- 2013-02355372"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>A growing number of studies are focusing on methods to estimate and analyze the functional connectome of the human brain. Graph theoretical measures are commonly employed to interpret and synthesize complex network-related information. While resting state functional MRI (rsfMRI) is often employed in this context, it is known to exhibit poor reproducibility, a key factor which is commonly neglected in typical cohort studies using connectomics-related measures as biomarkers. We aimed to fill this gap by analyzing and comparing the inter- and intra-subject variability of connectivity matrices, as well as graph-theoretical measures, in a large (n = 1003) database of young healthy subjects which underwent four consecutive rsfMRI sessions. We analyzed both directed (Granger Causality and Transfer Entropy) and undirected (Pearson Correlation and Partial Correlation) time-series association measures and related global and local graph-theoretical measures. While matrix weights exhibit a higher reproducibility in undirected, as opposed to directed, methods, this difference disappears when looking at global graph metrics and, in turn, exhibits strong regional dependence in local graphs metrics. Our results warrant caution in the interpretation of connectivity studies, and serve as a benchmark for future investigations by providing quantitative estimates for the inter- and intra-subject variabilities in both directed and undirected connectomic measures.<\/jats:p>","DOI":"10.3390\/e21070661","type":"journal-article","created":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T03:01:31Z","timestamp":1562554891000},"page":"661","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Variability and Reproducibility of Directed and Undirected Functional MRI Connectomes in the Human Brain"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4210-5065","authenticated-orcid":false,"given":"Allegra","family":"Conti","sequence":"first","affiliation":[{"name":"Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6030-5269","authenticated-orcid":false,"given":"Andrea","family":"Duggento","sequence":"additional","affiliation":[{"name":"Department of Biomedicine and Prevention, University of Rome Tor Vergata, 00133 Rome, Italy"}]},{"given":"Maria","family":"Guerrisi","sequence":"additional","affiliation":[{"name":"Department of Biomedicine and Prevention, University of Rome Tor Vergata, 00133 Rome, Italy"}]},{"given":"Luca","family":"Passamonti","sequence":"additional","affiliation":[{"name":"Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, UK"},{"name":"Institute of Bioimaging and Molecular Physiology, National Research Council, 20090 Milano, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1183-0013","authenticated-orcid":false,"given":"Iole","family":"Indovina","sequence":"additional","affiliation":[{"name":"Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy"},{"name":"Saint Camillus International University of Health and Medical Sciences, 00131 Rome, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1929-5833","authenticated-orcid":false,"given":"Nicola","family":"Toschi","sequence":"additional","affiliation":[{"name":"Department of Biomedicine and Prevention, University of Rome Tor Vergata, 00133 Rome, Italy"},{"name":"Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA 02129, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,6]]},"reference":[{"key":"ref_1","first-page":"8","article-title":"A systematic framework for functional connectivity measures","volume":"8","author":"Wang","year":"2014","journal-title":"Front. Mol. Neurosci."},{"key":"ref_2","first-page":"521","article-title":"Review of the methods of determination of directed connectivity from multichannel data","volume":"49","author":"Blinowska","year":"2011","journal-title":"Med. Boil. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1038\/nrn3214","article-title":"The economy of brain network organization","volume":"13","author":"Bullmore","year":"2012","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_4","unstructured":"Fornito, A., Zalesky, A., and Bullmore, E.T. (2016). Chapter 3\u2014Connectivity Matrices and Brain Graphs. 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