{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T14:21:30Z","timestamp":1761402090172,"version":"build-2065373602"},"reference-count":67,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,10,27]],"date-time":"2020-10-27T00:00:00Z","timestamp":1603756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Human brain, a dynamic complex system, can be studied with different approaches, including linear and nonlinear ones. One of the nonlinear approaches widely used in electroencephalographic (EEG) analyses is the entropy, the measurement of disorder in a system. The present study investigates brain networks applying approximate entropy (ApEn) measure for assessing the hemispheric EEG differences; reproducibility and stability of ApEn data across separate recording sessions were evaluated. Twenty healthy adult volunteers were submitted to eyes-closed resting EEG recordings, for 80 recordings. Significant differences in the occipital region, with higher values of entropy in the left hemisphere than in the right one, show that the hemispheres become active with different intensities according to the performed function. Besides, the present methodology proved to be reproducible and stable, when carried out on relatively brief EEG epochs but also at a 1-week distance in a group of 36 subjects. Nonlinear approaches represent an interesting probe to study the dynamics of brain networks. ApEn technique might provide more insight into the pathophysiological processes underlying age-related brain disconnection as well as for monitoring the impact of pharmacological and rehabilitation treatments.<\/jats:p>","DOI":"10.3390\/e22111220","type":"journal-article","created":{"date-parts":[[2020,10,27]],"date-time":"2020-10-27T09:22:45Z","timestamp":1603790565000},"page":"1220","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Approximate Entropy of Brain Network in the Study of Hemispheric Differences"],"prefix":"10.3390","volume":"22","author":[{"given":"Francesca","family":"Al\u00f9","sequence":"first","affiliation":[{"name":"Brain Connectivity Laboratory, Department of Neuroscience &amp; Neurorehabilitation, IRCCS San Raffaele Pisana, Via Val Cannuta, 247, 00166 Rome, Italy"}]},{"given":"Francesca","family":"Miraglia","sequence":"additional","affiliation":[{"name":"Brain Connectivity Laboratory, Department of Neuroscience &amp; Neurorehabilitation, IRCCS San Raffaele Pisana, Via Val Cannuta, 247, 00166 Rome, Italy"}]},{"given":"Alessandro","family":"Orticoni","sequence":"additional","affiliation":[{"name":"Brain Connectivity Laboratory, Department of Neuroscience &amp; Neurorehabilitation, IRCCS San Raffaele Pisana, Via Val Cannuta, 247, 00166 Rome, Italy"}]},{"given":"Elda","family":"Judica","sequence":"additional","affiliation":[{"name":"Department of Neurorehabilitation Sciences, Casa Cura Policlinico, 20144 Milano, Italy"}]},{"given":"Maria","family":"Cotelli","sequence":"additional","affiliation":[{"name":"Neuropsychology Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, 25125 Brescia, Italy"}]},{"given":"Paolo Maria","family":"Rossini","sequence":"additional","affiliation":[{"name":"Brain Connectivity Laboratory, Department of Neuroscience &amp; Neurorehabilitation, IRCCS San Raffaele Pisana, Via Val Cannuta, 247, 00166 Rome, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6004-9522","authenticated-orcid":false,"given":"Fabrizio","family":"Vecchio","sequence":"additional","affiliation":[{"name":"Brain Connectivity Laboratory, Department of Neuroscience &amp; Neurorehabilitation, IRCCS San Raffaele Pisana, Via Val Cannuta, 247, 00166 Rome, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.smrv.2017.01.003","article-title":"Nonlinear dynamical analysis of sleep electroencephalography using fractal and entropy approaches","volume":"37","author":"Ma","year":"2018","journal-title":"Sleep Med. 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