{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T01:04:23Z","timestamp":1768266263964,"version":"3.49.0"},"reference-count":33,"publisher":"World Scientific Pub Co Pte Lt","issue":"06","funder":[{"name":"Fundacion Seneca, Agencia de Ciencia y Tecnologia de la Region de Murcia","award":["RTI2018-098969-B-100"],"award-info":[{"award-number":["RTI2018-098969-B-100"]}]},{"name":"Ministerio de Ciencia, Innovacion y Universidades, y","award":["PROMETEO\/2019\/119"],"award-info":[{"award-number":["PROMETEO\/2019\/119"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"published-print":{"date-parts":[[2020,6]]},"abstract":"<jats:p> The cortical and subcortical circuit regulating both cognition and cardiac autonomic interactions are already well established. This circuit has mainly been analyzed from cortex to heart. Thus, the heart rate variability (HRV) is usually considered a reflection of cortical activity. In this paper, we investigate whether HRV changes affect cortical activity. Short-term local autonomic changes were induced by three breathing strategies: spontaneous (Control), normal (NB) and slow paced breathing (SB). We measured the performance in two cognition domains: executive functions and processing speed. Breathing maneuvres produced three clearly differentiated autonomic states, which preconditioned the cognitive tasks. We found that the SB significantly increased the HRV low frequency (LF) power and lowered the power spectral density (PSD) peak to 0.1[Formula: see text]Hz. Meanwhile, executive function was assessed by the working memory test, whose accuracy significantly improved after SB, with no significant changes in the response times. Processing speed was assessed by a multitasking test. Consistently, the proportion of correct answers (success rate) was the only dependent variable affected by short-term and long-term breath pacing. These findings suggest that accuracy, and not timing of these two cognitive domains would benefit from short-term SB in this study population. <\/jats:p>","DOI":"10.1142\/s0129065720500288","type":"journal-article","created":{"date-parts":[[2020,3,30]],"date-time":"2020-03-30T07:02:55Z","timestamp":1585551775000},"page":"2050028","source":"Crossref","is-referenced-by-count":15,"title":["The Effect of Breath Pacing on Task Switching and Working Memory"],"prefix":"10.1142","volume":"30","author":[{"given":"Maria Paula","family":"Bonomini","sequence":"first","affiliation":[{"name":"Instituto Argentino de Matem\u00e1ticas \u201cAlberto P. Calder\u00f3n\u201d (IAM), CONICET, Saavedra 15, CABA, Argentina"},{"name":"Instituto de Ingenier\u00eda Biom\u00e9dica, Fac. de Ingenier\u00eda, Univ. de Buenos Aires (UBA), Paseo Col\u00f3n 850, CABA, Argentina"}]},{"given":"Mikel Val","family":"Calvo","sequence":"additional","affiliation":[{"name":"Departamento de Inteligencia Artificial, Universidad Nacional de Educaci\u00f3n a Distancia (UNED), Juan del Rosal, 16, 28040, Madrid, Spain"},{"name":"Departamento Electr\u00f3nica, Tecnolog\u00eda de Computadoras y Proyectos, Universidad Polit\u00e9cnica de Cartagena, Cartagena, Spain"}]},{"given":"Alejandro Diaz","family":"Morcillo","sequence":"additional","affiliation":[{"name":"Departamento Tecnolog\u00edas de la Informaci\u00f3n y las Comunicaciones, Universidad Polit\u00e9cnica de Cartagena, Cartagena, Spain"}]},{"given":"Florencia","family":"Segovia","sequence":"additional","affiliation":[{"name":"Sanatorio Guemes, Francisco Acu\u00f1a de Figueroa 1240, CABA, Argentina"}]},{"given":"Jose Manuel Ferrandez","family":"Vicente","sequence":"additional","affiliation":[{"name":"Departamento Electr\u00f3nica, Tecnolog\u00eda de Computadoras y Proyectos, Universidad Polit\u00e9cnica de Cartagena, Cartagena, Spain"}]},{"given":"Eduardo","family":"Fernandez-Jover","sequence":"additional","affiliation":[{"name":"Instituto de Bioingenier\u00eda, Universidad Miguel Hern\u00e1ndez, Elche, Spain"}]}],"member":"219","published-online":{"date-parts":[[2020,5,27]]},"reference":[{"key":"S0129065720500288BIB001","doi-asserted-by":"publisher","DOI":"10.1016\/S0165-0173(97)00064-7"},{"key":"S0129065720500288BIB002","doi-asserted-by":"publisher","DOI":"10.1016\/S0025-6196(12)62272-1"},{"key":"S0129065720500288BIB003","doi-asserted-by":"publisher","DOI":"10.1016\/S0165-0327(00)00338-4"},{"key":"S0129065720500288BIB004","doi-asserted-by":"publisher","DOI":"10.1016\/j.neubiorev.2008.08.004"},{"key":"S0129065720500288BIB005","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijcard.2009.09.543"},{"key":"S0129065720500288BIB006","doi-asserted-by":"publisher","DOI":"10.1007\/s00421-004-1208-0"},{"key":"S0129065720500288BIB007","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.0000000000002499"},{"key":"S0129065720500288BIB008","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-8760(03)00073-4"},{"key":"S0129065720500288BIB009","doi-asserted-by":"publisher","DOI":"10.3758\/s13415-018-0600-x"},{"key":"S0129065720500288BIB010","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2018.00397"},{"key":"S0129065720500288BIB011","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2015.00145"},{"key":"S0129065720500288BIB012","doi-asserted-by":"publisher","DOI":"10.1152\/jn.00551.2017"},{"key":"S0129065720500288BIB013","volume-title":"From Bioinspired Systems and Biomedical Applications to Machine Learning, Proceeding th Int. 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