{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T15:35:17Z","timestamp":1790955317545,"version":"4.1.0"},"reference-count":107,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T00:00:00Z","timestamp":1785369600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T00:00:00Z","timestamp":1785369600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cogn Affect Behav Neurosci"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Human attention is inherently transient and limited in span. Intrinsic fluctuations of large-scale neurocognitive networks are thought to contribute to the unavoidable lapses in attention that constrain its span. However, it remains unclear how the millisecond temporal dynamics of specific electrophysiological brain states contribute to the endogenous maintenance of attention or the onset of attentional lapses. In the present study, we investigated whether the strength and millisecond dynamics of brain electric\n                    <jats:italic>microstates<\/jats:italic>\n                    differentiate states of focus from inattention and contribute to the maintenance of attention. We recorded 128-channel EEG while participants maintained their attention during the wait time delay of trials in the Sustained Attention to Cue Task (SACT) and segmented the EEG into a categorized time series of microstates based on data-driven topographic clustering. The findings revealed that the prevalence and rate of occurrence of microstates C and E in the wait time delay of trials differentiated correct from incorrect target detections. Their time-varying dynamics also changed systematically during wait time delays and across the task session, implicating these microstates in the maintenance of attention over short and long timescales. Furthermore, microstate C co-occurred with the greatest attention-related modulations in alpha power and predominated within alpha oscillatory episodes, suggesting its neural generators are the primary contributors to established associations between the alpha frequency band and inattention. Together, these findings demonstrate the sensitivity of microstates to variation in attentional states and suggest that their millisecond dynamics contribute to the maintenance of attention over time.\n                  <\/jats:p>","DOI":"10.3758\/s13415-026-01478-2","type":"journal-article","created":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T19:11:54Z","timestamp":1785438714000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["The maintenance of attention over time influences the dynamics of EEG microstates"],"prefix":"10.3758","author":[{"given":"Anthony P.","family":"Zanesco","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abigail M.","family":"Gross","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Delanie J.","family":"Spivey","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Braxton M.","family":"Stevenson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lexi F.","family":"Horn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sophia R.","family":"Zanelli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,30]]},"reference":[{"key":"1478_CR1","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.neuroimage.2019.01.076","volume":"191","author":"A Abbas","year":"2019","unstructured":"Abbas, A., Belloy, M., Kashyap, A., Billings, J., Nezafati, M., Schumacher, E. 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