{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T17:45:06Z","timestamp":1790963106160,"version":"4.1.0"},"reference-count":117,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T00:00:00Z","timestamp":1719360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Hum. Neurosci."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Numerous modes or patterns of neural activity can be seen in the brain of individuals during the resting state. However, those functions do not persist long, and they are continuously altering in the brain. We have hypothesized that the brain activations during the resting state should themselves be responsible for this alteration of the activities.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Using the resting-state fMRI data of 63 healthy young individuals, we estimated the causality effects of each resting-state activation map on all other networks. The resting-state networks were identified, their causality effects on the other components were extracted, the networks with the top 20% of the causality were chosen, and the networks which were under the influence of those causal networks were also identified.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Our results showed that the influence of each activation component over other components is different. The brain areas which showed the highest causality coefficients were subcortical regions, such as the brain stem, thalamus, and amygdala. On the other hand, nearly all the areas which were mostly under the causal effects were cortical regions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>In summary, our results suggest that subcortical brain areas exert a higher influence on cortical regions during the resting state, which could help in a better understanding the dynamic nature of brain functions.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fnhum.2024.1363125","type":"journal-article","created":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T04:40:59Z","timestamp":1719376859000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["The subcortical brain regions influence the cortical areas during resting-state: an fMRI study"],"prefix":"10.3389","volume":"18","author":[{"given":"Omid","family":"Moazeni","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Georg","family":"Northoff","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seyed Amir Hossein","family":"Batouli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,6,26]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1016\/j.neuroimage.2017.10.034","article-title":"Image processing and quality control for the first 10,000 brain imaging datasets from UK biobank","volume":"166","author":"Alfaro-Almagro","year":"2018","journal-title":"Neuroimage"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"1163111","DOI":"10.3389\/fnins.2023.1163111","article-title":"Functional and effective connectivity analysis of drug-resistant epilepsy: a resting-state fMRI analysis","volume":"17","author":"Bacon","year":"2023","journal-title":"Front. 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