Editor, Editors, USER, admin, Bureaucrats, Check users, dev, editor, founder, Interface administrators, oversight, Suppressors, Administrators, translator
10,785
edits
Tags: Reverted Visual edit |
Tags: Reverted Visual edit |
||
Line 1: | Line 1: | ||
=== Introduzione === | === Introduzione === | ||
An important but outstanding issue in contemporary cognitive neuroscience is understanding the organizational properties of neural activity. For instance, is there a fundamental structure to the spatial–temporal patterns neural brain activity across different conditions? One common approach used to address this question is to examine the brain at “rest”. Measures such as functional connectivity, independent component analysis and graph theoretic metrics, have been applied to data recorded using different imaging techniques (e.g., functional magnetic resonance imaging (fMRI) and electroencephalography (EEG)), to cluster brain areas that exhibit similar activity patterns. Numerous studies have shown that brain activity during “rest” can be grouped into distinct networks across<ref>{{cita libro | |||
| autore = Biswal B | | autore = Biswal B | ||
| autore2 = Zerrin Yetkin F | | autore2 = Zerrin Yetkin F | ||
Line 46: | Line 46: | ||
| LCCN = | | LCCN = | ||
| OCLC = | | OCLC = | ||
}}</ref> | }}</ref>; such as sensory (visual and auditory), default mode, executive, salience, and attentional (ventral and dorsal) networks that have been reliably reproduced across thousands of participants<ref>{{cita libro | ||
| autore = Eickhoff SB | | autore = Eickhoff SB | ||
| autore2 = Yeo BTT | | autore2 = Yeo BTT | ||
Line 64: | Line 64: | ||
| LCCN = | | LCCN = | ||
| OCLC = | | OCLC = | ||
}}</ref> | }}</ref>, and are predictive of phenotypic measures like cognition and clinical diagnoses<ref>{{cita libro | ||
| autore = Dajani DR | | autore = Dajani DR | ||
| autore2 = Burrows CA | | autore2 = Burrows CA | ||
Line 86: | Line 86: | ||
| LCCN = | | LCCN = | ||
| OCLC = | | OCLC = | ||
}}</ref><ref>Uddin LQ, Karlsgodt KH. Future directions for examination of brain networks in neurodevelopmental disorders. J. Clin. Child Adolesc. Psychol. 2018;47:483–497. doi: 10.1080/15374416.2018.1443461. [PMC free article] [PubMed] [CrossRef] [Google Scholar]</ref><ref>Sripada C, et al. Prediction of neurocognition in youth from resting state fMRI. Mol. Psychiatry. 2020;25:3413–3421. doi: 10.1038/s41380-019-0481-6. [PMC free article] [PubMed] [CrossRef] [Google Scholar]</ref> | }}</ref><ref>Uddin LQ, Karlsgodt KH. Future directions for examination of brain networks in neurodevelopmental disorders. J. Clin. Child Adolesc. Psychol. 2018;47:483–497. doi: 10.1080/15374416.2018.1443461. [PMC free article] [PubMed] [CrossRef] [Google Scholar]</ref><ref>Sripada C, et al. Prediction of neurocognition in youth from resting state fMRI. Mol. Psychiatry. 2020;25:3413–3421. doi: 10.1038/s41380-019-0481-6. [PMC free article] [PubMed] [CrossRef] [Google Scholar]</ref>. These results suggest these networks may be an intrinsic aspect of neural activity. |
edits