Difference between revisions of "Store:EEMIen02"
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=== 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 | ||
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| 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 | ||
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}}</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 | ||
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}}</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. | ||
Revision as of 15:46, 5 November 2022
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[1][2]; 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[3], and are predictive of phenotypic measures like cognition and clinical diagnoses[4][5][6]. These results suggest these networks may be an intrinsic aspect of neural activity.
- ↑ Biswal B, Zerrin Yetkin F, Haughton VM, Hyde JS, «Functional connectivity in the motor cortex of resting human brain using echo-planar MRI», in Magn Reson Med, 1995».
PMID:8524021
DOI:10.1002/mrm.1910340409 - ↑ Hutchison RM, Womelsdorfb T, Allenc EA, Bandettinie PA, Calhound VD, Corbetta M, Della Penna S, «Dynamic functional connectivity: Promise, issues, and interpretations», in Neuroimage, 2013».
PMID:23707587 - PMCID:PMC3807588
DOI:10.1016/j.neuroimage.2013.05.079 - ↑ Eickhoff SB, Yeo BTT, Genon S, «Imaging-based parcellations of the human brain», in Nat Rev Neurosci, 2018».
PMID:30305712
DOI:10.1038/s41583-018-0071-7 - ↑ Dajani DR, Burrows CA, Odriozola P, Baez A, Nebel MB, Mostofsky SH, Uddin LQ, «Investigating functional brain network integrity using a traditional and novel categorical scheme for neurodevelopmental disorders», in NeuroImage Clin, 2019».
PMID:30708240 - PMCID:PMC6356009
DOI:10.1016/j.nicl.2019.101678 - ↑ 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]
- ↑ 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]