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Difference between revisions of "Exploring electroencephalography with a model inspired by quantum mechanics"
Exploring electroencephalography with a model inspired by quantum mechanics (view source)
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(Created page with "{{transl}} {{FR | Title = Exploring electroencephalography with a model inspired by quantum mechanics | author1 = Nicholas J. M. Popiel | author2 = Colin Metrow | author3 = Geofrey Laforge | author4 = Adrian M. Owen | author5 = Bobby Stojanoski | author6 = Andrea Soddu | author7 = | author8 = | author9 = | author10 = | Source = https://pubmed.ncbi.nlm.nih.gov/34611185/<!-- where this work comes from or where was it was retrieved (URL) --> | Original =...") |
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=== Introduction === | === Introduction === | ||
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>Biswal | 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 | |||
| autore2 = Zerrin Yetkin F | |||
| autore3 = Haughton VM | |||
| autore4 = Hyde JS | |||
| titolo = Functional connectivity in the motor cortex of resting human brain using echo-planar MRI | |||
| url = https://pubmed.ncbi.nlm.nih.gov/8524021/ | |||
| volume = | |||
| opera = Magn Reson Med | |||
| anno = 1995 | |||
| editore = | |||
| città = | |||
| ISBN = | |||
| DOI = 10.1002/mrm.1910340409 | |||
| PMID = 8524021 | |||
| PMCID = | |||
| oaf = <!-- qualsiasi valore --> | |||
| LCCN = | |||
| OCLC = | |||
}}</ref><ref>{{cita libro | |||
| autore = Hutchison RM | |||
| autore2 = Womelsdorfb T | |||
| autore3 = Allenc EA | |||
| autore4 = Bandettinie PA | |||
| autore5 = Calhound VD | |||
| autore6 = Corbetta M | |||
| autore7 = Della Penna S | |||
| author8 = Duyni JH | |||
| author9 = Glover GH | |||
| author10 = Gonzalez-Castillo J | |||
| author11 = Handwerkere A | |||
| author12 = Keilholzk S <!-- ALTRI: Vesa Kiviniemi, David A. Leopold, Francesco de Pasquale, Olaf Sporns, Martin Waltero, and Catie Chang --> | |||
| titolo = Dynamic functional connectivity: Promise, issues, and interpretations | |||
| url = https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3807588/pdf/nihms519835.pdf | |||
| volume = | |||
| opera = Neuroimage | |||
| anno = 2013 | |||
| editore = | |||
| città = | |||
| ISBN = | |||
| DOI = 10.1016/j.neuroimage.2013.05.079 | |||
| PMID = 23707587 | |||
| PMCID = PMC3807588 | |||
| oaf = <!-- qualsiasi valore --> | |||
| LCCN = | |||
| OCLC = | |||
}}</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 | |||
| autore2 = Yeo BTT | |||
| autore3 = Genon S | |||
| titolo = Imaging-based parcellations of the human brain | |||
| url = https://orbi.uliege.be/bitstream/2268/229950/2/Eickhoff_Yeo_Genon_NRN_MainManuscript.pdf | |||
| volume = | |||
| opera = Nat Rev Neurosci | |||
| anno = 2018 | |||
| editore = | |||
| città = | |||
| ISBN = | |||
| DOI = 10.1038/s41583-018-0071-7 | |||
| PMID = 30305712 | |||
| PMCID = | |||
| oaf = <!-- qualsiasi valore --> | |||
| LCCN = | |||
| OCLC = | |||
}}</ref>, and are predictive of phenotypic measures like cognition and clinical diagnoses<ref>{{cita libro | |||
| autore = Dajani DR | |||
| autore2 = Burrows CA | |||
| autore3 = Odriozola P | |||
| autore4 = Baez A | |||
| autore5 = Nebel MB | |||
| autore6 = Mostofsky SH | |||
| autore7 = Uddin LQ | |||
| titolo = Investigating functional brain network integrity using a traditional and novel categorical scheme for neurodevelopmental disorders | |||
| url = https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6356009/pdf/main.pdf | |||
| volume = | |||
| opera = NeuroImage Clin | |||
| anno = 2019 | |||
| editore = | |||
| città = | |||
| ISBN = | |||
| DOI = 10.1016/j.nicl.2019.101678 | |||
| PMID = 30708240 | |||
| PMCID = PMC6356009 | |||
| oaf = <!-- qualsiasi valore --> | |||
| LCCN = | |||
| 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>. These results suggest these networks may be an intrinsic aspect of neural activity. | |||
Indeed, the same set of structured patterns of neural activity have been found during "active" states, such as, while completing different tasks<ref>Biswal BB, Eldreth DA, Motes MA, Rypma B. Task-dependent individual differences in prefrontal connectivity. Cereb. Cortex. 2010;20:2188–2197. doi: 10.1093/cercor/bhp284. [PMC free article][PubMed] [CrossRef] [Google Scholar]</ref><ref>Fox MD, Raichle ME. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 2007;8:700–711. doi: 10.1038/nrn2201. [PubMed] [CrossRef] [Google Scholar]</ref><ref>Kraus BT, et al. Network variants are similar between task and rest states. Neuroimage. 2021;229:117743. doi: 10.1016/j.neuroimage.2021.117743. [PMC free article][PubMed] [CrossRef] [Google Scholar]</ref>. For instance, there is a high degree of correspondence between networks extracted during rest and those extracted during tasks measuring sensorimotor<ref>Kristo G, et al. Task and task-free FMRI reproducibility comparison for motor network identification. Hum. Brain Mapp. 2014;35:340–352. doi: 10.1002/hbm.22180. [PMC free article][PubMed] [CrossRef] [Google Scholar]</ref><ref>Sui J, Adali T, Pearlson GD, Calhoun VD. An ICA-based method for the identification of optimal FMRI features and components using combined group-discriminative techniques. Neuroimage. 2009;46:73–86. doi: 10.1016/j.neuroimage.2009.01.026.[PMC free article] [PubMed] [CrossRef] [Google Scholar]</ref> and higher-level cognitive abilities (i.e., working memory)<ref>Calhoun VD, Kiehl KA, Pearlson GD. Modulation of temporally coherent brain networks estimated using ICA at rest and during cognitive tasks. Hum. Brain Mapp. 2008;29:828–838. doi: 10.1002/hbm.20581. [PMC free article] [PubMed] [CrossRef] [Google Scholar]</ref><ref>Xie H, et al. Whole-brain connectivity dynamics reflect both task-specific and individual-specific modulation: A multitask study. Neuroimage. 2018;180:495–504. doi: 10.1016/j.neuroimage.2017.05.050. [PMC free article] [PubMed] [CrossRef] [Google Scholar]</ref>.Even completing a task as complicated as following the plot of a movie elicits the same network architecture as observed in the resting brain<ref name=":0">Naci L, Cusack R, Anello M, Owen AM. A common neural code for similar conscious experiences in different individuals. Proc. Natl. Acad. Sci. U. S. A. 2014;111:14277–14282. doi: 10.1073/pnas.1407007111. [PMC free article] [PubMed] [CrossRef] [Google Scholar]</ref>. The correspondence between task and rest-based networks is so strong that task-based fMRI network activity can be predicted from the resting state<ref>Kannurpatti SS, Rypma B, Biswal BB. Prediction of task-related BOLD fMRI with amplitude signatures of resting-state fMRI. Front. Syst. Neurosci. 2012;6:7. doi: 10.3389/fnsys.2012.00007.[PMC free article] [PubMed] [CrossRef] [Google Scholar]</ref>, and rest-task network pairs can be identified at the individual level<ref>Elliott ML, et al. General functional connectivity: Shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks. Neuroimage. 2019;189:516–532. doi: 10.1016/j.neuroimage.2019.01.068. [PMC free article][PubMed] [CrossRef] [Google Scholar]</ref>16. Together, these results suggest that rest and task-based patterns of brain activity likely share a similar underlying neural architecture, despite distinct experiences and cognitive processes<ref>Cole MW, Ito T, Cocuzza C, Sanchez-Romero R. The functional relevance of task-state functional connectivity. J. Neurosci. 2021 doi: 10.1523/JNEUROSCI.1713-20.2021. [PMC free article][PubMed] [CrossRef] [Google Scholar]</ref>. | Indeed, the same set of structured patterns of neural activity have been found during "active" states, such as, while completing different tasks<ref>Biswal BB, Eldreth DA, Motes MA, Rypma B. Task-dependent individual differences in prefrontal connectivity. Cereb. Cortex. 2010;20:2188–2197. doi: 10.1093/cercor/bhp284. [PMC free article][PubMed] [CrossRef] [Google Scholar]</ref><ref>Fox MD, Raichle ME. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 2007;8:700–711. doi: 10.1038/nrn2201. [PubMed] [CrossRef] [Google Scholar]</ref><ref>Kraus BT, et al. Network variants are similar between task and rest states. Neuroimage. 2021;229:117743. doi: 10.1016/j.neuroimage.2021.117743. [PMC free article][PubMed] [CrossRef] [Google Scholar]</ref>. For instance, there is a high degree of correspondence between networks extracted during rest and those extracted during tasks measuring sensorimotor<ref>Kristo G, et al. Task and task-free FMRI reproducibility comparison for motor network identification. Hum. Brain Mapp. 2014;35:340–352. doi: 10.1002/hbm.22180. [PMC free article][PubMed] [CrossRef] [Google Scholar]</ref><ref>Sui J, Adali T, Pearlson GD, Calhoun VD. An ICA-based method for the identification of optimal FMRI features and components using combined group-discriminative techniques. Neuroimage. 2009;46:73–86. doi: 10.1016/j.neuroimage.2009.01.026.[PMC free article] [PubMed] [CrossRef] [Google Scholar]</ref> and higher-level cognitive abilities (i.e., working memory)<ref>Calhoun VD, Kiehl KA, Pearlson GD. Modulation of temporally coherent brain networks estimated using ICA at rest and during cognitive tasks. Hum. Brain Mapp. 2008;29:828–838. doi: 10.1002/hbm.20581. [PMC free article] [PubMed] [CrossRef] [Google Scholar]</ref><ref>Xie H, et al. Whole-brain connectivity dynamics reflect both task-specific and individual-specific modulation: A multitask study. Neuroimage. 2018;180:495–504. doi: 10.1016/j.neuroimage.2017.05.050. [PMC free article] [PubMed] [CrossRef] [Google Scholar]</ref>.Even completing a task as complicated as following the plot of a movie elicits the same network architecture as observed in the resting brain<ref name=":0">Naci L, Cusack R, Anello M, Owen AM. A common neural code for similar conscious experiences in different individuals. Proc. Natl. Acad. Sci. U. S. A. 2014;111:14277–14282. doi: 10.1073/pnas.1407007111. [PMC free article] [PubMed] [CrossRef] [Google Scholar]</ref>. The correspondence between task and rest-based networks is so strong that task-based fMRI network activity can be predicted from the resting state<ref>Kannurpatti SS, Rypma B, Biswal BB. Prediction of task-related BOLD fMRI with amplitude signatures of resting-state fMRI. Front. Syst. Neurosci. 2012;6:7. doi: 10.3389/fnsys.2012.00007.[PMC free article] [PubMed] [CrossRef] [Google Scholar]</ref>, and rest-task network pairs can be identified at the individual level<ref>Elliott ML, et al. General functional connectivity: Shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks. Neuroimage. 2019;189:516–532. doi: 10.1016/j.neuroimage.2019.01.068. [PMC free article][PubMed] [CrossRef] [Google Scholar]</ref>16. Together, these results suggest that rest and task-based patterns of brain activity likely share a similar underlying neural architecture, despite distinct experiences and cognitive processes<ref>Cole MW, Ito T, Cocuzza C, Sanchez-Romero R. The functional relevance of task-state functional connectivity. J. Neurosci. 2021 doi: 10.1523/JNEUROSCI.1713-20.2021. [PMC free article][PubMed] [CrossRef] [Google Scholar]</ref>. | ||