Academic Journal
Full-brain auto-regressive modeling (FARM) using fMRI
| Τίτλος: | Full-brain auto-regressive modeling (FARM) using fMRI |
|---|---|
| Συγγραφείς: | Garg, Rahul grahul@us.ibm.com, Cecchi, Guillermo A. gcecchi@us.ibm.com, Rao, A. Ravishankar ravirao@us.ibm.com |
| Πηγή: | NeuroImage. Sep2011, Vol. 58 Issue 2, p416-441. 26p. |
| Θεματικοί όροι: | *Magnetic resonance imaging of the brain, *Brain -- Mathematical models, *Neurophysiology, *Biological neural networks, *Parietal lobe, *Regression analysis |
| Περίληψη: | Abstract: In order to fully uncover the information potentially available in the fMRI signal, we model it as a multivariate auto-regressive process. To infer the model, we do not apply any form of clustering or dimensionality reduction, and solve the problem of under-determinacy using sparse regression. We find that only a few small clusters (with average size of 3–4 voxels) are useful in predicting the activity of other voxels, and demonstrate remarkable consistency within a subject as well as across multiple subjects. Moreover, we find that: (a) the areas that can predict activity of other voxels are consistent with previous results related to networks activated by the specific somatosensory task, as well as networks related to the default mode activity; (b) there is a global dynamical state dominated by two prominent (although not unique) streams, originating in the posterior parietal cortex and the posterior cingulate/precuneus cortex; (c) these streams span default mode and task-specific networks, and interact in several regions, notably the insula; and (d) the posterior cingulate is a central node of the default mode network, in terms of its ability to determine the future evolution of the rest of the nodes. [Copyright &y& Elsevier] |
| Βάση Δεδομένων: | Academic Search Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://www.doi.org/10.1016/j.neuroimage.2011.02.074? Name: ScienceDirect (all content) (s7799221) Category: fullText Text: View record from ScienceDirect MouseOverText: View record from ScienceDirect |
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| Items | – Name: Title Label: Title Group: Ti Data: Full-brain auto-regressive modeling (FARM) using fMRI – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Garg%2C+Rahul%22">Garg, Rahul</searchLink><i> grahul@us.ibm.com</i><br /><searchLink fieldCode="AR" term="%22Cecchi%2C+Guillermo+A%2E%22">Cecchi, Guillermo A.</searchLink><i> gcecchi@us.ibm.com</i><br /><searchLink fieldCode="AR" term="%22Rao%2C+A%2E+Ravishankar%22">Rao, A. Ravishankar</searchLink><i> ravirao@us.ibm.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22NeuroImage%22">NeuroImage</searchLink>. Sep2011, Vol. 58 Issue 2, p416-441. 26p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Magnetic+resonance+imaging+of+the+brain%22">Magnetic resonance imaging of the brain</searchLink><br />*<searchLink fieldCode="DE" term="%22Brain+--+Mathematical+models%22">Brain -- Mathematical models</searchLink><br />*<searchLink fieldCode="DE" term="%22Neurophysiology%22">Neurophysiology</searchLink><br />*<searchLink fieldCode="DE" term="%22Biological+neural+networks%22">Biological neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Parietal+lobe%22">Parietal lobe</searchLink><br />*<searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract: In order to fully uncover the information potentially available in the fMRI signal, we model it as a multivariate auto-regressive process. To infer the model, we do not apply any form of clustering or dimensionality reduction, and solve the problem of under-determinacy using sparse regression. We find that only a few small clusters (with average size of 3–4 voxels) are useful in predicting the activity of other voxels, and demonstrate remarkable consistency within a subject as well as across multiple subjects. Moreover, we find that: (a) the areas that can predict activity of other voxels are consistent with previous results related to networks activated by the specific somatosensory task, as well as networks related to the default mode activity; (b) there is a global dynamical state dominated by two prominent (although not unique) streams, originating in the posterior parietal cortex and the posterior cingulate/precuneus cortex; (c) these streams span default mode and task-specific networks, and interact in several regions, notably the insula; and (d) the posterior cingulate is a central node of the default mode network, in terms of its ability to determine the future evolution of the rest of the nodes. [Copyright &y& Elsevier] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asx&AN=64869285 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.neuroimage.2011.02.074 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 416 Subjects: – SubjectFull: Magnetic resonance imaging of the brain Type: general – SubjectFull: Brain -- Mathematical models Type: general – SubjectFull: Neurophysiology Type: general – SubjectFull: Biological neural networks Type: general – SubjectFull: Parietal lobe Type: general – SubjectFull: Regression analysis Type: general Titles: – TitleFull: Full-brain auto-regressive modeling (FARM) using fMRI Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Garg, Rahul – PersonEntity: Name: NameFull: Cecchi, Guillermo A. – PersonEntity: Name: NameFull: Rao, A. Ravishankar IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 09 Text: Sep2011 Type: published Y: 2011 Identifiers: – Type: issn-print Value: 10538119 Numbering: – Type: volume Value: 58 – Type: issue Value: 2 Titles: – TitleFull: NeuroImage Type: main |
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