- Abstract:
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Here we report an exploratory within-subject variance decomposition analysis conducted on a task-based fMRI dataset with an unusually large number of repeated measures (i.e., 500 trials in each of three different subjects) distributed across 100 functional scans and 9 to 10 different sessions. Within-subject variance was segregated into four primary components: variance across-sessions, variance across-runs within a session, variance across-blocks within a run, and residual measurement/modeli...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Publisher's Version
- Files:
-
-
(pdf, 3.2MB)
-
- Publisher copy:
- 10.1016/j.neuroimage.2016.10.024
-
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- Publisher:
- Elsevier Publisher's website
- Journal:
- NeuroImage Journal website
- Volume:
- 154
- Pages:
- 206-218
- Publication date:
- 2016-10-20
- Acceptance date:
- 2016-10-14
- DOI:
- EISSN:
-
1095-9572
- ISSN:
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1053-8119
- Pubs id:
-
pubs:908844
- URN:
-
uri:148ecce6-1db3-4762-a2c5-4509f7fa8f81
- UUID:
-
uuid:148ecce6-1db3-4762-a2c5-4509f7fa8f81
- Local pid:
- pubs:908844
- Copyright date:
- 2016
- Notes:
- Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).
Journal article
Variance decomposition for single-subject task-based fMRI activity estimates across many sessions
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National Institute of Neurological Disorders and Stroke
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