Sökning: onr:"swepub:oai:gup.ub.gu.se/133352" >
A Monte Carlo metho...
A Monte Carlo method for locally multivariate brain mapping.
-
- Björnsdotter, Malin (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för neurovetenskap och fysiologi, sektionen för fysiologi,Institute of Neuroscience and Physiology, Department of Physiology,Department of Physiology, Institute of Neuroscience and Physiology, University of Gothenburg, Sweden
-
- Rylander, Karin, 1977 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för neurovetenskap och fysiologi, sektionen för klinisk neurovetenskap och rehabilitering,Institute of Neuroscience and Physiology, Department of Clinical Neuroscience and Rehabilitation,Department of Physiology, Institute of Neuroscience and Physiology, University of Gothenburg, Sweden
-
- Wessberg, Johan, 1962 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för neurovetenskap och fysiologi, sektionen för fysiologi,Institute of Neuroscience and Physiology, Department of Physiology,Department of Physiology, Institute of Neuroscience and Physiology, University of Gothenburg, Sweden
-
(creator_code:org_t)
- Elsevier BV, 2011
- 2011
- Engelska.
-
Ingår i: NeuroImage. - : Elsevier BV. - 1095-9572 .- 1053-8119.
- Relaterad länk:
-
https://gup.ub.gu.se...
-
visa fler...
-
https://doi.org/10.1...
-
https://urn.kb.se/re...
-
visa färre...
Abstract
Ämnesord
Stäng
- Locally multivariate approaches to functional brain mapping offer a highly appealing complement to conventional statistics, but require restrictive region-of-interest hypotheses, or, in exhaustive search forms (such as the "searchlight" algorithm; Kriegeskorte et al., 2006), are excessively computer intensive. We therefore propose a non-restrictive, comparatively fast yet highly sensitive method based on Monte Carlo approximation principles where locally multivariate maps are computed by averaging across voxelwise condition-discriminative information obtained from repeated stochastic sampling of fixed-size search volumes. On simulated data containing discriminative regions of varying size and contrast-to-noise ratio (CNR), the Monte Carlo method reduced the required computer resources by as much as 75% compared to the searchlight with no reduction in mapping performance. Notably, the Monte Carlo mapping approach not only outperformed the general linear method (GLM), but also produced higher discriminative voxel detection scores than the searchlight irrespective of classifier (linear or nonlinear support vector machine), discriminative region size or CNR. The improved performance was explained by the information-average procedure, and the Monte Carlo approach yielded mapping sensitivities of a few percent lower than an information-average exhaustive search. Finally, we demonstrate the utility of the algorithm on whole-brain, multi-subject functional magnetic resonance imaging (fMRI) data from a tactile study, revealing that the central representation of gentle touch is spatially distributed in somatosensory, insular and visual regions.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinska och farmaceutiska grundvetenskaper -- Fysiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Basic Medicine -- Physiology (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
Hitta via bibliotek
Till lärosätets databas