Source:http://linkedlifedata.com/resource/pubmed/id/18702583
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| Predicate | Object |
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| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
5
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| pubmed:dateCreated |
2009-4-9
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| pubmed:abstractText |
We investigated the temporal dynamics and changes in connectivity in the mental rotation network through the application of spatio-temporal support vector machines (SVMs). The spatio-temporal SVM [Mourao-Miranda, J., Friston, K. J., et al. (2007). Dynamic discrimination analysis: A spatial-temporal SVM. Neuroimage, 36, 88-99] is a pattern recognition approach that is suitable for investigating dynamic changes in the brain network during a complex mental task. It does not require a model describing each component of the task and the precise shape of the BOLD impulse response. By defining a time window including a cognitive event, one can use spatio-temporal fMRI observations from two cognitive states to train the SVM. During the training, the SVM finds the discriminating pattern between the two states and produces a discriminating weight vector encompassing both voxels and time (i.e., spatio-temporal maps). We showed that by applying spatio-temporal SVM to an event-related mental rotation experiment, it is possible to discriminate between different degrees of angular disparity (0 degrees vs. 20 degrees , 0 degrees vs. 60 degrees , and 0 degrees vs. 100 degrees ), and the discrimination accuracy is correlated with the difference in angular disparity between the conditions. For the comparison with highest accuracy (0 degrees vs. 100 degrees ), we evaluated how the most discriminating areas (visual regions, parietal regions, supplementary, and premotor areas) change their behavior over time. The frontal premotor regions became highly discriminating earlier than the superior parietal cortex. There seems to be a parcellation of the parietal regions with an earlier discrimination of the inferior parietal lobe in the mental rotation in relation to the superior parietal. The SVM also identified a network of regions that had a decrease in BOLD responses during the 100 degrees condition in relation to the 0 degrees condition (posterior cingulate, frontal, and superior temporal gyrus). This network was also highly discriminating between the two conditions. In addition, we investigated changes in functional connectivity between the most discriminating areas identified by the spatio-temporal SVM. We observed an increase in functional connectivity between almost all areas activated during the 100 degrees condition (bilateral inferior and superior parietal lobe, bilateral premotor area, and SMA) but not between the areas that showed a decrease in BOLD response during the 100 degrees condition.
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| pubmed:language |
eng
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| pubmed:journal | |
| pubmed:citationSubset |
IM
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| pubmed:chemical | |
| pubmed:status |
MEDLINE
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| pubmed:month |
May
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| pubmed:issn |
0898-929X
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| pubmed:author | |
| pubmed:issnType |
Print
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| pubmed:volume |
21
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| pubmed:owner |
NLM
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| pubmed:authorsComplete |
Y
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| pubmed:pagination |
890-904
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| pubmed:meshHeading |
pubmed-meshheading:18702583-Adult,
pubmed-meshheading:18702583-Brain,
pubmed-meshheading:18702583-Brain Mapping,
pubmed-meshheading:18702583-Discrimination (Psychology),
pubmed-meshheading:18702583-Female,
pubmed-meshheading:18702583-Functional Laterality,
pubmed-meshheading:18702583-Humans,
pubmed-meshheading:18702583-Image Processing, Computer-Assisted,
pubmed-meshheading:18702583-Magnetic Resonance Imaging,
pubmed-meshheading:18702583-Nerve Net,
pubmed-meshheading:18702583-Neuropsychological Tests,
pubmed-meshheading:18702583-Nonlinear Dynamics,
pubmed-meshheading:18702583-Orientation,
pubmed-meshheading:18702583-Oxygen,
pubmed-meshheading:18702583-Pattern Recognition, Visual,
pubmed-meshheading:18702583-Photic Stimulation,
pubmed-meshheading:18702583-Reaction Time,
pubmed-meshheading:18702583-Time Factors,
pubmed-meshheading:18702583-User-Computer Interface,
pubmed-meshheading:18702583-Young Adult
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| pubmed:year |
2009
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| pubmed:articleTitle |
Dynamic changes in the mental rotation network revealed by pattern recognition analysis of fMRI data.
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| pubmed:affiliation |
King's College London.
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| pubmed:publicationType |
Journal Article
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