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Neuroimage. 2013 Feb 1;66:623-33. doi: 10.1016/j.neuroimage.2012.10.069. Epub 2012 Nov 2.

Accurate decoding of sub-TR timing differences in stimulations of sub-voxel regions from multi-voxel response patterns.

Author information

1
Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, 10 Center Dr. MSC 1148, Bethesda, MD 20892-1148 USA. Electronic address: mmisaki@laureateinstitute.org.
2
Functional MRI Facility, National Institute of Mental Health, National Institutes of Health. 10 Center Dr. MSC 1148, Bethesda, MD 20892-1148 USA.
3
Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, 10 Center Dr. MSC 1148, Bethesda, MD 20892-1148 USA; Functional MRI Facility, National Institute of Mental Health, National Institutes of Health. 10 Center Dr. MSC 1148, Bethesda, MD 20892-1148 USA.

Abstract

We investigated the decoding of ocular dominance stimulations with millisecond-order timing difference from the blood oxygen level dependent (BOLD) signal in human functional magnetic resonance imaging (fMRI). In our experiment, ocular dominance columns were activated by monocular visual stimulation with 500- or 100- ms onset differences. We observed that the event-related hemodynamic response (HDR) in the human visual cortex was sensitive to the subtle onset difference. The HDR shapes were related to the stimulus timings in various manners: the timing difference was represented in either the amplitude of positive peak, amplitude of negative peak, delay of peak time, or response duration of HDR. These complex relationships were different across voxels and subjects. To find an informative feature of HDR for discriminating the subtle timing difference of ocular dominance stimulations, we examined various characteristics of HDR including response amplitude, time to peak, full width at half-maximum response, as inputs for decoding analysis. Using a canonical HDR function for estimating the voxel's response did not yield good decoding scores, suggesting that information may reside in the variability of HDR shapes. Using all the values from the deconvolved HDR also showed low performance, which could be due to an over-fitting problem with the large data dimensionality. When using either positive or negative peak amplitude of the deconvolved HDR, high decoding performance could be achieved for both the 500ms and the 100ms onset differences. The high accuracy even for the 100ms difference, given that the signal was sampled at a TR of 250ms and 2×2×3-mm voxels, implies a possibility of spatiotemporally hyper-resolution decoding. Furthermore, both down-sampling and smoothing did not affect the decoding accuracies very much. These results suggest a complex spatiotemporal relationship between the multi-voxel pattern of the BOLD response and the population activation of neuronal columns. The demonstrated possibility of decoding stimulations for columnar-level organization with 100-ms onset difference using lower resolution imaging data may broaden the scope of application of the BOLD fMRI.

KEYWORDS:

Complex spatiotemporal filter voxel; Deconvolved hemodynamic response; Hyper-spatiotemporal resolution; Multi-voxel pattern analysis

PMID:
23128073
PMCID:
PMC3582847
DOI:
10.1016/j.neuroimage.2012.10.069
[Indexed for MEDLINE]
Free PMC Article
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