Volume 15 Supplement 1
Using fMRI to characterize how cortex represents limb motions
© Menon et al; licensee BioMed Central Ltd. 2014
Published: 21 July 2014
Neuroimaging experiments that map limb motions on to the brain observe fractured somatotopic maps, with correlated neural responses across functionally related joints in the arm . Analyzing such experiments involves visually comparing winner-takes-all neural activation maps for different subjects that are generated with generalized linear models . Such analyses, however, abstract cross-joint correlations and treat reliable deviations from canonical neural (haemodynamic) response functions as temporal noise. Here, using classification accuracy while delineating different limb motions as a metric, we demonstrate that the peak neural response amplitude---upon which winner-takes-all analyses are based---is the least informative part of the time-series. In contrast, our experiments suggest that neural responses are most informative after the initial response peak (t=4-10s). Our observations extend to primary motor (M1), pre-motor (PMd), somatosensory (S1), superior parietal (SupPar), and supplementary motor (SMA) cortices, matching prior region-agnostic results . As expected for open-loop limb motions, median M1 and S1 classification accuracies are greater than SupPar, PMd and SMA. All accuracies exceed the ventricles, which set a data-driven noise threshold at chance (50-55% accuracy; chance=50%) and demonstrate that our datasets lack task-correlated noise.
Our results suggest that Functional Magnetic Resonance Imaging (fMRI) time-series responses convey sufficient information to classify a variety of motor tasks in regions where neural activity is expected to be correlated across conditions. Reproducing our results, however, may require fMRI datasets with minimal (<1mm) head-motion, no spatial smoothing, and tests for null (baseline) results in regions with no expected effect.
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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.