Cies within each and every location (using GGcorrected rmANOVAs and False Discovery Rate [FDR]corrected followup paired sample ttests) revealed only several considerable effects in LSPOC, decoding accuracies for the hand were substantially higher than for the tool and for acrosseffector decoding (each at p.; F. p); in LSMG, decoding accuracies for the tool were PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/21480800 considerably larger than for the hand and for acrosseffector decoding (each at p.; F. p); in Pentagastrin Epigenetic Reader Domain Lmotor cortex, decoding accuracies for the hand had been considerably higherGallivan et al.eLife ;e..eLife.ofResearch articleNeuroscienceFigure .Hand and Tool movement plans decoded from the localizerdefined taIPS.(A) Blockdesign protocol and experimental timing from the Bodies, Objects, and Tools (BOT) localizer.(B) Overlay of tool and anterior parietal ROIs.The Motor experimentdefined anterior parietal ROIs (post.aIPS and aIPS; defined by the Figure .Continued on next pageGallivan et al.eLife ;e..eLife.ofResearch post Figure .ContinuedNeuroscience[(Program Execute) (Preview)] contrast) plus the Localizer experimentdefined anterior parietal ROI (taIPS; defined by the [(Tools Scrambled) AND (Tools Bodies) AND (Tools Objects)] conjunction contrast) are superimposed around the transverse anatomical slices of 3 representative subjects.Across all subjects we found a affordable degree of overlap between the Motor and Localizer experimentdefined anterior parietal ROIs.(C) SC timecourse activity and timeresolved and planepoch decoding accuracies from taIPS.See Figure caption for format..eLife.than acrosseffector decoding (p.; F. p); and lastly, in LpMTG, decoding accuracies for the tool were significantly higher than for the hand (p.; F. p) (note that in LEBA, though decoding accuracies for the hand showed a trend to be larger than for the tool, this did not reach significance; p.; F. p).Taken together, these analyses suggest toolspecific decoding in SMG and pMTG and handspecific decoding in SPOC and EBA.Voxel weight analysesTo further examine the underlying patterns of activity that led to accurate decoding and crossdecoding we investigated the voxel weights assigned by the classifier (where the path from the weight indicates the relationship of the voxel with all the class label, as learned by the classifier; see also the caption for Figure figure supplement).In distinct, we looked for correspondence inside the voxel weights across pairwise comparisons within single subjects as a prospective explanation for why the spatial activity patterns in certain regions could possibly show acrosseffector decoding (the data from two representative subjects is shown in Figure figure supplement ; see also Formisano et al for a similar approach).Which is, when the precise exact same population of voxels have been accountable for driving the observed acrosseffector classification effects than this very same voxel set could possibly be consistently biased towards coding 1 variety of action vs the other (i.e grasping or reaching) for each effectors (hand and tool).(Note that for the reason that our pattern classification analysis was performed on nonTalairached data [MVPA was the truth is performed on singlesubject ACPCaligned data], comparing the weights across subjects on a single cortical surface was not feasible).Visual inspection of your voxel weightings failed to reveal any structured or consistent topography inside or across subjects (for similar benefits, see also Harrison and Tong, Gallivan et al a).That is definitely, whilst the weightings of some voxels appeared to be constant acr.
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