Motor participation in phonological processing can be modulated by task nature across the speech perception to speech production range. The pars opercularis of the left inferior frontal gyrus (LIFG) would be increasingly active across this range, because of changing motor demands. Here, we investigated with simultaneous tDCS and fMRI whether the task load modulation of tDCS effects translates into predictable patterns of functional connectivity. Findings were analysed under the "multi-node framework", according to which task load and the network structure underlying cognitive functions are modulators of tDCS effects. In a within-subject study, participants (N = 20) performed categorical perception, lexical decision and word naming tasks [which differentially recruit the target of stimulation (LIFG)], which were repeatedly administered in three tDCS sessions (anodal, cathodal and sham). The LIFG, left superior temporal gyrus and their right homologues formed the target network subserving phonological processing. C-tDCS inhibition and A-tDCS excitation should increase with task load. Correspondingly, the larger the task load, the larger the relevance of the target for the task and smaller the room for compensation of C-tDCS inhibition by less relevant nodes. Functional connectivity analyses were performed with partial correlations, and network compensation globally inferred by comparing the relative number of significant connections each condition induced relative to sham. Overall, simultaneous tDCS and fMRI was adequate to show that motor participation in phonological processing is modulated by task nature. Network responses induced by C-tDCS across phonological processing tasks matched predictions. A-tDCS effects were attributed to optimisation of network efficiency.
Pairwise rigid registration aims to find the rigid transformation that best registers two surfaces represented by point clouds. This work presents a comparison between seven algorithms, with different strategies to tackle rigid registration tasks. We focus on the frame-to-frame problem, in which the point clouds are extracted from a video sequence with depth information generating partial overlapping 3D data. We use both point clouds and RGB-D video streams in the experimental results. The former is considered under different viewpoints with the addition of a case-study simulating missing data. Since the ground truth rotation is provided, we discuss four different metrics to measure the rotation error in this case. Among the seven considered techniques, the Sparse ICP and Sparse ICP-CTSF outperform the other five ones in the point cloud registration experiments without considering incomplete data. However, the evaluation facing missing data indicates sensitivity for these methods against this problem and favors ICP-CTSF in such situations. In the tests with video sequences, the depth information is segmented in the first step, to get the target region. Next, the registration algorithms are applied and the average root mean squared error, rotation and translation errors are computed. Besides, we analyze the robustness of the algorithms against spatial and temporal sampling rates. We conclude from the experiments using a depth video sequences that ICP-CTSF is the best technique for frame-to-frame registration.
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