Journal: Nature Communications

Article Title: Double dissociation of dynamic and static face perception provides causal evidence for a third visual pathway

doi: 10.1038/s41467-025-61395-9

Figure Lengend Snippet: Performance on static and dynamic emotion recognition tasks and brain lesion mapping in patients with focal brain lesions ( A ) 31 Patients with focal brain lesions involving the right posterior superior temporal sulcus (pSTS) but sparing the right fusiform face area (FFA) and occipital face area (OFA), showed normal performance in the static emotion recognition task but poor performance for dynamic emotion recognition. B 12 patients with focal brain lesions involving the right FFA or OFA, but sparing the right pSTS, showed normal performance in dynamic but poor performance in static emotion recognition. In ( A , B ), the rendered MNI152 template brain shows the extent of the associated lesions in green. The boxplots show the z-transformed performance accuracy of the static and dynamic emotion recognition tasks. The boxes denote the interquartile range, the whiskers extend to 1.5 × IQR from each quartile and the horizontal lines denote the median. The source data and code used for generating the plots are available at https://osf.io/ez9dp/ . C Rendered MNI152 template brain and orthogonal brain slices show results of the multivariate support vector regression lesion symptom mapping (SVR-LSM). Clusters show significant associations with behavioral performance (FWE-corrected p < 0.001) for dynamic (blue) and static (red) emotion recognition, identified using a two-tailed, permutation-based maximum statistic approach. A disconnectome analysis for the white matter revealed tracts associated with dynamic (in purple) and static (in yellow) emotion recognition. [AF Arcuate fasciculus, MDLF Middle longitudinal fasciculus, ILF Inferior longitudinal fasciculus, IFOF Inferior fronto-occipital fasciculus].

Article Snippet: Initial SVR-LSM was conducted using the SVR lesion symptom mapping toolbox ( https://github.com/atdemarco/svrlsmgui ) with functionalities of the Statistics and Machine Learning Toolbox within MATLAB (MATLAB 2022a, The MathWorks, Inc., Natick, Massachusetts, United States) , .

Techniques: Transformation Assay, Plasmid Preparation, Two Tailed Test

Journal: Scientific Reports

Article Title: Influence of stroke infarct location on quality of life assessed in a multivariate lesion-symptom mapping study

doi: 10.1038/s41598-021-92865-x

Figure Lengend Snippet: Results of SVR lesion-symptom mapping for the EQ-5D sum scores illustrated on a brain template in MNI standard space oriented in radiological convention. Three clusters with N > 50 significant voxels after permutation based on a threshold of p < 0.005 are shown. Clusters are color-coded to identify p-values after cluster-wise FWE correction (p = 0.036 [red]; p = 0.062 [green]; p = 0.298 [blue]). See also Table for statistical and anatomical details. MNI coordinates of each transverse section (z axis) are shown.

Article Snippet: SVR was conducted using a publicly available SVR lesion symptom mapping toolbox (SVR-LSM, available at https://github.com/atdemarco/svrlsmgui ) applying functionalities of the Statistics and Machine Learning Toolbox within MATLAB (MATLAB 2019b, The MathWorks, Inc., Natick, Massachusetts, United States) .

Techniques: