voxel based lesion symptom mapping toolbox for matlab  (MathWorks Inc)

Journal: Brain and language

Article Title: Subjective experience of inner speech in aphasia: Preliminary behavioral relationships and neural correlates

doi: 10.1016/j.bandl.2016.09.009

Figure Lengend Snippet: A. Lesion overlay map (N=36). B. SVR-LSM results showing lesion locations associated with higher reports of sIS, IwW, and ToT (p < .01, see figure legend for color representations). C. SVR-LSM results at p < .10 to illustrate the minimal overlap between lesions associated with sIS and IwW. Total lesion volume was controlled in both maps, using the direct lesion volume control method.

Article Snippet: This was performed using the support vector regression-based lesion-symptom mapping (SVR-LSM) toolbox in MATLAB ( https://cfn.upenn.edu/~zewang/ ), which is more resistant than voxel-based lesion-symptom mapping to theoretical bias in localization due to lesion covariance, especially when behaviors rely on multiple distinct brain regions ( Herbet, Lafargue, & Duffau, 2015 ; Mah, Husain, Rees, & Nachev, 2014 ).

Techniques:

Journal: Brain and language

Article Title: Subjective experience of inner speech in aphasia: Preliminary behavioral relationships and neural correlates

doi: 10.1016/j.bandl.2016.09.009

Figure Lengend Snippet: SVR-LSM results showing significant clusters (threshold = 200 mm 3 ) at a threshold of P <.01.

Article Snippet: This was performed using the support vector regression-based lesion-symptom mapping (SVR-LSM) toolbox in MATLAB ( https://cfn.upenn.edu/~zewang/ ), which is more resistant than voxel-based lesion-symptom mapping to theoretical bias in localization due to lesion covariance, especially when behaviors rely on multiple distinct brain regions ( Herbet, Lafargue, & Duffau, 2015 ; Mah, Husain, Rees, & Nachev, 2014 ).

Techniques: