matlab support Search Results


96
MathWorks Inc matlab toolbox support
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96
MathWorks Inc lesion symptom mapping svr lsm toolbox in matlab
A. Lesion overlay map (N=36). B. <t>SVR-LSM</t> 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.
Lesion Symptom Mapping Svr Lsm Toolbox In Matlab, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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MathWorks Inc deep learning toolbox support package
A. Lesion overlay map (N=36). B. <t>SVR-LSM</t> 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.
Deep Learning Toolbox Support Package, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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96
MathWorks Inc support vector machine
A. Lesion overlay map (N=36). B. <t>SVR-LSM</t> 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.
Support Vector Machine, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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MathWorks Inc matlab computer vision toolbox
A. Lesion overlay map (N=36). B. <t>SVR-LSM</t> 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.
Matlab Computer Vision Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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92
MathWorks Inc aerospace blockset
A. Lesion overlay map (N=36). B. <t>SVR-LSM</t> 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.
Aerospace Blockset, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 92/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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93
MathWorks Inc communications toolbox hardware support matlab
A. Lesion overlay map (N=36). B. <t>SVR-LSM</t> 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.
Communications Toolbox Hardware Support Matlab, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Image Search Results


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.

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:

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

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: