spm12 implemented in matlab 2019 Search Results


statistical parametric mapping spm12 toolbox  (MathWorks Inc)
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MathWorks Inc statistical parametric mapping spm12 toolbox
Statistical Parametric Mapping Spm12 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
https://www.bioz.com/product/spm12+implemented+in+matlab+2019/pmc08339567-47-5-12?v=MathWorks+Inc
Average 96 stars, based on 1 article reviews
statistical parametric mapping spm12 toolbox - by Bioz Stars, 2026-08
96/100 stars
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conn toolbox  (MathWorks Inc)
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MathWorks Inc conn toolbox
Figure 2. Group-level significant BOLD signal increases. A. Clusters showing significant BOLD signal increases due to residual head motion regressors (realignment parameters and scrubbed volumes). B. Clusters showing significant BOLD signal increases associated with CSF/edge effects (5 aCompCor components). C. Clusters showing significant global BOLD signal increases <t>(CONN</t> method; default mask value set at 80%). D. Clusters showing significant global BOLD signal increases (mask value set at 0%). E. Extracranial sources of significant global BOLD signal increases observed in the unmasked data from panel D, rendered on a single individual’s T1- weighted MRI scan (‘chris_t1’ in MRIcroGL, Version 13.6.1, https://www.nitrc.org/projects /mricrogl/; Rorden, 2017). A–D are shown on inflated surface renderings <t>from</t> <t>SPM12.</t> All results come from Pipeline 6 looking at the variance coming from each type of noise regressor when controlling for the others and are thresholded at p < 0.001 with a spatial extent cluster at p < 0.05 (FWE corrected).
Conn 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
https://www.bioz.com/product/spm12+implemented+in+matlab+2019/10__1162_slash_nol_a_00151-93-12-21?v=MathWorks+Inc
Average 96 stars, based on 1 article reviews
conn toolbox - by Bioz Stars, 2026-08
96/100 stars
  Buy from Supplier

Image Search Results


Figure 2. Group-level significant BOLD signal increases. A. Clusters showing significant BOLD signal increases due to residual head motion regressors (realignment parameters and scrubbed volumes). B. Clusters showing significant BOLD signal increases associated with CSF/edge effects (5 aCompCor components). C. Clusters showing significant global BOLD signal increases (CONN method; default mask value set at 80%). D. Clusters showing significant global BOLD signal increases (mask value set at 0%). E. Extracranial sources of significant global BOLD signal increases observed in the unmasked data from panel D, rendered on a single individual’s T1- weighted MRI scan (‘chris_t1’ in MRIcroGL, Version 13.6.1, https://www.nitrc.org/projects /mricrogl/; Rorden, 2017). A–D are shown on inflated surface renderings from SPM12. All results come from Pipeline 6 looking at the variance coming from each type of noise regressor when controlling for the others and are thresholded at p < 0.001 with a spatial extent cluster at p < 0.05 (FWE corrected).

Journal: Neurobiology of Language

Article Title: A comparison of denoising approaches for spoken word production related artefacts in continuous multiband fMRI data

doi: 10.1162/nol_a_00151

Figure Lengend Snippet: Figure 2. Group-level significant BOLD signal increases. A. Clusters showing significant BOLD signal increases due to residual head motion regressors (realignment parameters and scrubbed volumes). B. Clusters showing significant BOLD signal increases associated with CSF/edge effects (5 aCompCor components). C. Clusters showing significant global BOLD signal increases (CONN method; default mask value set at 80%). D. Clusters showing significant global BOLD signal increases (mask value set at 0%). E. Extracranial sources of significant global BOLD signal increases observed in the unmasked data from panel D, rendered on a single individual’s T1- weighted MRI scan (‘chris_t1’ in MRIcroGL, Version 13.6.1, https://www.nitrc.org/projects /mricrogl/; Rorden, 2017). A–D are shown on inflated surface renderings from SPM12. All results come from Pipeline 6 looking at the variance coming from each type of noise regressor when controlling for the others and are thresholded at p < 0.001 with a spatial extent cluster at p < 0.05 (FWE corrected).

Article Snippet: Preprocessing and statistical analyses were conducted using SPM12 (https://www.fil.ion.ucl.ac .uk/spm/software/spm12/) and the CONN toolbox (Version 22.a; Nieto-Castañón & WhitfieldGabrieli, 2022) in MATLAB R2019B (MathWorks, 2019).

Techniques: