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SPM fMRI Processing
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[en] SPM fMRI Processing This method performs the Preprocessing and the First Level Analysis of fMRI images using the SPM software. The preprocessing steps are: Slice Timing Correction, Motion Correction, Spatial Normalization into a standard space, and Spatial Smoothing. The statistical analysis is based on General Lineal Model (GLM). INPUTS T1-weighted 3D volume: Structural image. fMIR Images: 4D images. Reference slice for Slice Timing Correction: Reference slice for Slice Timing Correction. Spatial smoothing (mm) (Optional): This determines the extent of the spatial smoothing carried out on each volume of the functional data. The optimal value is the size of the voxels multiplied by 2. Default value: 6 mm. High Pass Filter (s) (Optional): High pass temporal filtering to remove low fequency artefacts. The optimal cutoff value is the time in seconds between a repetition of the same event or stimulus multiplied by 2. Default value: 128 s. Model HRF Derivatives: (Optional): No derivatives: 0, Time and Dispersion derivatives: 1. Default Value 0. P-value for the contrast: (Optional): The p-value for a t-statistic gives the probability that the difference between the experimental and control conditions arose by chance. Default Value 0.05. Name of Conditions: (e.g "Task-Odd;Task-Even") Name of each conditions. If you have two or more conditions, split the durations for each condition by ";". Avoid the use of spaces. Default Value: "Task-Odd;Task-Even" Onsets: (e.g. 15,25,35;20,30,40) Onsets for the model. Each onset must be split by ",". If you have two or more conditions, split the onsets by ";". Avoid the use of spaces. Default Value: 15,25,35;20,30,40 Duration: (e.g. 15;15) Duration for each condition. All the onsets for each condition must have the same duration. If you have two or more conditions, split the durations for each condition by ";". Avoid the use of spaces. Default Value: "15;15" Title of Contrast: Name for the contrasts that you want to perform. Avoid the use of spaces. Default Value: "Task>Baseline" Type of statistic for the Contrast: Write "T" (in capital letter) in case you want to perform a t-test. Default Value: "T" Names of the conditions to perform the contrast: Name of the columns to perform the contrast. The names must be the same that some of the names for the Conditions. Each name must be split by ",". Default Value: "Task-Odd,Task-Even" Weights for the contrast: Weights for the contrast. Split the weights for the different conditions to contrast by ",". Default Value: "0.5,0.5" OUTPUTS SPM_mat SPM data structure. This structure contains all the information about the design and contrasts. spmT This is the Threshold Statistical Parametric Map. References: (1) K.J. Friston. Introduction: Experimental design and statistical parametric mapping. In R.S.J. Frackowiak, K.J. Friston, C. Frith, R. Dolan, K.J. Friston, C.J. Price, S. Zeki, J. Ashburner, and W.D. Penny, editors, Human Brain Function. Academic Press, 2nd edition, 2003. (2) SPM - Statistical Parametric Mapping [es] Procesado fMRI con SPM This method performs the Preprocessing and the First Level Analysis of fMRI images using the SPM software. The preprocessing steps are: Slice Timing Correction, Motion Correction, Spatial Normalization into a standard space, and Spatial Smoothing. The statistical analysis is based on General Lineal Model (GLM). ENTRADAS T1-weighted 3D volume: Structural image. fMIR Images: 4D images. Reference slice for Slice Timing Correction: Reference slice for Slice Timing Correction. Spatial smoothing (mm) (Optional): This determines the extent of the spatial smoothing carried out on each volume of the functional data. The optimal value is the size of the voxels multiplied by 2. Default value: 6 mm. High Pass Filter (s) (Optional): High pass temporal filtering to remove low fequency artefacts. The optimal cutoff value is the time in seconds between a repetition of the same event or stimulus multiplied by 2. Default value: 128 s. Model HRF Derivatives: (Optional): No derivatives: 0, Time and Dispersion derivatives: 1. Default Value 0. P-value for the contrast: (Optional): The p-value for a t-statistic gives the probability that the difference between the experimental and control conditions arose by chance. Default Value 0.05. Name of Conditions: (e.g "Task-Odd;Task-Even") Name of each conditions. If you have two or more conditions, split the durations for each condition by ";". Avoid the use of spaces. Default Value: "Task-Odd;Task-Even" Onsets: (e.g. 15,25,35;20,30,40) Onsets for the model. Each onset must be split by ",". If you have two or more conditions, split the onsets by ";". Avoid the use of spaces. Default Value: 15,25,35;20,30,40 Duration: (e.g. 15;15) Duration for each condition. All the onsets for each condition must have the same duration. If you have two or more conditions, split the durations for each condition by ";". Avoid the use of spaces. Default Value: "15;15" Title of Contrast: Name for the contrasts that you want to perform. Avoid the use of spaces. Default Value: "Task>Baseline" Type of statistic for the Contrast: Write "T" (in capital letter) in case you want to perform a t-test. Default Value: "T" Names of the conditions to perform the contrast: Name of the columns to perform the contrast. The names must be the same that some of the names for the Conditions. Each name must be split by ",". Default Value: "Task-Odd,Task-Even" Weights for the contrast: Weights for the contrast. Split the weights for the different conditions to contrast by ",". Default Value: "0.5,0.5" SALIDAS SPM_mat SPM data structure. This structure contains all the information about the design and contrasts. spmT This is the Threshold Statistical Parametric Map. References: (1) K.J. Friston. Introduction: Experimental design and statistical parametric mapping. In R.S.J. Frackowiak, K.J. Friston, C. Frith, R. Dolan, K.J. Friston, C.J. Price, S. Zeki, J. Ashburner, and W.D. Penny, editors, Human Brain Function. Academic Press, 2nd edition, 2003. (2) SPM - Statistical Parametric Mapping

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