cortical_thickness
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cortical_thickness [2017/08/04 18:47] mpenrod |
cortical_thickness [2017/08/04 18:58] (current) mpenrod [Longitudinal Analysis] |
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| Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type: | Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type: | ||
| <code>recon-all -i /home/projects/MRI/[subjid]/[YYYYMMDD]/[subjid]_WIP_MEMP_VBM_SENSE_13_1_MSE.nii.gz -subjid [subid] -all</code> | <code>recon-all -i /home/projects/MRI/[subjid]/[YYYYMMDD]/[subjid]_WIP_MEMP_VBM_SENSE_13_1_MSE.nii.gz -subjid [subid] -all</code> | ||
| - | The MATLAB script **parallel_recon.m** has been written to (with slight adjustments in order to specify file input and output) automate and parallelize the process, which typically takes 6-10 hours per file. It is also recommended that the process be executed on multiple servers in order to further divide the work. | + | The MATLAB script ''parallel_recon.m'' has been written to (with slight adjustments in order to specify file input and output) automate and parallelize the process, which typically takes 6-10 hours per file. It is also recommended that the process be executed on multiple servers in order to further divide the work. |
| ====Longitudinal Analysis==== | ====Longitudinal Analysis==== | ||
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| ===Pre-processing pt. 2=== | ===Pre-processing pt. 2=== | ||
| - | In order to complete the following analyses, two steps are needed to collect and process data. The two commands used are ''mris_preproc'' and ''mri_surf2surf''. For example: <code>mris_preproc --qdec-long long.qdec.table.dat --target fsaverage --hemi lh --meas thickness --out lh.thickness.stack.mgh</code> | + | In order to complete the following analyses, there are two more steps of collecting and processign data. The two commands used are ''mris_preproc'' and ''mri_surf2surf''. For example: <code>mris_preproc --qdec-long long.qdec.table.dat --target fsaverage --hemi lh --meas thickness --out lh.thickness.stack.mgh</code> |
| + | Followed by: | ||
| <code>mri_surf2surf --hemi lh --s fsaverage --sval lh.thickness.stack.mgh --tval lh.thickness.stack.fwhm10.mgh --fwhm-trg 10 --cortex --noreshape</code> | <code>mri_surf2surf --hemi lh --s fsaverage --sval lh.thickness.stack.mgh --tval lh.thickness.stack.fwhm10.mgh --fwhm-trg 10 --cortex --noreshape</code> | ||
| Further documentation for both [[https://surfer.nmr.mgh.harvard.edu/fswiki/mris_preproc|mris_preproc]] and [[https://surfer.nmr.mgh.harvard.edu/fswiki/mri_surf2surf|mri_surf2surf]] can be found at these links. | Further documentation for both [[https://surfer.nmr.mgh.harvard.edu/fswiki/mris_preproc|mris_preproc]] and [[https://surfer.nmr.mgh.harvard.edu/fswiki/mri_surf2surf|mri_surf2surf]] can be found at these links. | ||
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| ===Linear Mixed Effects Modelling=== | ===Linear Mixed Effects Modelling=== | ||
| - | The linear mixed effects model is a useful and robust method of statistical analysis. In order to use this model, the 3D data matrix output by ''corrplot_ROIs.m'' must be reformatted to a 2D matrix using the function ''consol_long_mtx.m''. Once the 2D matrix is created, it can be used in the function ''lme_long_fitandplot.m'' which will calculate the significance and plot the change of the brain anatomy over time. | + | The linear mixed effects model is a useful and robust method of statistical analysis. In order to use this model, the 3D data matrix output of ''corrplot_ROIs.m'' must be reformatted to a 2D matrix using the function ''consol_long_mtx.m''. Once the 2D matrix is created, it can be used in the function ''lme_long_fitandplot.m'' which will calculate the significance and plot the change of the brain anatomy over time. |
| The linear mixed effects model can be used to statistically analyze ROIs found through other forms of analysis. However, it is also possible to use the model to identify regions of interest, based on the significance of their change over time. The function ''lme_whole_brain_analysis'' uses information from ''long_prepare_LME.m'' and analyzes the longitudinal change in each vertex of the brain across time. The output is a collection of significance files which can be mapped onto the cortical surface for visualization in freeview or used for ROI analysis. | The linear mixed effects model can be used to statistically analyze ROIs found through other forms of analysis. However, it is also possible to use the model to identify regions of interest, based on the significance of their change over time. The function ''lme_whole_brain_analysis'' uses information from ''long_prepare_LME.m'' and analyzes the longitudinal change in each vertex of the brain across time. The output is a collection of significance files which can be mapped onto the cortical surface for visualization in freeview or used for ROI analysis. | ||
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| ===ROI Analysis=== | ===ROI Analysis=== | ||
| - | As described above, analysis of ROIs provides a lot of useful information, such as change in those regions over time or correlations with behavioral measures. The freesurfer command ''mri_surfcluster'' takes an mgh file, among other inputs, and outputs several useful files containing information. The command can either be run in the command line or in MATLAB using the function ''do_mri_surfcluster.m''. Further documentation on ''mri_surfclsuter'' can be found [[https://surfer.nmr.mgh.harvard.edu/fswiki/mri_surfcluster|here]]. | + | As described above, analysis of ROIs provides a lot of useful information, such as change in those regions over time or correlations with behavioral measures. The freesurfer command ''mri_surfcluster'' takes an mgh file, among other inputs, and outputs several files containing useful information. The command can either be run in the command line or in MATLAB using the function ''do_mri_surfcluster.m''. Further documentation on ''mri_surfclsuter'' can be found [[https://surfer.nmr.mgh.harvard.edu/fswiki/mri_surfcluster|here]]. |
| - | In order to use functions such as ''lme_long_fitandplot.m'', the outputs of these files must be processed and reformatted into matrices. Regarding the analyses described above, ''corrplot_ROIs.m'' will reformat information for correlational analysis and ''cluster_2_lme_longmtx'' should be used to process the clusters extracted from the output of ''lme_whole_brain_analysis.m''. | + | In order to use functions such as ''lme_long_fitandplot.m'', the outputs of these files must be processed and reformatted into matrices. Regarding the analyses described above, ''corrplot_ROIs.m'' will reformat information for correlational analysis and ''cluster_2_lme_longmtx.m'' should be used to process the clusters extracted from the output of ''lme_whole_brain_analysis.m''. |
cortical_thickness.1501872467.txt.gz · Last modified: 2017/08/04 18:47 by mpenrod
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