Human Connectome Project (HCP) | Getting Access to the Data | Getting an Access/API Key | Installing the neurohcp package | Setting the API key | Getting Data: Downloading a Directory of Data | Getting Data: Downloading a Single File

Overview | Reading data | Standardizing | Resampling

Purpose | Basic Mapping | Combined and Wave-Specific References | Overall Age or Sex Strata | Single Values | Age Categories | References

What's Next? Additional Features and Future Plans | Conclusion

Exploring Freesurfer Output: A Practical Guide | Visualizing Segmented Brain Structures | Analyzing Anatomical Statistics for Brain Structures | Converting and Visualizing Brain Surfaces with mris_convert | Working with Label Files

Understanding Imaging Formats in freesurfer and R | The Freesurfer Reconstruction Pipeline | Converting MRI Images with mri_convert | Correcting for Bias Fields with nu_correct | Brain Extraction with mri_watershed

Setting Up Your R Environment for freesurfer | Setting Up Freesurfer Options | Check if Freesurfer is installed | Understanding Freesurfer Output Formats | Understanding how get_fs() fascilitates Freesurfer calls | Why is get_fs() important? | How it works | Example Usage | Organizing Your Freesurfer Analyses

Running the Freesurfer recon-all Pipeline | Restarting the Reconstruction Pipeline | Customizing the Pipeline with recon | Running All Steps (Default) | Customizing Steps | Adding Verbose Output | Running Specific Stages of Reconstruction | Advamced use with the Low-Level reconner Function | Basic Usage | Force Execution | Summary of Functions | Conclusion

GIFTI Files | Getting the Data

Load MSC data and test data from cifti package | For reference: MSC 01 (65890 * 818) (using matlab ft_read_cifti), showing first 5 rows & columns | MSC Sub-01 Session 1 (sub-MSC01_ses-func01_task-rest_bold_32k_fsLR.dtseries.nii) | Data where dimensions are row x dense loads fine. Updated function loads them the same way as before. | Conte69.MyelinAndCorrThickness.32k_fs_LR.dscalar.nii | Conte69.MyelinAndCorrThickness.32k_fs_LR.dtseries.nii | Conte69.MyelinAndCorrThickness.32k_fs_LR.ptseries.nii | Conte69.parcellations_VGD11b.32k_fs_LR.dlabel.nii (dense label on vertex level)

Resources and Goals | Data Location | Reading in the Data | Making b-vectors and b-values | Checking our data | Printing out FSL Version | Running eddy_correct | Running DTI Fitting as a cursor | Plotting an FA map | Fitting bedpostx

Setup | Creating a query | Running the query | Getting the content | Funding agencies | Number of author limits | More than 100 authors | Get all the authors | Conclusion

Glassdoor API Keys | Calling the API | Job Statistics | Employers

The nifti object | S4 Implementation | Accessing Information from a nifti | Accessing the "data" | Show all slots | Other objects | NIfTI Input/Output: readnii/writenii vs. readNIfTI/writeNIfTI | Option reorient = FALSE | Operations of nifti objects | Logical operators | Arithmetic on nifti objects | Summaries | Visualization of nifti objects | Orthographic view | Differences between orthographic and ortho2 | Bright values | Double orthographic view | Single slice view | Different Planes | Overlaying slices | Dropping empty dimensions | Dropping with multiple images | Adding back the dropped dimensions | Manipulating nifti images | Creating copies of nifti | Copying header information | Creating a "shell" | Masking | Vectorizing a nifti | Subsetting via masks

S4 Implementation | Accessing Information from a nifti | Accessing the "data" | Show all slots | Other objects | NIfTI Input/Output: readnii/writenii vs. readNIfTI/writeNIfTI | Option reorient = FALSE | Operations of nifti objects | Logical operators | Arithmetic on nifti objects | Summaries | Visualization of nifti objects | Orthographic view | Differences between orthographic and ortho2 | Bright values | Double orthographic view | Single slice view | Different Planes | Overlaying slices | Dropping empty dimensions | Dropping with multiple images | Adding back the dropped dimensions | Manipulating nifti images | Creating copies of nifti | Copying header information | Creating a "shell" | Masking | Vectorizing a nifti | Subsetting via masks | Creating data.frames | Bi-variate distributions | Marginal distributions

rscopus | Steps to get API key | A note about API keys and IP addresses | Example

The nifti object | Downloading an Image | Reading in an image | Writing Images | Operations of nifti objects | Logical operators | Arithmetic on nifti objects | Visualization of nifti objects | Orthographic view | Single slice view | Different Planes | Overlaying slices

Downloading the data | Getting the filenames | Reading in the data | Displaying the data | Running WhiteStripe | Displaying the output | Location of the white stripe | White stripe mask | Normalized intensities | Conclusion

Reading in Data | Checking if FSL exists | Printing out FSL Version | Returning Objects | Main functions

Running STAPLE | Multi-class STAPLE