Manual Statistical and Computational Methods in Brain Image Analysis

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Statistical methods are slow to be adopted.

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Statistical research has produced very advanced methodology, and yet a large part of science still uses very basic tools like linear regression and hypothesis tests for their inference. However, the need to properly quantify uncertainty is becoming more evident, particularly in areas where large amounts of complex data are being collected, and these are areas where modern statistical methods are more likely to take hold.

In my case, brain image and genomic analysis are probably the areas with the most statistical analysis software tools already and eager to continue the trend. I am working with collaborators to incorporate our new tools into the standard software analysis platforms for widespread use by neuroscientists. In the years to come, what role do you feel computational methods and machine learning algorithms will play for research purposes and within clinical settings? While computers and algorithms have been at the forefront of research in highly technical areas such as engineering, they are now making their way into all areas of research, from biology to social sciences, helping us make sense of large and complex data.

Medicine will be seeing this soon too as medical records become better organised and easier to analyse.

ICERM - Computational Imaging

However, while machine learning algorithms excel at performing complex tasks, we also desire scientific explanations of those complex phenomena and an understanding of the fundamental principles guiding them. For this reason, I believe mathematical and statistical models will continue to play a very important role in research alongside computational methods, both complementing each other and helping us better understand the workings of the world.

What are your plans for future investigation? I want to expand the use of statistical image analysis tools in important areas that still need them.

Computational Statistics - SciPy 2017 Tutorial - Allen Downey

Environmental research, in particular, is becoming increasingly important and I believe it can greatly benefit from the advanced analysis tools we have produced by working in other areas of science. His research interests are focused on signal and image analysis, with applications to biomedicine and the environment. Notify me of follow-up comments by email.

Notify me of new posts by email. This is also true for imaging and signal-related data, collected by scientists within a variety of fields. Professor Armin Schwartzman, working at the University of California in San Diego, has dedicated his career to the development of simple but effective statistical methods for signal and imaging data analysis, which could have significant biomedical and environmental applications.

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The statistical methods developed by Prof Schwartzman could greatly simplify analysis of imaging data within a variety of fields Quantifying spatial uncertainty of climate change projections. Like this: Like Loading September 10, 0. August 12, 0. We use cookies to gather data about how you use our site. This helps us improve how our site works and ensures we offer you the best content.

CRAN Task View: Medical Image Analysis

Results of this execution arranged in whatever way the pipeline prefers can be optionally processed in a group level analysis reduce step—see Fig 2. Workflows will need to decouple the individual level analysis process independent subjects from the group-level analysis.

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For the analysis of individual subjects, the workflow will require an understanding of the BIDS structure so that the required inputs for the designated subject are found. The optional reducer module will take up from results generated in the mapper and generate a group output. The overall workflow has an entrypoint and an endpoint responsible of setting-up the map-reduce tasks and the tear-down including organizing the outputs for its archiving, respectively.

Please mind that each app can implement multiple map and reduce steps see Advanced use cases. Each pipeline should have a simple wrapper script used to run it. The script should be a command-line interface and accept the following command-line arguments minimally :. Mind that the same set of extra arguments will be passed to the map single subject level and the reduce group level stage. Supervision: RAP.


Visualization: KJG. Writing — original draft: KJG. Abstract The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. Author summary Magnetic Resonance Imaging MRI is a non-invasive way to measure human brain structure and activity that has been used for over 25 years. Introduction The last 25 years have witnessed a proliferation of methods for imaging the human brain including structural, diffusion and functional Magnetic Resonance Imaging, Positron Emission Tomography, Electroencephalography and Magnetoencephalography.

Singularity—for running containers on HPCs [ 14 ]. BIDS Apps forge. Download: PPT. Dockerfile creation. However, because the container images built in the BIDS-Apps format are ultimately intended to alternatively run under Singularity, there are some additional requirements that must be followed: Apps cannot rely on having elevated permissions inside the container image in contrast to Docker, processes inside a Singularity container run with the privileges of the user running the container.

Command-line interface. Building and testing container images. Running a BIDS App on a cluster HPC On many academic clusters, Singularity can be used to run containers due to its minimal dependencies and security concerns Singularity is more likely to be approved for multi-tenant systems usage than Docker. Discussion We have proposed a new way of distributing easy-to-use, reproducible neuroimaging analysis workflows that can run on all three major operating systems as well as multi-tenant clusters. Command-line specification This approach to run our workflows requires sticking with three standards : 1 a common command-line interface, 2 a Docker container to ensure portability, and 3 a standard for organizing input data.

Command line interface Each pipeline should have a simple wrapper script used to run it. This directory is read only. This is the only directory the pipeline should write to. Can be used to store intermediate files, but they should be removed after the pipeline finishes. If this parameter is not provided all subjects should be analyzed. Multiple participants can be specified with a space separated list. Example: Run processing for every subject independently map step. Each of these operations can be performed in parallel. This directory is the same one that was used in the participant level analysis and should be prepopulated with participant level results before running the group level.

This can be useful if you want to do a group level analysis on a subset of all participants in your dataset Example:.

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  • Advanced use cases Multiple map reduce steps. In the description of your app please specify how many map reduce steps are necessary. Within-job multi-CPU parallelization. When running your app the execution system will pass available memory in megabytes. References 1. Hanke M, Halchenko YO. Front Neuroinform. View Article Google Scholar 2.

    Halchenko YO, Hanke M. Open is not enough. View Article Google Scholar 3. A purely confirmatory replication study of structural brain-behavior correlations. Provenance in neuroimaging. Hayasaka S, editor. PLoS One. Reproducibility of neuroimaging analyses across operating systems. Frontiers; ;9. View Article Google Scholar 7. CloVR: a virtual machine for automated and portable sequence analysis from the desktop using cloud computing. BMC Bioinformatics.