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Quality Score

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Overall Score : 88 / 100

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Course Description

Neurohacking describes how to use the R programming language (https://cran.r-project.org/) and its associated package to perform manipulation, processing, and analysis of neuroimaging data. We focus on publicly-available structural magnetic resonance imaging (MRI). We discuss concepts such as inhomogeneity correction, image registration, and image visualization.By the end of this course, you will be able to:Read/write images of the brain in the NIfTI (Neuroimaging Informatics Technology Initiative) formatVisualize and explore these imagesPerform inhomogeneity correction, brain extraction, and image registration (within a subject and to a template).

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Instructor Details

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Dr. Elizabeth Sweeney earned her PhD from the statistics department at the Johns Hopkins Bloomberg School of Public Health, under the supervision of Dr. Ciprian Crainiceanu at Johns Hopkins and Dr. Russell Shinohara at the University of Pennsylvania. Elizabeth's PhD research has made contributions to the improved analysis of neuroimaging data, as evidenced by numerous publications, presentations, and patents. Elizabeth's interest in this area began with a traineeship at the Nation Institute of Neurological Disease and Stroke, where she did research in Dr. Daniel Reich's lab on image analysis in multiple sclerosis. Elizabeth is passionate about both research and teaching. Elizabeth has co-taught a number of tutorials and courses on neuroimage data analysis. She also taught and introductory biostatistics course to masters of public health students at the American University of Armenia. Elizabeth now works on imaging in Alzheimer's disease at Rice University as a Rice Academy Postdoctoral Fellow. With mentors Dr. Genevera Allen of Rice Statistics and Dr. Joshua Shulman of the Baylor College of Medicine Neurology, Elizabeth is working to develop neuroimaging, epidemiological, and genetic biomarkers in Alzheimer's disease.

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Reviews

4.4

32 total reviews

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By Zaeem H on 10-Apr-19

I was expecting teaching but they are simply reading code from slides. Big let down.

By Jonathan G on 2-Feb-18

this course was misleading and boring as hell! You're supposed to learn the function of an MRI and what diseases to look for that would've been a more relevant course. It just talked about different computer systems and how an MRI functions.

By KJ B on 3-Aug-17

Very basic, could be clearer.

By Tiago A on 15-Sep-17

Nice contents but: questions not being answered in the forums. Online content from github (where in fact are the scripts and data) is somewhat confusing

By Fidel G on 17-Jul-19

It was a great experience going through the processes of ways of viewing, manipulating and extracting data from brain scan images. With few bumps on the way I keep focusing on the knowledge that I gain which I may apply into a future project of machine learn.

By Brion J on 7-Mar-18

Student presenters are not as good as professors.

By Srihari S on 21-Aug-17

Perhaps links to some materials regarding explanation of the data could be provided.

By Aman M on 5-Mar-17

All the lecture were thorough and good.

By ANSHAY A on 15-Feb-18

Should have more practical work

By Mehdi N T on 5-Apr-18

Great concise walk-through of neuro-imaging techniques. Low quality audio and a lot of background noise though.

By salvador g d l on 18-May-17

It was a really good course, thank you!

By Miriam M on 21-Nov-17

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