Merge pull request #13 from nikhil153/gh-pages

updated index.md

README.md

@@ -20,20 +20,27 @@ The **sMRI Analysis in Python** is a workshop series started up via a collaborat
 
 This lesson covers a typrical sMRI imaging pipeline by introducing 1) image modalities, 2) image preprocessing, 3) phoenotype quantification, and 4) statistical analyses.  
 
+The primary goals of this workshop are:
+1. Understand the basics of strcutural MR image acquisition
+2. Familiarize with structural MR image (pre)processing pipeline
+3. Perform and visualize group-level neuroanatomical analyses
+
+## You Are Here!
+
+![course_flow](../fig/episode_0/Course_flow_0.png)
+
 ### Episodes
 
 | Time | Episode | Question(s) Answered |
 | ---  | ---     | ---                  |
 ||Setup|Download files required for the lesson|
-| 00:00 | 0. Course Overview and Prereqs | What is a structural MR imaging pipeline? |
-| 00:30 | 1. sMRI modalities | How is MR image acquired? What anatomical features do different modalities capture?  |
-| 01:00 | 2. sMRI preprocessing (Part 1: image clean-up) | How do we clean-up MR images and extract brains? |
+| 00:00 | 1. sMRI modalities | How is MR image acquired? What anatomical features do different modalities capture?  |
+| 00:45 | 2. sMRI preprocessing (Part 1: image clean-up) | How do we clean-up MR images and extract brains? |
 | 01:30 | 3. sMRI preprocessing (Part 2: image registration) | What are "templates", "spaces", "atlases"? What is spatial normalization? |
 | 02:00 | 4. sMRI quantification | How do we delineate brain anatomy and quantify phenotypes? |
 | 02:30 | 5. sMRI quality-control | How do we identify image preprocessing failures? |
 | 03:00 | 6. Statistical analysis (Part 1: ROIs) | How to look at group differences in regional anatomical features? |
-| 03:30 | 7. Statistical analysis (Part 2: voxels) | How to look at group differences at voxel-level features? |
-| 04:00 | 8. Reproducibility considerations | How sensitive are the findings to your MR pipeline parameters? |
+| 04:00 | 7. Reproducibility considerations | How sensitive are the findings to your MR pipeline parameters? |
 | 04:30 | Finish | |
 
 

index.md

@@ -3,7 +3,26 @@ layout: lesson
 root: .  # Is the only page that doesn't follow the pattern /:path/index.html
 permalink: index.html  # Is the only page that doesn't follow the pattern /:path/index.html
 ---
-FIXME: sMRI analysis in Neuroimaging
+sMRI (pre)processing in neuroimaging
+
+Welcome to the **Structural Neuroimaging Analysis in Python** workshop! 
+
+The primary goals of this workshop are:
+1. Understand the basics of strcutural MR image acquisition
+2. Familiarize with structural MR image (pre)processing pipeline
+3. Perform and visualize group-level neuroanatomical analyses
+
+Things to keep in mind:
+1. Magnetic resonance (MR) imaging is a medical imaging technique used visualize anatomy and the physiological processes of the body. MR imaging scanners use strong magnetic fields, magnetic field gradients, and radio waves to generate images of the organs in the body.
+2. In structural neuroimaging, MR scans can refer to several different image modalities including, T1-weighted, T2-weighted, diffusion weighted images (DWI), proton-densty (PD), Fluid attenuation inversion recovery (FLAIR) etc. 
+3. MR (pre)processing pipeline is a set of sequential image processing tasks performed on acquired MR scans prior to the statistical analyese. 
+
+Notes:
+1. These days (year 2021), several software packages (e.g. FreeSurfer, FSL, SPM, fMRIprep) provide ready-to-use pipelines which will comprising commonly used pre(processesing) tasks. Thus as a user, you need not know the details of each algorithm. Nevertheless it is useful to understand the methods and their impact on the downstream analyses. This will 1) help developers to improve the underlying algorithms  and 2) help users to customize the neuorimaging pipelines according to their dataset requirements. 
+
+_All of this may sound complicated, but we'll explain things step-by-step in depth with practical examples as the course goes along. We will begin our computational journey stating from how an MR image is acquired, followed by several pre-processing tasks, with the end goal of conducting a statistical analysis to investigate volumetric hippocampal differences between Alzheimer's patients and healthy controls._
+
+![course_flow](../fig/index/Overview.png)
 
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