Updateed QC and reproducibility lessons

_episodes/05-Image_QC.md

@@ -18,31 +18,45 @@ keypoints:
 ### Acquistion
 #### Due to MR physics (e.g. Field of view (FOV), ghosting, aliasing) 
 Incorrect parameters can truncate and/or duplicate brain anatomy. 
-![MR_physics_QC](../fig/episode_5/MR_physics_QC.png) 
-
+
+<img src="../fig/episode_5/MR_physics_QC.png" alt="Drawing" align="middle" width="500px"/>    
+
 #### Due to participant (e.g. Motion artifacts) 
 - Participant specific issues such as motion artifcats in Parkinson's patients can manifest in the scan (e.g. ringing effect showing ripples or curved lines).
-![Motion_QC](../fig/episode_5/Motion_QC.png)
+
+<img src="../fig/episode_5/Motion_QC.png" alt="Drawing" align="middle" width="500px"/>    
 
 ### Quantification
 Exisiting image processing pipelines (e.g. FreeSurfer, CIVET) will have a few QC tools and examples that can help with failure detection and quality control of volumetric segmentations and surface parcellations.  
 
-![Segment_and_surface_QC](../fig/episode_5/Segment_and_surface_QC.png)
+<img src="../fig/episode_5/Segment_and_surface_QC.png" alt="Drawing" align="middle" width="500px"/>  
 
 Usage of new method will require your own QC protocols. Especially for highly specific segmentation methods require visual inspection from a neuroanatomy expert. Even for the qualitiative visual inspection, it is important create a QC protocol and share it with the results. 
+
 ![HC_and_CB_MAGeT](../fig/episode_5/HC_and_CB_MAGeT.png)
 
 _Note: see [Hippocampal](http://dx.doi.org/10.1016/j.neuroimage.2014.04.054) and [cerebellar](https://www.sciencedirect.com/science/article/pii/S1053811914001840?via%3Dihub) for segmentation method details._ 
 
 ## Automatic QC tools
-- Using reports from exisiting pipelines: https://fmriprep.org/en/stable/_static/sample_report.html
-![sMRIPrep_QC_report](../fig/episode_5/sMRIPrep_QC_report.png)
 
-- Using QC tools 
-    - [VisualQC](https://github.com/raamana/visualqc) : assistive tool to improve the quality control workflow of neuroimaging data (Author: Pradeep Reddy Raamana). 
+### Using reports from exisiting pipelines: https://fmriprep.org/en/stable/_static/sample_report.html
+
+<img src="../fig/episode_5/sMRIPrep_QC_report.png" alt="Drawing" align="middle" width="700px"/> 
+
+
+### Using QC tools 
+
+#### [MRIQC](https://github.com/poldracklab/mriqc): extracts no-reference IQMs (image quality metrics) from structural (T1w and T2w) and functional MRI (magnetic resonance imaging) data. _(Developed by the Poldrack Lab at Stanford University for use at the Center for Reproducible Neuroscience (CRN), as well as for open-source software distribution.)_
+
+|  Individual report |     Group report        |  
+| :-------------: | :-----------: |
+| <img src="../fig/episode_5/mriqc_individual_report.png" alt="Drawing" align="middle" width="700px"/>  | <img src="../fig/episode_5/mriqc_group_report.png" alt="Drawing" align="middle" width="450px"/> | 
+
+
+#### [VisualQC](https://github.com/raamana/visualqc): assistive tool to improve the quality control workflow of neuroimaging data (Author: Pradeep Reddy Raamana). 
 
 |  T1w acquisition |     Alignment        | Cortical Parcellation | 
 | :-------------: | :-----------: |:-----------: |
 | ![t1_mri_visual_QC](../fig/episode_5/t1_mri_visual_QC.png) | ![alignment_mismatched_colormix_visualQC](../fig/episode_5/alignment_mismatched_colormix_visualQC.png) | ![cortical_zoomed_in](../fig/episode_5/cortical_zoomed_in.png)| 
 
-{% include links.md %}
+{% include links.md %}

_episodes/07-Reproducibility.md

@@ -19,7 +19,7 @@ keypoints:
 
 - Software (algorithms and their versions)
     - Image clean-up
-    - Image preproc
+    - Image normalization 
     - Image quantification
 
 - Quality control
@@ -43,5 +43,13 @@ _Note: See [this]([https://academic.oup.com/cercor/article/30/9/5014/5831485]) f
 
 ![software_compare](../fig/episode_8/CT_compare_software.png)
 
+
+## Possible choices for FreeSurfer parcellations
+- Different parcellation different results? 
+- Inference pertaining to neuroantomical differences and/or prediction models based on individual neuroanatomical feature sets can be sensitive to parcellation choice. 
+
+![software_compare](../fig/episode_8/FreeSurfer_parcels.jpg)
+_Note: Image adopted from [Madan 2021](https://link.springer.com/article/10.1007/s12021-021-09519-6)
+
 {% include links.md %}