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more renaming and update to course_flow figure
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4 changes: 4 additions & 0 deletions README.md
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Expand Up @@ -41,6 +41,10 @@ The primary goals of this workshop are:
| 04:30 | Finish | |


## You Are Here!
![course_flow](fig/index/Course_flow_0.png)


## Contributing

We welcome all contributions to improve the lesson! Maintainers will do their best to help you if you have any
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3 changes: 2 additions & 1 deletion _episodes/04-Image_Quantification.md
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Expand Up @@ -14,7 +14,8 @@ keypoints:
- "sMRI pathology measures can have significant impact in diagnosis, treatment planning, and surgery"
---
## You Are Here!
Course flow image edited for this episode to be placed here.
![course_flow](../fig/episode_4/Course_flow_4.png)


**Assumptions:**
* the source of contrast in T1, T2, PD has been explained in another episode in terms of GM, WM , CSF. If possible, also include FLAIR.
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3 changes: 3 additions & 0 deletions _episodes/06-Statistical_Analysis.md
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Expand Up @@ -11,6 +11,9 @@ keypoints:
- "Atlas driven analysis can be a powerful technique for biomarker development. Nevertheless it is important to be cognizant of confounding factors."
---

## You Are Here!
![course_flow](../fig/episode_6/Course_flow_6.png)

# 5. Statistical Analysis
Structural resonance images (sMRIs) provide information about various tissues type in the brain (e.g. gray matter, white matter, cerbrospinal fluid). sMRI (like fMRI), help study underlying causes of neuropsychiatric illnesses and their mechanisms by studying regional brain activities or atrophies. Statistical analysis of MRIs in individuals over time or cohorts provide region specific neuroanatomical information related to clinical questions in studies related to neuropsychiatric.

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26 changes: 14 additions & 12 deletions _episodes/07-Reproducibility.md
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Expand Up @@ -10,7 +10,7 @@ keypoints:
- "It is crucial to assess biological vs methodological variation in your findings to avoid reproducibility crisis."
---
## You Are Here!
![course_flow](../fig/episode_8/Course_flow_8.png)
![course_flow](../fig/episode_7/Course_flow_7.png)

## MR image processing pipeline selection choices
- Compute environment
Expand All @@ -32,24 +32,26 @@ keypoints:

## Example software analysis

- Task: compare cortical thickness between FreeSurfer and CIVET
![reproducibility](../fig/episode_8/Reproducibility.png)
### Task: compare cortical thickness between FreeSurfer and CIVET

_Note: See [this]([https://academic.oup.com/cercor/article/30/9/5014/5831485]) for details on brain plots]_
<img src="../fig/episode_7/Reproducibility.png" alt="Drawing" align="middle" width="700px"/>

- Correlation between thickness measurements:
- Software: FreeSurfer (v5.1, v5.3, v6.0), CIVET (2.1) and ANTs
- Parcellation: DKT
_Note: See [this]([https://academic.oup.com/cercor/article/30/9/5014/5831485]) for details on brain plots_

## Correlation between thickness measurements:
### Software: FreeSurfer (v5.1, v5.3, v6.0), CIVET (2.1) and ANTs
### Parcellation: DKT

![software_compare](../fig/episode_8/CT_compare_software.png)
<img src="../fig/episode_7/CT_compare_software.png" alt="Drawing" align="middle" width="700px"/>


## 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.
### 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.

<img src="../fig/episode_7/FreeSurfer_parcels.jpg" alt="Drawing" align="middle" width="700px"/>

![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)
_Note: Image adopted from [Madan 2021](https://link.springer.com/article/10.1007/s12021-021-09519-6)_

{% include links.md %}

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3 changes: 2 additions & 1 deletion index.md
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Expand Up @@ -21,7 +21,8 @@ Notes:

_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)
## You Are Here!
![course_flow](fig/index/Course_flow_0.png)

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