Journal club

May 21st, 2019

1. extract image-related features

2. train model on normative dataset to predict age

 3. obtain predicted ages on test set

 4. compute
brain-predicted age difference

 5. make inference: accelerated/
delayed aging

BAG (brain age gap) = predicted age - real age

brainPAD (brain-predicted age difference)

brainAGE (brain age gap estimate)

BAG ~ MS diagnosis

longitudinal analysis within MS

1,118 features

(360 ROIs: thickness, area, volume)

3,208 HC

xgboost

10-fold CV r = 0.91

235 HC

76 MS

Four types of Multiple Sclerosis (MS)

rate of aging

= \frac{\Delta \textrm{BAG}}{\Delta t}

Increased brain age gaps for all brain regions except temporal

Significant accelerated rate of brain aging compared to chronological aging

apparent accelerated aging of the brain may partly be explained by chronic inflammatory processes that drive neurodegeneration in MS

Questions

  • circularity in the analysis of brain atrophy vs brain aging?

We estimated the annual global brain atrophy by comparing estimated total brain volume from the Freesurfer output (BrainSegVolNotVent) between time points.

Questions

  • Estimation model accuracy: r = 0.91, what about MAE, MSE, etc.?

Discussion...

BrainAGE MS

By Trang Le

BrainAGE MS

Presentation on 2019-05-21 for the journal club, Kim Lab.

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