1. J OURNAL C LUB : S Magon, et al. University Hospital Basel, Switzerland “Label-Fusion-Segmentation and Deformation-Based Shape Analysis of Deep Gray Matter in Multiple Sclerosis: The Impact of Thalamic Subnuclei on Disability” Feb 2, 2014 Jason Su

2. Motivation Many similarities to our own work – Label-fusion based thalamic nuclei segmentation – Our data comes from an MS cohort as well Thinking about adding a small clinical component to our paper

3. Background Deep GM (caudate and thalamus) neuronal loss and atrophy observed in early MS – May be linked to disease progression, esp. thalamic atrophy – However, connection to EDSS unclear

4. Aims 1.Relationship between striatal, pallidal, and thalamic volume and disability in RRMS 2.Relationship between thalamic nuclei volume and disability 3.Changes in shape of subcortical structures by WM lesions

5. Methods 118 RRMS patients with EDSS and FSS MRI Protocol at 1.5T – MPRAGE (TR/TI/TE = 2080/1100/3ms, α=15deg, 1mm 3 ) – PD/T2 double SE (TR/TE1/TE2 = 3980/14/108ms, 1x1x3mm 3 )

6. Segmentation Using MAGeT Brain algorithm (Chakravarty et al.) – Similar to data augmentation and ensemble methods in machine learning 1.Manually segment striatum, thalamus, and pallidum (Schaltenbrand, Gloor) and thalamic nuclei (Hirai and Jones) 2.Pre-register to 31 patients for template library (span age and disability range) 3.Register incoming subject to 31 templates and do majority vote on candidate labels

8. Shape Analysis and Volume Measures 1.Generate surface and normals for each structure 2.Compare nonlinear warp to normals – Measure inward and outward displacement (larger and smaller volume) SIENAX for GM with lesion filling using MPRAGE – SIENAX volume correction factor used to normalize volume of DGM and GM Lesion segmentation – Amira for intensity- thresholding pre-selection then edited manually Brain lobes segmented by using MNI atlas

9. Statistical Methods Hierarchical multiple linear regression of EDSS with 3 blocks: – log(EDSS) – Age, gender, duration – DGM volumes (stepwise) – WM lesion load and GM volume (stepwise) Linear regression of shape vertex displacements against: – EDSS, WM lesion load, lobe- wise lesion load – Accounting for age and gender Multinomial logistic regression – How significant predictors from MLR relate to FSS – Predicts categorical variables? Testing for linearity, constant variance, normality, correlation

10. Results: Regression VL, VA, VP highly correlated -> combined as VNC After accounting for age, gender, duration: – Thalamic and GM volume are significant predictors (R 2 =0.29) – VNC and GM volume were significant predictors (R 2 =0.3) Similar results with bootstrapping

11. Results: FSS Softmax Regression VNC and GMV for cerebellar FSS (Nagelkerke R 2 =0.36) GMV only for pyramidal and sensory FSS (0.24 and 0.16)

13. Results: Shape Analysis Significant relationship between frontal lesion load and shape of DGM structures – No significance w/ occipital lesion load or disease duration Bilateral outward displacement of thalamus and EDSS had a significant relationship Thalamus: outward displacement in anterior medial, inward in lateral medial Striatum: outward in various parts Global pallidus: outward displacements in anterior

14. Discussion Thalamus most relevant DGM for predicting EDSS – VNC combined nuclei within that – Thalamus may be vulnerable in MS as a widely connected structure VNC may serve as an important integrative center for behavior and motor output Previously inconsistent correlation of thalamus with EDSS reported

15. Discussion MAGeT for thalamic subregions validated in Chakravarty 2009 Maybe different types of disease at earlier stages – FSS regression showed VNC and GMV predict cerebellar FSS – VNC seems to relate to motor function Shape abnormalities in anterior of thalamus driven by WM lesion load were related to disability Using only T1w may be a limitation