Research Update Nov 18, 2016 Jason Su
3T Pre-MS In collaboration with Bordeaux (recruiting, scanning) 23 CIS patients, 16 controls Thomas Tourdias, Fanny Munsch, Sandy Mournet Study thalamic atrophy in CIS/early MS and relation to cognitive performance Detailed neuropsych testing Verbal memory learning and recall Visual memory learning and recall Working memory Executive function Information processing speed
Imaging and Post-Processing Acquiring CSFnMPRAGE (WM/GM structural), FLAIR (lesions), DTI, WMnMPRAGE (thalamic anatomy) Primarily been working with the WMnMPRAGE images Brain parenchyma and intracranial cavity (ICC) segmentation Thalamic anatomy segmentation (12 nuclei and whole thalamus)
Segmentation Thalamus Brain and Intra-Cranial Cavity Fanny sends images Jason applies THOMAS (v0, whole brain template variant), ~5-6h per brain per side Sandy edits segmentations with training from Thomas A label-fusion based algorithm to segment these quantities Priors derived from multi-contrast co-registered volumes from ET-FUS study Brain parenchyma priors from FSL’s BET of CSFnMPRAGE ICC priors from BET of CUBE Co-registered onto 3T WMnMPRAGE Majority voting with some smoothing Another WMn template specific to the FUS study, ~5-6h per brain Sandy edits ICC as well Should we define ICV (intra cranial volume) and TPV (total parenchymal volume) based on editing down to supra-tentorial only, as we / Hagen did in MSmcDESPOT?
Statistical Analysis Must be very careful about multiple comparisons problem 2x13 thalamic regions 10 cognitive scores Only 21 patients Want to narrow down the number of correlations we test to a few select high-value targets Anterior thalamus and memory-guided attention MD and learning and decision-making Narrow down which thalamic regions to study in multivariate analysis, by comparing patient group to control group We expect only those regions that are significantly altered from normal to be correlated to the disease state I believe we only have 20 complete patient datasets right now, isn’t that right? Because P057 had thalamic lesions so dropped out of the patient group? My master spreadsheet shows only 13 controls: T 001 to T 013. Question: based on the way the THOMAS template was created from single-sided manual segmentations of MS+HC brains, could this introduce any kind of net “bias” or offset in right vs left thalamic volumes for incoming brains? This could be something to be very careful with, and how we describe / justify this in the paper Mitchell references: Mitchell et al FrontSysNeurosci 2013.7.1 What does the mediodorsal thalamus do? Mitchell et al NeurosciBiobehavioralRev 2015.54.76 The mediodorsal thalamus as a higher order thalamic relay nucleus important for learning and decision making
Group Comparisons: Raw THOMAS ICV “regressed out”
Group Comparisons: Raw THOMAS ICV “regressed out”
Group Comparisons: Raw THOMAS ICV “regressed out”
Group Comparisons: Raw THOMAS ICV “regressed out”
Memory ~ Age + Sex + SNR + ICV + Mean_Thalamus + Mean_AV Adjusts for gender, SNR, age, ICV, whole thal
Memory ~ Age + Sex + SNR + ICV + Mean_Thalamus + Mean_AV Adjusts for gender, SNR, age, ICV, whole thal
Decision ~ Age + Sex + SNR + ICV + Mean_Thalamus + Mean_MDPf Adjusts for gender, SNR, age, ICV, whole thal
Decision ~ Age + Sex + SNR + ICV + Mean_Thalamus + Mean_MDPf Adjusts for gender, SNR, age, ICV, whole thal
Group Comparisons: Raw vs Edited ICV “regressed out”
Group Comparisons: Raw vs Edited ICV “regressed out”
Group Comparisons: Raw vs Edited ICV “regressed out”
Simple Correlation Other papers use simple correlations as a way to build multiple regression model in steps (only use covariates that are significant) However, there are critics of this approach Since our patient population is pretty uniform, we remove the 3 males and only regress out ICV
Simple Correlations (Spearman)
Simple Correlations (Spearman)
Decision ~ Age + Sex + SNR + ICV + Mean_Thalamus + Mean_VPL
Decision ~ Age + Sex + SNR + ICV + Mean_Thalamus + Mean_VPL
Future work Lesion segmentation (if any?) WM/GM atrophy? VBM/TBM/DBM