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Histogram-weighted cortical thickness networks for the detection of Alzheimer's disease

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Presentation delivered by Pradeep Reddy Raamana at 2016 international workshop on Pattern Recognition in Neuroimaging on the topic of histogram-weighted cortical thickness networks for the detection of Alzheimer's disease.

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Histogram-weighted cortical thickness networks for the detection of Alzheimer's disease

  1. 1. Histogram-weighted cortical thickness networks for the detection of Alzheimer's disease Pradeep Reddy Raamana, PhD Postdoctoral fellow Research interests: Machine learning Medical image analysis
  2. 2. Alzheimer’s Disease (AD) http://www.alz.org 2
  3. 3. Progression of AD http://www.alz.org 3 Asymptomatic Prodromal dementia Dementia
  4. 4. Cortical Thickness — Pial — White surfaces Thickness
  5. 5. Limited Power of Thickness • Cortical thickness is a great imaging biomarker, but its prognostic power is limited. • According to an extensive comparison study Cuingnet et al. (2011): Example Study 
 on CN vs. MCIc Type of 
 Cortical Thickness Sensitivity Specificity Satisfactory? Klöppel et al. (2008) Direct & Raw 54% 96% No Desikan et al. (2009) Summarized 57% 93% No Marcus et al. (2007) ROI-based 65% 94% No 5MCI: Mild cognitive impairment, MCIc: MCI converters.
  6. 6. Link/Edge Definition similar? — Pial — White surfaces Thickness
  7. 7. Linking Cortical Patches 7 Similarity criteria: = Mean Thickness in Patch i|MTi MTj| >
  8. 8. 8 Healthy Alzheimer’s #Patches = 680, Alpha = 0.30mm; Isolated nodes (with no links) are not drawn. Similarity Networks
  9. 9. Network Features: Examples 9 Nodal Degree Betweenness Centrality Clustering coefficient Illustration adapted from: Várkuti B, PLOS One, 2011.
  10. 10. Multiple Kernel Learning • Typically, many features combined into a single bag 
 to train SVM • Combining the ThickNet features to maximize collective predictive power • Using Variational Bayes probabilistic MKL (VBpML)1 10 Composite Classifier Optimized Kernel 1 ThickNet Feature 1 Optimized Kernel 2 ThickNet Feature 2 Optimized Kernel 3 ThickNet Feature 3 1 Damoulas, T., & Girolami, M. A. (2008). Bioinformatics, 24(10), 1264-1270.
  11. 11. • Class-imbalance is not uncommon. • Classifiers can be sensitive to class-imbalance • The most popular classifier SVM is. • This can result in biased estimates, making the classifier either too sensitive, or too specific. Repeated Holdout, Stratified Training Set ( RHsT ) Controls (n=159) MCIc (n=56) Training (MCIc)Training (CN) Test Set (CN) 11 Tes
  12. 12. Evaluation Procedure 12
  13. 13. Method Parameters 13 smaller 𝛼large 𝛼 largesmall Total number 
 of patches
  14. 14. Alzheimer’s Dataset • Evaluated on ADNI-1 to enable comparison to published literature. • Exact subset as published in Cuingnet et al., Neuroimage, 2011. • except for exclusions from quality-control. Diagnostic Group #Subjects Healthy controls (CN) 159 Alzheimer’s disease (AD) 136 MCI converters (MCIc) 56 MCI non-converters (MCInc) 130 Total 481 14ADNI: Alzheimer Disease Neuroimaging Initiative
  15. 15. Best Performance 15Results in Raamana et al. 2014, Neurobiol. Aging. 0.5 0.6 0.7 0.8 0.9 1 CN vs. AD CN vs. MCIc MCIc vs. MCInc 0.64 0.76 0.9 0.65 0.74 0.8 0.64 0.76 0.89 0.68 0.83 0.92 AUC Accuracy Sensitivity Specificity
  16. 16. Improvement over Thickness Full AUC 0.75 0.813 0.875 0.938 1 CN vs. AD CN vs. MCIc 0.832 0.924 0.807 0.916 Mean Thickness Thickness 16AUC: Area under ROC; Partial AUC is bounded by specificity > 85%
  17. 17. Partial AUC 0.05 0.063 0.075 0.088 0.1 CN vs. AD CN vs. MCIc 0.068 0.097 0.057 0.09 Mean Thickness ThickNet Improvement over Thickness 17AUC: Area under ROC; Partial AUC is bounded by specificity > 85%
  18. 18. Summary • A predictive model with attractive properties: • individual feature significance • most discriminative regions • improved classification power • intuitive interpretation. 18
  19. 19. Few applications 1. Early detection of Alzheimer Disease 2. Amnestic MCI 
 sub-classification 3. Differential diagnosis of AD and Frontotemporal Disease (FTD) 19 overlap Normal Aging ADFTD Others 
 (VaD etc.) Healthy Prodromal dementia Dementia Mild cognitive impairment (MCI) Single domain Multi-
 aMCI MCI AD: Alzheimer disease, MCI: Mild cognitive impairment, VaD: vascular dementia
  20. 20. Key choices 20 HighLow resolution Edge definition:
  21. 21. Defining Edges m=243 #vertices m=216 Examples cortical thickness n=100
  22. 22. Edge definitions Type of base distribution Type of 
 Edge Metric Acronym Definition Summarized Similarity (diff. in medians) MD exp(similarity) EMD raw distribution Wilcoxon ranksum statistic RS ranksum statistic normalized histogram histogram correlation HCOR 𝝌2 statistic CHI2 histogram intersection HINT spatial scale: m= 400, 1000, 2000, 3000, 5000 and 10000 vertices per patch. 718, 273, 136, 97, 74 and 68 patches per brain.
  23. 23. Examples: weighted networks Healthy controls
  24. 24. Alzheimer’s Dataset • Evaluated on ADNI-1 to enable comparison to published literature. • Only CN and AD are chosen to focus the comparison on edge metrics alone. Diagnostic Group N #Females Age MMSE Healthy controls (CN) 224 109 75.79 ( 4.99) 29.11 ( 1.01) Alzheimer’s disease (AD) 188 89 75.22 ( 7.49) 23.29 ( 2.04) Total 412 198 only MMSE differed significantly 24ADNI: Alzheimer Disease Neuroimaging Initiative
  25. 25. Evaluation of Predictive Power 25
  26. 26. Evaluation of Predictive Power 26
  27. 27. Comparison of Performance At m=400 vertices/patch
  28. 28. Comparison of Performance m=400 m=1000
  29. 29. Multi-network Multi-scale Analysis m=400, 1000, 2000, 3000, 5000 & 10000
  30. 30. Are the differences significant?
  31. 31. Summary • Simpler methods (MD) are just as predictive. • Impact of spatial scale m on predictive performance seems to be not significant. • If I may overstate it, the most popular way of computing edge weights in group-wise analysis i.e. correlation, seems to be the least-predictive of disease-status. 31
  32. 32. Future work • Further validate it on • another separability, such as MCI vs. CN. • another dataset, such as AIBL. • another disease, such as FTD. • another parcellation scheme! • Compare individual graph properties
  33. 33. Thank you. Questions?

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