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Machine Learning Algorithms for Neuroimaging-based Clinical Trials in Preclinical Alzheimers Disease Vamsi K. Ithapu Wisconsin Alzheimers Disease Research Center University of Wisconsin-Madison April 2, 2017 (BRAIN Initiative Symposium

  1. Machine Learning Algorithms for Neuroimaging-based Clinical Trials in Preclinical Alzheimer’s Disease Vamsi K. Ithapu Wisconsin Alzheimer’s Disease Research Center University of Wisconsin-Madison April 2, 2017 (BRAIN Initiative Symposium 2017) Learning methods for enrichment 1 / 18

  2. A Clinical Trial – The work flow Randomized Controlled Trial Study Population (BRAIN Initiative Symposium 2017) Learning methods for enrichment 2 / 18

  3. A Clinical Trial – The work flow Randomized Controlled Trial At enrollment Controls Study Population Intervention (BRAIN Initiative Symposium 2017) Learning methods for enrichment 2 / 18

  4. A Clinical Trial – The work flow Randomized Controlled Trial At enrollment Trial ends Controls Controls Study Population Intervention Intervention (BRAIN Initiative Symposium 2017) Learning methods for enrichment 2 / 18

  5. A Clinical Trial – The work flow Randomized Controlled Trial At enrollment Trial ends Controls Controls Study Population Do Controls differ from interneved Intervention Intervention (BRAIN Initiative Symposium 2017) Learning methods for enrichment 2 / 18

  6. Setting up a clinical trial – My work Who is participating in the trial? Clinical Trial Enrichment How to differentiate control from intervened? Trial Outcome Design (BRAIN Initiative Symposium 2017) Learning methods for enrichment 3 / 18

  7. Setting up a clinical trial – My work Who is participating in the trial? Clinical Trial Enrichment How to differentiate control from intervened? Trial Outcome Design (BRAIN Initiative Symposium 2017) Learning methods for enrichment 3 / 18

  8. Setting up a clinical trial – My work Who is participating in the trial? Clinical Trial Enrichment How to differentiate control from intervened? Trial Outcome Design trials aimed for Alzheimer’s Disease (BRAIN Initiative Symposium 2017) Learning methods for enrichment 3 / 18

  9. Alzheimer’s Disease Destroys memory and cognition Irreversible. Strongest risk factor is age Diagnosis ← { Age, Family History, Cognitive/Neuropsych/Physical Exams, Brain Scans } (BRAIN Initiative Symposium 2017) Learning methods for enrichment 4 / 18

  10. Alzheimer’s Disease Destroys memory and cognition Irreversible. Strongest risk factor is age Diagnosis ← { Age, Family History, Cognitive/Neuropsych/Physical Exams, Brain Scans } (BRAIN Initiative Symposium 2017) Learning methods for enrichment 4 / 18

  11. Alzheimer’s Disease Destroys memory and cognition Irreversible. Strongest risk factor is age Diagnosis ← { Age, Family History, Cognitive/Neuropsych/Physical Exams, Brain Scans } Mild Cognitive Impairment (MCI) Preclinical Normal/Healthy Dementia (BRAIN Initiative Symposium 2017) Learning methods for enrichment 4 / 18

  12. Landscape of AD Clinical Trials Clinicaltrials.gov lists 485 recruiting studies 225 in US; 147 in Europe; 68 are in Phase III and IV (BRAIN Initiative Symposium 2017) Learning methods for enrichment 5 / 18

  13. Landscape of AD Clinical Trials Clinicaltrials.gov lists 485 recruiting studies 225 in US; 147 in Europe; 68 are in Phase III and IV Very little success . . . more than 550 trials since 2002 (Cummings 2014) (BRAIN Initiative Symposium 2017) Learning methods for enrichment 5 / 18

  14. Landscape of AD Clinical Trials Clinicaltrials.gov lists 485 recruiting studies AD diagnosis itself is messy 225 in US; 147 in Europe; → Early diagnosis is much harder 68 are in Phase III and IV → CN vs. MCI ≈ 70% Very little success . . . more than 550 trials since 2002 (Cummings 2014) (BRAIN Initiative Symposium 2017) Learning methods for enrichment 5 / 18

  15. Landscape of AD Clinical Trials Clinicaltrials.gov lists 485 recruiting studies AD diagnosis itself is messy 225 in US; 147 in Europe; → Early diagnosis is much harder 68 are in Phase III and IV → CN vs. MCI ≈ 70% < 20% of MCIs convert to AD Very little success . . . more than 550 trials since 2002 (Cummings 2014) = ⇒ 8 out of 10 trial subjects are not-eligible!! (BRAIN Initiative Symposium 2017) Learning methods for enrichment 5 / 18

  16. . . . but there is light Imaging to the rescue Cognitive decline follows atypical brain scans (BRAIN Initiative Symposium 2017) Learning methods for enrichment 6 / 18

  17. . . . but there is light Imaging to the rescue Cognitive decline follows atypical brain scans Risk Factors Alzheimer’s Disease Clinical Markers Optimal Enricher High Design Dimensional Imaging Machine Learning (BRAIN Initiative Symposium 2017) Learning methods for enrichment 6 / 18

  18. Population enrichment Enrichment Cut-Off Discarded Included Healthy AD Worsening Disease (Enrichment Criterion) (BRAIN Initiative Symposium 2017) Learning methods for enrichment 7 / 18

  19. Population enrichment Enrichment Cut-Off Discarded Included Healthy AD Worsening Disease (Enrichment Criterion) Good enrichment criterion ⇐ ⇒ High correlation with disease Practical enrichment criterion ⇐ ⇒ High predictive power (BRAIN Initiative Symposium 2017) Learning methods for enrichment 7 / 18

  20. Designing a good enricher Given some marker δ : Longitudinal change σ : Pooled Variance (BRAIN Initiative Symposium 2017) Learning methods for enrichment 8 / 18

  21. Designing a good enricher Given some marker δ : Longitudinal change σ : Pooled Variance Optimal Enricher Small σ + Large δ (BRAIN Initiative Symposium 2017) Learning methods for enrichment 8 / 18

  22. Designing a good enricher Given some marker δ : Longitudinal change σ : Pooled Variance Optimal Enricher Low-Variance An Ensemble + + Un-Biased Neural Networks (BRAIN Initiative Symposium 2017) Learning methods for enrichment 8 / 18

  23. Randomized deep networks for enrichment (BRAIN Initiative Symposium 2017) Learning methods for enrichment 9 / 18

  24. Randomized deep networks for enrichment T outputs T networks L Layered NN L Layered NN Block 1 L Layered NN (BRAIN Initiative Symposium 2017) Learning methods for enrichment 9 / 18

  25. Randomized deep networks for enrichment T outputs T networks L Layered NN L Layered NN Block 1 L Layered NN T outputs Block 2 T networks L Layered NN L Layered NN L Layered NN (BRAIN Initiative Symposium 2017) Learning methods for enrichment 9 / 18

  26. Randomized deep networks for enrichment T outputs T networks L Layered NN L Layered NN Block 1 f 1 L Layered NN T outputs Block 2 T networks L Layered NN f 2 f B f 1 L Layered NN f 3 f 6 f 2 f 4 f 5 L Layered NN T outputs T networks L Layered NN f B L Layered NN Block B L Layered NN (BRAIN Initiative Symposium 2017) Learning methods for enrichment 9 / 18

  27. Randomized deep networks for enrichment T outputs T networks L Layered NN L Layered NN Block 1 L Layered NN T outputs Block 2 T networks L Layered NN L Layered NN Final Linear Combination Output L Layered NN T outputs T networks L Layered NN L Layered NN Block B L Layered NN (BRAIN Initiative Symposium 2017) Learning methods for enrichment 9 / 18

  28. Randomized deep network Markers – rDm Training baseline rDm Inputs → MRI and PET Images Labels → AD – 0, healthy – 1 (BRAIN Initiative Symposium 2017) Learning methods for enrichment 10 / 18

  29. Randomized deep network Markers – rDm Training baseline rDm Inputs → MRI and PET Images Labels → AD – 0, healthy – 1 rDm at test time Predict on MCI (BRAIN Initiative Symposium 2017) Learning methods for enrichment 10 / 18

  30. Randomized deep network Markers – rDm Training baseline rDm Inputs → MRI and PET Images Labels → AD – 0, healthy – 1 rDm at test time Choose a cut-off t ∈ [0 , 1] & filter out Predict on MCI subjects with rDm prediction > t (BRAIN Initiative Symposium 2017) Learning methods for enrichment 10 / 18

  31. Predictive power of baseline rDm Baseline rDm versus change (12 and 24 months) in outcomes Spearman correlation coefficient (and p -value) Marker 12m 24m MMSE 0.2123, p = 0.0008 0.3311, p = 0.0003 –0.5300, p < 10 − 4 ADAS 0.2139, p = 0.0007 0.5952, p = 10 − 4 MOCA 0.0568, p > 0.1 RAVLT 0.1285, p = 0.04 0.5702, p = 0.0008 0.2811, p < 10 − 4 PsyMEM 0.4207, p = 0.001 0.3262, p ≪ 10 − 4 0.4744, p ≪ 10 − 4 HippoVol –0.3643, p ≪ 10 − 4 –0.5344, p ≪ 10 − 4 CDR-SB DXConv 1 > 20, p ≪ 10 − 4 > 20, p ≪ 10 − 4 1ANOVA test results are reported since this variable is categorical (BRAIN Initiative Symposium 2017) Learning methods for enrichment 11 / 18

  32. Predictive power of baseline rDm Baseline rDm versus change (12 and 24 months) in outcomes Spearman correlation coefficient (and p -value) Marker 12m 24m MMSE 0.2123, p = 0.0008 0.3311, p = 0.0003 –0.5300, p < 10 − 4 ADAS 0.2139, p = 0.0007 0.5952, p = 10 − 4 MOCA 0.0568, p > 0.1 RAVLT 0.1285, p = 0.04 0.5702, p = 0.0008 0.2811, p < 10 − 4 PsyMEM 0.4207, p = 0.001 0.3262, p ≪ 10 − 4 0.4744, p ≪ 10 − 4 HippoVol –0.3643, p ≪ 10 − 4 –0.5344, p ≪ 10 − 4 CDR-SB DXConv 1 > 20, p ≪ 10 − 4 > 20, p ≪ 10 − 4 1ANOVA test results are reported since this variable is categorical (BRAIN Initiative Symposium 2017) Learning methods for enrichment 11 / 18

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