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BIRS 2016: Opening the analysis black box: Improving robustness and interpretation Matthew Brown, PhD University of Alberta, Canada
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Overview 1.About us 2.Preprocessing quality assurance 3.Interpretation of group vs. individual differences 4.Trial type fMRI signatures
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Dept. Psychiatry Dept. Computing Science Computational Psychiatry Group
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Diagnosis – What disease? Prognosis – Predict patient response to treatment options Clinical decision-making
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What are we detecting? 10 psychosis patients, 10 controls, fMRI Highly diagnostic Fourier power distribution from voxels IN THE EYES Eye movement disturbances in psychosis
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ADHD-200 and ABIDE datasets n=1000 approx. ADHD patients or autism patients Structural MRI, resting state fMRI Simple diagnosis – Classify patients vs. controls – Accuracy 50-70% in various papers Some papers reported higher 75%+ accuracy BUT cherry-picking sites?
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ADHD-200 Global Competition Best-performing algorithm, but did not win Used only non-imaging features: – Age, gender, handedness, IQ, site of scan – 3-class classification (ADHD-c, ADHD-i, control) – 63% hold-out accuracy (vs. 54% chance) Using non-imaging features Brown et al. 2012 Chance accuracy Validation Accuracy (%)
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Histogram of oriented gradient (HOG) features Image from Ghiassian et al. under review. Also see Dalal and Triggs 2005. IEEE Computer Society Conference on. vol. 1. IEEE, p.886–893.
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ADHD-200 and ABIDE datasets Ghiassian et al. under review State of the art (as of 1.5 years ago) 2-class classification (patients vs. controls) ADHD-200ABIDE Chance55%51% Non-imaging69%60% Non-imaging + Structural MRI 70%64% Non-imaging + Functional MRI 64%65%
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Overview 1.About us 2.Preprocessing quality assurance 3.Interpretation of group vs. individual differences 4.Trial type fMRI signatures
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Registration failure Subject 1Subject 15 Fixed -> Standard preprocessing methods failed for 1 of 21 subjects.
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Inter-site variability Sen et al. in preparation PCA Component 1 PCA Component 2 ADHD-200 Subjects Projected onto PCA component space Each colour is a different scanning site. Even with standard normalization procedures, inter-site structure remains in the data.
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Overview 1.About us 2.Preprocessing quality assurance 3.Interpretation of group vs. individual differences 4.Trial type fMRI signatures
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Clinical research Huntington’s Image from Wikipedia Healthy One goal: Associate disease with biological features
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ADHD-200 resting state fMRI functional connectivity analysis ICA Brown et al. 2012
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ADHD patients vs. controls “Default mode” networkPatients vs. controls Brown et al. 2012 “Desired” simple interpretation: “Patients are different from controls. This difference tells us something about the disease.”
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Group vs. individual differences Patients Controls Statistically significant group differences, but substantial overlap between individual patients and controls. Brown et al. 2012
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Interpretation Simple interpretation “patients are different from controls” Overlap precludes simple interpretation Yet many papers provide precisely and only the simple interpretation Patients Controls Brown et al. 2012
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Overview 1.About us 2.Preprocessing quality assurance 3.Interpretation of group vs. individual differences 4.Trial type fMRI signatures
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Black box analysis Analysis Software Analysis Software
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General linear model regression Model voxel i’s timecourse Model matrix for trial type k
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Two different models for hemodynamic response function SPM canonical model Finite impulse response model
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Check deconvolved timecourses Basically agree on shape (but not statistical differences in this case) SPM canonical model Finite impulse response model, same region
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Check deconvolved timecourses SPM canonical model Finite impulse response model, same region Noise in deconvolved timecourses
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Another example SPM canonical model Finite impulse response model, same region Noise in deconvolved timecourses
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GLM analysis Check deconvolved timecourses What is the model fitting – Noise vs. signal Model selection – regularization
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Summary Quality check everything Visualization Intermediate steps and final results Particularly important for non-technical end-users
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Acknowledgements People: Azad, Benoit, Dursun, Ghiassian, Greenshaw, Greiner, Juhas, Purdon, Ramasubbu, Rish, Sen, Silverstone Funding: AICML, AIHS, CIHR, Norlien Foundation, AHS, AMHB, UAlberta Questions?
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Invitation Continue informing other researchers about analysis pitfalls and caveats. Questions?
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