diagnosing alzheimer’s disease using machine learning techniques on neuroimaging data david nummey...
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Diagnosing Alzheimer’s DiseaseUsing Machine Learning
Techniqueson Neuroimaging Data
David NummeyAdvised by Prof. Fred L. Fontaine
May 10, 2011
The Cooper Union for the Advancement of Science and ArtAlbert Nerken School of Engineering
S*ProCom2
Center for Signal Processing,
Communications, and
Computer Engineering Research
Overview• Introduction• Alzheimer’s Disease
and Neuroimaging• Machine Learning Techniques• Implementation• Results• Conclusions
Overview• Introduction• Alzheimer’s Disease
and Neuroimaging• Machine Learning Techniques• Implementation• Results• Conclusions
Introduction
• AD a neurodegenerative ‘dementia’• 4.5M Americans in 2000; 5.1M in
2007• 13.2M projected by 2050!• Annual health care costs $148B & rising
• Research progressing:1. Early, accurate diagnosis2. Identify predictive biomarkers3. Treatment and prevention
measures
Our Approach• Example-based brain models
of AD and healthy elderly patients• Functional and structural brain images• Machine learning approach
Overview• Introduction• Alzheimer’s Diseaseand Neuroimaging• Machine Learning Techniques• Implementation• Results• Conclusions
Alzheimer’s Disease
• Disease course known; cause is not• Typical pattern of degeneration• Amyloid protein ‘plaque’ depositions• Tau protein ‘tangles’ strangle neurons
• Genetics reveal risk factors
• Current diagnostic methods• Clinical evaluations• Specific cognitive deficits must be present
• Diagnosis confirmed post-mortem
Neuroimaging for AD
• Structural imaging• MRI• PIB-PET• CT
• Functional imaging• FDG-PET• fMRI• DTI
Magnetic Resonance Imaging
• Create structural image from body’s magnetic properties• Water highly polar• Apply static B field• Perpendicular
pulses• Measure EM decay
FDG-PET
• Fluorodeoxyglucosepositron emission tomography• Radiopaque by
anaerobic activity• Corresponds to
brain functionalityby region
AD
MCI
HC
ADNI
• Alzheimer’s DiseaseNeuroimaging Initiative (ADNI)• International collaboration• Longitudinal studies of 800+ patients
• Third long-term study in progress• NIH and NIA funding since 2004
ADNI Data
Overview• Introduction• Alzheimer’s Disease
and Neuroimaging• Machine Learning Techniques• Implementation• Results• Conclusions
Machine Learning Overview
• Pattern recognition• Automatic classification• ‘Train’ models based on examples• Successful in many fields,and medical diagnoses
• Supervised learning problem• Inputs xi with known labels yi
• Images belong to AD, MCI, or HC classes
Machine Learning Overview• Feature extraction• PCA, the kernel trick
• Machine learning methods• LDA/QDA, GNB, KNN, SVM• Majority voting classifier
• Cross-validation• Prevent overfitting
Feature Extraction
• Principal Component Analysis (PCA)• Orthogonal dimensions w/highest variances• Project data onto this smaller space
Feature Extraction• The Kernel Trick• Allow non-linear solutionsto linear problems/classifiers• Replace inner products withkernel in higher-dim space• No need to determine higher-dimspace computational savings
Discriminant Analysis
• Linear Discriminant Analysis (LDA)• f(x) = wTx + b – Separating hyperplane• Minimize ||w|| ‘maximal margin’
• Quadratic Discriminant Analysis (QDA)• Statistical estimations
Gaussian Naïve Bayes
• Naïve Bayes• Assume independent processes• Estimate statistics, model classes• GNB – Gaussian PDF’s• ML prediction
K-Nearest Neighbors
• KNN – estimate class PDFs• Trained models depend onlabels of nearest neighbors
Support Vector Machines
• SVM – Maximum margin problem• Convex optimization• Rep. hyperplane by ‘support vectors’• Non-linear solutions with kernel trick• Multiclass solutions done heuristically
Majority Voting Classifier• Ensemble classifiers• Use results from multiple methods• Extract strengths of each• Simplest: Majority vote• Label is most popular output
Cross-Validation• Avoid overfitting!• Want to model general characteristics• Train & test on random subsets of data
• K-fold cross-validation• Partition training data into K groups• Iteratively verify features and models• Select best PC’s per model
Overview• Introduction• Alzheimer’s Disease
and Neuroimaging• Machine Learning Techniques• Implementation• Results• Conclusions
Implementation Overview
• ADNI data retrieval interface• Data sets constructed• Classification & evaluation
procedures
ADNI Data Extraction• ADNI data organized by visit IDs• Perl/SQL interface by Aleksey Orekhov
• SQL queries to create general list• Specifies file paths to best images
• Matlab interface• User selects image types & visit periods• ‘Least common denominator’3-class data set constructed
Data Sets Constructed1. Best-quality FDG-PET scans, BL
visit2. Restrict to also include best-
quality1.5T MRI’s from screening period
• Grey-matter mask applied toADNINEW & PETGREY
Classification & Evaluation• Subsets of data over many iterations• Feature extraction• Principal component analysis• Cross-validate training data
• Machine learning methods• LDA, QDA, GNB, KNN, SVM, Full SVM• One-vs.-one multiclass heuristic• Majority voting classifier
• Accuracy, sensitivity, specificity, etc.
Overview• Introduction• Alzheimer’s Disease
and Neuroimaging• Machine Learning Techniques• Implementation• Results• Conclusions
Results
• Attempted each classification task• AD vs. MCI vs. HC• AD/MCI vs. HC• AD vs. MCI/HC• AD vs. HC• AD vs. MCI• MCI vs. HC
• Results evaluated statistically
Results – Masked PET Scans
Results – Masked PET Scans
Results – Unmasked PET Scans
Results – MRI Scans
Performance Analysis• Three-class formulations
need more/better features• Simpler, shorter simulations are
fine• SVM best, GNB worst• Others very similar
• Grey-matter mask tradeoff• MRI data most separable
Overview• Introduction• Alzheimer’s Disease
and Neuroimaging• Machine Learning Techniques• Implementation• Results• Conclusions
Summary
• Taub interface to ADNI database• FDG-PET and MRI data sets
• Machine learning framework• Successful AD vs. HC diagnoses• 98%±6% MRI accuracy; 85±5% FDG-PET
• ‘Early detection’ (MCI vs. HC)requires further work• Combining data types• Longitudinal studies
Future Work• Further classifiers & cross-
validation• New feature extraction
techniques• Outlier detection• Expand breadth of studies• Longitudinal studies• Predict cognitive conversions• Clinician-friendly interface
Acknowledgements
• Prof. Fred Fontaineand S*ProCom2
• Dr. Christian Habeckand The Taub Institute• Kamran Mahbobi
and MaXentric Technologies• My mentors, family,
and friends here today
Thank you.
Any questions?
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Q&A – AD Biomarkers
Q&A – Regions of Interest
• Temporal lobe• Language• Hippocampus (memory)• Amygdala (emotion)
• Parietal lobe• Sensory integration
• Frontal lobe in later stages• Personality
Q&A – Benchmarking
• Unsimplified MRI data take~20-25min/iteration(12 patients/data set to train/test)• Unsimplified FDG-PET data take
~5-10 min/iteration(Same patient selection method)• FDG-PET data with grey matter
masktake ~1min/iteration, or ~5-10min/itwith univariate ROIs (~33 pats/each)
Q&A – Clinical Impact
• Research progressing:1. Early, accurate diagnosis2. Identify predictive biomarkers3. Treatment and prevention
measures
• Regulatory approval neededfor clinical implementation• Combined prediction &
preventiontrials may see some success