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