![Features
• We started with the our preprocessed values, and then looked at a
variety of transforms
• We chose the full vector and the PCA reduced version as our main
features of interest
• Principle Component Analysis (PCA) is a tool to reduce the dimensionality of a
dataset
PCA
Full Vector (Samples)
50 Highest Values
Histogram
[0.5, .01, -.02, 1.5, 2.0, … -3.0]576
One Volume](https://image.slidesharecdn.com/a61e9460-0531-471f-858a-734b26e6e60e-161104040013/85/CSU_comp-12-320.jpg)
CSU_comp
- 1. Computational Classification Techniques for Neuroimaging A Machine Learning Based Approach Adrian Smith – Undergraduate Computer Science Department Sonoma State University
- 2. Fundamentals • Understanding the human brain has been a central theme of human history • By growing our understanding of the brain, we improve our ability to treat diseases (Gur2002) • Understanding the brain helps us be aware of it’s limitations Artist’s Depiction of Neurons UCI Research Courtesy of OSA Student Chapter at UCI Art in Science Contest. Photo by: Ardy Rahman
- 3. fMRI Scanning • Functional Magnetic Resonance Imaging (fMRI) allows us to measure localized brain activity • This allows one to find relationships between cognition and brain activity • Blood oxygen is used as a measure of activity (BOLD imaging) • This technique produces rich data, but contains high levels of noise CSRB (Keck MRI Center)
- 4. Data Collection • One major advantage of researching fMRI data is it’s availability on a variety of online locations • We worked with 1452 total brain scans each corresponding to one of 9 categories • The categories refer to the image a subject was observing
- 5. Analysis Goals • Our goal was to be able to, given the fMRI scan of a subject, predict what image they were observing • This means differentiating scans based on the image the subject is observing • What is the relationship? Haxby2001 Stimulus Images
- 6. Machine Learning Techniques • Machine learning is an information processing technique • The field of machine learning is at the heart of understanding “Big Data” • We aimed to use modern machine learning techniques to help classify fMRI data.
- 7. How does Machine Learning Work? • Machine Learning classification focuses on designing algorithms which are trained to categorize objects • This is done by combining some defining characteristics and a label • The algorithm trains on one set of data, and then is tested to see how accurately it can predict the label of some piece of data. • What is the data? By Antti Ajanki AnAj (Own work) [GFDL (http://www.gnu.org/copyleft/fdl.html), CC-BY-SA- 3.0 (http://creativecommons.org/licenses/by-sa/3.0/)
- 8. Which is active before processing? Unprocessed Active Unprocessed Rest
- 9. Which is active after processing? Processed Active Processed Rest
- 10. Preprocessing • We applied masks that came with the dataset in order to focus on the Ventral Temporal cortex, our region of interest • We then applied a polynomial detrender, which eliminates systematic trends, such as signal increase as the machine warms up • This was followed by a key step, z- scoring against the rest position Graph of Normal Distribution Public Domain
- 11. Classification • We now had to decide how to process the image data • This meant choosing features that best represented the data we sought • We also tested a variety of classification algorithms which would label images based on the chosen feature
- 12. Features • We started with the our preprocessed values, and then looked at a variety of transforms • We chose the full vector and the PCA reduced version as our main features of interest • Principle Component Analysis (PCA) is a tool to reduce the dimensionality of a dataset PCA Full Vector (Samples) 50 Highest Values Histogram [0.5, .01, -.02, 1.5, 2.0, … -3.0]576 One Volume
- 13. Experimental Design • Data was split evenly and randomly into training and test • We used several feature vectors to test each classifier • We primarily focused on k Nearest Neighbor (kNN) and Support Vector Machine (SVM) classifiers • Tests were repeated 15 times and scores averaged Train Feature Training Label Trained Classifier Testing Feature Predicted Label Testing Label Comparison Accuracy and Confusion Matrix Classifier
- 14. kNN vs. SVM • SVM preforms better than kNN • Increase in accuracy is likely due to the weakness of kNN when dealing with high dimensionality SVM on samples, 90.9% accuracy kNN on samples, 75.6% accuracy
- 15. • We applied PCA to the processed data • This produced a vector over half the size of our original • This smaller vector produces more accurate results Samples vs. PCA PCA (SVM), 92.1% accuracy SVM on samples, 90.9% accuracy
- 16. • PCA and SVM in combination gave the best results after repeated testing • We achieved on average 92.1% accuracy among 9 labels, with a 2.0% standard deviation. • Our classification methods are effective and repeatable • We also gained a variety of insights about the nature of the data Classification Results: Accuracy
- 17. • We saw several labels which repeatedly misclassified, and saw accuracy improve as they were removed • One area of further study is investigating whether these patterns exist between multiple subjects, and why PCA (SVM), 92.1% accuracy Classification Results: Insights
- 18. Future Exploration • We intend to move towards classifying across multiple subjects • This is of utmost importance to clinical applications of fMRI data • Multisubject comparison presents challenges due to the variation in brain structure • We intend to build upon previous work on feature detection and scaling maps (Gill2014)
- 19. Sources • Gur, R. E., McGrath, C., Chan, R. M., Schroeder, L., Turner, T., Turetsky, B. I., ... & Gur, R. C. (2002). An fMRI study of facial emotion processing in patients with schizophrenia. American Journal of Psychiatry. • Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L., & Pietrini, P. (2001). Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293(5539), 2425-2430. • Gill, G., Bauer, C., & Beichel, R. R. (2014). A method for avoiding overlap of left and right lungs in shape model guided segmentation of lungs in CT volumes. Medical physics, 41(10), 101908. • Dataset: This data was obtained from the OpenfMRI database. Its accession number is ds000105. The original authors of :ref:`Haxby et al. (2001) <HGF+01>` hold the copyright of this dataset and made it available under the terms of the `Creative Commons Attribution-Share Alike 3.0`_ license.
- 20. Acknowledgments • Dr. Gurman Gill – Mentor • OpenfMRI – Source of all data, and amazing example of open data in science • pyMVPA – Python toolkit used in preprocessing • Scikit-learn – Python toolkit used in classification • Dr. Yaroslav Halchenko – Researcher who provided extensive aid in understanding and dealing with fMRI data
- 21. Questions?
- 22. Extra Graphics SVM of top 400 values. 30.9% accuracy SVM on 90% PCA. 92.2% accuracy