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Automated Feature Abstraction of the fMRI Signal using Neural

Network Clustering Techniques

Stefan Niculescu and Tom MitchellSiemens Medical Solutions, Carnegie Mellon University

December 8th, 2006

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Study: Pictures and Sentences

• Trial: read sentence, view picture, answer whether sentence describes picture

• Picture presented first in half of trials, sentence first in other half

• Image every 500 msec • 12 normal subjects• Three possible objects:

star, dollar, plus• Collected by Just et al.

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It is true that the star is above the plus?

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5

+

---

*

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Task and Goal

• Task: discriminate whether a subject is looking at a picture or is reading a sentence

• Trained per subject classifier– Only one subject analyzed, using only CALC

• Goal: find useful abstractions of the data that allow accurate classification

• Method proposed: – Abstract at the hidden layer level of neural networks

– Assign each voxel to a unique cluster (hidden unit) based on:• Distance to center of cluster

• The weight from that voxel to the cluster

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• After training, each hidden unit will summarize a useful feature extracted from a subset of voxels.

• Each voxel will belong to exactly one cluster

Each input unit represents a voxel in the brain

Learned feature abstraction / cluster

Output classification

The Model

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Modified Backpropagation Algorithm

1. Initialize the weights with small random numbers. Set learning rate to 0.1

2. Initialize the centers of the clusters to be all equal to the center of mass of the voxels.

3. Run stochastic backpropagation for each sample in the training set

4. For each input feature i (starting with i=1), find the hidden unit j = argmax ( || wik|| / dik )

5. Set wik = 0 except for k=j, assign voxel i to cluster j and recompute the center of the cluster j

6. Compute the error on the early stopping set. If it is smaller than before, save the current clustering and weights. If it is larger, check if there was no improvement in the last fixed number of epochs (1000 by default) and, if so, GO TO 7. Otherwise GO TO 3

7. Output clustering. Report accuracy on the validation set.

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Accuracies of trained classifiers 

 

Classifier \ Dataset 40 Examples 320 Examples

ANN (2 clusters) 1.00 0.94

ANN (3 clusters) 1.00 0.93

ANN (4 clusters) 1.00 0.90

ANN (5 clusters) 1.00 0.90

GNB 0.90 0.875

SVM 0.875 0.83

3 NN 0.875 0.77

*Four fold crossvalidation used.

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An example of clustering

Clustering of CALC (318 voxels) in two clusters using 320 examplesAccuracy ~ 0.94

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Summary

• Our method yields high classification accuracy,

comparable with all other classifiers we tried

• A way to abstract the data by mathematically

summarizing the important feature in a group of

voxels.

• The abstraction is not easy to interpret

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Directions for Future Research

• Apply this method for other subjects– Check if learnt clusters are similar

• Try automated feature abstraction for tasks where we did not get good accuracies– Discriminate between affirmative and negative

sentences

• Try other datasets – Semantic categories – 12 way classification

task