ABSTRACTS

Using Self-Organizing Maps for Abstraction of FMRI Images

Didem Gokcay, LiMin Fu Department of Computer and Information Sciences, University of Florida,

Gainesville, FL-32611

Stereotactic mapping of fMRI images provides interpretation of studies across subjects and better image registration and ROI placement. However, in this type of mapping, only brain shape and size is taken into account and the anatomic basis of cerebral structure and function relationships can not be captured (1). We believe that transformation of fMRI images to an abstract model is another alternative, which allows interpretations in a different domain. In this study, we develop abstract visualization of the FMRI images through self-organizing maps (SOM) of Kohonen (2). Two different models of SOM are designed and implemented on finger-tapping fMRI experiments. One SOM is trained and associated with each subject, such that the important features of task- activations are implied in the final configuration of the SOM.

I-SOM with standard deviation and correlation features: In this model, before training the SOM, the time series FMRI images from a specific subject is reduced to one image, by calculating standard deviation of activation and correlation with the square (ON/OFF) waveform for each pixel in the image. Therefore the image pixels (input to SOM) are represented by 4 features: x coordinate, y coordinate, standard deviation and correlation with task waveform. II-SOM with task-state-average features: In this model, average of the time series FMRI data for each task-state is fed as input features to the SOM. For each

pixel in FMRI images, the time series data is averaged in each task state separately. Additional to the state averages, the x and y coordinates of the pixels are also specified in the input feature vector.

These two models are explored in this study, for 2-D 50x50 SOM configurations. In order to visualize the data, the gray level projection method of (3) is used. Briefly, this method represents each unit j in the SOM with a circle of area that is proportional to the maximum distance between unit j's and its neighbors' weights. The results below illustrate one left-tap/rest experiment, such that the images on the left represent the subtracted fMRI and model II SOM, whereas images on the right represent the correlation fMRI and model I SOM. Note that the activity area is around location (79,156).

The method proposed in this study provides a means of abstracting the FMRI activations. It not only reduces the fMRI images from 256x256 to 50x50, but it also transforms the images according to functional features rather than only brain shape and size. This model partially removes head movement artifacts too.

Major References 1. S.E. Nadeau, B. Crosson, "A Guide to the Functional Imaging of Cognitive Processes", Neuropsychiatry, Neuropsychology, and Behavioral Neurology, Vol. 8, No. 3, pp. 143-162, 1995 2. T. Kohonen, "Self-Organizing Maps", Springer Verlag, 1995 3. M.A. Kraaijveld, J. Mao, A.K. Jain, "A Nonlinear Projection Method Based on Kohonen's Topology Preserving Maps", IEEE Transactions on Neural Networks, Vol. 6, No. 3, pp. 548-559, 1995

3OO

25O

~ 200 ~50

100

50

~,'~ i':~ '~':: ; = 7 . . " ::: : " : : ' �9 :," " ; ' ~{%~[ i::? :.~. �9 = . L:. :

50 I00 150 200 >~-#im of neuron i

250

300

25O

c ~ 200

c 150

IO0

5O

g'

e 50 1 O0 150 200

M-d im of neu ron i 250

$60