Indexing and Retrieval of Dynamic Brain Images: Construction of TimeSpace Graphs for Cognitive Proce

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Indexing and Retrieval of Dynamic Brain Images
Construction of Time-Space Graphs for Cognitive
Processes in Human Brain Ulukbek Ibraev, Ph.D.
Candidate, Rutgers University Sponsored by NSF
Grant EIA-0205178. P.B. Kantor, Principal
Investigator (PI), S.J. Hanson, Co-PI.
Centroid Matching Algorithms
Functional Magnetic Resonance Imaging
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• Functional Magnetic Resonance Imaging (fMRI)
  detects changes in blood flow to particular areas
  of the brain.
• Firing neurons consume oxygen. Therefore, to
  re-supply oxygen blood flows to those areas of
  the brain that are actively firing.
• fMRI detects these changes in blood flow and
  thus effectively provides both anatomical and
  functional views of the brain over time.

• We have developed three slightly different types
  of graph building (centroid matching) algorithms.
  The first algorithm, called local, connects
  only vertices that have shortest Euclidean
  distance and are adjacent in time.
• The second algorithm, called global, connects
  vertices so that the total length of all edges is
  minimized. This means that not only vertices
  adjacent in time can be connected.
• The second algorithm is very expensive
  computationally since it requires us to consider
  all possible edges for matching. Therefore, a new
  heuristic called sliding-window, was
  developed. It defines a window that slides over
  time and all matching is done inside that window.

Local
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Global
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Sliding-Window
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Conclusions and Results
Electrical Signals in Brain

• Neurons exchange electrical signals to
  communicate with each other.
• Our project suggested that traces of these
  electrical signals over time could be used for
  indexing and retrieval of dynamic brain images.
• For example, the figure shows imaginary trace of
  the signal that two areas of the brain might have
  exchanged over time.

• Testing on simulated data showed that the global
  and sliding-window algorithms performed better
  than local algorithm.
• Although the sliding-window algorithm is
  significantly less expensive in terms of the
  computational requirements, it did not perform
  worse than the global algorithm.
• Based on the results of testing we have modified
  our algorithms to allow splitting and merging of
  the signals.
• The figure shows results of running the
  sliding-window algorithm on simulated signals.

Some Future Work
Time-Space Graphs

• It is important to compare the performance of
  the centroid matching algorithms with other types
  of indexing and retrieval algorithms.
• The vector-space model has proved effective in
  textual information retrieval (IR). It can be
  used as the base case for retrieving dynamic
  brain images.
• One naïve way is to treat each volume file as a
  vector in high dimensional space. Thus, each 3D
  image is a point in this high dimensional space.
  fMRI scans are represented as a collection of
  vectors and can be compared using inner product
  or cosine measure.

• However, the time resolution of current fMRI
  doesnt allow us to see individual signals
  propagating in human brain. What one can see in
  fMRI images is that particular areas of brain
  were active during the period of the experiment.
• We could use thresholding and/or object
  segmentation to find these meaningful areas of
  activation and use them to build a graph, where
  vertices are activation centroids and edges are
  causational paths.

3D fMRI image
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Vector
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Futures (cont.)
Building and Comparing Graphs
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• Since the signal propagation speed in human
  brain is finite, it is more likely that active
  areas of the brain that are close in space-time
  exchanged electrical signals.
• The Euclidean distance function is computed in
  four dimensional space, where first three
  coordinates is space and the fourth coordinate is
  time.
• Given the time-space graphs for two different
  fMRI scans, we can use an algorithm developed by
  Sven Dickinson to compute their similarity.

• Another way to use vector-space model is to
  represent each 3D image as a color histogram.
  Histogram can be viewed as a multi-dimensional
  vector.
• fMRI scans are represented as a collection of
  color histogram vectors. Two fMRI scans can be
  compared by computing similarity between their
  vectors.
• Color histogram representation is more desirable
  than simply using activation values since it
  significantly reduces the dimensionality of the
  feature space.

3D fMRI image
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