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What are Estimates?

It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
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Localizing Function-specific Targets for Transcranial Magnetic Stimulation in the Absence of Navigation Equipment
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Think global, act local; projectome estimation with BlueMatter.

Anthony J Sherbondy1, Robert F Dougherty, Rajagopal Ananthanarayanan

  • 1IBM Almaden Reserach Center, Almaden, USA. anthony.sherbondy@stanford.edu

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|April 30, 2010
PubMed
Summary

BlueMatter is a novel parallel algorithm that improves human brain projectome mapping by incorporating global parameters like prediction error and volume conservation. This method generates the highest resolution, volume-conserved human brain projectome to date.

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Medical Imaging

Background:

  • Estimating the complete human brain projectome from diffusion data is computationally intensive.
  • Current greedy algorithms for pathway searching overlook crucial global parameters like prediction error and volume conservation.

Purpose of the Study:

  • To introduce BlueMatter, a parallel algorithm for global projectome evaluation.
  • To address the limitations of existing methods by incorporating global prediction error and volume conservation.

Main Methods:

  • Developed BlueMatter, a parallel algorithm utilizing the BlueGene/L supercomputing architecture.
  • Explored a database of 180 billion candidate fascicles derived from atlases and multiple tractography algorithms.
  • Integrated global prediction error and volume conservation into the projectome estimation process.

Main Results:

  • Generated the highest resolution, volume-conserved projectome of the human brain.
  • Successfully accounted for global prediction error and volume conservation in pathway evaluation.
  • Demonstrated the capability of BlueMatter to explore a massive number of candidate fascicles.

Conclusions:

  • BlueMatter offers a significant advancement in mapping the human brain projectome.
  • The algorithm provides a more accurate and comprehensive representation of white matter fascicles.
  • This approach enhances our understanding of brain connectivity and structure.