Automatic Bayesian Classification of Healthy Controls, Bipolar Disorder, and Schizophrenia Using Intrinsic Connectivity Maps From fMRI Data

@article{Arribas2010AutomaticBC,
  title={Automatic Bayesian Classification of Healthy Controls, Bipolar Disorder, and Schizophrenia Using Intrinsic Connectivity Maps From fMRI Data},
  author={Juan Ignacio Arribas and Vince D. Calhoun and T{\"u}lay Adali},
  journal={IEEE Transactions on Biomedical Engineering},
  year={2010},
  volume={57},
  pages={2850-2860}
}
We present a method for supervised, automatic, and reliable classification of healthy controls, patients with bipolar disorder, and patients with schizophrenia using brain imaging data. The method uses four supervised classification learning machines trained with a stochastic gradient learning rule based on the minimization of Kullback-Leibler divergence and an optimal model complexity search through posterior probability estimation. Prior to classification, given the high dimensionality of… CONTINUE READING
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