title: An empirical solution for over-pruning with a novel ensemble-learning method for fMRI decoding.
abstract: BACKGROUND
  Recent functional magnetic resonance imaging (fMRI) decoding techniques allow us to predict the contents of sensory and motor events or participants' mental states from multi-voxel patterns of fMRI signals. Sparse logistic regression (SLR) is a useful pattern classification algorithm that has the advantage of being able to automatically select voxels to avoid over-fitting. However, SLR suffers from over-pruning, in which many voxels that are potentially useful for prediction are discarded.
  
  
  METHODS
  We propose an ensemble solution for over-pruning, called "Iterative Recycling" (iRec), in which sparse classifiers are trained iteratively by recycling over-pruned voxels.
  
  
  RESULTS
  Our simulation demonstrates that iRec can effectively rectify over-pruning in SLR and improve its classification accuracy. We also conduct an fMRI experiment in which eight healthy volunteers perform a finger-tapping task with their index or middle fingers. The results indicate that SLR with iRec (iSLR) can predict the finger used more accurately than SLR. Further, iSLR is able to identify a voxel cluster representing the finger movements in the biologically plausible contralateral primary sensory-motor cortices in each participant. We also successfully dissociated the regularly arranged representation for each finger in the cluster.
  To the best of our knowledge, ours is the first study to propose a solution for over-pruning with ensemble-learning that is applicable to any sparse algorithm. In addition, from the viewpoint of machine learning, we provide the novel idea of using the sparse classification algorithm to generate accurate divergent base classifiers.
authors.affiliation: CiNet, National Institute of Information and Communications Technology, CiNet Bldg., 1-4 Yamadaoka, Suita, Osaka 565-0871, Japan. Electronic address: satoshi.hirose@nict.go.jp.
authors.forename: Satoshi
authors.initials: S
authors.lastname: Hirose
authors.affiliation: Department of Electrical Engineering, Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka, Niigata 940-2188, Japan. Electronic address: inambu@vos.nagaokaut.ac.jp.
authors.forename: Isao
authors.initials: I
authors.lastname: Nambu
authors.affiliation: CiNet, National Institute of Information and Communications Technology, CiNet Bldg., 1-4 Yamadaoka, Suita, Osaka 565-0871, Japan; Graduate School of Medicine, Osaka University, 1-4 Yamadaoka, Suita, Osaka 565-0871, Japan. Electronic address: eiichi.naito@nict.go.jp.
authors.forename: Eiichi
authors.initials: E
authors.lastname: Naito
mesh_terms: D001921:Brain / Q000098:blood supply* / Q000502:physiology*; D001931:Brain Mapping; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007858:Learning / Q000502:physiology*; D008279:Magnetic Resonance Imaging; D010100:Oxygen / Q000097:blood; D011597:Psychomotor Performance
chemical_list: D010100:Oxygen
keywords: Ensemble-learning; Iterative Recycling (iRec); Multi-voxel pattern classification; Over-pruning; Sparse logistic regression; iSLR