Regional homogeneity and anatomical parcellation for fMRI image classification: application to schizophrenia and normal controls

  • Authors:

  • Feng Shi;Yong Liu;Tianzi Jiang;Yuan Zhou;Wanlin Zhu;Jiefeng Jiang;Haihong Liu;Zhening Liu

  • Affiliations:

  • Department of National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Department of National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Department of National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Department of National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Department of National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Department of National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Institute of Mental Health, Second Xiangya Hospital, Central South University, Changsha, Hunan, China;Institute of Mental Health, Second Xiangya Hospital, Central South University, Changsha, Hunan, China

  • Venue:
  • MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
  • Year:
  • 2007

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Abstract

This paper presents a discriminative model of multivariate pattern classification, based on functional magnetic resonance imaging (fMRI) and anatomical template. As a measure of brain function, Regional homogeneity (ReHo) is calculated voxel by voxel, and then a widely used anatomical template is applied on ReHo map to parcelate it into 116 brain regions. The mean and standard deviation of ReHo values in each region are extracted as features. Pseudo-Fisher Linear Discriminant Analysis (PFLDA) is performed for training samples to generate discriminative model. Classification experiments have been carried out in 48 schizophrenia patients and 35 normal controls. Under a full leave-one-out (LOO) cross-validation, correct prediction rate of 80% is achieved. Anatomical parcellation process is proved useful to improve classification rate by a control experiment. The discriminative model shows its ability to reveal abnormal brain functional activities and identify people with schizophrenia.