Regional Homogeneity and Anatomical Parcellation for fMRI Image Classification: Application to Schizophrenia and Normal Controls

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

doi:10.1007/978-3-540-75759-7_17

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Regional Homogeneity and Anatomical Parcellation for fMRI Image Classification: Application to Schizophrenia and Normal Controls

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Regional Homogeneity and Anatomical Parcellation for fMRI Image Classification: Application to Schizophrenia and Normal Controls

Feng Shi, Yong Liu, Tianzi Jiang, Yuan Zhou, Wanlin Zhu, Jiefeng Jiang, Haihong Liu and Zhening Liu

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007, pp.136-143

Lecture Notes in Computer Science, Springer Berlin Heidelberg

2007

DOI: 10.1007/978-3-540-75759-7_17

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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.

Details

Title: Subtitle: Regional Homogeneity and Anatomical Parcellation for fMRI Image Classification: Application to Schizophrenia and Normal Controls
Creators: Feng Shi - Department of National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China
Yong Liu - Department of National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China
Tianzi Jiang - Department of National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China
Yuan Zhou - Department of National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China
Wanlin Zhu - Department of National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China
Jiefeng Jiang - Department of National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China
Haihong Liu - Institute of Mental Health, Second Xiangya Hospital, Central South University, Changsha 410011, Hunan, China
Zhening Liu - Institute of Mental Health, Second Xiangya Hospital, Central South University, Changsha 410011, Hunan, China
Resource Type: Book chapter
Publication Details: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007, pp.136-143
Series: Lecture Notes in Computer Science
DOI: 10.1007/978-3-540-75759-7_17
eISSN: 1611-3349
ISSN: 0302-9743
Publisher: Springer Berlin Heidelberg; Berlin, Heidelberg
Language: English
Date published: 2007
Academic Unit: Psychological and Brain Sciences; Iowa Neuroscience Institute
Record Identifier: 9984065739102771

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