Chapter title |
Regional Homogeneity and Anatomical Parcellation for fMRI Image Classification: Application to Schizophrenia and Normal Controls
|
---|---|
Chapter number | 17 |
Book title |
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007
|
Published in |
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, October 2007
|
DOI | 10.1007/978-3-540-75759-7_17 |
Pubmed ID | |
Book ISBNs |
978-3-54-075758-0, 978-3-54-075759-7
|
Authors |
Shi, Feng, Liu, Yong, Jiang, Tianzi, Zhou, Yuan, Zhu, Wanlin, Jiang, Jiefeng, Liu, Haihong, Liu, Zhening, Feng Shi, Yong Liu, Tianzi Jiang, Yuan Zhou, Wanlin Zhu, Jiefeng Jiang, Haihong Liu, Zhening Liu |
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. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Germany | 2 | 4% |
United States | 1 | 2% |
United Kingdom | 1 | 2% |
Unknown | 53 | 93% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 13 | 23% |
Student > Ph. D. Student | 10 | 18% |
Student > Master | 8 | 14% |
Professor | 4 | 7% |
Student > Bachelor | 3 | 5% |
Other | 8 | 14% |
Unknown | 11 | 19% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 8 | 14% |
Psychology | 8 | 14% |
Agricultural and Biological Sciences | 7 | 12% |
Neuroscience | 7 | 12% |
Computer Science | 3 | 5% |
Other | 7 | 12% |
Unknown | 17 | 30% |