Articles

Improved temporal clustering analysis method applied to whole-brain data in acupuncture fMRI study

Author

Lu N., Shan B. C., Xu J. Y., Wang W., Li K. C.

Journal

Magnetic-resonance-imaging

Volume

25

Issue ID

8

Page

1190-5

Date

2007 October

Keyword

Alternative medicine
Acupuncture Therapy/methods*
Algorithms
Artificial Intelligence
Brain/anatomy & histology
Brain/physiology*
Brain Mapping/methods
Cluster Analysis*
Evoked Potentials/physiology*
Humans
Image Enhancement/methods*
Image Interpretation, Computer-Assisted/methods*
Magnetic Resonance Imaging/methods*
Pattern Recognition, Automated/methods
Reproducibility of Results
Sensitivity and Specificity

Abstract

Temporal clustering analysis (TCA) has been proposed as a method for detecting the brain responses of a functional magnetic resonance imaging (fMRI) time series when the time and location of activation are completely unknown. But TCA is not suitable for treating the time series of the whole brain due to the existence of many inactive pixels. In theory, active pixels are located only in gray matter (GM). In this study, SPM2 was used to segment functional images into GM, white matter and cerebrospinal fluid, and only the pixels in GM were considered. Thus, most of inactive pixels are deleted, so that the sensitivity of TCA is greatly improved in the analysis of the whole brain. The same set of acupuncture fMRI data was treated using both conventional TCA and modified TCA (MTCA) for comparing their analytical ability. The results clearly show a significant improvement in the sensitivity achieved by MTCA.

Language

English