A novel approach for fMRI data analysis based on the combination of sparse approximation and affinity propagation clustering.
Clustering analysis has been widely used to detect the functional connectivity from functional magnetic resonance imaging (fMRI) data. However, it has some limitations such as enormous computer memory requirement, and difficulty in estimating the number of clusters. In this study, in order to effectually resolve the deficiencies mentioned above, we have proposed a novel approach (SAAPC) for fMRI data analysis, which combines sparsity, an effective assumption for analyzing fMRI signal, with affinity propagation clustering (APC). The SAAPC method is composed of three parts: to obtain the sparse approximation coefficients set through wavelet packet decomposition and sparsity measuring and selection, which contributes a lot in the brain functional connectivity detection accuracy; to implement a split APC algorithm, which is put forward in this paper to overcome the computer memory shortage problem and to reduce the time cost in basic APC; to reconstruct the source signal by unmixing the mixed fMRI data using the time courses which are derived from the ultimate exemplars. In the task-related experiments, we can see that SAAPC is more accurate to detect the functional networks than basic APC, and it significantly reduces the time cost relative to basic APC. In addition, in the resting-state data experiments, the SAAPC method can successfully identify typical resting-state networks from the resting-state data set, while this performance is seldom reported by the classical cluster method and the basic APC method. This proposed clustering analysis method is expected to have wide applicability.
Digital Image and Intelligent computation Laboratory, College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
Tianlong
T
Ren
Digital Image and Intelligent computation Laboratory, College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China. Electronic address: zwmcd@yahoo.com.
Weiming
W
Zeng
Digital Image and Intelligent computation Laboratory, College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
Nizhuan
N
Wang
Digital Image and Intelligent computation Laboratory, College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
Lei
L
Chen
Digital Image and Intelligent computation Laboratory, College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
Chenglin
C
Wang
D000328:Adult; D000465:Algorithms; D016000:Cluster Analysis; D005260:Female; D064368:Healthy Volunteers; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008962:Models, Theoretical; D059906:Neuroimaging / Q000379:methods*; D014793:Visual Cortex / Q000033:anatomy & histology*
APC; Affinity propagation clustering; Sparse approximation; fMRI