Resting state fMRI data (TR = 2 s, 250 volumes) of one subject are provided as an example data. The data have been preprocessed such as slice time correction, realignment and spatial normalization (3 mm × 3 mm × 3 mm), and the nuisance signals such as linear drift, headmotion, white and CSF signals also have been removed from data. The brain mask is also provided.
The results of FOCA are provided as .img files.
"Spatio-temporal Consistency of Local Neural Activities: A New Imaging Measure for functional Magnetic Resonance Data". Li Dong, Cheng Luo, Weifang Cao, Rui Zhang, Jinnan Gong, Diankun Gong, Dezhong Yao*. J Magn Reson Imaging 42(3): 729-736.
Brain is amazing. We may understand the brain function spatio-temporally as we understand the time and space. Here, we hypothesized that the local functional brain consistency could contain two aspects: a temporal correlation between voxels and a local spatial correlation between neighboring time points. And, we proposed a new measure, named Four-dimensional (spatio-temporal) Consistency of local neural Activities (FOCA), to characterize the local consistency by integrating temporal and spatial information in the local region. The FOCA measure has several advantages. 1) Because FOCA measure integrates temporal and spatial information in a local region, it is more flexible to characterize the local spontaneous activity. It emphasizes both temporal homogeneity of local adjacent voxels, and regional stability of brain activity states (local functional states) between neighboring time points. 2) It is a data-driven method without prior knowledge and practical choices of the key parameter settings. 3) It also has a good reproducibility and reliability.
More details about the FOCA can been seen in our original paper. And, taking into account that FOCA represents the local spatio-temporal consistency, FOCA may have provide additional information that will help in the understanding of brain function and dysfunction.