2019/11/02 by Johnatan Cardona Jiménez, Jiménez, Johnatan Cardona
Computer Science · Neuroscience · #Applications (stat.AP) #Blind Source Separation Techniques #FOS: Computer and information sciences #Functional Brain Connectivity Studies #Neural dynamics and brain function
paper · pdf · doi:10.48550/arxiv.1911.00708
openalex publication_date 2019/11/02 · openalex created_date 2022/09/15 · openalex updated_date 2026/07/28
In this work, we describe in more detail how to perform fMRI group analysis\nusing inputs from modeling fMRI signal using Matrix-Variate Dynamic Linear\nModels (MDLM) at the individual level. After computing a posterior distribution\nfor the average group activation, the three algorithms (FEST, FSTS, and FFBS)\nproposed from the previous work by Jim 'enez et al. [2019] can be easily\nimplemented. We also propose an additional algorithm, which we call\nAG-algorithm, to draw on-line trajectories of the state parameter and therefore\nassess voxel activation at the group level. The performance of our method is\nillustrated through one practical example using real fMRI data from a\n"voice-localizer" experiment.\n