Neural Activation Estimation in Brain Networks During Task and Rest Using BOLD-fMRI

摘要

Since the introduction of BOLD (Blood Oxygen Level Dependent) imaging, the hemodynamic response model has remained the standard analysis approach to relate activated brain areas to extrinsic task conditions. Ongoing brain activity unrelated to the task is neglected and considered noise. By contrast, model-free blind source separation techniques such as Independent Component Analysis (ICA) have been used in intrinsic task-free experiments to reveal functional systems usually referred to as "resting-state" networks. However, matrix factorization techniques applied to BOLD imaging do not model the translation of neuronal activity into BOLD fluctuations and depend on arbitrarily chosen regularization measures such as statistical independence or sparsity. We present a novel neurobiologically-driven matrix factorization approach. Our matrix factorization model incorporates the hemodynamic response function that enables the estimation of underlying neural activity in individual brain networks that present during task- and task-free BOLD-fMRI experiments. We validate our model on the recently published Midnight Scanning Club dataset including five hours of task-free and six hours of various task experiments per subject. The resulting temporal and spatial activation patterns obtained from our matrix factorization technique resemble individual task profiles and known functional brain networks, which are either correlated with the task or spontaneously activating unrelated to the task.