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

TY - CHAP

T1 - Neural Activation Estimation in Brain Networks During Task and Rest Using BOLD-fMRI

AU - Hütel, Michael

AU - Melbourne, Andrew

AU - Ourselin, Sebastien

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85053933559&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-00931-1_25

DO - 10.1007/978-3-030-00931-1_25

M3 - Conference paper

AN - SCOPUS:85053933559

SN - 978-3-030-00930-4

VL - 11072

T3 - Lecture Notes in Computer Science

SP - 215

EP - 222

BT - MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, PT III

T2 - 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018

Y2 - 16 September 2018 through 20 September 2018

ER -