TY - GEN
T1 - A region-of-interest-reweight 3D convolutional neural network for the analytics of brain information processing
AU - Ni, Xiuyan
AU - Yan, Zhennan
AU - Wu, Tingting
AU - Fan, Jin
AU - Chen, Chao
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - We study how human brains activate to process input information and execute necessary cognitive tasks. Understanding the process is crucial in improving our diagnostic and treatment of different neurological disorders. Given functional MRI images recorded when human subjects execute tasks with different levels of information uncertainty, we need to identify the similarity and difference between brain activities at different regions of interest (ROIs), and thus gain insights into the underlying mechanism. To achieve this goal, we propose a new ROI-reweight 3D convolutional neural network (CNN). Our CNN not only learns to classify the task-evoked fMRIs with a high accuracy, but also locates crucial ROIs based on a reweight layer. Our findings reveal several brain regions to be crucial in differentiating brain activity patterns facing tasks of different uncertainty levels.
AB - We study how human brains activate to process input information and execute necessary cognitive tasks. Understanding the process is crucial in improving our diagnostic and treatment of different neurological disorders. Given functional MRI images recorded when human subjects execute tasks with different levels of information uncertainty, we need to identify the similarity and difference between brain activities at different regions of interest (ROIs), and thus gain insights into the underlying mechanism. To achieve this goal, we propose a new ROI-reweight 3D convolutional neural network (CNN). Our CNN not only learns to classify the task-evoked fMRIs with a high accuracy, but also locates crucial ROIs based on a reweight layer. Our findings reveal several brain regions to be crucial in differentiating brain activity patterns facing tasks of different uncertainty levels.
KW - CNN
KW - Task-evoked fMRI
KW - Uncertainty representation
UR - http://www.scopus.com/inward/record.url?scp=85053904628&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00931-1_35
DO - 10.1007/978-3-030-00931-1_35
M3 - Conference contribution
AN - SCOPUS:85053904628
SN - 9783030009304
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 302
EP - 310
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
A2 - Frangi, Alejandro F.
A2 - Davatzikos, Christos
A2 - Fichtinger, Gabor
A2 - Alberola-López, Carlos
A2 - Schnabel, Julia A.
PB - Springer Verlag
T2 - 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Y2 - 16 September 2018 through 20 September 2018
ER -