Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Milad Makkie"'
Autor:
Milad Makkie, Xiang Li, Wei Zhang, Tianming Liu, Shijie Zhao, Yu Zhao, Quanzheng Li, Mo Zhang, Heng Huang
Publikováno v:
IEEE Transactions on Cognitive and Developmental Systems. 12:451-460
Since the human brain functional mechanism has been enabled for investigation by the functional magnetic resonance imaging (fMRI) technology, simultaneous modeling of both the spatial and temporal patterns of brain functional networks from 4-D fMRI d
Publikováno v:
IEEE Trans Big Data
Since the BRAIN Initiative and Human Brain Project began, a few efforts have been made to address the computational challenges of neuroscience Big Data. The promises of these two projects were to model the complex interaction of brain and behavior an
Publikováno v:
University of Technology Sydney
In recent years, analyzing task-based fMRI (tfMRI) data has become an essential tool for understanding brain function and networks. However, due to the sheer size of tfMRI data, its intrinsic complex structure, and lack of ground truth of underlying
Autor:
Jinglei Lv, Milad Makkie, Bao Ge, Tianming Liu, Xiang Li, Wei Zhang, Xi Jiang, Shu Zhang, Jin Wang, Junwei Han, Shijie Zhao, Lei Guo
Publikováno v:
ISBI
As the size of brain imaging data such as fMRI grows explosively, it provides us with unprecedented and abundant information about the brain. How to reduce the size of fMRI data but not lose much information becomes a more and more pressing issue. Re
Autor:
Milad Makkie, Wei Zhang, Tianming Liu, Quanzheng Li, Xiang Li, Mo Zhang, Yu Zhao, Shijie Zhao
Publikováno v:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 ISBN: 9783030009304
MICCAI (3)
MICCAI (3)
Simultaneous modeling of the spatio-temporal variation patterns of brain functional network from 4D fMRI data has been an important yet challenging problem for the field of cognitive neuroscience and medical image analysis. Inspired by the recent suc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a2d94f7bf396df506da40bd626cd126e
https://doi.org/10.1007/978-3-030-00931-1_21
https://doi.org/10.1007/978-3-030-00931-1_21
Autor:
Armin Iraji, Yujie Li, Qinglin Dong, Milad Makkie, Zhifeng Kou, Yu Zhao, Hanbo Chen, Tianming Liu
State-of-the-art functional brain network reconstruction methods such as independent component analysis (ICA) or sparse coding of whole-brain fMRI data can effectively infer many thousands of volumetric brain network maps from a large number of human
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::634266fe804945004383ff1b550dd923
https://europepmc.org/articles/PMC5654647/
https://europepmc.org/articles/PMC5654647/
Autor:
Tianming Liu, Lei Guo, Milad Makkie, Yu Zhao, Xintao Hu, Shijie Zhao, Heng Huang, Qinglin Dong
Publikováno v:
IEEE transactions on medical imaging. 37(7)
Task-based functional magnetic resonance imaging (tfMRI) has been widely used to study functional brain networks under task performance. Modeling tfMRI data is challenging due to at least two problems: the lack of the ground truth of underlying neura
Publikováno v:
ISBI
Functional network analysis based on matrix decomposition/factorization methods including ICA and dictionary learning models have become a popular approach in fMRI study. Yet it is still a challenging issue in interpreting the result networks because
Autor:
Ian Davidson, Jieping Ye, Xiang Li, Binbin Lin, Mojtaba Sedigh Fazli, Tianming Liu, Milad Makkie, Shannon Quinn
Publikováno v:
KDD
It has been shown from various functional neuroimaging studies that sparsity-regularized dictionary learning could achieve superior performance in decomposing comprehensive and neuroscientifically meaningful functional networks from massive fMRI sign
Autor:
Junwei Han, Tianming Liu, Milad Makkie, Jinglei Lv, Xiang Li, Bao Ge, Yu Zhao, Shijie Zhao, Xi Jiang
Publikováno v:
Brain Informatics
Tremendous efforts have thus been devoted on the establishment of functional MRI informatics systems that recruit a comprehensive collection of statistical/computational approaches for fMRI data analysis. However, the state-of-the-art fMRI informatic