TY - CHAP
T1 - How to Incorporate Prior Knowledge for Effective FMRI Data Analysis
T2 - A Comprehensive Review and Future Direction
AU - Shi, Yuhu
AU - Wang, Nizhuan
AU - Zeng, Weiming
N1 - Publisher Copyright:
© 2025 selection and editorial matter, Temitope Emmanual Komolafe, Patrice Monkam, Blessing Funmi Komolafe and Nizhuan Wang.
PY - 2025/5/5
Y1 - 2025/5/5
N2 - How to effectively conduct functional magnetic resonance imaging (fMRI) data analysis is very important for the promotion and application of fMRI technology. Compared with the traditional fMRI data analysis methods, the strategy has unique advantages in fMRI data analysis by incorporating prior knowledge into the algorithm process. In this chapter, the introduction of prior knowledge into independent component analysis is given as an example to illustrate the advantages of prior knowledge in many aspects, such as reducing the computational complexity and improving the analysis accuracy. Then, this chapter summarizes the main results of such methods in existing research and further discusses the ways of introducing different types of prior knowledge into the model and the limitations of existing methods. Finally, this chapter explains how to mine deeper prior knowledge from the data itself, and how to introduce prior knowledge into other fMRI data analysis methods in the future.
AB - How to effectively conduct functional magnetic resonance imaging (fMRI) data analysis is very important for the promotion and application of fMRI technology. Compared with the traditional fMRI data analysis methods, the strategy has unique advantages in fMRI data analysis by incorporating prior knowledge into the algorithm process. In this chapter, the introduction of prior knowledge into independent component analysis is given as an example to illustrate the advantages of prior knowledge in many aspects, such as reducing the computational complexity and improving the analysis accuracy. Then, this chapter summarizes the main results of such methods in existing research and further discusses the ways of introducing different types of prior knowledge into the model and the limitations of existing methods. Finally, this chapter explains how to mine deeper prior knowledge from the data itself, and how to introduce prior knowledge into other fMRI data analysis methods in the future.
UR - https://www.taylorfrancis.com/chapters/edit/10.1201/9781003481959-3/incorporate-prior-knowledge-effective-fmri-data-analysis-yuhu-shi-nizhuan-wang-weiming-zeng
UR - http://www.scopus.com/inward/record.url?scp=105002847101&partnerID=8YFLogxK
U2 - 10.1201/9781003481959-3
DO - 10.1201/9781003481959-3
M3 - Chapter in an edited book (as author)
SN - 9781032772332
VL - 1
T3 - Analytics and AI for Healthcare
SP - 41
EP - 55
BT - Modern Technologies in Healthcare
A2 - Komolafe, Temitope Emmanuel
A2 - Monkam, Patrice
A2 - Komolafe, Blessing Funmi
A2 - Wang, Nizhuan
PB - CRC Press
CY - Boca Raton
ER -