Transfer Learning to Decode Brain States Reflecting the Relationship Between Cognitive Tasks

Qu, Youzhi1; Jian, Xinyao2; Che, Wenxin1; Du, Penghui1; Fu, Kai1; Liu, Quanying1

10.1007/978-981-19-8222-4_10

2023

International Workshop on Human Brain and Artificial Intelligence (HBAI)

1865-0929

1865-0937

978-981-19-8221-7

HUMAN BRAIN AND ARTIFICIAL INTELLIGENCE, HBAI 2022

1692

JUL 23, 2022

null,Vienna,AUSTRIA

GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND

SPRINGER INTERNATIONAL PUBLISHING AG

Transfer learning improves the performance of the target task by leveraging the data of a specific source task: the closer the relationship between the source and the target tasks, the greater the performance improvement by transfer learning. In neuroscience, the relationship between cognitive tasks is usually represented by similarity of activated brain regions or neural representation. However, no study has linked transfer learning and neuroscience to reveal the relationship between cognitive tasks. In this study, we propose a transfer learning framework to reflect the relationship between cognitive tasks, and compare the task relations reflected by transfer learning and by the overlaps of brain regions (e.g., neurosynth). Our results of transfer learning create cognitive taskonomy to reflect the relationship between cognitive tasks which is well in line with the task relations derived from neurosynth. Transfer learning performs better in task decoding with fMRI data if the source and target cognitive tasks activate similar brain regions. Our study uncovers the relationship of multiple cognitive tasks and provides guidance for source task selection in transfer learning for neural decoding based on small-sample data.

Transfer learning Task relationship Cognitive tasks

First ; Corresponding

English

CPCI-S

National Key Research and Development Program of China[2021YFF1200804] ; National Natural Science Foundation of China[62001205] ; Guangdong Natural Science Foundation[2019A1515111038] ; Shenzhen Science and Technology Innovation Committee["20200925155957004","KCXFZ2020122117340001"] ; Shenzhen-Hong Kong-Macao Science and Technology Innovation Project[SGDX2020110309280100] ; Shenzhen Key Laboratory of Smart Healthcare Engineering[ZDSYS20200811144003009] ; Guangdong Provincial Key Laboratory of Advanced Biomaterials[2022B1212010003]

Computer Science ; Neurosciences & Neurology

Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Neurosciences

WOS:000925059700010

Web of Science

1.Southern Univ Sci & Technol, Dept Biomed Engn, Shenzhen Key Lab Smart Healthcare Engn, Shenzhen 518055, Peoples R China
2.Southern Univ Sci & Technol, Dept Stat & Data Sci, Shenzhen 518055, Peoples R China

Qu, Youzhi,Jian, Xinyao,Che, Wenxin,et al. Transfer Learning to Decode Brain States Reflecting the Relationship Between Cognitive Tasks[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG,2023.