Individualized Prediction of Task Performance Decline Using Pre-Task Resting-State Functional Connectivity

被引:0
|
作者
Qi, Peng [1 ]
Zhang, Xiaobing [2 ]
Kakkos, Ioannis [3 ]
Wu, Kuijun [4 ]
Wang, Sujie [4 ]
Yuan, Jingjia [4 ]
Gao, Lingyun [4 ]
Matsopoulos, George K. [3 ]
Sun, Yu [4 ,5 ,6 ,7 ]
机构
[1] Tongji Univ, Coll Elect & Informat Engn, Dept Control Sci & Engn, Shanghai 200092, Peoples R China
[2] Shaoxing Peoples Hosp, Dept Neurosurg, Shaoxing 312068, Peoples R China
[3] Natl Tech Univ Athens, Sch Elect & Comp Engn, Biomed Engn Lab, Athens 15790, Greece
[4] Zhejiang Univ, Minist Educ China, Key Lab Biomed Engn, Hangzhou 310030, Peoples R China
[5] State Key Lab Brain Machine Intelligence, Hangzhou 310030, Peoples R China
[6] MOE Frontier Sci Ctr Brain Sci & Brain Machine Int, Hangzhou 310030, Peoples R China
[7] Zhejiang Univ, Sir Run Run Shaw Hosp, Sch Med, Dept Neurol, Hangzhou 310030, Peoples R China
基金
中国国家自然科学基金;
关键词
Connectome-based prediction model (CPM); time-on-task (TOT); individualized prediction; resting-state fMRI; FATIGUE; TIME; CONNECTOME; ATTENTION; PATTERNS;
D O I
10.1109/JBHI.2023.3307578
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
As a common complaint in contemporary so-ciety, mental fatigue is a key element in the deterioration ofthe daily activities known as time-on-task (TOT) effect, mak-ing the prediction of fatigue-related performance declineexceedingly important. However, conventional group-levelbrain-behavioral correlation analysis has the limitation of generalizability to unseen individuals and fatigue predic-tion at individual-level is challenging due to the significantdifferences between individuals both in task performanceefficiency and brain activities. Here, we introduced a cross-validated data-driven analysis framework to explore, for thefirst time, the feasibility of utilizing pre-task idiosyncraticresting-state functional connectivity (FC) on the predictionof fatigue-related task performance degradation at indi-vidual level. Specifically, two behavioral metrics, namely Delta RT (between the most vigilant and fatigued states) andTOTslopeover the course of the 15-min sustained attentiontask, were estimated among three sessions from 37 healthysubjects to represent fatigue-related individual behavioralimpairment. Then, a connectome-based prediction modelwas employed on pre-task resting-state FC features, iden-tifying the network-related differences that contributed tothe prediction of performance deterioration. As expected,prominent populational TOT-related performance declineswere revealed across three sessions accompanied withsubstantial inter-individual differences. More importantly,we achieved significantly high accuracies for individual-ized prediction of both TOT-related behavioral impairmentmetrics using pre-task neuroimaging features. Despite thedistinct patterns between both behavioral metrics, the iden-tified top FC features contributing to the individualized pre-dictions were mainly resided within/between frontal, tem-poral and parietal areas. Overall, our results of individual-ized prediction framework extended conventional correla-tion/classification analysis and may represent a promisingavenue for the development of applicable techniques thatallow precaution of the TOT-related performance declinesin real-world scenarios
引用
收藏
页码:4971 / 4982
页数:12
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