Predicting Attentional Vulnerability to Sleep Deprivation: A Multivariate Pattern Analysis of DTI Data.

Chen WangPeng FangYa LiLin WuTian HuQi YangAiping HanYingjuan ChangXing TangXiuhua LvZiliang XuYongqiang XuLeilei LiMinwen ZhengYuanqiang Zhu

Published in: Nature and science of sleep (2022)

These findings suggest that WM fibers connecting (1) regions within frontal-parietal attention network, (2) the thalamus to the prefrontal cortex, and (3) the left and right hemispheres contributed the most to classification accuracy.

Keyphrases

working memory
prefrontal cortex
data analysis
machine learning
deep learning
climate change
electronic health record
big data
physical activity
sleep quality
deep brain stimulation
white matter
artificial intelligence
depressive symptoms
network analysis