Dynamic functional network connectivity reveals the brain functional alterations in lung cancer patients after chemotherapy.

Abstract

This study aimed to investigate alterations of brain functional network connectivity (FNC) in lung cancer patients after chemotherapy and explore links between these FNC differences and cognitive impairment. Twenty-two lung cancer patients receiving chemotherapy and 26 healthy controls (HCs) underwent resting-state functional MRI (rs-fMRI) and neuropsychological testing. Group independent component analysis (GICA) was applied to rs-fMRI data to extract whole-brain resting state networks (RSNs). Static and dynamic FNC (dFNC) were constructed to reveal RSNs connectivity alterations between lung cancer patients and HCs group, and the correlations between the group differences in RSNs and cognitive performance were analyzed. Our findings revealed that chemotherapeutics can produce widespread connectivity abnormalities in RSNs, mainly focused on default mode network (DMN) and executive control network. Furthermore, the dFNC analysis help identify network configurations of each state and capture more chemotherapy-induced disorders of interactions between and within RSNs, which mainly includes sensorimotor network, attentional network and auditory network. In addition, after chemotherapy, the lung cancer patients spend shorter mean dwell time (MDT) in state 2. The decreased dFNC between DMN [independent component 5 (IC5)] and DMN (IC6) in the lung cancer patients after chemotherapy in state 4 was negatively correlated with Montreal Cognitive Assessment (MoCA) scores (r=-0.447, p=0.042). The dFNC analysis enrich our understanding of the neural mechanisms underlying the chemobrain, and suggested that the temporal dynamics of FNC could be a potential effective method to detect cognitive changes in lung cancer patients receiving chemotherapy.

• Authors: Lanyue Hu, Shaohua Ding, Yujie Zhang, Jia You, Song'an Shang, Peng Wang, Xindao Yin, Wenqing Xia, Yu-Chen Chen
• Journal: Brain imaging and behavior (Brain Imaging Behav) Vol. 16 Issue 3 Pg. 1040-1048 (Jun 2022) ISSN: 1931-7565 [Electronic] United States
• PMID: 34718941 (Publication Type: Journal Article)
• Copyright: © 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Topics

• Brain (diagnostic imaging)
• Brain Mapping
• Humans
• Lung Neoplasms (diagnostic imaging, drug therapy)
• Magnetic Resonance Imaging
• Nerve Net