Uncovering brain functional connectivity disruption patterns of lung cancer-related pain

肺癌 默认模式网络 神经影像学 前额叶皮质 医学 神经心理学 癌症 疾病 功能连接 神经科学 心理学 内科学 认知
作者
Xiaotong Wei,Yong Lai,Xiaosong Lan,Yong Tan,Jing Zhang,Jiang Liu,Jiao Chen,Chengfang Wang,Xiaoyu Zhou,Yu Tang,Daihong Liu,Jiuquan Zhang
出处
期刊:Brain Imaging and Behavior [Springer Science+Business Media]
卷期号:18 (3): 576-587
标识
DOI:10.1007/s11682-023-00836-9
摘要

Pain is a pervasive symptom in lung cancer patients during the onset of the disease. This study aims to investigate the connectivity disruption patterns of the whole-brain functional network in lung cancer patients with cancer pain (CP+). We constructed individual whole-brain, region of interest (ROI)-level functional connectivity (FC) networks for 50 CP+ patients, 34 lung cancer patients without pain-related complaints (CP-), and 31 matched healthy controls (HC). Then, a ROI-based FC analysis was used to determine the disruptions of FC among the three groups. The relationships between aberrant FCs and clinical parameters were also characterized. The ROI-based FC analysis demonstrated that hypo-connectivity was present both in CP+ and CP- patients compared to HC, which were particularly clustered in the somatomotor and ventral attention, frontoparietal control, and default mode modules. Notably, compared to CP- patients, CP+ patients had hyper-connectivity in several brain regions mainly distributed in the somatomotor and visual modules, suggesting these abnormal FC patterns may be significant for cancer pain. Moreover, CP+ patients also showed increased intramodular and intermodular connectivity strength of the functional network, which could be replicated in cancer stage IV and lung adenocarcinoma. Finally, abnormal FCs within the prefrontal cortex and somatomotor cortex were positively correlated with pain intensity and pain duration, respectively. These findings suggested that lung cancer patients with cancer pain had disrupted connectivity in the intrinsic brain functional network, which may be the underlying neuroimaging mechanisms.

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