基于Connectome的吸烟严重性的预测建模,使用吸烟者的个性化结构共变性网络
Weijian Wang1, Yimeng Kang1, Xiaoyu Niu1
1Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Frontiers in neuroscience
|August 7, 2023
概括
这项研究揭示了大脑结构共变网络与吸烟严重程度之间的联系. 机器学习确定了与吸烟者尼古丁依赖程度相关的特定大脑网络模式.
科学领域:
- 神经成像是一种神经成像.
- 成神经科学成神经科学
- 计算精神病学是一种计算精神病学.
背景情况:
- 异常的大脑网络相互作用与尼古丁成有关.
- 结构性共变网络与吸烟严重程度之间的关系尚未得到充分理解.
研究的目的:
- 调查结构共变网络与吸烟者吸烟严重程度之间的关联.
- 为了确定吸烟严重性的神经支.
主要方法:
- 101名男性吸烟者和51名男性非吸烟者接受了T1加权的MRI扫描.
- 个性化结构共变性网络是使用刀偏差估计创建的.
- 基于Connectome的预测建模 (CPM) 用于预测吸烟严重程度 (FTND分数).
主要成果:
- 在CPM的研究中,他们成功地发现了与吸烟严重程度相关的结构性共变性网络.
- 在预测和实际FTND得分之间发现了显著的相关性 (r = 0.23,p = 0.020).
- 关键的网络连接涉及皮下-小脑区域和前端-双面,默认模式,运动和视觉网络.
结论:
- 这项研究确定了与吸烟严重程度相关的结构性共变性网络.
- 这些发现为吸烟严重程度和尼古丁成的神经基础提供了新的见解.
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