Classifying and characterizing nicotine use disorder with high accuracy using machine learning and resting-state fMRI.
Classifying and characterizing nicotine use disorder with high accuracy using machine learning and resting-state fMRI.
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DOI：
10.1111/adb.12644
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发表时间：
2019-07
影响因子：
3.4
通讯作者：
Fan Y
中科院分区：
文献类型：
--
作者：
Wetherill RR;Rao H;Hager N;Wang J;Franklin TR;Fan Y
吸烟仍然是可预防的发病和死亡的主要原因。尽管大多数吸烟者报告在过去一年中尝试戒烟，但戒烟率仍然不高。因此，开发准确的、基于数据的方法来对尼古丁使用障碍（NUD）的神经特征进行分类和描述，将是一种强大的临床工具，有助于优化治疗方案的制定并指导治疗调整。本研究将基于支持向量机的分类方法应用于被诊断为尼古丁使用障碍（n = 108；63名男性）和匹配的非吸烟对照组（n = 108；63名男性）的静息态功能连接（rsFC）数据，并使用多维尺度分析（MDS）根据rsFC指标可视化个体吸烟者中尼古丁使用障碍的异质性。基于机器学习的模型确定了五个在区分吸烟者和对照组中起作用的静息态网络（RSNs）：后默认模式网络、前默认模式网络、感觉运动网络、突显网络和右侧执行控制网络。该分类方法构建的分类器平均正确分类率为88.1%，曲线下面积平均为0.93。与对照组相比，尼古丁使用障碍患者在这些网络中的功能连接指标较弱（p < 0.05，错误发现率校正）。此外，MDS可视化显示对照组彼此相似；而尼古丁使用障碍患者与对照组以及其他尼古丁使用障碍患者的相似性较低。我们的研究结果建立在先前文献的基础上，先前文献表明基于机器学习的rsFC数据分类方法为理解尼古丁相关神经生物学的网络层面差异提供了一种有价值的技术，并且通过提高分类准确性和展示尼古丁使用障碍患者静息态网络的异质性扩展了先前的研究结果。
Cigarette smoking continues to be a leading cause of preventable morbidity and mortality. Although the majority of smokers report making a quit attempt in the past year, smoking cessation rates remain modest. Thus, developing accurate, data-driven methods that can classify and characterize the neural features of nicotine use disorder (NUD) would be a powerful clinical tool that could aid in optimizing treatment development and guide treatment modifications. This investigation applied support-vector machine-based classification to resting-state functional connectivity (rsFC) data from individuals diagnosed with NUD (n=108; 63 males) and matched nonsmoking controls (n=108; 63 males) and multidimensional scaling (MDS) to visualize the heterogeneity of NUD in individual smokers based on rsFC measures. Machine-based learning models identified five resting state networks (RSNs) that played a role in distinguishing smokers from controls: the posterior and anterior default mode networks, the sensorimotor network, the salience network, and the right executive control network. The classification method constructed classifiers with an average correct classification rate of 88.1% and an average area under the curve of 0.93. Compared to controls, individuals with NUD had weaker functional connectivity measures within these networks (p<0.05, false discovery rate corrected). Further, MDS visualization demonstrated that controls were similar to each other; whereas, individuals with NUD had less similarity to controls and to other individuals with NUD. Our findings build upon previous literature demonstrating that machine-learning based approaches to classifying rsFC data offer a valuable technique to understanding network-level differences in nicotine-related neurobiology and extend previous findings by improving classification accuracy and demonstrating the heterogeneity in RSNs of individuals with NUD.
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影响因子：
25.8
作者：
Lerman, Caryn;Gu, Hong;Loughead, James;Ruparel, Kosha;Yang, Yihong;Stein, Elliot A.
通讯作者：
Stein, Elliot A.
DOI：
10.1073/pnas.0504136102
发表时间：
2005-07-05
影响因子：
11.1
作者：
Fox, MD;Snyder, AZ;Raichle, ME
通讯作者：
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影响因子：
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作者：
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影响因子：
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作者：
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通讯作者：
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用于 fMRI 数据分析的群体信息引导 ICA
影响因子：
5.7
作者：
Du, Yuhui;Fan, Yong
通讯作者：
Fan, Yong