可卡因成瘾是一种严重的神经精神疾病，其核心特征是对药物的强烈渴求欲望和难以控制的成瘾行为。目前，对可卡因成瘾的神经机制尚不清楚，对成瘾欲望的评估缺乏有效的客观依据，且无有效的治疗手段。本研究基于可卡因成瘾患者重复经颅磁刺激（rTMS）治疗前后的脑网络特征，研究可卡因成瘾及其治疗的神经机制，并为成瘾欲望提供潜在的神经标志物。

构建了可卡因成瘾患者的静态功能连接网络，采用分层模块划分方法刻画了网络整合和分离特征，分析网络指标与可卡因渴求欲望的统计学关系。发现可卡因成瘾患者表现出较高的整合过程和较低的分离过程；可卡因渴求欲望与右脑视觉皮层更高的整合成分和更低的分离成分在统计学上相关。高阶模态脑网络中可卡因成瘾患者的功能连接低于健康对照组。建立了基于贝叶斯优化算法和脑网络高阶模态的机器学习分类与预测模型，发现右脑的视觉皮层具有最高的特征重要性。

构建了可卡因成瘾患者的动态功能连接网络，分析整合和分离的动态变化及其与成瘾欲望的统计学关系。发现可卡因成瘾患者视觉系统的整合和分离成分的动态变化规律，视觉系统和控制系统具有更高的分离变异性。右脑扣带回皮层的整合成分和变异程度与渴求欲望呈负相关，表明控制系统在调节渴求欲望中具有关键作用。发现默认模式网络在分类模型中具有重要作用，且默认模式网络和控制系统在预测症状时具有相反作用。

研究rTMS治疗可卡因成瘾前后脑网络特征变化，发现rTMS干预能有效改善可卡因成瘾患者的临床症状。建立了治疗效果预测模型，发现右半球视觉皮层及视觉系统网络在渴求程度预测中具有关键作用。发现当成瘾渴求欲望降低时，右侧控制系统外侧后额叶皮层与视觉皮层之间的静态功能连接强度减弱，右侧控制系统外侧后额叶皮层与左外侧后额叶皮层的动态功能整合成分减少。

Cocaine addiction is a severe neuropsychiatric disorder characterized by intense drug craving and compulsive addictive behaviors. Currently, the neural mechanisms underlying cocaine addiction remain unclear, and there is a lack of effective objective markers for assessing craving, as well as a lack of effective treatment options. This study investigates the neural mechanisms of cocaine addiction and its treatment by examining brain network features before and after repetitive transcranial magnetic stimulation (rTMS) therapy in individuals with cocaine addiction, aiming to identify potential neural biomarkers for craving.

We constructed  static functional connectivity networks were constructed for individuals with cocaine addiction. A hierarchical modular partitioning method was used to characterize network integration and segregation, and statistical relationships between network metrics and cocaine craving were analyzed. The results showed that individuals with cocaine addiction characterized by increased integration and decreased segregation processes. Statistically significant correlations were found between cocaine craving and both higher integration and lower segregation components in the right visual cortex. The functional connectivity in higher-order modal brain networks is lower in cocaine-addicted patients compared to healthy controls.A machine learning classification and prediction model was developed using Bayesian optimization algorithms and high-order brain network modalities, revealing that the right visual cortex had the highest feature importance.

We constructed dynamic functional connectivity networks to analyze temporal changes in integration and segregation and their statistical associations with craving. It was found that individuals with cocaine addiction exhibited increased dynamic fluctuations in integration and segregation within the visual system, and greater variability in segregation between the visual and control systems. The integration component and variability in the right cingulate cortex were negatively correlated with craving, suggesting that the control system plays a crucial role in regulating craving. The default mode network (DMN) was found to be important in classification models, and the DMN and control system showed opposite effects in symptom prediction.

We examined changes in brain network features before and after rTMS treatment for cocaine addiction. The results showed that rTMS intervention effectively improved clinical symptoms in individuals with cocaine addiction. A treatment outcome prediction model was developed, identifying the right visual cortex and visual network as key regions in craving prediction. When craving decreased, the static functional connectivity strength between the right lateral posterior prefrontal cortex (part of the control system) and the visual cortex was reduced, and the dynamic functional integration component between the right and left lateral posterior prefrontal cortices was also diminished.

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