Department of Physics of Complex Systems, The Weizmann Institute of Science, Rehovot, Israel.
Sagol Center for Brain and Mind, Baruch Ivcher School of Psychology, Interdisciplinary Center (IDC), Herzliya, Israel.
Addict Biol. 2022 Mar;27(2):e13146. doi: 10.1111/adb.13146.
Although previous research in alcohol dependent populations identified alterations within local structures of the addiction 'reward' circuitry, there is limited research into global features of this network, especially in early recovery. Transcranial magnetic stimulation (TMS) is capable of non-invasively perturbing the brain network while electroencephalography (EEG) measures the network response. The current study is the first to apply a TMS inhibitory paradigm while utilising network science (graph theory) to quantify network anomalies associated with alcohol dependence. Eleven individuals with alcohol-dependence (ALD) in early recovery and 16 healthy controls (HC) were administered 75 single pulses and 75 paired-pulses (inhibitory paradigm) to both the left and right prefrontal cortex (PFC). For each participant, Pearson cross-correlation was applied to the EEG data and correlation matrices constructed. Global network measures (mean degree, clustering coefficient, local efficiency and global efficiency) were extracted for comparison between groups. Following administration of the inhibitory paired-pulse TMS to the left PFC, the ALD group exhibited altered mean degree, clustering coefficient, local efficiency and global efficiency compared to HC. Decreases in local efficiency increased the prediction of being in the ALD group, while all network metrics (following paired-pulse left TMS) were able to adequately discriminate between the groups. In the ALD group, reduced mean degree and global clustering was associated with increased severity of past alcohol use. Our study provides preliminary evidence of altered network topology in patients with alcohol dependence in early recovery. Network anomalies were predictive of high alcohol use and correlated with clinical features of alcohol dependence. Further research using this novel brain mapping technique may identify useful network biomarkers of alcohol dependence and recovery.
虽然之前在酒精依赖人群中的研究已经确定了成瘾“奖励”回路的局部结构发生了改变，但对于该网络的全局特征，尤其是在早期康复阶段，研究还很有限。经颅磁刺激（TMS）能够在不损伤大脑的情况下对大脑网络进行干扰，而脑电图（EEG）则可以测量网络的反应。目前的研究首次应用 TMS 抑制范式，同时利用网络科学（图论）来量化与酒精依赖相关的网络异常。11 名处于早期康复阶段的酒精依赖个体（ALD）和 16 名健康对照（HC）接受了 75 个单脉冲和 75 个成对脉冲（抑制范式）刺激左、右前额叶皮层（PFC）。对于每个参与者，都将 Pearson 交叉相关应用于 EEG 数据，并构建相关矩阵。为了比较组间差异，提取了全局网络指标（平均度、聚类系数、局部效率和全局效率）。与 HC 相比，ALD 组在接受左侧 PFC 抑制性成对脉冲 TMS 后，表现出平均度、聚类系数、局部效率和全局效率的改变。局部效率的降低增加了被归入 ALD 组的预测，而所有网络指标（在左侧 TMS 抑制后）都能够充分区分组间差异。在 ALD 组中，平均度和全局聚类的降低与过去饮酒严重程度的增加有关。我们的研究初步证明了早期康复阶段酒精依赖患者网络拓扑结构的改变。网络异常可以预测高饮酒量，并与酒精依赖的临床特征相关。使用这种新的脑映射技术进行进一步研究，可能会确定有用的酒精依赖和康复的网络生物标志物。
