Defining neurobiological links between substance use and mental illness

简斯博士继续发展她新成立的实验室，并开始在NID-IRP收集数据。她专注于工作人员招聘，并出版了2篇手稿，如下所述。 1.Wanger T，Janes AC，Frederick BF 2023。整体信号回归后功能连通性重测可靠性变化的空间差异：考虑血流动力学延迟的影响。人脑图谱 全局信号回归(GSR)是一种有争议的分析方法，因为它去除了信号，从而降低了功能连通性估计的可靠性。在这里，我们使用重测信度来表征传统的静态GSR(SGSR)和一种新的动态形式的GSR(DGSR)在空间格局上的潜在差异。与sGSR不同，dGSR在时间延迟时对全局信号进行建模，以校正血液到达时间。因此，dGSR解决了全局信号的更大差异，去除了与血流相关的干扰信号，并留下了更高质量的神经元信号。我们使用组内相关系数(ICCs)来评估来自人类连接组项目的462名健康对照的功能连接性的可靠性。我们测试了两个因素：使用的去噪方法(对照、sGSR和dGSR)和采集间隔(天数之间，或会话内同时改变相位编码方向)。可靠性是按地区评估的，以确定每种情况下的地形模式。与非GSR控制相比，sGSR和dGSR在可靠性方面提供了全局降低。在所有情况下，重测信度在额顶叶和默认模式区域最高，在感觉运动皮质最低。在这两种策略都大大降低可靠性的地区，dGSR提供了更有效的去噪。这两种GSR方法都大大降低了重测的可靠性，这在去噪之前可靠性较低的大脑区域最为明显。这些发现表明，全球信号可能夸大了区域间相关性的可靠性，而全球信号被认为主要反映了动态血流。 2.Biernacki K，Molokotos E，han C，Dillon D，Leventhal AM，Janes AC(印刷中)。戒烟的尼古丁依赖者的增强决策：使用分层驱动扩散模型的计算分析。药物和酒精依赖。 背景：决策能力和奖励反应的可变性可能是戒烟能力差异的基础。选择行为的计算模型，就像分层漂移扩散模型(HDDM)一样，可以帮助将奖励反应与决策的基本组成部分分离开来。在这里，我们使用HDDM来识别从奖励处理任务中获得的数据中提取的哪些决策或奖励相关参数，有助于没有寻求治疗的吸烟者在实验室任务中戒烟。 方法：80名吸烟的成年人在吸烟后照常完成概率奖赏任务(PRT)和复发模拟任务(RAT)。PRT是一项信号检测任务，具有不同的强化时间表。在一夜戒烟后，参与者可以通过推迟吸烟50分钟来赚钱。由老鼠定义了两个队列：那些在吸烟前等待0分钟(n=36)或整整50分钟(n=44)的人。 结果：PRT信号检测结果显示，所有被试都学习了任务的偶然性，两组在反应偏差和辨别能力上没有差异。然而，HDDM分析表明，50分钟的服务员比0分钟的服务员漂移速度更快。 结论：相对于那些没有戒烟的人，计算模型表明戒烟50分钟的人在基于奖励的决策过程中表现出更快的证据积累。这些结果强调了决策机制对戒烟的重要性，并表明关注证据积累过程可能会产生新的治疗目标。

Dr. Janes continued to grow her newly established lab and began data collection at NID-IRP. She focused on staff recruitment and published 2 manuscripts, which are described below. 1. Wanger T, Janes AC, Frederick BF 2023. Spatial variation of changes in test-retest reliability of functional connectivity after global signal regression: the effect of considering hemodynamic delay. Human Brain Mapping Global signal regression (GSR) is a controversial analysis method, since its removal of signal has been observed to reduce the reliability of functional connectivity estimates. Here, we used test-retest reliability to characterize potential differences in spatial patterns between conventional, static GSR (sGSR) and a novel dynamic form of GSR (dGSR). In contrast with sGSR, dGSR models the global signal at a time delay to correct for blood arrival time. Thus, dGSR accounts for greater variation in global signal, removes blood-flow-related nuisance signal, and leaves higher quality neuronal signal remaining. We used intraclass correlation coefficients (ICCs) to estimate the reliability of functional connectivity in 462 healthy controls from the Human Connectome Project. We tested across two factors: denoising method used (control, sGSR, and dGSR), and interacquisition interval (between days, or within session while varying phase encoding direction). Reliability was estimated regionally to identify topographic patterns for each condition. sGSR and dGSR provided global reductions in reliability compared with the non-GSR control. Test-retest reliability was highest in the frontoparietal and default mode regions, and lowest in sensorimotor cortex for all conditions. dGSR provides more effective denoising in regions where both strategies greatly reduce reliability. Both GSR methods substantially reduced test-retest reliability, which was most evident in brain regions that had low reliability prior to denoising. These findings suggest that reliability of interregional correlation is likely inflated by the global signal, which is thought to primarily reflect dynamic blood flow. 2. Biernacki K, Molokotos E, Han C, Dillon D, Leventhal AM, Janes AC (In Press). Enhanced decision-making in nicotine dependent individuals who abstain: A computational analysis using Hierarchical Drive Diffusion Modeling. Drug and Alcohol Dependence. Background: Variability in decision-making capacity and reward responsiveness may underlie differences in the ability to abstain from smoking. Computational modeling of choice behavior, as with the Hierarchical Drift Diffusion Model (HDDM), can help dissociate reward responsiveness from underlying components of decision-making. Here we used the HDDM to identify which decision-making or reward-related parameters, extracted from data acquired in a reward processing task, contributed to the ability of people who smoke that are not seeking treatment to abstain from cigarettes during a laboratory task. Methods: 80 adults who smoke cigarettes completed the Probabilistic Reward Task (PRT) - a signal detection task with a differential reinforcement schedule - following smoking as usual, and the Relapse Analogue Task (RAT) - a task in which participants could earn money for delaying smoking up to 50min - after a period of overnight abstinence. Two cohorts were defined by the RAT; those who waited either 0-min (n=36) or the full 50-min (n=44) before smoking. Results: PRT signal detection metrics indicated all subjects learned the task contingencies, with no differences in response bias or discriminability between the two groups. However, HDDM analyses indicated faster drift rates in 50-min vs. 0-min waiters. Conclusions: Relative to those who did not abstain, computational modeling indicated that people who abstained from smoking for 50min showed faster evidence accumulation during reward-based decision-making. These results highlight the importance of decision-making mechanisms to smoking abstinence, and suggest that focusing on the evidence accumulation process may yield new targets for treatment.