The Neural Underpinnings of Depression and Cannabis Use in Young PLWH

项目摘要/摘要 对RFA-DA-21-012的回应，“阐明了SUD和其他复杂疾病的发病机制 艾滋病患者的精神疾病“(PLWH)，我们建议研究 大麻使用和抑郁共病是PLWH中两种非常普遍的疾病。我们将专注于年轻一代 成年人(18-34岁)，以尽量减少艾滋病毒对神经元的慢性影响，并鉴于药物使用率高 并在这个年龄段减少了对艾滋病毒治疗的坚持。我们提出的模型是：1)奖赏功能障碍 (奖赏学习、期望、成就、积极预测错误方面的缺陷)和疼痛过敏(疼痛 敏感性、厌恶感、负面预测错误)导致青少年吸食大麻和抑郁共病 PLWH。2)缰核(Hb)，一个小的边缘中枢，通过抑制在这些过程中起着关键的调节作用 腹侧被盖区(VTA)在疼痛和丧失后向伏隔核(NAC)发出奖赏信号。3)货柜码头处理费： 大麻素是大麻的主要成分，通过与大麻素1受体结合发挥其精神活性镇痛作用。 在奖痛系统，包括前扣带回(ACC)、中脑导水管周围灰质(PAG)、丘脑、 杏仁核、VTA、NAC和Hb，暂时缓解情绪和疼痛症状，但导致长期 奖赏回路的改变，加剧了抑郁和物质使用。4)利用改进 在fmri分辨率方面，我们的新成像方法克服了以前研究Hb和其他 对奖赏和痛苦处理至关重要的小结构。18-34岁PLWH对我们健康的支持数据 系统显示，43%的人患有抑郁症，21%的人有大麻使用障碍，只有68%的人感染了无法检测到的艾滋病毒 病毒载量(VL)。使用奖励侧翼(RFT)和奖励预测误差(RPET)fMRI任务，我们记录了 在奖励预期、成就和预测误差过程中的不同大脑活动，这预示着未来 抑郁症的严重程度。此外，我们在RPET和疼痛任务中检测到Hb的激活，并绘制了Hb的内在图谱 与奖励(VTA)、疼痛(岛、PAG)或两个回路(NAC、ACC)的关键区域的功能连接(IFC)。 不同的Hb IFC与抑郁、快感缺乏和大麻使用有关。我们假设 年轻的PLWH吸食大麻和抑郁具有相加效应，导致奖赏不足和疼痛。 超敏反应，这一模式将预测更糟糕的结果在1年的随访。我们将利用2×2阶乘 设计：1)70名抑郁大麻使用者；2)70名抑郁大麻非使用者；3)70名非抑郁大麻使用者 以及4)70名非抑郁大麻非吸毒者。参与者将在以下位置进行全面评估 基线、6个月和12个月，包括抑郁、物质、奖励、疼痛、焦虑、创伤、艾滋病毒治疗、CD4+ 伯爵和VL。将进行基线认知测试和功能磁共振成像(静息状态、RFT、RPET、疼痛)。分析性 方法将包括机器学习分类。

PROJECT SUMMARY / ABSTRACT In response to RFA-DA-21-012, “Elucidation of mechanisms underlying complex morbidities of SUD and other mental Illnesses in people living with HIV/AIDS” (PLWH), we propose to investigate reward and pain circuitry in cannabis use and depression comorbidity, two highly prevalent conditions in PLWH. We will focus on young adults (ages 18-34) to minimize HIV neuronal chronicity effects and in light of the high rates of substance use and reduced adherence to HIV treatment in this age group. Our proposed model is: 1) Both reward dysfunction (deficits in reward learning, expectancy, attainment, positive prediction errors) and pain hypersensitivity (pain sensitivity, aversion, negative prediction errors) contribute to cannabis use and depression comorbidity in young PLWH. 2) The habenula (Hb), a small limbic hub, plays a pivotal regulatory role in these processes by inhibiting ventral tegmental area (VTA) reward signals to the nucleus accumbens (NAc) following pain and loss. 3) THC, a major component of cannabis, exerts its psychoactive analgesic effects by binding to cannabinoid 1 receptors in the reward and pain systems, including the anterior cingulate (ACC), periaqueductal gray (PAG), thalamus, amygdala, VTA, NAc, and Hb, creating temporary relief of mood and pain symptoms but resulting in long-term alterations in reward circuitry that exacerbate depression and substance use. 4) Capitalizing on improvements in fMRI resolution, our novel imaging methods overcome prior technical constraints to study the Hb and other small structures critical to reward and pain processing. Supporting data from PLWH ages 18-34 in our health system show that 43% have depression, 21% have cannabis use disorders, and only 68% had undetectable HIV viral load (VL). Using the reward flanker (RFT) and reward prediction error (RPET) fMRI tasks, we documented distinct brain activity during reward anticipation, attainment and prediction error, which predicted future depression severity. Further, we detected Hb activation during RPET and a pain task, and mapped Hb intrinsic functional connectivity (iFC) with regions critical to reward (VTA), pain (insula, PAG), or both circuits (NAc, ACC). Distinct Hb iFC were documented in relation to depression, anhedonia and cannabis use. We hypothesize that cannabis use and depression in young PLWH have an additive effect, inducing both reward deficits and pain hypersensitivity and that this pattern will predict worse outcomes at 1 year follow-up. We will utilize a 2×2 factorial design: 1) 70 depressed cannabis users; 2) 70 depressed cannabis non-users; 3) 70 non-depressed cannabis users; and 4) 70 non-depressed cannabis non-users. Participants will have comprehensive evaluations at baseline, 6- and 12-months including depression, substance, reward, pain, anxiety, trauma, HIV treatment, CD4+ count, and VL. Baseline cognitive testing and fMRI (resting-state, RFT, RPET, pain) will be performed. Analytical approaches will include machine learning classifications.