【摘要】 抑郁症早期识别技术至今仍未解决，需要厘清研究思路。回顾以往研究可梳理出三类方法，即：法、法、法。前驱症状法是依据前驱期症状识别早期患者，但现象学指标的客观性和特异性较差；易感性法是依据家系或个体风险因素界定风险人群，但缺少纵向研究支持，很难直接用于预警；生物内表型法是寻找与疾病相关的潜在生物学标记，但多为临床期研究，不能确定其中哪些可作为前驱期征象。今后的研究可考虑整合三类方法，即利用家系高风险设计寻找生物内表型，并以此识别可靠前驱症状，构建更精确的风险预测模型。
【关键词】 抑郁症 早期识别 前驱症状 易感性 生物内表型
1 引言
抑郁症的早期识别对预防、阻止或延缓其发展、改善其预后至关重要(Leopold et al., 2014)。然而抑郁症的预防及监测技术至今仍未解决(Miret, Ayuso-Mateos, Sanchez-Moreno, & Vieta, 2013)。对抑郁症早期识别的研究是精神医学前沿课题，但文献相对较少(Bagana, 2013; Iacoviello, Alloy, Abramson, & Choi, 2010)。尽管早期识别这一课题的研究难度较大，但研究者们还是从不同角度进行了一些有益探索。通过回顾以往各种研究，可以梳理归纳出当前抑郁症早期识别的三大类方法，即：前驱症状法、易感性法、生物内表型法。本文拟对这些方法的研究进展进行综述。
2 前驱症状法
作为一个医学概念，前驱症状是指一种疾病的前兆症状，标志着疾病进程的开始(Kovacs & Lopez-Duran, 2010)。前驱症状法是指寻找这些前兆症状来识别那些抑郁症早期患者的方法。
抑郁症的纵向发展过程大致呈现为几个不同的阶段，即：病前期、前驱期、临床期。由于病因不明，针对病前期的一级预防缺乏明确目标。而针对前驱期的二级预防成为相对可行的研究靶点，且被早期预防的概念所涵盖。这一构想围绕的中心是最大限度地减少误诊、促进及早治疗(Madhussodanam, 2010)。有研究支持抑郁症存在前驱期阶段的观点(Iacoviello et al., 2010)。抑郁症前驱期是指从易感个体首次发生变化到其首次出现明显临床症状之间的一段时期，在此期间出现的前驱症状是疾病的先兆标志(Kovacs & Lopez-Duran, 2010)。不同研究所发现的抑郁症前驱期的持续时间很不一致，平均而言，抑郁症前驱期的时间跨度大约为6周至23个月(Iacoviello et al., 2010)。
抑郁症前驱症状的特征是心境和精力的调节出现异常(Skjelstad, Malt, & Holte, 2010)。研究发现，抑郁症常见的前驱症状有：、紧张、易激惹、兴趣丧失、障碍、动机减少、心境沮丧、胃肠功能紊乱、疲乏、注意力不集中、精力下降、食欲减退或增强、体重减少或增加、失眠、犹豫不决、绝望、念头、沉思、忧虑、自信不足、依赖、身体不适、效率下降、无助感等(Fava & Tossani, 2007; Iacoviello et al., 2010; Kovacs & Lopez-Duran, 2010)。
抑郁症早期症状的探测，在多数研究中是通过临床精神检查或症状评定等行为学方法(McGillivray & McCabe, 2007; Pietsch et al., 2012; Reay, Matthey, Ellwood, & Scott, 2011)，有些研究所采用的调查方法是回顾性的(Iacoviello et al., 2010)。由于前驱症状的特异性不强，仅凭早期现象学的测量来预警疾病的发生是不可能的，也是不可取的(Skjelstad et al., 2010)。
3 易感性法
易感性是指增加个体患病风险的稳定特质。遗传因素决定了个体对某种疾病的易感倾向，认知、发展与情绪调节本身属于个体特质。易感性法就是指通过寻找个体的这些倾向或特质来识别那些具有高危风险人群的方法。
3.1 家系风险研究
遗传因素在传统研究中被视为抑郁症发生的危险因素(Hammen, 2009)，遗传度的影响大约是40-50%，证据显示抑郁症患者一级亲属发生抑郁症的危险性大约为10~13% (Bagana, 2013)。同时有研究发现早发型抑郁症、反复发作抑郁症（发病年龄 < 30岁）患者亲属患病的相对风险度（Relative Risk, RR）可达到4~5(杨福中，禹顺英，施慎逊，2008)。抑郁症家族史是最常被研究的抑郁症易感性，被认为有多个遗传组分(Kovacs & Lopez-Duran, 2010)。一个国际性的共识是，遗传和家庭相关因素促进抑郁症发作频率及严重程度的增加(Bagana, 2013)。因此，家系高风险研究，是抑郁症早期识别研究中较为常用和可靠的研究方式。
3.2 个体风险研究
3.2.1 认知风险研究
首个抑郁认知理论提出那些易罹患抑郁症的人群会按照更消极的方式看待周围环境(Beck, 1967)。在面对应激条件时，抑郁认知易感者比非认知易感者会感受到更高水平的应激，表现出更明显的抑郁与焦虑症状(蚁金瑶等，2012)。
有关认知方面的实证研究发现成人和抑郁患者效价刺激加工存在负性偏差(李斌彬，周东丰，2015; Gotlib, Joormann, & Foland-Ross, 2014)。采用最弱连接计算法识别抑郁认知易感者的研究也发现在效价刺激加工中，认知易感者存在负性注意偏向(肖倩，2014；钟明天等，2012)。同时，抑郁症家族史研究表明抑郁患者一级亲属在情绪材料知觉、注意、理解和过程中存在负性认知偏差(Dearing & Gotlib, 2009; Joormann, Talbot, & Gotlib, 2007; Kujawa et al., 2011)。这些研究在一定程度上表明负性认知偏差可作为抑郁症发作的一个重要预测因素。
3.2.2 情绪发展与情绪调节风险研究
理学研究的理论认为，积极情绪偏低是抑郁症的易罹患因素，且代表了一种气质风险因素(Clark, Watson, & Mineka, 1994)。采用报告方法测量被试积极与消极情绪的研究发现，积极情绪偏低对抑郁症具有特异性(Gen..z, 2002)。抑郁症患者后代积极情绪与消极情绪发展研究表明，抑郁症患者高风险子女的情绪发展在幼儿期可能已发生扭曲，且以积极情绪偏低为主要特征(Olino et al., 2011)。可见，弱化的积极情绪功能可能是抑郁症易感性的来源之一。
采用自我报告法研究抑郁症患者情绪调节或调节能力，发现情绪调节困难不仅与抑郁症患者的当前症状有关(Garnefski & Kraaij, 2006)，且在抑郁症康复人群中表现明显(Ehring, Fischer, Schnülle, B.sterling, & Tuschen-Caffier, 2008)。采用片段诱发悲伤情绪以评估被试自发使用情绪抑制或认知重评策略，发现抑郁恢复组比健康控制组报告更多的情绪抑制(Ehring, Tuschen-Caffier, Schnülle, Fischer, & Gross, 2010)。研究双相I型患者及健康亲属情绪调节策略发现与健康控制组相比，双相I型患者和健康亲属组均报告更多使用不恰当的情绪调节策略(Green et al., 2011)。这些结果显示情绪调节缺陷可能是抑郁症的重要易感因素。
4 生物内表型法
生物内表型是指介于型与表现型之间的中间标记。生物内表型法是指通过寻找这些中间标记来预测易感群体疾病发展可能性的方法。
4.1 神经影像学研究
4.1.1 神经形态结构研究
研究表明抑郁症患者背外侧前额叶皮层体积减少(Bora, Fornito, Pantelis, & Yücel, 2012)。而物理或药物治疗可改变前额叶皮层的代谢状况(Li et al., 2010; Seminowicz et al., 2004)。这在一定程度上增加了该标记的生物学可信度。有研究发现高风险人群右侧额顶叶皮层厚度明显小于健康人，而这种标记在无抑郁症史的高风险人群中也有发现(Dubin et al., 2012)，表明额叶皮层体积减少并非抑郁症发病或治疗的结果。还有研究指出抑郁症患者前扣带回体积减少(Bora et al., 2012; Du et al., 2012)，且有证据显示该部位体积减少在抑郁症发病早期就已出现(Han et al., 2014)。
4.1.2 神经功能活动研究
采用奖赏学习任务和金钱奖赏延迟任务研究抑郁症患者的奖赏加工，发现患者在奖赏结果处理或奖赏预期时其腹侧纹状体信号异常降低(Kumar et al., 2008; Pizzagalli et al., 2009)。采用卡片猜测任务研究抑郁症患者的未患病女儿与患病女儿的奖赏加工，发现患病女儿和未患病女儿在奖赏结果处理时其右腹侧纹状激活均比健康控制组降低(Sharp et al., 2014)。采用单光子发射计算机断层成像技术检查抑郁症患者多巴胺转运体损伤状况，发现抑郁症患者纹状体多巴胺转运体不足，表明该区域存在多巴胺能功能下降(Wu, Lou, Huang, & Shi, 2011)。这些结果显示腹侧纹状体功能异常可能有预警作用。采用情绪记忆任务对抑郁症亲属的情绪加工进行纵向研究，发现在正性和中性情绪条件下，亲属患病组的丘脑激活比亲属健康组增加；在正性情绪条件下，亲属健康组2年后转变为患病组的被试的丘脑、脑岛和前扣带回的激活比未转变为患病组的被试增加(Whalley et al., 2015)。有关抑郁症患者情绪和认知任务条件下神经功能影响研究的元分析显示，抑郁症患者在正性条件下左侧丘脑激活增加(Diener et al., 2012)。这提示正性条件下的丘脑激活增加可能与抑郁症发展密切相关。
由于静息态自发脑活动持续进行且消耗大量能量，因此有研究者认为该种脑活动可能受基因控制(Hasler & Northoff, 2011)。抑郁症患者静息态活动研究的元分析结果表明，抑郁症患者在前扣带回、腹内侧前额叶皮层、背内侧丘脑和中脑区域静息态活动增强(Greicius et al., 2007; Kühn & Gallinat, 2013)。类似结果在动物抑郁模型的静息态活动研究中也得到证实(Alcaro, Panksepp, Witczak, Hayes, & Northoff, 2010)。Hasler等采用正电子发射断层扫描技术研究临床痊愈抑郁症患者的葡萄糖代谢状况，发现他们在腹内侧前额叶、右侧颞叶和右侧背外侧前额叶皮层的代谢活动降低(Hasler et al., 2008)。以上研究显示，旁边缘皮质-中线脑区的异常活动可能为抑郁症重要内表型。静息态功能连接活动主要集中于情感网络(Wang, Hermens, Hickie, & Lagopoulos, 2012)，其中，内侧前额叶、杏仁核、海马又是默认网络的重要节点。Zhang对30例首发未用药患者的静息态脑网络进行研究发现，患者组默认网络节点中心度增加，表明该网络这些节点与其他节点之间的功能同步性比健康控制组异常升高(Zhang et al., 2011)。有研究发现病人和家系高风险人群默认网络的功能连接强度异常增加(Posner et al., 2016)，显示其可能为抑郁症的重要生物标记。
4.2 神经生理学与神经化学研究
4.2.1 REM睡眠研究
Rao等采用多导睡眠描记法测量非双相抑郁症患者睡眠状况发现，相比随后转化为双相抑郁症的患者和健康人群，未转化的患者表现出REM睡眠潜伏期缩短、密度增高、周期增加的特点(Rao et al., 2002)。张娜等对首发抑郁症患者及一级亲属的类似研究发现，抑郁症患者也有以上特点，且与一级亲属均伴有REM睡眠潜伏期缩短问题(张娜，马辛，任艳萍，2011)。有研究显示抑郁症患者一级亲属表现出REM睡眠密度显著更高的特征(Pillai, Kalmbach, & Ciesla, 2011)。这些研究表明REM睡眠异常可能为抑郁症的素质指标。
4.2.2 HPA轴研究
下丘脑-垂体-肾上腺皮质(hypothalamic-pituitary-adrenocortical, HPA)轴活动过度是抑郁症研究中最一致的生物学发现之一，主要表现为皮质醇分泌过多(Stetler & Miller, 2011)。处于康复期和缓解期的抑郁症患者唾液皮质醇浓度仍然高于健康者(Bhagwagar, Hafizi, & Cowen, 2003; Holsen et al., 2013)，表明皮质醇反应异常为一种状态独立的生物学标记。Baeken等发现，高频重复经颅磁刺激治疗后病人唾液皮质醇浓度显著下降，提示该治疗对抑郁症起效可能是通过抑制HPA轴而取得的，从治疗角度反证了HPA轴异常在抑郁症发病中的作用(Baeken et al., 2009)。
4.2.3 神经递质研究
采用磁共振波谱成像技术在中度抑郁症患者背侧前额叶皮层和痊愈患者枕叶区域发现γ-氨基丁酸浓度降低(Bhagwagar et al., 2007; Hasler et al., 2007)，提示皮层γ-氨基丁酸浓度可能与抑郁症易感性相关。在动物抑郁模型和抑郁症患者健康亲属中发现类似结果(Alcaro et al., 2010; Bjork et al., 2001)，表明低γ-氨基丁酸浓度不仅是抑郁症的一种生物标记，且可能是重要的内表型。有研究认为脑内去甲肾上腺素和5-羟色胺功能的改变与抑郁症病理学和抗抑郁药作用机制密切相关(Castrén, 2005)。研究发现5-羟色胺、儿茶酚胺或色氨酸的耗竭可使处于缓解期的抑郁症患者和有抑郁症家族史的健康人出现明显的抑郁症状(Ruhé, Mason, & Schene, 2007; Young, 2013)，这提示可将脑内单胺类神经递质降低作为抑郁症的可靠生物标记。
5 小结与展望
以上三类方法从不同角度为抑郁症早期识别提供了有用的策略，有各自的优点，但仍存在各自的局限性。前驱症状法虽能用于识别那些具有早期临床指标的人群，但其研究多数属于追溯性研究(Iacoviello et al., 2010)，且局限于单一的临床现象学视角(Nishioka & Ozaki, 2007)，现象学指标的客观性和特异性较差，不是理想的预警指标。易感性研究有助于早期识别是因为其能帮助界定抑郁症风险群体。然而，此类研究缺少纵向研究结果支持，尚不能确定还需哪些其他因素参与才会导致抑郁症的发作。因此很难将易感性直接用于预警。识别生物内表型可为预测易感群体疾病的发展提供可能性(Cook, Hunter, Abrams, Siegman, & Leuchter, 2009)，但生物内表型法存在的问题是：多数研究为临床期研究，不能确定这些研究所发现的生物内表型中哪些可作为前驱期征象。
抑郁症的早期识别是一个难度较高的课题，今后的研究或许可以考虑以下两个思路。第一个思路是，针对现有的三类方法各自的局限性，进一步改进和完善这些方法。例如，为完善前驱症状法，可考虑开发和使用对早期症状具有更高敏感性和特异性的筛查量表，以提高现象学研究的客观性；还可考虑将早期症状筛查法简易化，以便普及到高危家庭及其社区，通过医生与家庭和社区的协作，将筛查起点前移，提高此类研究的前瞻性。为完善易感性法，可考虑开展长期追踪随访研究，以期弄清各种风险因素对抑郁症形成的不同作用及其程度，找出具有预警价值的风险因素。为完善生物内表型法，可考虑更多地开展以未发病的高危人群为对象的研究，并进行纵向追踪随访，以明确哪些内表型可以用于早期预警。第二个思路是，可考虑把三类方法各自的优势整合起来，形成系统的综合性方法。例如，利用家系高风险设计寻找生物易感性标记，并以该标记为生物学基础识别可靠前驱症状，构建更精确的风险预测模型；同时，采用纵向追踪研究验证该风险预测模型的适用性和可靠性，以便推广到实际应用之中。在抑郁症的病因和病理机制尚不十分清楚的现状下，综合性方法也许是目前的最佳选择。使用这样的思路和方法，不但有利于找到早期诊断的更全面、更客观、更可靠的参考指标，而且有可能为探索抑郁症的病因和病理机制提供线索，从而有望在将来从根本上解决抑郁症早期识别和防治的问题。
参考文献
李斌彬, 周东丰. (2015). 抑郁症对负性情绪的认知偏向——记忆和注意. 临床精神医学杂志, 25(2), 133-135.
肖倩. (2014). 大学生抑郁认知易感者对任务无关情绪面孔注意偏向研究. 首都师范大学硕士学位论文.
杨福中, 禹顺英, 施慎逊. (2008). 国内抑郁症遗传学研究. 上海精神医学, 20(4), 245-248.
蚁金瑶, 钟明天, 凌宇, 张丽, 朱熊兆, 姚树桥. (2012). 抑郁认知易感性在应激一抑郁中的中介效应. 中国临床心理学杂志, 20(6), 831-834.
钟明天, 蚁金瑶, 凌宇, 王海星, 朱熊兆, 姚树桥. (2012). 抑郁认知易感者对负性面孔的注意特征. 中国心理卫生杂志, 26(2), 151-156.
Alcaro, A., Panksepp, J., Witczak, J., Hayes, D. J., & Northoff, G. (2010). Is subcortical-cortical midline activity in depression mediated by glutamate and GABA? A cross-species translational approach. Neuroscience and Biobehavioral Reviews, 34(4), 592-605.
Baeken, C., De Raedt, R., Leyman, L., Schiettecatte, J., Kaufman, L., Poppe, K., et al. (2009). The impact of one HF-rTMS session on mood and salivary cortisol in treatment resistant unipolar melancholic depressed patients. Journal of Affective Disorders, 113(1-2), 100-108.
Bagana, E. (2013). Prodromal symptoms and initial episode of depression-genetic and emotional correlations. Procedia-Social and Behavioral Sciences, 78, 451-455.
Beck, A. T. (1967). Depression: Clinical, experimental, and theoretical aspects. Philadelphia: University of Pennsylvania Press.
Bhagwagar, Z., Hafizi, S., & Cowen, P. J. (2003). Increase in concentration of waking salivary cortisol in recovered patients with depression. American Journal of Psychiatry, 160(10), 1890-1891.
Bhagwagar, Z., Wylezinska, M., Jezzard, P., Evans, J., Ashworth, F., Sule, A., et al. (2007). Reduction in occipital cortex γ-aminobutyric acid concentrations in medication-free recovered unipolar depressed and bipolar subjects. Biological Psychiatry, 61(6), 806-812.
Bjork, J. M., Moeller, F. G., Kramer, G. L., Kram, M., Suris, A., Rush, A. J., & Petty, F. (2001). Plasma GABA levels correlate with aggressiveness in relatives of patients with unipolar depressive disorder. Psychiatry Research, 101(2), 131-136.
Bora, E., Fornito, A., Pantelis, C., & Yücel, M. (2012). Gray matter abnormalities in major depressive disorder: A meta-analysis of voxel based morphometry studies. Journal of Affective Disorders, 138(1-2), 9-18.
Castrén, E. (2005). Is mood chemistry? Nature Reviews Neuroscience, 6(3), 241-246.
Clark, L. A., Watson, D., & Mineka, S. (1994). Temperament, personality, and the mood and anxiety disorders. Journal of Abnormal Psychology, 103(1), 103-116.
Cook, I. A., Hunter, A. M., Abrams, M., Siegman, B., & Leuchter, A. F. (2009). Midline and right frontal brain function as a physiologic biomarker of remission in major depression. Psychiatry Research: NeuroImaging, 174(2), 152-157.
Dearing, K. F., & Gotlib, I. H. (2009). Interpretation of ambiguous information in girls at risk for depression. Journal of Abnormal Child Psychology, 37(1), 79-91.
Diener, C., Kuehner, C., Brusniak, W., Ubl, B., Wessa, M., & Flor, H. (2012). A meta-analysis of neurofunctional imaging studies of emotion and cognition in major depression. NeuroImage, 61(3), 677-685.
Du, M. Y., Wu, Q. Z., Yue, Q., Li, J., Liao, Y., Kuang, W. H., et al. (2012). Voxelwise meta-analysis of gray matter reduction in major depressive disorder. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 36(1), 11-16.
Dubin, M. J., Weissman, M. M., Xu, D. R., Bansal, R., Hao, X. J., Liu, J., et al. (2012). Identification of a circuit-based endophenotype for familial depression. Psychiatry Research: NeuroImaging, 201(3), 175-181.
Ehring, T., Fischer, S., Schnülle, J., B.sterling, A., & Tuschen-Caffier, B. (2008). Characteristics of emotion regulation in recovered depressed versus never depressed individuals. Personality and Individual Differences, 44(7), 1574-1584.
Ehring, T., Tuschen-Caffier, B., Schnülle, J., Fischer, S., & Gross, J. J. (2010). Emotion regulation and vulnerability to depression: Spontaneous versus instructed use of emotion suppression and reappraisal. Emotion, 10(4), 563-572.
Fava, G. A., & Tossani, E. (2007). Prodromal stage of major depression. Early Intervention in Psychiatry, 1(1), 9-18.
Garnefski, N., & Kraaij, V. (2006). Relationships between cognitive emotion regulation strategies and depressive symptoms: A comparative study of five specific samples. Personality and Individual Differences, 40(8), 1659-1669.
Gen..z, T. (2002). Discriminant validity of low positive affect: Is it specific to depression? Personality and Individual Differences, 32(6), 991-999.
Gotlib, I. H., Joormann, J., & Foland-Ross, L. C. (2014). Understanding familial risk for depression: A 25-year perspective. Perspectives on Psychological Science, 9(1), 94-108.
Green, M. J., Lino, B. J., Hwang, E. J., Sparks, A., James, C., & Mitchell, P. B. (2011). Cognitive regulation of emotion in bipolar I disorder and unaffected biological relatives. Acta Psychiatrica Scandinavica, 124(4), 307-316.
Greicius, M. D., Flores, B. H., Menon, V., Glover, G. H., Solvason, H. B., Kenna, H., et al. (2007). Resting-state functional connectivity in major depression: Abnormally increased contributions from subgenual cingulate cortex and thalamus. Biological Psychiatry, 62(5), 429-437.
Hammen, C. (2009). Adolescent depression stressful interpersonal contexts and risk for recurrence. Current Directions in Psychological Science, 18(4), 200-204.
Han, K. M., Choi, S., Jung, J., Na, K. S., Yoon, H. K., Lee, M. S., & Ham, B. J. (2014). Cortical thickness, cortical and subcortical volume, and white matter integrity in patients with their first episode of major depression. Journal of Affective Disorders, 155, 42-48.
Hasler, G., Fromm, S., Carlson, P. J., Luckenbaugh, D. A., Waldeck, T., Geraci, M., et al. (2008). Neural response to catecholamine depletion in unmedicated subjects with major depressive disorder in remission and healthy subjects. Archives of General Psychiatry, 65(5), 521-531.
Hasler, G., & Northoff, G. (2011). Discovering imaging endophenotypes for major depression. Molecular Psychiatry, 16(6), 604-619.
Hasler, G., van der Veen, J. W., Tumonis, T., Meyers, N., Shen, J., & Drevets, W. C. (2007). Reduced prefrontal glutamate/glutamine and γ-aminobutyric acid levels in major depression determined using proton magnetic resonance spectroscopy. Archives of General Psychiatry, 64(2), 193-200.
Holsen, L. M., Lancaster, K., Klibanski, A., Whitfield-Gabrieli, S., Cherkerzian, S., Buka, S., & Goldstein, J. M. (2013). HPA-axis hormone modulation of stress response circuitry activity in women with remitted major depression. Neuroscience, 250, 733-742.
Iacoviello, B. M., Alloy, L. B., Abramson, L. Y., & Choi, J. Y. (2010). The early course of depression: A longitudinal investigation of prodromal symptoms and their relation to the symptomatic course of depressive episodes. Journal of Abnormal Psychology, 119(3), 459-467.
Joormann, J., Talbot, L., & Gotlib, I. H. (2007). Biased processing of emotional information in girls at risk for depression. Journal of Abnormal Psychology, 116(1), 135-143.
Kühn, S., & Gallinat, J. (2013). Resting-state brain activity in schizophrenia and major depression: A quantitative meta-analysis. Schizophrenia Bulletin, 39(2), 358-365.
Kovacs, M., & Lopez-Duran, N. (2010). Prodromal symptoms and atypical affectivity as predictors of major depression in juveniles: Implications for prevention. Journal of Child Psychology and Psychiatry, 51(4), 472-496.
Kujawa, A. J., Torpey, D., Kim, J., Hajcak, G., Rose, S., Gotlib, I. H., & , D. N. (2011). Attentional biases for emotional faces in young children of mothers with chronic or recurrent depression. Journal of Abnormal Child Psychology, 39(1), 125-135.
Kumar, P., Waiter, G., Ahearn, T., Milders, M., Reid, I., & Steele, J. (2008). Abnormal temporal difference reward-learning signals in major depression. Brain, 131(8), 2084-2093.
Leopold, K., Ratzer, S., Correll, C. U., Rottmann-Wolf, M., Pfeiffer, S., Ritter, P., et al. (2014). Characteristics, symptomatology and naturalistic treatment in individuals at-risk for bipolar disorders: Baseline results in the first 180 help-seeking individuals assessed at the Dresden high-risk project. Journal of Affective Disorders, 152-154, 427-433.
Li, C. T., Wang, S. J., Hirvonen, J., Hsieh, J. C., Bai, Y. M., Hong, C. J., et al. (2010). Antidepressant mechanism of add-on repetitive transcranial magnetic stimulation in medication-resistant depression using cerebral glucose metabolism. Journal of Affective Disorders, 127(1-3), 219-229.
Madhussodanam, S. (2010). Preventive psychiatry: We are getting closer to fulfilling the promise of reducing mental illness. Annals of Clinical Psychiatry, 22(4), 217-218.
McGillivray, J. A., & McCabe, M. P. (2007). Early detection of depression and associated risk factors in adults with mild/moderate intellectual disability. Research in Developmental Disabilities, 28(1), 59-70.
Miret, M., Ayuso-Mateos, J. L., Sanchez-Moreno, J., & Vieta, E. (2013). Depressive disorders and suicide: Epidemiology, risk factors, and burden. Neuroscience and Biobehavioral Reviews, 37(10), 2372-2374.
Nishioka, K., & Ozaki, N. (2007). Early detection and initial treatment of depression. Nihon Rinsho. Japanese Journal of Clinical Medicine, 65(9), 1660-1664.
Olino, T. M., Lopez-Duran, N. L., Kovacs, M., George, C. J., Gentzler, A. L., & Shaw, D. S. (2011). Developmental trajectories of positive and negative affect in children at high and low familial risk for depressive disorder. Journal of Child Psychology and Psychiatry, 52(7), 792-799.
Pietsch, K., Hoyler, A., Frühe, B., Kruse, J., Schulte-K.rne, G., & Allgaier, A. K. (2012). Early detection of major depression in paediatric care: Validity of the beck depression inventory-(BDI-II) and the beck depression inventory-fast screen for medical patients (BDI-FS). Psychotherapie, Psychosomatik, Medizinische Psychologie, 62(11), 418-424.
Pillai, V., Kalmbach, D. A., & Ciesla, J. A. (2011). A meta-analysis of electroencephalographic sleep in depression: Evidence for genetic biomarkers. Biological Psychiatry, 70(10), 912-919.
Pizzagalli, D. A., Holmes, A. J., Dillon, D. G., Goetz, E. L., Birk, J. L., Ryan Bogdan, A., et al. (2009). Reduced caudate and nucleus accumbens response to rewards in unmedicated individuals with major depressive disorder. American Journal of Psychiatry, 166(6),702-710.
Posner, J., Cha, J., Wang, Z. S., Talati, A., Warner, V., Gerber, A., et al. (2016). Increased default mode network connectivity in individuals at high familial risk for depression. Neuropsychopharmacology, 41(7), 1759-1767.
Rao, U., Dahl, R. E., Ryan, N. D., Birmaher, B., Williamson, D. E., Rao, R., & Kaufman, J. (2002). Heterogeneity in EEG sleep findings in adolescent depression: Unipolar versus bipolar clinical course. Journal of Affective Disorders, 70(3), 273-280.
Reay, R., Matthey, S., Ellwood, D., & Scott, M. (2011). Long-term outcomes of participants in a perinatal depression early detection program. Journal of Affective Disorders, 129(1-3), 94-103.
Ruhé, H. G., Mason, N. S., & Schene, A. H. (2007). Mood is indirectly related to serotonin, norepinephrine and dopamine levels in humans: A meta-analysis of monoamine depletion studies. Molecular Psychiatry, 12(4), 331-359.
Seminowicz, D. A., Mayberg, H. S., McIntosh, A. R., Goldapple, K., Kennedy, S., Segal, Z., & Rafi-Tari, S. (2004). Limbic-frontal circuitry in major depression: A path modeling metanalysis. NeuroImage, 22(1), 409-418.
Sharp, C., Kim, S., Herman, L., Pane, H., Reuter, T., & Strathearn, L. (2014). Major depression in mothers predicts reduced ventral striatum activation in adolescent female offspring with and without depression. Journal of Abnormal Psychology, 123(2), 298-309.
Skjelstad, D. V., Malt, U. F., & Holte, A. (2010). Symptoms and signs of the initial prodrome of bipolar disorder: A systematic review. Journal of Affective Disorders, 126(1-2), 1-13.
Stetler, C., & Miller, G. E. (2011). Depression and hypothalamic-pituitary-adrenal activation: A quantitative summary of four decades of research. Psychosomatic Medicine, 73(2), 114-126.
Wang, L., Hermens, D. F., Hickie, I. B., & Lagopoulos, J. (2012). A systematic review of resting-state functional-MRI studies in major depression. Journal of Affective Disorders, 142(1-3), 6-12.
Whalley, H. C., Sussmann, J. E., Romaniuk, L., Stewart, T., Kielty, S., Lawrie, S. M., et al. (2015). Dysfunction of emotional brain systems in individuals at high risk of mood disorder with depression and predictive features prior to illness. Psychological Medicine, 45(6), 1207-1218.
Wu, H., Lou, C., Huang, Z. K., & Shi, G. H. (2011). SPECT imaging of dopamine transporters with 99mTc-TRODAT-1 in major depression and Parkinson's disease. The Journal of Neuropsychiatry and Clinical Neurosciences, 23(1), 63-67.
Young, S. N. (2013). The effect of raising and lowering tryptophan levels on human mood and social behaviour. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 368(1615), 20110375.
Zhang, J. R., Wang, J. H., Wu, Q. Z., Kuang, W. H., Huang, X. Q., He, Y., & Gong, Q. Y. (2011). Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder. Biological Psychiatry, 70(4), 334-342.
Early Detection of Major Depressive Disorder
Guo Huaibin1,2,3, Wang Donglin1,2,3,4
(1Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, 311121)
(2Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, 311121) (3Center for Cognition and Brain Disorder, Hangzhou Normal University, Hangzhou, 311121) (4The Affiliated Hospital of Hangzhou Normal University, Hangzhou, 310015)
Abstract The early detection of major depressive disorder (MDD) is critical for timely intervention and better outcomes of treatment. Extensive efforts have been made to evaluate methods of detecting MDD in its early stages of development. The methods that have been empirically shown promising can be roughly classified into three categories: focusing on the prodromal symptoms, the risk factors and the biological endophenotype of MDD.
The prodromal approach identifies individuals with early MDD through screening prodromal symptoms, such as insomnia, fatigue, difficulty in concentrating, indecision, contemplation, lack of self-confidence, decreased efficiency, helplessness, reduced motivation, tension, irritability, physical discomfort, decreased energy, loss of appetite, gastrointestinal disorders among other symptoms commonly associated with MDD. The estimated duration of the prodromal period varies across studies, spanning between 6 weeks to 23 months. In most studies, early depressive symptoms were identified by means of clinical interview or mental symptom rating.
The second approach, which aims at determining individuals’ susceptibility to MDD, emphasizes the importance of examining the family history and personal risk factors of potential MDD patients. Family history is a major risk factor for MDD. If a first-degree relative has MDD, the chance of an individual developing this disorder is about 10-13%. As for personal risk factors, they could be either cognitive or emotional. Studies have shown that the first-degree relatives of MDD patients are more likely to have negative cognitive biases and they show low level of positive emotions.
A third and promising approach focuses on identifying potential biological markers of MDD. Previous neuroimaging studies have shown that MDD patients have abnormalities in brain structure and function. The dorsal anterolateral prefrontal cortex and the anterior cingulate cortex of MDD patients are reduced in volume. In addition, MDD patients also have reduced activity in the ventral striatum, increased activity of the thalamus in the positive condition, increased activity in cortical midline structures, increased connectivity in the default mode network (DMN), decreased regional homogeneity (ReHo) in the left insula and left cerebellum, shortened latency and increased density of REM sleep. Furthermore, previous studies have found that MMD patients have increased activity in the hypothalamic-pituitary-adrenocortical (HPA) axis, and lowered concentration level of neurotransmitters, such as serotonin, catecholamines, tryptophan, and γ-aminobutyric acid, in some neural pathways and brain regions.
These three approaches are all successful, to varied extents, in detecting early stage patients of MDD. However, one needs to be mindful of the disadvantages of these research approaches. The prodromal approach is phenomenological in nature, the prodromal symptoms usually lacks specificity and the methods used to identify them are too often subjective. Longitudinal studies that focus on individuals’ susceptibility to MDD is scarce in the literature; it remains unclear how various family and personal risk factors collaborate to deliver the end result of MDD. In clinical situations, it is not possible for a psychiatrist to make diagnoses based solely on these risk factors. As for the biological endophenotype approach, most previous studies have examined clinical stage patients rather than those in the early stages of developing MDD. Thus, it is difficult to determine which of the identified biomarkers are reliable warning signs of MDD. To identify reliable and objective precursors of MDD and to build an accurate risk prediction model, future studies should consider the use of high risk family designs to trace down the biological endophenotype of MDD.
Key words major depressive disorder, early detection, prodromal symptoms, susceptibility, biological endophenotype
www.nmgpsy.com内蒙古心理网
