脑部扫描可以预测重度忧郁的复发风险


  【24drs.com】根据一篇新研究,功能性核磁共振造影(fMRI)可以帮助确认从重度忧郁症(major depressive disorder,MDD)缓解的病患中,有哪里些人最可能复发。
  
  英国伦敦国王学院精神病学、心理学和神经科学研究中心的Roland Zahn医师表示,关键在于,罪恶感等自责情绪时的fMRI影像,可以预测哪里些人在未来一年会再度发生忧郁,哪里些人依旧维持缓解。
  
  他指出,使用机器学习模型分析的案例中,预测正确率为75%;而使用临床资料时,预测准确率只有50%─很像用猜的。
  
  这篇研究在线发表于10月7日的JAMA精神病学期刊。
  
  在之前的一篇fMRI研究中,研究者观察发现,从重度忧郁缓解的病患,相较于与责备他人(对他人愤怒或生气)的情绪,当其发生自责情绪时,在右上前颞叶(RSATL)和膝下扣带皮层与相邻隔区(SCSR)之间的功能性连结低下。
  
  他们表示,这是首度有研究指出,偏见的神经签名指向过度类化的自责情绪(例如:对一切感到内疚),是形成重度忧郁之认知脆弱因子的关键。他们的最新研究认为,这个神经签名可以预测复发风险,这是建立它作为预后生物标记的重要一步。
  
  他们的研究对象是64名没有使用药物的病患,这些人已经从重度忧郁缓解至少6个月,另有39名个人或家庭无重度忧郁史的健康对照组。
  
  进行fMRI时,参与者被要求想象对他们最好的朋友做一些很糟糕的举动,他们会感到自责情绪,如罪恶感;接下来的14个月中,37名病患依旧缓解(稳定组),27人复发重度忧郁。
  
  研究者报告指出,在感到罪恶感的期间,相较于稳定组与对照组,复发重度忧郁者有比较高的RSATL-SCSR关联(P < .05) ,他们写道,我们证实了我们的假设,比较自责情绪和责怪他人的情绪发现,RSATL-SCSR连结性可预测后续的复发风险。
  
  复发重度忧郁组中,右腹壳区和屏状核及颞顶交界处展现出RSATL高度连结性,研究者表示,整体而言,这些区域预测复发的准确度有75%(64例预测案例中有48例正确)。
  
  Zahn医师表示,需要更多研究才能把这个方法运用到临床。目前还无法应用到临床实务,除非有研究验证且达到80%准确度,才是临床有用的生物标记。
  
  不过,这些研究结果的重要性是,相对于责怪他人,选择性的自责在忧郁和神经网络改变功能连结之间显示可能有因果关系。这与常见的假设「负面情绪相关之脑部反应的整体增加情况是了解忧郁的关键」不同。
  
  英国医学研究委员会神经科学与心理健康负责人Kathryn Adcock博士在一篇声明中表示,这篇令人振奋的研究有潜力确认哪里些人比较可能会复发忧郁症,因此,可以从长期治疗与用药中获益。这篇研究也有助于发现新的忧郁疗法,因为临床试验更能聚焦在更有可能出现异常和有此经历者。
  
  加州大学洛杉矶分校Cyrus Raji博士对研究结果发表评论时表示,这篇文章是基本概念的一种创新诠释:在此情况下,神经影像可以在精神病学提升价值,用于重度忧郁症,不过,还需要多篇设计良好的多中心随机临床试验、或大型的资料分析方法,才能说服医疗保健专业人士将这些研究结果应用到每天的临床实务。
  
  资料来源:http://www.24drs.com/
  
  Native link:Brain Scans May Predict Recurrence Risk in Major Depression

Brain Scans May Predict Recurrence Risk in Major Depression

By Megan Brooks
Medscape Medical News

Functional magnetic resonance imaging (fMRI) may help identify patients in remission from major depressive disorder (MDD) who are most apt to experience relapse, according to a new study.

"The key finding is that fMRI of self-blaming emotions such as guilt predicts who will go on to develop another depressive episode in the next year and who will remain in remission from a previous episode," Roland Zahn, MD, who led the research at the Institute of Psychiatry, Psychology and Neuroscience, Kings College London, United Kingdom, told Medscape Medical News.

"The prediction is correct in 75% of the cases using a machine learning model. Using clinical information, the prediction accuracy is at 50% ─ like guessing," he noted.

The study was published online October 7 in JAMA Psychiatry.

Neural Signature of Relapse Risk

In an earlier fMRI study, the researchers observed that patients in remission from MDD exhibited lower functional connectivity between the right superior anterior temporal lobe (RSATL) and the subgenual cingulate cortex and adjacent septal region (SCSR) when experiencing self-blaming emotions, as opposed to emotions related to blaming others ("indignation or anger toward others").

"This finding provided the first neural signature of biases toward overgeneralized self-blaming emotions (eg, 'feeling guilty for everything'), known to have a key role in cognitive vulnerability to MDD," they say. Their latest study suggests that this neural signature predicts risk for recurrence, "a crucial step in establishing its potential as a prognostic biomarker."

They focused on 64 nonmedicated patients who had been in remission from MDD for at least 6 months and 39 healthy control participants who had no personal or family history of MDD.

During fMRI, participants were asked to imagine acting badly toward their best friends, and they experienced self-blaming emotions such as guilt. Over the next 14 months, 37 patients remained in remission (stable group), and 27 developed a recurrent major depressive episode.

During the experience of emotions of guilt, the group with recurring MDD showed higher RSATL-SCSR connectivity than the group with stable MDD (P < .05) and the control group, the researchers report. "We corroborated our hypothesis that during the experience of self-blaming vs other-blaming emotions, RSATL-SCSR connectivity predicted risk of subsequent recurrence," they write.

The group with recurring MDD also exhibited RSATL hyperconnectivity with the right ventral putamen and claustrum and the temporoparietal junction. Together, these regions predicted recurrence with 75% accuracy (48 out of 64 predicted cases), the researchers say.

Ready for Prime Time?

Dr Zahn said more study is needed before this approach could be used in the clinic. "It cannot be used in clinical practice until replicated and until reaching an agreed benchmark of 80% accuracy for clinically useful biomarkers," he told Medscape Medical News.

"The importance of the finding is, however, to show a likely causal relationship between depression and altered functional connections in a neural network that is selective for blaming oneself relative to blaming others. This is in contrast with a common assumption that an overall increase in negative emotion–related brain responses is key to understanding depression," Dr Zahn said.

"This exciting research has the potential to help identify those individuals who are more likely to suffer from recurrent episodes of depression and will therefore benefit most from long-term treatment and medication," Kathryn Adcock, PhD, head of neurosciences and mental health at the Medical Research Council (United Kingdom), said in a statement. "This work could aid the discovery of new treatments for depression because clinical trials will be better able to focus on people with a more comparable disorder and experience."

Commenting on the results for Medscape Medical News, Cyrus Raji, MD, PhD, from the University of California, Los Angeles, said this article is "an innovative demonstration of a basic concept: neuroimaging can add value in psychiatry, in this case, major depressive disorder. However, it will take either several well-executed, multicenter, randomized clinical trials or large data-analytic approaches to convince healthcare stakeholders to apply these findings to daily clinical practice."

The study was funded by the Medical Research Council. One author has participated in consultancy and speaking engagements for Bristol-Myers Squibb, AstraZeneca, Eli Lilly, Schering-Plough, Janssen-Cilag, and Servier and owns share options in P1vital Limited. Dr Raji consults for Brainreader, which makes Neuroreader, an FDA-approved software application that measures brain volumes on MRI scans.

JAMA Psychiatry. Published online October 7, 2015.

    
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