Department of Psychiatry, Stony Brook University, HSC T-10-040D, Stony Brook, NY 11794.
Department of Psychiatry, Stony Brook University, Stony Brook, New York, USA.
J Clin Psychiatry. 2018 Jul 10;79(4):17m11901. doi: 10.4088/JCP.17m11901.
A major target in suicide prevention is interrupting the progression from suicidal thoughts to action. Use of complex algorithms in large samples has identified individuals at very high risk for suicide. We tested the ability of data-driven pattern classification analysis of brain functional connectivity to differentiate recent suicide attempters from patients with suicidal ideation.
We performed a cross-sectional study using resting-state functional magnetic resonance imaging in depressed inpatients and outpatients of both sexes recruited from a university hospital between March 2014 and June 2016: recent suicide Attempters within 3 days of an attempt (n = 10), Suicidal Ideators (n = 9), Depressed Non-Suicidal Controls (n = 17), and Healthy Controls (n = 18). All depressed patients fulfilled DSM-IV-TR criteria for major depressive episode and either major depressive disorder, bipolar disorder, or depression not otherwise specified. A subset of suicide attempters (n = 7) were rescanned within 7 days. We used a support vector machine data-driven neural pattern classification analysis of resting-state functional connectivity to characterize recent suicide attempters and then tested the classifier's specificity.
A binary classifier trained to discriminate patterns of resting-state functional connectivity robustly differentiated Suicide Attempters from Suicidal Ideators (mean accuracy = 0.788, signed rank test: P = .002; null hypothesis: area under the curve = 0.5), with distinct functional connectivity between the default mode and the limbic, salience, and central executive networks. The classifier did not discriminate stable Suicide Attempters from Suicidal Ideators (mean accuracy = 0.58, P = .33) or presence from absence of lifetime suicidal behavior (mean accuracy = 0.543, P = .348) and was not improved by modeling clinical variables (mean accuracy = 0.736, P = .002).
Measures of intrinsic brain organization may have practical value as objective measures of suicide risk and its underlying mechanisms. Further incorporation of serum or cognitive markers and use of a prospective study design are needed to validate and refine the clinical relevance of this candidate biomarker of suicide risk.
预防自杀的一个主要目标是中断从自杀意念到自杀行为的发展。在大样本中使用复杂的算法已经确定了自杀风险极高的个体。我们测试了基于数据的大脑功能连接模式分类分析的能力，以区分近期自杀未遂者和有自杀意念的患者。
我们进行了一项横断面研究，使用 2014 年 3 月至 2016 年 6 月期间从一所大学医院招募的男女抑郁症住院患者和门诊患者的静息状态功能磁共振成像：在尝试自杀后 3 天内的近期自杀未遂者（n=10）、有自杀意念者（n=9）、抑郁非自杀对照组（n=17）和健康对照组（n=18）。所有抑郁症患者均符合 DSM-IV-TR 重性抑郁发作标准，且符合重性抑郁障碍、双相障碍或未特定的抑郁障碍。一部分自杀未遂者（n=7）在 7 天内重新扫描。我们使用支持向量机基于数据的静息状态功能连接的神经模式分类分析来描述近期自杀未遂者，然后测试分类器的特异性。
一个训练来区分静息状态功能连接模式的二元分类器，能够可靠地区分自杀未遂者和有自杀意念者（平均准确率=0.788，符号秩检验：P=0.002；零假设：曲线下面积=0.5），默认模式与边缘、突显和中央执行网络之间存在明显的功能连接。该分类器不能区分稳定的自杀未遂者和有自杀意念者（平均准确率=0.58，P=0.33）或有或无终生自杀行为（平均准确率=0.543，P=0.348），并且通过建模临床变量也不能提高准确性（平均准确率=0.736，P=0.002）。
内在大脑组织的测量值可能具有作为自杀风险及其潜在机制的客观测量值的实际价值。需要进一步纳入血清或认知标志物，并使用前瞻性研究设计，以验证和完善这种自杀风险候选生物标志物的临床相关性。
