Structural MRI and functional connectivity features predict current clinical status and persistence behavior in prescription opioid users.
Structural MRI and functional connectivity features predict current clinical status and persistence behavior in prescription opioid users.
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结构 MRI 和功能连接特征可预测处方阿片类药物使用者当前的临床状态和持续行为。
DOI：
10.1016/j.nicl.2021.102663
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Ray S
中科院分区：
文献类型：
--
作者：
Mill RD;Winfield EC;Cole MW;Ray S
研究了sMRI和restFC在区分POUD特征方面的有效性。sMRI的单变量分析显示POUD受试者的皮质下结构较低。使用多模态特征的多变量分类器可靠地识别POUD受试者。类似的模型也可靠地预测临床相关的持久性行为。多变量多模态神经影像学方法对POUD的研究具有实用性。处方阿片类药物使用障碍（POUD）在美国已达到流行病的程度，迫切需要诊断生物学工具，可以改善疾病特征的预测。使用神经成像方法来开发此类生物标志物在应用于神经退行性疾病和精神疾病时已经产生了有希望的结果，但尚未扩展到处方阿片类药物成瘾。考虑到这一长期目标，我们在这个研究不足的临床组中进行了初步研究。基于结构MRI（sMRI）和静息状态功能连接（restFC）特征，研究了区分POUD（n = 26）和健康对照（n = 21）的单变量和多变量方法。单变量方法显示，在POUD受试者中，先前报道的成瘾相关网络的皮质下范围的结构完整性降低。未观察到皮质结构或侧倾静息FC的可靠单变量组间差异。与这些混合单变量结果相比，多变量机器学习分类方法恢复了统计学上更可靠的组间差异，特别是当sMRI和restFC特征在多模态模型中结合时（分类准确率= 66.7%，p <.001）。同样的多变量多模态方法也产生了可靠的预测个体差异的临床相关行为措施（持久性行为;预测到实际重叠r = 0.42，p = 0.009）。我们的研究结果表明，sMRI和restFC测量可用于可靠地区分长期阿片类药物使用的神经效应，并且这种努力在数值上受益于多变量预测方法和多模态特征集。这可以作为未来从神经影像学特征预测POUD特征的纵向建模的理论概念验证，这将具有更清晰的临床实用性。
The efficacy of sMRI and restFC in distinguishing POUD features was investigated. Univariate analyses of sMRI revealed lower subcortical structure in POUD subjects. Multivariate classifiers using multi-modal features reliably identified POUD subjects. Similar models also reliably predicted clinically relevant persistence behavior. Multivariate multi-modal neuroimaging approaches have utility for the study of POUD. Prescription opioid use disorder (POUD) has reached epidemic proportions in the United States, raising an urgent need for diagnostic biological tools that can improve predictions of disease characteristics. The use of neuroimaging methods to develop such biomarkers have yielded promising results when applied to neurodegenerative and psychiatric disorders, yet have not been extended to prescription opioid addiction. With this long-term goal in mind, we conducted a preliminary study in this understudied clinical group. Univariate and multivariate approaches to distinguishing between POUD (n = 26) and healthy controls (n = 21) were investigated, on the basis of structural MRI (sMRI) and resting-state functional connectivity (restFC) features. Univariate approaches revealed reduced structural integrity in the subcortical extent of a previously reported addiction-related network in POUD subjects. No reliable univariate between-group differences in cortical structure or edgewise restFC were observed. Contrasting these mixed univariate results, multivariate machine learning classification approaches recovered more statistically reliable group differences, especially when sMRI and restFC features were combined in a multi-modal model (classification accuracy = 66.7%, p < .001). The same multivariate multi-modal approach also yielded reliable prediction of individual differences in a clinically relevant behavioral measure (persistence behavior; predicted-to-actual overlap r = 0.42, p = .009). Our findings suggest that sMRI and restFC measures can be used to reliably distinguish the neural effects of long-term opioid use, and that this endeavor numerically benefits from multivariate predictive approaches and multi-modal feature sets. This can serve as theoretical proof-of-concept for future longitudinal modeling of prognostic POUD characteristics from neuroimaging features, which would have clearer clinical utility.
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影响因子：
4.3
作者：
Diedrichsen J;Kriegeskorte N
通讯作者：
Kriegeskorte N
神经影像学中脑部疾病的单受试者预测：前景和陷阱
影响因子：
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作者：
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影响因子：
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Drysdale AT;Grosenick L;Downar J;Dunlop K;Mansouri F;Meng Y;Fetcho RN;Zebley B;Oathes DJ;Etkin A;Schatzberg AF;Sudheimer K;Keller J;Mayberg HS;Gunning FM;Alexopoulos GS;Fox MD;Pascual-Leone A;Voss HU;Casey BJ;Dubin MJ;Liston C
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影响因子：
4.1
作者：
Bhanji, Jamil P.;Kim, Eunbin S.;Delgado, Mauricio R.
通讯作者：
Delgado, Mauricio R.
影响因子：
8.2
作者：
Jasinska, Agnes J.;Stein, Elliot A.;Kaiser, Jochen;Naumer, Marcus J.;Yalachkov, Yavor
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Yalachkov, Yavor