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根据对细胞c-Fos表达的全脑成像进行迷幻药物和精神活性药物的分类

Farid Aboharb^1,2, Pasha A Davoudian^1,3,4, Ling-Xiao Shao^1,5

¹Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA.

Nature communications

|February 12, 2025

概括

这项研究开发了一种机器学习方法,根据神经可塑性标志物对精神活性药物进行分类. 这种方法可以准确地区分西和胺等化合物,有助于治疗的发展.

科学领域:

神经科学是一个神经科学.
药理学药理学是指药理学的学科.
计算生物学计算生物学

背景情况:

精神活性化合物 (psilocybin,ketamine,MDMA) 显示出治疗潜力,但需要精确的临床前查.
现有的测试可能无法完全捕捉这些物质对神经可塑性的微妙影响.

研究的目的:

开发和验证使用神经可塑性标记物对精神活性药物进行分类的新管道.
为了区分具有迷幻性质的化合物和相关的类似物.

主要方法:

利用光板光显微镜可视化小鼠大脑组织中即时早期基因表达.
应用机器学习算法用于基于细胞分辨率基因表达模式的药物分类.
采用沙普利增量解释来识别影响预测的关键大脑区域.

主要成果:

从一个包括 псилоцибин,胺和MDMA在内的小组中分类特定药物的准确率达到了67%.
在对对比中,在区分 пси洛西宾与其他测试化合物的准确性 >95%.
成功地确定了对准确药物分类至关重要的大脑区域.

结论:

开发的管道提供了一种强大的方法来表征精神活性药物及其对神经可塑性的影响.
这种方法促进了潜在的迷幻疗法的临床前评估和验证.
直接早期基因表达的机器学习分析为了解药物诱导的神经生物学变化提供了一个独特的镜头.

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