个性化神经调节
Personalizing neuromodulation.
作者信息
Medaglia John D, Erickson Brian, Zimmerman Jared, Kelkar Apoorva
机构信息
Department of Psychology, Drexel University, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Drexel University, Philadelphia, PA, 19104, USA.
Department of Psychology, Drexel University, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
出版信息
Int J Psychophysiol. 2020 Aug;154:101-110. doi: 10.1016/j.ijpsycho.2019.01.002. Epub 2019 Jan 24.
In the era of "big data", we are gaining rich person-specific information about neuroanatomy, neural function, and cognitive functions. However, the optimal ways to create precise approaches to optimize individuals' mental functions in health and disease are unclear. Multimodal analysis and modeling approaches can guide neuromodulation by combining anatomical networks, functional signal analysis, and cognitive neuroscience paradigms in single subjects. Our progress could be improved by progressing from statistical fits to mechanistic models. Using transcranial magnetic stimulation as an example, we discuss how integrating methods with a focus on mechanisms could improve our predictions TMS effects within individuals, refine our models of health and disease, and improve our treatments.
在“大数据”时代，我们正在获取关于神经解剖学、神经功能和认知功能的丰富的个人特定信息。然而，在健康和疾病状态下创建精确方法以优化个体心理功能的最佳方式尚不清楚。多模态分析和建模方法可以通过结合单一个体的解剖网络、功能信号分析和认知神经科学范式来指导神经调节。从统计拟合发展到机制模型可以推动我们取得更大进展。以经颅磁刺激为例，我们讨论了聚焦于机制的整合方法如何能够改善我们对个体经颅磁刺激效应的预测、完善我们的健康和疾病模型以及改进我们的治疗方法。
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