Motiwala Asma, Soldado-Magraner Joana, Batista Aaron P, Smith Matthew A, Yu Byron M
Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Center for the Neural Basis of Cognition, Carnegie Mellon University and University of Pittsburgh, Pittsburgh, PA 15213, USA; Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA; Center for the Neural Basis of Cognition, Carnegie Mellon University and University of Pittsburgh, Pittsburgh, PA 15213, USA.
Trends Cogn Sci. 2025 Jul 28. doi: 10.1016/j.tics.2025.06.017.
Establishing causal relationships between neural activity and brain function requires experimental perturbations of neural activity. Many existing perturbation methods modify activity by directly applying external signals to the brain. We review an alternative approach where brain-computer interfaces (BCIs) leverage volitional control of neural activity to manipulate and causally perturb it. We highlight the potential of BCIs to manipulate neural activity in ways that are flexible, accurate, and adhere to intrinsic biophysical and network-level constraints to investigate the consequences of configuring neural population activity in specified ways. We discuss the advantages and disadvantages of using BCIs as a perturbation tool compared with other perturbation methods and how BCIs can expand the scope of questions that can be addressed about brain function.
建立神经活动与脑功能之间的因果关系需要对神经活动进行实验性扰动。许多现有的扰动方法通过直接向大脑施加外部信号来改变活动。我们回顾了一种替代方法，即脑机接口（BCI）利用对神经活动的意志控制来操纵并对其进行因果扰动。我们强调了BCI以灵活、准确且符合内在生物物理和网络层面约束的方式操纵神经活动的潜力，以便研究以特定方式配置神经群体活动的后果。我们讨论了与其他扰动方法相比，使用BCI作为扰动工具的优缺点，以及BCI如何能够扩展关于脑功能可解决问题的范围。
