Feasibility of Engineered Neural Networks for Neuro-Restoration After Cortical Stroke

美国有超过800万人患有慢性中风。由此产生的残疾导致公共卫生成本巨大，生活质量下降。由于卒中后功能无法令人满意地恢复，迫切需要新的有效的再生方法。为此，提出了工程神经网络(ENN)的新概念。ENN被设想为包含来自人类成年干细胞的神经元，并被构建成具有精确的输入和反馈回路。这些连接将使ENN与大脑和身体的其他区域整合，从而学习行为。最终，这种整合过程可以导致脑神经系统取代中风受损的大脑和任何相关的功能。在实现这一目标之前，第一步是开发一个台式试验台培养神经网络(CNN)，并验证它可以被训练成执行任意感觉任务和运动行为。这项研究的成功完成将证明，CNN可以被训练成编码任意行为，并与大脑和身体的其他区域相互作用。这将证明进一步的研究是合理的，以追求脑神经系统的发展，以及将这种系统植入中风损伤的大脑的方法。最终，这可能会以一种有意义的方式恢复大脑资源，进而可能导致突破性的中风康复再生治疗。这项研究还将：(I)促进生物医学工程学生和研究实习生的教育和终身学习；(Ii)扩大未被充分代表的群体对神经工程的参与；(Iii)提高科学素养。该项目将开发一种新的神经修复概念--工程神经网络(ENN)，以包含来自人类诱导多能干细胞(HiPSCs)的神经元。ENN将有一个精确的结构，由兴奋性和抑制性神经元组成的输入和反馈回路组成，以促进中风后与大脑/身体其他区域的沟通。通过将ENN与微电子学接口，将提供一致和精确的兴奋和抑制输入模式以及错误驱动的反馈，以训练ENN与大脑/身体的其他区域交互，并学习人类的任意行为。最终，这可以建立ENN与中风后剩余大脑的双向通讯，从而取代中风受损的皮质和辅助功能。为了评估这一概念的可行性，第一步是开发一个台式试验台培养神经网络(CNN)，并验证它可以被训练成执行任意感觉任务和运动行为。科学地讲，CNN平台可以提供一种学习神经编码和记忆提取基本概念的手段。从神经工程学的角度来看，这项研究可能会为干细胞来源的神经元组织的优化设计提供参考。如果成功，这项研究将证明，CNN可以被训练成编码任意行为，并与其他大脑和身体区域相互作用，并将证明进一步的研究，以追求脑神经的发展和方法，将这种系统植入中风受损的大脑。最终，这项技术可能会以一种有意义的方式恢复大脑资源，进而可能导致突破性的中风康复再生治疗。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Over 8 million people in the US are living with chronic stroke. The resulting disabilities lead to significant public health costs and decreased quality of life. With no means to satisfactorily restore functions after stroke, new effective regenerative approaches are much needed. Therefore, a new concept of “engineered neural networks” (ENN) is proposed. The ENN is envisioned to contain neurons derived from human adult stem cells and be structured to have precise inputs and feedback loops. These connections will enable the ENN’s integration with other brain and body areas and thereby learn behaviors. Ultimately, this integration process can lead to ENNs replacing stroke-damaged brain and any associated functions. Before this goal can be achieved, the first step is to develop a benchtop testbed cultured neural network (CNN) and verify that it can be trained to perform an arbitrary sensory task and motor behavior. Successful completion of this study would demonstrate that CNNs can be trained to encode arbitrary behaviors and interact with other brain and body areas. This would justify further research to pursue the development of ENNs and methods to implant such systems into the stroke-damaged brain. Ultimately, this may restore brain resources in a functionally meaningful manner, which may in turn lead to ground-breaking regenerative treatments for stroke rehabilitation. This study will also: (i) promote education and lifelong learning in biomedical engineering students and research trainees; (ii) broaden the participation of underrepresented groups in neuroengineering; (iii) increase scientific literacy. This project will develop a novel neurorestorative concept of “engineered neural networks” (ENN) envisioned to contain neurons derived from human induced pluripotent stem cells (HIPSCs). The ENN will have a precise structure consisting of inputs and feedback loops, formed by excitatory and inhibitory neurons, to facilitate communication with other brain/body areas post-stroke. By interfacing the ENN with microelectronics, consistent and precise patterns of excitatory and inhibitory inputs and error-driven feedback will be delivered to train the ENN to interface with other brain/body areas and learn arbitrary human behaviors. Ultimately, this can establish bidirectional communication of the ENN with the remaining post-stroke brain, thereby replacing the stroke-damaged cortex and subserved functions. To gauge the feasibility of this concept, the first step is to develop a benchtop testbed cultured neural network (CNN) and verify that it can be trained to perform an arbitrary sensory task and motor behavior. Scientifically, the CNN platform can provide a means to study basic concepts of neural encoding and memory retrieval. From a neuroengineering perspective, this study may inform the optimal design of stem cell-derived neuronal tissue. If successful, this study would demonstrate that CNNs can be trained to encode arbitrary behaviors and interact with other brain and body areas and would justify further research to pursue the development of ENNs and methods to implant such systems into the stroke-damaged brain. Ultimately, this technology may restore brain resources in a functionally meaningful manner, which may, in turn, lead to ground-breaking regenerative treatments for stroke rehabilitation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.