Hadar Peter N, Zelmann Rina, Salami Pariya, Cash Sydney S, Paulk Angelique C
Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, United States.
Front Hum Neurosci. 2024 Sep 4;18:1439541. doi: 10.3389/fnhum.2024.1439541. eCollection 2024.
As the pace of research in implantable neurotechnology increases, it is important to take a step back and see if the promise lives up to our intentions. While direct electrical stimulation applied intracranially has been used for the treatment of various neurological disorders, such as Parkinson's, epilepsy, clinical depression, and Obsessive-compulsive disorder, the effectiveness can be highly variable. One perspective is that the inability to consistently treat these neurological disorders in a standardized way is due to multiple, interlaced factors, including stimulation parameters, location, and differences in underlying network connectivity, leading to a trial-and-error stimulation approach in the clinic. An alternate view, based on a growing knowledge from neural data, is that variability in this input (stimulation) and output (brain response) relationship may be more predictable and amenable to standardization, personalization, and, ultimately, therapeutic implementation. In this review, we assert that the future of human brain neurostimulation, via direct electrical stimulation, rests on deploying standardized, constrained models for easier clinical implementation and informed by intracranial data sets, such that diverse, individualized therapeutic parameters can efficiently produce similar, robust, positive outcomes for many patients closer to a prescriptive model. We address the pathway needed to arrive at this future by addressing three questions, namely: (1) why aren't we already at this prescriptive future?; (2) how do we get there?; (3) how far are we from this Neurostimulationist prescriptive future? We first posit that there are limited and predictable ways, constrained by underlying networks, for direct electrical stimulation to induce changes in the brain based on past literature. We then address how identifying underlying individual structural and functional brain connectivity which shape these standard responses enable targeted and personalized neuromodulation, bolstered through large-scale efforts, including machine learning techniques, to map and reverse engineer these input-output relationships to produce a good outcome and better identify underlying mechanisms. This understanding will not only be a major advance in enabling intelligent and informed design of neuromodulatory therapeutic tools for a wide variety of neurological diseases, but a shift in how we can predictably, and therapeutically, prescribe stimulation treatments the human brain.
随着植入式神经技术研究步伐的加快，退一步审视其前景是否符合我们的预期显得尤为重要。虽然颅内直接电刺激已被用于治疗各种神经系统疾病，如帕金森病、癫痫、临床抑郁症和强迫症，但其有效性差异很大。一种观点认为，无法以标准化方式持续治疗这些神经系统疾病是由于多种相互交织的因素，包括刺激参数、位置以及潜在网络连接的差异，这导致临床上采用试错性的刺激方法。另一种基于神经数据不断增长的知识的观点认为，这种输入（刺激）与输出（大脑反应）关系的变异性可能更具可预测性，并且更易于标准化、个性化，最终实现治疗应用。在本综述中，我们断言，通过直接电刺激进行人类脑神经刺激的未来，取决于部署标准化、受限的模型，以便于临床实施，并以颅内数据集为依据，从而使多样化的个性化治疗参数能够有效地为许多患者产生类似、稳健、积极的结果，更接近规定性模型。我们通过回答三个问题来探讨实现这一未来所需的途径，即：（1）为什么我们还没有达到这个规定性的未来？；（2）我们如何到达那里？；（3）我们距离这个神经刺激规定性的未来还有多远？我们首先假定，根据以往文献，直接电刺激在基于潜在网络的限制下，诱导大脑变化的方式有限且可预测。然后我们探讨如何识别塑造这些标准反应的潜在个体结构和功能脑连接，从而实现有针对性的个性化神经调节，这通过大规模努力得到加强，包括机器学习技术，以绘制和逆向工程这些输入 - 输出关系，从而产生良好的结果并更好地识别潜在机制。这种理解不仅将在为多种神经系统疾病设计智能且信息充分的神经调节治疗工具方面取得重大进展，而且将改变我们可预测地、治疗性地为人类大脑规定刺激治疗的方式。
