A formalism for sequential estimation of neural membrane time constant and input–output curve towards selective and closed-loop transcranial magnetic stimulation* Matlab Code is available at https://github.com/smmalavi/TMS-tau-IOC-estim-fixed-tp. (1st October 2022)

• Record Type: Journal Article
• Authors: Alavi, S M Mahdi; Vila-Rodriguez, Fidel; Mahdi, Adam; Goetz, Stefan M
• Is Part Of: Journal of neural engineering. Volume 19:Number 5(2022)
• Publication Date: 2022-10-01
• Subjects: transcranial magnetic stimulation -- input–output curve -- neural membrane time-constant -- closed-loop TMS -- selective stimulation; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/ac8ed5
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: Objective. To obtain a formalism for real-time concurrent sequential estimation of neural membrane time constant and input–output (IO) curve with transcranial magnetic stimulation (TMS). Approach. First, the neural membrane response and depolarization factor, which leads to motor evoked potentials with TMS are analytically computed and discussed. Then, an integrated model is developed which combines the neural membrane time constant and IO curve. Identifiability of the proposed integrated model is discussed. A condition is derived, which assures estimation of the proposed integrated model. Finally, sequential parameter estimation (SPE) of the neural membrane time constant and IO curve is described through closed-loop optimal sampling and open-loop uniform sampling TMS. Without loss of generality, this paper focuses on a specific case of commercialized TMS pulse shapes. The proposed formalism and SPE method are directly applicable to other pulse shapes. Main results. The results confirm satisfactory estimation of the membrane time constant and IO curve parameters. By defining a stopping rule based on five times consecutive convergence of the estimation parameters with a tolerances of 0.01, the membrane time constant and IO curve parameters are estimated with 82 TMS pulses with absolute relative estimation errors (AREs) of less than 4% with the optimal sampling SPE method. At this point, the uniform sampling SPE method leads to AREs up to 16%. The uniform sampling method does not satisfy the stopping rule due to the large estimation variations. Significance. This paper provides a tool for real-time closed-loop SPE of the neural time constant and IO curve, which can contribute novel insights in TMS studies. SPE of the membrane time constant enables selective stimulation, which can be used for advanced brain research, precision medicine and personalized medicine.