Biology subjects

Mahdi, A.

Publications and source records attributed to Mahdi, A..

4 recordsLinked to original sources

bioRxiv2022

Identifiability analysis and noninvasive online estimation of the first-order neural activation dynamics in the brain with transcranial magnetic stimulation

BackgroundNeurons demonstrate very distinct nonlinear activation dynamics, influenced by the neuron type, morphology, ion channel expression, and various other factors. The measurement of the activation dynamics can identify the neural target of stimulation and detect deviations, e.g., for diagnosis. This paper describes a tool for closed-loop sequential parameter estimation (SPE) of the activation dynamics through transcranial magnetic stimulation (TMS). The proposed SPE method operates in real time, selects ideal stimulus parameters, detects and processes the response, and concurrently estimates the input-output (IO) curve and the first-order approximation of the activated neural target. ObjectiveTo develop a method for concurrent SPE of the first-order activation dynamics and IO curve with closed-loop TMS. MethodFirst, identifiability of an integrated model of the first-order neural activation dynamics and IO curve is assessed, demonstrating that at least two IO curves need to be acquired with different pulse widths. Then, a two-stage SPE method is proposed. It estimates the IO curve by using Fisher information matrix (FIM) optimization in the first stage and subsequently estimates the membrane time constant as well as the coupling gain in the second stage. The procedure continues in a sequential manner until a stopping rule is satisfied. ResultsThe results of 73 simulation cases confirm the satisfactory estimation of the membrane time constant and coupling gain with average absolute relative errors (AREs) of 6.2% and 5.3%, respectively, with an average of 344 pulses (172 pulses for each IO curve or pulse width). The method estimates the IO curves lower and upper plateaus, mid-point, and slope with average AREs of 0.2%, 0.7%, 0.9%, and 14.5%, respectively. The conventional time constant estimation method based on the strength-duration (S-D) curve leads to 33.3% ARE, which is 27.0% larger than 6.2% ARE obtained through the proposed real-time FIM-based SPE method in this paper. ConclusionsSPE of the activation dynamics requires acquiring at least two IO curves with different pulse widths, which needs a controllable TMS (cTMS) device with adjustable pulse duration. SignificanceThe proposed SPE method enhances the cTMS functionality, which can contribute novel insights in research and clinical studies.

neuroscience↗

bioRxiv2022

Closed-loop and automatic tuning of pulse amplitude and width in EMG-guided controllable transcranial magnetic stimulation (cTMS)

This paper proposes a tool for automatic and optimal tuning of pulse amplitude and width for sequential parameter estimation (SPE) of the membrane time constant and input-output curve in closed-loop electromyography-guided (EMG-guided) controllable transcranial magnetic stimulation (cTMS). A normalized depolarization factor is defined which separates the optimization of the pulse amplitude and width. Then, the pulse amplitude is chosen by the maximization of the Fisher information matrix (FIM), while the pulse width is chosen by the maximization of the normalized depolarization factor. The simulation results confirm satisfactory estimation. The results show that the normalized depolarization factor maximization can identify the critical pulse width, which is an important parameter in the identifiability analysis, without any prior neurophysiological or anatomical knowledge of the neural membrane.

neuroscience↗

bioRxiv2022

Formalism for the sequential estimation of neural membrane time constant and input-output curve towards selective and closed-loop transcranial magnetic stimulation

ObjectiveTo obtain a formalism for real-time concurrent sequential estimation of neural membrane time constant and input-output (IO) curve with transcranial magnetic stimulation (TMS). ApproachFirst, the neural membrane response and depolarization factor, which leads to motor evoked potentials (MEPs) with TMS are analytically computed and discussed. Then, an integrated model is developed which combines the neural membrane time constant and input-output 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 resultsThe 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. SignificanceThis 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.

neuroscience↗

bioRxiv2022

Temporal responses of bumblebee gustatory neurons encode sugar identity

The sense of taste permits the recognition of valuable nutrients and the avoidance of potential toxins. Models of gustatory coding propose that within modalities (e.g. sweet, bitter, etc.), taste ligands are not distinct stimuli. However, these models are based on data from mice or flies that have omnivorous, non-specialist diets. A specialist feeder might, however, be expected to have acuity within modality if stimulus resolution was critical to survival. Previously, we found that bumblebees have a specialized mechanism for sensing sugars whereby two gustatory receptor neurons (GRNs) within the galeal sensilla of the bees mouthparts exhibit burst of spikes. Here, we show that the temporal firing patterns of these GRNs separate sugars into four distinct groups that correlate with sugar nutritional value and palatability. We also identified a third GRN that responded to stimulation with relatively high concentrations of fructose, sucrose, and maltose. Sugars that were non-metabolizable or toxic suppressed the responses of bursting GRNs to sucrose. These abilities to encode information about sugar value are a refinement to the bumblebees sense of sweet taste that could be an adaptation that enables precise calculations of the nature and nutritional value of floral nectar.

neuroscience↗