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Sefati, S.

Publications and source records attributed to Sefati, S..

2 recordsLinked to original sources

Inside-out tracking and projection mapping for robot-assisted transcranial magnetic stimulation

Transcranial Magnetic Stimulation (TMS) is a neurostimulation technique based on the principle of electromagnetic induction of an electric field in the brain with both research and clinical applications. To produce an optimal neuro-modulatory effect, the TMS coil must be placed on the head and oriented accurately with respect to the region of interest within the brain. A robotic method can enhance the accuracy and facilitate the procedure for TMS coil placement. This work presents two system improvements for robot-assisted TMS (RA-TMS) application. Previous systems have used outside-in tracking method where a stationary external infrared (IR) tracker is used as a reference point to track the head and TMS coil position. This method is prone to losing track of the coil or the head if the IR camera is blocked by the robotic arm during its motion. To address this issue, we implemented an inside-out tracking method by mounting a portable IR camera on the robot end-effector. This method guarantees that the line of sight of the IR camera is not obscured by the robotic arm at any time during its motion. We also integrated a portable projection mapping device (PPMD) into the RA-TMS system to provide visual guidance during TMS application. PPMD can track the head via an IR tracker, and can project a planned contact point of the TMS coil on the head or overlay the underlying brain anatomy in real-time.

bioengineering↗

Variability in locomotor dynamics reveals the critical role of feedback in task control

Animals vary considerably in size, shape, and physiological features across individuals, but yet achieve behavioral performances that are virtually indistinguishable between conspecifics. We examined how animals compensate for morphophysiological variation by measuring the system dynamics of individual knifefish (Eigenmannia virescens) in a refuge tracking task. Kinematic measurements of Eigenmannia were used to generate individualized estimates of each fishs locomotor plant and controller revealing substantial variability between fish. To test the impact of this variability on behavioral performance, these models were used to perform simulated brain transplants--computationally swapping controllers and plants between individuals. We found that simulated closed-loop performance was robust to mismatch between plant and controller. This suggests that animals rely on feedback rather than precisely tuned neural controllers to compensate for morphophysiological variability.

neuroscience↗