Biology subjects

Memarian Sorkhabi, M.

Publications and source records attributed to Memarian Sorkhabi, M..

3 recordsLinked to original sources

bioRxiv2021

The sensitivity of ECG contamination to surgical implantation site in adaptive closed-loop neurostimulation systems

BackgroundBrain sensing devices are approved today for Parkinsons, essential tremor, and epilepsy therapies. Clinical decisions for implants are often influenced by the premise that patients will benefit from using sensing technology. However, artifacts, such as ECG contamination, can render such treatments unreliable. Therefore, clinicians need to understand how surgical decisions may affect artifact probability. ObjectivesInvestigate neural signal contamination with ECG activity in sensing enabled neurostimulation systems, and in particular clinical choices such as implant location that impact signal fidelity. MethodsElectric field modelling and empirical signals from 85 patients were used to investigate the relationship between implant location and ECG contamination.a ResultsThe impact on neural recordings depends on the difference between ECG signal and noise floor of the electrophysiological recording. Empirically, we demonstrate that severe ECG contamination was more than 3.2x higher in left-sided subclavicular implants (48.3%), when compared to right-sided implants (15.3%). Cranial implants did not show ECG contamination. ConclusionsGiven the relative frequency of corrupted neural signals, we conclude that implant location will impact the ability of brain sensing devices to be used for "closed-loop" algorithms. Clinical adjustments such as implant location can significantly affect signal integrity and need consideration. HighlightsO_LIChronic embedded brain sensing promises algorithm-based neurostimulation C_LIO_LIAlgorithms for closed-loop stimulation can be impaired by artifacts C_LIO_LIThe relationship of implant location to cardiac dipole has relevant impact on neural signal fidelity; simple models can provide guidance on the sensitivity C_LIO_LIECG artifacts are present in up to 50% of neural signals from left subclavicular DBS systems C_LIO_LIImplanting DBS in a right subclavicular location significantly reduces frequency of ECG artifacts C_LIO_LICranial-mounted implants are relatively immune to artifacts C_LI

neuroscience↗

bioRxiv2020

Practical Design and Implementation of Animal Movements Tracking System for Neuroscience Trials

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSThe nervous system functions of an animal are predominantly reflected in the behaviour and the movement, therefore the movement-related data and measuring behavior quantitatively are crucial for behavioural analyses. The animal movement is traditionally recorded, and human observers follow the animal behaviours; if they recognize a certain behaviour pattern, they will note it manually, which may suffer from observer fatigue or drift. ObjectiveAutomating behavioural observations with computer-vision algorithms are becoming essential equipment to the brain function characterization in neuroscience trials. In this study, the proposed tracking module is eligible to measure the locomotor behaviour (such as speed, distance, turning) over longer time periods that the operator is unable to precisely evaluate. For this aim, a novel animal cage is designed and implemented to track the animal movement. The frames received from the camera are analyzed by the 2D bior 3.7 Wavelet transform and SURF feature points. ResultsImplemented video tracking device can report the location, duration, speed, frequency and latency of each behavior of an animal. Validation tests were conducted on the auditory stimulation trial and the magnetic stimulation treatment of hemi-Parkinsonian rats. Conclusion/ SignificanceThe proposed toolkit can provide qualitative and quantitative data on animal behaviour in an automated fashion, and precisely summarize an animals movement at an arbitrary time and allows operators to analyse movement patterns without requiring to check full records for every experiment.

animal behavior and cognition↗

bioRxiv2020

Physiological Artifacts and the Implications for Brain-Machine-Interface Design

The accurate measurement of brain activity by Brain-Machine-Interfaces (BMI) and closed-loop Deep Brain Stimulators (DBS) is one of the most important steps in communicating between the brain and subsequent processing blocks. In conventional chest-mounted systems, frequently used in DBS, a significant amount of artifact can be induced in the sensing interface, often as a common-mode signal applied between the case and the sensing electrodes. Attenuating this common-mode signal can be a serious challenge in these systems due to finite commonmode-rejection-ratio (CMRR) capability in the interface. Emerging BMI and DBS devices are being developed which can mount on the skull. Mounting the system on the cranial region can potentially suppress these induced physiological signals by limiting the artifact amplitude. In this study, we model the effect of artifacts by focusing on cardiac activity, using a current-source dipole model in a torso-shaped volume conductor. Performing finite element simulation with the different DBS architectures, we estimate the ECG common mode artifacts for several device architectures. Using this model helps define the overall requirements for the total system CMRR to maintain resolution of brain activity. The results of the simulations estimate that the cardiac artifacts for skull-mounted systems will have a significantly lower effect than non-cranial systems that include the pectoral region. It is expected that with a pectoral mounted device, a minimum of 60-80 dB CMRR is required to suppress the ECG artifact, while in cranially-mounted devices, a 20 dB CMRR is sufficient, in the worst-case scenario. The methods used for estimating cardiac artifacts can be extended to other sources such as motion/muscle sources. The susceptibility of the device to artifacts has significant implications for the practical translation of closed-loop DBS and BMI, including the choice of biomarkers and the design requirements for insulators and lead systems.

bioengineering↗