P213 logLIRA: a novel algorithm for intracortical microstimulation artifacts suppression
Francesco Negri*1, David J. Guggenmos2, Federico Barban1,3
1Department of Informatics, Bioengineering, Robotics, System Engineering (DIBRIS), University of Genova, Genova, Italy
2Department of Rehabilitation Medicine and the Landon Center on Aging, University of Kansas Medical Center, Kansas City, KS, United States
3IRCSS Ospedale Policlinico San Martino, Genova, Italy
*Email: francesco.negri@edu.unige.it
Introduction
Intracortical microstimulation is a key tool to study neuropathologies and ultimately develop novel therapies [1, 2]. The analysis of short-latency evoked activity is essential to understand cortical reorganization driven by targeted electrical pulses [1, 3]. However, large voltage fluctuations known as stimulation artifacts hinder recording and analysis of neural response [4-6]. Existing rejection methods struggle with high spatially and temporally variable stimulus artifacts or rely on restrictive assumptions (e.g., absence of signal saturation) [5-8]. We propose a novel algorithm using piece-wise linear interpolation of logarithmically distributed points, alongside a framework to generate a semisynthetic dataset for benchmarking.
Methods
Our method, logLIRA, begins with a 1 ms blanking interval, dynamically extended to the end of signal saturation if present. Interpolation points are then sampled logarithmically, ensuring denser sampling where the signal changes rapidly. Piecewise linear interpolation estimates the artifact which is later subtracted. Possibly remaining secondary artifacts are mitigated by clustering the first 2 ms of recovered signals across trials, averaging and subtracting highly time-locked components. Finally, trial discontinuities are adjusted, and the same spike detection is applied to both ground truth and cleaned data for comparison.
Results
We evaluated logLIRA against three stimulus artifact rejection algorithms (dynamic averaging [5], global polynomial fitting [10], and SALPA [4]) using a semisynthetic dataset as ground truth. Root-mean-square error and cross-correlation at zero lag were calculated for varying mean firing and artifact rates. SALPA and logLIRA outperformed their competitors, excelling in both metrics (Fig. 1A). Notably, logLIRA significantly reduced the blanking interval duration (Fig. 1B), enabling better recovery of short-latency evoked responses while controlling secondary artifacts and thus false positives. Though not fully evident in the semisynthetic dataset lacking direct stimulus-spike correlation, this advantage is obvious in real data (Fig. 1C).
Discussion
With this work we introduced a reliable and effective method for the rejection of stimulus artifacts, highlighting the importance of handling secondary artifacts emerging from a reduced blanking interval or poor suppression due to numerous factors, including signal saturation. A trustworthy recovery of short-latency evoked activity is poised to greatly benefit neuroscientific research: logLIRA could improve the estimation of mesoscale effective connectivity by means of SEEC method [10], aiding in the understanding of cortex stimulation-driven functional reorganization [1, 3], and eventually enhancing the effectiveness of neuroprosthetic systems aimed at treating neuropathologies, improving the life quality of millions of patients [1-3, 11].
Figure 1. Performance comparison of stimulus artifact rejection algorithms on both semisynthetic and real data. A. Cross-correlation at zero lag for different values of mean artifact rate. B. Blanking intervals distribution for logLIRA and SALPA in the benchmark dataset. C. Example of recovered short-latency evoked activity from a real signal. The red vertical bars depict the 1 ms blanking interval.
Acknowledgements
Work supported by #NextGenerationEU (NGEU) and funded by the Italian Ministry of University and Research (MUR), National Recovery and Resilience Plan (PNRR), project MNESYS (PE0000006) - (DN. 1553 11.10.2022).
References
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