Diversify your workflow! - An inconvenient suggestion to analyze spike data from intracranial recordings

Abstract

An important challenge of neuroscience research and future brain machine interfacing is the reliable assignment of spikes to putative neurons. By means of extracellular recordings, researchers try to match different types action potentials with their putative neuronal source and timing. Unfortunately, this procedure is by far not standardized and reliable, leading to many different suggestions and as many differing results. It appears that sharing of data is thus hampered by different processing pipelines in different labs, thus playing along the reproducibility crisis in neurosciences. To systematically shed light on this issue, we present preliminary results of several easy event detection schemes on one data set, meant to illustrate the inconsistencies arising from different processing pipelines already in its initial step. The results indicate that thresholding choices alter findings due to a lack of a ground truth for spike sorting. We suggest to increase reliability in findings by only accepting and further processing events accepted by more than one processing pipeline.

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

“…As it can be seen, filtering and spike detection are the lead-in operations in spike sorting; therefore, the quality of feature extraction and clustering is greatly impacted by detection algorithm performance, but even if data have been vigorously curated, spotting spike candidates remains a challenge (Okkesim et al, 2021 ). Filters may be an excellent support for threshold crossing event detection algorithms (Yang et al, 2017 ; Saggese et al, 2021 ), although more complicated methods, such as correlation-based detection, wavelet decomposition (Gao et al, 2018 ), Bayesian shrinkage methods (Sousa et al, 2021 ), and Teager or smoothed non-linear energy operators may also profit from them (Pagin and Ortmanns, 2017 ; Tambaro et al, 2020 ).…”

“…In the search for accurate and real-time functioning, computationally efficient algorithms, difficulties such as involuntary drifts, temporally coincident, overlapping, spikes, or even obstacles given by the ever-increasing recording capacity are generated. Disregarding energy consumption especially when implantable devices are considered can lead to obstacles in practice (Mukhopadhyay et al, 2018 ; Okkesim et al, 2021 ). Last but not least, we should also engage in assumptions that are made before recording, since electromagnetic field theory presumes extracellular space isotropy and homogeneity; however, e.g., privileged cell orientations and the bare existence of neural probes in a tissue render simple models inaccurate (Buccino et al, 2019 ).…”