Alice Tor

All Publications

• Material damage to multielectrode arrays after electrolytic lesioning is insignificant. eLife Tor, A., Clarke, S. E., Bray, I. E., Nuyujukian, P. 2026; 14

  Abstract

  The quality of stable long-term recordings from chronically implanted electrode arrays is essential for experimental neuroscience and brain-computer interfaces. This work uses scanning electron microscopy (SEM) to image and analyze eight 96-channel Utah arrays previously implanted in motor cortical regions of four subjects (subject H = 2242 days implanted, F = 1875, U = 2680, C = 594), providing important contributions to a growing body of long-term implant research leveraging this imaging technology. Four of these arrays have been used in electrolytic lesioning experiments (H = 10 lesions, F = 1, U = 4, C = 1), a recently developed electrolytic perturbation technique demonstrated compatible with continued neuroelectrophysiology using small direct currents. Previously, our group showed that electrolytic lesioning can be used as a technique to create regions of controlled neuron loss without significantly changing recording quality (Bray, Clarke et al., 2024). Here, by surveying physical damage such as biological debris and material deterioration, we show that electrolytic lesioning causes no statistically significant material damage to the implanted electrode arrays. In addition to surveying physical damage, such as biological debris and material deterioration, this work also analyzes whether electrolytic lesioning created damage beyond what is typical for these arrays. These findings also indicate that there are no statistically significant differences between the damage observed on normal electrodes versus those used for electrolytic lesioning, yielding no evidence that electrolytic lesioning significantly affects the material quality of chronically implanted electrode arrays. Finally, this work also includes the largest collection of single-electrode SEM images for previously implanted multielectrode Utah arrays, spanning 11 different intact arrays and one broken array. As the clinical relevance of chronically implanted electrodes with single-neuron resolution continues to grow, these images may be used to provide the foundation for a larger public database and inform further electrode design and analyses.

  View details for DOI 10.7554/eLife.106452

  View details for PubMedID 42318605

• Compression detects changes in spiking neural data from cortical lesions. Journal of neural engineering Tor, A., Wu, Y., Clarke, S. E., Yamada, L., Weissman, T., Nuyujukian, P. 2026

  Abstract

  The complexity of neural data changes as the brain processes information during events. Universal lossless compression algorithms, which are broadly applicable and grounded in information theory, identify and exploit redundancies in data in order to compress it to essentially-optimal sizes regardless of underlying statistics. These algorithms may be used to efficiently estimate a signal's Shannon entropy rate, a biologically relevant measure of the complexity of a signal. It is therefore natural to explore their effectiveness in the analysis of spiking neural data. Approach: This work uses the inverse compression ratio (ICR) to analyze recordings (Utah arrays) taken from motor cortex of animals performing reaching tasks three days before and three days after administering electrolytic lesions (Subject U: 4 lesions, H: 3). We calculate ICR with temporally-independent lossless compression (gzip) and temporally-dependent lossy compression (H.264, MPEG-2). Compression-based ICR was compared to single-neuron measures used to understand spiking data (average firing rates and Fano factor), as well as common dimensionality reduction techniques (principal component analysis and factor analysis). Main Results: ICR is able to significantly (Mann-Whitney U test, p<0.01) detect lesions with higher accuracy than single-neuron metrics, but not dimensionality reduction (ICR methods: 85.7%, single-neuron methods: 78.6%, dimensionality reduction: 100%). Additionally, statistical results on the same data show that ICR metrics remain more stable than single-neuron methods after lesion. The bitrate parameter of lossy compression algorithms is swept to better understand the effect of information rates and "optimal" compression on lesion detection performance. Simulated data shows that ICR is computationally advantageous. Significance: These results suggest that compression algorithms may be a useful tool to detect and better understand perturbations to the underlying structure of neural data. Information-theoretic analyses may complement techniques like dimensionality reduction and firing rate tuning as a convenient and useful tool to characterize neural data.

  View details for DOI 10.1088/1741-2552/ae555b

  View details for PubMedID 41861401