Yuxin Wu

All Publications

• 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

• MRI Retrospective Respiratory Gating and Cardiac Sensing by CW Doppler Radar: A Feasibility Study IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING Lee, W., Ryu, K., Li, Z., Oscanoa, J., Wu, Y., Robb, F., Vasanawala, S., Pauly, J., Scott, G. 2025; 72 (1): 112-122

  Abstract

  This study investigates the feasibility of non-contact retrospective respiratory gating and cardiac sensing using continuous wave Doppler radar deployed in an MRI system. The proposed technique can complement existing sensors which are difficult to apply for certain patient populations.We leverage a software-defined radio for continuous wave radar at 2.4 GHz to detect in-vivo respiratory and cardiac timescrolled signals. In-bore radar signal demodulation is verified with full electromagnetic simulations, and its functionality is validated on a test bench and within the MR bore with four normal subjects. Radar sensing was compared against well-known references: electrocardiography on a test bench, system bellows, and pulsed plethysmography sensors with in the MRI bore.The feasibility of noncontact cardiac rate sensing, dynamic breathing sequence synchronization, and in-bore motion correction for retrospective respiratory gating applications was demonstrated. Optimal radar front-end system arrangement, along with spectral isolation and narrow bandwidth of operation, enable MRI-compatible and interference-free motion sensing. The signal-to-noise-ratio degradation by the radar integration was within 4.5% on phantom images.We confirmed that in-bore retrospective motion correction using CW Doppler radar is feasible without MRI system constraints.Non-contact motion correction sensing in MRI may provide better patient handling and through put by complementing existing system sensors and motion correction algorithms.

  View details for DOI 10.1109/TBME.2024.3440317

  View details for Web of Science ID 001398974700003

  View details for PubMedID 39115989