Abstract
BACKGROUND: Covert stroke is understudied despite occurring ten times for every symptomatic stroke and contributing to stroke's enormous global disease burden. For instance, it is unknown whether covert stroke replicates the peri-infarct neuroelectrophysiological frequency spectrum impact of symptomatic stroke. This motivated the use of our novel electrolytic lesioning platform to explore the spectral consequences of covert neuron loss. METHODS: During a multi-month period of participation in an arm reaching task, neuron loss was induced via electrolytic lesions to the motor cortex of two large animals (U: n = 4; H: n = 7). Behavioral metrics were paired with spectrum estimates from local field potential recordings. These spectra were represented as bandpowers, aperiodic/periodic parameters, and decomposed time-frequency tensors. Lesion impact was assessed using nonparametric permutation tests of next-day effects and state space model parameters. RESULTS: Task success rate was unaffected by lesions, but shifts in aperiodic structure reduced next-day γ bandpower (30 - 100 Hz; U: -1.38µV (2) , p < 1 × 10 (-) (3) ; H: -1.66µV (2) , p = 0.001) while sensorimotor rhythms spanning 8 - 45 Hz (Σ SMR ) were amplified (Monkey U: 8.68µV (2) , p < 1 × 10 (-) (3) ; Monkey H: 2.40µV (2) , p = 0.004). Additionally, state space modeling showed that spectral perturbations to γ and Σ SMR outlasted any behavioral impact of the lesions (Monkey U: Behavior=1 d, γ =2 d, Σ SMR =2 d; Monkey H: Behavior=0 d, γ =3 d, Σ SMR =1 d). Finally, tensor decomposition revealed interpretable, animal-specific perturbations to time-frequency dynamics. CONCLUSION: Covert neuron loss induced multi-day spectral perturbations similar to those observed after symptomatic stroke. The neural spectrum is thus more sensitive to neuron loss than previously understood and could be responsive to neuron loss caused by covert stroke. This work also motivates the use of electrolytic lesions to bridge covert and symptomatic regimes of neuron loss, advancing our causal understanding of post-stroke interactions between spectrum and behavior.