Pre-stimulus neural dynamics predict TMS responses: The role of fractal dimension and oscillatory activity

The brain operates near 'criticality' balancing stability and adaptability for optimal function. Deviations from this state alter brain responses or signal neurological disorders. TMS-EEG is a powerful tool for studying criticality. In this study, we investigate how pre-stimulus features of activity (power) and signal complexity influence the amplitude of stimulus-related TMS evoked potentials (TEPs).

We used a publicly available TMS-EEG dataset from 20 healthy individuals. TMS was applied to the left primary motor area (M1). We analyzed pre-stimulus features to measure power across frequency bands and Higuchi's Fractal Dimension (HFD), a measure of signal complexity. We examined the relationship between pre-stimulus estimated features and post-stimulus TEP amplitude across trials using machine learning.

Stronger TEPs were measured when pre-stimulus activity contained lower gamma power and higher alpha power. Interestingly, lower pre-stimulus fractal dimension values, reflecting less complex baseline activity, were associated with increased TEP amplitudes. Partial correlation analysis showed that HFD (R = -0.30) was the most influential feature in predicting post-stimulus TEP. Machine learning models provided accurate predictions of post-stimulus TEP using the pre-stimulus EEG features (R² = 0.6867).

Our findings are consistent with the brain criticality framework highlighting how shifts in pre-stimulus TEP dynamics influence cortical responsiveness. Reduced gamma power and fractal dimension were associated with stronger TEP amplitudes, suggesting that states closer to order optimize responsiveness-conversely, higher gamma power and increased HFD, indicative of chaotic neuronal dynamics, dampened perturbation responses. The opposite effect of alpha power highlights the frequency-specific nuances of criticality, where inhibitory mechanisms may fine-tune responsiveness.