Abstract
Because multivariate autoregressive models have failed to adequately account for the complexity of neural signals, researchers have predominantly relied on non-parametric methods when studying the relations between brain and behavior. Using medial temporal lobe (MTL) recordings from 96 neurosurgical patients, we show that time series models with volatility described by a multivariate stochastic latent-variable process and lagged interactions between signals in different brain regions provide new insights into the dynamics of brain function. The implied volatility inferred from our process positively correlates with high-frequency spectral activity, a signal that correlates with neuronal activity. We show that volatility features derived from our model can reliably decode memory states, and that this classifier performs as well as those using spectral features. Using the directional connections between brain regions during complex cognitive process provided by the model, we uncovered perirhinal-hippocampal desynchronization in the MTL regions that is associated with successful memory encoding.
Highlights
Recent advances in neuroscience have enabled researchers to measure brain function with both high spatial and temporal resolution, leading to significant advances in our ability to relate complex behaviors to underlying neural signals
The multivariate stochastic volatility framework that we propose allows for non-stationary variance in the signals
The multivariate stochastic volatility (MSV) models proposed in this paper provide a new framework for studying multi-channel neural data and relating them to cognition
Summary
Recent advances in neuroscience have enabled researchers to measure brain function with both high spatial and temporal resolution, leading to significant advances in our ability to relate complex behaviors to underlying neural signals. This generalization is feasible due to high-temporal-resolution neural time series collected using the intracranial electroencephalography. Having established that the MSV model outperforms the more traditional SV approach, and having shown that the implied volatility of the series reliably correlates with high-frequency neural activity, we asked whether we can use the model-derived time series of volatility to predict subjects’ behavior in a memory task. To benchmark our MSV findings, we conducted parallel analyses of wavelet-derived spectral power at frequencies ranging between 3 and 180 Hz. To aggregate across MTL electrodes within each subject we applied an L2penalized logistic regression classifier using features extracted during the encoding period to predict subsequent memory performance (Ezzyat et al, 2017; Ezzyat et al, 2018). Find any other significant directional connections among the remaining regions (Figure 5, Appendix 7—tables 1 and 2)
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