Memory-related processing is the primary driver of human hippocampal theta oscillations.

Decades of work in rodents suggest that movement is a powerful driver of hippocampal low-frequency "theta" oscillations. Puzzlingly, such movement-related theta increases in primates are less sustained and of lower frequency, leading to questions about their functional relevance. Verbal memory encoding and retrieval lead to robust increases in low-frequency oscillations in humans, and one possibility is that memory might be a stronger driver of hippocampal theta oscillations in humans than navigation. Here, neurosurgical patients navigated routes and then immediately mentally simulated the same routes while undergoing intracranial recordings. We found that mentally simulating the same route that was just navigated elicited oscillations that were of greater power, higher frequency, and longer duration than those involving navigation. Our findings suggest that memory is a more potent driver of human hippocampal theta oscillations than navigation, supporting models of internally generated theta oscillations in the human hippocampus.

Machine learning classifiers for electrode selection in the design of closed-loop neuromodulation devices for episodic memory improvement.

Successful neuromodulation approaches to alter episodic memory require closed-loop stimulation predicated on the effective classification of brain states. The practical implementation of such strategies requires prior decisions regarding electrode implantation locations. Using a data-driven approach, we employ support vector machine (SVM) classifiers to identify high-yield brain targets on a large data set of 75 human intracranial electroencephalogram subjects performing the free recall (FR) task. Further, we address whether the conserved brain regions provide effective classification in an alternate (associative) memory paradigm along with FR, as well as testing unsupervised classification methods that may be a useful adjunct to clinical device implementation. Finally, we use random forest models to classify functional brain states, differentiating encoding versus retrieval versus non-memory behavior such as rest and mathematical processing. We then test how regions that exhibit good classification for the likelihood of recall success in the SVM models overlap with regions that differentiate functional brain states in the random forest models. Finally, we lay out how these data may be used in the design of neuromodulation devices.