Existing data from noninvasive studies have led researchers to posit that the posterior cingulate cortex (PCC) supports mnemonic processes: It exhibits degeneration in memory disorders, and fMRI investigations have demonstrated memory-related activation principally during the retrieval of memory items. Despite these data, the role of the PCC in episodic memory has received only limited treatment using the spatial and temporal precision of intracranial EEG, with previous analyses focused on item retrieval. Using data gathered from 21 human participants who underwent stereo-EEG for seizure localization, we characterized oscillatory patterns in the PCC during the encoding and retrieval of episodic memories. We identified a subsequent memory effect during item encoding characterized by increased gamma band oscillatory power and a low-frequency power desynchronization. Fourteen participants had stereotactic electrodes located simultaneously in the hippocampus and PCC, and with these unique data, we describe connectivity changes between these structures that predict successful item encoding and that precede item retrieval. Oscillatory power during retrieval matched the pattern we observed during encoding, with low-frequency (below 15 Hz) desynchronization and a gamma band (especially high gamma, 70-180 Hz) power increase. Encoding is characterized by synchrony between the hippocampus and PCC, centered at 3 Hz, consistent with other observations of properties of this oscillation akin to those for rodent theta activity. We discuss our findings in light of existing theories of episodic memory processing, including the information via desynchronization hypothesis and retrieved context theory, and examine how our data fit with existing theories for the functional role of the PCC. These include a postulated role for the PCC in modulating internally directed attention and for representing or integrating contextual information for memory items.

Memory encoding is an essential step for all learning. However, the genetic and molecular mechanisms underlying human memory encoding remain poorly understood, and how this molecular framework permits the emergence of specific patterns of brain oscillations observed during mnemonic processing is unknown. Here, we directly compare intracranial electroencephalography recordings from the neocortex in individuals performing an episodic memory task with human gene expression from the same areas. We identify genes correlated with oscillatory memory effects across 6 frequency bands. These genes are enriched for autism-related genes and have preferential expression in neurons, in particular genes encoding synaptic proteins and ion channels, supporting the idea that the genes regulating voltage gradients are involved in the modulation of oscillatory patterns during successful memory encoding across brain areas. Memory-related genes are distinct from those correlated with other forms of cognitive processing and resting state fMRI. These data are the first to identify correlations between gene expression and active human brain states as well as provide a molecular window into memory encoding oscillations in the human brain.

Surgical resection of portions of the temporal lobe is the standard of care for patients with refractory mesial temporal lobe epilepsy. While this reduces seizures, it often results in an inability to form new memories, which leads to difficulties in social situations, learning, and suboptimal quality of life. Learning about the success or failure to form new memory in such patients is critical if we are to generate neuromodulation-based therapies. To this end, we tackle the many challenges in analyzing memory formation when their brains are recorded using stereoencephalography (sEEG) in a Free Recall task. Our contributions are threefold. First, we compute a rich measure of brain connectivity by computing the phase locking value statistic (synchrony) between pairs of regions, over hundreds of word memorization trials. Second, we leverage the rich information (over 400 values per pair of probed brain regions) to form consistent length feature vectors for classifier training. Third, we train and evaluate seven different types of classifier models and identify which ones achieve the highest accuracy and which brain features are most important for high accuracy. We assess our approach on data from 37 patients pre-resection surgery. We achieve up to 73% accuracy distinguishing successful from unsuccessful memory formation in the human brain from just 1.6 sec epochs of sEEG data.

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