Identifying deficient memory replay as a potentially treatable mechanism of impaired sleep-dependent memory consolidation in schizophrenia

About This Grant

Cognitive deficits are the strongest predictor of functional outcome in in schizophrenia (SZ). Even after psychotic symptoms have been controlled with antipsychotic drugs, debilitating cognitive deficits persist. The lack of a mechanistic understanding of cognitive deficits is a major impediment to developing effective treatments to improve cognition. Novel approaches to understanding the mechanisms of cognitive deficits are needed to guide the development and evaluation of interventions that promote recovery. This is the unmet need that this research proposal addresses. Sleep oscillations are causally related to memory consolidation. In SZ, a deficit in sleep spindles correlates with impaired sleep-dependent memory consolidation (SDMC), suggesting it as a potential mechanism. But increasing spindles with drugs has not led to memory improvement. This likely reflects that spindles do not act alone. Memory consolidation also requires the participation of the hippocampus, which replays memories during sharp wave ripples in the sleep that follows learning. The precisely timed dialogue between cortical slow oscillations, thalamocortical spindles, and hippocampal ripples mediates the transfer of memories from temporary representation in the hippocampus to longer term storage in the cortex (i.e., SDMC). Converging evidence from studies of hippocampal structure and function in SZ, from neuropathology to animal models to neuroimaging, all point to deficient replay as a mechanism of impaired SDMC in SZ. Yet, the contribution of hippocampal replay to SDMC deficits in SZ is yet to be examined. This likely reflects that reliable measurement of hippocampal replay depends on invasive recordings. In this 5-year Mentored Research Scientist Development Award the applicant will use data from epilepsy patients undergoing clinically indicated scalp EEG and intracranial hippocampal recordings to develop a machine learning algorithm to detect hippocampal replay noninvasively based on scalp EEG; apply it to archival data to examine, for the first time, whether individuals with SZ have memory replay deficits during sleep; and evaluate closed-loop auditory stimulation during sleep (CLASS) as a potential intervention to augment memory replay and improve SDMC in SZ. If successful, this research will develop memory replay as a novel EEG biomarker of SDMC; demonstrate that it is impaired in SZ, introduce CLASS as a promising, safe, and potentially scalable intervention to improve SDMC in SZ; and generate pilot data for a future R01. To ensure its success, the applicant will engage in advanced courses and individual instruction in (1) the pathophysiology of SZ, cognitive neuroscience, and clinical trials methodology; (2) machine learning to detect memory replay; and (3) hippocampal physiology and intracranial data analysis. The proposed research, training plan, and the mentorship/advisory team will all support the applicant’s transition to independence and long-term goal of leading a laboratory focused on identifying neurophysiological biomarkers of cognitive deficits in neuropsychiatric disorders and neuromodulation-based interventions to treat these.