# Neocortical-Hippocampal Circuits Underlying Pattern Separation in Humans

> **NIH NIH F30** · UNIVERSITY OF CALIFORNIA-IRVINE · 2020 · $40,794

## Abstract

Project Summary
Memories are crucial to our lives. With them, we generate a unique history, connect with the world around us,
and make informed decisions. Memory loss occurs as we get older, and more pervasive changes in memory
function can be among the earliest signs of Alzheimer’s disease, a major public health challenge affecting more
than 5.6 million Americans. Past studies have shown that the ability to discriminate among similar mnemonic
experiences (i.e. pattern separation) is an early sign of memory impairment in older adults at risk for Alzheimer’s
disease. This disruption in memory is thought to be an early harbinger of subsequent cognitive decline and may
be a suitable therapeutic target in the earliest stages of the disease. However, such targeting is impossible
without a complete understanding of the circuit-level dynamics that support pattern separation in humans. While
computational theories have long suggested a necessary role for interactions between the hippocampus and the
neocortex, the field has struggled with challenges in identifying such network level dynamics in humans due to
limited temporal resolution using fMRI and limited spatial resolution using EEG. I propose to fill this gap in
knowledge using a rare and unique opportunity to record from both the neocortex and the hippocampus with
superior spatial and temporal resolution in humans implanted with intracranial electrodes for clinical monitoring
while they engage in a pattern separation memory task. I will collect neural recording data from a minimum of
15 patients undergoing clinical monitoring with surgically implanted depth electrodes in the hippocampus and
the neocortex at the UCI Comprehensive Epilepsy Unit. I propose three specific aims: (1) Test the hypothesis
that increased theta power in the hippocampus and neocortex will predict successful discrimination performance
on pattern separation task; (2) Test the hypothesis that during encoding, hippocampal-neocortical interactions
will be directionally biased such that the hippocampus leads the cortex, reflecting the integration of newly learned
information into neocortical sites; and (3) Test the hypothesis that during retrieval, hippocampal-neocortical
interactions will be directionally biased such that the cortex leads the hippocampus, reflecting the access of
memory content from neocortical sites. The proposed studies are feasible with the excellent research and
training environment at UC Irvine and the availability of clinical infrastructure and an epilepsy monitoring unit with
research-dedicated recording equipment. Using these resources, I have already collected preliminary data in
support of all three hypotheses. With the joint mentorship of Dr. Michael Yassa and Dr. Jack Lin, I will receive
advanced training in clinical and cognitive neuroscience. With additional mentorship of Dr. Lee Swindlehurst in
electrical engineering, I am employing techniques to analyze high dimensional data and directionality measu...

## Key facts

- **NIH application ID:** 10068898
- **Project number:** 1F30AG069406-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA-IRVINE
- **Principal Investigator:** Sandra Gattas
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $40,794
- **Award type:** 1
- **Project period:** 2020-09-30 → 2025-02-24

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10068898

## Citation

> US National Institutes of Health, RePORTER application 10068898, Neocortical-Hippocampal Circuits Underlying Pattern Separation in Humans (1F30AG069406-01). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10068898. Licensed CC0.

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