# Computational mechanisms of memory disruption in depression

> **NIH NIH R01** · MCLEAN HOSPITAL · 2020 · $410,000

## Abstract

Relative to healthy adults, depressed individuals typically show excellent memory for negative material but
poor memory for positive material. Furthermore, depression impairs recollection—the ability to retrieve vivid,
contextual details about an event. These abnormalities trouble patients and appear to prolong depressive
episodes, but they are not well understood. Therefore, this proposal will use multi-modal neuroimaging and
computational modeling to investigate the encoding and retrieval of emotional memories in depressed adults.
To investigate categorical effects of depression, electroencephalogram (EEG)/event-related potential
(ERP) data will be collected from 64 unmedicated adults with MDD and 64 healthy controls (n = 128). To
investigate dimensional effects of depression, functional magnetic resonance imaging (fMRI) data will be
acquired from adults selected for minimal, mild, moderate, or severe depressive symptoms (n = 36). On Day 1,
the participants will study negative and positive words in the context of two encoding tasks. On Day 2, they will
return for a recognition memory test in which the “old” encoded words will be presented with similar “new”
words. When a participant recognizes an old word, source memory (recollection) will be tested by asking which
task the word was encoded with. Day 2 will include exposure to acute stress, to potentiate emotional biases.
 This comprehensive design will support three aims. Aim 1 will use EEG/ERP to test the hypothesis that
MDD blunts cortical responses to positive vs. negative stimuli at encoding and retrieval. We expect ERPs
linked to memory formation and retrieval to be reduced for positive material, but not negative material, in adults
with MDD vs. controls. Moreover, we expect such effects to be exaggerated after stress exposure. Importantly,
the EEG/ERP methodology cannot detect activity in subcortical brain regions important for memory, such as
the amygdala. Therefore, Aim 2 will use fMRI to test the hypothesis that depressive severity correlates with
activation in subcortical structures that support retrieval. We expect that as depressive severity increases,
activation of the amygdala, hippocampus, and parietal cortex to negative memory probes will increase. By
contrast, activation of the striatum, hippocampus, and parietal cortex to positive memory probes should
decrease. Finally, to gain insight into the underlying mechanisms that support memory, Aim 3 will use the
HDDM to reveal the impact of depression on decision-making at retrieval. The Hierarchical Drift Diffusion
Model (HDDM) is a computational model that can estimate the evidence accumulation process that enables us
to choose between two options (e.g., old vs. new). We predict that the speed of evidence accumulation—drift
rate—will be reduced for positive, but not negative, memory probes in depressed adults. Moreover, increased
depression is expected to weaken relationships between drift rate and EEG/fMRI signals that support me...

## Key facts

- **NIH application ID:** 9823890
- **Project number:** 5R01MH111676-02
- **Recipient organization:** MCLEAN HOSPITAL
- **Principal Investigator:** DANIEL G DILLON
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $410,000
- **Award type:** 5
- **Project period:** 2018-12-01 → 2023-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9823890

## Citation

> US National Institutes of Health, RePORTER application 9823890, Computational mechanisms of memory disruption in depression (5R01MH111676-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9823890. Licensed CC0.

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