# Combined EEG and in silico modeling to investigate the mechanisms of ketamine's sustained antidepressant effect in patients

> **NIH NIH R21** · YALE UNIVERSITY · 2022 · $196,350

## Abstract

ABSTRACT
 Depression affects around ten percent of people in the United States. Two-thirds of these patients do
not respond to traditional antidepressants and are diagnosed with treatment-resistant depression (TRD). Also,
conventional antidepressants take approximately three to four weeks to show effects. Ketamine, a drug that
has been traditionally used as an anesthetic agent, represents a promising hope. At doses lower than those
utilized for anesthesia, ketamine has been found to improve depressive symptoms in more than half of the
patients diagnosed with TRD. Recently, an isomer of ketamine, esketamine, has been approved by the FDA
for the treatment of TRD.
 Repeated dosing of ketamine and esketamine augment their antidepressant effect, prolonging the
therapeutic benefit from a few days to up to a few weeks and increasing response and remission rates. Animal
models of depression have shown that a single dose of ketamine works by increasing neuroplasticity, the
ability of the brain to change and adapt. However, how repeated ketamine treatments augment the
antidepressant effect is not known. Understanding the underlying mechanism of augmented therapeutic effect
in humans would make it possible to a) prolong ketamine’s antidepressant effect beyond a few weeks, b)
increase response and remission rates, and c) develop novel molecules with better response and remission
rates. Therefore, we are proposing to combine two techniques to understand the brain changes associated
with the augmented improvements in patients.
 The first technique is electroencephalography (EEG), which monitors the electrical activity of the
neocortex, the part of the brain involved in memory, decision making, and mood, features that are affected in
depression. Using an EEG task that measures neuroplasticity, we will probe the neocortex to identify changes
in neuroplasticity associated with the sustained antidepressant effects in patients diagnosed with TRD treated
with repeated ketamine dosing. They will undergo the EEG task before they begin their treatment, after their
first treatment, and then again after they finish all their treatments.
 The second technique is computer modeling of the neocortex using a computer model that has been
developed to understand the brain mechanisms generating EEG, called the Human Neocortical Neurosolver
(HNN). We will use HNN to make mechanistic interpretations of the neocortical mechanisms underlying the
EEG changes associated with the sustained antidepressant effect. This will identify the neocortical changes
responsible for the sustained therapeutic response, which may allow for better treatment targeting.

## Key facts

- **NIH application ID:** 10376804
- **Project number:** 5R21MH125199-02
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** PATRICK David SKOSNIK
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $196,350
- **Award type:** 5
- **Project period:** 2021-03-23 → 2023-08-21

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10376804

## Citation

> US National Institutes of Health, RePORTER application 10376804, Combined EEG and in silico modeling to investigate the mechanisms of ketamine's sustained antidepressant effect in patients (5R21MH125199-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10376804. Licensed CC0.

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