# Validating of Machine Learning-Based EEG Treatment Biomarkers in Depression

> **NIH NIH R44** · ALTO NEUROSCIENCE, INC. · 2022 · $1,016,513

## Abstract

SUMMARY/ABSTRACT
The overarching aim of Alto Neuroscience is to advance brain-based biomarkers for psychiatric disorders in
order to both optimize treatment pathways and drive the development of novel pharmacological and non-
pharmacological interventions. Alto does this by developing and applying sophisticated machine learning
computational models to electroencephalography (EEG) data collected at scale in real-world clinical treatment
contexts. Specifically, in this direct-to-phase II SBIR proposal we will refine, and then independently validate,
two EEG-based candidate biomarkers we have identified for stratifying patients with depression in a manner that
both factors biological heterogeneity and informs treatment response. One of our biomarkers was derived in a
“top-down” (i.e. supervised) manner by trying to directly predict treatment outcome, while the other biomarker
presents a complimentary “bottom-up” (i.e. unsupervised) approach that begins by first identifying the most
biologically homogeneous subset of patients and then testing the treatment relevance of the subtyping. Together,
these findings represent very robust individual patient-level treatment-relevant EEG biomarkers, and in both
cases, help define a critically-important objective approach to prospectively identifying and treating treatment-
resistant depressed patients. A successful outcome of the proposed work would yield the first FDA-cleared
biomarkers for stratifying psychiatric conditions. It would also provide a basis for targeted development of
pharmacological and non-pharmacological interventions based on the EEG biomarkers. Both outcomes hold
substantial commercial value and exciting potential for transforming psychiatry.

## Key facts

- **NIH application ID:** 10366060
- **Project number:** 5R44MH123373-03
- **Recipient organization:** ALTO NEUROSCIENCE, INC.
- **Principal Investigator:** Amit Etkin
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $1,016,513
- **Award type:** 5
- **Project period:** 2020-03-01 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10366060

## Citation

> US National Institutes of Health, RePORTER application 10366060, Validating of Machine Learning-Based EEG Treatment Biomarkers in Depression (5R44MH123373-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10366060. Licensed CC0.

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