# Neural Correlates of Complex Multi-Choice Decisions

> **NIH NIH K00** · JOHNS HOPKINS UNIVERSITY · 2021 · $68,742

## Abstract

The brain can be viewed as an extremely complex and high-dimensional dynamical system. Despite
its complexity, only very limited measures of brain activity are generally accessible to recording–e.g.
the electroencephalogram (EEG). Nonlinear dynamics provides the tools to extract information from
a limited measurement to determine the invariant nonlinear properties of the underlying dynamical
system. In Delay Differential Analysis (DDA), a low-dimensional nonlinear functional embedding is
built from the dynamical structure of the data; this serves as a basis onto which the data can be
mapped. By constraining the models used to low dimensionality, we ensure that DDA is immune to
overfitting, insensitive to noise, and generalizes well to new data. DDA has already been applied to
human intracranial recordings of sleep to detect sleep spindles and characterize their spatiotemporal
development. In the proposed project, this method will also be applied to EEG data from a large study
of schizophrenia. In both of these datasets, distinct observed phenomena can be linked to different
underlying cortical states. By finding DDA models which detect sleep spindles, insights can be gained
into their dynamics, and this information can be used to refine sophisticated circuit models for their
generation. Likewise, by finding models which reliably distinguish schizophrenia patients from control
subjects, we can develop a better understanding of the dynamical differences that might give rise to
sensory processing deficits and other symptoms of schizophrenia. Further extensions of this work
could help to address aditional questions related to functionally distinct states of the brain including in
additional neurological and psychiatric disorders.

## Key facts

- **NIH application ID:** 10241377
- **Project number:** 5K00NS105204-04
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Aaron L Sampson
- **Activity code:** K00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $68,742
- **Award type:** 5
- **Project period:** 2019-09-30 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10241377

## Citation

> US National Institutes of Health, RePORTER application 10241377, Neural Correlates of Complex Multi-Choice Decisions (5K00NS105204-04). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10241377. Licensed CC0.

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