# A novel approach based on dynamical systems theory to unravel large-scale dysfunction underlying neuropsychiatric disorders

> **NIH NIH F30** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2020 · $50,520

## Abstract

Project Summary/Abstract
One of the fundamental challenges that exist in the field of psychiatry and cognitive science is to better under-
stand how multiple brain areas work together to produce emergent behavior and cognition. The brain's ability to
precisely coordinate these areas in the face of a constantly changing environment is important for maintaining
normal psychosocial and cognitive functioning. Complex mental disorders, such as schizophrenia, affect multiple
interacting dynamical systems (including sensory processing and attention) subserving cognition. The degree to
which each system is affected may be related to the varying degrees of cognitive impairment observed in these
disorders. Characterizing the dynamical states of these systems non-invasively via electroencephalography
(EEG) can provide an avenue for identifying clinically useful biomarkers.
This research proposal seeks to develop a novel computational method based on dynamical systems theory
to detect nonlinear systems architectures hidden in EEG signals and to relate these dynamical signatures to
psychosocial/cognitive functioning and the underlying brain network dynamics. Specifically, the three aims of
the proposal are: (1) to develop a new clustering method to group brain signals based on nonlinear dynamical
states, (2) to apply the method to a large EEG dataset to identify nonlinear dynamical features associated with
psychosocial functioning, and (3) to relate nonlinear dynamical states of EEG signals to the underlying network
interactions. A large EEG recording dataset from patients diagnosed with schizophrenia will be used to probe
dynamical states corresponding to neurocognitive deficits, and a simultaneous EEG-fMRI dataset from patients
with absence epilepsy will be used to establish a relationship between EEG dynamical features and functional
networks critical for cognition and attention. Together, these aims have potential to identify unique EEG dynam-
ical states that are indicative of cognitive deficits and network dysfunction present in neuropsychiatric disorders.
In summary, investigating EEG nonlinear system features can reveal disorder-specific biomarkers that can help
identify individuals at risk, make early diagnosis, and monitor intervention/treatment responses.

## Key facts

- **NIH application ID:** 9984528
- **Project number:** 5F30MH115605-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Robert Kim
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $50,520
- **Award type:** 5
- **Project period:** 2018-09-01 → 2021-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9984528

## Citation

> US National Institutes of Health, RePORTER application 9984528, A novel approach based on dynamical systems theory to unravel large-scale dysfunction underlying neuropsychiatric disorders (5F30MH115605-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9984528. Licensed CC0.

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