# Disentangling periodic and aperiodic neural activity in the first two years of life: Contributions of perinatal maternal distress on neurodevelopment and childhood psychopathology risk

> **NIH NIH K99** · PENNSYLVANIA STATE UNIVERSITY, THE · 2022 · $102,846

## Abstract

Project Summary
 Cognition depends on efficient and flexible neural communication. Disrupted neuronal synchronization may
thwart efficient neural communication and undercut cognitive mechanisms that support engagement of
attentional resources. Disrupted information processing can be indexed by the aperiodic exponent, which
describes the expected exponential decrease in power across increasing frequencies of the
electroencephalogram (EEG) power spectrum. This novel neural marker is associated with cognitive deficits and
psychopathology from late childhood through old age. We do not know, however, whether changes in the
aperiodic exponent affect cognitive and behavioral development in infancy. Behavioral inhibition (BI) and reactive
attentional processes (i.e., attention bias to threat, vigilance) are two core risk factors that may be influenced by
individual differences in aperiodic neural activity. Early attentional biases operate as a cognitive mechanism
prospectively linking BI to childhood anxiety. A smaller aperiodic exponent indexes greater excitation relative to
inhibition in cortical circuits. This pattern of neural activity may affect how infants access and process
environmental input, which may potentiate attentional biases and increase risk for BI. Aperiodic exponent
trajectories may be also susceptible to environmental influences, such as maternal distress (e.g., perceived
stress, anxiety), that may be amenable to targeted prevention efforts. The proposed K99 study will chart the
development of the aperiodic exponent across the first two years of life, identify the attentional and behavioral
correlates of the aperiodic exponent, and describe how aperiodic exponent trajectories vary as a function of
fluctuations in maternal distress. Participants will be drawn from an ongoing R01 (Pérez-Edgar, Buss, LoBue,
MPIs) that examines how early attentional biases contribute to BI through a detailed assessment of temperament
and biopsychosocial risk (N = 357). Leveraging this richly phenotyped and large sample, we will chart trajectories
of the aperiodic exponent across the first two years of life (8, 12, 18, and 24 months) in relation to attentional,
behavioral, and environmental risk for childhood psychopathology. The proposed R00 study will build on this
foundational knowledge by recruiting a new sample of pregnant women to investigate whether coordinated
fluctuations in a mothers’ distress and health-promoting behaviors (i.e., physical activity, sleep quality) while
pregnant predict neural, cognitive, and behavioral difficulties in her infant. Ultimately, the aperiodic exponent of
the EEG power spectrum may serve as a non-invasive and economical biomarker for attentional and behavioral
risk, marking a key neural dysfunction to target with preventative services, a NIMH priority (Strategic Objective
2.2). Facilitated by this K99/R00 “Pathway to Independence” Award, my research program will examine how
functional trajectories of brain maturation...

## Key facts

- **NIH application ID:** 10449449
- **Project number:** 1K99MH126071-01A1
- **Recipient organization:** PENNSYLVANIA STATE UNIVERSITY, THE
- **Principal Investigator:** Brendan Dale Ostlund
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $102,846
- **Award type:** 1
- **Project period:** 2022-04-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10449449

## Citation

> US National Institutes of Health, RePORTER application 10449449, Disentangling periodic and aperiodic neural activity in the first two years of life: Contributions of perinatal maternal distress on neurodevelopment and childhood psychopathology risk (1K99MH126071-01A1). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10449449. Licensed CC0.

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