# Adaptive and Maladaptive Neural Network Responses to Inhibitory Challenges

> **NIH NIH R01** · UNIVERSITY OF DELAWARE · 2022 · $580,630

## Abstract

ABSTRACT
Impulse control predicts a myriad of serious public health problems that substantially reduce life expectancy,
including suicide, violence, substance use, and other risky behaviors. Although it is known that inhibitory
control deficits confer risk for these clinical problems, one critical barrier to progress in this field is that much
less is known about how this core regulatory process interacts with other cognitive and affective systems.
Given that impulse control failures that occur in everyday life reflect the interaction of multiple cognitive and
affective systems, this knowledge is critical for mapping impulse control failures onto neural circuits and
accurately modeling the impulsivity that occurs in psychopathology as disruptions in neural systems. The
objective of this application is to determine how functional brain networks supporting inhibitory control respond
to cognitively- and affectively-challenging contexts and to evaluate the relevance of these networks for
explaining clinical problems with impulsivity. We propose a novel investigation aimed at better understanding
how three contexts known to challenge impulse control (cognitive resource depletion, competing appetitive
cues, and negative mood induction) impact the functional brain networks that support successful inhibitory
control and, ultimately, self-regulation in mental illness. First, healthy adults will undergo a thorough clinical
diagnostic assessment and complete a battery of functional magnetic resonance imaging (fMRI) tasks in the
MRI scanner that assess inhibitory control in different challenging contexts. These data are expected to
contribute a precise working model of how functional brain networks compensate to meet the unique inhibitory
control demands present in different challenging contexts. They will also add context as a level of analysis to
existing neural models of inhibition, bringing them closer to capturing the multidimensional nature of inhibitory
control failures in everyday life. Next, given that replication failures are common in neuroimaging research, a
subset of participants will undergo a second MRI scan after a three-month period to establish the reliability of
the novel functional brain metrics of inhibitory control we propose to investigate. Finally, a central goal of the
NIMH RDoC initiative is to link clinical problems to neural systems (Cuthbert & Insel, 2013). Consistent with
this initiative, we propose to examine disruptions in inhibitory control networks as potential transdiagnostic risk
factors for externalizing disorders (e.g., alcohol/ substance use disorders, antisocial personality disorder) and
as biomarkers for identifying subtypes of inhibition. This approach has the potential to create a more
neuroscience-based classification of clinical problems related to impulsivity. Together, this research is
expected to ultimately aid in the treatment of clinical impulsivity by leading to a deeper understanding of the
brain networks t...

## Key facts

- **NIH application ID:** 10318933
- **Project number:** 5R01MH116228-04
- **Recipient organization:** UNIVERSITY OF DELAWARE
- **Principal Investigator:** Naomi Samimi-Sadeh
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $580,630
- **Award type:** 5
- **Project period:** 2019-04-01 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10318933

## Citation

> US National Institutes of Health, RePORTER application 10318933, Adaptive and Maladaptive Neural Network Responses to Inhibitory Challenges (5R01MH116228-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10318933. Licensed CC0.

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