# The effects of cumulative concussion, repetitive head impacts, and comorbid cardiovascular risk factors on MRI metrics of pathological aging and neurobehavioral functioning across the lifespan

> **NIH NIH K23** · MEDICAL COLLEGE OF WISCONSIN · 2021 · $132,171

## Abstract

Project Summary/Abstract (1 page)
Exposure to mild traumatic brain injury (mTBI) and repetitive head impacts (RHI) during sport participation has
been associated with adverse long-term consequences (e.g., cognitive decline). Research investigating these
associations has been limited to cross-sectional studies at single points of the lifespan without consideration of
factors that can influence these associations. Greater mTBI/RHI history is associated with cerebral blood flow
(CBF) and white matter (WM) abnormalities, even within younger athletes. CBF and WM abnormalities are two
indicators of pathological aging and longitudinal decline in the general population. Age and cardiovascular risk
factors across the lifespan can influence these abnormalities. The scientific objective of this proposal is to
determine the influence of mTBI/RHI on MRI metrics of pathological aging and neurobehavioral function across
the lifespan. Our central hypothesis is that mTBI/RHI will be associated with neurobehavioral function and
CBF/WM abnormalities, and that the strength of this association is independently influenced by increasing age
and the presence of cardiovascular risk factors. The rationale for this project is that determining the influence
of mTBI/RHI on MRI metrics of pathological aging and neurobehavioral function across the lifespan, as well as
factors that influence this relationship, will provide insight into the process underlying cumulative head injury
and neurological decline later in life. Our hypothesis will be tested with two specific and one exploratory aims:
1) Determine the associations between mTBI/RHI and neurobehavioral function across the lifespan and the
influence of comorbid cardiovascular risk factors; 2) Determine the associations between mTBI/RHI and MRI
metrics of pathological aging across the lifespan and the influence of comorbid cardiovascular risk factors;
Exploratory) Examine if MRI metrics of pathological aging mediate the association between cumulative
mTBI/RHI and neurobehavioral function across the lifespan. To establish an independent research program
that identifies the mechanisms by which mTBI/RHI can result in long-term neurological disease burden, the
training objective of this proposal is to receive expert training/mentorship in a) the analysis and interpretation of
neuroimaging data, b) the cumulative effects of aging and cardiovascular risk factors on long-term brain health
and function, and c) the molecular mechanisms and progression of neuropathology as a consequence of
mTBI/RHI. We will employ an innovative lifespan approach by leveraging data from four large-scale,
prospective, multi-center studies of athletes across several decades of life. Harmonization of several mentor-
directed, large-scale multisite datasets of athletes will allow a hybrid prospective, cross-sectional design. We
will utilize advanced analytical approaches to examine the potential influence of aging and comorbid
cardiovascular risk factors ...

## Key facts

- **NIH application ID:** 10282566
- **Project number:** 1K23AG073528-01
- **Recipient organization:** MEDICAL COLLEGE OF WISCONSIN
- **Principal Investigator:** Benjamin L Brett
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $132,171
- **Award type:** 1
- **Project period:** 2021-08-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10282566

## Citation

> US National Institutes of Health, RePORTER application 10282566, The effects of cumulative concussion, repetitive head impacts, and comorbid cardiovascular risk factors on MRI metrics of pathological aging and neurobehavioral functioning across the lifespan (1K23AG073528-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10282566. Licensed CC0.

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