Brain structure can mediatemoderate the relationship of behavior to brain function and transcriptome a preliminary study Sumra Bari1 Nicole L Vike1 Khrystyna Stetsiv2 Anne J Blood3-5 Eric A Nauman6

2025-04-30 1 0 1.11MB 62 页 10玖币

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Brain structure can mediate/moderate the relationship of behavior to brain function and

transcriptome: a preliminary study

Sumra Bari1,†, Nicole L Vike1,†, Khrystyna Stetsiv2, %, Anne J Blood3-5,%, Eric A Nauman6%,

Thomas M Talavage6,%, Semyon Slobounov7,&, Hans C Breiter1,4,&

1Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA

2Warren Wright Adolescent Center Department of Psychiatry and Behavioral Sciences, Feinberg

School of Medicine, Northwestern University, Chicago, IL, USA

3Mood and Motor Control Laboratory, Departments of Neurology and Psychiatry, Massachusetts

General Hospital and Harvard Medical School, Boston, MA 02129, USA

4Laboratory of Neuroimaging and Genetics, Department of Psychiatry, Massachusetts General

Hospital and Harvard Medical School, Boston, MA 02129, USA

5Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General

Hospital and Harvard Medical School, Boston, MA 02129, USA

6Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, USA

7Department of Kinesiology, Pennsylvania State University, University Park, PA, USA

† indicate co-equal first authorship

% indicate co-equal second authorship

& indicate co-equal senior authorship

* Correspondence:

For project design, management, and data collection: Semyon Slobounov, 268P Recreation

Building, The Pennsylvania State University, University Park, PA 16802, USA. Email:

sms18@psu.edu

For hypotheses and conceptual framework, data analysis, and paper development: Hans C.

Breiter, Department of Computer Science, College of Engineering and Applied Science,

University of Cincinnati, 2901 Woodside Drive, Cincinnati, OH 45219, USA. Email:

breitehs@ucmail.uc.edu

Keywords: miR-30d; DWI; rs-fMRI; motor control; head impacts; football

Abstract

Abnormalities in motor-control behavior, which have been associated with concussion and head

acceleration events (HAE), can be quantified using virtual reality (VR) technologies. Motor-

control behavior has been consistently mapped to the brain’s somatomotor network (SM) using

both structural (sMRI) and functional MRI (fMRI). However, no studies have integrated HAE,

motor-control behavior, sMRI and fMRI measures. Here, brain networks important for motor-

control were hypothesized to show changes in tractography-based diffusion weighted imaging

[difference in fractional anisotropy (FA)] and resting-state fMRI (rs-fMRI) measures in

collegiate American football players across the season, and that these measures would relate to

VR-based motor-control. We further tested if nine inflammation-related miRNAs were associated

with behavior-structure-function variables. Using permutation-based mediation/moderation

methods, we found that across-season FA from the SM structural connectome (SM-FA)

mediated the relationship between across-season VR-based Sensory-motor Reactivity (SR) and

rs-fMRI SM fingerprint similarity (

 = 0.007 and  ). The interaction between SR

and SM-FA also predicted (

= 0.036, 

= 0.058) across-season levels of miRNA-30d

through permutation-based moderation analysis. These results suggest (1) that motor-control is in

a feedback relationship with brain structure and function, (2) behavior-structure-function can be

connected to HAE, and (3) behavior-structure might predict molecular biology measures.

Introduction

Core features of both concussion and accumulated head acceleration events (HAE) include

disturbances in balance, sensory-motor reactivity (SR), and spatial navigation – features

quantifiable with virtual reality (VR) tasks1–3. Following Luria’s framework4, these three VR tasks

can detect residual neurologic abnormalities in concussed athletes with cleared symptoms, unlike

many computer-based neuropsychological tests5 (see Supplemental Material for details and

validation of the VR tasks).

Two of these three motor-control behavior processes (balance and SR) rely fundamentally

on brain regions in the somatomotor resting-state network (SM). Somatomotor circuitry has been

mapped extensively via structural MRI measures6–13 and functional MRI (fMRI)- or PET-based

assessments14,15,24,16–23. Brain regions in other resting-state networks, like subcortical (SUBC)25,26,

dorsal attention (DA)27,28, and default mode network (DMN)29, are also linked with motor-control

behavior processes underlying balance and SR, whereas the fronto-parietal (FP)30–32 and DA

networks32,33 are involved with spatial navigation (SN). The dearth of studies relating both

structural and functional MRI to computational behavior (i.e., mathematical psychology and the

advanced technology used to study behavior) measures of motor-control raises a question of what

organizational framework the structure-function-behavior relationship might follow. Recent

studies have constrained motor-control and other behaviors to be dependent variables (DV) in

imaging34,35. Because it is now acknowledged that the adult brain is far more plastic than

previously recognized36–39, this raises the question whether changes in motor-control behavior

might drive features of brain structure and/or function (i.e., that it may in some cases be the

independent variable (IV) or mediator/moderator (Me/Mo) for structure-function-behavior

relationships). The same question extends to whether any observation involving behavior and brain

structure or function would predict a molecular biology metric such as micro-RNA (miRNA)

levels, which have been reported to be elevated in football players and related to accumulated

HAE3,34,40,41. The miRNA studied here have been implicated in multiple cellular processes,

including those that follow the mechanical phase of impact, such as the initiation of inflammation

and its longer-term resolution42,43. Accumulated HAE have been implicated in one of the

mechanisms hypothesized to underlie brain injury, namely chronic inflammation which is energy

intensive and can alter cellular processes44–48. Accumulated HAE have also been implicated in two

other hypothesized mechanisms of brain injury: 1) alterations in cerebrovascular blood flow

control49–52, and 2) axonal abnormalities caused by traumatic shearing and stretching forces53–56.

These models have been studied independently, and recently assessed together for relationships

between markers of neuroinflammation and markers of cerebral blood flow34.

The current study sought to connect markers of neuroinflammation with markers of

structural abnormalities, as from axonal stretching, using a cohort of collegiate American football

players. Prior work, using the same cohort of athletes, showed that across-season rs-fMRI network

fingerprint similarity measures in motor-control networks (i.e., SM, DMN, SUBC, DA, and FP)

were lower than in age-matched, non-athletes40. For this study we hypothesized that these athletes

would exhibit across-season changes in resting-state fMRI (rs-fMRI) network connectivity (i.e.,

fingerprint similarity) in brain regions involved with motor-control, and that these measures would

show a relationship with changes in diffusion tensor imaging (DTI) measures (i.e., changes in

fractional anisotropy, FA) in the same brain networks. Further, it was hypothesized that these

measures would be integrated with across-season changes in motor-control behavior (i.e., balance,

SR, or spatial navigation measures). Our hypotheses further implemented permutation-based

mediation (PMe)57 and moderation (PMo)58–60 to test if a panel of nine miRNAs (i.e.,

transcriptomic measures), previously implicated in inflammation and HAE3, were associated with

any two or three variables in a structure-function-behavior framework.

Given the focus on extending prior work to indirect measures of axonal abnormality, the

FA was always required in any three-way analysis involving structure, function, behavior, or

miRNA variables with PMe/PMo. The use of permutation-based statistics with

mediation/moderation analysis provides significant advantages for small-to-medium sized human

subject cohorts, as detailed elsewhere40,41 (see Supplemental Material). Permutation-based

statistics with mediation/moderation analysis moves past standard association to identify more

statistically mechanistic relationships. Unlike standard parametric statistics, permutation-based

methods produce a true data distribution, thus protecting against false positives and increasing the

overall power of the analysis. Variables involved in PMe and PMo were further analyzed against

HAE measures collected during the football season. This integrated statistical approach applied

across structure, function, behavior and omic measures suggest a viable methodology for

investigating complex multi-scale biology in humans, opening up avenues for bringing human

studies in closer approximation to pre-clinical studies in animals. This approach can further

identify potential biomarkers and expand our understanding of disease onset and progression.

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Brainstructurecanmediate/moderatetherelationshipofbehaviortobrainfunctionandtranscriptome:apreliminarystudySumraBari1,†,NicoleLVike1,†,KhrystynaStetsiv2,%,AnneJBlood3-5,%,EricANauman6%,ThomasMTalavage6,%,SemyonSlobounov7,&,HansCBreiter1,4,&1DepartmentofComputerScience,UniversityofCincinnati,Cincinna...

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Brain structure can mediatemoderate the relationship of behavior to brain function and transcriptome a preliminary study Sumra Bari1 Nicole L Vike1 Khrystyna Stetsiv2 Anne J Blood3-5 Eric A Nauman6

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