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. Author manuscript; available in PMC: 2023 Jun 1.
Published in final edited form as: Brain Imaging Behav. 2022 Jan 6;16(3):1275–1283. doi: 10.1007/s11682-021-00617-2

Investigating the overlapping associations of prior concussion, default mode connectivity, and executive function-based symptoms

Benjamin L Brett 1,2, Andrew M Bryant 2, Lezlie Y España 1, Andrew R Mayer 3, Timothy B Meier 1,4,5,*
PMCID: PMC9107488  NIHMSID: NIHMS1770457  PMID: 34989980

Abstract

Growing evidence suggests that younger athletes with greater concussion history are more likely to endorse greater subjective cognitive (e.g., executive function) symptoms, but not perform worse on objective cognitive testing. We sought to identify biological correlates of elevated cognitive symptoms in 100 healthy, collegiate-aged athletes with varying degrees of concussion history. Associations between concussion history with subjectively-rated executive function were assessed with generalized linear models. Using resting state fMRI, we examined associations between concussion history and between-and within-network connectivity across three networks integral to executive function; default mode network (DMN), frontoparietal network (FPN), and ventral attention network (VAN). Relationships of between-and within-network connectivity with subjective executive function were assessed. Although the large majority of participants did not report clinically relevant levels of executive difficulties, there was a significant association between concussion history and higher behavioral regulation-related symptoms; B=.04[.01, .07], p=.011. A significant elevation in total within-network connectivity was observed among those with a greater concussion history, B=.02[.002, .03], p=.028, which was primarily driven by a positive association between concussion history and within DMN connectivity, B=.02[.004, .04], p=.014. Higher behavioral regulation-related symptoms were associated with greater total within-network connectivity, B=0.57[0.18, 0.96], p=.005, and increased within-network connectivity for the DMN, B=.49[.12, .86], p=.010). The current study identified a distinct biological correlate, increased within-DMN connectivity, which was associated with both a greater history of concussion and greater behavioral regulation symptoms. Future studies are required to determine the degree to which these changes associated with concussion history may evolve toward objective cognitive decline over the lifespan.

Keywords: Executive function, concussion, mTBI, resting-state fMRI, default mode network, subjective cognitive function

Introduction

Executive function deficits have been suggested as being one of the primary areas of clinical impairment among older former athletes with a history of multiple concussions (Esopenko et al., 2017; Mez et al., 2017; Seichepine et al., 2013; Terpstra et al., 2019). In older athletes years removed from sport, multiple studies have shown greater history of concussion to be associated with worse performance on executive-based measures when specifically examined (Esopenko et al., 2017; Hampshire, MacDonald, & Owen, 2013). While studies have reported an association between general subjective–but not objective–cognition in younger healthy athletes with a greater history of concussion (Caccese et al., 2021; Mannix et al., 2014), the association between concussion history and endorsement of executive function symptoms, specifically, has been notably understudied among younger, active athletes.

Resting state functional magnetic resonance imaging (rs-fMRI) is a modality that is utilized to assesses intrinsic connectivity of brain networks that are associated with executive function and subjective executive function complaints. Within-network connectivity of the ventral attention network (VAN; also known as the salience network), default mode network (DMN), and frontoparietal network (FPN), as well as the strength of their inter-connectivity, have been identified as critical to aspects of executive function and subjective executive difficulties (Bonnelle et al., 2012; Chen et al., 2020; Jilka et al., 2014; Sridharan, Levitin, & Menon, 2008; Zanchi et al., 2017). For example, alterations in DMN functional connectivity are associated with greater subjective cognitive complaints in samples of healthy adults, and these functional alterations are predictive of longitudinal decline (Chen et al., 2020; Wang et al., 2013; Zanchi et al., 2017). Though several studies have shown alterations in network connectivity in the three executive function-related networks described above (VAN, DMN, and/or FPN), or associated nodes/regions, following acute concussion (Iyer, Zalesky, Barlow, & Cocchi, 2019; Johnson et al., 2012; T. B. Meier, Bellgowan, & Mayer, 2017; T. B. Meier et al., 2020), the relationships among concussion history, functional connectivity of these networks, and subjective executive function have not been thoroughly investigated.

Accordingly, the aims of the current study were to investigate the following within a sample of healthy (i.e., strict exclusion criteria) collegiate-aged athletes: 1) the association of history of concussion with executive function-related symptoms; 2) whether concussion history is associated with alterations in connectivity within- and between- three large scale functional networks implicated in executive function, and 3) the association between functional connectivity in the large-scale networks with subjective measures of executive function (i.e., level of executive function-related symptoms).

Material and Methods

Participants and clinical data

Study enrollment occurred between December 2018 and March 2020 as part of an ongoing project assessing the chronic effects of concussions and contact sport exposure (Project ChronEx). Inclusion criteria included sport participation within the last 12 months and being between the ages of 18 and 26 years. As described previously, individuals with current or prior co-morbidities and recent concussion were excluded (see supplement for strict exclusion criteria). The study was approved by the Institutional Review Board at the Medical College of Wisconsin and participants provided written informed consent.

A comprehensive clinical outcome assessment was performed in order to examine the associations between prior concussion and clinical measures, which have been previously reported (T. Meier et al., 2021). Subjective executive function was evaluated using the Behavior Rating Inventory of Executive Functions for Adults (BRIEF-A) Behavior Regulation (BRI) and Metacognition (MI) indices. The National Institutes of Health Toolbox Cognitive Battery (iPad version) Crystalized and Fluid Composite measures characterized objective performance-based cognitive function. Semi-structured interviews were performed to retrospectively diagnose and quantify concussions based on the American Congress of Rehabilitation Medicine criteria (see supplement) (Kay et al., 1993; T. Meier et al., 2021). Based upon the distribution of the sample, concussion history was binned into four groups including 0, 1, 2, and 3 or more (3+) prior concussion(s).

MRI data and connectivity metric calculation

MRI data were collected on a General Electric HealthCare SIGNA Premier 3 Tesla scanner. A 10 minute gradient-echo echo-planar imaging resting state scan (TR/TE = 1800/30 ms) was acquired with participants instructed to fixate on a cross. T1-weighted and T2-weighted images were collected for anatomical reference. Detailed information regarding scan parameters, processing steps, and resting state metric calculation are provided in the supplement. In brief, resting state scans were despiked, slice-time and susceptibility distortion corrected, and non-linearly normalized to standard space. Nuisance signals, including the six motion parameters and their derivatives, the zero- through fourth-order polynomial trends, local white matter signal (i.e., ANATICOR(Jo, Saad, Simmons, Milbury, & Cox, 2010) and the top three principal components of the CSF signal were regressed from the time series. Volumes with excessive head motion were censored from analyses with the preceding volume; participations with more than 15% of their total volumes removed due to motion censoring or with an average Euclidian norm of motion parameters > 0.2 were excluded from analyses.

Regions-of-interest (ROI) were obtained from the 400-parcel, 7-network Schaefer atlas (Schaefer et al., 2018). A priori networks of interest included the DMN, FPN, and VAN due to their role in executive function and subject cognitive complaints (Bonnelle et al., 2012; Chen et al., 2020; Zanchi et al., 2017; Zanto & Gazzaley, 2013). A connectivity matrix of Fisher-Z transformed Pearson correlation coefficients between each ROI pair was generated, with negative correlations were set to zero. A single metric of the total within-network connectivity across all networks of interest (i.e., DMN, FPN, and VAN) was calculated as the mean ROI-to-ROI Fisher-Z-value of all ROI-pairs belonging to the same network (e.g., DMN to DMN). A single metric of between-network connectivity across all networks of interest was calculated as the mean ROI-to-ROI Fisher-Z-value of all ROI-pairs belonging to different networks (e.g., DMN to VAN or FPN). Between- and within-network connectivity was also calculated for each network (or network pairs) separately to facilitate follow-up analyses with more anatomical specificity, when indicated.

Statistical analysis

Statistical analyses were conducted using IBM SPSS, Version 24. Three sets of a priori generalized linear models (GLMs) were fit in order to test the three aims of the study. The first set of GLMs examined the association between concussion history (independent variable) with the subjective executive function measures (BRIEF subscales BRI and MI raw scores, separately) as the dependent variables. The second set of GLMs assessed the association of history of concussion (independent variable) and the metric of total within-network connectivity across all three networks of interest (FPN, VAN, and DMN), as well as with the metric of total between-network connectivity across the three networks (run separately). The third set of GLMs examined associations of the metrics of total within- and between-network connectivity described above with subjective executive function as the dependent variables. For resting state analyses, the total within- and between-network connectivity across the three networks of interest were assessed as the primary outcomes; secondary analysis of within- (e.g., DMN-DMN only) and between-network connectivity of (e.g., DMN-VAN only) individual networks were only assessed following significant effects on the total connectivity metrics to allow greater anatomical specificity. Age and sex were included as covariates for all models. Based upon the distribution of raw BRIEF-A MI and BRI scores (i.e., positively skewed) and relative fit statistics (Akaike information criterion and Bayesian information criterion), the associations between concussion history and resting state connectivity with BRIEF-A MI and BRI scores were estimated using a Poisson distribution. Statistical significance was evaluated at the 0.05 level for each aim. For follow-up analyses investigating specific networks, when indicated, p<.017 was considered significant (i.e., correction for 3 follow-up tests).

Results

A total of 106 athletes met inclusion criteria and were enrolled in the study; six were excluded due to excessive motion during the scan, resulting in a final sample of N=100 (Table 1). The mean performance-based cognitive performance of the sample was in the average range and neither the Crystalized (M=108.92, SD=12.68; B=.02[−.98, 1.01], p=.975) or Fluid (M=103.36, SD=11.94; B=.45[−1.02, 1.91], p=.548) Composite Scores were significantly associated with concussion history.

Table 1:

Sample characteristics

Demographic/History Total
Total No. 100
Sex (No. Male) 69
Age M (SD) 21.40 (1.68)
WTAR Std. Score M (SD) 107.89 (10.18)
Race (No.)
Black or African-American 7
White 92
Not Reported 1
Ethnicity (No.)
Hispanic or Latino 7
Not Hispanic or Latino 92
Unknown 1
Concussion History (No.)
0 53
1 19
2 13
3+ 15
Subjective Executive Function
BRIEF-BRI t-score M (SD) 41.29 (7.22)
BRIEF-MI t-score MSD) 41.82 (6.25)
Sport (No.)
Baseball/Softball 11
Cross Country/Track & Field 11
Football 38
Ice Hockey/Lacrosse 9
Rugby 8
Soccer 16
Other 7
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Basic demographic information. No. = number. WTAR = Wechsler’s Test of Adult Reading. M = mean. SD = standard deviation BRIEF BRI = Behavior Rating Inventory of Executive Function Behavioral Regulation Index. BRIEF MI = Behavior Rating Inventory of Executive Function Metacognition Index.

There was a significant positive association between concussion history and subjective executive function (Table 2; Figure 1). Those with a greater history of concussion endorsed more executive function-based symptoms on the BRIEF-A BRI (beta[95% CI]; B=.04[.01, .07], p=.011) and BRIEF-A MI (B=.04[.01, .07], p=.002).

Table 2:

Association of prior concussion with executive function and resting-state connectivity

Model M (SD) Statistic Beta SE 95% CI [lower, upper] p-value
Subjective Executive Function
BRIEF BRI* 36.29 (7.21) Wald X2(1)=6.45 0.04 0.02 [0.01, 0.07] 0.011
BRIEF MI* 48.35 (8.53) Wald X2(1)=9.35 0.04 0.01 [0.01, 0.07] 0.002
Resting-state Connectivity
Within-Network* 0.37 (0.09) Wald X2(1)=4.83 0.02 0.01 [0.002, 0.03] 0.028
DMN-DMN 0.37 (0.09) Wald X2(1)=6.06 0.02 0.01 [0.004, 0.04] 0.014 ^
VAN-VAN 0.40 (0.10) Wald X2(1)=0.89 0.01 0.01 [−0.009, 0.03] 0.346
FPN-FPN 0.35 (0.09) Wald X2(1)=2.76 0.01 0.01 [−0.002, 0.03] 0.097
Between-Network* 0.22 (0.09) Wald X2(1)=2.86 0.01 0.01 [−0.002, 0.03] 0.091
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Statistical results of measures of executive function. BRIEF BRI = Behavior Rating Inventory of Executive Function Behavioral Regulation Index. BRIEF MI = Behavior Rating Inventory of Executive Function Metacognition Index. DMN = Default Mode Network. VAN = Ventral Attention Network. FPN = Frontoparietal Network.

*

Indicates primary analyses. Significant p-values are underlined and italicized.

^

Indicates significant follow-up test when accounting for multiple comparisons.

Figure 1. Association amongst subjective executive function, default mode network connectivity, and concussion history.

Figure 1.

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Top left: Violin plots showing the association between concussion history and scores on the Behavior Rating Inventory of Executive Functions for Adults (BRIEF-A) Behavioral Regulation Index (BRI). Top right: Illustrative example of the resting state networks assessed in the current study, including the default mode network (DMN), ventral attention network (VAN), and the frontoparietal network (FPN). Bottom left: Violin plots showing the association between concussion history and within-network DMN connectivity (z-scores). Bottom right: Scatter plot showing the association between within-network DMN connectivity (z-scores) and BRIEF-BRI scores.

Significant elevations in the total within-network connectivity metric were observed among those with a greater concussion history (B=.02[.002, .03], p=.028; Table 2; Figure 1). Follow-up analyses showed that this effect was primarily driven by a strong, positive association between concussion history and within-network connectivity of the DMN (B=.02[.004, .04], p=.014). No significant associations between concussion history and VAN or FPN within-network connectivity were observed, ps >.05. The metric of total between-network connectivity was not significantly associated with concussion history (B=.01[−.002, .03], p=.091).

Greater executive function-related symptoms on the BRIEF-A BRI were positively associated with the metric of total within-network connectivity (B=0.57[0.18, 0.96], p=.005; Table 3). Follow-up analyses of individual networks showed this effect was evident in the VAN (B=.55[.21, .89], p=.001) and DMN (B=.49[.12, .86], p=.010; Figure 1). A similar pattern was observed for the FPN, though it did not survive strict multiple comparison correction accounting for the three follow-up tests (B=.40[.04, .77], p=.030). A positive association between BRIEF-A BRI scores and the metric of total between-network connectivity was also observed (B=.50[.13, .87], p=.009). Follow-up analyses at the network level showed that this effect was observed for all network pairs including the VAN-DMN (B=.47[.12, .82], p=.008), VAN-FPN, (B=.42[.08, .77], p=.015), and FPN-DMN (B=.47[.09, .84], p=.014). No significant associations of executive function-related symptoms on the BRIEF-A MI with the metrics of total within- or between-network connectivity were observed, ps >.05. Sensitivity analyses demonstrated that the results reported above were robust and remained significant when total years of contact sport exposure was included within the GLMs. Total years of participation was significantly associated with higher BRIEF-MI scores, but not functional connectivity or other clinical outcomes of interest (see supplement).

Table 3:

Associations of executive function with resting-state connectivity

Model Statistic Beta SE 95% CI [lower, upper] p-value
Subjective Executive Function
BRIEF BRI RAW
Within-Network* Wald X2(1)=8.03 0.57 0.20 [0.18, 0.96] 0.005
DMN-DMN Wald X2(1)=6.71 0.49 0.19 [0.12, 0.86] 0.010 ^
VAN-VAN Wald X2(1)=10.25 0.55 0.17 [0.21, 0.89] 0.001 ^
FPN-FPN Wald X2(1)=4.70 0.40 0.19 [0.04, 0.77] 0.030
Between-Network* Wald X2(1)=6.87 0.50 0.19 [0.13, 0.87] 0.009
VAN-FPN Wald X2(1)=5.90 0.42 0.17 [0.08, 0.77] 0.015 ^
VAN-DMN Wald X2(1)=7.04 0.47 0.18 [0.12, 0.82] 0.008 ^
FPN-DMN Wald X2(1)=5.99 0.47 0.19 [0.09, 0.84] 0.014 ^
BRIEF MI RAW
Within-Network* Wald X2(1)=2.88 0.29 0.17 [−.05, 0.64] 0.090
Between-Network* Wald X2(1)=1.68 0.21 0.16 [−0.11, 0.54] 0.195
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Statistical results of clinical measures of executive function mediated by resting-state executive function networks. BRIEF BRI = Behavior Rating Inventory of Executive Function Behavioral Regulation Index. BRIEF MI = Behavior Rating Inventory of Executive Function Metacognition Index. DMN = Default Mode Network. VAN = Ventral Attention Network. FPN = Frontoparietal Network.

*

Indicates primary analyses. Significant p-values are underlined and italicized.

^

Indicates significant follow-up test when accounting for multiple comparisons.

Discussion

Executive function difficulties are commonly reported as one of the main clinical sequelae of repeated concussion in older athletes. Little work has investigated whether endorsement of symptoms within this domain are observable in younger athletes or the potential physiological underpinnings of these deficits. In the current study, a history of concussion was associated with increased resting state functional connectivity of the DMN and higher executive function-based symptom severity (i.e., behavioral regulation symptoms) in healthy younger athletes. Furthermore, athletes that reported the most behavioral regulation symptoms also had the greatest within-network DMN connectivity. Taken together, these results identify a shared neural correlate of history of concussion and executive function-related symptoms, signifying a potential biological substrate for the consistent pattern of cognitive symptoms commonly observed in younger active athletes with greater history of concussion.

Given the emphasis of executive function deficits in older athletes with a history of multiple concussions, the limited number of studies examining subjective or objective executive function in younger athletes is surprising (Esopenko et al., 2017; Hampshire et al., 2013; Montenigro et al., 2017; Terpstra et al., 2019). Nevertheless, current results are consistent with the limited number of prior studies of younger athletes ranging from pediatric to collegiate-aged that have reported that a greater history of concussion is associated with higher levels of general cognitive symptoms, but not objective cognitive performance (Brett, Huber, Wild, Nelson, & McCrea, 2019; Caccese et al., 2021; Mannix et al., 2014). Factors underlying the discrepancy between the associations of subjective and objective executive function with prior concussion are not entirely understood. In older, healthy non-athlete samples, subjective cognitive complaints and perceived decline in the absence of impairment increases an individual’s likelihood of exhibiting objective decline at longitudinal follow-up (Jorm et al., 1997; Numbers et al., 2020; Rabin et al., 2020). It should be noted that the large majority of participants in the current study do not report clinically relevant levels of executive difficulties and higher symptom endorsement is relative to those with lower concussion history, which is unsurprising given the strict exclusion criteria and the focus on younger, active athletes. Future studies are needed to test the hypothesis that these subtle subjective changes may precede an eventual decline in objective executive functioning later in life.

The current study also found that prior concussion was associated with increased total within-network connectivity, with follow-up analyses showing that this effect was driven by within-network connectivity of the DMN. Investigations into the association between prior concussion and functional connectivity changes have been limited, particularly within younger athletes, with some reports showing that prior concussion is associated with both increased and decreased connectivity across several regions in collegiate athletes (Churchill, Hutchison, Leung, Graham, & Schweizer, 2017; T. B. Meier, Lancaster, Mayer, Teague, & Savitz, 2017). Consistent with the current results, a smaller study of active hockey players with a history of concussion observed alterations in DMN connectivity as compared to their teammates without a history of concussion (Orr et al., 2016). Within the current study, elevated behavioral regulation-related executive function ratings were associated with both increased within- and between-network connectivity for the DMN, VAN, and FPN. The nature of these associations is unknown (i.e., directionality), though it is hypothesized that greater previous concussions can lead to alterations in DMN connectivity, which can the result in higher executive-based symptoms. However, given that higher symptom endorsement and functional connectivity alterations have been observed as risk factors for sustaining a concussion within the same sport season, the alternative direction of the three factors cannot be ruled out (Churchill et al., 2021).

It is noteworthy that alterations in DMN connectivity are also commonly reported in the acute and sub-acute phase post-concussion, with general resolution of these abnormalities throughout the recovery period (D’Souza et al., 2020; Iyer et al., 2019; Johnson et al., 2012; T. B. Meier, Bellgowan, et al., 2017; T. B. Meier et al., 2020). Prior reports have highlighted that midline structures, such as key hubs of the DMN (e.g., medial prefrontal cortex and posterior cingulate cortex), are particularly susceptible to shearing forces associated with rotational head injury (Zhang, Yang, & King, 2004). Thus, the current finding that greater concussion history was associated specifically with DMN connectivity is consistent with accumulating evidence the DMN is particularly sensitive to the effects of concussion.

Associations between subjective cognitive complaints and alterations in resting state functional connectivity have been primarily studied in the context of aging, mild cognitive impairment, and Alzheimer’s disease. Although several studies have reported positive associations between functional connectivity and subjectively endorsed cognitive change (Dillen et al., 2016; Hafkemeijer et al., 2013; Verfaillie et al., 2018), as in the current study of young, healthy athletes, the literature is relatively mixed, with other studies reporting an inverse association between subjective cognitive complaints and functional connectivity (i.e., hypoconnectivity) or a complex combination of both increased and decreased connectivity changes (Wang et al., 2013; Yasuno et al., 2015; Zanchi et al., 2017).

Regardless of the conflicting findings in functional connectivity described above, the current results are consistent with a conceptual model presented by Viviano and Damoiseaux (Viviano & Damoiseaux, 2020) which posits that subjective cognitive difficulties are associated with greater connectivity when subjective decline is first reported, possibly reflecting noisy signal propagation due to early synaptic changes and/or compensatory mechanisms, and that these changes progressively evolve toward decreased functional connectivity over time due to neurodegeneration and physical disconnection. This course of functional changes from an onset of increased connectivity that progress toward decreased connectivity would also be consistent with the cascade hypothesis of other disease models. In such models, initial hyperconnectivity acts as a compensatory mechanism to neurological injury (e.g., axonal injury due to TBI, synaptic dysfunction due to neuropathological deposition, etc.) that over time results in an energy or metabolic demand that is not sustainable, leaving individuals vulnerable to secondary detrimental processes over subsequent years (Hillary & Grafman, 2017; Jones et al., 2016). Within the context of this framework, the increases in DMN connectivity and higher levels of subjective cognitive symptoms observed in the current study may conceivably represent the initial responses to cumulative concussion and present a potential pathway for vulnerability to secondary pathological processes later in the lifespan. Additional research is needed to test this hypothesis.

Study Strengths and Limitations

The current study contains a number of strengths that increase confidence in the observed results. The current study was designed and performed to exclusively examine the potential chronic or early long-term effects of remote concussion, and was not a secondary analysis of pre-season baseline sampling enrollment. Relatedly, the study employed strict enrollment criteria of other potentially confounding conditions associated with functional connectivity or neurobehavioral function (ADHD, depression, concussion within the last 6-months, etc.). As a study intended to assess the early long-term chronic effects of concussion, the current study also employed a rigorous standardized interview for retrospective identification and characterization of concussion history based on established criteria for a more accurate assessment of the number of prior concussions in each participant. In parallel, empirically-validated self-report and objective performance based measures of executive function were utilized in the study to ensure standardized assessment of these neurobehavioral constructs.

However, the generalizability of the current findings to other developmental age groups besides collegiate-aged athletes cannot be determined. The current sample is relatively homogeneous in regard to representation and the generalizability of these findings to historically marginalized groups should be interpreted with some caution. Further work should look to replicate the current results in more diverse samples with consideration of social determinants of health that have been identified as influencing functional network connectivity throughout development (Tooley et al., 2020). Finally, the study is cross-sectional in nature, and we cannot infer the temporal relationship between repeat concussion, subjective executive function deficits, and resting state functional connectivity.

Conclusion

Taken together, current findings suggest that while within- and between-network functional connectivity patterns across multiple networks may influence individuals’ subjective experience of executive function symptoms, cumulative concussion is specifically associated with both subjective behavioral regulation-related executive function symptoms and alterations of within-DMN connectivity. Further studies are required to investigate whether these functional abnormalities put athletes with an extensive history of concussion at risk for cognitive decline later in life.

Supplementary Material

1770457_Sup_material

Acknowledgements

The authors thank Luisa Bohorquez-Montoya, Jennifer Powell, Alexander Kirk, Amy Nader, and Dan Huber at the Medical College of Wisconsin for data collection, quality assurance, and data management; and Andrew S. Nencka, Brad Swearingen, and the MRI technicians at the Center for Imaging Research at the Medical College of Wisconsin for assistance in MRI data collection.

Funding

Research reported in this publication was supported by the National Institute Of Neurological Disorders And Stroke of the National Institutes of Health under Award Number R01NS102225. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The REDCap electronic database and the Adult Translational Research Unit used for this project were supported by the National Center for Advancing Translational Sciences, National Institutes of Health, Award Number UL1TR001436. BLB acknowledges support from the National Institute of Neurological Disorders and Stroke (Award Number L30NS113158) and National Institute on Aging (Award Number K23 AG073528).This research was completed in part with computational resources and technical support provided by the Research Computing Center at the Medical College of Wisconsin.

Footnotes

Competing Interests

The Authors declare that there is no conflict of interest.

Availability of data and materials

The data utilized as part of this study that support the reported findings are available from the corresponding author, upon reasonable request.

Ethical Approval

The study was approved by the Institutional Review Board at the Medical College of Wisconsin.

Consent to Participate

All participants provided written informed consent as part of study enrollment.

Consent to Publish

The authors have the right to publish the data.

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