Associations of lifetime concussion history and repetitive head impact exposure with resting-state functional connectivity in former collegiate American football players: An NCAA 15-year follow-up study

Samuel R Walton; Jacob R Powell; Benjamin L Brett; Weiyan Yin; Zachary Yukio Kerr; Mingxia Liu; Michael A McCrea; Kevin M Guskiewicz; Kelly S Giovanello

doi:10.1371/journal.pone.0273918

. 2022 Sep 9;17(9):e0273918. doi: 10.1371/journal.pone.0273918

Associations of lifetime concussion history and repetitive head impact exposure with resting-state functional connectivity in former collegiate American football players: An NCAA 15-year follow-up study

Samuel R Walton ^1,^2,^3,^4,^*, Jacob R Powell ^2,³, Benjamin L Brett ⁵, Weiyan Yin ⁶, Zachary Yukio Kerr ^1,², Mingxia Liu ⁶, Michael A McCrea ⁵, Kevin M Guskiewicz ^1,², Kelly S Giovanello ^3,^6,⁷

Editor: Jacob Resch⁸

¹Center for the Study of Retired Athletes, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America

²Department of Exercise and Sport Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America

³Cognitive Neuroscience of Memory Laboratory, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America

⁴Physical Medicine and Rehabilitation, School of Medicine, Virginia Commonwealth University, Richmond, VA, United States of America

⁵Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, United States of America

⁶Department of Radiology, Biomedical Research Imaging Center, University of North Carolina School of Medicine, Chapel Hill, NC, United States of America

⁷Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America

⁸University of Virginia, UNITED STATES

Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: BLB reports grants and support from the National Institute of Neurological Disorders and Stroke and National Institute on Aging under the National Institutes of Health. KMG reports compensation from National Collegiate Athletic Association for other services and grants from Boston Children’s Hospital (sub-award from the National Football League). MAM acknowledges researching funding from the National Institutes of Health, U.S. Department of Defense, Centers for Disease Control and Prevention, National Collegiate Association and National Football League (via subaward from Boston Children’s Hospital). No other conflicts of interest are declared for the remaining authors.

^✉

* E-mail: samuel.walton@vcuhealth.org

Roles

Samuel R Walton: Conceptualization, Data curation, Formal analysis, Methodology, Visualization, Writing – original draft, Writing – review & editing

Jacob R Powell: Conceptualization, Data curation, Formal analysis, Writing – review & editing

Benjamin L Brett: Conceptualization, Methodology, Writing – review & editing

Weiyan Yin: Data curation, Formal analysis, Software, Writing – review & editing

Zachary Yukio Kerr: Data curation, Funding acquisition, Investigation, Methodology, Supervision, Writing – review & editing

Mingxia Liu: Methodology, Writing – review & editing

Michael A McCrea: Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – review & editing

Kevin M Guskiewicz: Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – review & editing

Kelly S Giovanello: Conceptualization, Data curation, Investigation, Methodology, Project administration, Resources, Supervision, Writing – review & editing

Jacob Resch: Editor

PMCID: PMC9462826 PMID: 36084077

Abstract

The objective of this study was to examine associations of lifetime concussion history (CHx) and an advanced metric of lifetime repetitive head impact exposure with resting-state functional connectivity (rsFC) across the whole-brain and among large-scale functional networks (Default Mode; Dorsal Attention; and Frontoparietal Control) in former collegiate football players. Individuals who completed at least one year of varsity collegiate football were eligible to participate in this observational cohort study (n = 48; aged 36–41 years; 79.2% white/Caucasian; 12.5±4.4 years of football played; all men). Individuals were excluded if they reported history/suspicion of psychotic disorder with active symptoms, contraindications to participation in study procedures (e.g., MRI safety concern), or inability to travel. Each participant provided concussion and football playing histories. Self-reported concussion history was analyzed in two different ways based on prior research: dichotomous “High” (≥3 concussions; n = 28) versus “Low” (<3 concussions; n = 20); and four ordinal categories (0–1 concussion [n = 19]; 2–4 concussions [n = 8]; 5–7 concussions [n = 9]; and ≥8 concussions [n = 12]). The Head Impact Exposure Estimate (HIEE) was calculated from football playing history captured via structured interview. Resting-state fMRI and T1-weighted MRI were acquired and preprocessed using established pipelines. Next, rsFC was calculated using the Seitzman et al., (2020) 300-ROI functional atlas. Whole-brain, within-network, and between-network rsFC were calculated using all ROIs and network-specific ROIs, respectively. Effects of CHx and HIEE on rsFC values were examined using separate multivariable linear regression models, with a-priori α set to 0.05. We observed no statistically significant associations between rsFC outcomes and either CHx or HIEE (ps ≥ .12). Neither CHx nor HIEE were associated with neural signatures that have been observed in studies of typical and pathological aging. While CHx and repetitive head impacts have been associated with changes in brain health in older former athletes, our preliminary results suggest that associations with rsFC may not be present in early midlife former football players.

Introduction

Several factors increase the risk for clinically significant cognitive decline in aging individuals (e.g., mild cognitive impairment [MCI] and dementia) [1–3]. Among the most reported risk factors for collision-sport athletes are exposures to repetitive head impacts and traumatic brain injuries (TBIs) [4–8]. Sport-related concussions (SRCs), a form of mild TBI [9], occur commonly in American football players [10, 11]. Studies have suggested that former football players may have earlier onset of, and/or increased risk for, cognitive decline or neurodegenerative disease diagnoses compared with the general population, purportedly due to their exposure to repetitive head impacts—SRC or otherwise—during football play [4–6, 8]. It is vital, therefore, to develop the scientific understanding of what constitutes normal versus abnormal changes in brain function as these players age. To do so requires measuring the associations between pathological changes and pertinent risk factors for neurodegenerative changes such as repetitive head impact exposure and traumatic brain injuries (e.g., SRC) sustained throughout a sporting career.

One method for examining biologic changes in brain health is resting-state functional activity as measured with magnetic resonance imaging (MRI). Changes in functional connectivity (correlated patterns of neural activity between brain regions) within large-scale functional brain networks, such as the Default Mode (DMN) [12, 13], Dorsal Attention (DAN) [14], and Frontoparietal Control (FPCN) [15] networks, have been characterized among aging individuals with age-related cognitive changes as well as those with clinically significant cognitive declines (e.g., those with MCI and dementia-related disorders) [16–22]. Specifically, overall resting-state functional connectivity (rsFC) may be decreased between regions within the DMN and DAN and may be stronger between the FPCN and both the DMN and DAN in cognitively normal older individuals (e.g., 60 years of age and over) compared with younger adults [17–20]. Hyperconnectivity between networks may represent an altered ability of the FPCN to serve as a “circuit breaker” [15] for activity within the DMN and DAN, which may partially explain age-related cognitive and mood-related behavior declines [17, 20]. The similarity between these characteristic network-based rsFC changes in those who experience age-related cognitive changes and what has been reported in studies of individuals with clinically significant cognitive declines (e.g., MCI and dementia) theoretically suggests that individuals with clinically significant cognitive declines may be experiencing exacerbated or accelerated biological aging processes [23].

Short-term and long-term changes to rsFC have been described in active adolescent and young adult athletes following SRC, and these changes largely resemble the patterns of functional reorganization (i.e., changes to within- and between-network rsFC in large-scale networks) observed in individuals experiencing typical aging and neurodegenerative disease processes [7, 18, 19, 21, 22, 24, 25]. There is mixed evidence as to whether rsFC changes persist beyond clinical recovery from SRC [7, 24, 26], and studies have reported neural recruitment differences when performing a cognitive task in former football players aged between 50–75 years with three or more lifetime SRCs as compared to those with fewer, despite no observed differences in task performance [27, 28]. Further evidence suggests that rsFC changes also occur in relation to repetitive head impacts, even without overt clinical signs or symptomsthat are consistent with a concussion diagnosis [29]. Taken together, long-term alterations in functional connectivity may result from greater exposure to SRC and repetitive head impacts, even in relatively young individuals, and these changes may resemble those observed in individuals experiencing cognitive decline. In this light, it is also reasonable to consider functional connectivity changes as potential biomarkers for the advanced aging and the early onset of pathological changes in former football players. In this preliminary study, we measured rsFC across the whole brain, and among large-scale functional brain networks (DMN, DAN, and FPCN) in a sample of early midlife former collegiate football players to examine whether rsFC was associated with lifetime concussion history or an advanced metric of lifetime repetitive head impact exposure. We hypothesized that alterations in rsFC as the result of head impacts sustained while playing football, if present, would manifest with network connectivity patterns similar to those described above in healthy older adults, including: 1) lower whole-brain rsFC; 2) lower within-network rsFC in the DMN and DAN; and, 3) higher between-network connectivity between the FPCN and both the DMN and DAN.

Materials & methods

Participants

Participants included former collegiate football players who completed an online general health survey in 2014 as part of a larger study of former collegiate athletes approximately 15-years after completion of their collegiate sport participation (the National Collegiate Athletic Association [NCAA] 15-Year Follow-up Study) [30]. The general health survey used in this study was adapted from previous studies of former football players with input from epidemiologists, athletic trainers, neuropsychologists, physicians, and former football players [4, 30]. Former collegiate football players who completed the survey were recruited to participate in a comprehensive in-person evaluation (e.g., neuroimaging, neurocognitive testing, and patient-reported outcome measures). Each player was contacted by a research coordinator, and those who responded were screened for eligibility. Inclusion criteria for the in-person visit was participation in at least one year of collegiate football. Exclusion criteria were a history of psychotic disorder with active symptoms, any contraindications to participation in study procedures (e.g., MRI safety concern), or inability to travel. This study was approved by the Institutional Review Boards at the University of North Carolina at Chapel Hill and the Medical College of Wisconsin, and all participants provided written informed consent prior to participation.

Image acquisition

Participants completed study visits at one of two separate institutions, and all images were acquired via 3T magnetic resonance scanner with a 32-channel head coil (Siemens MAGNETOM Prisma at the University of North Carolina at Chapel Hill; GE Healthcare Signa Premier at the Medical College of Wisconsin). During the course of data collection, there was a scanner software update at the Medical College of Wisconsin, resulting in three closely-related sets of acquisition parameters for each of the structural and functional MRI series (Table 1). T1-weighted Magnetic Prepared Rapid Gradient Echo (MPRAGE) and resting-state functional MRI (rsfMRI) blood oxygen level-dependent (BOLD) scans were collected for each participant.

Table 1. Magnetic resonance imaging (MRI) acquisition parameters across study sites.

	University of North Carolina at Chapel Hill Siemens Magnetom Prisma		Medical College of Wisconsin GE Premier (pre-software update)		Medical College of Wisconsin GE Premier (post-software update)
	n = 38		n = 4		n = 6
Label	T1w	rsfMRI	T1w	rs-fMRI	T1w	rsfMRI
Acquisition time	4m:54s	7m:00s	4m:07s	6m:51s	5m:21s	6m:50s
Plane	Sagittal	Axial	Sagittal	Sagittal	Sagittal	Axial
Slices	176	72	160	72	184	72
Matrix	256x208	104x104	256x204	104x104	256x256	104x104
TE (ms)	2.03	33	7.592	33.1	2.016	21.8
TR (ms)	2540	802	3.008	802	4.672	800
FOV (cm)	256x208	210x210	256x256	210x210	256x256	208x208
Thickness (mm)	1.0	2.0	1.0	2.0	1.0	1.5
Volumes	1	512	1	512	1	512

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All series were acquired using a 32-channel head coil and a 3T magnet at each study site. T1w = T1-weighted images (3D Magnetization Prepared Rapid Gradient Echo [MPRAGE]); rsfMRI = resting-state functional MRI (Blood-Oxygen-Level-Dependent [BOLD] signal).

Data processing

Structural and functional MRI images were processed following a typically used pipeline [31, 32]. Specifically, T1 MPRAGE images were pre-processed for each participant using Freesurfer. The brain structural images were segmented into grey matter, white matter, and cerebrospinal fluid (CSF), and each resultant image was visually inspected by two of the study team members (SRW & JRP) for reconstruction errors. Functional data (rsfMRI) were preprocessed using FSL [33–35]. The preprocessing steps included discarding the first 10 volumes for magnetization equilibrium before processing, motion correction, and bandpass filtering (0.01–0.08 Hz). Mean signal from white matter, CSF, whole brain, and 24 motion parameters were removed using a linear regression model. In order to further reduce the motion effect, FD-DVARS “scrubbing” approach was applied [36]. Subsequently, rsfMRI images were aligned to the corresponding T1-weighted images by using linear alignment. And the alignment between T1-weighed images and Montreal Neurological Institute (MNI) template was performed by using the advanced normalization tools (ANTs) [37]. To improve the accuracy of registration, brain tissue segmentation images were employed to calculate the deformation field to the MNI template, as well as the reverse deformation field from MNI template to each individual subject.

Using the brain atlas provided by Seitzman et al. [38], deformation back to the individual space was performed to extract the mean time-series BOLD signal for each of 300 regions of interest (ROIs). This atlas was selected as it contains multiple ROIs in subcortical grey matter structures and pre-defines large-scale resting-state networks by uniquely assigning specific ROIs to a single network (or otherwise “unassigned” designation). Pearson’s correlation coefficients (r) were calculated between all pairs of ROIs for each subject.

Outcome measures (rsFC values)

Whole-brain rsFC was calculated as the average Pearson r correlation value across the BOLD signal time-series between each of the 300 individual functional ROIs. To test our hypotheses, within-network and between-network average Pearson r correlation values were calculated using the pre-defined DMN (65 ROIs), DAN (16 ROIs), and FPCN (36 ROIs) network nodes [38]. Within-network average rsFC was calculated as the average between ROI Pearson r correlation value between all pairs of ROIs dwelling within a given network (e.g., DMN). Between-network average rsFC was calculated as the average between ROI Pearson r correlation value between each individual node of one specific network and each individual node of another specific network. These operationalizations resulted in seven dependent variables: 1) whole-brain average rsFC; 2) within-DMN average rsFC; 3) within-DAN average rsFC; 4) within-FPCN average rsFC; 5) DMN-DAN average rsFC; 6) DMN-FPCN average rsFC; and 7) DAN-FPCN average rsFC.

Concussion history & head impact exposure estimate

History of concussion was self-reported by each participant using a definition that has been employed in previous research with current and former athletes [30, 39]. This operational definition described concussion as, “an injury occurring typically, but not necessarily, from a blow to the head, followed by a variety of symptoms that may include any of the following: headache, dizziness, loss of balance, blurred vision, ‘seeing stars,’ feeling in a fog or slowed down, memory problems, poor concentration, nausea, throwing up, and loss of consciousness” [30, 39]. This method of self-reporting concussion history has shown moderate levels of consistency (weighted Cohen κ = 0.48) over repeated administrations separated by many years [40]. After reading the operational definition of a concussion, participants then reported the total number of lifetime concussions they had sustained through sport or other mechanisms (e.g., military service, motor vehicle accidents).

Lifetime exposure to repetitive head impacts without diagnosed injury (e.g., head impacts that did not result in overt clinical signs or symptoms) were estimated using the adjusted Head Impact Exposure Estimate (HIEE) [41, 42]. A 30-minute structured interview was used to gather information regarding participation in contact football games and practices across each individual year of football participation at the high school, collegiate, and professional levels [41, 42]. For each year of play, participants detailed their primary and secondary playing positions, the average number and length (hours) of practices during each week of the pre-, regular, and post-season participation, the number of games played, and an estimate of the percentage of time playing in each game that year (0%; 25%; 50%; 75%; or 100%). These data were used to calculate a “number of contact hours” estimate for each participant. Those estimates were then adjusted to account for the number of head contacts that might be sustained for each individual by player position and level of play based on previous reports that utilized helmet-mounted accelerometers to measure head impacts [41, 43, 44]. The resultant number (HIEE) serves as a surrogate for the estimated number of head impacts to which that individual had been exposed during football participation in high school and beyond.

Analyses

Primary independent variables were lifetime self-reported concussion history and HIEE. Concussion history was operationalized in two separate ways based on common standards in the existing literature: A) dichotomous “High” (≥ 3 concussions; n = 28) versus “Low” (< 3 concussions; n = 20) history groups; and B) four ordinal categories (0 or 1 concussion [n = 19]; 2 to 4 concussions [n = 8]; 5 to 7 concussions [n = 9]; and 8 or more concussions [n = 12]). The dichotomous operationalization was selected to be similar to prior research examining the long-term effects of concussions on fMRI outcomes and cognitive function, where outcomes from participants three or more lifetime concussions were contrasted with those reporting fewer [4, 27, 28]. Further, ordinal operationalization of concussion history in recent studies has allowed for a more granular investigation of the effects of multiple concussions on long-term brain health [8, 45, 46], and we opted to explore (i.e., as a sensitivity analysis) whether the same phenomenon would be observed in the present study by ascribing previously used concussion history groupings from overlapping study samples [42, 45]. The HIEE measure was included in each model as a continuous variable, regardless of the concussion history operationalization.

Potential covariates were participant age, body mass index (BMI), and MRI acquisition site. Analyses were performed to test univariable associations between each potential covariate and all seven of the rsFC outcomes of interest (S1 File). Both BMI and MRI acquisition site were related to one or more of the outcomes individually, and were therefore included as covariates in the multivariable models.

Separate multivariable linear regression models were fit for each of the seven rsFC outcomes including our primary exposures of interest (concussion history and HIEE) as well as covariates (BMI, and MRI acquisition site) as independent variables. Altogether, there were a total of 14 models fit (seven for each operationalization of lifetime concussion history). As this was a preliminary study, we set a-priori α at 0.05, and we’ve interpreted results based on these values as well as measures of effect size (standardized beta values [β]). All analyses were performed with SPSS version 28.0 (Armonk, NY). Post hoc observed power for each regression was calculated in G*Power v3.1.9.7.

Results

Participants

Participants were recruited from a sample of former collegiate athletes who previously completed a general health survey [30]. Initially, 123 former collegiate football players were able to be reached for in-person visit screening. Among these former players, 65 opted not to participate, met exclusion criteria (e.g., for MRI safety reasons), or did not respond to the study team. As a result, 58 former players completed in-person visits. Generally speaking, the cohort fell within the average range across indices of neurobehavioral function as described previously by Brett et al. [42]. Three of these participants did not participate in the MRI portion of the study due to claustrophobia (n = 2) or an acquisition protocol deviation (n = 1). A total of 55 male former collegiate football players participated in the MRI study. Among these participants, 7 (12.7%) had missing (n = 1) or unusable rsfMRI data (n = 6) due to poor functional image resolution precluding measurement of BOLD signal in one or more ROI. The resulting sample size was 48 participants across both research sites (Table 2).

Table 2. Participant characteristics.

	Full sample	“Low” lifetime concussion history group (<3 concussions)	“High” lifetime concussion history group (≥3 concussions)
	n = 48	n = 20	n = 28
Age^a, mean (standard deviation)	37.9 (1.5) years	37.9 (1.3) years	37.9 (1.6) years
Body Mass Index (BMI), mean (standard deviation)	30.6 (4.3) kg·(m²)^-1	31.1 (4.6) kg·(m²)^-1	30.2 (4.1) kg·(m²)^-1
Race, n (%)
White or Caucasian	38 (79.2)	14 (70)	24 (85.7)
Black or African American	7 (14.6)	3 (15)	4 (14.3)
Multiracial	3 (6.3)	3 (15)	0 (0)
MRI acquisition site, n (%)
University of North Carolina at Chapel Hill	38 (79.2)	16 (80.0)	22 (78.6)
Medical College of Wisconsin	10 (20.8)	4 (20.0)	6 (21.4)
Lifetime concussion history, median (lowest, highest)	4 (0,24)	1 (0,2)	6.5 (3,24)
Played professional football after college, n (%)	7 (14.6)	2 (10)	5 (17.9)
Total years of football play, mean (standard deviation)	12.5 (4.4)	13.2 (3.2)	11.9 (5.0)
Adjusted Head Impact Exposure Estimate (HIEE), mean (standard deviation)	1292.0 (448.3)	1262.4 (504.7)	1313.0 (411.8)

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All participants were male.

^a Ages ranged from 36 to 41 years old.

Resting state functional connectivity

After adjusting for BMI and MRI acquisition site, there were no statistically significant differences (ps ≥ 0.30) for any of the rsFC outcomes between those with “High” (three or more) versus “Low” (less than three) lifetime concussion history (Table 3; Figs 1 and 2). Similarly, when concussion history was operationalized into 4 ordinal categories, no statistically significant associations were observed (ps ≥ 0.12) between concussion history and any of the rsFC outcomes (Table 3; Figs 3 and 4). Finally, HIEE was not significantly associated with rsFC outcomes in any of the multivariable models (ps ≥ 0.14). We also computed standardized effect sizes for both concussion history and HIEE (i.e., β-values). The largest β-values were observed for HIEE in models for within-FPCN rsFC and between-network rsFC for DMN-DAN (Table 3). Specifically, larger HIEE was associated with lower within-FPCN and higher DMN-DAN rsFC values, regardless of the operationalization variable used for concussion history. Plots of bivariate associations between HIEE and each of the seven rsFC outcomes are in Fig 5. Observed power estimates for each model ranged from 25–91% and are presented in S1 File.

Table 3. Standardized β-values from multivariable linear regression models.

	“High” vs. “Low” Concussion History		4-Category Concussion History
Self-Reported Concussion History
Outcome	Standardized β	p-value	Standardized β	p-value
Whole-brain	.115	.41	.061	.66
Within-network
DMN	.010	.95	.089	.54
DAN	.021	.88	.137	.33
FPCN	.071	.62	-.024	.87
Between-network
DMN-DAN	.001	.99	-.169	.24
DMN-FPCN	.152	.30	.009	.95
DAN-FPCN	.054	.70	.122	.37
Adjusted Head Impact Exposure Estimate
Outcome	Standardized β	p-value	Standardized β	p-value
Whole-brain	-.045	.75	-.037	.80
Within-network
DMN	.011	.94	.008	.96
DAN	-.159	.29	-.163	.27
FPCN	-.231	.12	-.224	.14
Between-network
DMN-DAN	.181	.24	.188	.21
DMN-FPCN	-.041	.72	.067	.66
DAN-FPCN	.027	.85	.026	.85

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Multivariable models included self-reported concussion history and adjusted Head Impact Exposure Estimates (HIEE) as predictors for each resting-state functional connectivity outcome. Two sets of models were fit with concussion history operationalized as either binary (“High” vs. “Low”) or the 4-category operationalization (0 or 1 concussion; 2 to 4 concussions; 5 to 7 concussions; 8 or more concussions) based on previous studies with these participants. Both body mass index (BMI) and acquisition site were observed to have significant univariable associations with one or more of the outcome variables of interest and were included in each multivariable linear regression model as covariates. Adjusted R² values for multivariable linear regression models with concussion history operationalized as “High” vs. “Low” were: whole-brain (.120); within-DMN (.000); within-DAN (.066); within-FPCN (.078); DMN-DAN (.012); DMN-FPCN (.028); and DAN-FPCN (.137). Adjusted R² values for multivariable linear regression models with concussion history operationalized as 4 categories were: whole-brain (.110); within-DMN (.009); within-DAN (.087); within-FPCN (.073); DMN-DAN (.043); DMN-FPCN (.003); and DAN-FPCN (.150).

Fig 1 — Whole-brain rsFC (top-left) was calculated as the average Pearson r correlation (y-axes) between each of the 300 regions of interest (ROIs) and all other ROIs across the time-series. Within-network rsFC for the Dorsal Attention Network (DAN; top-right), the Default Mode Network (DMN; bottom-left), and the Frontoparietal Control Network (FPCN; bottom-right) was calculated as the average Pearson r correlation between each of the individual ROIs from the given network and all other ROIs within that same network across the time-series. Individual points represent participant-level outcomes for each measure within each of the concussion history groups (White = “Low” [fewer than 3 lifetime concussions]; Blue =“High” [3 or more lifetime concussions]). Boxplots represent the median and interquartile range, and the whiskers extend to 1.5 times the interquartile range. The violin plot is a depiction of the density of individual rsFC values for each measure.

Fig 2 — Between-network rsFC was calculated as the average Pearson r correlation (y-axes) between each of the individual ROIs from one specific network and all individual ROIs in another specific network across the time-series: Dorsal Attention Network and Default Mode Network (top-left); Default Mode Network and Frontoparietal Control Network (top-right); and Dorsal Attention Network and Frontoparietal Control Network (bottom). Individual points represent participant-level outcomes for each measure within each of the concussion history groups (White = “Low” [fewer than 3 lifetime concussions]; Blue =“High” [3 or more lifetime concussions]). Boxplots represent the median and interquartile range, and the whiskers extend to 1.5 times the interquartile range. The violin plot is a depiction of the density of individual rsFC values for each measure.

Fig 3 — Whole-brain rsFC (top-left) was calculated as the average Pearson r correlation (y-axes) between each of the 300 regions of interest (ROIs) and all other ROIs across the time-series. Within-network rsFC for the Dorsal Attention Network (DAN; top-right), the Default Mode Network (DMN; bottom-left), and the Frontoparietal Control Network (FPCN; bottom-right) was calculated as the average Pearson r correlation between each of the individual ROIs from the given network and all other ROIs within that same network across the time-series. Individual points represent participant-level outcomes for each measure within each of the concussion history groups (1 = 0 or 1 concussion [n = 19]; 2 = 2 to 4 concussions [n = 8]; 3 = 5 to 7 concussions [n = 9]; and 4 = 8 or more concussions [n = 12]). Boxplots represent the median and interquartile range, and the whiskers extend to 1.5 times the interquartile range. The violin plot is a depiction of the density of individual rsFC values for each measure.

Fig 4 — Between-network rsFC was calculated as the average Pearson r correlation (y-axes) between each of the individual ROIs from one specific network and all individual ROIs in another specific network across the time-series: Dorsal Attention Network and Default Mode Network (top-left); Default Mode Network and Frontoparietal Control Network (top-right); and Dorsal Attention Network and Frontoparietal Control Network (bottom). Individual points represent participant-level outcomes for each measure within each of the concussion history groups (1 = 0 or 1 concussion [n = 19]; 2 = 2 to 4 concussions [n = 8]; 3 = 5 to 7 concussions [n = 9]; and 4 = 8 or more concussions [n = 12]). Boxplots represent the median and interquartile range, and the whiskers extend to 1.5 times the interquartile range. The violin plot is a depiction of the density of individual rsFC values for each measure.

Fig 5 — Whole-brain rsFC was calculated as the average Pearson r correlation (y-axes) between each of the 300 regions of interest (ROIs) and all other ROIs across the time-series. Within-network rsFC for the Dorsal Attention Network (DAN), the Default Mode Network (DMN), and the Frontoparietal Control Network (FPCN) was calculated as the average Pearson r correlation between each of the individual ROIs from the given network and all other ROIs within that same network across the time-series. Between-network rsFC was calculated as the average Pearson r correlation between each of the individual ROIs from one specific network and all individual ROIs in another specific network across the time-series: DMN-DAN; DMN-FPCN; and DAN-FPCN. Individual points represent each of the individual participants.

Discussion

Our findings suggest that lifetime concussion history and accumulated head impact exposure among younger former collegiate football players were not significantly associated with functional connectivity of large-scale brain networks associated with aging during early midlife. However, there were notable effect sizes suggesting a relationship between repetitive head impacts (HIEE) and functional connectivity profiles that have been associated with the aging process (lower within-network connectivity and higher between-network connectivity). Previously reported findings related to age-related cognitive decline, MCI, and dementia in older former football players suggest that these players may be at increased risk of developing dementia-related disorders or accelerated cognitive aging [4, 5, 8]; however evidence for causal relationships between concussion history, repetitive head impacts, and these clinical outcomes have not been established. The present study provides evidence that broad changes to neural activity in the brain in relation to accumulated head trauma from football participation may not be readily detectable (i.e., too subtle to measure) or absent in individuals approximately 15-years after their collegiate sport participation. Continued follow-up with these participants and further evidence from prospective, longitudinal monitoring of brain health in former athletes is imperative to develop an understanding of change over time and associations of long-term brain health with head trauma.

In addition to our primary categorization of concussion history into “High” (three or more) and “Low” (less than three) lifetime injury groups, we sought to explore whether a more granular, ordinal categorization scheme might identify patterns with increasing exposure to injury that could be hidden in the traditional dichotomous operationalization, as has been seen in recent research [8, 45, 46]. Neither of the operationalizations of concussion history (dichotomous or ordinal categories) used in our study were associated with intra- or inter-network rsFC of the DMN, DAN, and FPCN networks or whole-brain rsFC. There are limitations inherent to retrospective recall of lifetime concussion history that warrant consideration when interpreting these findings, and previous work has described moderate consistency in recall over time [40]. Despite this, self-reported concussion history has previously been associated with increased rsFC within the DMN in current collegiate athletes [7], altered neural recruitment patterns in former football players aged between 50–75 years when completing a cognitive task [27, 28], and with self-reported cognitive dysfunction and atypical cognitive decline [4, 8, 46]. However, there is little research with former football players under 50 years of age related to the long-term effects of concussion history and repetitive head impacts on brain health.

Our recent work with an overlapping sample of relatively young (36–41 years of age) former football players noted significant associations between greater lifetime HIEE and multiple aspects of neurobehavioral functioning–including worse subjectively reported cognitive function, general psychological distress, and executive functioning alongside worse objectively measured memory and processing speed task performances [42]. In that study, we also observed that concussion history did not significantly alter the associations between neurobehavioral functioning and HIEE, despite these two measures of exposure to head trauma being distinct from one another [42]. The lack of statistically significant associations between HIEE and rsFC outcomes in the present study suggests that alterations in rsFC within and between large-scale networks may not be the underlying processes (i.e., neural correlates) for the relationship between HIEE and neurobehavioral functioning, or they may require more precise measurement to detect. It is possible that the association between exposure to head trauma and rsFC may be observed by using other approaches that describe functional organization in the brain (e.g., graph theoretical measures like small world topology) [47, 48]. These approaches warrant investigation in former football players as they may provide insight into latent constructs of brain health such as communication efficiency and resilience [49, 50], and some graph measures have even been associated with early recognition of neurodegenerative changes [51, 52].

The functional connectivity data reported in this study are the first to be reported in relation to concussion and repetitive head impact history in participants of this age. Specifically, objective markers of biological brain health in former athletes below 50 years of age are relatively understudied compared to their older counterparts. It is notable that only a few (n = 7) of the participants in this study played football after their collegiate careers while also reporting 12.5 years of football play, on average. This sample is therefore mostly representative of amateur athletes who began playing football at youth levels. Research with former athletes in early midlife—especially longitudinal studies—are essential to understanding the relationships between head trauma and the aging process across the lifespan. Participants in this study with more self-reported lifetime concussions and greater HIEE did not exhibit functional connectivity differences when compared to those with fewer concussions and/or lower HIEE; however, these participants are purportedly at higher risk of developing early cognitive decline and neuropathology associated with head trauma according to previous research findings [4, 5, 8]. Therefore, this timepoint is a key contribution to the literature in that longitudinal follow-up will allow us to investigate the interaction between head trauma and age, especially over the next decade as these participants approach 50 years of age.

One limitation of this study is the potential for error in data relying on self-report. Specifically, participants self-reported the primary exposure measures used in this study (concussion history & HIEE), and it is possible that self-reported exposure differs from the true incidence of exposure. Further, data were collected at two research sites and under three separate image acquisition protocols. To address this acquisition heterogeneity, we evaluated the association between study site and the rsFC outcomes (S1 File), and ultimately controlled for site in the analyses. Data from the present study may also not be representative of former collegiate football players at large, and should be considered in this light. Namely, there were only 48 participants included in our analyses, and most of them identified as White/non-Hispanic. Lack of racial and ethnic diversity, as well as other potential determinants of brain health, limit the generalizability of our findings to the population. On the whole, our sample reports limited health conditions and functional limitations [42, 45]; however, there are thousands of former collegiate football players who may or may not be similar in their current health status. With this cross-sectional data, we cannot yet determine whether trajectories of brain health-related outcomes as players age are associated with accumulated concussion injuries or repetitive head impact exposures, and whether changes in brain health are different for former football players than non-football players. Future work should prospectively examine the time course of changes in functional connectivity in aging former football players and evaluate potential modifiers of these changes over time (i.e., expected brain health changes due to aging versus accelerated decline resulting from acquired brain trauma).

Among former collegiate football players at early midlife, we did not observe associations among concussion history or repetitive head impact exposure and neural signatures of altered large-scale functional network connectivity that have been observed in studies of age-related or clinically significant cognitive declines, as well as acute SRC. While SRC history and repetitive head impacts have been associated with changes in brain health and function in former football players and other collision and contact sport athletes, our preliminary data suggest that associations with functional network connectivity, if they exist, may not be detectable prior to older ages. Continuing to study these former players prospectively will be useful in identifying predispositions to potential accelerated aging processes, and the age at which they are most likely to present.

Supporting information

S1 File. Supplemental results.

Linear regressions for the effect of individual covariates on each of the resting-state functional connectivity (rsFC) outcomes described in body of the manuscript.

(DOCX)

Click here for additional data file.^{(54.9KB, docx)}

Acknowledgments

First, we would like to thank the athletes who participated in this study. We would also like to thank Candice Goerger, Robyn Furger, and Leah Thomas for their invaluable efforts in study coordination and management. Finally, we are also grateful for the many important contributions to this study made by Drs. Weili Lin, Hao Guan, and Andrew Nencka.

Data Availability

Data are available in an Open Science Framework repository, which may be found using the following DOI: 10.17605/OSF.IO/B5ZN4.

Funding Statement

KMG and MAM received funding for this study from the National Collegiate Athletic Association (https://www.ncaa.org/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0273918.r001

Decision Letter 0

Jacob Resch

21 Mar 2022

PONE-D-22-05923Associations of Lifetime Concussion History and Repetitive Head Impact Exposure with Resting-State Functional Connectivity in Former Collegiate American Football Players: An NCAA 15-Year Follow-Up StudyPLOS ONE

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Reviewer #1: Walton and colleagues report data from a subset of former collegiate American football athletes who underwent in-person data collection through the 15-year follow up of the original NCAA study. This report focused on rs-fMRI data. Somewhat similar to other recent reports from this dataset, results suggested minimal support for an association between self-reported concussion history or repetitive head impact exposure and brain health, operationalized here as specific within- and between-network connectivity strengths.

The small N of this study makes it difficult to draw meaningful conclusions from the null findings. The authors generally did a good job of interpreting the data cautiously and pointing out the limitations of the study sample and design, but given these, I am on the fence about the appropriateness of this study as a standalone publication in its current form. Conceptually, I felt the introduction did not adequately set up the rationale for a study of presumably healthy men in their 30s and 40s. The hypothesis-driven approach is much appreciated, but I am not sure that data from older adult studies mostly focused on patients with MCI/dementia due to Alzheimer’s disease is ideal here. I was not convinced that we should have ever expected to find the hypothesized changes in this particular sample in the first place (young, healthy males) simply because they have varying degrees of prior head trauma exposure. This would have required a biomarker with a sensitivity to preclinical neurodegenerative pathophysiology that I don’t think exists anywhere. Even well-validated biomarkers of AD pathophysiology require relatively widespread AD pathology and, usually, symptomatic patients before they are clearly altered.

As the authors acknowledge, the real goal is longitudinal tracking. I am a little worried that the current study N is only 55 and further attrition is expected over time. I view this study as basically a report of baseline rs-fMRI data in a sample of former collegiate American football players that notes no clear association with prior head trauma, which by itself (i.e., an isolated biomarker without other biologic or clinical measurement) is not terribly compelling given the primary conclusion is essentially “we’ll now wait and see if things change over time.” The data may be better presented as something like a short report.

I thank the authors for beginning to organize the important data expected to come from the follow-up NCAA study and look forward to seeing future results from this unique cohort. I provide other comments and questions for the authors to consider:

1) L73-75: This opening statement is a little confusing. Why is MCI called out specifically (also no need to capitalize Mild Cognitive Impairment)? Also not clear what is meant by “typical aging.”

2) L80-82: Can the authors be more specific than “neurodegenerative changes”? I caution against using vague terminology (including “typical aging”). If you mean cognitive healthy older adults, or clinically normal older adults, etc., that would be preferred over “typical aging” in this context (applicable throughout the manuscript).

3) L85-86: Is rs-fMRI still considered a burgeoning method?

4) L97: MCI is not an example of a neurodegenerative disease. MCI is a cluster of symptoms potentially resulting from an underlying neurodegenerative disease. Alzheimer’s disease is one example of a neurodegenerative disease, which can manifest as symptoms that get classified as either MCI (objective cognitive/behavioral changes without impact on functional independence) or dementia (same as MCI, but now with loss of functional independence). Alzheimer’s disease is not synonymous with dementia, nor is it a more severe form of MCI. I strongly recommend modifying terminology throughout the paper to more accurately represent symptom-based/syndromic phenomenon (e.g., MCI, dementia) distinct from the neurodegenerative disease causing those symptoms (e.g., Alzheimer’s disease).

5) L107: I recommend saying “even without overt symptoms” instead of “injury.”

6) L108-109: Authors previously stated that rsFC changes in “those with MCI and AD are similar to changes observed with typical aging processes.” Concerns with terminology aside, it is unclear then what “alterations in functional connectivity that are similar to those observed in pathological aging” is referring to if rsFC changes are similar between healthy and unhealthy aging groups.

7) L244: While I would not personally consider multiple comparison adjustments a hard and fast rule, it would be helpful for the authors to provide rationale for an a priori alpha of p < .05 given the number of models, or include interpretation of alternate metrics to complement the p values.

8) L248-249: It is necessary to give readers a sense of the sample being studied here. How many athletes were contacted to participate in the in-person phase? How many outright declined compared to the 55 who were enrolled? Are there any metrics that can be provided to determine potential demographic/exposure differences between those who enrolled and those who declined? Were any clinical evaluations performed for these participants to gauge cognitive/behavioral status?

9) L277-280: Some of the adjusted R-squared values for these models are decently high. While not statistically significant, the standardized beta-weights for HIEE in a handful of the models are intriguing (e.g., within DAN and FPCN, between DMN-DAN). Given the low N for this study, and at least one of these associations being in the hypothesized direction (lower within DAN), it may be worth incorporating effect size estimates into your interpretation and also providing readers with a sense of study power in the methods (i.e., what effect size would have been required to be detected as statistically significant given your N?).

10) General points regarding the Discussion: It is exceedingly difficult to “prove the null” hypothesis and draw firm conclusions about associations between head trauma exposure and rsFC based on this study. First, I have concerns about the underlying conceptual model of aging/neurodegenerative disease considering this was a sample of men in their 30s and 40s and presumably all are cognitively healthy (there were no details provided about cognitive/behavioral health). Therefore, identifying rsFC changes depended on methodology being so exquisitely sensitive to pathological brain changes (if they existed) that it would detect them decades prior to symptom onset (should that ultimately occur). I don’t know that we can assume that. 2) The ordinal categorization of concussion history is better than the dichotomization, but there remain questions about self-report accuracy given that some studies show self-report numbers on the order of 10s (such as this study) and others show self-report numbers on the order of hundreds to thousands. 3) The limitations section is well thought out and transparent, though I worry that acknowledgment of the limitations alone is insufficient and wonder whether we can really draw meaningful conclusions in light of these limitations.

11) The choice of figure(s) is unclear. Why decide to show only the group comparisons (null) for the Low vs. High concussion hx groups rather than the ordinal characterization and/or scatterplots depicting the HIEE associations?

Reviewer #2: The authors present an important paper on brain health (based on functional connectivity imaging metrics) in former collegiate football players and this manuscript will make a valuable addition to the literature. One primary concern is the limited description of the participants and how this relates to the larger story. The recent TES NINDS statement (Katz 2021) suggests 5 years of collision sports is needed to reach some magical “threshold”, it could be really interesting to see if this population sample reaches that threshold especially given the results. Presumably, collegiate football players also participated in high school, so one would suspect that all participants herein meet the TES criteria. Similarly, did these participants continue sports participation post-college (either RHI or non-RHI sports) given the known benefits of exercise on broadly stated brain health. Further, there is a real concern about a healthy person selection bias in the Methods (e.g., can’t travel). Can the authors elaborate on how they addressed this limitation? Otherwise, it seems to be the opposite of the BU studies with their unhealthy person bias.

Minor comments

Abstract – suggest adding some simplistic demographics to the participants section.

Methods – The rationale for “one or more years” of college football needs to be provided. I would also suggest adding career duration (years played) in Table 2 (acknowledging HIEE is a better metric) to make it easier to compare to other studies.

I commend the authors for their transparency in Table 1 on the different scanners, did the authors perform any post-hoc comparisons to ensure the scanner switch at institution B didn’t influence the outcomes (while recognizing that “location” was an appropriate covariate).

Line #99 – “changes” – can this be directionally defined? From the literature, it seems that either higher or lower is “bad” which makes findings hard to compare and interpret.

Line #133 – Can you clarify “suspicion” of psychotic disorder? Was anyone removed based on this criterion?

Line #194 – a very old reference for the definition of a concussion. Why not use a more contemporary definition (e.g., 5th CIS, CARE, etc).

In the Discussion the authors comment on the lack of studies investigating the middle-aged former football player (line #333 +/-), however Iverson has a couple of studies in this area (CJSM 2021, Frontiers 2021, J Neurotrauma 2021) which address this issue and their inclusion would provide a more comprehensive discussion.

Similarly, there are some studies outside of football (e.g., rugby) which address midlife health in collision sports athletes (e.g., Hunzinger MSSE 2021; Van Patten 2021 Frontiers; Inversion 2021 Frontiers)

I commend the authors for their transparency in the Figures.

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2022 Sep 9;17(9):e0273918. doi: 10.1371/journal.pone.0273918.r002

Author response to Decision Letter 0

20 May 2022

Author Response: We understand the reviewer’s concerns and have made changes throughout the manuscript to better present this study as a preliminary investigation of a potential objective marker of declining brain health. Specifically, language has been revised, including in the hypotheses, to frame our study purpose through an inductive lens—that is, if long-term changes in functional connectivity exists in those with history of one or more mild TBI and/or exposure to repetitive head trauma, we hypothesize that rsFC in this sample would appear to be similar to connectivity patterns observed in older individuals. This was our original study purpose, and the reviewer’s feedback has been very helpful in recognizing where it was not well communicated.

Author Response: In line with our response to the preceding comment, we have revised the language in this paper to better present this as a preliminary report of rsFC in this sample. We have also acknowledged the recent work with a heavily overlapping sample detailing the general health and well-being of the sample via objective and subjective measures of cognitive and neurobehavioral functioning.

Author Response: This comment was very helpful, thank you. MCI was called out specifically as it has been a focus of recent work in former football players, and it has been associated with concussion history and repetitive head impacts in those players. We have changed our opening statement to use MCI as an example of abnormal cognitive decline, and the capitalization has been removed. This change was also in line with other comments from this reviewer to use more specific terminology rather than “typical aging”. We have made similar changes throughout the manuscript.

Author Response: Similar to the reviewer’s previous comment, this was helpful feedback. Changes have been made in the introduction and throughout the manuscript to be less vague regarding these important points.

3) L85-86: Is rs-fMRI still considered a burgeoning method?

Author Response: The word burgeoning has been removed, and the statement now reads: “One method for examining biologic changes in brain health is resting-state functional activity as measured with magnetic resonance imaging (MRI).”

Author Response: Thank you for this clarification. We have made efforts to better distinguish these important characteristics in the language used throughout the paper, in line with the reviewer’s previous comments as well.

5) L107: I recommend saying “even without overt symptoms” instead of “injury.”

Author Response: Great suggestion. We have changed this statement accordingly: “Further evidence suggests that rsFC changes also occur in relation to repetitive head impacts, even without overt clinical signs or symptoms (e.g., concussion).”

Author Response: This is helpful feedback. We have modified the final section of the introduction to address this comment as well as to help address the concern noted in other reviewer comments about the underlying conceptual framework for this study.

The reviewer’s comment that, “[a]uthors previously stated that rsFC changes in ‘those with MCI and AD are similar to changes observed with typical aging processes,’” is important, and we have attempted to qualify that initial statement by indicating that changes in the presence of MCI and AD diagnoses suggest an exacerbated or accelerated process of functional changes when compared to normal age-related changes, theoretically relating to accelerated biological aging.

We have changed the statement, “alterations in functional connectivity that are similar to those observed in pathological aging,” to address the notion that the functional connectivity changes exist in normal aging but may appear in younger individuals: “Taken together, long-term alterations in functional connectivity may result from greater exposure to SRC and repetitive head impacts, even in relatively young individuals, and these changes may resemble those observed in individuals experiencing cognitive decline. In this light, it is also reasonable to consider functional connectivity changes as potential biomarkers for the advanced aging and the early onset of pathological changes in former football players.”

Author Response: This is a fair point. We have added language about the preliminary nature of this study and to acknowledge that we will interpret the results based on both p-values and measures of effect size (standardized beta values). We have also included estimates of post hoc power for each regression model in the supplement (S1).

Author Response: Thank you, this is a great suggestion. We have included the available information regarding recruitment and retention of study participants in the methods and results sections. Additionally, both objective and subjective measures of neurobehavioral function were reported in a recently published paper, so we’ve cited that paper in the results section accompanying a statement that the generally fell within the average range across indices of neurobehavioral function.

Author Response: This is an important suggestion. We have included interpretations of the standardized beta-weights in the paper as well as post hoc power estimates in the paper and supplemental file.

Author Response: Thank you for this discussion. It relates to prior comments from this reviewer as well, and we have attempted to address key components of this in the revised manuscript. Specifically: 1— We have provided more details regarding the conceptual model. We have additionally provided a statement about, and citation for, the participants’ current cognitive/neurobehavioral health. Further, we have more clearly stated that this is a preliminary study throughout the manuscript; 2¬—Self-reported concussion history is indeed a notable consideration, and we’ve added text to address this in the body of the discussion rather than just in the limitations; 3—We have hopefully addressed the reviewer’s concerns in response to the first two points.

Author Response: The choice to present one set of graphs was to give a visual example of the spread of the data in relation to a predictor of interest. Since all the associations we tested were not statistically significant, we opted for what we felt to be the simplest depiction (Low vs. High concussion history). However, after considering the reviewer’s comment, we have included graphical representations of the associations between both concussion history categorizations and rsFC values as well as between HIEE and rsFC values. There are now 5 total figures included in the paper.

Author Response: Thank you for bringing up this important context. We have added both total years of football play as well as the proportion who played professional football to Table 2 alongside other demographic data. Beyond participation in professional football, we do not possess data on post-collegiate participation in other sports or other types of physical activity.

Regarding the “healthy person bias”, it would be hard to say that this was definitely the case with our sample. In the table below, we have data from a separate manuscript which is currently in review elsewhere (please keep these data confidential) on rates of lower-than-average function/performance. The data indicate that not all our participants were within normal limits. Our participants may also have been interested in the current state of their personal health and/or concerned with their long-term brain health, which could have been the driving factor for their choice to participate in this longitudinal follow-up. We did not directly measure this, and therefore cannot account for personal reasons for engaging in our study. Relatedly, in response to a comment by another reviewer, we did add text and an accompanying citation to the results section (Participants) stating that, “…participants were relatively healthy with regard to performance-based and self-reported measures of cognitive function as described previously by Brett et al.[44]” Finally, we tested the hypothesis that exposure leads to differences in an outcome (rsFC). There was a significant range (extending to the upward limit) in each of our exposure variables, suggesting that if more concussions and RHI would associate with differences in rsFC, we would in theory observe that association here.

Table 4.

Proportion of scores equal to or exceeding one standard deviation on objective and subjective measures.

Neuro-QoL Cognition BRIEF-A MI BRIEF-A BRI

Sample (N = 57) WNL Low WNL Low WNL Low

Objective Cognitive Functioning n (%) n (%) n (%) n (%) n (%) n (%) n (%)

HVLT-R Immediate Recall a WNL 45 (78.9) 42 (93.3) 3 (6.7) — — — —

Low 12 (21.0) 10 (83.3) 2 (16.7) — — — —

HVLT- R Delayed Recall a WNL 43 (75.4) 42 (97.7) 1 (2.3) — — — —

Low 14 (24.6) 10 (71.4) 4 (28.6) — — — —

SDMT a WNL 55 (96.5) 50 (90.9) 5 (9.1) — — — —

Low 2 (3.5) 2 (100) 0 (0) — — — —

TMT-A a WNL 38 (66.7) 34 (89.5) 4 (10.5) — — — —

Low 19 (33.3) 18 (94.7) 1 (5.3) — — — —

TMT-B a WNL 34 (59.6) 30 (88.2) 4 (11.8) 27 (79.4) 7 (20.6) 27 (79.4) 7 (20.6)

Low 23 (40.4) 22 (95.7) 5 (4.3) 21 (91.3) 2 (8.7) 19 (82.6) 4 (17.4)

F-A-S a WNL 50 (87.7) 48 (96.0) 2 (4.0) 44 (88.0) 6 (12.0) 42 (84.0) 8 (16.0)

Low 7 (12.3) 4 (57.1) 3 (42.9) 4 (57.1) 3 (42.9) 4 (57.1) 3 (42.9)

Subjective Cognitive Functioning

Neuro-QoL Cognition a WNL 52 (91.2) — — — — — —

Low 5 (8.8) — — — — — —

BRIEF-MI b WNL 48 (84.2) 46 (95.8) 2 (4.2) — — — —

Low 9 (15.8) 6 (66.7) 3 (33.3) — — — —

BRIEF-BRI b WNL 46 (80.7) 44 (95.7) 2 (4.3) — — — —

Low 11 (19.3) 8 (72.7) 3 (27.3) — — — —

Note. Low = lower than average function/performance (≥ 1 SD); BRI = Behavioral Regulation Index; BRIEF-A = Behavior Rating Inventory of Executive Function – Adult; BSI-18 GSI= Brief Symptom Inventory-18 Global Severity Index; F-A-S = Verbal Fluency; HVLT-R = Hopkins Verbal Learning Test-Revised; MI = Metacognition Index; Neuro-QoL Cognition = Quality of Life in Neurological Disorders Cognitive Functioning Short-form; SDMT = Symbol Digit Modalities Test; TMT = Trail Making Test; WNL = within normal limits (± 1 SD).

a = Low defined as ≤1 SD of the mean.

b = Low defined as ≥1 SD of mean.

Minor comments

Abstract – suggest adding some simplistic demographics to the participants section.

Author Response: Good suggestion. In addition to age and gender, we have also added the proportion identifying as white/Caucasian and the average total number of years playing football. Concussion histories are detailed later in the abstract.

Author Response: Thank you for these suggestions. We have added years of football play to Table 2 as well as the proportion of participants who went on to play professional football after college. We have edited the Participants paragraph of the Materials & Methods section to detail inclusion and exclusion criteria for our study. The rationale for one or more years of college football play was that this was a study of former collegiate football players, and they needed to have this experience in order to be included in the study.

Author Response: The reviewer brings up a reasonable suggestion. There were no statistically significant differences between the two protocols at Institution B for any of the seven study outcomes (ps ≥ .059).

Line #99 – “changes” – can this be directionally defined? From the literature, it seems that either higher or lower is “bad” which makes findings hard to compare and interpret.

Author Response: This is a great point. In response to this comment as well as those from the other reviewer, we have tried to clarify this instance and other similar vague statements throughout the introduction and discussion.

Line #133 – Can you clarify “suspicion” of psychotic disorder? Was anyone removed based on this criterion?

Author Response: We have removed the term “suspicion” from this statement and have included a statement prior that all potential participants were screened for the presence of psychotic disorder with active symptoms as part of the recruitment process. We do not have a specific number to report regarding how many people were screened out as a result of that particular exclusion criterion.

Line #194 – a very old reference for the definition of a concussion. Why not use a more contemporary definition (e.g., 5th CIS, CARE, etc).

Author Response: This is a good point from the reviewer. Provided that this study was a longitudinal follow-up from a study performed 15 years prior, we wanted to keep the same definition of concussion across time points to ensure that participants were being asked to report their lifetime concussion history using a consistent measure. We acknowledge that self-reported concussion history, in general, has flaws in both the early part of the discussion section and the limitations paragraph.

Author Response: This is a good point, and these are valuable studies to the understanding of brain health in former athletes. Thank you for providing this perspective. We have clarified our statement in the paper to say, “Specifically, objective markers of biological brain health in former athletes below 50 years of age are relatively understudied compared to their older counterparts.” This is the point that was originally intended, and we believe the revised statement better reflects that intent.

I commend the authors for their transparency in the Figures.

Author Response: Thank you very much. In response to a comment from the other reviewer regarding the selection of a single set of figures (dichotomous concussion history), we have also included figures depicting the outcome rsFC data in relation to the 4-category concussion history as well as HIEE.

PLoS One. doi: 10.1371/journal.pone.0273918.r003

Decision Letter 1

Jacob Resch

16 Jun 2022

PONE-D-22-05923R1Associations of Lifetime Concussion History and Repetitive Head Impact Exposure with Resting-State Functional Connectivity in Former Collegiate American Football Players: An NCAA 15-Year Follow-Up StudyPLOS ONE

Dear Dr. Walton,

More specifically, I would ask that you and the authorship pay close attention to Reviewer 1's comments regarding the clarification of definitions used for neurodegenerative disease. Reviewer 1's thoughtful comments will assist the readership in further understanding what your findings are and what they are not. Reviewer 2's comments will also help craft a better manuscript in terms of readability and a more thoughtful discussion.

Please submit your revised manuscript by Jul 31 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
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We look forward to receiving your revised manuscript.

Kind regards,

Jacob Resch, Ph.D.

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: No

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

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6. Review Comments to the Author

Reviewer #1: Walton et al have significantly improved the organization and conceptual framework for this preliminary study. My remaining suggestions largely surround continued need for clarifying use of the terms MCI, dementia, AD, etc. in the paper (I commend the attempted accommodations already and am sympathetic to terminology being less-than-intuitive outside of aging/dementia specializations). Otherwise, I have no further content-specific recommendations.

As an additional point, I support the authors decision to appropriately steer clear of attempting to incorporate a TES framework or discussion into the current paper. The other reviewer indicated that “one would suspect that all participants herein meet the TES criteria,” which is not the case given that this study sample does not have evidence of a neurodegenerative disease and would not fulfill almost any of the TES criteria beyond having played 5 or more years of American football. As this sample continues to age, this is of course a worthy consideration and the data collected from these preliminary studies while still in their 30s and 40s could prove invaluable.

1) There remains confusion with the use of MCI, dementia, Alzheimer’s disease, etc. As stated in the prior review, MCI and dementia are clinically-defined entities with many different causes. One potential cause of MCI and dementia is Alzheimer’s disease. It would be more appropriate to simply open the introduction with “Several factors increase the risk for clinically significant cognitive decline in aging individuals (e.g., mild cognitive impairment [MCI] or dementia). If your specific hypotheses are around Alzheimer’s disease as an etiology for the MCI/dementia, or its associations with head trauma, then perfectly fine to call out AD specifically. Otherwise, probably better to just broadly mention the broad clinical syndrome categories (MCI, dementia) that reflect the presence of an underlying neurodegenerative disease (whether AD or something else).

2) L101-102: The terminology is off here. I suggest saying “…among individuals with age-related cognitive changes as well as clinically significant decline (e.g., MCI or dementia).” AD is not a more severe version of MCI and is not synonymous with dementia. It is one potential CAUSE of MCI/dementia. The cause of many adults’ MCI is Alzheimer’s disease (and, many cognitively normal older adults have Alzheimer’s disease but are resilient to the underlying brain changes, for many reasons). Please check the rest of the manuscript for instances where phrases like “MCI and AD” are used and modify accordingly.

3) L110: Not sure what “expected age-related functional declines…” refers to. Does this mean “functional” in the sense of rsFC changes? Or actual changes in daily function? The latter is not a part of normal or healthy aging.

4) L113: Consider “…individuals with clinically significant cognitive decline may be experiencing exacerbated or accelerated…” (similar suggestion throughout)

5) Author Response: Great suggestion. We have changed this statement accordingly: “Further evidence suggests that rsFC changes also occur in relation to repetitive head impacts, even without overt clinical signs or symptoms (e.g., concussion).”

Reviewer response: a little confusing as worded since at first glance it seems like you are saying that this sentence is describing an example of (“e.g.”) what a concussion is. Consider “…even without the overt clinical signs or symptoms consistent with a concussion diagnosis.”

Reviewer #2: I commend the authors for their response and revision of their manuscript to reflect the concerns of the two reviewers and I am now generally supportive of publication. However, a few smaller comments/considerations remain.

In response to reviewer #1, it is worth noting that cognitive and behavioral deficits (including CTE – e.g., Chris Henry died at 26 with CTE and cognitive/behavioral deficits) have been identified in this age group and this is probably worth noting in the introduction.

It might be simply a formatting issue on the track changes version, but the “burgeoning” sentence is currently a one sentence paragraph.

In regard to the Table in the response, unfortunately the formatting from the journal to the reviewer makes this largely unreadable; however, the text response explains. However, this raises an interesting point regarding the below average characteristics. One certainly understands the unfortunate need for salami science in the current environment, but in this case it really weakens this paper. The ability to link and compare the rsfMRI data to the cognitive/behavioral data could have been a real strength especially if the below average functional “group” had poorer fMRI outcomes.

The fact that the average participant had ~12.5 years of playing experience is a real strength of the study and suggests these are players who likely started in grade school and played to/through college. This is the critical question, in my opinion, from a public health perspective. The NFL reflects so few people as compared to youth through high school and the lack of findings here (in view of reviewer’s #1 comments on extrapolation) is noteworthy and, in my opinion, warrants stronger commentary in the Discussion. Certainly this does not fully answer the public health question, but it contributes to the discussion in a meaningful way.

The only “concern” that remains is the ability to identify how many participants failed their screening – if the data isn’t available for this specific reason, then can the authors provide an overall number of participants who failed their screening for any reason? Surely this would have tracked for IRB purposes. This would alleviate (or perhaps exacerbate) the healthy person bias concern.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

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PLoS One. 2022 Sep 9;17(9):e0273918. doi: 10.1371/journal.pone.0273918.r004

Author response to Decision Letter 1

31 Jul 2022

Dear Dr. Resch,

We want to thank you and the reviewers for your continued review of our work, and for providing feedback that will help strengthen the paper. Below we have provided detailed responses to each of the reviewer comments. Of note, the reviewers appear to disagree on the importance of discussing criteria for Traumatic Encephalopathy Syndrome, and similarly “cognitive and behavioral deficits” in this cohort (and general age group). We do not feel it is appropriate to discuss this in the present paper as it was not within the scope of the research question—relating to imaging biomarkers of brain health—and that discussion of such may lead readers to believe that we are insinuating that these are measurable through fMRI studies. In the context of comments provided by “Reviewer 1” previously, clear rsFC biomarkers for well-known pathologies like Alzheimer’s Disease are pre-clinical (or non-existent) and we would prefer to avoid any suggestion that we were attempting to observe one for TES. These are indeed important research questions, with powerful clinical implications, but are outside the scope of the present study and not feasibly addressed with our present data. We are open to further comments and discussion from the Editor if deemed necessary.

Thank you again,

Samuel R. Walton, PhD

AUTHOR RESPONSE: We thank the reviewer for their continued review and support of our decision to not discuss TES criteria specifically in this sample.

AUTHOR RESPONSE: These suggestions are well received and have been incorporated as recommended. We do not have AD-specific hypotheses and any mention of AD specifically was meant to acknowledge the content of the cited works. We have made efforts throughout to remove specific mentions of AD where it could potentially be misleading to the reader.

AUTHOR RESPONSE: This comment as well as the following two, which are similar, has helped us address these multiple instances of unclear nomenclature. We have made changes throughout to avoid imprecise words like “expected”, “atypical”, etc. and have opted to use language as suggested by the reviewer in this comment.

AUTHOR RESPONSE: See the response to item ‘2)’ above.

4) L113: Consider “…individuals with clinically significant cognitive decline may be experiencing exacerbated or accelerated…” (similar suggestion throughout)

AUTHOR RESPONSE: See the response to item ‘2)’ above.

AUTHOR RESPONSE: Thank you for helping to clarify this statement. The reviewer’s suggestion has been incorporated.

AUTHOR RESPONSE: We thank the reviewer for their continued efforts in reviewing this manuscript and for providing meaningful feedback.

It might be simply a formatting issue on the track changes version, but the “burgeoning” sentence is currently a one sentence paragraph.

AUTHOR RESPONSE: We agree with the reviewer that cognitive and behavioral deficits in relatively young former collision sport athletes is concerning, especially as there may be associations with neuropathological changes in the brain. As we discussed above, we feel that it would be inappropriate to specifically call out to CTE and/or TES in the present paper as recognition of these issues is beyond the scope of this paper. Similarly, and in response to Reviewer #1 above, we have also removed mention of other potentially misleading statements including specific conditions (such as Alzheimer’s Disease) to avoid confusion for the reader. We did not aim to study clinical diagnoses in the present paper, and have left presentation of these conditions to more general terms—for example, “Studies have suggested that former football players may have earlier onset of, and/or increased risk for, cognitive decline or neurodegenerative disease diagnoses compared with the general population, purportedly due to their exposure to repetitive head impacts—SRC or otherwise—during football play.[4–6,8].”

AUTHOR RESPONSE: The concern with having no clinical data (e.g., cognitive test performance) is an important consideration. In the present study, we based our hypotheses and analyses on previous work that focused on how resting-state functional connectivity patterns are associated with age-related changes and changes observed in following head trauma—which may or may not co-occur with cognitive changes. While resting-state networks have been correlated with specific domains of cognitive testing outcomes in prior studies, these relationships are not well established and we did not feel that it would be appropriate to layer these additional exploratory analyses on top of this preliminary investigation.

AUTHOR RESPONSE: Thank you for this suggestion. We have added the following statements in the fourth paragraph of the discussion section: “It is notable that only a few (n = 7) of the participants in this study played football after their collegiate careers while also reporting 12.5 years of football play, on average. This sample is therefore mostly representative of amateur athletes who began playing football at youth levels.”

AUTHOR RESPONSE: Unfortunately, the tracking data for this study did not allow us to parse out who met which exclusion criteria vs. those who opted not to participate (via communication with the study team or via non-response to inquiry). Reasons for choosing not to participate and choosing not to respond were not reported, but could have been due to health-related concerns about travel and/or study participation. At this time, we cannot provide more information than has presently been given in the manuscript.

PLoS One. doi: 10.1371/journal.pone.0273918.r005

Decision Letter 2

Jacob Resch

18 Aug 2022

Associations of Lifetime Concussion History and Repetitive Head Impact Exposure with Resting-State Functional Connectivity in Former Collegiate American Football Players: An NCAA 15-Year Follow-Up Study

PONE-D-22-05923R2

Dear Dr. Walton,

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PLoS One. doi: 10.1371/journal.pone.0273918.r006

Acceptance letter

Jacob Resch

1 Sep 2022

PONE-D-22-05923R2

Dear Dr. Walton:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 File. Supplemental results.

Linear regressions for the effect of individual covariates on each of the resting-state functional connectivity (rsFC) outcomes described in body of the manuscript.

(DOCX)

Click here for additional data file.^{(54.9KB, docx)}

Data Availability Statement

Data are available in an Open Science Framework repository, which may be found using the following DOI: 10.17605/OSF.IO/B5ZN4.

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PERMALINK

Associations of lifetime concussion history and repetitive head impact exposure with resting-state functional connectivity in former collegiate American football players: An NCAA 15-year follow-up study

Samuel R Walton

Jacob R Powell

Benjamin L Brett

Weiyan Yin

Zachary Yukio Kerr

Mingxia Liu

Michael A McCrea

Kevin M Guskiewicz

Kelly S Giovanello

Roles

Abstract

Introduction

Materials & methods

Participants

Image acquisition

Table 1. Magnetic resonance imaging (MRI) acquisition parameters across study sites.

Data processing

Outcome measures (rsFC values)

Concussion history & head impact exposure estimate

Analyses

Results

Participants

Table 2. Participant characteristics.

Resting state functional connectivity

Table 3. Standardized β-values from multivariable linear regression models.

Fig 1. Whole-brain and within-network resting-state functional connectivity (rsFC) in large-scale networks separated by dichotomous concussion history group.

Fig 2. Between-network resting-state functional connectivity (rsFC) in large-scale networks separated by dichotomous concussion history group.

Fig 3. Whole-brain and within-network resting-state functional connectivity (rsFC) in large-scale networks separated by four ordinal concussion history groups.

Fig 4. Between-network resting-state functional connectivity (rsFC) in large-scale networks separated by four ordinal concussion history groups.

Fig 5. Plots of head impact exposure estimates (HIEE) and average resting-state functional connectivity (rsFC) measures in large-scale networks.

Discussion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Jacob Resch

Roles

Author response to Decision Letter 0

Decision Letter 1

Jacob Resch

Roles

Author response to Decision Letter 1

Decision Letter 2

Jacob Resch

Roles

Acceptance letter

Jacob Resch

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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