Microstructural Organization of Distributed White Matter Associated With Fine Motor Control in US Service Members With Mild Traumatic Brain Injury

Abstract

Mild traumatic brain injury (mTBI) is the most common form of brain injury. While most individuals recover from mTBI, roughly 20% experience persistent symptoms, potentially including reduced fine motor control. We investigate relationships between regional white matter organization and subcortical volumes associated with performance on the Grooved Pegboard (GPB) test in a large cohort of military Service Members and Veterans (SM&Vs) with and without a history of mTBI(s). Participants were enrolled in the Long-term Impact of Military-relevant Brain Injury Consortium-Chronic Effects of Neurotrauma Consortium. SM&Vs with a history of mTBI(s) (n = 847) and without mTBI (n = 190) underwent magnetic resonance imaging and the GPB test. We first examined between-group differences in GPB completion time. We then investigated associations between GPB performance and regional structural imaging measures (tractwise diffusivity, subcortical volumes, and cortical thickness) in SM&Vs with a history of mTBI(s). Lastly, we explored whether mTBI history moderated associations between imaging measures and GPB performance. SM&Vs with mTBI(s) performed worse than those without mTBI(s) on the non-dominant hand GPB test at a trend level (p < 0.1). Higher fractional anisotropy (FA) of tracts including the posterior corona radiata, superior longitudinal fasciculus, and uncinate fasciculus were associated with better GPB performance in the dominant hand in SM&Vs with mTBI(s). These findings support that the organization of several white matter bundles are associated with fine motor performance in SM&Vs. We did not observe that mTBI history moderated associations between regional FA and GPB test completion time, suggesting that chronic mTBI may not significantly influence fine motor control.

Introduction

Traumatic brain injury (TBI) is a highly heterogenous and prevalent injury. Mild TBI (mTBI), or concussions, account for approximately 82% of all TBIs in the general public.^1,2 In 2016, they accounted for 85.9% of all TBIs in United States military personnel³ and pose a serious concern as a history of mTBI(s) can cause chronic cognitive and physical disabilities.⁴

Mild TBIs may result from a variety of forces to the head, including blunt force trauma and intense over-pressurization from nearby explosions. These insults often cause histological changes to the underlying neural tissue,⁵ which may have functional consequences on several domains, including motor control. Several studies have investigated magnetic resonance imaging (MRI) signatures of mTBI and have identified candidate biomarkers of injury in distributed white and gray matter brain regions. A large meta-analysis of military brain injury, the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium, reported pronounced alterations in the diffusivity of the superior longitudinal fasciculi (SLF) in Service Members and Veterans (SM&Vs) with a history of mTBI(s) relative to controls.⁶ A meta-analysis of white matter alterations following mTBI found frequent abnormalities in the corpus callosum, internal capsules, SLF, and corona radiata.⁷ Recent investigations have also highlighted gray matter alterations in the cingulate gyrus,⁸ insular cortex,⁹ thalamus,¹⁰ and accumbens.¹⁰

Although the mechanisms, locations, and force of the TBI-producing insults are heterogeneous, the adverse effects of mTBI(s) on cognitive domains, postural control, and motor functions are generally convergent across studies.^11

–14 This convergence has mechanistic underpinnings, and in particular, fine motor control (i.e., the coordinated and controlled movement of small muscles such as those in the hands and fingers) is supported by distributed gray and white matter regions that are affected by mTBI, including the SLF, corona radiata, internal capsule, thalamus, and basal ganglia.^7,10 However, less is known about the long-term impact of mTBI occurring in military settings compared to civilian settings. Brain and behavioral differences have been reported in studies that compare military and civilian groups.¹⁵ Further, in an investigation of military mTBI, Tate and colleagues¹⁰ reported several aberrant associations between subcortical shape and cognitive functions.

The purpose of this study was to identify associations between regional white matter organization, cortical thickness, and subcortical volumes and fine motor performance evaluated by the Grooved Pegboard (GPB) test in a large cohort of SM&Vs with and without a history of mTBI(s). We first hypothesized that GPB performance would be significantly worse on dominant and non-dominant hands among SM&Vs with a history of mTBI(s) relative to those without a history of mTBI(s). Second, we hypothesized that lower regional white matter organization of frontoparietal tracts and subcortical volumes of the basal ganglia would be associated with poorer performance on the GPB test among SM&Vs with a history of mTBI(s). Lastly, we hypothesized that we would observe interactive effects—that is, associations between regional imaging measures with respect to GPB performance would be moderated by mTBI history.

Methods

Participants

This study is a secondary analysis of data collected for the Long-term Impact of Military-relevant Brain Injury Consortium-Chronic Effects of Neurotrauma Consortium (LIMBIC-CENC) Prospective Longitudinal Study (PLS). We included two groups: 1) SM&Vs with a history of at least one mTBI (n = 847, mean age = 39.9 ± 9.5, 88% male); and 2) SM&Vs with a negative history of mTBI (n = 190, mean age = 39.9 ± 10.4, 78% male). A detailed description of the LIMBIC-CENC PLS cohort has been previously published.^16,17 The ongoing parent study collects longitudinal cognitive, mental health, neuroimaging, and physical data on post-9/11 era SM&Vs with deployment histories and either positive or negative mTBI histories at sites distributed throughout the U.S. All participants in the current report were recruited and evaluated at eight participating sites. Inclusion criteria were: 1) deployment in Operation Enduring Freedom, Operation Iraqi Freedom, Operation New Dawn, or follow-on conflicts; 2) history of combat exposure defined by the Deployment Risk and Resiliency Inventory Section D (DRRI-2-D) score >1 on any item¹⁸; and 3) at least 18 years of age. Exclusionary criteria were: 1) any history of moderate, severe, or penetrating TBI; or 2) history of major neurologic disorder or psychiatric disorder causing a significant reduction in independent living status (e.g., complete spinal cord injury, schizophrenia). This study was approved by the Institutional Review Boards at all performing sites and each participant signed an informed consent document prior to undergoing study activities.

Assessment of TBI history

Lifetime history of all potential concussive events (PCEs) was evaluated using a modified version of the Ohio State University TBI Identification interview.¹⁹ A detailed structured interview with an embedded algorithmic preliminary diagnosis based on the Department of Defense/Veterans Affairs common definition of mild TBI, the Virginia Commonwealth University retrospective Concussion Diagnostic Interview was employed for each identified PCE and was followed by site PI review and central quality assurance to confirm or rule out mTBI diagnosis.²⁰ A binary approach was used in this investigation and each participant was assigned as “with mTBI” or “without mTBI” based on the results of that structured interview and adjudication. Participants' demographics and deployment-related information were also collected. TBI history was collected at baseline and updated at each follow-up visit.

Grooved Pegboard test

The Grooved Pegboard (GPB) test (Lafayette Instruments, Lafayette, IN) is a timed test used to evaluate visual-motor coordination on the dominant and non-dominant hand.²¹ The pegboard is a square 5 × 5 grid of 25 holes, with slots oriented randomly on the edge of each hole. Pegs have a one-sided notch that must be manipulated and rotated between two fingers (usually the thumb and index finger) to be inserted into a pegboard hole. The score of the GPB test is the time (in seconds) taken to fill all 25 pegboard holes (completion time) for each hand. The GPB Test was collected on the same day as imaging.

Image acquisition

At their baseline visit, participants underwent MRI scanning at 3T field strengths following a standardized protocol overseen by a central site to ensure compliance. Site-specific scan parameters are reported in Supplementary Table S1.

Image processing

All MRI images were centrally processed on the high-performance computing system at the Office of Research Computing at Brigham Young University. The raw MRI DICOM-format data was converted to the NIfTI format, using the dcm2niix conversion software, which automatically created the magnetic resonance-images' relevant sidecar files in Brain Imaging Data Structure²² format, containing information about the MR acquisition parameters. The acpcdetect tool from the Automatic Registration Toolbox was used to put T1-weighted images into standard alignment. Intensity correction on T1-weighted data was performed using the Advanced Normalization Tools (ANTs; version 2.3.1.dev37-gf78b2) N4 Bias Field Correction tool.²³ T2-weighted images were then co-registered to the preprocessed T1-weighted images for each participant using ANTs registration (antsRegistrationSyNQuick.sh). FreeSurfer v6.0.0 parcellated cortical and segmented subcortical brain regions from the T1- and co-registered T2-weighted images using the recon-all pipeline.

Diffusion MRI (dMRI) data was similarly centrally processed using standardized Enhancing Neuroimaging and Genetics Meta-Analysis (ENIGMA) diffusion tensor imaging (DTI) Working Group pipelines. dMRI data preprocessing steps included eddy current correction, brain extraction, and tensor fitting. Diffusion metrics, including fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD), for 24 white matter tracts based on the Johns Hopkins University white matter atlas were extracted using fMRI (functional magnetic resonance imaging) Software Library (FSL) tract-based spatial statistics (TBSS) tools.^24

–27 In brief, subjectwise diffusion data was nonlinearly aligned to a common space. A skeletonized image representing the averaged centers of each tract was created and aligned diffusion data was then projected onto the skeletonized image. The projected data were then used for analysis.

As neuroimaging measures may be affected by non-biological scanner and site-specific artifacts^28,29 including manufacturer, field strength, gradient nonlinearity, and subject positioning, we used the ComBat algorithm^30,31 to harmonize all region of interest (ROI) properties to reduce these effects. A summary of the non-harmonized imaging data distributions is given in Supplementary Table S2.

Statistical analysis

Our primary outcome for all analyses was GPB completion time of the participant's dominant hand; however, post hoc analyses also evaluated GPB completion times of the non-dominant hand. We used linear regression models to identify associations between GPB completion time and regional subcortical volumes, cortical thickness, and tract-wise FA measures; collectively referred to as ROIs. Within the cohort of SM&Vs with a history of mTBI, we regressed GPB completion times on ROI values while adjusting for participant age, sex, time since index injury (years), number of lifetime mTBIs, alcohol use disorder (assessed by the Alcohol Use Disorders Identification Test-Concise [AUDIT-C]), handedness, and acquisition site; i.e., $\begin{matrix} y & = α + β_{1} * R O I + β_{2} * a g e + β_{3} * s e x + β_{4} * T S I \\ + β_{5} * T B I c o u n t + β_{6} * a u d i t_c + β_{7} * h a n d + β_{8} * s i t e \end{matrix}$

where $α$ is the model intercept. To determine whether associations between GPB completion times and ROI characteristics were mediated by mTBI history (any vs. no history of mTBI), we repeated the above regression models with an added ROI-by-TBI history interaction term in the joint set of SM&Vs with and without a history of mTBI(s): thus, $\begin{matrix} y & = α + β_{1} * R O I + β_{2} * a g e + β_{3} * s e x + β_{4} * T S I \\ + β_{5} * T B I c o u n t + β_{6} * a u d i t_c + β_{7} * h a n d \\ + β_{8} * s i t e + β_{9} * m T B I s t a t u s + β_{10} * R O I x m T B I s t a t u s \end{matrix}$

Here, with negative and positive mTBI histories coded as 0 and 1, respectively, the interaction term is zero for those without an mTBI history, and the effect of ROI on GPB completion time is $β_{1} + β_{10}$

for those with a positive mTBI history. We adjusted for multiple comparisons across all ROIs grouped within the set of volumetric and diffusion models (and separately for FA, RD, MD, and AD diffusion models) and again by models including an interaction term. Multiple comparisons corrections were done using the false discovery rate method with a standard 0.05 alpha level of significance. All statistics were computed using R version 3.6.3.

Results

Sample characteristics

Our study included 1037 SM&Vs. Of these, 863 had usable diffusion data, and an overlapping 970 had usable subcortical volume and cortical thickness data. Clinical and demographic characteristics of the full cohort and modality-specific cohorts are provided in Table 1. The mean age of the full cohort was 39.9 ± 9.6 years and age did not differ by TBI status. In total, 847 SM&Vs reported at least one mTBI. Our sample included 139 women, and the proportion of female participants with a positive mTBI history was significantly lower than males (χ² = 10.8, df = 1, p < 0.001). The mean dominant hand GPB completion time was 71.8 ± 13.9 seconds. On average, SM&Vs with a history of at least one mTBI took 1.41 sec longer than the comparison group to complete the dominant hand GPB task; however, this difference was non-significant (t = -1.30, df = 289.06, p > 0.05). Age was associated with GPB completion times (t = 8.00, df = 1034, p < 0.001) in the whole sample, with older participants performing more slowly. Female SM&Vs had shorter GPB completion times than males (t = 2.24, df = 169.45, p < 0.05), on average. Time since injury was not significantly associated with GPB completion time after adjustment for participant age. Non-dominant hand GPB completion time was higher in SM&Vs with at least one mTBI compared with those without a history of mTBI(s) at a trend level (mean difference = 2.3 sec, t = -1.93, df = 312.22, p < 0.1). This trend persisted following adjustment for age, sex, and time since injury.

Table 1.

Clinical and Demographic Data

	Full cohort		Volumetric cohort		Diffusion cohort
TBI status	TBI-	TBI+	TBI-	TBI+	TBI-	TBI+
n	190	847	179	791	153	710
Age, mean (SD)	39.9 (10.4)	39.9 (9.5)	40.3 (10.5)	39.9 (9.6)	40.0 (10.6)	40.0 (9.6)
Males, n	150	747	142	695	119	624
Time since injury, years, mean (SD), [range]	–	9.6 (4.7) [0.1, 47]	–	9.6 (4.6) [0.3, 47]	–	9.5 (4.7) [0.1, 47]
Number of mTBIs, mean (SD), [range]	–	2.72 (1.9) [1, 15]	–	2.67 (1.9) [1, 15]	–	2.69 (1.9) [1, 12]
GPB completion time dominant hand, mean (SD)	70.6 (13.4)	72.0 (14.0)	70.7 (13.5)	72.2 (13.8)	71.2 (13.7)	72.0 (13.9)
GPB completion time non-dominant hand, mean (SD)	75.8 (14.8)	78.2 (17.1)	76.1 (15.1)	78.5 (16.9)	76.6 (14.8)	78.5 (16.8)

TBI; traumatic brain injury; SD, standard deviation; mTBI, mild TBI; GPB, Grooved Pegboard.

Fractional anisotropy of several white matter tracts is inversely related to GPB completion time

We identified no significant associations between GPB completion time and interactions between imaging measures and mTBI status, suggesting the observed associations are not moderated by mTBI history. Within the cohort of SM&Vs with a history of mTBI(s), we identified associations between GPB completion time on the dominant hand and the FA of several white matter tracts. Whole–brain average FA was assessed alongside specific white matter structures including the bilateral anterior and posterior subdivisions of the corona radiata (CR); the whole CR; left and whole superior longitudinal fasciculus (SLF); the left and whole uncinate fasciculus (UNC). Several tracts exhibited significant inverse associations between GPB completion time and tract-wise FA: the whole posterior corona radiata (PCR; B = -62.32, t = -3.08, q < 0.05), right PCR (B = -54.13 t = -2.97, q < 0.05), left SLF (B = -61.52 t = -3.12, q < 0.05), and left UNC (B = -28.67, t = -2.97, q < 0.05). No associations between regional volume, cortical thickness, or other diffusion metrics (MD, AD, or RD), and GPB completion time were observed on the dominant hand. Model parameters are tabulated in Table 2. Significant associations between GPB completion time on the dominant hand and regional FA measures are illustrated in Figure 1. No associations between GPB completion time and regional diffusivity or volume were observed in the non-dominant hand.

FIG. 1.

Significant associations between dominant hand Grooved Pegboard (GPB) completion time (y-axis) and regional fractional anisotropy (x-axis).

Table 2.

Model Estimates for Significant Associations Between Fractional Anisotropy and Dominant Hand GPB Completion Time

Regions	Beta (SD)	T	p	q
PCR	-62.3 (20.2)	-3.0	< 0.01	< 0.05
PCR right	-54.1 (18.2)	-2.9	< 0.01	< 0.05
SLF left	-61.5 (19.6)	-3.1	< 0.01	< 0.05
UNC left	-28.6 (9.6)	-2.9	< 0.01	< 0.05

PCR, posterior corona radiata; SLF, superior longitudinal fasciculus; UNC, uncinate fasciculus

Discussion

We examined relationships between regional white matter microstructural organization and gray matter volumes with respect to motor function captured by the Grooved Pegboard (GPB) test in a large cohort of SM&Vs with and without a history of mTBI(s). We hypothesized that GPB completion time would be significantly slower in SM&Vs with a history of mTBI(s), that regional diffusivity and volumetric measures associated with motor functioning would be associated with GPB completion time, and that mTBI history would moderate associations between GPB completion times and regional imaging measures—that is, that magnitudes of associations between imaging measures and GBP time would vary between SM&Vs with and without history of mTBI(s). Notably, dominant hand GPB completion time did not significantly differ between SM&Vs mTBI groups; however, non-dominant hand time differed at a trend level between groups.

Within the mTBI group, we observed several associations between GPB completion time on dominant hand and regional white matter organization of the whole PCR, right PCR, left SLF, and left UNC. Two of the associations with GPB completion time were tracts in the left-hemisphere, which may reflect the contralateral control of participant's dominant hands in a predominantly right-handed sample (∼88% of participants were right-handed)³²; however, our models adjusted for handedness. We also observed that mTBI status did not moderate associations between regional imaging measures and GBP completion times. Further, no associations were observed between regional gray matter volumes and GPB completion time.

Lesion-based studies have previously highlighted that global white matter lesion (WML) burden worsens individual performances on motor and manual dexterity tasks. For example, the Northern Manhattan Study reported that participants with WML volumes at or above the 75th percentile performed more poorly on tests of manual dexterity.³³ Additional studies by the Leukoaraiosis And DISability Study (LADIS) group have highlighted associations between age-related white matter changes and disorders of gait and balance.^34,35 WML volumes in middle-aged individuals have also been associated with poorer performance on the GPB test,³⁶ suggesting that this effect is not restricted to elderly individuals.

The FA of the PCR was associated with GPB completion time for the dominant hand. To date, only a limited number of studies have reported on associations between motor function and the organization of the broader set of CR white matter fibers. The CR is composed, in part, of fibers descending from both frontal and parietal lobes projecting to the basal ganglia and spinal column that subserve a variety of motor and cognitive functions. An earlier study by Moulton and colleagues reported that axial diffusivity of the CR 24 h post-stroke was predictive of upper limb NIH Stroke Scale motor scores 7 days following a stroke.³⁷ In a smaller cohort of 15 patients with CR strokes, Koyama and colleagues reported that the FA of the cerebral peduncles was associated with subsequent upper and lower extremity function.³⁸ Taken together, these findings support that the organization of CR white matter is critical to the execution of fine motor or manual dexterity tasks.

Diffusivity of the SLF was also significantly associated with GPB completion time in the dominant hand. The SLF is a large fiber bundle connecting regions of the occipital, parietal, and temporal lobes to the frontal lobe^39,40 and is classically divided into four components, SLF I – IV.⁴⁰ Although we did not report on SLF subdivisions, SLF I projects from the superior parietal lobule to the supplementary and premotor areas of the frontal lobe and thus is thought to contribute to regulation of higher-order motor functions involving proprioception and initiation of motor activity.^40,41 Reported associations between SLF diffusivity and GPB performance are sparse; however, in a recent study of 55 children with ADHD and 61 matched controls, Hyde and colleagues²¹ reported reduced performance on the GPB test on the non-dominant hand in children with ADHD. The degree of impairment in the non-dominant hand was further associated with reduced fiber density in SLF I. Recent studies in typically-developing controls have also demonstrated that microstructural properties and volumes of the SLF and its subcomponents are strongly associated with individual differences in fine motor control.^42,43 Collectively, these findings, along with our own, further support that the organization of white matter fibers such as the SLF, which subserve a broad range of visuospatial and higher-order motor functions, are key in supporting fine motor control.

We further identified an association between the UNC FA and dominant hand GPB completion time. The UNC projects from medial and lateral regions of the orbitofrontal cortex to the anterior portion of the temporal lobes⁴⁴ and is thought to subserve language processing as well as other cognitive functions, including object recognition.^45,46 To our knowledge, the UNC has not previously been implicated in serving a role in fine motor function. However, a study of patients undergoing surgical resection of portions of the UNC to treat gliomas found no significant effect of UNC resection, Trail Making, Verbal Fluency, or Word Learning tasks.⁴⁶ The same study, however, found no significant effect of UNC resection on other tasks including Trail Making, Verbal Fluency, or Word Learning. Further work needs to be done to characterize the role of the UNC in fine motor or visuospatial skills.

Our divergent findings identifying trend-level differences in GPB completion time on the non-dominant, but not dominant, hand is consistent with the limited literature in this area. While previous studies on mTBI have not observed this association, two previous studies in adolescents with ADHD have reported poorer GPB performance in the non-dominant, but not the dominant, hand relative to controls.^21,47 Although it is unclear why differences may be more pronounced on the non-dominant hand, it has been suggested that practice effects (i.e., the daily use of the dominant hand) may diminish between-group differences.⁴⁸ Additionally, we did not observe differential associations between regional patterns of diffusivity and GPB performance as a function of mTBI history. Nevertheless, previous reports have highlighted that the organization of major fiber bundles including the CR, SLF, and UNC, are widely affected by mTBI/TBI both acutely and longitudinally.^6,49
-51

Limitations

There are several limitations to consider for this study. Multi-site neuroimaging data are commonly confounded by site- and scanner-specific idiosyncrasies; thus, there may have been systematic imaging and GPB performance differences across sites. ComBat harmonization was used to mitigate potential site-based differences in imaging measures; though, GPB scores were not harmonized. Additionally, ComBat harmonization is applied to post-processed data and successfully removed site-based associations in our imaging data (Supplementary Fig. S1); however, it remains unclear whether this method of harmonization is as effective as harmonization methods applied directly to magnetic resonance images. It is also noted that antidepressant medications can alter regional brain structures, but medication status was not evaluated in this study. We were further unable to account for pre-deployment mood disorders. Additional limitations include the wide range of times since injury within the SM&V cohort with a history of mTBI(s) and the cross-sectional nature of the study.

Conclusions

In summary, our findings indicate that among SM&Vs with a history of at least one mild TBI, performance on fine motor tasks captured by the GPB test is associated with the organization of major WM tracts, including the posterior corona radiata, superior longitudinal fasciculus, and uncinate fasciculus, and that these associations did not differ significantly from SM&Vs without a history of mTBI(s). In SM&Vs with a positive mTBI history, we also observed trend-level reductions in GPB performance only in the non-dominant hand, which may relate to effects of consistent dominant hand usage overriding mTBI-related deficits in manual dexterity or enhanced resilience of the motor skills in the dominant hand to mild injury. However, we did not observe differences in associations between white matter organization and GPB performance as a function of mTBI status, suggesting that mTBI-related abnormalities in fiber organization may not contribute to diminished performance on manual dexterity in this cohort. Future work should focus on elucidating specific effects of injury mechanisms; for example, whether blast-related mTBI(s) or repeated mTBI(s) relate to manual dexterity performance.

Footnotes

Acknowledgments

Disclaimer: The views, opinions, interpretations, conclusions and recommendations expressed in this manuscript are those of the authors and do not reflect the official policy of the U.S. Department of the Navy, Department of the Army, Department of Defense, Department of Veterans Affairs, or the U.S. government.

Ethics approval: This study was approved by the local Institutional Review Boards at all eleven prospective longitudinal study enrollment sites.

Consent to participate: All study participants signed an informed consent document prior to undergoing study procedures.

Consent for publication: Consent form signed by all participants included consent for publication of their deidentified data.

Availability of data and material: Available to the public in the Federal Interagency Brain Injury Research (FITBIR) Informatics System.

Code availability: not applicable.

Authors' Contributions

Benjamin Wade: Conceptualization, methodology, writing—original draft, software, investigation, formal analysis. David Tate: Supervision, data curation, investigation, conceptualization, funding acquisition, writing—review and editing. Eamonn Kennedy: Methodology, writing—review and editing. Erin Bigler: Supervision, writing—review and editing. Gerald York: Funding acquisition, writing—review and editing. Brian Taylor: Funding acquisition, project administration, writing—review and editing, resources. Maya Troyanskaya: Writing—review and editing. Elizabeth Hovenden: Writing—review and editing, data curation. Naomi Goodrich-Hunsaker: Data curation, writing—review and editing, software. Mary Newsome: Writing—review and editing. Emily Dennis: Writing—review and editing, funding afgcquisition. Tracy Abildskov: Data curation, resources. Mary Jo Pugh: Funding acquisition, writing—review and editing. William Walker: Funding acquisition, resources, writing—review and editing. Kimbra Kenney: Funding acquisition, writing—review and editing. Aaron Betts: Data curation, writing—review and editing. Robert Shih: Data curation, writing—review and editing. Robert Welsh: Funding acquisition, writing—review and editing. Elisabeth Wilde: Funding acquisition, project administration, supervision, resources, conceptualization, writing—review and editing.

Funding Information

This work is supported in part by a NARSAD Young Investigator Grant (27786 to BW) and a K99 Pathway to Independence Award (MH119314 to BW). The United States (U.S.) Department of Veterans Affairs Merit Review Awards Numbers I01 CX001820 (RS). R61NS120249 to ELD, DFT, and EAW. This work was supported by the Assistant Secretary of Defense for Health Affairs endorsed by the Department of Defense, through the Psychological Health/Traumatic Brain Injury Research Program Long-Term Impact of Military-Relevant Brain Injury Consortium (LIMBIC) Award/W81XWH-18-PH/TBIRP-LIMBIC under Awards No. W81XWH1920067 and W81XWH-13-2-0095, and by the U.S. Department of Veterans Affairs Awards No. I01 CX002097, I01 CX002096, I01 HX003155, I01 RX003444, I01 RX003443, I01 RX003442, I01 CX001135, I01 CX001246, I01 RX001774, I01 RX 001135, I01 RX 002076, I01 RX 001880, I01 RX 002172, I01 RX 002173, I01 RX 002171, I01 RX 002174, and I01 RX 002170, I01 CX001820. The U.S. Army Medical Research Acquisition Activity, 839 Chandler Street, Fort Detrick, MD 21702-5014 is the awarding and administering acquisition office. Additional funding was provided by VA Health Services Research and Development (IK6HX002608).

Author Disclosure Statement

No competing financial interests exist.

Supplementary Material

Supplementary Table S1

Supplementary Table S2

Supplementary Figure S1

References

Skandsen

, Nilsen

, Einarsen

, et al. Incidence of mild traumatic brain injury: a prospective hospital, emergency room and general practitioner-based study. Front Neurol, 2019; 10:638; doi: 10.3389/fneur.2019.00638

Department of Defense. DOD TBI Worldwide Numbers. 2021. Available from: https://health.mil/Reference-Center/Reports/2023/06/12/2021-DOD-Worldwide-Numbers-for-TBI [Last accessed October 6, 2023 ].

Department of Defense. DoD Numbers for Traumatic Brain InjuryWorldwide—Totals. 2016. Available from: https://www.biami.org/wp-content/uploads/2017/09/DoD-TBI-Worldwide-Totals_2016_Feb-17-2017_v1.0_2017-04-06.pdf [Last accessed September 26, 2023 ].

Kushner

Mild traumatic brain injury: toward understanding manifestations and treatment. Arch Intern Med, 1998; 158(15):1617–1624; doi: 10.1001/archinte.158.15.1617

Aubry

, Cantu

, Dvorak

, et al. Summary and agreement statement of the First International Conference on Concussion in Sport, Vienna 2001. Recommendations for the improvement of safety and health of athletes who may suffer concussive injuries. Br J Sports Med, 2002; 36(1):6–10; doi: 10.1136/bjsm.36.1.6

Dennis

, Wilde

, Newsome

, et al. ENIGMA military brain injury: a coordinated meta-analysis of diffusion MRI from multiple cohorts. Proc IEEE Int Symp Biomed Imaging, 2018; 2018:1386–1389; doi: 10.1109/ISBI.2018.8363830

Narayana

PA.

White matter changes in patients with mild traumatic brain injury: MRI perspective. Concussion, 2017; 2(2):CNC35; doi: 10.2217/cnc-2016-0028

Zhou

, Kierans

, Kenul

, et al. Mild traumatic brain injury: longitudinal regional brain volume changes. Radiology, 2013; 267(3):880–890; doi: 10.1148/radiol.13122542

Patel

, Wilson

, Oakes

, et al. Structural and volumetric brain MRI findings in mild traumatic brain injury. AJNR Am J Neuroradiol, 2020; 41(1):92–99; doi: 10.3174/ajnr.A6346

10.

Tate

, Wade

BSC

, Velez

, et al. Volumetric and shape analyses of subcortical structures in United States service members with mild traumatic brain injury. J Neurol, 2016; 263(10); doi: 10.1007/s00415-016-8236-7

11.

Cavanaugh

, Guskiewicz

, Giuliani

, et al. Detecting altered postural control after cerebral concussion in athletes with normal postural stability. Br J Sports Med, 2005; 39(11):805–811; doi: 10.1136/bjsm.2004.015909

12.

Guskiewicz

, McCrea

, Marshall

, et al. Cumulative effects associated with recurrent concussion in collegiate football players: the NCAA Concussion Study. JAMA, 2003; 290(19):2549–2555; doi: 10.1001/jama.290.19.2549

13.

McCrea

, Guskiewicz

, Marshall

, et al. Acute effects and recovery time following concussion in collegiate football players: the NCAA Concussion Study. JAMA, 2003; 290(19):2556–2563; doi: 10.1001/jama.290.19.2556

14.

Tate

, Wade

BSC

, Velez

, et al. Subcortical shape and neuropsychological function among U.S. service members with mild traumatic brain injury. Brain Imaging Behav, 2018; doi: 10.1007/s11682-018-9854-8

15.

Fischer

, Parsons

, Durgerian

, et al. Neural activation during response inhibition differentiates blast from mechanical causes of mild to moderate traumatic brain injury. J Neurotrauma, 2014; 31(2):169–179; doi: 10.1089/neu.2013.2877

16.

Cifu

, Dixon

. Chronic effects of neurotrauma consortium. Brain Inj, 2016; 30(12):1397–1398; doi: 10.1080/02699052.2016.1219065

17.

Walker

, Carne

, Franke

, et al. The Chronic Effects of Neurotrauma Consortium (CENC) multi-centre observational study: description of study and characteristics of early participants. Brain Inj, 2016; 30(12):1469–1480; doi: 10.1080/02699052.2016.1219061

18.

Vogt

, Smith

, King

, et al. Deployment risk and resilience inventory-2 (DRRI-2): an updated tool for assessing psychosocial risk and resilience factors among service members and veterans. J Trauma Stress, 2013; 26(6):710–717; doi: 10.1002/jts.21868

19.

Corrigan

, Bogner

. Initial Reliability and Validity of the Ohio State University TBI Identification Method. J Head Trauma Rehabil, 2007; 22(6) :318–329;doi: 10.1097/01.HTR.0000300227.67748.77

20.

Walker

, Cifu

, Hudak

, et al. Structured interview for mild traumatic brain injury after military blast: inter-rater agreement and development of diagnostic algorithm. J Neurotrauma, 2015; 32(7):464–473; doi: 10.1089/neu.2014.3433

21.

Hyde

, Sciberras

, Efron

, et al. Reduced fine motor competence in children with ADHD is associated with atypical microstructural organization within the superior longitudinal fasciculus. Brain Imaging Behav, 2021; 15(2):727–737; doi: 10.1007/s11682-020-00280-z

22.

Gorgolewski

, Auer

, Calhoun

, et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data, 2016; 3(1):160044; doi: 10.1038/sdata.2016.44

23.

Tustison

, Avants

, Cook

, et al. N4ITK: improved N3 bias correction. IEEE Trans Med Imaging, 2010; 29(6):1310–1320; doi: 10.1109/TMI.2010.2046908

24.

Kochunov

, Jahanshad

, Sprooten

, et al. Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: comparing meta and megaanalytical approaches for data pooling. Neuroimage, 2014; 95:136–150; doi: 10.1016/j.neuroimage.2014.03.033

25.

Mori

, Oishi

, Jiang

, et al. Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. Neuroimage, 2008; 40(2):570–582; doi: 10.1016/j.neuroimage.2007.12.035

26.

Smith

, Jenkinson

, Johansen-Berg

, et al. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage, 2006; 31(4):1487–1505; doi: 10.1016/j.neuroimage.2006.02.024

27.

Smith

, Johansen-Berg

, Jenkinson

, et al. Acquisition and voxelwise analysis of multi-subject diffusion data with tract-based spatial statistics. Nat Protoc, 2007; 2(3):499–503; doi: 10.1038/nprot.2007.45

28.

Takao

, Hayashi

, Kabasawa

, et al. Effect of scanner in longitudinal diffusion tensor imaging studies. Hum Brain Mapp, 2012; 33(2):466–477; doi: 10.1002/hbm.21225

29.

Han

, Jovicich

, Salat

, et al. Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer. Neuroimage, 2006; 32(1):180–194; doi: https://doi.org/10.1016/j.neuroimage.2006.02.051

30.

Fortin

, Parker

, Tunç

, et al. Harmonization of multi-site diffusion tensor imaging data. Neuroimage, 2017; 161(March):149–170; doi: 10.1016/j.neuroimage.2017.08.047

31.

Fortin

J-P

, Cullen

, Sheline

, et al. Harmonization of cortical thickness measurements across scanners and sites. Neuroimage, 2018; 167:104–120; doi: 10.1016/j.neuroimage.2017.11.024

32.

Budisavljevic

, Castiello

, Begliomini

. Handedness and white matter networks. Neuroscientist, 2021; 27(1):88–103; doi: 10.1177/1073858420937657

33.

Holtrop

, Loucks

, Sosnoff

, et al. Investigating age-related changes in fine motor control across different effectors and the impact of white matter integrity. Neuroimage, 2014; 96:81–87; doi: 10.1016/j.neuroimage.2014.03.045

34.

Baezner

, Blahak

, Poggesi

, et al. Association of gait and balance disorders with age-related white matter changes: the LADIS study. Neurology, 2008; 70(12):935–942; doi: 10.1212/01.wnl.0000305959.46197.e6

35.

Blahak

, Baezner

, Pantoni

, et al. Deep frontal and periventricular age related white matter changes but not basal ganglia and infratentorial hyperintensities are associated with falls: cross sectional results from the LADIS study. J Neurol Neurosurg Psychiatry, 2009; 80(6):608–613; doi: 10.1136/jnnp.2008.154633

36.

Nyquist

, Yanek

, Bilgel

, et al. Effect of white matter lesions on manual dexterity in healthy middle-aged persons. Neurology, 2015; 84(19):1920–1926; doi: 10.1212/WNL.0000000000001557

37.

Moulton

, Amor-Sahli

, Perlbarg

, et al. Axial diffusivity of the corona radiata at 24 hours post-stroke: a new biomarker for motor and global outcome. PLoS One, 2015; 10(11):e0142910; doi: 10.1371/journal.pone.0142910

38.

Koyama

, Marumoto

, Miyake

, et al. Relationship between diffusion tensor fractional anisotropy and motor outcome in patients with hemiparesis after corona radiata infarct. J Stroke Cerebrovasc Dis, 2013; 22(8):1355–1360; doi: 10.1016/j.jstrokecerebrovasdis.2013.02.017

39.

Kamali

, Flanders

, Brody

, et al. Tracing superior longitudinal fasciculus connectivity in the human brain using high resolution diffusion tensor tractography. Brain Struct Funct, 2014; 219(1):269–281; doi: 10.1007/s00429-012-0498-y

40.

Schmahmann

, Smith

, Eichler

, et al. Cerebral white matter: neuroanatomy, clinical neurology, and neurobehavioral correlates. Ann N Y Acad Sci, 2008; 1142:266–309; doi: 10.1196/annals.1444.017

41.

Chang

, Raygor

, Berger

. Contemporary model of language organization: an overview for neurosurgeons. J Neurosurg, 2015; 122(2):250–261; doi: 10.3171/2014.10.JNS132647

42.

Budisavljevic

, Dell'Acqua

, Zanatto

, et al. Asymmetry and structure of the fronto-parietal networks underlie visuomotor processing in humans. Cereb Cortex, 2017; 27(2):1532–1544; doi: 10.1093/cercor/bhv348

43.

Howells

, Thiebaut de Schotten

, Dell'Acqua

, et al. Frontoparietal tracts linked to lateralized hand preference and manual specialization. Cereb Cortex, 2018; 28(7):2482–2494; doi: 10.1093/cercor/bhy040

44.

Catani

, Howard

, Pajevic

, et al. Virtual in vivo interactive dissection of white matter fasciculi in the human brain. Neuroimage, 2002; 17(1):77–94; doi: 10.1006/nimg.2002.1136

45.

Papagno

Naming and the role of the uncinate fasciculus in language function. Curr Neurol Neurosci Rep, 2011; 11(6):553–559; doi: 10.1007/s11910-011-0219-6

46.

Papagno

, Miracapillo

, Casarotti

, et al. What is the role of the uncinate fasciculus? Surgical removal and proper name retrieval. Brain, 2011; 134(Pt 2):405–414; doi: 10.1093/brain/awq283

47.

Fliers

, Vermeulen

, Rijsdijk

, et al. ADHD and poor motor performance from a family genetic perspective. J Am Acad Child Adolesc Psychiatry, 2009; 48(1):25–34; doi: 10.1097/CHI.0b013e31818b1ca2

48.

Rommelse

NNJ

, Altink

, Oosterlaan

, et al. Motor control in children with ADHD and non-affected siblings: deficits most pronounced using the left hand. J Child Psychol Psychiatry, 2007; 48(11):1071–1079; doi: 10.1111/j.1469-7610.2007.01781.x

49.

Bendlin

, Ries

, Lazar

, et al. Longitudinal changes in patients with traumatic brain injury assessed with diffusion-tensor and volumetric imaging. Neuroimage, 2008; 42(2):503–514; doi: 10.1016/j.neuroimage.2008.04.254

50.

Aoki

, Inokuchi

. A voxel-based meta-analysis of diffusion tensor imaging in mild traumatic brain injury. Neurosci Biobehav Rev, 2016; 66:119–126; doi: 10.1016/j.neubiorev.2016.04.021

51.

Hellstrøm

, Westlye

, Kaufmann

, et al. White matter microstructure is associated with functional, cognitive and emotional symptoms 12 months after mild traumatic brain injury. Sci Rep, 2017; 7(1):13795; doi: 10.1038/s41598-017-13628-1

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.02 MB

0.60 MB

0.02 MB