Concussions can damage networks of connections in the brain. Scientists have spent decades and millions of dollars studying concussions and potential treatments. Yet, no new treatments are available or in the pipeline. A major reason for this stagnation is that no two concussions are exactly alike. People affected by concussions may have different genetic or socioeconomic backgrounds. The nature of the injury or how its effects change over time may also vary among people with concussions.

One central question facing scientists is whether there are multiple types of concussions. If so, what distinguishes them and what characteristics do they share. Some studies have looked at differences among subgroups of patients with concussions. But questions remain about whether – beyond differences between the patients – the brain injury itself differs and what impact that has on symptoms or patient trajectory.

To better characterize different types of concussion, Guberman et al. analyzed diffusion magnetic resonance imaging scans from 306 nine or ten-year-old children with a previous concussion. The children were participants in the Adolescent Brain Cognitive Development Study. Using specialized statistical techniques, the researchers outlined subgroups of concussions in terms of connections and symptoms and studied how many of these subgroups each patient had. Some types of injury were linked with a category of symptoms like cognitive, mood, or physical symptoms. Some types of damage were linked with specific symptoms. Guberman et al. also found that one symptom, sleep problems, was part of many different injury subtypes. Sleep problems may occur in different patients for different reasons. For example, one patient with sleep difficulties may have experienced damage in brain regions controlling sleep and wakefulness. Another person with sleep problems may have injured parts of the brain responsible for mood and may have depression, which causes excessive sleepiness and difficulties waking up.

Guberman et al. suggest a new way of thinking about concussions. If more studies confirm these concussion subgroups, scientists might use them to explore which types of therapies might be beneficial for patients with specific subgroups. Developing subgroup-targeted treatments may help scientists overcome the challenges of trying to develop therapies that work across a range of injuries. Similar disease subgrouping strategies may also help researchers study other brain diseases that may vary from patient to patient.

Concussion afflicts approximately 600 per 100,000 individuals every year (Cassidy et al., 2004). It is associated with several psychiatric conditions, such as post-traumatic stress disorder and attention-deficit/hyperactivity disorder (ADHD) (Orlovska et al., 2014). Its incidence rate is rising in children and adolescents (Zhang et al., 2016), and compared to adult populations, the impact of concussions on pediatric brains is understudied (Mayer et al., 2018). Despite considerable funding devoted to clinical and basic research, no major advances in therapeutics have been achieved to date (Kenzie et al., 2017). A root cause of this stagnation appears to be a contradiction: while all concussions are treated equally in clinical trials and research studies, they are characterized by extensive heterogeneity in their pathophysiology, clinical presentation, symptom severity and duration (Kenzie et al., 2017; Hawryluk and Bullock, 2016). Concussion heterogeneity across patients has been identified as a major hurdle in advancing concussion care (Kenzie et al., 2017; Hawryluk and Bullock, 2016).

Due to shearing forces transmitted during injury, the brain’s white matter is especially vulnerable to concussion (Armstrong et al., 2016; Bigler and Maxwell, 2012). Decades of research have studied white matter structure in individuals who sustain concussions. However, most studies continue to assume consistent, one-to-one structure/symptom relationships and employ traditional group comparisons (Dodd et al., 2014; Hulkower et al., 2013), averaging out the diffuse and likely more idiosyncratic patterns of brain structure abnormalities in favor of shared ones. Hence, the extant literature suggests that a large proportion of the clinical and research studies have not adequately accounted for clinical and neuropathological concussion heterogeneity.

To remedy this shortcoming, a growing number of studies aim to parse the clinical heterogeneity in concussions by algorithmically partitioning patients into discrete subgroups based on symptoms (Langdon et al., 2020; Si et al., 2018; Yeates et al., 2019). Other studies aim instead to account for heterogeneity in white matter structure alterations (Stojanovski et al., 2019b; Taylor et al., 2020; Ware et al., 2017). Ware et al., 2017 built individualized maps of white matter abnormalities which revealed substantial inter-subject variability in traumatic axonal injury and minimal consistency of subject-level effects. Taylor et al., 2020 computed a multivariate summary measure of white matter structure across 22 major white matter bundles which achieved better classification accuracy of concussed patients from healthy controls compared to single tract measures. Hence, studies have attempted to address heterogeneity in symptoms and in white matter structure across concussed patients.

However, white matter alterations due to concussion are diffuse and can elicit several symptoms that may interact with each other in complex and variable ways (Kenzie et al., 2017; Hawryluk and Bullock, 2016; Iverson, 2019). For instance, two individuals may suffer a concussion and develop sleep problems. The first may have damaged white matter tracts related to sleep/wakefulness control, whereas the second may have damaged tracts related to mood, causing depression-like symptoms, which include sleep problems. These two individuals will thus display a common symptom but will have overall different symptom profiles and different white matter damage profiles. This example illustrates an additional, hitherto ignored source of heterogeneity, whereby a variety of white matter structure alterations (‘multi-tract’) may be related to a variety of symptoms (‘multi-symptoms’) in different ways. This disease-specific type of heterogeneity will henceforth be referred to as multi-tract multi-symptom heterogeneity for brevity. Parsing concussion heterogeneity requires accounting for these dynamic, multi-tract multi-symptom relationships.

The objective of the present study was to leverage advanced diffusion MRI (dMRI) methods as well as a double-multivariate approach to parse multi-tract multi-symptom heterogeneity in a large sample of previously concussed children. Multi-tract multi-symptom relationships captured more information than traditional univariate approaches. Expression of multi-tract connectivity features was not driven by sociodemographic strata, injury characteristics, or clinical subgroups. Analyses comparing clinical subgroups defined by the presence of attention-deficit/hyperactivity disorder showed that multi-tract multi-symptom analyses identified disease-specific connectivity patterns that were missed by single-tract single-symptom approaches.

In the present study, we leveraged novel dMRI methods and a double-multivariate approach to parse heterogeneity in the relationship between white matter structure and symptoms in a large sample of previously-concussed children. By applying PLSc on biologically interpretable measures of dMRI obtained from PCA, we found cross-demographic multi-tract multi-symptom features that captured information about structure/symptom relationships that traditional approaches missed. More representative multi-tract multi-symptom pairs represented broader symptom categories and implicated wider networks of connections, whereas more idiosyncratic pairs represented more specific symptom combinations and implicated more localized connections. Whereas certain symptom/tract correspondences were consistent across the pairs that implicated them, more often than not, tracts were not consistently associated with the same symptoms across pairs. This finding was especially apparent for sleep problems, which were implicated across most pairs. These results suggest that rather than a clean and consistent set of one-to-one symptom/tract relationships, concussions may instead be composed of subsets of symptom combinations that are associated with combinations of structural alterations of different white matter tracts.

Defining concussions as a clinical syndrome characterized by a set of symptoms stemming from a set of alterations of brain structure and function, multi-tract multi-symptom pairs can be thought of as subtypes of concussion (i.e.: of structure/symptom relationships), not of concussion patients. Patients with concussions can express these different concussion subtypes to varying degrees. We theorize that the combination of these different subtypes and how much they are expressed is what determines the clinical syndrome a person will display. The pairs explaining the most covariance can be interpreted as the subtypes that are most commonly expressed across individuals. Whether these subtypes are dataset-specific remains to be explored. Multi-tract relationships may be driven by the metabolic demands imposed by the network structure of the brain, which is known to predict the course of several brain diseases (Crossley et al., 2014), by biomechanical constraints imposed by the skull and other structures exposing certain areas to more shearing strain (Hernandez et al., 2019), or by both factors simultaneously (Anderson et al., 2020). These possibilities need to be tested further. Multi-symptom relationships may be driven by feedback mechanisms, with symptoms potentiating each other. One example is sleep: sleep disturbances were implicated in nearly all multi-tract multi-symptom pairs, but showed no consistent correspondence with any particular connection. This result suggests that sleep disturbances across concussed patients may not be associated with the same neural substrate, but may instead arise, in some concussions, as a consequence of other symptoms. Previous theoretical work has proposed that sleep may play a central role in concussions, given that it can arise as a consequence of certain symptoms but can also potentiate other symptoms (Kenzie et al., 2018). The current findings are consistent with this previous work.

Concussion heterogeneity has been identified as a major obstacle (Kenzie et al., 2017; Hawryluk and Bullock, 2016) in response to decades of failed attempts to translate basic science findings into successful clinical trials and novel therapies. Heterogeneity in symptoms, impact of injury on brain structure and function, and pre-injury factors pose a particular problem for most concussion neuroimaging studies which have traditionally employed univariate comparisons between concussed and healthy or orthopedic injury control groups, or between patients with and without persistent symptoms (Dodd et al., 2014; Hulkower et al., 2013). These sources of inter-subject variability are believed to be problematic because they decrease the statistical power needed for group comparisons and multivariable models to detect the often-subtle effects of concussions (Maas et al., 2013). To overcome this challenge, landmark initiatives such as the IMPACT (Maas et al., 2013), InTBIR (Tosetti et al., 2013), CENTER TBI (Maas et al., 2015), and TRACK TBI (Bodien et al., 2018) aim to standardize and pool multi-center data collected across sociodemographic strata, to identify and statistically correct for pre-injury factors known to impact brain structure, and develop diagnostic and prognostic tools leveraging multimodal data and increasingly sophisticated machine-learning approaches.

In this study, we posited that disease-specific concussion heterogeneity is also problematic because by pooling across patients, idiosyncratic patterns of connectivity that may be more symptom-specific are sacrificed in favour of shared ones. By assuming that symptoms map cleanly and consistently onto shared connectivity abnormalities in a one-to-one fashion, erroneous inferences could be made about relationships between group-level patterns of connectivity differences and specific symptoms. Our results are consistent with this idea: univariate comparisons between a clinical subgroup defined by a diagnosis of ADHD and the rest of the sample identified connectivity features that mostly overlapped with the first multi-tract multi-symptom pair obtained from both PLSc analyses. These pairs, which accounted for the most covariance, reflected general problems and not specifically ADHD. Both these pairs and the univariate ‘ADHD-related’ connections implicated a distributed network of tracts. Instead, a multi-tract multi-symptom pair that more uniquely represented attention problems implicated mostly frontal connections, including a connection that was part of the corticospinal tract. These findings are consistent with prior literature showing differences in white matter structure, especially in the corticospinal tract, among children with ADHD (Silk et al., 2016; Wu et al., 2020; Puzzo et al., 2018). Nonetheless, children with ADHD were heterogeneous in the expression of this more attention-specific multi-tract multi-symptom pair, suggesting that this clinical subgroup of children with TBIs may have important differences that can be further investigated. Across pairs, nearly every time attention problems were implicated, they were found alongside two connections with trajectories that correspond to parts of the right superior longitudinal fasciculus. This result is consistent with previous work that has shown differences in the structure of the right superior longitudinal fasciculus in children and adults with ADHD (Cortese et al., 2013; Hamilton et al., 2008; Konrad et al., 2010; Makris et al., 2008; Wolfers et al., 2015). Overall, these results suggest that univariate comparisons in concussed children, even when performed in such a way as to identify a diagnosis-specific set of connectivity features, identified only the most consistent group-level connectivity differences at the expense of more symptom-specific idiosyncratic ones.

The present findings must be contrasted to the nascent literature addressing concussion heterogeneity. A few recent studies have parsed inter-subject heterogeneity in concussion symptoms, using clustering analyses to group concussion patients into discrete subtypes (Langdon et al., 2020; Si et al., 2018; Yeates et al., 2019). The symptoms displayed by these reported subgroups differed from those implicated in the multi-symptom features found in the present study. Differences between symptom profiles arose because our multi-symptom features are associated with white matter structure and not driven by variability in symptoms alone. Other prior studies have attempted to address inter-subject heterogeneity in white matter structure in concussions (Stojanovski et al., 2019b; Taylor et al., 2020; Ware et al., 2017). Using two different approaches, these studies generated point summaries that accounted for the high-dimensional variability of white matter structure to better distinguish patients from controls. These prior studies, using a variety of approaches, have all focused on parsing down inter-subject heterogeneity in symptoms or white matter structure. Our approach focuses instead on disease-specific heterogeneity in structure/symptom relationships.

The present results should be considered in light of methodological limitations. Data on mTBI occurrence was collected retrospectively. Participants did not have baseline data, and additionally had highly variable times since injury. Most individuals with concussions recover from their injury (Leddy et al., 2012) which should have led to a concussed group where most participants were similar to healthy controls. Interestingly, our PCA yielded combinations of diffusion measures that differed from those of two prior studies that have used this approach (Chamberland et al., 2019; Geeraert et al., 2020). These prior studies used samples of typically developing children without neurological insults. To the extent that the PCs reported in these prior studies reflect healthy neurotypical brains, our PCs suggest that our concussed sample was not as similar in white matter structure to healthy controls as expected. However, variable time since injury made the interpretation of patterns of microstructure difficult. Further, due to the cross-sectional nature of this data, the difference between symptoms and pre-existing characteristics are difficult to discern, especially since some behavioral measures often believed to be symptoms of concussion, such as attention problems, can also be risk factors for brain injury (Guberman et al., 2020b). Heterogeneity has several forms, including in symptoms, duration, severity, neuropathology (Bigler and Maxwell, 2012), lesion location (Ware et al., 2017), sociodemographics (Maas et al., 2013), genetics (Stojanovski et al., 2019a), behavior (Guberman et al., 2020b), pre-injury comorbidities (Yue et al., 2019), and environmental differences, including access to and quality of care (Yue et al., 2020). These factors have been theorized to interact in complex ways (Kenzie et al., 2017). This study only addressed a minority of these complex relationships, further studies integrating more variable sets are needed to address these other drivers of heterogeneity. The connectivity features that were studied came from connectomes, not from well-known bundles. This choice was made to obtain a larger coverage of white matter structure, but as a result, sacrificed interpretability. The reason is that the connectivity features studied here may or may not represent true anatomical units, and hence cannot be interpreted easily on their own. Instead, we relied on patterns across tracts, such as counting the number of connections that corresponded to the same symptom across different multi-tract multi-symptom pairs. Future studies should contrast this approach against procedures used to extract large well-known bundles. Lastly, methodological choices are a central part of this study. As illustrated by the sensitivity analyses and analyses performed on the replication set, some parts of the analytical pipeline were robust to methodological variations, whereas others were not. For instance, the PLSc procedure was robust to variations in input data. This was evidenced by the non-significant correlations between multi-tract multi-symptom feature expressions and a variable indexing the dataset, as well as the similarities observed after running the PLSc analyses on the replication set. However, the univariate feature selection approach was not as robust, as shown by the low overlap of connections when using different input datasets, as well as the loss of significant permutation test results when using a stricter resampling method that reshuffled prior to feature selection. Importantly, this study was not attempting to identify a single best analytical approach for studying multi-tract multi-symptom relationships. Rather, this project aimed to present a fundamentally different way of understanding concussions. Among the myriad of options available, we found a compromise between novel, cutting-edge techniques, and established, well-known ones. However, for parts of the analytical pipeline, refinements can be made. Future iterations of this work will need to exert better control of time since injury, perform longitudinal follow-up, develop predictive models for clinical outcomes, and assess the impact of choices made among the panoply of alternatives along key steps of the analytical pipeline.

Conversely, this study leveraged some of the most recent and important advances in dMRI to address the major limitations of conventional approaches. We used high-quality multi-shell dMRI data (Jones et al., 2013), as well as modeling approaches, tractography techniques, and microstructural measures robust to crossing fibers, partial volume effects, and connectivity biases (Girard et al., 2014; Descoteaux et al., 2009; Tournier et al., 2007; Raffelt et al., 2012; Dell’Acqua et al., 2013; Chamberland et al., 2019). We used PCA to combine dMRI measures into meaningful indices of white matter structure. Lastly, we used gold-standard measures of psychiatric illness to divide the sample into meaningful clinical subgroups.

In conclusion, leveraging advanced dMRI and a pattern-learning algorithm to parse white matter structure/symptom heterogeneity, we have found clinically-meaningful, cross-demographic multi-tract multi-symptom relationships. As the field moves towards large-scale studies which aim to statistically control for sociodemographic sources of heterogeneity to detect a putative consistent white matter signature of concussion across patients, the fundamental insight of this study should be taken into consideration: when pooling across patients, disease-specific multi-tract multi-symptom heterogeneity is lost, leading to the loss of informative, clinically-meaningful, symptom-specific patterns of connectivity abnormalities. Future studies aiming to better understand the relationship between white matter abnormalities and concussion symptomatology should look beyond the group-comparison design and consider multi-tract multi-symptom heterogeneity.

All data used in this project were obtained from the Adolescent Brain Cognitive Development Study. This dataset is administered by the National Institutes of Mental Health Data Archive and is freely available to all qualified researchers upon submission of an access request. All relevant instructions to obtain the data can be found in https://nda.nih.gov/abcd/request-access. The Institutional Review Board of the McGill University Faculty of Medicine and Health Sciences reviewed the application and confirmed that no further ethics approvals were required.

1. 1. Casey BJ
   2. Cannonier T
   3. Conley MI
   4. Cohen AO
   5. Barch DM
   6. Heitzeg MM
   7. Soules ME
   8. Teslovich T
   9. Dellarco DV
   10. Garavan H
   11. Orr CA
   12. Wager TD
   13. Banich MT
   14. Speer NK
   15. Sutherland MT
   16. Riedel MC
   17. Dick AS
   18. Bjork JM
   19. Thomas KM
   20. Chaarani B
   21. Mejia MH
   22. Hagler DJ
   23. Cornejo DM
   24. Sicat CS
   25. Harms MP
   26. Dosenbach NUF
   27. Rosenberg M
   28. Earl E
   29. Bartsch H
   30. Watts R
   31. Polimeni JR
   32. Kuperman JM
   33. Fair DA
   34. Dale AM

   (2018) NIMH Data Archive Collection

   ID #2573. Adolescent Brain Cognitive Development Study.

   https://nda.nih.gov/abcd

1. 1. Anderson ED
   2. Giudice JS
   3. Wu T
   4. Panzer MB
   5. Meaney DF

   (2020) Predicting Concussion Outcome by Integrating Finite Element Modeling and Network Analysis

   Frontiers in Bioengineering and Biotechnology 8:309.

   https://doi.org/10.3389/fbioe.2020.00309

   • PubMed
   • Google Scholar
2. 1. Armstrong RC
   2. Mierzwa AJ
   3. Marion CM
   4. Sullivan GM

   (2016) White matter involvement after TBI: Clues to axon and myelin repair capacity

   Experimental Neurology 275 Pt 3:328–333.

   https://doi.org/10.1016/j.expneurol.2015.02.011

   • PubMed
   • Google Scholar
3. 1. Beaton D
   2. Chin Fatt CR
   3. Abdi H

   (2014) An ExPosition of multivariate analysis with the singular value decomposition in R

   Computational Statistics & Data Analysis 72:176–189.

   https://doi.org/10.1016/j.csda.2013.11.006

   • Google Scholar
4. 1. Bigler ED
   2. Maxwell WL

   (2012) Neuropathology of mild traumatic brain injury: relationship to neuroimaging findings

   Brain Imaging and Behavior 6:108–136.

   https://doi.org/10.1007/s11682-011-9145-0

   • PubMed
   • Google Scholar
5. 1. Bodien YG
   2. McCrea M
   3. Dikmen S
   4. Temkin N
   5. Boase K
   6. Machamer J
   7. Taylor SR
   8. Sherer M
   9. Levin H
   10. Kramer JH
   11. Corrigan JD
   12. McAllister TW
   13. Whyte J
   14. Manley GT
   15. Giacino JT
   16. TRACK-TBI Investigators

   (2018) Optimizing Outcome Assessment in Multicenter TBI Trials: Perspectives From TRACK-TBI and the TBI Endpoints Development Initiative

   The Journal of Head Trauma Rehabilitation 33:147–157.

   https://doi.org/10.1097/HTR.0000000000000367

   • PubMed
   • Google Scholar
6. 1. Boulesteix AL

   (2004) PLS dimension reduction for classification with microarray data

   Statistical Applications in Genetics and Molecular Biology 3:Article33.

   https://doi.org/10.2202/1544-6115.1075

   • PubMed
   • Google Scholar
7. 1. Casey BJ
   2. Cannonier T
   3. Conley MI
   4. Cohen AO
   5. Barch DM
   6. Heitzeg MM
   7. Soules ME
   8. Teslovich T
   9. Dellarco DV
   10. Garavan H
   11. Orr CA
   12. Wager TD
   13. Banich MT
   14. Speer NK
   15. Sutherland MT
   16. Riedel MC
   17. Dick AS
   18. Bjork JM
   19. Thomas KM
   20. Chaarani B
   21. Mejia MH
   22. Hagler DJ
   23. Daniela Cornejo M
   24. Sicat CS
   25. Harms MP
   26. Dosenbach NUF
   27. Rosenberg M
   28. Earl E
   29. Bartsch H
   30. Watts R
   31. Polimeni JR
   32. Kuperman JM
   33. Fair DA
   34. Dale AM
   35. ABCD Imaging Acquisition Workgroup

   (2018) The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites

   Developmental Cognitive Neuroscience 32:43–54.

   https://doi.org/10.1016/j.dcn.2018.03.001

   • PubMed
   • Google Scholar
8. 1. Cassidy JD
   2. Carroll L
   3. Peloso P
   4. Borg J
   5. von Holst H
   6. Holm L
   7. Kraus J
   8. Coronado V

   (2004) Incidence, risk factors and prevention of mild traumatic brain injury: results of the WHO collaborating centre task force on mild traumatic brain injury

   Journal of Rehabilitation Medicine 36:28–60.

   https://doi.org/10.1080/16501960410023732

   • Google Scholar
9. 1. Chamberland M
   2. Raven EP
   3. Genc S
   4. Duffy K
   5. Descoteaux M
   6. Parker GD
   7. Tax CMW
   8. Jones DK

   (2019) Dimensionality reduction of diffusion MRI measures for improved tractometry of the human brain

   NeuroImage 200:89–100.

   https://doi.org/10.1016/j.neuroimage.2019.06.020

   • PubMed
   • Google Scholar
10. 1. Corrigan JD
    2. Bogner J

    (2007) Initial reliability and validity of the Ohio State University TBI Identification Method

    The Journal of Head Trauma Rehabilitation 22:318–329.

    https://doi.org/10.1097/01.HTR.0000300227.67748.77

    • PubMed
    • Google Scholar
11. 1. Cortese S
    2. Imperati D
    3. Zhou J
    4. Proal E
    5. Klein RG
    6. Mannuzza S
    7. Ramos-Olazagasti MA
    8. Milham MP
    9. Kelly C
    10. Castellanos FX

    (2013) White matter alterations at 33-year follow-up in adults with childhood attention-deficit/hyperactivity disorder

    Biological Psychiatry 74:591–598.

    https://doi.org/10.1016/j.biopsych.2013.02.025

    • PubMed
    • Google Scholar
12. 1. Crossley NA
    2. Mechelli A
    3. Scott J
    4. Carletti F
    5. Fox PT
    6. McGuire P
    7. Bullmore ET

    (2014) The hubs of the human connectome are generally implicated in the anatomy of brain disorders

    Brain 137:2382–2395.

    https://doi.org/10.1093/brain/awu132

    • PubMed
    • Google Scholar
13. 1. Daducci A
    2. Dal Palù A
    3. Lemkaddem A
    4. Thiran J-P

    (2015) COMMIT: Convex optimization modeling for microstructure informed tractography

    IEEE Transactions on Medical Imaging 34:246–257.

    https://doi.org/10.1109/TMI.2014.2352414

    • PubMed
    • Google Scholar
14. 1. Dell’Acqua F
    2. Simmons A
    3. Williams SCR
    4. Catani M

    (2013) Can spherical deconvolution provide more information than fiber orientations? Hindrance modulated orientational anisotropy, a true-tract specific index to characterize white matter diffusion

    Human Brain Mapping 34:2464–2483.

    https://doi.org/10.1002/hbm.22080

    • PubMed
    • Google Scholar
15. 1. Descoteaux M
    2. Deriche R
    3. Knösche TR
    4. Anwander A

    (2009) Deterministic and probabilistic tractography based on complex fibre orientation distributions

    IEEE Transactions on Medical Imaging 28:269–286.

    https://doi.org/10.1109/TMI.2008.2004424

    • PubMed
    • Google Scholar
16. 1. Dinga R
    2. Schmaal L
    3. Penninx B
    4. van Tol MJ
    5. Veltman DJ
    6. van Velzen L
    7. Mennes M
    8. van der Wee NJA
    9. Marquand AF

    (2019) Evaluating the evidence for biotypes of depression: Methodological replication and extension of

    NeuroImage. Clinical 22:101796.

    https://doi.org/10.1016/j.nicl.2019.101796

    • PubMed
    • Google Scholar
17. 1. Dodd AB
    2. Epstein K
    3. Ling JM
    4. Mayer AR

    (2014) Diffusion tensor imaging findings in semi-acute mild traumatic brain injury

    Journal of Neurotrauma 31:1235–1248.

    https://doi.org/10.1089/neu.2014.3337

    • PubMed
    • Google Scholar
18. 1. Drysdale AT
    2. Grosenick L
    3. Downar J
    4. Dunlop K
    5. Mansouri F
    6. Meng Y
    7. Fetcho RN
    8. Zebley B
    9. Oathes DJ
    10. Etkin A
    11. Schatzberg AF
    12. Sudheimer K
    13. Keller J
    14. Mayberg HS
    15. Gunning FM
    16. Alexopoulos GS
    17. Fox MD
    18. Pascual-Leone A
    19. Voss HU
    20. Casey BJ
    21. Dubin MJ
    22. Liston C

    (2017) Resting-state connectivity biomarkers define neurophysiological subtypes of depression

    Nature Medicine 23:28–38.

    https://doi.org/10.1038/nm.4246

    • PubMed
    • Google Scholar
19. 1. Garavan H
    2. Bartsch H
    3. Conway K
    4. Decastro A
    5. Goldstein RZ
    6. Heeringa S
    7. Jernigan T
    8. Potter A
    9. Thompson W
    10. Zahs D

    (2018) Recruiting the ABCD sample: Design considerations and procedures

    Developmental Cognitive Neuroscience 32:16–22.

    https://doi.org/10.1016/j.dcn.2018.04.004

    • PubMed
    • Google Scholar
20. 1. Geeraert BL
    2. Chamberland M
    3. Lebel RM
    4. Lebel C

    (2020) Multimodal principal component analysis to identify major features of white matter structure and links to reading

    PLOS ONE 15:e0233244.

    https://doi.org/10.1371/journal.pone.0233244

    • PubMed
    • Google Scholar
21. 1. Girard G
    2. Whittingstall K
    3. Deriche R
    4. Descoteaux M

    (2014) Towards quantitative connectivity analysis: reducing tractography biases

    NeuroImage 98:266–278.

    https://doi.org/10.1016/j.neuroimage.2014.04.074

    • PubMed
    • Google Scholar
22. 1. Girard G
    2. Caminiti R
    3. Battaglia-Mayer A
    4. St-Onge E
    5. Ambrosen KS
    6. Eskildsen SF
    7. Krug K
    8. Dyrby TB
    9. Descoteaux M
    10. Thiran JP
    11. Innocenti GM

    (2020) On the cortical connectivity in the macaque brain: A comparison of diffusion tractography and histological tracing data

    NeuroImage 221:117201.

    https://doi.org/10.1016/j.neuroimage.2020.117201

    • PubMed
    • Google Scholar
23. 1. Guberman GI
    2. Houde JC
    3. Ptito A
    4. Gagnon I
    5. Descoteaux M

    (2020a) Structural abnormalities in thalamo-prefrontal tracks revealed by high angular resolution diffusion imaging predict working memory scores in concussed children

    Brain Structure & Function 225:441–459.

    https://doi.org/10.1007/s00429-019-02002-8

    • PubMed
    • Google Scholar
24. 1. Guberman GI
    2. Robitaille MP
    3. Larm P
    4. Ptito A
    5. Vitaro F
    6. Tremblay RE
    7. Hodgins S

    (2020b) A Prospective Study of Childhood Predictors of Traumatic Brain Injuries Sustained in Adolescence and Adulthood

    Canadian Journal of Psychiatry. Revue Canadienne de Psychiatrie 65:36–45.

    https://doi.org/10.1177/0706743719882171

    • PubMed
    • Google Scholar
25. Software

    1. Guberman G

    (2022) Multi-tract-multi-symptom-relationships-in-pediatric-concussion, version swh:1:rev:4c30fa113b2e0d24305a6e82fe8af54a3ed5af1a

    GitHub.

    https://github.com/GuidoGuberman/Multi-tract-multi-symptom-relationships-in-pediatric-concussion
26. 1. Hamilton LS
    2. Levitt JG
    3. O’Neill J
    4. Alger JR
    5. Luders E
    6. Phillips OR
    7. Caplan R
    8. Toga AW
    9. McCracken J
    10. Narr KL

    (2008) Reduced white matter integrity in attention-deficit hyperactivity disorder

    Neuroreport 19:1705–1708.

    https://doi.org/10.1097/WNR.0b013e3283174415

    • PubMed
    • Google Scholar
27. 1. Hawryluk GWJ
    2. Bullock MR

    (2016) Past, Present, and Future of Traumatic Brain Injury Research

    Neurosurgery Clinics of North America 27:375–396.

    https://doi.org/10.1016/j.nec.2016.05.002

    • PubMed
    • Google Scholar
28. Preprint

    1. Heeringa SG

    (2020) Meaningful Effects in the Adolescent Brain Cognitive Development Study

    bioRxiv.

    https://doi.org/10.1101/2020.09.01.276451

    • Google Scholar
29. 1. Hernandez F
    2. Giordano C
    3. Goubran M
    4. Parivash S
    5. Grant G
    6. Zeineh M
    7. Camarillo D

    (2019) Lateral impacts correlate with falx cerebri displacement and corpus callosum trauma in sports-related concussions

    Biomechanics and Modeling in Mechanobiology 18:631–649.

    https://doi.org/10.1007/s10237-018-01106-0

    • PubMed
    • Google Scholar
30. 1. Hulkower MB
    2. Poliak DB
    3. Rosenbaum SB
    4. Zimmerman ME
    5. Lipton ML

    (2013) A decade of DTI in traumatic brain injury: 10 years and 100 articles later

    AJNR. American Journal of Neuroradiology 34:2064–2074.

    https://doi.org/10.3174/ajnr.A3395

    • PubMed
    • Google Scholar
31. 1. Iverson GL

    (2019) Network Analysis and Precision Rehabilitation for the Post-concussion Syndrome

    Frontiers in Neurology 10:489.

    https://doi.org/10.3389/fneur.2019.00489

    • PubMed
    • Google Scholar
32. 1. Jones DK
    2. Knösche TR
    3. Turner R

    (2013) White matter integrity, fiber count, and other fallacies: the do’s and don’ts of diffusion MRI

    NeuroImage 73:239–254.

    https://doi.org/10.1016/j.neuroimage.2012.06.081

    • PubMed
    • Google Scholar
33. 1. Kaufman J
    2. Birmaher B
    3. Brent D
    4. Rao U
    5. Flynn C
    6. Moreci P
    7. Williamson D
    8. Ryan N

    (1997) Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL): initial reliability and validity data

    Journal of the American Academy of Child and Adolescent Psychiatry 36:980–988.

    https://doi.org/10.1097/00004583-199707000-00021

    • PubMed
    • Google Scholar
34. 1. Kenzie ES
    2. Parks EL
    3. Bigler ED
    4. Lim MM
    5. Chesnutt JC
    6. Wakeland W

    (2017) Concussion As a Multi-Scale Complex System: An Interdisciplinary Synthesis of Current Knowledge

    Frontiers in Neurology 8:513.

    https://doi.org/10.3389/fneur.2017.00513

    • PubMed
    • Google Scholar
35. 1. Kenzie ES
    2. Parks EL
    3. Bigler ED
    4. Wright DW
    5. Lim MM
    6. Chesnutt JC
    7. Hawryluk GWJ
    8. Gordon W
    9. Wakeland W

    (2018) The Dynamics of Concussion: Mapping Pathophysiology, Persistence, and Recovery With Causal-Loop Diagramming

    Frontiers in Neurology 9:203.

    https://doi.org/10.3389/fneur.2018.00203

    • PubMed
    • Google Scholar
36. 1. Klein A
    2. Tourville J

    (2012) 101 labeled brain images and a consistent human cortical labeling protocol

    Frontiers in Neuroscience 6:171.

    https://doi.org/10.3389/fnins.2012.00171

    • PubMed
    • Google Scholar
37. 1. Konrad A
    2. Dielentheis TF
    3. El Masri D
    4. Bayerl M
    5. Fehr C
    6. Gesierich T
    7. Vucurevic G
    8. Stoeter P
    9. Winterer G

    (2010) Disturbed structural connectivity is related to inattention and impulsivity in adult attention deficit hyperactivity disorder

    The European Journal of Neuroscience 31:912–919.

    https://doi.org/10.1111/j.1460-9568.2010.07110.x

    • PubMed
    • Google Scholar
38. 1. Langdon S
    2. Königs M
    3. Adang EAMC
    4. Goedhart E
    5. Oosterlaan J

    (2020) Subtypes of Sport-Related Concussion: a Systematic Review and Meta-cluster Analysis

    Sports Medicine (Auckland, N.Z.) 50:1829–1842.

    https://doi.org/10.1007/s40279-020-01321-9

    • PubMed
    • Google Scholar
39. 1. Leddy JJ
    2. Sandhu H
    3. Sodhi V
    4. Baker JG
    5. Willer B

    (2012) Rehabilitation of Concussion and Post-concussion Syndrome

    Sports Health 4:147–154.

    https://doi.org/10.1177/1941738111433673

    • PubMed
    • Google Scholar
40. 1. Maas AIR
    2. Murray GD
    3. Roozenbeek B
    4. Lingsma HF
    5. Butcher I
    6. McHugh GS
    7. Weir J
    8. Lu J
    9. Steyerberg EW
    10. International Mission on Prognosis Analysis of Clinical Trials in Traumatic Brain Injury IMPACT Study Group

    (2013) Advancing care for traumatic brain injury: findings from the IMPACT studies and perspectives on future research

    The Lancet. Neurology 12:1200–1210.

    https://doi.org/10.1016/S1474-4422(13)70234-5

    • PubMed
    • Google Scholar
41. 1. Maas AIR
    2. Menon DK
    3. Steyerberg EW
    4. Citerio G
    5. Lecky F
    6. Manley GT
    7. Hill S
    8. Legrand V
    9. Sorgner A
    10. CENTER-TBI Participants and Investigators

    (2015) Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI): a prospective longitudinal observational study

    Neurosurgery 76:67–80.

    https://doi.org/10.1227/NEU.0000000000000575

    • PubMed
    • Google Scholar
42. 1. Maier-Hein KH
    2. Neher PF
    3. Houde J-C
    4. Côté M-A
    5. Garyfallidis E
    6. Zhong J
    7. Chamberland M
    8. Yeh F-C
    9. Lin Y-C
    10. Ji Q
    11. Reddick WE
    12. Glass JO
    13. Chen DQ
    14. Feng Y
    15. Gao C
    16. Wu Y
    17. Ma J
    18. He R
    19. Li Q
    20. Westin C-F
    21. Deslauriers-Gauthier S
    22. González JOO
    23. Paquette M
    24. St-Jean S
    25. Girard G
    26. Rheault F
    27. Sidhu J
    28. Tax CMW
    29. Guo F
    30. Mesri HY
    31. Dávid S
    32. Froeling M
    33. Heemskerk AM
    34. Leemans A
    35. Boré A
    36. Pinsard B
    37. Bedetti C
    38. Desrosiers M
    39. Brambati S
    40. Doyon J
    41. Sarica A
    42. Vasta R
    43. Cerasa A
    44. Quattrone A
    45. Yeatman J
    46. Khan AR
    47. Hodges W
    48. Alexander S
    49. Romascano D
    50. Barakovic M
    51. Auría A
    52. Esteban O
    53. Lemkaddem A
    54. Thiran J-P
    55. Cetingul HE
    56. Odry BL
    57. Mailhe B
    58. Nadar MS
    59. Pizzagalli F
    60. Prasad G
    61. Villalon-Reina JE
    62. Galvis J
    63. Thompson PM
    64. Requejo FDS
    65. Laguna PL
    66. Lacerda LM
    67. Barrett R
    68. Dell’Acqua F
    69. Catani M
    70. Petit L
    71. Caruyer E
    72. Daducci A
    73. Dyrby TB
    74. Holland-Letz T
    75. Hilgetag CC
    76. Stieltjes B
    77. Descoteaux M

    (2017) The challenge of mapping the human connectome based on diffusion tractography

    Nature Communications 8:1349.

    https://doi.org/10.1038/s41467-017-01285-x

    • PubMed
    • Google Scholar
43. 1. Makris N
    2. Buka SL
    3. Biederman J
    4. Papadimitriou GM
    5. Hodge SM
    6. Valera EM
    7. Brown AB
    8. Bush G
    9. Monuteaux MC
    10. Caviness VS
    11. Kennedy DN
    12. Seidman LJ

    (2008) Attention and executive systems abnormalities in adults with childhood ADHD: A DT-MRI study of connections

    Cerebral Cortex (New York, N.Y) 18:1210–1220.

    https://doi.org/10.1093/cercor/bhm156

    • PubMed
    • Google Scholar
44. 1. Mayer AR
    2. Kaushal M
    3. Dodd AB
    4. Hanlon FM
    5. Shaff NA
    6. Mannix R
    7. Master CL
    8. Leddy JJ
    9. Stephenson D
    10. Wertz CJ
    11. Suelzer EM
    12. Arbogast KB
    13. Meier TB

    (2018) Advanced biomarkers of pediatric mild traumatic brain injury: Progress and perils

    Neuroscience and Biobehavioral Reviews 94:149–165.

    https://doi.org/10.1016/j.neubiorev.2018.08.002

    • PubMed
    • Google Scholar
45. 1. McIntosh AR
    2. Lobaugh NJ

    (2004) Partial least squares analysis of neuroimaging data: applications and advances

    NeuroImage 23 Suppl 1:S250–S263.

    https://doi.org/10.1016/j.neuroimage.2004.07.020

    • PubMed
    • Google Scholar
46. 1. Orlovska S
    2. Pedersen MS
    3. Benros ME
    4. Mortensen PB
    5. Agerbo E
    6. Nordentoft M

    (2014) Head injury as risk factor for psychiatric disorders: a nationwide register-based follow-up study of 113,906 persons with head injury

    The American Journal of Psychiatry 171:463–469.

    https://doi.org/10.1176/appi.ajp.2013.13020190

    • PubMed
    • Google Scholar
47. 1. Puzzo I
    2. Seunarine K
    3. Sully K
    4. Darekar A
    5. Clark C
    6. Sonuga-Barke EJS
    7. Fairchild G

    (2018) Altered White-Matter Microstructure in Conduct Disorder Is Specifically Associated with Elevated Callous-Unemotional Traits

    Journal of Abnormal Child Psychology 46:1451–1466.

    https://doi.org/10.1007/s10802-017-0375-5

    • PubMed
    • Google Scholar
48. 1. Raffelt D
    2. Tournier J-D
    3. Rose S
    4. Ridgway GR
    5. Henderson R
    6. Crozier S
    7. Salvado O
    8. Connelly A

    (2012) Apparent Fibre Density: a novel measure for the analysis of diffusion-weighted magnetic resonance images

    NeuroImage 59:3976–3994.

    https://doi.org/10.1016/j.neuroimage.2011.10.045

    • PubMed
    • Google Scholar
49. Conference

    1. Rheault F
    2. Houde JC

    (2021)

    Connectoflow: A cutting-edge Nextflow pipeline for structural connectomics

    International Society for Magnetic Resonance in Medicine Annual Meeting & Exhibition.

    • Google Scholar
50. Software

    1. Sherbrooke Connectivity Imaging Lab

    (2022) Scilpy, version 1.0.0

    GitHub.

    https://github.com/scilus/scilpy
51. 1. Sherif T
    2. Rioux P
    3. Rousseau M-E
    4. Kassis N
    5. Beck N
    6. Adalat R
    7. Das S
    8. Glatard T
    9. Evans AC

    (2014) CBRAIN: a web-based, distributed computing platform for collaborative neuroimaging research

    Frontiers in Neuroinformatics 8:54.

    https://doi.org/10.3389/fninf.2014.00054

    • PubMed
    • Google Scholar
52. 1. Si B
    2. Dumkrieger G
    3. Wu T
    4. Zafonte R
    5. Valadka AB
    6. Okonkwo DO
    7. Manley GT
    8. Wang L
    9. Dodick DW
    10. Schwedt TJ
    11. Li J

    (2018) Sub-classifying patients with mild traumatic brain injury: A clustering approach based on baseline clinical characteristics and 90-day and 180-day outcomes

    PLOS ONE 13:e0198741.

    https://doi.org/10.1371/journal.pone.0198741

    • PubMed
    • Google Scholar
53. 1. Silk TJ
    2. Vilgis V
    3. Adamson C
    4. Chen J
    5. Smit L
    6. Vance A
    7. Bellgrove MA

    (2016) Abnormal asymmetry in frontostriatal white matter in children with attention deficit hyperactivity disorder

    Brain Imaging and Behavior 10:1080–1089.

    https://doi.org/10.1007/s11682-015-9470-9

    • PubMed
    • Google Scholar
54. 1. Stojanovski S
    2. Felsky D
    3. Viviano JD
    4. Shahab S
    5. Bangali R
    6. Burton CL
    7. Devenyi GA
    8. O’Donnell LJ
    9. Szatmari P
    10. Chakravarty MM
    11. Ameis S
    12. Schachar R
    13. Voineskos AN
    14. Wheeler AL

    (2019a) Polygenic Risk and Neural Substrates of Attention-Deficit/Hyperactivity Disorder Symptoms in Youths With a History of Mild Traumatic Brain Injury

    Biological Psychiatry 85:408–416.

    https://doi.org/10.1016/j.biopsych.2018.06.024

    • PubMed
    • Google Scholar
55. 1. Stojanovski S
    2. Nazeri A
    3. Lepage C
    4. Ameis S
    5. Voineskos AN
    6. Wheeler AL

    (2019b) Microstructural abnormalities in deep and superficial white matter in youths with mild traumatic brain injury

    NeuroImage. Clinical 24:102102.

    https://doi.org/10.1016/j.nicl.2019.102102

    • PubMed
    • Google Scholar
56. 1. Taylor PN
    2. Moreira da Silva N
    3. Blamire A
    4. Wang Y
    5. Forsyth R

    (2020) Early deviation from normal structural connectivity: A novel intrinsic severity score for mild TBI

    Neurology 94:e1021–e1026.

    https://doi.org/10.1212/WNL.0000000000008902

    • PubMed
    • Google Scholar
57. 1. Theaud G
    2. Houde JC
    3. Boré A
    4. Rheault F
    5. Morency F
    6. Descoteaux M

    (2020) TractoFlow: A robust, efficient and reproducible diffusion MRI pipeline leveraging Nextflow & Singularity

    NeuroImage 218:116889.

    https://doi.org/10.1016/j.neuroimage.2020.116889

    • PubMed
    • Google Scholar
58. 1. Tosetti P
    2. Hicks RR
    3. Theriault E
    4. Phillips A
    5. Koroshetz W
    6. Draghia-Akli R
    7. Workshop Participants

    (2013) Toward an international initiative for traumatic brain injury research

    Journal of Neurotrauma 30:1211–1222.

    https://doi.org/10.1089/neu.2013.2896

    • PubMed
    • Google Scholar
59. 1. Tournier JD
    2. Calamante F
    3. Connelly A

    (2007) Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution

    NeuroImage 35:1459–1472.

    https://doi.org/10.1016/j.neuroimage.2007.02.016

    • PubMed
    • Google Scholar
60. 1. Volkow ND
    2. Koob GF
    3. Croyle RT
    4. Bianchi DW
    5. Gordon JA
    6. Koroshetz WJ
    7. Pérez-Stable EJ
    8. Riley WT
    9. Bloch MH
    10. Conway K
    11. Deeds BG
    12. Dowling GJ
    13. Grant S
    14. Howlett KD
    15. Matochik JA
    16. Morgan GD
    17. Murray MM
    18. Noronha A
    19. Spong CY
    20. Wargo EM
    21. Warren KR
    22. Weiss SRB

    (2018) The conception of the ABCD study: From substance use to a broad NIH collaboration

    Developmental Cognitive Neuroscience 32:4–7.

    https://doi.org/10.1016/j.dcn.2017.10.002

    • PubMed
    • Google Scholar
61. 1. Wang HT
    2. Smallwood J
    3. Mourao-Miranda J
    4. Xia CH
    5. Satterthwaite TD
    6. Bassett DS
    7. Bzdok D

    (2020) Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists

    NeuroImage 216:116745.

    https://doi.org/10.1016/j.neuroimage.2020.116745

    • PubMed
    • Google Scholar
62. 1. Ware JB
    2. Hart T
    3. Whyte J
    4. Rabinowitz A
    5. Detre JA
    6. Kim J

    (2017) Inter-Subject Variability of Axonal Injury in Diffuse Traumatic Brain Injury

    Journal of Neurotrauma 34:2243–2253.

    https://doi.org/10.1089/neu.2016.4817

    • PubMed
    • Google Scholar
63. 1. Wolfers T
    2. Onnink AMH
    3. Zwiers MP
    4. Arias-Vasquez A
    5. Hoogman M
    6. Mostert JC
    7. Kan CC
    8. Slaats-Willemse D
    9. Buitelaar JK
    10. Franke B

    (2015) Lower white matter microstructure in the superior longitudinal fasciculus is associated with increased response time variability in adults with attention-deficit/ hyperactivity disorder

    Journal of Psychiatry & Neuroscience 40:344–351.

    https://doi.org/10.1503/jpn.140154

    • PubMed
    • Google Scholar
64. 1. Wu ZM
    2. Wang P
    3. Yang L
    4. Liu L
    5. Sun L
    6. An L
    7. Cao QJ
    8. Chan RCK
    9. Yang BR
    10. Wang YF

    (2020) Altered brain white matter microstructural asymmetry in children with ADHD

    Psychiatry Research 285:112817.

    https://doi.org/10.1016/j.psychres.2020.112817

    • PubMed
    • Google Scholar
65. 1. Xia M
    2. Wang J
    3. He Y

    (2013) BrainNet Viewer: a network visualization tool for human brain connectomics

    PLOS ONE 8:e68910.

    https://doi.org/10.1371/journal.pone.0068910

    • PubMed
    • Google Scholar
66. 1. Yeates KO
    2. Tang K
    3. Barrowman N
    4. Freedman SB
    5. Gravel J
    6. Gagnon I
    7. Sangha G
    8. Boutis K
    9. Beer D
    10. Craig W
    11. Burns E
    12. Farion KJ
    13. Mikrogianakis A
    14. Barlow K
    15. Dubrovsky AS
    16. Meeuwisse W
    17. Gioia G
    18. Meehan WP
    19. Beauchamp MH
    20. Kamil Y
    21. Grool AM
    22. Hoshizaki B
    23. Anderson P
    24. Brooks BL
    25. Vassilyadi M
    26. Klassen T
    27. Keightley M
    28. Richer L
    29. DeMatteo C
    30. Osmond MH
    31. Zemek R
    32. Pediatric Emergency Research Canada PERC Predicting Persistent Postconcussive Problems in Pediatrics 5P Concussion Team

    (2019) Derivation and Initial Validation of Clinical Phenotypes of Children Presenting with Concussion Acutely in the Emergency Department: Latent Class Analysis of a Multi-Center, Prospective Cohort, Observational Study

    Journal of Neurotrauma 36:1758–1767.

    https://doi.org/10.1089/neu.2018.6009

    • PubMed
    • Google Scholar
67. 1. Yue JK
    2. Cnossen MC
    3. Winkler EA
    4. Deng H
    5. Phelps RRL
    6. Coss NA
    7. Sharma S
    8. Robinson CK
    9. Suen CG
    10. Vassar MJ
    11. Schnyer DM
    12. Puccio AM
    13. Gardner RC
    14. Yuh EL
    15. Mukherjee P
    16. Valadka AB
    17. Okonkwo DO
    18. Lingsma HF
    19. Manley GT
    20. TRACK-TBI Investigators

    (2019) Pre-injury Comorbidities Are Associated With Functional Impairment and Post-concussive Symptoms at 3- and 6-Months After Mild Traumatic Brain Injury: A TRACK-TBI Study

    Frontiers in Neurology 10:343.

    https://doi.org/10.3389/fneur.2019.00343

    • PubMed
    • Google Scholar
68. 1. Yue JK
    2. Upadhyayula PS
    3. Avalos LN
    4. Phelps RRL
    5. Suen CG
    6. Cage TA

    (2020) Concussion and Mild-Traumatic Brain Injury in Rural Settings: Epidemiology and Specific Health Care Considerations

    Journal of Neurosciences in Rural Practice 11:23–33.

    https://doi.org/10.1055/s-0039-3402581

    • PubMed
    • Google Scholar
69. 1. Zhang AL
    2. Sing DC
    3. Rugg CM
    4. Feeley BT
    5. Senter C

    (2016) The Rise of Concussions in the Adolescent Population

    Orthopaedic Journal of Sports Medicine 4:2325967116662458.

    https://doi.org/10.1177/2325967116662458

    • PubMed
    • Google Scholar

Author details

1. Guido I Guberman

   Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, Canada

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review and editing

   For correspondence

   guido.guberman@mail.mcgill.ca

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4422-2225

2. Sonja Stojanovski

   1. Department of Physiology, Faculty of Medicine, University of Toronto, Toronto, Canada
   2. Neuroscience and Mental Health, The Hospital for Sick Children, Toronto, Canada

   Contribution

   Conceptualization, Data curation, Methodology, Visualization, Writing – review and editing

   Competing interests

   No competing interests declared

3. Eman Nishat

   1. Department of Physiology, Faculty of Medicine, University of Toronto, Toronto, Canada
   2. Neuroscience and Mental Health, The Hospital for Sick Children, Toronto, Canada

   Contribution

   Data curation, Resources, Writing – review and editing

   Competing interests

   No competing interests declared

4. Alain Ptito

   Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, Canada

   Contribution

   Resources, Supervision, Writing – review and editing

   Competing interests

   No competing interests declared

5. Danilo Bzdok

   1. McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
   2. Department of Biomedical Engineering, Faculty of Medicine, School of Computer Science, McGill University, Montreal, Canada
   3. Mila - Quebec Artificial Intelligence Institute, Montreal, Canada

   Contribution

   Conceptualization, Supervision, Writing – review and editing

   Competing interests

   No competing interests declared

6. Anne L Wheeler

   1. Department of Physiology, Faculty of Medicine, University of Toronto, Toronto, Canada
   2. Neuroscience and Mental Health, The Hospital for Sick Children, Toronto, Canada

   Contribution

   Conceptualization, Supervision, Writing – review and editing

   Competing interests

   No competing interests declared

7. Maxime Descoteaux

   1. Department of Computer Science, Université de Sherbrooke, Sherbrooke, Canada
   2. Imeka Solutions Inc, Sherbrooke, Canada

   Contribution

   Conceptualization, Resources, Software, Supervision, Writing – review and editing

   Competing interests

   works as Chief Scientific Officer for IMEKA. He holds the following patents: DETERMINATION OF WHITE-MATTER NEURODEGENERATIVE DISEASE BIOMARKERS (Patent Application No.: 63/222,914), PROCESSING OF TRACTOGRAPHY RESULTS USING AN AUTOENCODER (Patent Application No.: 17/337,413)

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