Longitudinal change in regional brain volumes with exposure to repetitive head impacts

Bernick, Charles; Shan, Guogen; Zetterberg, Henrik; Banks, Sarah J.; Mishra, Virendra; Bekris, Lynn M.; Leverenz, James B.; Blennow, Kaj

doi:10.1212/wnl.0000000000008817

Cited by 44 publications

(49 citation statements)

References 35 publications

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“…These affected dFC still minimally demonstrate a medium effect size of | d | = 0.46. Both weaker sFC and structural damages have been observed among these regions from the current and previous reports ( 18 , 55 ). Therefore, overall, from our analysis, it can only be concluded that dFC measure of temporal variations across all windows in the sliding-window approach might not be sensitive enough to uncover significant between-group differences in fighters with repetitive head trauma.…”

Section: Discussionsupporting

confidence: 73%

“…The Professional Fighters Brain Health Study (PFBHS) launched at our center in 2011 ( 17 ) has focused on investigating brain changes related to repetitive head trauma and their associations with clinical and neuropsychological deficits in active professional fighters. Specifically, in PFBHS, structural MRI studies have revealed both cross-sectional and longitudinal brain volumes loss in fighters ( 18 ); and in a subset of fighters with neuropsychological impairments, abnormal white matter microstructural organization ( 19 ) and white matter topological reorganizations ( 20 ) have been observed with diffusion-weighted MRI studies.…”

Section: Introductionmentioning

confidence: 99%

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Resting-State Static and Dynamic Functional Abnormalities in Active Professional Fighters With Repetitive Head Trauma and With Neuropsychological Impairments

et al. 2020

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Previous neuroimaging studies have identified structural brain abnormalities in active professional fighters with repetitive head trauma and correlated these changes with fighters' neuropsychological impairments. However, functional brain changes in these fighters derived using neuroimaging techniques remain unclear. In this study, both static and dynamic functional connectivity alterations were investigated (1) between healthy normal control subjects (NC) and fighters and (2) between non-impaired and impaired fighters. Resting-state fMRI data were collected on 35 NC and 133 active professional fighters, including 68 impaired fighters and 65 non-impaired fighters, from the Professional Fighters Brain Health Study at our center. Impaired fighters performed worse on processing speed (PSS) tasks with visual-attention and working-memory demands. The static functional connectivity (sFC) matrix was estimated for every pair of regions of interest (ROI) using a subject-specific parcellation. The dynamic functional connectivity (dFC) was estimated using a sliding-window method, where the variability of each ROI pair across all windows represented the temporal dynamics. A linear regression model was fitted for all 168 subjects, and different t-contrast vectors were used for between-group comparisons. An association analysis was further conducted to evaluate FC changes associated with PSS task performances without creating artificial impairment group-divisions in fighters. Following corrections for multiple comparisons using network-based statistics, our study identified significantly reduced long-range frontal-temporal, frontal-occipital, temporal-occipital, and parietal-occipital sFC strengths in fighters than in NCs, corroborating with previously observed structural damages in corresponding white matter tracts in subjects experiencing repetitive head trauma. In impaired fighters, significantly decreased sFC strengths were found among key regions involved in visual-attention, executive and cognitive process, as compared to non-impaired fighters. Association analysis further reveals similar sFC deficits to worse PSS task performances in all 133 fighters. With our choice of dFC indices, we were not able to observe any significant dFC changes beyond a trend-level increased temporal variability among similar regions with weaker sFC strengths in impaired fighters. Collectively, our functional brain findings supplement previously reported structural brain abnormalities in fighters and are important to comprehensively understand brain changes in fighters with repetitive head trauma.

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Section: Discussionsupporting

confidence: 73%

Section: Introductionmentioning

confidence: 99%

Resting-State Static and Dynamic Functional Abnormalities in Active Professional Fighters With Repetitive Head Trauma and With Neuropsychological Impairments

et al. 2020

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“…This often means an AD diagnosis can be missed or delayed for years 60 . The impact is even more dramatic on the AD drug development pipeline which has seen significant bottlenecks in recruitment for early stage trials and studies composed of largely homogeneous populations 61 , 62 .…”

Section: Discussionmentioning

confidence: 99%

Machine learning methods to predict amyloid positivity using domain scores from cognitive tests

Shan

Bernick

Caldwell

et al. 2021

Sci Rep

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Amyloid-$$\beta$$ β (A$$\beta$$ β ) is the target in many clinical trials for Alzheimer’s disease (AD). Preclinical AD patients are heterogeneous with regards to different backgrounds and diagnosis. Accurately predicting A$$\beta$$ β status of participants by using machine learning (ML) models based on easily accessible data, could improve the effectiveness of AD clinical trials. We will develop optimal ML models for each subpopulation stratified by sex and disease stages using sub scores from screening neurological tests. Data from the AD Neuroimaging Initiative (ADNI) were used to build the ML models, for three groups: individuals with significant memory concern, early mild cognitive impairment (MCI), and late MCI. Data were further separated into 6 groups by disease stage (3 levels) and sex (2 categories). The outcome was defined as the A$$\beta$$ β status confirmed by the PET imaging, and the features include demographic data, newly identified risk factors, screening tests, and the domain scores from screening tests. Monte Carlo simulation studies were used together with k-fold cross-validation technique to compute model performance metric. We also develop a new feature selection method based on the stochastic ordering to avoiding searching all possible combinations of features. Accuracy of the identified optimal model for SMC male was over 90% by using domain scores, and accuracy for LMCI female was above 86%. Domain scores can improve the ML model prediction as compared to the total scores. Accurate ML prediction models can identify the proper population for AD clinical trials.

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“…The use of composite measures as endpoints in clinical trials have been encouraged by the Food and Drug Administration (FDA) and European Medical Agency (EMA) provided that the measures are carefully designed, are relevant to existing tools for which historical evidence exists, and are validated in independent prospective cohorts (20,21). Given that the RHICI is based on data derived from commonly used neuropsychological tools, the rst two requirements are met.…”

Section: Discussionmentioning

confidence: 99%

Assessing Clinical Change in Individuals Exposed to Repetitive Head Impacts: The Repetitive Head Impact Composite Index

Bernick

Shan

Bennett

et al. 2020

Preprint

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Background: There is a current lack of any composite measure for the effective tracking and monitoring of executive function in individuals exposed to repetitive head impacts (RHI). The aim of this study is to create a composite instrument for the purposes of detecting change over time in executive function in individuals exposed to RHI.Methods: The data to derive the composite instrument came from the Professional Fighters Brain Health Study (PFBHS), a longitudinal study of active and retired professional fighters (boxers and mixed martial arts [MMA] fighters) and healthy controls. Participants in the PFBHS underwent assessment on an annual basis that included computerized cognitive testing and behavioral questionnaires. Multivariate logistic regression models were employed to compare active fighters (n=117) with controls (n=22), and retired fighters (n=26) with controls to identify the predictors that could be used to differentiate the groups over time. In a second step, linear discriminant analysis was performed to derive the linear discriminant coefficients for the three groups by using the predictors from the two separate logistic regression models.Results: The composite scale is a weighted linear value of 12 standardized scores consisting of both current and yearly change scores in domains including: processing speed, choice reaction time, semantic fluency, letter fluency, and Barrett Impulsiveness Scale. Because the weighting of values differed between active and retired fighters, two versions emerged. The mean and standard deviation ratio (MSDR) showed that the new index had better sensitivity compared to the individual measures, with the ratio of MSDR of the new index to that of the existing measures of at least 1.84.Conclusion: With the increasing need for tools to follow individuals exposed to RHI and the potential of clinical trials on the horizon for CTE, the RHICI is poised to serve as an initial approach to a composite clinical measure.

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Longitudinal change in regional brain volumes with exposure to repetitive head impacts

Cited by 44 publications

References 35 publications

Resting-State Static and Dynamic Functional Abnormalities in Active Professional Fighters With Repetitive Head Trauma and With Neuropsychological Impairments

Resting-State Static and Dynamic Functional Abnormalities in Active Professional Fighters With Repetitive Head Trauma and With Neuropsychological Impairments

Machine learning methods to predict amyloid positivity using domain scores from cognitive tests

Assessing Clinical Change in Individuals Exposed to Repetitive Head Impacts: The Repetitive Head Impact Composite Index

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