Driving is the preferred mode of transportation for adults across the healthy age span. However, motor vehicle crashes are among the leading causes of injury and death, especially for older adults, ...and under distracted driving conditions. Understanding the neuroanatomical basis of driving may inform interventions that minimize crashes. This exploratory study examined the neuroanatomical correlates of undistracted and distracted simulated straight driving.
One-hundred-and-thirty-eight participants (40.6% female) aged 17-85 years old (mean and SD = 58.1 ± 19.9 years) performed a simulated driving task involving straight driving and turns at intersections in a city environment using a steering wheel and foot pedals. During some straight driving segments, participants responded to auditory questions to simulate distracted driving. Anatomical T1-weighted MRI was used to quantify grey matter volume and cortical thickness for five brain regions: the middle frontal gyrus (MFG), precentral gyrus (PG), superior temporal cortex (STC), posterior parietal cortex (PPC), and cerebellum. Partial correlations controlling for age and sex were used to explore relationships between neuroanatomical measures and straight driving behavior, including speed, acceleration, lane position, heading angle, and time speeding or off-center. Effects of interest were noted at an unadjusted
-value threshold of 0.05.
Distracted driving was associated with changes in most measures of straight driving performance. Greater volume and cortical thickness in the PPC and cerebellum were associated with reduced variability in lane position and heading angle during distracted straight driving. Cortical thickness of the MFG, PG, PPC, and STC were associated with speed and acceleration, often in an age-dependent manner.
Posterior regions were correlated with lane maintenance whereas anterior and posterior regions were correlated with speed and acceleration, especially during distracted driving. The regions involved and their role in straight driving may change with age, particularly during distracted driving as observed in older adults. Further studies should investigate the relationship between distracted driving and the aging brain to inform driving interventions.
We explored the effects of aging on 2 large-scale brain networks, the default mode network (DMN) and the task-positive network (TPN). During functional magnetic resonance imaging scanning, young and ...older participants carried out 4 visual tasks: detection, perceptual matching, attentional cueing, and working memory. Accuracy of performance was roughly matched at 80% across tasks and groups. Modulations of activity across conditions were assessed, as well as functional connectivity of both networks. Younger adults showed a broader engagement of the DMN and older adults a more extensive engagement of the TPN. Functional connectivity in the DMN was reduced in older adults, whereas the main pattern of TPN connectivity was equivalent in the 2 groups. Age-specific connectivity also was seen in TPN regions. Increased activity in TPN areas predicted worse accuracy on the tasks, but greater expression of a connectivity pattern associated with a right dorsolateral prefrontal TPN region, seen only in older adults, predicted better performance. These results provide further evidence for age-related differences in the DMN and new evidence of age differences in the TPN. Increased use of the TPN may reflect greater demand on cognitive control processes in older individuals that may be partially offset by alterations in prefrontal functional connectivity.
Neuroimaging has identified significant disturbances in cerebrovascular reactivity (CVR) in the early symptomatic phase of sport-related concussion. However, less is known about how whole-brain ...alterations in CVR evolve after concussion and whether they remain present beyond medical clearance to return to play (RTP). In the present study, CVR was evaluated using blood-oxygenation-level-dependent functional magnetic resonance imaging (BOLD fMRI) during a respiratory challenge. Imaging data were collected for 110 university-level athletes, including 39 concussed athletes and 71 athletic controls. The concussed athletes were imaged at the acute phase of injury (1–7 days post-injury), the subacute phase (8-14 days post-injury), medical clearance to RTP, 1 month post-RTP, and 1 year post-RTP. Enhanced negative BOLD response to controlled breathing was seen at acute injury, with attenuation of the effect mainly occurring by 1 year post-RTP. Secondary analyses showed that greater symptom severity and prolonged recovery were associated with enhanced BOLD response in the acute phase of injury, but a more attenuated BOLD response in the subacute phase. This study provides novel information characterizing the CVR response after concussion and shows CVR to be a sensitive technique for evaluating long-term brain recovery.
The Trail Making Test (TMT) is widely used to probe brain function and is performed with pen and paper, involving Parts A (linking numbers) and B (alternating between linking numbers and letters). ...The relationship between TMT performance and the underlying brain activity remains to be characterized in detail. Accordingly, sixteen healthy young adults performed the TMT using a touch-sensitive tablet to capture enhanced performance metrics, such as the speed of linking movements, during simultaneous electroencephalography (EEG). Linking and non-linking periods were derived as estimates of the time spent executing and preparing movements, respectively. The seconds per link (SPL) was also used to quantify TMT performance. A strong effect of TMT Part A and B was observed on the SPL value as expected (Part B showing increased SPL value); whereas the EEG results indicated robust effects of linking and non-linking periods in multiple frequency bands, and effects consistent with the underlying cognitive demands of the test.
Interest is increasing in applying discriminative multivariate analysis techniques to the analysis of functional neuroimaging data. Model interpretation is of great importance in the neuroimaging ...context, and is conventionally based on a ‘brain map’ derived from the classification model. In this study we focus on the relative influence of model regularization parameter choices on both the model generalization, the reliability of the spatial patterns extracted from the classification model, and the ability of the resulting model to identify relevant brain networks defining the underlying neural encoding of the experiment. For a support vector machine, logistic regression and Fisher's discriminant analysis we demonstrate that selection of model regularization parameters has a strong but consistent impact on the generalizability and both the reproducibility and interpretable sparsity of the models for both ℓ2 and ℓ1 regularization. Importantly, we illustrate a trade-off between model spatial reproducibility and prediction accuracy. We show that known parts of brain networks can be overlooked in pursuing maximization of classification accuracy alone with either ℓ2 and/or ℓ1 regularization. This supports the view that the quality of spatial patterns extracted from models cannot be assessed purely by focusing on prediction accuracy. Our results instead suggest that model regularization parameters must be carefully selected, so that the model and its visualization enhance our ability to interpret the brain.
► We consider classification models widely used within the neuroimaging community. ► Within a resampling framework we evaluate the importance of appropriate selection of model regularization parameters. ► We illustrate a trade-off between model visualization reproducibility and prediction accuracy. ► The quality of spatial patterns extracted from models cannot be assessed purely by focusing on prediction accuracy. ► Optimizing prediction accuracy does not ensure discovery of the relevant brain networks.
Inflammation is considered a hallmark of concussion pathophysiology in experimental models, yet is understudied in human injury. Despite the growing use of blood biomarkers in concussion, ...inflammatory biomarkers have not been well characterized. Furthermore, it is unclear if the systemic inflammatory response to concussion differs from that of musculoskeletal injury. The purpose of this paper was to characterize systemic inflammation after injury in athletes with sport-related concussion or musculoskeletal injury.
A prospective, observational cohort study was conducted employing 175 interuniversity athletes (sport-related concussion, n = 43; musculoskeletal injury, n = 30; healthy, n = 102) from 12 sports at a sports medicine clinic at an academic institution. High-sensitivity immunoassay was used to evaluate 20 inflammatory biomarkers in the peripheral blood of athletes within 7 days of injury (subacute) and at medical clearance. Healthy athletes were sampled prior to the start of their competitive season. Partial least squares regression analyses were used to identify salient biomarker contributions to class separation between injured and healthy athletes, as well as to evaluate the relationship between biomarkers and days to recovery in injured athletes.
In the subacute period after injury, compared to healthy athletes, athletes with sport-related concussion had higher levels of the chemokines' monocyte chemoattractant protein-4 (p < 0.001) and macrophage inflammatory protein-1β (p = 0.001); athletes with musculoskeletal injury had higher levels of thymus and activation-regulated chemokine (p = 0.001). No significant differences in biomarker profiles were observed at medical clearance. Furthermore, concentrations of monocyte chemoattractant protein-1 (p = 0.007) and monocyte chemoattractant protein-4 (p < 0.001) at the subacute time point were positively correlated with days to recovery in athletes with sport-related concussion, while thymus and activation-regulated chemokine was (p = 0.001) positively correlated with days to recovery in athletes with musculoskeletal injury.
Sport-related concussion is associated with perturbations to systemic inflammatory chemokines that differ from those observed in athletes with a musculoskeletal injury. These results support inflammation as an important facet of secondary injury after sport-related concussion that can be measured systemically in a human model of injury.
Concussion is associated with significant adverse effects within the first week post-injury, including physical complaints and altered cognition, sleep and mood. It is currently unknown whether these ...subjective disturbances have reliable functional brain correlates. Resting-state functional magnetic resonance imaging (rs-fMRI) has been used to measure functional connectivity of individuals after traumatic brain injury, but less is known about the relationship between functional connectivity and symptom assessments after a sport concussion. In this study, rs-fMRI was used to evaluate whole-brain functional connectivity for seventy (70) university-level athletes, including 35 with acute concussion and 35 healthy matched controls. Univariate analyses showed that greater symptom severity was mainly associated with lower pairwise connectivity in frontal, temporal and insular regions, along with higher connectivity in a sparser set of cerebellar regions. A novel multivariate approach also extracted two components that showed reliable covariation with symptom severity: (1) a network of frontal, temporal and insular regions where connectivity was negatively correlated with symptom severity (replicating the univariate findings); and (2) a network with anti-correlated elements of the default-mode network and sensorimotor system, where connectivity was positively correlated with symptom severity. These findings support the presence of connectomic signatures of symptom complaints following a sport-related concussion, including both increased and decreased functional connectivity within distinct functional brain networks.
•Analyzed relationship between resting brain function and symptoms of concussion•Whole-brain analysis, using both univariate and multivariate methods•Symptoms associated with lower connectivity for frontal/temporal/insular network•Symptoms associated with higher connectivity for default-mode/sensorimotor network
Major Depressive Disorder (MDD) is characterized by rumination. Prior research suggests that resting-state brain activation reflects rumination when depressed individuals are not task engaged. ...However, no study has directly tested this. Here we investigated whether resting-state epochs differ from induced ruminative states for healthy and depressed individuals. Most previous research on resting-state networks comes from seed-based analyses with the posterior cingulate cortex (PCC). By contrast, we examined resting state connectivity by using the complete multivariate connectivity profile (i.e., connections across all brain nodes) and by comparing these results to seeded analyses. We find that unconstrained resting-state intervals differ from active rumination states in strength of connectivity and that overall connectivity was higher for healthy vs. depressed individuals. Relationships between connectivity and subjective mood (i.e., behavior) were strongly observed during induced rumination epochs. Furthermore, connectivity patterns that related to subjective mood were strikingly different for MDD and healthy control (HC) groups suggesting different mood regulation mechanisms.
•MDDs show heighted connectivity in the default network during induced rumination.•HCs show overall greater global connectivity compared to MDDs.•Greater coherence between mood and connectivity exists during induced rumination.•Mood changes relate to different networks for MDDs and HCs.•Resting-states do not identically reflect depressive rumination neurally or behaviorally.
The brain consists of specialized cortical regions that exchange information between each other, reflecting a combination of segregated (local) and integrated (distributed) processes that define ...brain function. Functional magnetic resonance imaging (fMRI) is widely used to characterize these functional relationships, although it is an ongoing challenge to develop robust, interpretable models for high-dimensional fMRI data. Gaussian mixture models (GMMs) are a powerful tool for parcellating the brain, based on the similarity of voxel time series. However, conventional GMMs have limited parametric flexibility: they only estimate segregated structure and do not model interregional functional connectivity, nor do they account for network variability across voxels or between subjects. To address these issues, this letter develops the functional segregation and integration model (FSIM). This extension of the GMM framework simultaneously estimates spatial clustering and the most consistent group functional connectivity structure. It also explicitly models network variability, based on voxel- and subject-specific network scaling profiles. We compared the FSIM to standard GMM in a predictive cross-validation framework and examined the importance of different model parameters, using both simulated and experimental resting-state data. The reliability of parcellations is not significantly altered by flexibility of the FSIM, whereas voxel- and subject-specific network scaling profiles significantly improve the ability to predict functional connectivity in independent test data. Moreover, the FSIM provides a set of interpretable parameters to characterize both consistent and variable aspects functional connectivity structure. As an example of its utility, we use subject-specific network profiles to identify brain regions where network expression predicts subject age in the experimental data. Thus, the FSIM is effective at summarizing functional connectivity structure in group-level fMRI, with applications in modeling the relationships between network variability and behavioral/demographic variables.
High-performance university athletes experience frequent exertion, resulting in disrupted biological homeostasis, but it is unclear to what extent brain physiology is affected. We examined whether ...athletes without overtraining symptoms show signs of increased neurophysiological stress over the course of a single athletic season, and whether the effects are modified by demographic factors of age, sex and concussion history, and sport-related factors of contact exposure and season length. Fifty-three university-level athletes were recruited from multiple sports at a single institution and followed longitudinally from beginning of season (BOS) to end of season (EOS) and 1 month afterwards, with a subset followed up at the subsequent beginning of season. MRI was used to comprehensively assess white matter (WM) diffusivity, cerebral blood flow (CBF), and brain activity, while overtraining symptoms were assessed with Hooper's Index (HI). Although athletes did not report increased HI scores, they showed significantly increased white matter diffusivity and decreased CBF at EOS and 1 month afterwards, with recovery at follow-up. Global brain activity was not significantly altered though, highlighting the ability of the brain to adapt to exercise-related stressors. Male athletes had greater white matter diffusivity at EOS, but female athletes had greater declines in CBF at 1 month afterwards. Post-season changes in MRI measures were not related to change in HI score, age, concussion history, contact exposure, or length of athletic season. Hence, the brain shows substantial but reversible neurophysiological changes due to season play in the absence of overtraining symptoms, with effects that are sex-dependent but otherwise insensitive to demographic variations. These findings provide new insights into the effects of training and competitive play on brain health.
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