Rayus Kuplicki

Recovery of Cerebral Blood Flow Following Sports-Related Concussion

IMPORTANCE Animal models suggest that reduced cerebral blood flow (CBF) is one of the most enduring physiological deficits following concussion. Despite this, longitudinal studies documenting serial changes in regional CBF following human concussion have yet to be performed. OBJECTIVE To longitudinally assess the recovery of CBF in a carefully selected sample of collegiate athletes and compare time course of CBF recovery with that of cognitive and behavioral symptoms. DESIGN, SETTING, AND PARTICIPANTS A cohort of collegiate football athletes (N = 44) participated in this mixed longitudinal and cross-sectional study at a private research institute specializing in neuroimaging between March 2012 and December 2013. Serial imaging occurred approximately 1 day, 1 week, and 1 month postconcussion for a subset of participants (n = 17). All athletes reported no premorbid mood disorders, anxiety disorders, substance abuse, or alcohol abuse. MAIN OUTCOMES AND MEASURES Arterial spin labeling magnetic resonance imaging was used to collect voxelwise relative CBF at each visit. Neuropsychiatric evaluations and a brief cognitive screen were also performed at all 3 points. Clinicians trained in sports medicine provided an independent measure of real-world concussion outcome (ie, number of days withheld from competition). RESULTS The results indicated both cognitive (simple reaction time) and neuropsychiatric symptoms at 1 day postinjury that resolved at either 1 week (cognitive; P < .005) or 1 month (neuropsychiatric; P < .005) postinjury. Imaging data suggested both cross-sectional (ie, healthy vs concussed athletes; P < .05) and longitudinal (1 day and 1 week vs 1 month postinjury; P < .001) evidence of CBF recovery in the right insular and superior temporal cortex. Importantly, CBF in the dorsal midinsular cortex was both decreased at 1 month postconcussion in slower-to-recover athletes (t11 = 3.45; P = .005) and was inversely related to the magnitude of initial psychiatric symptoms (Hamilton Depression Scale: r = -0.64, P = .02; Hamilton Anxiety Scale: r = -0.56, P = .046), suggesting a potential prognostic indication for CBF as a biomarker. CONCLUSIONS AND RELEVANCE To our knowledge, these results provide the first prospective evidence of reduced CBF in human concussion and subsequent recovery. The resolution of CBF abnormalities closely mirrors previous reports from the animal literature and show real-world validity for predicting outcome following concussion.

Relationship of Collegiate Football Experience and Concussion With Hippocampal Volume and Cognitive Outcomes

IMPORTANCE Concussion and subconcussive impacts have been associated with short-term disrupted cognitive performance in collegiate athletes, but there are limited data on their long-term neuroanatomic and cognitive consequences. OBJECTIVE To assess the relationships of concussion history and years of football experience with hippocampal volume and cognitive performance in collegiate football athletes. DESIGN, SETTING, AND PARTICIPANTS Cross-sectional study conducted between June 2011 and August 2013 at a US psychiatric research institute specializing in neuroimaging among collegiate football players with a history of clinician-diagnosed concussion (n = 25), collegiate football players without a history of concussion (n = 25), and non-football-playing, age-, sex-, and education-matched healthy controls (n = 25). EXPOSURES History of clinician-diagnosed concussion and years of football experience. MAIN OUTCOMES AND MEASURES High-resolution anatomical magnetic resonance imaging was used to quantify brain volumes. Baseline scores on a computerized concussion-related cognitive battery were used for cognitive assessment in athletes. RESULTS Players with and without a history of concussion had smaller hippocampal volumes relative to healthy control participants (with concussion: t48 = 7.58; P < .001; mean difference, 1788 μL; 95% CI, 1317-2258 μL; without concussion: t48 = 4.35; P < .001, mean difference, 1027 μL; 95% CI, 556-1498 μL). Players with a history of concussion had smaller hippocampal volumes than players without concussion (t48 = 3.15; P < .001; mean difference, 761 μL; 95% CI, 280-1242 μL). In both athlete groups, there was a statistically significant inverse relationship between left hippocampal volume and number of years of football played (t46 = -3.62; P < .001; coefficient = -43.54; 95% CI, -67.66 to -19.41). Behavioral testing demonstrated no differences between athletes with and without a concussion history on 5 cognitive measures but did show an inverse correlation between years of playing football and reaction time (ρ42 = -0.43; 95% CI, -0.46 to -0.40; P = .005). CONCLUSIONS AND RELEVANCE Among a group of collegiate football athletes, there was a significant inverse relationship of concussion and years of football played with hippocampal volume. Years of football experience also correlated with slower reaction time. Further research is needed to determine the temporal relationships of these findings.

Comparison of two different analysis approaches for DTI free-water corrected and uncorrected maps in the study of white matter microstructural integrity in individuals with depression

Diffusion tensor imaging (DTI) has often been used to examine white matter (WM) tract abnormalities in depressed subjects, but these studies have yielded inconsistent results, probably, due to gender composition or small sample size. In this study, we applied different analysis pipelines to a relatively large sample of individuals with depression to determine whether previous findings in depression can be replicated with these pipelines. We used a “standard” DTI algorithm and maps computed through a free‐water (FW) corrected DTI. This latter algorithm is able to identify and separate the effects of extracellular FW on DTI metrics. Additionally, skeletonized and WM voxel‐based analysis (VBA) methods were used. Using the skeletonized method, DTI maps showed lower fractional anisotropy (FA) in depressed subjects in the left brain hemisphere, including the anterior thalamic radiation (ATR L), cortical spinal tract (CST L), inferior fronto‐occipital fasciculus, inferior longitudinal fasciculus, and superior longitudinal fasciculus (SLF L). Differences in radial diffusivity (RD) were also found. For the VBA using RD, we found different results when we used FW uncorrected and corrected DTI metrics. Relative to the VBA approach, the skeletonized analysis was able to identify more clusters where WM integrity was altered in depressed individuals. Different significant correlations were found between RD and the Patient Health Questionnaire in the CST L, and SLF L. In conclusion, the skeletonized method revealed more clusters than the VBA and individuals with depression showed multiple WM abnormalities, some of which were correlated with disease severity Hum Brain Mapp 38:4690–4702, 2017.

Screen media activity and brain structure in youth: Evidence for diverse structural correlation networks from the ABCD study

&NA; The adolescent brain undergoes profound structural changes which is influenced by many factors. Screen media activity (SMA; e.g., watching television or videos, playing video games, or using social media) is a common recreational activity in children and adolescents; however, its effect on brain structure is not well understood. A multivariate approach with the first cross‐sectional data release from the Adolescent Brain Cognitive Development (ABCD) study was used to test the maturational coupling hypothesis, i.e. the notion that coordinated patterns of structural change related to specific behaviors. Moreover, the utility of this approach was tested by determining the association between these structural correlation networks and psychopathology or cognition. ABCD participants with usable structural imaging and SMA data (N = 4277 of 4524) were subjected to a Group Factor Analysis (GFA) to identify latent variables that relate SMA to cortical thickness, sulcal depth, and gray matter volume. Subject scores from these latent variables were used in generalized linear mixed‐effect models to investigate associations between SMA and internalizing and externalizing psychopathology, as well as fluid and crystalized intelligence. Four SMA‐related GFAs explained 37% of the variance between SMA and structural brain indices. SMA‐related GFAs correlated with brain areas that support homologous functions. Some but not all SMA‐related factors corresponded with higher externalizing (Cohens d effect size (ES) 0.06–0.1) but not internalizing psychopathology and lower crystalized (ES: 0.08–0.1) and fluid intelligence (ES: 0.04–0.09). Taken together, these findings support the notion of SMA related maturational coupling or structural correlation networks in the brain and provides evidence that individual differences of these networks have mixed consequences for psychopathology and cognitive performance. HighlightsScreen media activity is a common recreational activity in children and adolescents.The manuscript focuses on how screen media activity is related to structural brain characteristics.Structural correlation networks were identified supporting the maturational coupling hypothesis.Some networks were associated with for externalizing psychopathology, fluid and crystallized intelligence.

Latent variable analysis of negative affect and its contributions to neural responses during shock anticipation

Negative affect is considered an important factor in the etiology of depression and anxiety, and is highly related to pain. However, negative affect is not a unitary construct. To identify specific targets for treatment development, we aimed to derive latent variables of negative affect and test their unique contributions to affective processing during anticipation of unpredictable, painful shock. Eighty-three subjects (43 with depression and anxiety spectrum disorders and 40 healthy controls) completed self-report measures of negative valence and underwent neuroimaging while exploring computer-simulated contexts with and without the threat of a painful, but tolerable, shock. Principal component analysis (PCA) extracted distinct components of general negative affect (GNA) and pain-related negative affect (PNA). While elevated GNA and PNA were both indicative of depression and anxiety disorders, greater PNA was more strongly related to task-specific anxious reactivity during shock anticipation. GNA was associated with increased precuneus and middle frontal gyrus activity, whereas PNA was related to increased bilateral anterior insula activity. Anterior insula activity mediated the relationship between PNA and task-specific anxious reactivity. In conclusion, GNA and PNA have distinct neural signatures and uniquely contribute to anxious anticipation. PNA, via insula activity, may relate to arousal in ways that could contribute to affective dysregulation, and thus may be an important treatment target.

A Nonlinear Simulation Framework Supports Adjusting for Age When Analyzing BrainAGE

Several imaging modalities, including T1-weighted structural imaging, diffusion tensor imaging, and functional MRI can show chronological age related changes. Employing machine learning algorithms, an individuals imaging data can predict their age with reasonable accuracy. While details vary according to modality, the general strategy is to: (1) extract image-related features, (2) build a model on a training set that uses those features to predict an individuals age, (3) validate the model on a test dataset, producing a predicted age for each individual, (4) define the “Brain Age Gap Estimate” (BrainAGE) as the difference between an individuals predicted age and his/her chronological age, (5) estimate the relationship between BrainAGE and other variables of interest, and (6) make inferences about those variables and accelerated or delayed brain aging. For example, a group of individuals with overall positive BrainAGE may show signs of accelerated aging in other variables as well. There is inevitably an overestimation of the age of younger individuals and an underestimation of the age of older individuals due to “regression to the mean.” The correlation between chronological age and BrainAGE may significantly impact the relationship between BrainAGE and other variables of interest when they are also related to age. In this study, we examine the detectability of variable effects under different assumptions. We use empirical results from two separate datasets [training = 475 healthy volunteers, aged 18–60 years (259 female); testing = 489 participants including people with mood/anxiety, substance use, eating disorders and healthy controls, aged 18–56 years (312 female)] to inform simulation parameter selection. Outcomes in simulated and empirical data strongly support the proposal that models incorporating BrainAGE should include chronological age as a covariate. We propose either including age as a covariate in step 5 of the above framework, or employing a multistep procedure where age is regressed on BrainAGE prior to step 5, producing BrainAGE Residualized (BrainAGER) scores.

Predicting age from brain EEG signals – a machine learning approach

Objective: The brain age gap estimate (BrainAGE) is the difference between the estimated age and the individual chronological age. BrainAGE was studied primarily using MRI techniques. EEG signals in combination with machine learning (ML) approaches were not commonly used for the human age prediction, and BrainAGE. We investigated whether age-related changes are affecting brain EEG signals, and whether we can predict the chronological age and obtain BrainAGE estimates using a rigorous ML framework with a novel and extensive EEG features extraction. Methods: EEG data were obtained from 468 healthy, mood/anxiety, eating and substance use disorder participants (297 females) from the Tulsa-1000, a naturalistic longitudinal study based on Research Domain Criteria framework. Five sets of preprocessed EEG features across channels and frequency bands were used with different ML methods to predict age. Using a nested-cross-validation (NCV) approach and stack-ensemble learning from EEG features, the predicted age was estimated. The important features and their spatial distributions were deduced. Results: The stack-ensemble age prediction model achieved R2 = 0.37 (0.06), Mean Absolute Error (MAE) = 6.87(0.69) and RMSE = 8.46(0.59) in years. The age and predicted age correlation was r = 0.6. The feature importance revealed that age predictors are spread out across different feature types. The NCV approach produced a reliable age estimation, with features consistent behavior across different folds. Conclusion: Our rigorous ML framework and extensive EEG signal features allow a reliable estimation of chronological age, and BrainAGE. This general framework can be extended to test EEG association with and to predict/study other physiological relevant responses.

Recruitment of orbitofrontal cortex during unpredictable threat among adults at risk for affective disorders

Mood and anxiety disorders are characterized by altered prefrontal‐amygdala function and increased behavioral inhibition (BI) in response to potential threat. Whether these alterations constitute a vulnerability or a symptom of illness remains unclear. The medial orbitofrontal cortex (mOFC) is thought to play a central role in estimating probability and cost of threat, in turn informing selection of subsequent behaviors. To better understand the behavioral and neural processes that may be associated with risk for psychopathology, we used a virtual reality paradigm to examine behavioral and neural responses of psychiatrically healthy adults with familial history of affective disorders during anticipation of unpredictable threat.

GridSPiM: A Framework for Simple Locality and Containment in the Stochastic π-Calculus

Process calculi hold great promise for modeling and analysis of cellular mechanics and behavior. While measured success has been achieved in their simulation of specific biochemical pathways and molecular mechanisms within the cell, several obstacles remain to their widespread adoption and use. Chiefly, these have to with the difficulty of modeling cell membranes and localized behavior, and limitations on the scalability of the execution model. This paper describes a multi-layered formalism --- GridSPiM --- that engages notions of concurrency, locality and encapsulation to provide a framework suitable for capturing the key aspects of cellular processes.