Eye movement performance and clinical outcomes among female athletes post-concussion.

BACKGROUND: Most post-concussion eye movement (EM) research involves predominantly male samples. We evaluated pro- (PRO; reflexive shift of visual attention to target) and anti- (ANTI; executive control of visual attention away from target) computer-based saccade task performance among female, collegiate athletes with recent concussion (CON) versus healthy-control athletes (HC). We evaluated the relationship between EM performance and post-concussion outcomes. We hypothesized ANTI performance would differ among CON and HC due to greater executive control demands, and that EM performance (both tasks) would be associated with clinical outcomes in CON. METHODS: 16 CON (assessed 4-10 days post-injury [M = 6.87, SD = 2.15 days]) and 16 age-matched HC athletes were recruited. General linear mixed modeling and Pearson's correlations were used. RESULTS: On ANTI, CON demonstrated higher error rate [F(1,2863) = 12.650, p<.001] and shorter latency on error trials [F(1,469) = 5.976, p=.015] relative to HC. Multiple EM measures were associated with clinical outcomes: PRO duration predicted days to symptom remission (r=.44, p <.05); ANTI error rate was associated with symptom burden on the day of testing (r=.27, p <.05). CONCLUSION: This study demonstrates promising utility of EM measures to detect cognitive control and sensorimotor effects of concussion among female athletes and their use as a prognostic indicators of recovery.

A leptin-mediated central mechanism in analgesia-enhanced opioid reward in rats.

Opioid analgesics are commonly used in chronic pain management despite a potential risk of rewarding. However, it remains unclear whether opioid analgesia would enhance the opioid rewarding effect thereby contributing to opioid rewarding. Utilizing a rat paradigm of conditioned place preference (CPP) combined with ankle monoarthritis as a condition of persistent nociception, we showed that analgesia induced by either morphine or the nonsteroid anti-inflammatory drug ibuprofen increased CPP scores in arthritic rats, suggesting that analgesia itself had a rewarding effect. However, arthritic rats exhibited a significantly higher CPP score in response to morphine than ibuprofen. Thus, the rewarding effect of morphine was enhanced in the presence of persistent nociception, producing a phenomenon of analgesia-enhanced opioid reward. At the cellular level, administration of morphine activated a cascade of leptin expression, glial activation, and dopamine receptor upregulation in the nucleus accumbens (NAc), while administration of ibuprofen decreased glial activation with no effect on leptin expression in the NAc. Furthermore, the morphine rewarding effect was blocked in leptin deficient ob/ob mice or by neutralizing leptin or interleukin-1ß in the NAc without diminishing morphine analgesia. The data indicate that systemic opioid can activate a leptin-mediated central mechanism in the NAc that led to the enhanced opioid rewarding effect. These findings provide evidence for an interaction between opioid analgesia and opioid rewarding, which may have implications in clinical opioid dose escalation in chronic pain management.

Cannabis use is quantitatively associated with nucleus accumbens and amygdala abnormalities in young adult recreational users.

Marijuana is the most commonly used illicit drug in the United States, but little is known about its effects on the human brain, particularly on reward/aversion regions implicated in addiction, such as the nucleus accumbens and amygdala. Animal studies show structural changes in brain regions such as the nucleus accumbens after exposure to Δ9-tetrahydrocannabinol, but less is known about cannabis use and brain morphometry in these regions in humans. We collected high-resolution MRI scans on young adult recreational marijuana users and nonusing controls and conducted three independent analyses of morphometry in these structures: (1) gray matter density using voxel-based morphometry, (2) volume (total brain and regional volumes), and (3) shape (surface morphometry). Gray matter density analyses revealed greater gray matter density in marijuana users than in control participants in the left nucleus accumbens extending to subcallosal cortex, hypothalamus, sublenticular extended amygdala, and left amygdala, even after controlling for age, sex, alcohol use, and cigarette smoking. Trend-level effects were observed for a volume increase in the left nucleus accumbens only. Significant shape differences were detected in the left nucleus accumbens and right amygdala. The left nucleus accumbens showed salient exposure-dependent alterations across all three measures and an altered multimodal relationship across measures in the marijuana group. These data suggest that marijuana exposure, even in young recreational users, is associated with exposure-dependent alterations of the neural matrix of core reward structures and is consistent with animal studies of changes in dendritic arborization.

Cannabis-related episodic memory deficits and hippocampal morphological differences in healthy individuals and schizophrenia subjects.

Cannabis use has been associated with episodic memory (EM) impairments and abnormal hippocampus morphology among both healthy individuals and schizophrenia subjects. Considering the hippocampus' role in EM, research is needed to evaluate the relationship between cannabis-related hippocampal morphology and EM among healthy and clinical groups. We examined differences in hippocampus morphology between control and schizophrenia subjects with and without a past (not current) cannabis use disorder (CUD). Subjects group-matched on demographics included 44 healthy controls (CON), 10 subjects with a CUD history (CON-CUD), 28 schizophrenia subjects with no history of substance use disorders (SCZ), and 15 schizophrenia subjects with a CUD history (SCZ-CUD). Large-deformation, high-dimensional brain mapping with MRI produced surface-based representations of the hippocampus that were compared across all four groups and correlated with EM and CUD history. Surface maps of the hippocampus were generated to visualize morphological differences. CON-CUD and SCZ-CUD were characterized by distinct cannabis-related hippocampal shape differences and parametric deficits in EM performance. Shape differences observed in CON-CUD were associated with poorer EM performance, while shape differences observed in SCZ-CUD were associated with a longer duration of CUD and shorter duration of CUD remission. A past history of CUD may be associated with notable differences in hippocampal morphology and EM impairments among adults with and without schizophrenia. Although the results may be compatible with a causal hypothesis, we must consider that the observed cannabis-related shape differences in the hippocampus could also be explained as biomarkers of a neurobiological susceptibility to poor memory or the effects of cannabis.

Characterizing major depressive disorder and substance use disorder using heatmaps and variable interactions: The utility of operant behavior and brain structure relationships.

BACKGROUND: Rates of depression and addiction have risen drastically over the past decade, but the lack of integrative techniques remains a barrier to accurate diagnoses of these mental illnesses. Changes in reward/aversion behavior and corresponding brain structures have been identified in those with major depressive disorder (MDD) and cocaine-dependence polysubstance abuse disorder (CD). Assessment of statistical interactions between computational behavior and brain structure may quantitatively segregate MDD and CD. METHODS: Here, 111 participants [40 controls (CTRL), 25 MDD, 46 CD] underwent structural brain MRI and completed an operant keypress task to produce computational judgment metrics. Three analyses were performed: (1) linear regression to evaluate groupwise (CTRL v. MDD v. CD) differences in structure-behavior associations, (2) qualitative and quantitative heatmap assessment of structure-behavior association patterns, and (3) the k-nearest neighbor machine learning approach using brain structure and keypress variable inputs to discriminate groups. RESULTS: This study yielded three primary findings. First, CTRL, MDD, and CD participants had distinct structure-behavior linear relationships, with only 7.8% of associations overlapping between any two groups. Second, the three groups had statistically distinct slopes and qualitatively distinct association patterns. Third, a machine learning approach could discriminate between CTRL and CD, but not MDD participants. CONCLUSIONS: These findings demonstrate that variable interactions between computational behavior and brain structure, and the patterns of these interactions, segregate MDD and CD. This work raises the hypothesis that analysis of interactions between operant tasks and structural neuroimaging might aide in the objective classification of MDD, CD and other mental health conditions.

Predicting COVID-19 Vaccination Uptake Using a Small and Interpretable Set of Judgment and Demographic Variables: Cross-Sectional Cognitive Science Study.

BACKGROUND: Despite COVID-19 vaccine mandates, many chose to forgo vaccination, raising questions about the psychology underlying how judgment affects these choices. Research shows that reward and aversion judgments are important for vaccination choice; however, no studies have integrated such cognitive science with machine learning to predict COVID-19 vaccine uptake. OBJECTIVE: This study aims to determine the predictive power of a small but interpretable set of judgment variables using 3 machine learning algorithms to predict COVID-19 vaccine uptake and interpret what profile of judgment variables was important for prediction. METHODS: We surveyed 3476 adults across the United States in December 2021. Participants answered demographic, COVID-19 vaccine uptake (ie, whether participants were fully vaccinated), and COVID-19 precaution questions. Participants also completed a picture-rating task using images from the International Affective Picture System. Images were rated on a Likert-type scale to calibrate the degree of liking and disliking. Ratings were computationally modeled using relative preference theory to produce a set of graphs for each participant (minimum R2>0.8). In total, 15 judgment features were extracted from these graphs, 2 being analogous to risk and loss aversion from behavioral economics. These judgment variables, along with demographics, were compared between those who were fully vaccinated and those who were not. In total, 3 machine learning approaches (random forest, balanced random forest [BRF], and logistic regression) were used to test how well judgment, demographic, and COVID-19 precaution variables predicted vaccine uptake. Mediation and moderation were implemented to assess statistical mechanisms underlying successful prediction. RESULTS: Age, income, marital status, employment status, ethnicity, educational level, and sex differed by vaccine uptake (Wilcoxon rank sum and chi-square P<.001). Most judgment variables also differed by vaccine uptake (Wilcoxon rank sum P<.05). A similar area under the receiver operating characteristic curve (AUROC) was achieved by the 3 machine learning frameworks, although random forest and logistic regression produced specificities between 30% and 38% (vs 74.2% for BRF), indicating a lower performance in predicting unvaccinated participants. BRF achieved high precision (87.8%) and AUROC (79%) with moderate to high accuracy (70.8%) and balanced recall (69.6%) and specificity (74.2%). It should be noted that, for BRF, the negative predictive value was <50% despite good specificity. For BRF and random forest, 63% to 75% of the feature importance came from the 15 judgment variables. Furthermore, age, income, and educational level mediated relationships between judgment variables and vaccine uptake. CONCLUSIONS: The findings demonstrate the underlying importance of judgment variables for vaccine choice and uptake, suggesting that vaccine education and messaging might target varying judgment profiles to improve uptake. These methods could also be used to aid vaccine rollouts and health care preparedness by providing location-specific details (eg, identifying areas that may experience low vaccination and high hospitalization).

A novel approach to anxiety level prediction using small sets of judgment and survey variables.

Anxiety, a condition characterized by intense fear and persistent worry, affects millions each year and, when severe, is distressing and functionally impairing. Numerous machine learning frameworks have been developed and tested to predict features of anxiety and anxiety traits. This study extended these approaches by using a small set of interpretable judgment variables (n = 15) and contextual variables (demographics, perceived loneliness, COVID-19 history) to (1) understand the relationships between these variables and (2) develop a framework to predict anxiety levels [derived from the State Trait Anxiety Inventory (STAI)]. This set of 15 judgment variables, including loss aversion and risk aversion, models biases in reward/aversion judgments extracted from an unsupervised, short (2-3 min) picture rating task (using the International Affective Picture System) that can be completed on a smartphone. The study cohort consisted of 3476 de-identified adult participants from across the United States who were recruited using an email survey database. Using a balanced Random Forest approach with these judgment and contextual variables, STAI-derived anxiety levels were predicted with up to 81% accuracy and 0.71 AUC ROC. Normalized Gini scores showed that the most important predictors (age, loneliness, household income, employment status) contributed a total of 29-31% of the cumulative relative importance and up to 61% was contributed by judgment variables. Mediation/moderation statistics revealed that the interactions between judgment and contextual variables appears to be important for accurately predicting anxiety levels. Median shifts in judgment variables described a behavioral profile for individuals with higher anxiety levels that was characterized by less resilience, more avoidance, and more indifference behavior. This study supports the hypothesis that distinct constellations of 15 interpretable judgment variables, along with contextual variables, could yield an efficient and highly scalable system for mental health assessment. These results contribute to our understanding of underlying psychological processes that are necessary to characterize what causes variance in anxiety conditions and its behaviors, which can impact treatment development and efficacy.

Vulnerable consumers: marketing research needs to pay more attention to the brain health of consumers.

We propose here that marketing research should increase consideration of the brain health of consumers, and argue that it would help both extend our current knowledge of vulnerable and other marginalised groups, as well as extend generalizability and external validity of marketing research in general. We show that such a focus would help enrich methodology, especially around causal inference, as well as impact on our understanding of a number of key emerging themes in marketing research. We particularly focus on the consumer behaviour around digitalisation, as well as compulsive buying behaviour. Further, we show that increasing consideration of consumer brain health will further efforts towards inclusivity of marketing, and help continue progress towards marketing research as a force for good.

The Relationship Between a History of High-risk and Destructive Behaviors and COVID-19 Infection: Preliminary Study.

BACKGROUND: The COVID-19 pandemic has heightened mental health concerns, but the temporal relationship between mental health conditions and SARS-CoV-2 infection has not yet been investigated. Specifically, psychological issues, violent behaviors, and substance use were reported more during the COVID-19 pandemic than before the pandemic. However, it is unknown whether a prepandemic history of these conditions increases an individual's susceptibility to SARS-CoV-2. OBJECTIVE: This study aimed to better understand the psychological risks underlying COVID-19, as it is important to investigate how destructive and risky behaviors may increase a person's susceptibility to COVID-19. METHODS: In this study, we analyzed data from a survey of 366 adults across the United States (aged 18 to 70 years); this survey was administered between February and March of 2021. The participants were asked to complete the Global Appraisal of Individual Needs-Short Screener (GAIN-SS) questionnaire, which indicates an individual's history of high-risk and destructive behaviors and likelihood of meeting diagnostic criteria. The GAIN-SS includes 7 questions related to externalizing behaviors, 8 related to substance use, and 5 related to crime and violence; responses were given on a temporal scale. The participants were also asked whether they ever tested positive for COVID-19 and whether they ever received a clinical diagnosis of COVID-19. GAIN-SS responses were compared between those who reported and those who did not report COVID-19 to determine if those who reported COVID-19 also reported GAIN-SS behaviors (Wilcoxon rank sum test, α=.05). In total, 3 hypotheses surrounding the temporal relationships between the recency of GAIN-SS behaviors and COVID-19 infection were tested using proportion tests (α=.05). GAIN-SS behaviors that significantly differed (proportion tests, α=.05) between COVID-19 responses were included as independent variables in multivariable logistic regression models with iterative downsampling. This was performed to assess how well a history of GAIN-SS behaviors statistically discriminated between those who reported and those who did not report COVID-19. RESULTS: Those who reported COVID-19 more frequently indicated past GAIN-SS behaviors (Q<0.05). Furthermore, the proportion of those who reported COVID-19 was higher (Q<0.05) among those who reported a history of GAIN-SS behaviors; specifically, gambling and selling drugs were common across the 3 proportion tests. Multivariable logistic regression revealed that GAIN-SS behaviors, particularly gambling, selling drugs, and attention problems, accurately modeled self-reported COVID-19, with model accuracies ranging from 77.42% to 99.55%. That is, those who exhibited destructive and high-risk behaviors before and during the pandemic could be discriminated from those who did not exhibit these behaviors when modeling self-reported COVID-19. CONCLUSIONS: This preliminary study provides insights into how a history of destructive and risky behaviors influences infection susceptibility, offering possible explanations for why some persons may be more susceptible to COVID-19, potentially in relation to reduced adherence to prevention guidelines or not seeking vaccination.

In humans, striato-pallido-thalamic projections are largely segregated by their origin in either the striosome-like or matrix-like compartments.

Cortico-striato-thalamo-cortical (CSTC) loops are fundamental organizing units in mammalian brains. CSTCs process limbic, associative, and sensorimotor information in largely separated but interacting networks. CTSC loops pass through paired striatal compartments, striosome (aka patch) and matrix, segregated pools of medium spiny projection neurons with distinct embryologic origins, cortical/subcortical structural connectivity, susceptibility to injury, and roles in behaviors and diseases. Similarly, striatal dopamine modulates activity in striosome and matrix in opposite directions. Routing CSTCs through one compartment may be an anatomical basis for regulating discrete functions. We used differential structural connectivity, identified through probabilistic diffusion tractography, to distinguish the striatal compartments (striosome-like and matrix-like voxels) in living humans. We then mapped compartment-specific projections and quantified structural connectivity between each striatal compartment, the globus pallidus interna (GPi), and 20 thalamic nuclei in 221 healthy adults. We found that striosome-originating and matrix-originating streamlines were segregated within the GPi: striosome-like connectivity was significantly more rostral, ventral, and medial. Striato-pallido-thalamic streamline bundles that were seeded from striosome-like and matrix-like voxels transited spatially distinct portions of the white matter. Matrix-like streamlines were 5.7-fold more likely to reach the GPi, replicating animal tract-tracing studies. Striosome-like connectivity dominated in six thalamic nuclei (anteroventral, central lateral, laterodorsal, lateral posterior, mediodorsal-medial, and medial geniculate). Matrix-like connectivity dominated in seven thalamic nuclei (centromedian, parafascicular, pulvinar-anterior, pulvinar-lateral, ventral lateral-anterior, ventral lateral-posterior, ventral posterolateral). Though we mapped all thalamic nuclei independently, functionally-related nuclei were matched for compartment-level bias. We validated these results with prior thalamostriate tract tracing studies in non-human primates and other species; where reliable data was available, all agreed with our measures of structural connectivity. Matrix-like connectivity was lateralized (left > right hemisphere) in 18 thalamic nuclei, independent of handedness, diffusion protocol, sex, or whether the nucleus was striosome-dominated or matrix-dominated. Compartment-specific biases in striato-pallido-thalamic structural connectivity suggest that routing CSTC loops through striosome-like or matrix-like voxels is a fundamental mechanism for organizing and regulating brain networks. Our MRI-based assessments of striato-thalamic connectivity in humans match and extend the results of prior tract tracing studies in animals. Compartment-level characterization may improve localization of human neuropathologies and improve neurosurgical targeting in the GPi and thalamus.

Sustained activation in basal ganglia and cerebellum after repetitive movement in a non-task-specific dystonia.

We previously observed sustained fMRI BOLD signal in the basal ganglia in focal hand dystonia patients after a repetitive finger tapping task. Since this was observed in a task-specific dystonia, for which excessive task repetition may play a role in pathogenesis, in the current study we asked if this effect would be observed in a focal dystonia (cervical dystonia [CD]) that is not considered task-specific or thought to result from overuse. We evaluated fMRI BOLD signal time courses before, during, and after the finger tapping task in CD patients. We observed patient/control differences in post-tapping BOLD signal in left putamen and left cerebellum during the non-dominant (left) hand tapping condition, reflecting abnormally sustained BOLD signal in CD. BOLD signals in left putamen and cerebellum were also abnormally elevated in CD during tapping itself and escalated as tapping was repeated. There were no cerebellar differences in the previously studied FHD cohort, either during or after tapping. We conclude that some elements of pathogenesis and/or pathophysiology associated with motor task execution/repetition may not be limited to task-specific dystonias, but there may be regional differences in these effects across dystonias, associated with different types of motor control programs.

Anxiety, Post-COVID-19 Syndrome-Related Depression, and Suicidal Thoughts and Behaviors in COVID-19 Survivors: Cross-sectional Study.

BACKGROUND: Although the mental health impacts of COVID-19 on the general population have been well studied, studies of the long-term impacts of COVID-19 on infected individuals are relatively new. To date, depression, anxiety, and neurological symptoms associated with post-COVID-19 syndrome (PCS) have been observed in the months following COVID-19 recovery. Suicidal thoughts and behavior (STB) have also been preliminarily proposed as sequelae of COVID-19. OBJECTIVE: We asked 3 questions. First, do participants reporting a history of COVID-19 diagnosis or a close relative having severe COVID-19 symptoms score higher on depression (Patient Health Questionnaire-9 [PHQ-9]) or state anxiety (State Trait Anxiety Index) screens than those who do not? Second, do participants reporting a COVID-19 diagnosis score higher on PCS-related PHQ-9 items? Third, do participants reporting a COVID-19 diagnosis or a close relative having severe COVID-19 symptoms score higher in STB before, during, or after the first year of the pandemic? METHODS: This preliminary study analyzed responses to a COVID-19 and mental health questionnaire obtained from a US population sample, whose data were collected between February 2021 and March 2021. We used the Mann-Whitney U test to detect differences in the medians of the total PHQ-9 scores, PHQ-9 component scores, and several STB scores between participants claiming a past clinician diagnosis of COVID-19 and those denying one, as well as between participants claiming severe COVID-19 symptoms in a close relative and those denying them. Where significant differences existed, we created linear regression models to predict the scores based on COVID-19 response as well as demographics to identify potential confounding factors in the Mann-Whitney relationships. Moreover, for STB scores, which corresponded to 5 questions asking about 3 different time intervals (i.e., past 1 year or more, past 1 month to 1 year, and past 1 month), we developed repeated-measures ANOVAs to determine whether scores tended to vary over time. RESULTS: We found greater total depression (PHQ-9) and state anxiety (State Trait Anxiety Index) scores in those with COVID-19 history than those without (Bonferroni P=.001 and Bonferroni P=.004) despite a similar history of diagnosed depression and anxiety. Greater scores were noted for a subset of depression symptoms (PHQ-9 items) that overlapped with the symptoms of PCS (all Bonferroni Ps<.05). Moreover, we found greater overall STB scores in those with COVID-19 history, equally in time windows preceding, during, and proceeding infection (all Bonferroni Ps<.05). CONCLUSIONS: We confirm previous studies linking depression and anxiety diagnoses to COVID-19 recovery. Moreover, our findings suggest that depression diagnoses associated with COVID-19 history relate to PCS symptoms, and that STB associated with COVID-19 in some cases precede infection.

Metabolomic response to collegiate football participation: Pre- and Post-season analysis.

Contact sports participation has been shown to have both beneficial and detrimental effects on health, however little is known about the metabolic sequelae of these effects. We aimed to identify metabolite alterations across a collegiate American football season. Serum was collected from 23 male collegiate football athletes before the athletic season (Pre) and after the last game (Post). Samples underwent nontargeted metabolomic profiling and 1131 metabolites were included for univariate, pathway enrichment, and multivariate analyses. Significant metabolites were assessed against head acceleration events (HAEs). 200 metabolites changed from Pre to Post (P < 0.05 and Q < 0.05); 160 had known identity and mapped to one of 57 pre-defined biological pathways. There was significant enrichment of metabolites belonging to five pathways (P < 0.05): xanthine, fatty acid (acyl choline), medium chain fatty acid, primary bile acid, and glycolysis, gluconeogenesis, and pyruvate metabolism. A set of 12 metabolites was sufficient to discriminate Pre from Post status, and changes in 64 of the 200 metabolites were also associated with HAEs (P < 0.05). In summary, the identified metabolites, and candidate pathways, argue there are metabolic consequences of both physical training and head impacts with football participation. These findings additionally identify a potential set of objective biomarkers of repetitive head injury.

A multicohort geometric deep learning study of age dependent cortical and subcortical morphologic interactions for fluid intelligence prediction.

The relationship of human brain structure to cognitive function is complex, and how this relationship differs between childhood and adulthood is poorly understood. One strong hypothesis suggests the cognitive function of Fluid Intelligence (Gf) is dependent on prefrontal cortex and parietal cortex. In this work, we developed a novel graph convolutional neural networks (gCNNs) for the analysis of localized anatomic shape and prediction of Gf. Morphologic information of the cortical ribbons and subcortical structures was extracted from T1-weighted MRIs within two independent cohorts, the Adolescent Brain Cognitive Development Study (ABCD; age: 9.93 ± 0.62 years) of children and the Human Connectome Project (HCP; age: 28.81 ± 3.70 years). Prediction combining cortical and subcortical surfaces together yielded the highest accuracy of Gf for both ABCD (R = 0.314) and HCP datasets (R = 0.454), outperforming the state-of-the-art prediction of Gf from any other brain measures in the literature. Across both datasets, the morphology of the amygdala, hippocampus, and nucleus accumbens, along with temporal, parietal and cingulate cortex consistently drove the prediction of Gf, suggesting a significant reframing of the relationship between brain morphology and Gf to include systems involved with reward/aversion processing, judgment and decision-making, motivation, and emotion.

The Prevalence of Psychotic Symptoms, Violent Ideation, and Disruptive Behavior in a Population With SARS-CoV-2 Infection: Preliminary Study.

BACKGROUND: The COVID-19 disease results from infection by the SARS-CoV-2 virus to produce a range of mild to severe physical, neurological, and mental health symptoms. The COVID-19 pandemic has indirectly caused significant emotional distress, triggering the emergence of mental health symptoms in individuals who were not previously affected or exacerbating symptoms in those with existing mental health conditions. Emotional distress and certain mental health conditions can lead to violent ideation and disruptive behavior, including aggression, threatening acts, deliberate harm toward other people or animals, and inattention to or noncompliance with education or workplace rules. Of the many mental health conditions that can be associated with violent ideation and disruptive behavior, psychosis can evidence greater vulnerability to unpredictable changes and being at a greater risk for them. Individuals with psychosis can also be more susceptible to contracting COVID-19 disease. OBJECTIVE: This study aimed to investigate whether violent ideation, disruptive behavior, or psychotic symptoms were more prevalent in a population with COVID-19 and did not precede the pandemic. METHODS: In this preliminary study, we analyzed questionnaire responses from a population sample (N=366), received between the end of February 2021 and the start of March 2021 (1 year into the COVID-19 pandemic), regarding COVID-19 illness, violent ideation, disruptive behavior, and psychotic symptoms. Using the Wilcoxon rank sum test followed by multiple comparisons correction, we compared the self-reported frequency of these variables for 3 time windows related to the past 1 month, past 1 month to 1 year, and >1 year ago among the distributions of people who answered whether they tested positive or were diagnosed with COVID-19 by a clinician. We also used multivariable logistic regression with iterative resampling to investigate the relationship between these variables occurring >1 year ago (ie, before the pandemic) and the likelihood of contracting COVID-19. RESULTS: We observed a significantly higher frequency of self-reported violent ideation, disruptive behavior, and psychotic symptoms, for all 3 time windows of people who tested positive or were diagnosed with COVID-19 by a clinician. Using multivariable logistic regression, we observed 72% to 94% model accuracy for an increased incidence of COVID-19 in participants who reported violent ideation, disruptive behavior, or psychotic symptoms >1 year ago. CONCLUSIONS: This preliminary study found that people who reported a test or clinician diagnosis of COVID-19 also reported higher frequencies of violent ideation, disruptive behavior, or psychotic symptoms across multiple time windows, indicating that they were not likely to be the result of COVID-19. In parallel, participants who reported these behaviors >1 year ago (ie, before the pandemic) were more likely to be diagnosed with COVID-19, suggesting that violent ideation, disruptive behavior, in addition to psychotic symptoms, were associated with COVID-19 with an approximately 70% to 90% likelihood.

A preliminary model of football-related neural stress that integrates metabolomics with transcriptomics and virtual reality.

Research suggests contact sports affect neurological health. This study used permutation-based mediation statistics to integrate measures of metabolomics, neuroinflammatory miRNAs, and virtual reality (VR)-based motor control to investigate multi-scale relationships across a season of collegiate American football. Fourteen significant mediations (six pre-season, eight across-season) were observed where metabolites always mediated the statistical relationship between miRNAs and VR-based motor control ( p S o b e l p e r m ≤ 0.05; total effect > 50%), suggesting a hypothesis that metabolites sit in the statistical pathway between transcriptome and behavior. Three results further supported a model of chronic neuroinflammation, consistent with mitochondrial dysfunction: (1) Mediating metabolites were consistently medium-to-long chain fatty acids, (2) tricarboxylic acid cycle metabolites decreased across-season, and (3) accumulated head acceleration events statistically moderated pre-season metabolite levels to directionally model post-season metabolite levels. These preliminary findings implicate potential mitochondrial dysfunction and highlight probable peripheral blood biomarkers underlying repetitive head impacts in otherwise healthy collegiate football athletes.

Preliminary Report: Localized Cerebral Blood Flow Mediates the Relationship between Progesterone and Perceived Stress Symptoms among Female Collegiate Club Athletes after Mild Traumatic Brain Injury.

Female athletes are under-studied in the field of concussion research, despite evidence of higher injury prevalence and longer recovery time. Hormonal fluctuations caused by the natural menstrual cycle (MC) or hormonal contraceptive (HC) use impact both post-injury symptoms and neuroimaging findings, but the relationships among hormone, symptoms, and brain-based measures have not been jointly considered in concussion studies. In this preliminary study, we compared cerebral blood flow (CBF) measured with arterial spin labeling between concussed female club athletes 3-10 days after mild traumatic brain injury (mTBI) and demographic, HC/MC matched controls (CON). We tested whether CBF statistically mediates the relationship between progesterone serum levels and post-injury symptoms, which may support a hypothesis for progesterone's role in neuroprotection. We found a significant three-way relationship among progesterone, CBF, and perceived stress score (PSS) in the left middle temporal gyrus for the mTBI group. Higher progesterone was associated with lower (more normative) PSS, as well as higher (more normative) CBF. CBF mediates 100% of the relationship between progesterone and PSS (Sobel p value = 0.017). These findings support a hypothesis for progesterone having a neuroprotective role after concussion and highlight the importance of controlling for the effects of sex hormones in future concussion studies.

Brain Perfusion Bridges Virtual-Reality Spatial Behavior to TPH2 Genotype for Head Acceleration Events.

Neuroimaging demonstrates that athletes of collision sports can suffer significant changes to their brain in the absence of concussion, attributable to head acceleration event (HAE) exposure. In a sample of 24 male Division I collegiate football players, we examine the relationships between tryptophan hydroxylase 2 (TPH2), a gene involved in neurovascular function, regional cerebral blood flow (rCBF) measured by arterial spin labeling, and virtual reality (VR) motor performance, both pre-season and across a single football season. For the pre-season, TPH2 T-carriers showed lower rCBF in two left hemisphere foci (fusiform gyrus/thalamus/hippocampus and cerebellum) in association with higher (better performance) VR Reaction Time, a dynamic measure of sensory-motor reactivity and efficiency of visual-spatial processing. For TPH2 CC homozygotes, higher pre-season rCBF in these foci was associated with better performance on VR Reaction Time. A similar relationship was observed across the season, where TPH2 T-carriers showed improved VR Reaction Time associated with decreases in rCBF in the right hippocampus/amygdala, left middle temporal lobe, and left insula/putamen/pallidum. In contrast, TPH2 CC homozygotes showed improved VR Reaction Time associated with increases in rCBF in the same three clusters. These findings show that TPH2 T-carriers have an abnormal relationship between rCBF and the efficiency of visual-spatial processing that is exacerbated after a season of high-impact sports in the absence of diagnosable concussion. Such gene-environment interactions associated with behavioral changes after exposure to repetitive HAEs have been unrecognized with current clinical analytical tools and warrant further investigation. Our results demonstrate the importance of considering neurovascular factors along with traumatic axonal injury to study long-term effects of repetitive HAEs.

Eye Movements Detect Differential Change after Participation in Male Collegiate Collision versus Non-Collision Sports.

Although neuroimaging studies of collision (COLL) sport athletes demonstrate alterations in brain structure and function from pre- to post-season, reliable tools to detect behavioral/cognitive change relevant to functional networks associated with participation in collision sports are lacking. This study evaluated the use of eye-movement testing to detect change in cognitive and sensorimotor processing among male club collegiate athletes after one season of participation in collision sports of variable exposure. We predicted that COLL (High Dose [hockey], n = 8; Low Dose [rugby], n = 9) would demonstrate longer reaction times (antisaccade and memory-guided saccade [MGS] latencies), increased inhibitory errors (antisaccade error rate), and poorer spatial working memory (MGS spatial accuracy) at post-season, relative to pre-season, whereas non-collision collegiate athletes (NON-COLL; n = 17) would remain stable. We also predicted that whereas eye-movement performance would detect pre- to post-season change, ImPACT (Immediate Post-Concussion Assessment and Cognitive Test) performance would remain stable. Our data showed that NON-COLL had shorter (improved performance) post- versus pre-season antisaccade and MGS latencies, whereas COLL groups showed stable, longer, or attenuated reduction in latency (ps ≤ 0.001). Groups did not differ in antisaccade error rate. On the MGS task, NON-COLL demonstrated improved spatial accuracy over time, whereas COLL groups showed reduced spatial accuracy (p < 0.05, uncorrected). No differential change was observed on ImPACT. This study provides preliminary evidence for eye-movement testing as a sensitive marker of subtle changes in attentional control and working memory resulting from participation in sports with varying levels of subconcussive exposure.

A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale.

In this era of complete genomes, our knowledge of neuroanatomical circuitry remains surprisingly sparse. Such knowledge is critical, however, for both basic and clinical research into brain function. Here we advocate for a concerted effort to fill this gap, through systematic, experimental mapping of neural circuits at a mesoscopic scale of resolution suitable for comprehensive, brainwide coverage, using injections of tracers or viral vectors. We detail the scientific and medical rationale and briefly review existing knowledge and experimental techniques. We define a set of desiderata, including brainwide coverage; validated and extensible experimental techniques suitable for standardization and automation; centralized, open-access data repository; compatibility with existing resources; and tractability with current informatics technology. We discuss a hypothetical but tractable plan for mouse, additional efforts for the macaque, and technique development for human. We estimate that the mouse connectivity project could be completed within five years with a comparatively modest budget.