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.
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.
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.
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.
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.
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
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.
Human brains develop across the life span and largely vary in morphology. Adolescent collision-sport athletes undergo repetitive head impacts over years of practices and competitions, and therefore ...may exhibit a neuroanatomical trajectory different from healthy adolescents in general. However, an unbiased brain atlas targeting these individuals does not exist. Although standardized brain atlases facilitate spatial normalization and voxel-wise analysis at the group level, when the underlying neuroanatomy does not represent the study population, greater biases and errors can be introduced during spatial normalization, confounding subsequent voxel-wise analysis and statistical findings. In this work, targeting early-to-middle adolescent (EMA, ages 13-19) collision-sport athletes, we developed population-specific brain atlases that include templates (T1-weighted and diffusion tensor magnetic resonance imaging) and semantic labels (cortical and white matter parcellations). Compared to standardized adult or age-appropriate templates, our templates better characterized the neuroanatomy of the EMA collision-sport athletes, reduced biases introduced during spatial normalization, and exhibited higher sensitivity in diffusion tensor imaging analysis. In summary, these results suggest the population-specific brain atlases are more appropriate towards reproducible and meaningful statistical results, which better clarify mechanisms of traumatic brain injury and monitor brain health for EMA collision-sport athletes.
Abstract 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.
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.
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.
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 R
>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.
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.
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).
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.
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.
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.
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.
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.
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.
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 (pSobelperm≤ 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.
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•Permutation-based mediation statistics can be applied to multi-scale biology problems•Fatty acids were a critical link between elevated miRNAs and motor control•HAEs interacted with pre-season metabolite levels to model post-season levels•Together, our observations point to brain-related mitochondrial dysfunction
Trauma; Metabolomics; Transcriptomics; Computer graphics
Contact sports affect measures at multiple scales such as transcriptomics, metabolomics, brain function, and behavior, but studies have not yet studied the statistical structure of how they are ...integrated. This preliminary study, examining collegiate American football players, integrated across-season changes (Δ) from transcriptomic and metabolomic variables (neuroinflammatory miRNAs and metabolites), neuroimaging (resting-state fMRI network fingerprint similarity), and virtual reality (VR)-based motor control. These findings were then assessed against head acceleration events (HAE). Using permutation-based moderation analysis (all pFperm,pβ3perm≤0.05), we observed that (1) Δtridecenedioate, a mono-unsaturated fatty acid, interacted with ΔmiR-505 to predict default mode network (DMN) fingerprint similarity, meaning the interaction between two molecular biology measures predicted a neuroimaging measure. Further, (2) Δtridecenedioate and DMN fingerprint similarity interacted to predict motor control, indicating the interaction of a molecular and a neuroimaging measure predicted behavior. ΔmiR-505 was positively related to HAE and DMN fingerprint similarity was negatively related to HAE and reduced relative to non-athlete subjects. These multi-scale, moderating relationships between a potential ROS scavenger, neuroinflammatory miRNA, reduced brain connectivity, and diminished motor control argue that seemingly healthy athletes with frequent HAE may experience chronic neuroinflammation. This imaging-omics framework using permutation-based mediation/moderation analysis has general applicability for human-animal translational studies.
•First study integrating multi-omic measures with imaging and behavior in humans.•First study showing two molecular measures predict a neuroimaging measure.•First study showing interaction of molecular and imaging measures predicts behavior.•Omic measures suggest chronic inflammation in otherwise healthy football players.•Permutation with mediation/moderation statistics promotes integratory neuroscience.
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