Brain structure can mediate or moderate the relationship of behavior to brain function and transcriptome. A preliminary study

Abnormalities in motor-control behavior, which have been with concussion and head acceleration events (HAE), can be quantified using virtual reality (VR) technologies. Motor-control behavior has been consistently mapped to the brain's somatomotor network (SM) using both structural (sMRI) and functional MRI (fMRI). However, no studies habe integrated HAE, motor-control behavior, sMRI and fMRI measures. Here, brain networks important for motor-control were hypothesized to show changes in tractography-based diffusion weighted imaging [difference in fractional anisotropy (dFA)] and resting-state fMRI (rs-fMRI) measures in collegiate American football players across the season, and that these measures would relate to VR-based motor-control. We firther tested if nine inflammation-related miRNAs were associated with behavior-structure-function variables. Using permutation-based mediation and moderation methods, we found that across-season dFA from the SM structural connectome (SM-dFA) mediated the relationship between across-season VR-based Sensory-motor Reactivity (dSR) and rs-fMRI SM fingerprint similarity (p = 0.007 and Teff = 47%). The interaction between dSR and SM-dFA also predicted (pF = 0.036, pbeta3 = 0.058) across-season levels of dmiRNA-30d through permutation-based moderation analysis. These results suggest (1) that motor-control is in a feedback relationship with brain structure and function, (2) behavior-structure-function can be connected to HAE, and (3) behavior-structure might predict molecular biology measures.Comment: 62 pages, 4 figures, 2 table

Advancements in Neuroimaging for Mild Traumatic Brain Injury and Multi-Site Reliability

Head injuries in collision sports have been linked to long-term neurological disorders. High school collision sport athletes, a population vulnerable to head injuries, are at a greater risk of chronic damage. Various studies have indicated significant deviations in brain function due to the accumulation of repetitive low-level subconcussive impacts to the head without externally observable cognitive symptoms. The aim of this study was to investigate metabolic changes in asymptomatic collision sport athletes across time within their competition season and as a function of mechanical force to their head. For this purpose, Proton Magnetic Resonance Spectroscopy (MRS) was used as a tool to detect altered brain metabolism in high school collision sport athletes (football and soccer) without diagnosed concussion. Also, sensors were attached to each athletes head to collect the count and magnitude of head impacts during their games and practices. Transient neurometabolic alterations along with prolonged recovery were observed in collision sport athletes. Multi-site studies are becoming important to increase statistical power, enhance generalizability, and to improve the likelihood of pooling relevant subgroups together, activities which are otherwise limited by the availability of patients or funds at a single site. Even with harmonized imaging sequences, site-dependent variability can mask the advantages of these multi-site studies. The aim of this study was to assess multi-site reproducibility in resting-state functional connectivity fingerprints, and to improve identifiability of obtained functional connectomes. We evaluated individual fingerprints in test- retest visit pairs within and across two sites and present a generalized framework based on principal component analysis (PCA) to improve identifiability. The optimally reconstructed functional connectomes using PCA showed a substantial improvement in individual fingerprinting of the subjects within and across the two sites and test-retest visit pairs relative to the original data. Results demonstrate that the data-driven method presented in the study can improve identifiability in resting-state functional connectomes in multi-site studies

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

BackgroundRates 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.MethodsHere, 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.ResultsThis 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.ConclusionsThese 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

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

BackgroundAlthough 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. ObjectiveWe 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? MethodsThis 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. ResultsWe 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). ConclusionsWe 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

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

BackgroundThe 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. ObjectiveThis 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. MethodsIn 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. ResultsThose 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. ConclusionsThis 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

Relative Preference Features and Age for 3 independent cohorts.

The dataset includes the relative preference variables that were calculated from 3 independent cohorts: 1) Emotion and Behavior Study, an online study of U.S. adult consumers (year 2016) .2) The Automated Mental Health Assessment Study, referred to as the AMHA-1 cohort. Random sample from the general U.S. population. (early 2021)3) The Automated Mental Health Assessment Study, referred to as the AMHA-2 cohort. Random sample from the general U.S. population. (late 2021)</p