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

Spectroscopic Investigation of a Novel Traumatic Brain Injury Biomarker and Analysis of Neurometabolic Changes in Youth American Football Athletes

Recent advances in Magnetic Resonance Imaging (MRI), a noninvasive imaging technique, have spurred the exploration of poorly understood physiological phenomena in vivo. Applications of MRI vary greatly, from anatomical evaluation to complex functional analysis. The body of this dissertation presents four applications of MRI: 1) investigation of a novel traumatic brain injury (TBI) biomarker, 2) analysis of position-specific head acceleration events on neurometabolic profiles in high school football athletes, 3) the first reporting of neurometabolic changes in middle school football athletes, and 4) a novel application of diffusion-weighted imaging (DWI) to characterize implantable drug-delivery depots (Appendix A). Magnetic resonance spectroscopy (MRS) is an MRI method used to evaluate the metabolic profiles of tissues. Certain brain metabolites (N-acetyl aspartate, myoinositol, choline, creatine, and glutamate/glutamine) offer unique information regarding brain homeostasis following TBI. When coupled with additional metrics, such as head acceleration events recorded during collision-sport participation, the mechanisms of neurophysiological changes can be further elucidated. Here, player position-specific neurometabolic changes were evaluated in high school and middle school football athletes. Striking differences were noted between linemen and non-linemen as well as high school and middle school athletes. However, in most clinical cases of TBI, information regarding head acceleration events is unknown and baseline scans are not available. Therefore, it is critical to evaluate candidate biomarkers which increase solely in response to injury. Acrolein, a toxic reactive oxygen species, has been shown to increase following injury to the central nervous system in animal models. Hence, acrolein is a prime TBI biomarker candidate and has been investigated using nuclear magnetic resonance and MRS at 7 Tesla. Applications of MRI are not limited to the brain, or even tissues. Studies have reported that up to 50% of patients fail to take their medications correctly - resulting in disease progression and medication waste. In situ forming implants (ISFIs) offer an alternative to oral dosage regimens but have not been validated in vivo. Using DWI, ISFIs can be characterized noninvasively and their design can be refined, ultimately improving patient outcomes. Taken together, MRI is powerful tool that can be used to investigate a wide range of physiological questions. Chapters 2-4 will emphasize efforts to improve TBI diagnostics and better understand neurometabolic changes in youth football athletes. Appendix A offers insights into the DWI-guided characterization of in situforming implants

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

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

Development of brain atlases for early-to-middle adolescent collision-sport athletes

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.</p