Reverse Engineering the Human Brain: An Evolutionary Computation Approach to the Analysis of fMRI

The field of neuroimaging has truly become data rich, and as such, novel analytical methods capable of gleaning meaningful information from large stores of imaging data are in high demand. Those methods that might also be applicable on the level of individual subjects, and thus potentially useful clinically, are of special interest. In this dissertation we introduce just such a method, called nonlinear functional mapping (NFM), and demonstrate its application in the analysis of resting state fMRI (functional Magnetic Resonance Imaging) from a 242-subject subset of the IMAGEN project, a European study of risk-taking behavior in adolescents that includes longitudinal phenotypic, behavioral, genetic, and neuroimaging data. Functional mapping employs a computational technique inspired by biological evolution to discover and mathematically characterize interactions among ROI (regions of interest), without making linear or univariate assumptions. Statistics of the resulting interaction relationships comport with recent independent work, constituting a preliminary cross-validation. Furthermore, nonlinear terms are ubiquitous in the models generated by NFM, suggesting that some of the interactions characterized here are not discoverable by standard linear methods of analysis. One such nonlinear interaction is discussed in the context of a direct comparison with a procedure involving pairwise correlation, designed to be an analogous linear version of functional mapping. Another such interaction suggests a novel distinction in brain function between drinking and non-drinking adolescents: a tighter coupling of ROI associated with emotion, reward, and interceptive processes such as thirst, among drinkers. Finally, we outline many improvements and extensions of the methodology to reduce computational expense, complement other analytical tools like graph-theoretic analysis, and possibly allow for voxel level functional mapping to eliminate the necessity of ROI selection

Risks and harms of binge drinking in young people: Bridging neurobiological, cognitive, and psychological perspectives

Binge drinking is highly prevalent among young people and can lead to health harms and engagement with other high-risk behaviors. While neurobiology, cognition, and psychopathology are central pathways to binge drinking, limited research bridges these perspectives, examines the developmental dynamics between them, or applies a multigenerational approach. To address these knowledge gaps, this thesis aims to examine the inter-related precursory risks of binge drinking, explore the added impact of multigenerational alcohol use, and determine the severity and recoverability of alcohol-related harms in young people. Study 1 is the first rigorous review of the neurobiological and cognitive precursory risks and harms of binge drinking. Findings show that aberrant neurodevelopment increases risk, with aberrations further exacerbated by binge drinking. Study 2 explores the dynamics between cognitive and psychological risk factors for binge drinking. The world-first study indicates that psychopathology in combination with poor executive functioning is associated with greater consumption. Studies 3–5 investigate the impact of multigenerational alcohol use. The mega-analyses show that preadolescents with familial alcohol use problems or low- to moderate-level prenatal alcohol exposure exhibit established risk markers of binge drinking. Study 6 examines cognitive harms following binge drinking in young people. Outcomes show that binge drinking is associated with inhibitory control deficits and demonstrate, for the first time, that these deficits do not recover over the short term. The research in this thesis is the first to robustly show that 1) precursory neurobiological features predate binge drinking and co-occurring psychopathology plays a key role; 2) these precursors are particularly prevalent among young people with added familial risk; and 3) neurobiological and cognitive harms follow binge drinking and do not recede in the short term. The findings provide critical evidence from a multidisciplinary and developmental perspective for global prevention and intervention efforts as well as positive alcohol use policies. Greater prioritization of targeting the whole family will significantly reduce the prevalence of binge drinking and related disabling consequences across the lifespan

Is (poly-) substance use associated with impaired inhibitory control? A mega-analysis controlling for confounders.

Many studies have reported that heavy substance use is associated with impaired response inhibition. Studies typically focused on associations with a single substance, while polysubstance use is common. Further, most studies compared heavy users with light/non-users, though substance use occurs along a continuum. The current mega-analysis accounted for these issues by aggregating individual data from 43 studies (3610 adult participants) that used the Go/No-Go (GNG) or Stop-signal task (SST) to assess inhibition among mostly "recreational" substance users (i.e., the rate of substance use disorders was low). Main and interaction effects of substance use, demographics, and task-characteristics were entered in a linear mixed model. Contrary to many studies and reviews in the field, we found that only lifetime cannabis use was associated with impaired response inhibition in the SST. An interaction effect was also observed: the relationship between tobacco use and response inhibition (in the SST) differed between cannabis users and non-users, with a negative association between tobacco use and inhibition in the cannabis non-users. In addition, participants' age, education level, and some task characteristics influenced inhibition outcomes. Overall, we found limited support for impaired inhibition among substance users when controlling for demographics and task-characteristics

Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research

By promising more accurate diagnostics and individual treatment recommendations, deep neural networks and in particular convolutional neural networks have advanced to a powerful tool in medical imaging. Here, we first give an introduction into methodological key concepts and resulting methodological promises including representation and transfer learning, as well as modelling domain-specific priors. After reviewing recent applications within neuroimaging-based psychiatric research, such as the diagnosis of psychiatric diseases, delineation of disease subtypes, normative modeling, and the development of neuroimaging biomarkers, we discuss current challenges. This includes for example the difficulty of training models on small, heterogeneous and biased data sets, the lack of validity of clinical labels, algorithmic bias, and the influence of confounding variables

An Automated Mobile Game-based Screening Tool for Patients with Alcohol Dependence

Traditional methods for screening and diagnosis of alcohol dependence are typically administered by trained clinicians in medical settings and often rely on interview responses. These self-reports can be unintentionally or deliberately false, and misleading answers can, in turn, lead to inaccurate assessment and diagnosis. In this study, we examine the use of user-game interaction patterns on mobile games to develop an automated diagnostic and screening tool for alcohol-dependent patients. Our approach relies on the capture of interaction patterns during gameplay, while potential patients engage with popular mobile games on smartphones. The captured signals include gameplay performance, touch gestures, and device motion, with the intention of identifying patients with alcohol dependence. We evaluate the classification performance of various supervised learning algorithms on data collected from 40 patients and 40 age-matched healthy adults. The results show that patients with alcohol dependence can be automatically identified accurately using the ensemble of touch, device motion, and gameplay performance features on 3-minute samples (accuracy=0.95, sensitivity=0.95, and specificity=0.95). The present findings provide strong evidence suggesting the potential use of user-game interaction metrics on existing mobile games as discriminant features for developing an implicit measure to identify alcohol dependence conditions. In addition to supporting healthcare professionals in clinical decision-making, the game-based self-screening method could be used as a novel strategy to promote alcohol dependence screening, especially outside of clinical settings

An Automated Mobile Game-based Screening Tool for Patients with Alcohol Dependence

Traditional methods for screening and diagnosis of alcohol dependence are typically administered by trained clinicians in medical settings and often rely on interview responses. These self-reports can be unintentionally or deliberately false, and misleading answers can, in turn, lead to inaccurate assessment and diagnosis. In this study, we examine the use of user-game interaction patterns on mobile games to develop an automated diagnostic and screening tool for alcohol-dependent patients. Our approach relies on the capture of interaction patterns during gameplay, while potential patients engage with popular mobile games on smartphones. The captured signals include gameplay performance, touch gestures, and device motion, with the intention of identifying patients with alcohol dependence. We evaluate the classification performance of various supervised learning algorithms on data collected from 40 patients and 40 age-matched healthy adults. The results show that patients with alcohol dependence can be automatically identified accurately using the ensemble of touch, device motion, and gameplay performance features on 3-minute samples (accuracy=0.95, sensitivity=0.95, and specificity=0.95). The present findings provide strong evidence suggesting the potential use of user-game interaction metrics on existing mobile games as discriminant features for developing an implicit measure to identify alcohol dependence conditions. In addition to supporting healthcare professionals in clinical decision-making, the game-based self-screening method could be used as a novel strategy to promote alcohol dependence screening, especially outside of clinical settings

Advancing our understanding of general psychopathology among young people: Optimising prevention targets and timing

Reconceptualising psychopathology in a hierarchical-dimensional framework has gained momentum in recent years. There is strong evidence for a single general psychopathology dimension that reflects the shared elements across mental and substance use disorders and captures their co-occurrence. Yet very little is known about what general psychopathology represents or whether it is a suitable intervention target. This thesis investigates the underlying structure, development, and prevention of general psychopathology among young people. Chapter 2 is the first systematic review of empirically based models of psychopathology among young people aged 10-24 years. The review identified a wide range of risk factors associated with general psychopathology, as well as critical gaps and methodological shortcomings in existing research. Chapters 3-4 examine cross-sectional and longitudinal associations between psychopathology and four high-risk personality traits (anxiety sensitivity, negative thinking, impulsivity, and sensation seeking) highlighting the complex and dynamic interplay between personality and psychopathology. Chapter 5 explores the impact of a selective, personality targeted prevention program on general and specific dimensions of psychopathology providing some of the first evidence world-wide that growth in general psychopathology can be reduced through a brief, school-based intervention. Together, these novel empirical studies make a highly significant contribution to our understanding of general psychopathology in adolescence and provide a critical foundation upon which prevention and intervention efforts can be personalised and optimised to reduce the considerable burden, harms and costs associated with mental and substance use disorders