Modern Views of Machine Learning for Precision Psychiatry

In light of the NIMH's Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of the ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. Additionally, we review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We further discuss explainable AI (XAI) and causality testing in a closed-human-in-the-loop manner, and highlight the ML potential in multimedia information extraction and multimodal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research

Extracting Generalizable Hierarchical Patterns Of Functional Connectivity In The Brain

The study of the functional organization of the human brain using resting-state functional MRI (rsfMRI) has been of significant interest in cognitive neuroscience for over two decades. The functional organization is characterized by patterns that are believed to be hierarchical in nature. From a clinical context, studying these patterns has become important for understanding various disorders such as Major Depressive Disorder, Autism, Schizophrenia, etc. However, extraction of these interpretable patterns might face challenges in multi-site rsfMRI studies due to variability introduced due to confounding variability introduced by different sites and scanners. This can reduce the predictive power and reproducibility of the patterns, affecting the confidence in using these patterns as biomarkers for assessing and predicting disease. In this thesis, we focus on the problem of robustly extracting hierarchical patterns that can be used as biomarkers for diseases. We propose a matrix factorization based method to extract interpretable hierarchical decomposition of the rsfRMI data. We couple the method with adversarial learning to improve inter-site robustness in multi-site studies, removing non-biological variability that can result in less interpretable and discriminative biomarkers. Finally, a generative-discriminative model is built on top of the proposed framework to extract robust patterns/biomarkers characterizing Major Depressive Disorder. Results on large multi-site rsfMRI studies show the effectiveness of our method in uncovering reproducible connectivity patterns across individuals with high predictive power while maintaining clinical interpretability. Our framework robustly identifies brain patterns characterizing MDD and provides an understanding of the manifestation of the disorder from a functional networks perspective which can be crucial for effective diagnosis, treatment and prevention. The results demonstrate the method\u27s utility and facilitate a broader understanding of the human brain from a functional perspective

Evaluating causal psychological models: A study of language theories of autism using a large sample

We used a large convenience sample (n = 22,223) from the Simons Powering Autism Research (SPARK) dataset to evaluate causal, explanatory theories of core autism symptoms. In particular, the data-items collected supported the testing of theories that posited altered language abilities as cause of social withdrawal, as well as alternative theories that competed with these language theories. Our results using this large dataset converge with the evolution of the field in the decades since these theories were first proposed, namely supporting primary social withdrawal (in some cases of autism) as a cause of altered language development, rather than vice versa.To accomplish the above empiric goals, we used a highly theory-constrained approach, one which differs from current data-driven modeling trends but is coherent with a very recent resurgence in theory-driven psychology. In addition to careful explication and formalization of theoretical accounts, we propose three principles for future work of this type: specification, quantification, and integration. Specification refers to constraining models with pre-existing data, from both outside and within autism research, with more elaborate models and more veridical measures, and with longitudinal data collection. Quantification refers to using continuous measures of both psychological causes and effects, as well as weighted graphs. This approach avoids “universality and uniqueness” tests that hold that a single cognitive difference could be responsible for a heterogeneous and complex behavioral phenotype. Integration of multiple explanatory paths within a single model helps the field examine for multiple contributors to a single behavioral feature or to multiple behavioral features. It also allows integration of explanatory theories across multiple current-day diagnoses and as well as typical development

Functional network and spectral analysis of clinical EEG data to identify quantitative biomarkers and classify brain disorders

Many cognitive and neurological disorders today, such as Autism Spectrum Disorders (ASD) and various forms of epilepsy such as infantile spasms (IS), manifest as changes in voltage activity recorded in scalp electroencephalograms (EEG). Diagnosis of brain disease often relies on the interpretation of complex EEG features through visual inspection by clinicians. Although clinically useful, such interpretation is subjective and suffers from poor inter-rater reliability, which affects clinical care through increased variability and uncertainty in diagnosis. In addition, such qualitative assessments are often binary, and do not parametrically measure characteristics of disease manifestations. Many cognitive disorders are grouped by similar behaviors, but may arise from distinct biological causes, possibly represented by subtle electrophysiological differences. To address this, quantitative analytical tools - such as functional network connectivity, frequency-domain, and time-domain features - are being developed and applied to clinically obtained EEG data to identify electrophysiological biomarkers. These biomarkers enhance a clinician’s ability to accurately diagnose, categorize, and select treatment for various neurological conditions. In the first study, we use spectral and functional network analysis of clinical EEG data recorded from a population of children to propose a cortical biomarker for autism. We first analyze a training set of age-matched (4–8 years) ASD and neurotypical children to develop hypotheses based on power spectral features and measures of functional network connectivity. From the training set of subjects, we derive the following hypotheses: 1) The ratio of the power of the posterior alpha rhythm (8–14 Hz) peak to the anterior alpha rhythm peak is significantly lower in ASD than control subjects. 2) The functional network density is lower in ASD subjects than control subjects. 3) A select group of edges provide a more sensitive and specific biomarker of ASD. We then test these hypotheses in a validation set of subjects and show that both the first and third hypotheses, but not the second, are validated. The validated features successfully classified the data with significant accuracy. These results provide a validated study for EEG biomarkers of ASD based on changes in brain rhythms and functional network characteristics. We next perform a follow-up study that utilizes the same group of ASD and neurotypical subjects, but focuses on differences between these two groups in the sleep state. Motivated by the results from the previous study, we utilize the previously validated biomarkers, including the alpha ratio and the subset of edges found to be a sensitive biomarker of ASD, and test their effectiveness in the sleep state. To complement these frequency domain features, we also investigate the efficacy of several time domain measures. This investigation did not lead to significant findings, which may have important implications for the differences between sleep and wake states in ASD, or perhaps generally for clinical assessment, as well as for the effect of noise on signal in clinically obtained data. Finally, we design a similar analysis framework to investigate a set of clinical EEG data recorded from a population of children with active infantile spasms (IS) (2-16 months), and age-matched neurotypical children, in both wake and sleep states. The goal of this analysis is to develop a quantitative biomarker from the EEG signal, which ultimately we will apply to predict the clinical outcome of children with IS. In addition to spectral and functional network analysis, we calculate time domain features previously found to correlate with seizures. We compare the two populations by each feature individually, test the effects of age on these features, use all features in a linear discriminant model to categorize IS versus neurotypical EEG, and test the findings using a leave-one-out validation test. We find almost every feature tested shows significant population differences between IS and control groups, and that taken together they serve as an effective classifier, with potential to be informative as to disease severity and long-term outcome. Furthermore, analysis of these features reveals two groups, indicating a possibility that these features reflect two distinct qualitative characteristics of IS and seizures

Epidemiology of prenatal alcohol use and fetal alcohol spectrum disorder

Prenatal alcohol exposure (PAE) can lead to fetal alcohol spectrum disorder (FASD). FASD refers to a range of lifelong conditions caused by PAE, characterised by a distinctive facial phenotype, growth deficiencies and/or neurobehavioural impairments. This thesis presents four studies that I conducted to address knowledge gaps relevant to the epidemiology of PAE and FASD. First, objective measures of PAE are essential for identifying children at risk of adverse outcomes. Biomarkers have been advocated for use in universal PAE screening programs but their validity had not been comprehensively evaluated. I conducted a systematic review and found that biomarker test performance varied widely across studies. The quality of published studies was low, resulting in insufficient evidence to support the use of objective measures of PAE in practice. Second, the prevalence of FASD in the UK was unknown. Active case ascertainment studies have not been possible due to funding and ethical issues. To overcome these issues, I developed an algorithm to estimate FASD prevalence using existing data from a population-based birth cohort in England (ALSPAC). Up to 17% of children met criteria for FASD, indicating that it is a significant public health concern. Third, although PAE is the sole necessary cause of FASD, it is not always sufficient. Understanding risk factors for FASD is important for informing prevention strategies. However, existing studies have mostly been limited to discussion of association, rather than causation. I produced a causal diagram to depict hypothesised causal pathways to FASD. I used this diagram to guide analyses in a FASD risk factor study, reported below. Finally, I investigated FASD risk factors using multivariable logistic regression within the ALSPAC cohort. Prenatal stress, smoking and mental health problems increased the odds of FASD. Social support and folic acid supplementation were protective. These results indicate novel potential targets for FASD intervention

Mediated Cognition: Information Technologies and the Sciences of Mind

This dissertation investigates the interconnections between minds, media, and the cognitive sciences. It asks what it means for media to have effects upon the mind: do our tools influence the ways that we think? It considers what scientific evidence can be brought to bear on the question: how can we know and measure these effects? Ultimately, it looks to the looping pathways by which science employs technological media in understanding the mind, and the public comes to understand and respond to these scientific discourses. I contend that like human cognition itself, the enterprise of cognitive science is a deeply and distinctively mediated phenomenon. This casts a different light on contemporary debates about whether television, computers, or the Internet are changing our brains, for better or for worse. Rather than imagining media effects as befalling a fictive natural mind, I draw on multiple disciplines to situate mind and the sciences thereof as shaped from their origins through interaction with technology. Our task is then to interrogate the forms of cognition and attention fostered by different media, alongside their attendant costs and benefits. The first chapter positions this dissertation between the fields of media studies and STS, developing a case for the reality of media effects without the implication of technological determinism. The second considers the history of technological metaphor in scientific characterizations of the mind. The third section consists of three separate chapters on the history of cognitive science, presenting the core of my case for its uniquely mediated character. Across three distinct eras, what unifies cognitive science is the quest to understand the mind using computational systems, operating by turns as generative metaphors and tangible models. I then evaluate the contemporary cognitive-scientific research on the question of media effects, and the growing role of electronic media in science. My fifth and final section develops a content analysis: what is said in the media about the popular theory that media themselves, in one way or another, are causing attention deficit disorders? The work concludes with a summary and some reflections on mind, culture, technoscience and markets as recursively interwoven causal systems