Leveraging big data for causal understanding in mental health: a research framework.

Over the past 30 years there have been numerous large-scale and longitudinal psychiatric research efforts to improve our understanding and treatment of mental health conditions. However, despite the huge effort by the research community and considerable funding, we still lack a causal understanding of most mental health disorders. Consequently, the majority of psychiatric diagnosis and treatment still operates at the level of symptomatic experience, rather than measuring or addressing root causes. This results in a trial-and-error approach that is a poor fit to underlying causality with poor clinical outcomes. Here we discuss how a research framework that originates from exploration of causal factors, rather than symptom groupings, applied to large scale multi-dimensional data can help address some of the current challenges facing mental health research and, in turn, clinical outcomes. Firstly, we describe some of the challenges and complexities underpinning the search for causal drivers of mental health conditions, focusing on current approaches to the assessment and diagnosis of psychiatric disorders, the many-to-many mappings between symptoms and causes, the search for biomarkers of heterogeneous symptom groups, and the multiple, dynamically interacting variables that influence our psychology. Secondly, we put forward a causal-orientated framework in the context of two large-scale datasets arising from the Adolescent Brain Cognitive Development (ABCD) study, the largest long-term study of brain development and child health in the United States, and the Global Mind Project which is the largest database in the world of mental health profiles along with life context information from 1.4 million people across the globe. Finally, we describe how analytical and machine learning approaches such as clustering and causal inference can be used on datasets such as these to help elucidate a more causal understanding of mental health conditions to enable diagnostic approaches and preventative solutions that tackle mental health challenges at their root cause.

The Heterogeneity of Mental Health Assessment.

Across the landscape of mental health research and diagnosis, there is a diverse range of questionnaires and interviews available for use by clinicians and researchers to determine patient treatment plans or investigate internal and external etiologies. Although individually, these tools have each been assessed for their validity and reliability, there is little research examining the consistency between them in terms of what symptoms they assess, and how they assess those symptoms. Here, we provide an analysis of 126 different questionnaires and interviews commonly used to diagnose and screen for 10 different disorder types including depression, anxiety, obsessive compulsive disorder (OCD), post-traumatic stress disorder (PTSD), attention deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), addiction, bipolar disorder, eating disorder, and schizophrenia, as well as comparator questionnaires and interviews that offer an all-in-one cross-disorder assessment of mental health. We demonstrate substantial inconsistency in the inclusion and emphasis of symptoms assessed within disorders as well as considerable symptom overlap across disorder-specific tools. Within the same disorder, similarity scores across assessment tools ranged from 29% for assessment of bipolar disorder to a maximum of 58% for OCD. Furthermore, when looking across disorders, 60% of symptoms were assessed in at least half of all disorders illustrating the extensive overlap in symptom profiles between disorder-specific assessment tools. Biases in assessment toward emotional, cognitive, physical or behavioral symptoms were also observed, further adding to the heterogeneity across assessments. Analysis of other characteristics such as the time period over which symptoms were assessed, as well as whether there was a focus toward frequency, severity or duration of symptoms also varied substantially across assessment tools. The consequence of this inconsistent and heterogeneous assessment landscape is that it hinders clinical diagnosis and treatment and frustrates understanding of the social, environmental, and biological factors that contribute to mental health symptoms and disorders. Altogether, it underscores the need for standardized assessment tools that are more disorder agnostic and span the full spectrum of mental health symptoms to aid the understanding of underlying etiologies and the discovery of new treatments for psychiatric dysfunction.

EEG Frequency Bands in Psychiatric Disorders: A Review of Resting State Studies.

A significant proportion of the electroencephalography (EEG) literature focuses on differences in historically pre-defined frequency bands in the power spectrum that are typically referred to as alpha, beta, gamma, theta and delta waves. Here, we review 184 EEG studies that report differences in frequency bands in the resting state condition (eyes open and closed) across a spectrum of psychiatric disorders including depression, attention deficit-hyperactivity disorder (ADHD), autism, addiction, bipolar disorder, anxiety, panic disorder, post-traumatic stress disorder (PTSD), obsessive compulsive disorder (OCD) and schizophrenia to determine patterns across disorders. Aggregating across all reported results we demonstrate that characteristic patterns of power change within specific frequency bands are not necessarily unique to any one disorder but show substantial overlap across disorders as well as variability within disorders. In particular, we show that the most dominant pattern of change, across several disorder types including ADHD, schizophrenia and OCD, is power increases across lower frequencies (delta and theta) and decreases across higher frequencies (alpha, beta and gamma). However, a considerable number of disorders, such as PTSD, addiction and autism show no dominant trend for spectral change in any direction. We report consistency and validation scores across the disorders and conditions showing that the dominant result across all disorders is typically only 2.2 times as likely to occur in the literature as alternate results, and typically with less than 250 study participants when summed across all studies reporting this result. Furthermore, the magnitudes of the results were infrequently reported and were typically small at between 20% and 30% and correlated weakly with symptom severity scores. Finally, we discuss the many methodological challenges and limitations relating to such frequency band analysis across the literature. These results caution any interpretation of results from studies that consider only one disorder in isolation, and for the overall potential of this approach for delivering valuable insights in the field of mental health.