Hierarchy of demographic and social determinants of mental health: analysis of cross-sectional survey data from the Global Mind Project

OBJECTIVES

To understand the extent to which various demographic and social determinants predict mental health status and their relative hierarchy of predictive power in order to prioritise and develop population-based preventative approaches.

DESIGN

Cross-sectional analysis of survey data.

SETTING

Internet-based survey from 32 countries across North America, Europe, Latin America, Middle East and North Africa, Sub-Saharan Africa, South Asia and Australia, collected between April 2020 and December 2021.

PARTICIPANTS

270 000 adults aged 18-85+ years who participated in the Global Mind Project.

OUTCOME MEASURES

We used 120+ demographic and social determinants to predict aggregate mental health status and scores of individuals (mental health quotient (MHQ)) and determine their relative predictive influence using various machine learning models including gradient boosting and random forest classification for various demographic stratifications by age, gender, geographical region and language. Outcomes reported include model performance metrics of accuracy, precision, recall, F1 scores and importance of individual factors determined by reduction in the squared error attributable to that factor.

RESULTS

Across all demographic classification models, 80% of those with negative MHQs were correctly identified, while regression models predicted specific MHQ scores within ±15% of the position on the scale. Predictions were higher for older ages (0.9+ accuracy, 0.9+ F1 Score; 65+ years) and poorer for younger ages (0.68 accuracy, 0.68 F1 Score; 18-24 years). Across all age groups, genders, regions and language groups, lack of social interaction and sufficient sleep were several times more important than all other factors. For younger ages (18-24 years), other highly predictive factors included cyberbullying and sexual abuse while not being able to work was high for ages 45-54 years.

CONCLUSION

Social determinants of traumas, adversities and lifestyle can account for 60%-90% of mental health challenges. However, additional factors are at play, particularly for younger ages, that are not included in these data and need further investigation.

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.

High Variability Periods in the EEG Distinguish Cognitive Brain States

OBJECTIVE

To describe a novel measure of EEG signal variability that distinguishes cognitive brain states.

METHOD

We describe a novel characterization of amplitude variability in the EEG signal termed "High Variability Periods" or "HVPs", defined as segments when the standard deviation of a moving window is continuously higher than the quartile cutoff. We characterize the parameter space of the metric in terms of window size, overlap, and threshold to suggest ideal parameter choice and compare its performance as a discriminator of brain state to alternate single channel measures of variability such as entropy, complexity, harmonic regression fit, and spectral measures.

RESULTS

We show that the average HVP duration provides a substantially distinct view of the signal relative to alternate metrics of variability and, when used in combination with these metrics, significantly enhances the ability to predict whether an individual has their eyes open or closed and is performing a working memory and Raven's pattern completion task. In addition, HVPs disappear under anesthesia and do not reappear in early periods of recovery.

CONCLUSIONS

HVP metrics enhance the discrimination of various brain states and are fast to estimate.

SIGNIFICANCE

HVP metrics can provide an additional view of signal variability that has potential clinical application in the rapid discrimination of brain states.

Assessment of Population Well-being With the Mental Health Quotient: Validation Study

BACKGROUND

The Mental Health Quotient (MHQ) is an anonymous web-based assessment of mental health and well-being that comprehensively covers symptoms across 10 major psychiatric disorders, as well as positive elements of mental function. It uses a novel life impact scale and provides a score to the individual that places them on a spectrum from Distressed to Thriving along with a personal report that offers self-care recommendations. Since April 2020, the MHQ has been freely deployed as part of the Mental Health Million Project.

OBJECTIVE

This paper demonstrates the reliability and validity of the MHQ, including the construct validity of the life impact scale, sample and test-retest reliability of the assessment, and criterion validation of the MHQ with respect to clinical burden and productivity loss.

METHODS

Data were taken from the Mental Health Million open-access database (N=179,238) and included responses from English-speaking adults (aged≥18 years) from the United States, Canada, the United Kingdom, Ireland, Australia, New Zealand, South Africa, Singapore, India, and Nigeria collected during 2021. To assess sample reliability, random demographically matched samples (each 11,033/179,238, 6.16%) were compared within the same 6-month period. Test-retest reliability was determined using the subset of individuals who had taken the assessment twice ≥3 days apart (1907/179,238, 1.06%). To assess the construct validity of the life impact scale, additional questions were asked about the frequency and severity of an example symptom (feelings of sadness, distress, or hopelessness; 4247/179,238, 2.37%). To assess criterion validity, elements rated as having a highly negative life impact by a respondent (equivalent to experiencing the symptom ≥5 days a week) were mapped to clinical diagnostic criteria to calculate the clinical burden (174,618/179,238, 97.42%). In addition, MHQ scores were compared with the number of workdays missed or with reduced productivity in the past month (7625/179,238, 4.25%).

RESULTS

Distinct samples collected during the same period had indistinguishable MHQ distributions and MHQ scores were correlated with r=0.84 between retakes within an 8- to 120-day period. Life impact ratings were correlated with frequency and severity of symptoms, with a clear linear relationship (R²>0.99). Furthermore, the aggregate MHQ scores were systematically related to both clinical burden and productivity. At one end of the scale, 89.08% (8986/10,087) of those in the Distressed category mapped to one or more disorders and had an average productivity loss of 15.2 (SD 11.2; SEM [standard error of measurement] 0.5) days per month. In contrast, at the other end of the scale, 0% (1/24,365) of those in the Thriving category mapped to any of the 10 disorders and had an average productivity loss of 1.3 (SD 3.6; SEM 0.1) days per month.

CONCLUSIONS

The MHQ is a valid and reliable assessment of mental health and well-being when delivered anonymously on the web.

The Acceptability, Feasibility, and Utility of Portable Electroencephalography to Study Resting-State Neurophysiology in Rural Communities

Electroencephalography (EEG) provides a non-invasive means to advancing our understanding of the development and function of the brain. However, the majority of the world’s population residing in low and middle income countries has historically been limited from contributing to, and thereby benefiting from, such neurophysiological research, due to lack of scalable validated methods of EEG data collection. In this study, we establish a standard operating protocol to collect approximately 3 min each of eyes-open and eyes-closed resting-state EEG data using a low-cost portable EEG device in rural households through formative work in the community. We then evaluate the acceptability of these EEG assessments to young children and feasibility of administering them through non-specialist workers. Finally, we describe properties of the EEG recordings obtained using this novel approach to EEG data collection. The formative phase was conducted with 9 families which informed protocols for consenting, child engagement strategies and data collection. The protocol was then implemented on 1265 families. 977 children (Mean age = 38.8 months, SD = 0.9) and 1199 adults (Mean age = 27.0 years, SD = 4) provided resting-state data for this study. 259 children refused to wear the EEG cap or removed it, and 58 children refused the eyes-closed recording session. Hardware or software issues were experienced during 30 and 25 recordings in eyes-open and eyes-closed conditions respectively. Disturbances during the recording sessions were rare and included participants moving their heads, touching the EEG headset with their hands, opening their eyes within the eyes-closed recording session, and presence of loud sounds in the testing environment. Similar to findings in laboratory-based studies from high-income settings, the percentage of recordings which showed an alpha peak was higher in eyes-closed than eyes-open condition, with the peak occurring most frequently in electrodes at O1 and O2 positions, and the mean frequency of the alpha peak was found to be lower in children (8.43 Hz, SD = 1.73) as compared to adults (10.71 Hz, SD = 3.96). We observed a deterioration in the EEG signal with prolonged device usage. This study demonstrates the acceptability, feasibility and utility of conducting EEG research at scale in a rural low-resource community, while highlighting its potential limitations, and offers the impetus needed to further refine the methods and devices and validate such scalable methods to overcome existing research inequity.

The impact of socioeconomic and stimulus inequality on human brain physiology

The brain undergoes profound structural and dynamical alteration in response to its stimulus environment. In animal studies, enriched stimulus environments result in numerous structural and dynamical changes along with cognitive enhancements. In human society factors such as education, travel, cell phones and motorized transport dramatically expand the rate and complexity of stimulus experience but diverge in access based on income. Correspondingly, poverty is associated with significant structural and dynamical differences in the brain, but it is unknown how this relates to disparity in stimulus access. Here we studied consumption of major stimulus factors along with measurement of brain signals using EEG in 402 people in India across an income range of $0.82 to $410/day. We show that the complexity of the EEG signal scaled logarithmically with overall stimulus consumption and income and linearly with education and travel. In contrast phone use jumped up at a threshold of $30/day corresponding to a similar jump in key spectral parameters that reflect the signal energy. Our results suggest that key aspects of brain physiology increase in lockstep with stimulus consumption and that we have not fully appreciated the profound way that stimulus expanding aspects of modern life are changing our brain physiology.

Poor Separation of Clinical Symptom Profiles by DSM-5 Disorder Criteria

Assessment of mental illness typically relies on a disorder classification system that is considered to be at odds with the vast disorder comorbidity and symptom heterogeneity that exists within and across patients. Patients with the same disorder diagnosis exhibit diverse symptom profiles and comorbidities creating numerous clinical and research challenges. Here we provide a quantitative analysis of the symptom heterogeneity and disorder comorbidity across a sample of 107,349 adult individuals (aged 18-85 years) from 8 English-speaking countries. Data were acquired using the Mental Health Quotient, an anonymous, online, self-report tool that comprehensively evaluates symptom profiles across 10 common mental health disorders. Dissimilarity of symptom profiles within and between disorders was then computed. We found a continuum of symptom prevalence rather than a clear separation of normal and disordered. While 58.7% of those with 5 or more clinically significant symptoms did not map to the diagnostic criteria of any of the 10 DSM-5 disorders studied, those with symptom profiles that mapped to at least one disorder had, on average, 20 clinically significant symptoms. Within this group, the heterogeneity of symptom profiles was almost as high within a disorder label as between 2 disorder labels and not separable from randomly selected groups of individuals with at least one of any of the 10 disorders. Overall, these results quantify the scale of misalignment between clinical symptom profiles and DSM-5 disorder labels and demonstrate that DSM-5 disorder criteria do not separate individuals from random when the complete mental health symptom profile of an individual is considered. Greater emphasis on empirical, disorder agnostic approaches to symptom profiling would help overcome existing challenges with heterogeneity and comorbidity, aiding clinical and research outcomes.

Assessment of Population Well-Being With the Mental Health Quotient (MHQ): Development and Usability Study

BACKGROUND

Existing mental health assessment tools provide an incomplete picture of symptom experience and create ambiguity, bias, and inconsistency in mental health outcomes. Furthermore, by focusing on disorders and dysfunction, they do not allow a view of mental health and well-being across a general population.

OBJECTIVE

This study aims to demonstrate the outcomes and validity of a new web-based assessment tool called the Mental Health Quotient (MHQ), which is designed for the general population. The MHQ covers the complete breadth of clinical mental health symptoms and also captures healthy mental functioning to provide a complete profile of an individual's mental health from clinical to thriving.

METHODS

The MHQ was developed based on the coding of symptoms assessed in 126 existing Diagnostic and Statistical Manual of Mental Disorders (DSM)-based psychiatric assessment tools as well as neuroscientific criteria laid out by Research Domain Criteria to arrive at a comprehensive set of semantically distinct mental health symptoms and attributes. These were formulated into questions on a 9-point scale with both positive and negative dimensions and developed into a web-based tool that takes approximately 14 min to complete. As its output, the assessment provides overall MHQ scores as well as subscores for 6 categories of mental health that distinguish clinical and at-risk groups from healthy populations based on a nonlinear scoring algorithm. MHQ items were also mapped to the DSM fifth edition (DSM-5), and clinical diagnostic criteria for 10 disorders were applied to the MHQ outcomes to cross-validate scores labeled at-risk and clinical. Initial data were collected from 1665 adult respondents to test the tool.

RESULTS

Scores in the normal healthy range spanned from 0 to 200 for the overall MHQ, with an average score of approximately 100 (SD 45), and from 0 to 100 with average scores between 48 (SD 21) and 55 (SD 22) for subscores in each of the 6 mental health subcategories. Overall, 2.46% (41/1665) and 13.09% (218/1665) of respondents were classified as clinical and at-risk, respectively, with negative scores. Validation against DSM-5 diagnostic criteria showed that 95% (39/41) of those designated clinical were positive for at least one DSM-5-based disorder, whereas only 1.14% (16/1406) of those with a positive MHQ score met the diagnostic criteria for a mental health disorder.

CONCLUSIONS

The MHQ provides a fast, easy, and comprehensive way to assess population mental health and well-being; identify at-risk individuals and subgroups; and provide diagnosis-relevant information across 10 disorders.

Proof of Concept of a Gamified DEvelopmental Assessment on an E-Platform (DEEP) Tool to Measure Cognitive Development in Rural Indian Preschool Children

Over 250 million children in developing countries are at risk of not achieving their developmental potential, and unlikely to receive timely interventions because existing developmental assessments that help identify children who are faltering are prohibitive for use in low resource contexts. To bridge this “detection gap,” we developed a tablet-based, gamified cognitive assessment tool named DEvelopmental assessment on an E-Platform (DEEP), which is feasible for delivery by non-specialists in rural Indian households and acceptable to all end-users. Here we provide proof-of-concept of using a supervised machine learning (ML) approach benchmarked to the Bayley’s Scale of Infant and Toddler Development, 3rd Edition (BSID-III) cognitive scale, to predict a child’s cognitive development using metrics derived from gameplay on DEEP. Two-hundred children aged 34–40 months recruited from rural Haryana, India were concurrently assessed using DEEP and BSID-III. Seventy percent of the sample was used for training the ML algorithms using a 10-fold cross validation approach and ensemble modeling, while 30% was assigned to the “test” dataset to evaluate the algorithm’s accuracy on novel data. Of the 522 features that computationally described children’s performance on DEEP, 31 features which together represented all nine games of DEEP were selected in the final model. The predicted DEEP scores were in good agreement (ICC [2,1] > 0.6) and positively correlated (Pearson’s r = 0.67) with BSID-cognitive scores, and model performance metrics were highly comparable between the training and test datasets. Importantly, the mean absolute prediction error was less than three points (<10% error) on a possible range of 31 points on the BSID-cognitive scale in both the training and test datasets. Leveraging the power of ML which allows iterative improvements as more diverse data become available for training, DEEP, pending further validation, holds promise to serve as an acceptable and feasible cognitive assessment tool to bridge the detection gap and support optimum child development.

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.

Coherence potentials: loss-less, all-or-none network events in the cortex

Transient associations among neurons are thought to underlie memory and behavior. However, little is known about how such associations occur or how they can be identified. Here we recorded ongoing local field potential (LFP) activity at multiple sites within the cortex of awake monkeys and organotypic cultures of cortex. We show that when the composite activity of a local neuronal group exceeds a threshold, its activity pattern, as reflected in the LFP, occurs without distortion at other cortex sites via fast synaptic transmission. These large-amplitude LFPs, which we call coherence potentials, extend up to hundreds of milliseconds and mark periods of loss-less spread of temporal and amplitude information much like action potentials at the single-cell level. However, coherence potentials have an additional degree of freedom in the diversity of their waveforms, which provides a high-dimensional parameter for encoding information and allows identification of particular associations. Such nonlinear behavior is analogous to the spread of ideas and behaviors in social networks.

The organizing principles of neuronal avalanches: cell assemblies in the cortex?

Neuronal avalanches are spatiotemporal patterns of neuronal activity that occur spontaneously in superficial layers of the mammalian cortex under various experimental conditions. These patterns reflect fast propagation of local synchrony, display a rich spatiotemporal diversity and recur over several hours. The statistical organization of pattern sizes is invariant to the choice of spatial scale, demonstrating that the functional linking of cortical sites into avalanches occurs on all spatial scales with a fractal organization. These features suggest an underlying network of neuronal interactions that balances diverse representations with predictable recurrence, similar to what has been theorized for cell assembly formation. We propose that avalanches reflect the transient formation of cell assemblies in the cortex and discuss various models that provide mechanistic insights into the underlying dynamics, suggesting that they arise in a critical regime.

LTP and adaptation to inactivity: Overlapping mechanisms and implications for metaplasticity

LTP and other rapidly induced forms of synaptic modification tune individual synaptic weights, whereas slower forms of plasticity such as adaptation to inactivity are thought to keep neurons within their firing limits and preserve their capability for information processing. Here we describe progress in understanding the relationship between LTP and adaptation to inactivity. A prevailing view is that adaptation to inactivity is purely postsynaptic, scales synaptic strength uniformly across all synapses, and thus preserves relative synaptic weights without interfering with signatures of prior LTP or the relative capacity for future LTP. However, recent evidence in hippocampal neurons indicates that, like LTP, adaptation to AMPA receptor blockade can draw upon a repertoire of synaptic expression mechanisms including enhancement of presynaptic vesicular turnover and increased quantal amplitude mediated by recruitment of homomeric GluR1 AMPA receptors. These pre- and postsynaptic changes appeared coordinated and preferentially expressed at subset of synapses, thereby increasing the variability of miniature EPSCs. In contrast to the NMDA receptor-, Ca2+ entry-dependent induction of LTP, adaptation to inactivity may be mediated by attenuation of voltage-sensitive L-type Ca2+ channel function. The associated intracellular signaling involves elevation of betaCaMKII, which in turn downregulates alphaCaMKII, a key player in LTP. Thus, adaptation to inactivity and LTP are not strictly independent with regard to mechanisms of signaling and expression. Indeed, we and others have found that responses to LTP-inducing stimuli can be sharply altered by prior inactivity, suggesting that the slow adaptation changes the rules of plasticity-an interesting example of "metaplasticity".

Adaptation to synaptic inactivity in hippocampal neurons

In response to activity deprivation, CNS neurons undergo slow adaptive modification of unitary synaptic transmission. The changes are comparable in degree to those induced by brief intense stimulation, but their molecular basis is largely unknown. Our data indicate that prolonged AMPAR blockade acts through loss of Ca2+ entry through L-type Ca2+ channels to bring about an increase in both vesicle pool size and turnover rate, as well as a postsynaptic enhancement of the contribution of GluR1 homomers, concentrated at the largest synapses. The changes were consistent with a morphological scaling of overall synapse size, but also featured a dramatic shift toward synaptic drive contributed by the Ca2+-permeable homomeric GluR1 receptors. These results extend beyond "synaptic homeostasis" to involve more profound changes that can be better described as "metaplasticity".

alpha- and betaCaMKII. Inverse regulation by neuronal activity and opposing effects on synaptic strength

We show that alpha and betaCaMKII are inversely regulated by activity in hippocampal neurons in culture: the alpha/beta ratio shifts toward alpha during increased activity and beta during decreased activity. The swing in ratio is approximately 5-fold and may help tune the CaMKII holoenzyme to changing intensities of Ca(2+) signaling. The regulation of CaMKII levels uses distinguishable pathways, one responsive to NMDA receptor blockade that controls alphaCaMKII alone, the other responsive to AMPA receptor blockade and involving betaCaMKII and possibly further downstream effects of betaCaMKII on alphaCaMKII. Overexpression of alphaCaMKII or betaCaMKII resulted in opposing effects on unitary synaptic strength as well as mEPSC frequency that could account in part for activity-dependent effects observed with chronic blockade of AMPA receptors. Regulation of CaMKII subunit composition may be important for both activity-dependent synaptic homeostasis and plasticity.