Big Data Begin in Psychiatry Weissman, Myrna M
JAMA psychiatry (Chicago, Ill.),
09/2020, Volume:
77, Issue:
9
Journal Article
Peer reviewed
The last 40 years of JAMA Psychiatry are reviewed as a celebration of its achievements. The focus of this article is on the evolution of big data as reflected in key journal articles. The review ...begins in 1984 with the introduction of the Epidemiology Catchment Area (ECA) study and Freedman’s editorial “Psychiatric Epidemiology Counts.” The ECA study (N = 17 000), for the first time in a survey, used clinical diagnosis in 5 urban communities, thus linking research and care to population rates of psychiatric diagnosis. The review then traces the subsequent evolution of big data to 5 overlapping phases, other population surveys in the US and globally, cohort studies, administrative claims, large genetic data sets, and electronic health records. Each of these topics are illustrated in articles in JAMA Psychiatry. The many caveats to these choices, the historical roots before 1984, as well as the controversy around the choice of topics and the term big data are acknowledged. The foundation for big data in psychiatry was built on the development of defined and reliable diagnosis, assessment tools that could be used in large samples, the computational evolution for handling large data sets, hypothesis generated by smaller studies of humans and animals with carefully crafted phenotypes, the welcoming of investigators from all over the world with calls for broader diversity, open access and the sharing of data, and introduction of electronic health records more recently. Future directions as well as the opportunities for the complementary roles of big and little data are described. JAMA Psychiatry will continue to be a rich resource of these publications.
...depression is a heterogeneous entity experienced with various combinations of signs and symptoms, severity levels, and longitudinal trajectories. ...core features of the condition have been ...described over thousands of years, long before the advent of contemporary classifications, and in diverse communities and cultures. More efficient prevention of depression is likely to have powerful impacts on the Sustainable Development Goals for a country and the health of individuals and families. 5 The experiences of depression and recovery are unique for each individual Depression is the result of a set of factors, typically the interaction of proximal adversities with genetic, social, environmental, and developmental risk and resilience factors. Empowering individuals, families, and communities to work with professionals who can learn from their experiences and help demand the implementation of known preventive and therapeutic strategies and to hold health-care systems and decision makers accountable is vital. 7 A formulation is needed to personalise care Detection and diagnosis of depression on the basis of symptoms, function, and duration should be accompanied by a clinical review or formulation for each person, which takes into account individual values and preferences, life stories, and circumstances.
With the proliferation of multi-site neuroimaging studies, there is a greater need for handling non-biological variance introduced by differences in MRI scanners and acquisition protocols. Such ...unwanted sources of variation, which we refer to as “scanner effects”, can hinder the detection of imaging features associated with clinical covariates of interest and cause spurious findings. In this paper, we investigate scanner effects in two large multi-site studies on cortical thickness measurements across a total of 11 scanners. We propose a set of tools for visualizing and identifying scanner effects that are generalizable to other modalities. We then propose to use ComBat, a technique adopted from the genomics literature and recently applied to diffusion tensor imaging data, to combine and harmonize cortical thickness values across scanners. We show that ComBat removes unwanted sources of scan variability while simultaneously increasing the power and reproducibility of subsequent statistical analyses. We also show that ComBat is useful for combining imaging data with the goal of studying life-span trajectories in the brain.
•Cortical thickness (CT) measurements are highly scanner specific.•Identifying scanner effects is crucial for inference and biomarker development.•We propose to use ComBat to harmonize cortical thickness values across scanners.
Acquiring resting‐state functional magnetic resonance imaging (fMRI) datasets at multiple MRI scanners and clinical sites can improve statistical power and generalizability of results. However, ...multi‐site neuroimaging studies have reported considerable nonbiological variability in fMRI measurements due to different scanner manufacturers and acquisition protocols. These undesirable sources of variability may limit power to detect effects of interest and may even result in erroneous findings. Until now, there has not been an approach that removes unwanted site effects. In this study, using a relatively large multi‐site (4 sites) fMRI dataset, we investigated the impact of site effects on functional connectivity and network measures estimated by widely used connectivity metrics and brain parcellations. The protocols and image acquisition of the dataset used in this study had been homogenized using identical MRI phantom acquisitions from each of the neuroimaging sites; however, intersite acquisition effects were not completely eliminated. Indeed, in this study, we found that the magnitude of site effects depended on the choice of connectivity metric and brain atlas. Therefore, to further remove site effects, we applied ComBat, a harmonization technique previously shown to eliminate site effects in multi‐site diffusion tensor imaging (DTI) and cortical thickness studies. In the current work, ComBat successfully removed site effects identified in connectivity and network measures and increased the power to detect age associations when using optimal combinations of connectivity metrics and brain atlases. Our proposed ComBat harmonization approach for fMRI‐derived connectivity measures facilitates reliable and efficient analysis of retrospective and prospective multi‐site fMRI neuroimaging studies.
This commentary, as requested, presents the highlights of my research career. The epidemiology of psychiatric disorders study, challenged in a small study, the notion that diagnosis for psychiatric ...disorders could be made in a community survey. This pilot study was the basis for the Epidemiology Catchment Area Study (ECA) with 18,000 participants and the many more updated surveys, which followed. The families at High and Low Risk for Depression study in its 40th year challenged the notion that children didn't get depressed and showed that parental depression was the major risk for depression, which began in youth and reoccurred over the lifespan. Interpersonal psychotherapy (IPT), now has been tested in over 150 clinical trials, recommended by the World Health Organization (WHO), globally in China, Germany, Ukraine, and many more countries.
Weissman reflects on the study regarding the nature or nurture of depression. She cites a study that uses a cleverly designed natural experiment and its findings confirmed the strong protective ...effect of a nurturing rearing environment on the child's well-being. However, the author recognizes the limitation of the study which should be considered for the future directions on the topic and asserts that depression remains nature and nurture and the relative proportions, for which types of depression, are under study.
Objective:Interpersonal psychotherapy (IPT) has been developed for the treatment of depression but has been examined for several other mental disorders. A comprehensive meta-analysis of all ...randomized trials examining the effects of IPT for all mental health problems was conducted.Method:Searches in PubMed, PsycInfo, Embase, and Cochrane were conducted to identify all trials examining IPT for any mental health problem.Results:Ninety studies with 11,434 participants were included. IPT for acute-phase depression had moderate-to-large effects compared with control groups (g=0.60; 95% CI=0.45–0.75). No significant difference was found with other therapies (differential g=0.06) and pharmacotherapy (g=–0.13). Combined treatment was more effective than IPT alone (g=0.24). IPT in subthreshold depression significantly prevented the onset of major depression, and maintenance IPT significantly reduced relapse. IPT had significant effects on eating disorders, but the effects are probably slightly smaller than those of cognitive-behavioral therapy (CBT) in the acute phase of treatment. In anxiety disorders, IPT had large effects compared with control groups, and there is no evidence that IPT was less effective than CBT. There was risk of bias as defined by the Cochrane Collaboration in the majority of studies. There was little indication that the presence of bias influenced outcome.Conclusions:IPT is effective in the acute treatment of depression and may be effective in the prevention of new depressive disorders and in preventing relapse. IPT may also be effective in the treatment of eating disorders and anxiety disorders and has shown promising effects in some other mental health disorders.
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