Editorial: Neuroimaging in clinical psychiatry--when will the pay off begin?

The Utility of Event-Related Potentials in Clinical Psychology

Event-related potentials (ERPs) are direct measures of brain activity that can be leveraged for clinically meaningful research. They can relate robustly both to continuous measures of individual difference and to categorical diagnoses in ways that clarify similarities and distinctions between apparently related disorders and traits. ERPs can be linked to genetic risk, can act as moderators of developmental trajectories and responses to stress, and can be leveraged to identify those at greater risk for psychopathology, especially when used in combination with other neural and self-report measures. ERPs can inform models of the development of, and risk for, psychopathology. Finally, ERPs can be used as targets for existing and novel interventions and prevention efforts. We provide concrete examples for each of these possibilities by focusing on programmatic research on the error-related negativity and anxiety, and thus show that ERPs are poised to make greater contributions toward the identification, prediction, treatment, and prevention of mental disorders.

The Value of Neuroimaging Techniques in the Translation and Transdiagnostic Validation of Psychiatric Diagnoses - Selective Review

Psychiatric diagnosis has long been perceived as more of an art than a science since its foundations lie within the observation, and the self-report of the patients themselves and objective diagnostic biomarkers are lacking. Furthermore, the diagnostic tools in use not only stray away from the conventional medical framework but also remain invalidated with evidence-based concepts. However, neuroscience, as a source of valid objective knowledge has initiated the process of a paradigm shift underlined by the main concept of psychiatric disorders being "brain disorders". It is also a bridge closing the explanatory gap among the different fields of medicine via the translation of the knowledge within a multidisciplinary framework. The contemporary neuroimaging methods, such as fMRI provide researchers with an entirely new set of tools to reform the current status quo by creating an opportunity to define and validate objective biomarkers that can be translated into clinical practice. Combining multiple neuroimaging techniques with the knowledge of the role of genetic factors, neurochemical imbalance and neuroinflammatory processes in the etiopathophysiology of psychiatric disorders is a step towards a comprehensive biological explanation of psychiatric disorders and a final differentiation of psychiatry as a well-founded medical science. In addition, the neuroscientific knowledge gained thus far suggests a necessity for directional change to exploring multidisciplinary concepts, such as multiple causality and dimensionality of psychiatric symptoms and disorders. A concomitant viewpoint transition of the notion of validity in psychiatry with a focus on an integrative validatory approach may facilitate the building of a collaborative bridge above the wall existing between the scientific fields analyzing the mind and those studying the brain.

The neural markers of MRI to differentiate depression and panic disorder

Depression and panic disorder (PD) share the common pathophysiology from the perspectives of neurotransmitters. The relatively high comorbidity between depression and PD contributes to the substantial obstacles to differentiate from depression and PD, especially for the brain pathophysiology. There are significant differences in the diagnostic criteria between depression and PD. However, the paradox of similar pathophysiology and different diagnostic criteria in these two disorders were still the issues needing to be addressed. Therefore the clarification of potential difference in the field of neuroscience and pathophysiology between depression and PD can help the clinicians and scientists to understand more comprehensively about significant differences between depression and PD. The researchers should be curious about the underlying difference of pathophysiology beneath the significant distinction of clinical symptoms. In this review article, I tried to find some evidences for the differences between depression and PD, especially for neural markers revealed by magnetic resonance imaging (MRI). The distinctions of structural and functional alterations in depression and PD are reviewed. From the structural perspectives, PD seems to have less severe gray matter alterations in frontal and temporal lobes than depression. The study of white matter microintegrity reveals more widespread alterations in fronto-limbic circuit of depression patients than PD patients, such as the uncinate fasciculus and anterior thalamic radiation. PD might have a more restrictive pattern of structural alterations when compared to depression. For the functional perspectives, the core site of depression pathophysiology is the anterior subnetwork of resting-state network, such as anterior cingulate cortex, which is not significantly altered in PD. A possibly emerging pattern of fronto-limbic distinction between depression and PD has been revealed by these explorative reports. The future trend for machine learning and pattern recognition might confirm the differentiation pattern between depression and PD based on the explorative results.

Application of machine learning classification for structural brain MRI in mood disorders: Critical review from a clinical perspective

Mood disorders are a highly prevalent group of mental disorders causing substantial socioeconomic burden. There are various methodological approaches for identifying the underlying mechanisms of the etiology, symptomatology, and therapeutics of mood disorders; however, neuroimaging studies have provided the most direct evidence for mood disorder neural substrates by visualizing the brains of living individuals. The prefrontal cortex, hippocampus, amygdala, thalamus, ventral striatum, and corpus callosum are associated with depression and bipolar disorder. Identifying the distinct and common contributions of these anatomical regions to depression and bipolar disorder have broadened and deepened our understanding of mood disorders. However, the extent to which neuroimaging research findings contribute to clinical practice in the real-world setting is unclear. As traditional or non-machine learning MRI studies have analyzed group-level differences, it is not possible to directly translate findings from research to clinical practice; the knowledge gained pertains to the disorder, but not to individuals. On the other hand, a machine learning approach makes it possible to provide individual-level classifications. For the past two decades, many studies have reported on the classification accuracy of machine learning-based neuroimaging studies from the perspective of diagnosis and treatment response. However, for the application of a machine learning-based brain MRI approach in real world clinical settings, several major issues should be considered. Secondary changes due to illness duration and medication, clinical subtypes and heterogeneity, comorbidities, and cost-effectiveness restrict the generalization of the current machine learning findings. Sophisticated classification of clinical and diagnostic subtypes is needed. Additionally, as the approach is inevitably limited by sample size, multi-site participation and data-sharing are needed in the future.

Neuromarkers for Mental Disorders: Harnessing Population Neuroscience

Despite abundant research into the neurobiology of mental disorders, to date neurobiological insights have had very little impact on psychiatric diagnosis or treatment. In this review, we contend that the search for neuroimaging biomarkers-neuromarkers-of mental disorders is a highly promising avenue toward improved psychiatric healthcare. However, many of the traditional tools used for psychiatric neuroimaging are inadequate for the identification of neuromarkers. Specifically, we highlight the need for larger samples and for multivariate analysis. Approaches such as machine learning are likely to be beneficial for interrogating high-dimensional neuroimaging data. We suggest that broad, population-based study designs will be important for developing neuromarkers of mental disorders, and will facilitate a move away from a phenomenological definition of mental disorder categories and toward psychiatric nosology based on biological evidence. We provide an outline of how the development of neuromarkers should occur, emphasizing the need for tests of external and construct validity, and for collaborative research efforts. Finally, we highlight some concerns regarding the development, and use of, neuromarkers in psychiatric healthcare.

Time-Frequency Reward-Related Delta Prospectively Predicts the Development of Adolescent-Onset Depression

BACKGROUND

A blunted reward positivity (RewP), an event-related potential elicited by feedback indicating monetary gain relative to loss, was recently shown to prospectively predict the development of adolescent-onset depression. Time-frequency-based representations of this activity (e.g., reward-related delta) have also been associated with depression. This study is a reanalysis of the time-domain RewP investigation to examine the incremental value of time-frequency indices in the prediction of adolescent-onset depression.

METHODS

The sample included 444 13- to 15-year-old girls with no lifetime history of a depressive disorder. At baseline, adolescents completed a monetary guessing task, and both time-domain and time-frequency analyses were conducted on the event-related potential response to gain and loss feedback. Lifetime psychiatric history in the adolescent and a biological parent were evaluated with diagnostic interviews, and adolescents' current depressive symptoms were assessed using a self-report questionnaire. Adolescents were interviewed again approximately 18 months later to identify first-onset depressive disorder.

RESULTS

Blunted reward-related delta predicted first-onset depressive disorder 18 months later, independent of the time-domain RewP and psychosocial risk factors (i.e., adolescent baseline depressive symptoms, adolescent and parental psychiatric history). In contrast, loss-related theta did not predict the development of depression. Reward-related delta increased sensitivity (73.8% to 82.8%) and positive predictive value (45.0% to 70.9%) for first-onset depressive disorder when applied in parallel and in series, respectively, with baseline depressive symptoms and the time-domain RewP.

CONCLUSIONS

This study provides evidence that frequency-based representations of event-related potentials provide incremental value in the prediction of psychiatric disorders.