Differential Abnormal Pattern of Anterior Cingulate Gyrus Activation in Unipolar and Bipolar Depression: an fMRI and Pattern Classification Approach

Biomarkers for bipolar disorder: current status and challenges ahead

INTRODUCTION

Bipolar disorder (BD) is a chronic psychiatric disorder marked by clinical and pathophysiological heterogeneity. There is a high expectation that personalized approaches can improve the management of patients with BD. For that, identification and validation of potential biomarkers are fundamental. Areas covered: This manuscript will critically review the current status of different biomarkers for BD, including peripheral, genetic, neuroimaging, and neurophysiological candidates, discussing the challenges to move the field forward. Expert commentary: There are no lab or complementary tests currently recommended for the diagnosis or management of patients with BD. Panels composed by multiple biomarkers will probably contribute to stratifying patients according to their clinical stage, therapeutic response, and prognosis.

Neural Correlates of Affective Disturbances: A Comparative Meta-analysis of Negative Affect Processing in Borderline Personality Disorder, Major Depressive Disorder, and Posttraumatic Stress Disorder

BACKGROUND

Borderline personality disorder (BPD), major depressive disorder (MDD), and posttraumatic stress disorder (PTSD) are prominent examples of mental disorders with affective disturbances. Notably, all three disorders share a generally heightened negative affect, which is presumably the result of shared neural abnormalities in affective processing. In this meta-analysis, we aimed to identify transdiagnostic and disorder-specific abnormalities during the processing of negative compared with neutral stimuli.

METHODS

We synthesized neuroimaging findings of affect processing in BPD, MDD, and PTSD and calculated combined coordinate- and image-based meta-analyses. The analysis comprised 70 distinct study samples with a total of 31 unthresholded statistical parametric maps. Twenty-four studies had a focus on BPD (431 individuals with BPD, 436 healthy control subjects [HCs]), 32 studies on MDD (789 individuals with current MDD, 870 HCs), and 14 studies on PTSD (247 individuals with PTSD, 245 HCs).

RESULTS

Findings showed limbic hyperactivations in BPD and PTSD compared with limbic activation of HCs. In contrast, patients with MDD showed blunted amygdala activation in comparison with that of HCs. Additionally, the calculation of overlapping brain abnormalities in BPD, MDD, and PTSD highlighted transdiagnostic hyperactivation of the right median cingulate gyri and hypoactivation of the right middle frontal gyrus and the right middle occipital gyrus. Finally, disorder-specific comparisons also illustrate unique abnormalities for each mental disorder.

CONCLUSIONS

The present results support shared and disorder-specific neural abnormalities in patients with affective disturbances.

Machine learning in major depression: From classification to treatment outcome prediction

AIMS

Major depression disorder (MDD) is the single greatest cause of disability and morbidity, and affects about 10% of the population worldwide. Currently, there are no clinically useful diagnostic biomarkers that are able to confirm a diagnosis of MDD from bipolar disorder (BD) in the early depressive episode. Therefore, exploring translational biomarkers of mood disorders based on machine learning is in pressing need, though it is challenging, but with great potential to improve our understanding of these disorders.

DISCUSSIONS

In this study, we review popular machine-learning methods used for brain imaging classification and predictions, and provide an overview of studies, specifically for MDD, that have used magnetic resonance imaging data to either (a) classify MDDs from controls or other mood disorders or (b) investigate treatment outcome predictors for individual patients. Finally, challenges, future directions, and potential limitations related to MDD biomarker identification are also discussed, with a goal of offering a comprehensive overview that may help readers to better understand the applications of neuroimaging data mining in depression.

CONCLUSIONS

We hope such efforts may highlight the need for an urgently needed paradigm shift in treatment, to guide personalized optimal clinical care.

Neuroimaging Advances for Depression

Depression is one of the world's most prevalent mental health problems, with as many as 350 million sufferers worldwide and close to 20 million sufferers in the US. While neuroimaging applications for identifying various types of depression have made enormous strides in recent years, no findings have been sufficiently replicated or considered significant enough to warrant application in clinical settings. Our authors are well equipped to tell us what the future may bring.