Fifteen years of NESDA Neuroimaging: An overview of results related to clinical profile and bio-social risk factors of major depressive disorder and common anxiety disorders.

Abstract

The longitudinal Netherlands Study of Depression and Anxiety (NESDA) Neuroimaging study was set up in 2003 to investigate whether neuroanatomical and functional abnormalities during tasks of primary emotional processing, executive planning and memory formation, and intrinsic brain connectivity are i) shared by individuals with major depressive disorder (MDD) and common anxiety disorders; and ii) characterized by symptomatology-specific abnormalities. Furthermore, questions related to individual variations in vulnerability for onset, comorbidity, and longitudinal course could be investigated. Between 2005 and 2007, 233 individuals fulfilling a diagnosis of MDD, panic disorder, social anxiety disorder and/or generalized anxiety disorder and 68 healthy controls aging between 18 and 57 were invited from the NESDA main sample (n\u202f=\u202f2981). An emotional faces processing task, an emotional word-encoding task, and an executive planning task were administered during 3T BOLD-fMRI acquisitions. In addition, resting state BOLD-fMRI was acquired and T1-weighted structural imaging was performed. All participants were invited to participate in the two-year and nine-year follow-up MRI measurement. Fifteen years of NESDA Neuroimaging demonstrated common morphological and neurocognitive abnormalities across individuals with depression and anxiety disorders. It however provided limited support for the idea of more extensive abnormalities in patients suffering from both depression and anxiety, despite their worse prognosis. Risk factors including childhood maltreatment and specific risk genes had an emotion processing modulating effect, apparently stronger than effects of diagnostic labels. Furthermore, brain imaging data, especially during emotion processing seemed valuable for predicting the long-term course of affective disorders, outperforming prediction based on clinical information alone.