Differing brain structural correlates of familial and environmental risk for major depressive disorder revealed by a combined VBM/pattern recognition approach. Issue 2 (10th September 2015)

• Record Type: Journal Article
• Authors: Opel, N.; Zwanzger, P.; Redlich, R.; Grotegerd, D.; Dohm, K.; Arolt, V.; Heindel, W.; Kugel, H.; Dannlowski, U.
• Is Part Of: Psychological medicine. Volume 46:Issue 2(2016)
• Page Start: 277
• Page End: 290
• Publication Date: 2015-09-10
• Subjects: Childhood maltreatment, -- depression, -- magnetic resonance imaging, -- morphometry, -- risk; Psychiatry -- Periodicals; Medicine and psychology -- Periodicals; Clinical psychology -- Periodicals; 616.89
• DOI: 10.1017/S0033291715001683
• Languages: English
• ISSNs: 0033-2917

Abstract

Abstract : Background: Neuroimaging traits of either familial or environmental risk for major depressive disorder (MDD) have been interpreted as possibly useful vulnerability markers. However, the simultaneous occurrence of familial and environmental risk might prove to be a major obstacle in the attempt of recent studies to confine the precise impact of each of these conditions on brain structure. Moreover, the exclusive use of group-level analyses does not permit prediction of individual illness risk which would be the basic requirement for the clinical application of imaging vulnerability markers. Hence, we aimed to distinguish between brain structural characteristics of familial predisposition and environmental stress by using both group- and individual-level analyses. Method: We investigated grey matter alterations between 20 healthy control subjects (HC) and 20 MDD patients; 16 healthy first-degree relatives of MDD patients (FH+) and 20 healthy subjects exposed to former childhood maltreatment (CM+) by using a combined VBM/pattern recognition approach. Results: We found similar grey matter reductions in the insula and the orbitofrontal cortex in patients and FH+ subjects and in the hippocampus in patients and CM+ subjects. No direct overlap in grey matter alterations was found between FH+ and CM+ subjects. Pattern classification successfully detected subjects at risk for the disease even by strictly focusing on morphological traits of MDD. Conclusions: Familial and environmental risk factors for MDD are associated with differing morphometric anomalies. Pattern recognition might be a promising instrument in the search for and future application of vulnerability markers for MDD.