Brain grey matter volume alterations in late-life depression

Abstract

IntroductionLate-life depression (LLD) is a common and disabling disorder, which is typically defined as depression in individuals older than 60 years. Estimates of the prevalence of clinically relevant depressive symptoms in older adults typically range from 10% to 15%, and rates of major depression range from 1% to 5%.1-3 Late-life depression is characterized by diverse etiological factors that remain poorly understood.4,5 Successes in delineating the neurobiology of LLD have closely paralleled progress in neuroimaging and have provided evidence that LLD is associated with underlying brain anatomic and functional abnormalities. Structural neuroimaging findings have revealed grey matter volume reductions in multiple fronto-striatal-limbic regions of older depressed patients, including the anterior cingulate cortex (ACC), the prefrontal cortices, the striatum, the hippocampus and the amygdala, 4,6,7 and these regions are The aim of the present study was to conduct a meta-analysis that integrated the reported VBM studies, to determine consistent grey matter alterations in individuals with LLD. Methods: A systematic search was conducted to identify VBM studies that compared patients with LLD and healthy controls. We performed a meta-analysis using the effect size signed differential mapping method to quantitatively estimate regional grey matter abnormalities in patients with LLD. Results: We included 9 studies with 11 data sets comprising 292 patients with LLD and 278 healthy controls in our meta-analysis. The pooled and subgroup meta-analyses showed robust grey matter reductions in the right lentiform nucleus extending into the parahippocampus, the hippocampus and the amygdala, the bilateral medial frontal gyrus and the right subcallosal gyrus as well as a grey matter increase in the right lingual gyrus. Meta-regression analyses showed that mean age and the percentage of female patients with LLD were not significantly related to grey matter changes. Limitations: The analysis techniques, patient characteristics and clinical variables of the studies included were heterogeneous, and most participants were medicated. Conclusion: The present meta-analysis is, to our knowledge, the first to overcome previous inconsistencies in the VBM studies of LLD and provide robust evidence for grey matter alterations within fronto-striatal-limbic networks, thereby implicating them in the pathophysiology of LLD. The mean age and the percentage of female patients with LLD did not appear to have a measurable impact on grey matter changes, although we cannot rule out the contributory effects of medication.

Microstructural brain abnormalities in medication-free patients with major depressive disorder: a systematic review and meta-analysis of diffusion tensor imaging

“…46 The AES-SDM results are represented on a 3-dimensionally rendered brain, removing part of the left or right hemisphere and highlighting areas of the brain where FA alterations reached significant values. 55 …”

Voxel-wise meta-analyses of brain blood flow and local synchrony abnormalities in medication-free patients with major depressive disorder

“…This checklist was based on previous metaanalytic studies. [24][25][26] Although the checklist was not designed as an as sessment tool, it provided some objective indication of the rigour of individual studies. At least 2 authors reviewed every paper and independently determined a completeness rating.…”

Brain Structural Connectivity in Late-Life Major Depressive Disorder

“…A subsequent meta-analysis argued that a voxel-based approach provides a more comprehensive strategy to identify the grey matter correlates of depression that is not biased by the need to pre-specify regions of interest (84); this study found, compared with healthy controls, LLD patients had lower grey matter volume in the hippocampus, parahippocampus, lentiform nucleus, amygdala, medial frontal gyrus, and right subcallosal gyrus, but had significantly larger right lingual gyrus volumes. Discrepancies between the results of these studies may reflect differences in the analytic approach (e.g.…”