| 1. |
- Landén, Mikael, 1966, et al.
(författare)
-
Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group
- 2021
-
Ingår i: Molecular Psychiatry. - : Springer Science and Business Media LLC. - 1359-4184 .- 1476-5578. ; 26
-
Tidskriftsartikel (refereegranskat)abstract
- Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18–75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted “brain age” and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen’s d = 0.14, 95% CI: 0.08–0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates. © 2020, The Author(s).
|
|
| 2. |
- Mann, P. J., et al.
(författare)
-
The biogeochemistry of carbon across a gradient of streams and rivers within the Congo Basin
- 2014
-
Ingår i: Journal of Geophysical Research: Biogeosciences. - 2169-8953. ; 119:4, s. 687-702
-
Tidskriftsartikel (refereegranskat)abstract
- Dissolved organic carbon (DOC) and inorganic carbon (DIC, pCO(2)), lignin biomarkers, and theoptical properties of dissolved organic matter (DOM) were measured in a gradient of streams and rivers within the Congo Basin, with the aim of examining how vegetation cover and hydrology influences the composition and concentration of fluvial carbon (C). Three sampling campaigns (February 2010, November 2010, and August 2011) spanning 56 sites are compared by subbasin watershed land cover type (savannah, tropical forest, and swamp) and hydrologic regime (high, intermediate, and low). Land cover properties predominately controlled the amount and quality of DOC, chromophoric DOM (CDOM) and lignin phenol concentrations (Sigma(8)) exported in streams and rivers throughout the Congo Basin. Higher DIC concentrations and changing DOM composition (lower molecular weight, less aromatic C) during periods of low hydrologic flow indicated shifting rapid overland supply pathways in wet conditions to deeper groundwater inputs during drier periods. Lower DOC concentrations in forest and swamp subbasins were apparent with increasing catchment area, indicating enhanced DOC loss with extended water residence time. Surface water pCO(2) in savannah and tropical forest catchments ranged between 2,600 and 11,922 mu atm, with swamp regions exhibiting extremely high pCO(2) (10,598-15,802 mu atm), highlighting their potential as significant pathways for water-air efflux. Our data suggest that the quantity and quality of DOM exported to streams and rivers are largely driven by terrestrial ecosystem structure and that anthropogenic land use or climate change may impact fluvial C composition and reactivity, with ramifications for regional C budgets and future climate scenarios.
|
|
