Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset

Diabetes like many diseases and biological processes is not mono-causal. On the one hand multifactorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics

Resting-State Functional Connectivity in Patients with Long-Term Remission of Cushing’s Disease

Glucocorticoid disturbance can be a cause of psychiatric symptoms. Cushing's disease represents a unique model for examining the effects of prolonged exposure to high levels of endogenous cortisol on the human brain as well as for examining the relation between these effects and psychiatric symptomatology. This study aimed to investigate resting-state functional connectivity (RSFC) of the limbic network, the default mode network (DMN), and the executive control network in patients with long-term remission of Cushing's disease. RSFC of these three networks of interest was compared between patients in remission of Cushing's disease (n=24; 4 male, mean age=44.96 years) and matched healthy controls (n=24; 4 male, mean age=46.5 years), using probabilistic independent component analysis to extract the networks and a dual regression method to compare both groups. Psychological and cognitive functioning was assessed with validated questionnaires and interviews. In comparison with controls, patients with remission of Cushing's disease showed an increased RSFC between the limbic network and the subgenual subregion of the anterior cingulate cortex (ACC) as well as an increased RSFC of the DMN in the left lateral occipital cortex. However, these findings were not associated with psychiatric symptoms in the patient group. Our data indicate that previous exposure to hypercortisolism is related to persisting changes in brain function

Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group

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