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Contains fulltext : 307363.pdf (Publisher’s version ) (Open Access)

15 februari 2023, Item does not contain fulltext, The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN), and performance was evaluated using 5-fold cross-validation. Features were visualized using GCN-Explainer, an ablation study and univariate t-testing. The results showed a mean classification accuracy of 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Sex classification accuracy was substantially better across datasets (73-81%). Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes. These results suggest that whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity as further supported by the higher accuracy for sex classification using the same methods. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies.

Item does not contain fulltext, BACKGROUND: Individuals with major depressive disorder (MDD) are at higher risk for obesity. In turn, weight gain is a predisposing factor for depression. Although clinical data are sparse, suicide risk also appears to be elevated in obese patients. This study used data from the European Group for the Study of Resistant Depression (GSRD) to investigate clinical outcomes associated with body mass index (BMI) in MDD. METHODS: Data were drawn from 892 participants with MDD over the age of 18 years (580 female, 50.5 ± 13.6 years). Response and resistance to antidepressant medication, depression rating scale scores, and further clinical and sociodemographic variables were compared using multiple logistic and linear regressions controlled for age, sex, and risk of weight gain due to psychopharmacotherapy. RESULTS: Of the 892 participants, 323 were categorized as treatment-responsive and 569 as treatment-resistant. Within this cohort, 278 (31.1 %) were overweight (BMI = 25-29.9 kg/m(2)) and 151 (16.9 %) were obese (BMI > 30 kg/m(2)). Elevated BMI was significantly associated with higher suicidality, longer duration of psychiatric hospitalizations over their lifetimes, earlier age of onset of MDD, and comorbidities. There was a trend-wise association of BMI with treatment resistance. LIMITATIONS: Data were analyzed in a retrospective, cross-sectional design. BMI was used as an exclusive measure of overweight and obesity. CONCLUSIONS: Participants with comorbid MDD and overweight/obesity were at risk for worse clinical outcomes, suggesting that weight gain should be closely monitored in individuals with MDD in daily clinical practice. Further studies are needed to explore the neurobiological mechanisms linking elevated BMI to impaired brain health.

Item does not contain fulltext, BACKGROUND: Serotonin-norepinephrine reuptake inhibitors (SNRIs) are among the most frequently prescribed antidepressants (ADs) for major depressive disorder (MDD), with an increasing trend in the last decade. Given the relative dearth of information regarding rationales for their preferred use as first-line ADs in the broad clinical routine, the present study systematically investigated real-world characteristics of MDD patients prescribed either SNRIs or other AD substances across different countries and treatment settings. METHODS: In the present secondary analyses based on a large European, multi-site, naturalistic and cross-sectional investigation with a retrospective assessment of treatment outcome, we firstly defined the proportion of MDD patients receiving SNRIs as first-line AD psychopharmacotherapy and secondly compared their sociodemographic and clinical characteristics to those patients prescribed alternative first-line ADs during their current major depressive episode (MDE). RESULTS: Within the total sample of 1410 MDD patients, 336 (23.8 %) received first-line SNRIs. Compared to other ADs, SNRIs were significantly associated with inpatient care, suicidality and treatment resistance during the current MDE, and a longer lifetime duration of psychiatric hospitalizations. Moreover, greater severity of depressive symptoms at study entry, higher daily doses of the administered ADs, as well as more frequent prescriptions of psychopharmacotherapeutic add-on strategies in general and antipsychotic augmentation in particular, were significantly related to first-line SNRIs. CONCLUSIONS: Considering the limitations of a cross-sectional and retrospective study design, our data point towards a preferred use of first-line SNRIs in a generally more severely ill MDD patients, although they did not lead to superior treatment outcomes compared to alternative ADs.

Contains fulltext : 242755.pdf (Publisher’s version ) (Open Access)

Major depressive disorder (MDD) is associated with abnormal neural circuitry. It can be measured by assessing functional connectivity (FC) at resting-state functional MRI, that may help identifying neural markers of MDD and provide further efficient diagnosis and monitor treatment outcomes. The main aim of the present study is to investigate, in an unbiased way, functional alterations in patients with MDD using a large multi-center dataset from the PsyMRI consortium including 1546 participants from 19 centers ( www.psymri.com ). After applying strict exclusion criteria, the final sample consisted of 606 MDD patients (age: 35.8 ± 11.9 y.o.; females: 60.7%) and 476 healthy participants (age: 33.3 ± 11.0 y.o.; females: 56.7%). We found significant relative hypoconnectivity within somatosensory motor (SMN), salience (SN) networks and between SMN, SN, dorsal attention (DAN), and visual (VN) networks in MDD patients. No significant differences were detected within the default mode (DMN) and frontoparietal networks (FPN). In addition, alterations in network organization were observed in terms of significantly lower network segregation of SMN in MDD patients. Although medicated patients showed significantly lower FC within DMN, FPN, and SN than unmedicated patients, there were no differences between medicated and unmedicated groups in terms of network organization in SMN. We conclude that the network organization of cortical networks, involved in processing of sensory information, might be a more stable neuroimaging marker for MDD than previously assumed alterations in higher-order neural networks like DMN and FPN.