Publications by authors named "Stephan Feder"

Structured patient data play a key role in all types of clinical research. They are often collected in study databases for research purposes. In order to describe characteristics of a next-generation study database and assess the feasibility of its implementation a proof-of-concept study in a German university hospital was performed.

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Objectives: Combinations of resting-state fMRI and machine-learning techniques are increasingly employed to develop diagnostic models for mental disorders. However, little is known about the neurobiological heterogeneity of depression and diagnostic machine learning has mainly been tested in homogeneous samples. Our main objective was to explore the inherent structure of a diverse unipolar depression sample.

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In small, selected samples, an approach combining resting-state functional connectivity MRI and multivariate pattern analysis has been able to successfully classify patients diagnosed with unipolar depression. Purposes of this investigation were to assess the generalizability of this approach to a large clinically more realistic sample and secondarily to assess the replicability of previously reported methodological feasibility in a more homogeneous subgroup with pronounced depressive symptoms. Two independent subsets were drawn from the depression and control cohorts of the BiDirect study, each with 180 patients with and 180 controls without depression.

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Depression has been associated with various alterations in magnetic resonance imaging (MRI) derived resting-state functional connectivity. Recently, homotopic connectivity, defined as functional connectivity between homotopic regions across hemispheres, has been reported to be reduced in patients with major depressive disorder (MDD). However, little is known about structural factors underlying alterations of homotopic connectivity, which would contribute to the understanding of the altered neurophysiological architecture in patients with MDD.

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