Flow cytometry (FC) is an analytical technique which is used in biological research to identify cell types and in the clinical context to diagnose human diseases including hematological malignancies[1]. FC characterizes cell types by measuring the light scatter and fluorescence emission properties of fluorochrome-labeled antibodies from each of the thousands of cells a sample contains[2]. Based on the measured intensity of the fluorescence and the light scatter of these cell events, cells are distinguished from contaminants, and then each cell is classified into a specific cell population. Traditionally, this classification is done by manually identifying and partitioning (i.e. 'gating') these populations based on visual inspection of mostly two-dimensional intensity histograms of two respective fluorescence emission detectors (Figure 1). Figure 1 Schematic manual gating workflow which corrects for measurement variances across samples caused by the batch effect. The first obstacle during gating is the batch effect, i.e. technical variance of event measurements across samples, caused e.g. by the variability of the staining procedure or by the decay of the exciting laser and the fluorescence emissions of fluorophore-bound antibodies.

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing an extensive sample of people with MDD (N=2,772) and HC (N=4,240), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible.

The brain-age gap is one of the most investigated risk markers for brain changes across disorders. While the field is progressing towards large-scale models, recently incorporating uncertainty estimates, no model to date provides the single-subject risk assessment capability essential for clinical application. In order to enable the clinical use of brain-age as a biomarker, we here combine uncertainty-aware deep Neural Networks with conformal prediction theory. This approach provides statistical guarantees with respect to single-subject uncertainty estimates and allows for the calculation of an individual's probability for accelerated brain-aging. Building on this, we show empirically in a sample of N=16,794 participants that 1. a lower or comparable error as state-of-the-art, large-scale brain-age models, 2. the statistical guarantees regarding single-subject uncertainty estimation indeed hold for every participant, and 3. that the higher individual probabilities of accelerated brain-aging derived from our model are associated with Alzheimer's Disease, Bipolar Disorder and Major Depressive Disorder.

This article describes the implementation and use of PHOTON, a high-level Python API designed to simplify and accelerate the process of machine learning model development. It enables designing both basic and advanced machine learning pipeline architectures and automatizes the repetitive training, optimization and evaluation workflow. PHOTON offers easy access to established machine learning toolboxes as well as the possibility to integrate custom algorithms and solutions for any part of the model construction and evaluation process. By adding a layer of abstraction incorporating current best practices it offers an easy-to-use, flexible approach to implementing fast, reproducible, and unbiased machine learning solutions.