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Found 11 papers, 2 papers with code
GateNet: A novel Neural Network Architecture for Automated Flow Cytometry Gating
While ubiquitous in research and clinical practice, flow cytometry requires gating, i. e. cell type identification which requires labor-intensive and error-prone manual adjustments.
Deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks
Brain extraction in magnetic resonance imaging (MRI) data is an important segmentation step in many neuroimaging preprocessing pipelines.
From Group-Differences to Single-Subject Probability: Conformal Prediction-based Uncertainty Estimation for Brain-Age Modeling
The brain-age gap is one of the most investigated risk markers for brain changes across disorders.
More Alike than Different: Quantifying Deviations of Brain Structure and Function in Major Depressive Disorder across Neuroimaging Modalities
Discussion: We provide a large-scale, multimodal analysis of univariate biological differences between MDD patients and controls and show that even under near-ideal conditions and for maximum biological differences, deviations are extremely small and similarity dominates.
Towards a Network Control Theory of Electroconvulsive Therapy Response
We show that whole-brain controllability metrics based on pre-ECT structural connectome data predict ECT response in accordance with our hypotheses.
A Network Control Theory Approach to Longitudinal Symptom Dynamics in Major Depressive Disorder
We quantified the theoretical energy required for each patient and time-point to reach a symptom-free state given individual symptom-network topology (E 0 ) and 1) tested if E 0 predicts future symptom improvement and 2) whether this relationship is moderated by Polygenic Risk Scores (PRS) of mental disorders, childhood maltreatment experience, and self-reported resilience.
Genetic, Individual, and Familial Risk Correlates of Brain Network Controllability in Major Depressive Disorder
Conclusions: We show that network controllability is related to genetic, individual, and familial risk in MDD patients.
An Uncertainty-Aware, Shareable and Transparent Neural Network Architecture for Brain-Age Modeling
The deviation between chronological age and age predicted from neuroimaging data has been identified as a sensitive risk-marker of cross-disorder brain changes, growing into a cornerstone of biological age-research.
Predicting brain-age from raw T 1 -weighted Magnetic Resonance Imaging data using 3D Convolutional Neural Networks
For comparison, state-of-the-art models using preprocessed neuroimaging data are trained and validated on the same samples.
PHOTONAI -- A Python API for Rapid Machine Learning Model Development
PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development.
Systematic Misestimation of Machine Learning Performance in Neuroimaging Studies of Depression
We currently observe a disconcerting phenomenon in machine learning studies in psychiatry: While we would expect larger samples to yield better results due to the availability of more data, larger machine learning studies consistently show much weaker performance than the numerous small-scale studies.
