Daniel Emden
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View article: MRI-derived estimation of biological aging in patients with affective disorders in a 9-year follow-up - a prospective marker of future recurrence
MRI-derived estimation of biological aging in patients with affective disorders in a 9-year follow-up - a prospective marker of future recurrence Open
We investigated whether the brain age gap (BAG)—the difference between chronological age and age estimated from structural MRI scans—is associated with long-term disease course in affective disorders, using a prospective nine-year follow-u…
View article: Symptom trajectories and their predictive factors in psychiatric inpatients with depression
Symptom trajectories and their predictive factors in psychiatric inpatients with depression Open
Our findings highlight reproducible inpatient symptom trajectories shaped by shared risk factors and the potential of longitudinal phenotyping to guide individualized care in depression.
View article: Scalable depression monitoring with smartphone speech: a multimodal benchmark and topic analysis
Scalable depression monitoring with smartphone speech: a multimodal benchmark and topic analysis Open
Objective, scalable biomarkers are needed for continuous monitoring of major depressive disorder (MDD). Smartphone-collected speech is promising, yet extracting clinically useful signals remains difficult. We analysed 3 151 weekly voice di…
View article: MRI-derived estimation of biological aging in patients with affective disorders in a 9-year follow-up - a prospective marker of future recurrence
MRI-derived estimation of biological aging in patients with affective disorders in a 9-year follow-up - a prospective marker of future recurrence Open
Background We investigated associations of the brain age gap (BAG), the difference between actual and estimated age derived from MRI scans, with disease course over nine years in patients with affective disorders in a long-term prospective…
View article: Momentary Depression Severity Prediction in Patients With Acute Depression Who Undergo Sleep Deprivation Therapy: Speech-Based Machine Learning Approach
Momentary Depression Severity Prediction in Patients With Acute Depression Who Undergo Sleep Deprivation Therapy: Speech-Based Machine Learning Approach Open
Background Mobile devices for remote monitoring are inevitable tools to support treatment and patient care, especially in recurrent diseases such as major depressive disorder. The aim of this study was to learn if machine learning (ML) mod…
View article: deepmriprep: Voxel-based Morphometry (VBM) Preprocessing via Deep Neural Networks
deepmriprep: Voxel-based Morphometry (VBM) Preprocessing via Deep Neural Networks Open
Voxel-based Morphometry (VBM) has emerged as a powerful approach in neuroimaging research, utilized in over 7,000 studies since the year 2000. Using Magnetic Resonance Imaging (MRI) data, VBM assesses variations in the local density of bra…
View article: Speech-based Machine Learning for Momentary Depression-Severity Prediction in Acutely Depressed Patients undergoing Sleep Deprivation Therapy (Preprint)
Speech-based Machine Learning for Momentary Depression-Severity Prediction in Acutely Depressed Patients undergoing Sleep Deprivation Therapy (Preprint) Open
BACKGROUND Mobile devices for remote monitoring are inevitable tools to support treatment and patient care, especially in recurrent diseases such as Major Depressive Disorder. The aim of this study was to learn if machine learning (ML) mod…
View article: GateNet: A novel neural network architecture for automated flow cytometry gating
GateNet: A novel neural network architecture for automated flow cytometry gating Open
GateNet enables fully end-to-end automated gating in flow cytometry, overcoming the labor-intensive and error-prone nature of manual adjustments. The neural network achieves human-level performance on unseen samples and generalizes well to…
Generalizability of Clinical Prediction Models in Mental Health - Real-World Validation of Machine Learning Models for Depressive Symptom Prediction Open
Mental health research faces the challenge of developing machine learning models for clinical decision support. Concerns about the generalizability of such models to real-world populations due to sampling effects and disparities in availab…
View article: A Systematic Evaluation of Machine Learning–Based Biomarkers for Major Depressive Disorder
A Systematic Evaluation of Machine Learning–Based Biomarkers for Major Depressive Disorder Open
Importance Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, major depressive disorder (MDD), no informative bioma…
View article: GateNet: A novel Neural Network Architecture for Automated Flow Cytometry Gating
GateNet: A novel Neural Network Architecture for Automated Flow Cytometry Gating Open
Flow cytometry is widely used to identify cell populations in patient-derived fluids such as peripheral blood (PB) or cerebrospinal fluid (CSF). While ubiquitous in research and clinical practice, flow cytometry requires gating, i.e. cell …
The impact of depression and childhood maltreatment experiences on psychological adaptation from lockdown to relaxation periods during the COVID-19 pandemic Open
The COVID-19 pandemic has presented a significant challenge to societal mental health. Yet, it remains unknown which factors influence the mental adaptation from lockdown to subsequent relaxation periods, particularly for vulnerable groups…
Remote monitoring of depression severity: A machine learning approach Open
Depression is a widely prevalent psychiatric illness with variable levels of severity that necessitate different approaches to treatment. To enhance the management of this condition, there is a growing interest in utilizing mobile devices,…
View article: Deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks
Deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks Open
Brain extraction in magnetic resonance imaging (MRI) data is an important segmentation step in many neuroimaging preprocessing pipelines. Image segmentation is one of the research fields in which deep learning had the biggest impact in rec…
View article: From Group-Differences to Single-Subject Probability: Conformal Prediction-based Uncertainty Estimation for Brain-Age Modeling
From Group-Differences to Single-Subject Probability: Conformal Prediction-based Uncertainty Estimation for Brain-Age Modeling Open
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 singl…
View article: Towards a network control theory of electroconvulsive therapy response
Towards a network control theory of electroconvulsive therapy response Open
Electroconvulsive Therapy (ECT) is arguably the most effective intervention for treatment-resistant depression. While large interindividual variability exists, a theory capable of explaining individual response to ECT remains elusive. To a…
A complex systems model of temporal fluctuations in depressive symptomatology Open
Symptom fluctuation is one of the hallmarks of major depression. However, a principled, quantitative framework explaining when and how symptoms change remains elusive. Following a complex systems perspective, we model clusters of depressiv…
View article: Quantifying Deviations of Brain Structure and Function in Major Depressive Disorder Across Neuroimaging Modalities
Quantifying Deviations of Brain Structure and Function in Major Depressive Disorder Across Neuroimaging Modalities Open
Results of this case-control study suggest that even for maximum univariate biological differences, deviations between patients with MDD and healthy controls were remarkably small, single-participant prediction was not possible, and simila…
View article: An uncertainty-aware, shareable, and transparent neural network architecture for brain-age modeling
An uncertainty-aware, shareable, and transparent neural network architecture for brain-age modeling Open
A network-based quantification of brain aging uncovers and fixes a fundamental problem of all previous approaches.
View article: More Alike than Different: Quantifying Deviations of Brain Structure and Function in Major Depressive Disorder across Neuroimaging Modalities
More Alike than Different: Quantifying Deviations of Brain Structure and Function in Major Depressive Disorder across Neuroimaging Modalities Open
Introduction: Identifying neurobiological differences between patients suffering from Major Depressive Disorder (MDD) and healthy individuals has been a mainstay of clinical neuroscience for decades. However, recent meta- and mega-analyses…
View article: Towards a Network Control Theory of Electroconvulsive Therapy Response
Towards a Network Control Theory of Electroconvulsive Therapy Response Open
Electroconvulsive Therapy (ECT) is arguably the most effective intervention for treatment-resistant depression. While large interindividual variability exists, a theory capable of predicting individual response to ECT remains elusive. To a…
View article: Genetic, Individual, and Familial Risk Correlates of Brain Network Controllability in Major Depressive Disorder
Genetic, Individual, and Familial Risk Correlates of Brain Network Controllability in Major Depressive Disorder Open
Background: A therapeutic intervention in psychiatry can be viewed as an attempt to influence the brain's large-scale, dynamic network state transitions underlying cognition and behavior. Building on connectome-based graph analysis and con…
A Network Control Theory Approach to Longitudinal Symptom Dynamics in\n Major Depressive Disorder Open
Background: The evolution of symptoms over time is at the heart of\nunderstanding and treating mental disorders. However, a principled,\nquantitative framework explaining symptom dynamics remains elusive. Here, we\npropose a Network Contro…
A Network Control Theory Approach to Longitudinal Symptom Dynamics in Major Depressive Disorder Open
Background: The evolution of symptoms over time is at the heart of understanding and treating mental disorders. However, a principled, quantitative framework explaining symptom dynamics remains elusive. Here, we propose a Network Control T…
PHOTONAI—A Python API for rapid machine learning model development Open
PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development. It functions as a unifying framework allowing the user to easily access and combine algorithms from different toolboxes into custom…