Vincent Holstein
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View article: Ecological Assessment of Transdiagnostic Clinical Symptoms in Serious Mental Illness with Daily Smartphone Surveys
Ecological Assessment of Transdiagnostic Clinical Symptoms in Serious Mental Illness with Daily Smartphone Surveys Open
Clinical symptoms in serious mental illness (SMI) fluctuate dynamically, yet standard interview-based assessments often fail to capture these daily changes. Smartphone-based ecological surveys offer a scalable approach to monitoring sympto…
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: Judged by your neighbors: Brain structural normativity profiles for large and heterogeneous samples
Judged by your neighbors: Brain structural normativity profiles for large and heterogeneous samples Open
The detection of norm deviations is fundamental to clinical decision making and impacts our ability to diagnose and treat diseases effectively. Current normative modeling approaches rely on generic comparisons and quantify deviations in re…
View article: Systems biology dissection of PTSD and MDD across brain regions, cell types, and blood
Systems biology dissection of PTSD and MDD across brain regions, cell types, and blood Open
The molecular pathology of stress-related disorders remains elusive. Our brain multiregion, multiomic study of posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) included the central nucleus of the amygdala, hippocamp…
View article: Predicting dimensions of depression from smartphone data
Predicting dimensions of depression from smartphone data Open
Depressive disorders are highly prevalent but demand nuanced personalized treatment that traditional approaches in psychiatry cannot address. This gap has prompted a surge of interest in leveraging digital technology, particularly smartpho…
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: PHOTONAI-Graph - A Python Toolbox for Graph Machine Learning
PHOTONAI-Graph - A Python Toolbox for Graph Machine Learning Open
Graph data is an omnipresent way to represent information in machine learning. Especially, in neuroscience research, data from Diffusion-Tensor Imaging (DTI) and functional Magnetic Resonance Imaging (fMRI) is commonly represented as graph…
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: 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: 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…
An Uncertainty-Aware, Shareable and Transparent Neural Network Architecture for Brain-Age Modeling Open
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. However, Machine Learni…
Predicting brain-age from raw T 1 -weighted Magnetic Resonance Imaging data using 3D Convolutional Neural Networks Open
Age prediction based on Magnetic Resonance Imaging (MRI) data of the brain is a biomarker to quantify the progress of brain diseases and aging. Current approaches rely on preparing the data with multiple preprocessing steps, such as regist…