Explainable AI Methods for Neuroimaging: Systematic Failures of Common Tools, the Need for Domain-Specific Validation, and a Proposal for Safe Application.

Trustworthy interpretation of deep learning models is critical for neuroimaging applications, yet commonly used Explainable AI (XAI) methods lack rigorous validation, risking misinterpretation. We performed the first large-scale, systematic comparison of XAI methods on ~45,000 structural brain MRIs using a novel XAI validation framework. This framework establishes verifiable ground truth by constructing prediction tasks with known signal sources - from localized anatomical features to subject-specific clinical lesions - without artificially altering input images.

Machine Learning Classification of Smoking Behaviours-From Social Environment to the Prefrontal Cortex.

The pronounced heterogeneity in smoking trajectories-ranging from occasional or heavy use to successful quitting -highlights substantial interindividual variation within the smoking population. Machine learning is particularly well suited to capture these complex patterns that may be challenging for traditional inferential statistics to uncover. In this study, we applied machine learning to data from a population-based cohort to identify multimodal markers that distinguish smokers from never smokers at baseline and predict long-term cessation success at a 10-year follow-up.

Generating Synthetic Task-based Brain Fingerprints for Population Neuroscience Using Deep Learning.

Task-based functional magnetic resonance imaging (tb-fMRI) reveals individual differences in the neural basis of cognitive functions by linking specific tasks to neural responses. However, scaling tb-fMRI to population-level studies is challenging due to its cognitive demands, variations in task design across studies, and the limited scope of tasks in large datasets. To address this, we propose DeepTaskGen, a deep-learning approach that generates non-acquired task-based contrast maps from resting-state fMRI (rs-fMRI) data.

Do Transformers and CNNs Learn Different Concepts of Brain Age?

"Predicted brain age" refers to a biomarker of structural brain health derived from machine learning analysis of T1-weighted brain magnetic resonance (MR) images. A range of machine learning methods have been used to predict brain age, with convolutional neural networks (CNNs) currently yielding state-of-the-art accuracies. Recent advances in deep learning have introduced transformers, which are conceptually distinct from CNNs, and appear to set new benchmarks in various domains of computer vision.

Design and methods of the research unit 5187 PREACT (towards precision psychotherapy for non-respondent patients: from signatures to predictions to clinical utility) - a study protocol for a multicentre observational study in outpatient clinics.

Introduction: Cognitive-behavioural therapy (CBT) works-but not equally well for all patients. Less than 50% of patients with internalising disorders achieve clinically meaningful improvement, with negative consequences for patients and healthcare systems. The research unit (RU) 5187 seeks to improve this situation by an in-depth investigation of the phenomenon of treatment non-response (TNR) to CBT.

The ReCoDe addiction research consortium: Losing and regaining control over drug intake-Findings and future perspectives.

Substance use disorders (SUDs) are seen as a continuum ranging from goal-directed and hedonic drug use to loss of control over drug intake with aversive consequences for mental and physical health and social functioning. The main goals of our interdisciplinary German collaborative research centre on Losing and Regaining Control over Drug Intake (ReCoDe) are (i) to study triggers (drug cues, stressors, drug priming) and modifying factors (age, gender, physical activity, cognitive functions, childhood adversity, social factors, such as loneliness and social contact/interaction) that longitudinally modulate the trajectories of losing and regaining control over drug consumption under real-life conditions. (ii) To study underlying behavioural, cognitive and neurobiological mechanisms of disease trajectories and drug-related behaviours and (iii) to provide non-invasive mechanism-based interventions.

Evaluation of machine learning-based classification of clinical impairment and prediction of clinical worsening in multiple sclerosis.

Background: Robust predictive models of clinical impairment and worsening in multiple sclerosis (MS) are needed to identify patients at risk and optimize treatment strategies.

Objective: To evaluate whether machine learning (ML) methods can classify clinical impairment and predict worsening in people with MS (pwMS) and, if so, which combination of clinical and magnetic resonance imaging (MRI) features and ML algorithm is optimal.

Methods: We used baseline clinical and structural MRI data from two MS cohorts (Berlin: n = 125, Amsterdam: n = 330) to evaluate the capability of five ML models in classifying clinical impairment at baseline and predicting future clinical worsening over a follow-up of 2 and 5 years.

Secure federated learning for Alzheimer's disease detection.

Machine Learning (ML) is considered a promising tool to aid and accelerate diagnosis in various medical areas, including neuroimaging. However, its success is set back by the lack of large-scale public datasets. Indeed, medical institutions possess a large amount of data; however, open-sourcing is prevented by the legal requirements to protect the patient's privacy.

Benchmarking the influence of pre-training on explanation performance in MR image classification.

Convolutional Neural Networks (CNNs) are frequently and successfully used in medical prediction tasks. They are often used in combination with transfer learning, leading to improved performance when training data for the task are scarce. The resulting models are highly complex and typically do not provide any insight into their predictive mechanisms, motivating the field of "explainable" artificial intelligence (XAI).

Performance reserves in brain-imaging-based phenotype prediction.

This study examines the impact of sample size on predicting cognitive and mental health phenotypes from brain imaging via machine learning. Our analysis shows a 3- to 9-fold improvement in prediction performance when sample size increases from 1,000 to 1 M participants. However, despite this increase, the data suggest that prediction accuracy remains worryingly low and far from fully exploiting the predictive potential of brain imaging data.

Uncontrolled eating and sensation-seeking partially explain the prediction of future binge drinking from adolescent brain structure.

Binge drinking behavior in early adulthood can be predicted from brain structure during early adolescence with an accuracy of above 70%. We investigated whether this accurate prospective prediction of alcohol misuse behavior can be explained by psychometric variables such as personality traits or mental health comorbidities in a data-driven approach. We analyzed a subset of adolescents who did not have any prior binge drinking experience at age 14 (IMAGEN dataset, n = 555, 52.

Artificial Intelligence Bias in Health Care: Web-Based Survey.

Background: Resources are increasingly spent on artificial intelligence (AI) solutions for medical applications aiming to improve diagnosis, treatment, and prevention of diseases. While the need for transparency and reduction of bias in data and algorithm development has been addressed in past studies, little is known about the knowledge and perception of bias among AI developers.

Objective: This study's objective was to survey AI specialists in health care to investigate developers' perceptions of bias in AI algorithms for health care applications and their awareness and use of preventative measures.

Higher performance for women than men in MRI-based Alzheimer's disease detection.

Introduction: Although machine learning classifiers have been frequently used to detect Alzheimer's disease (AD) based on structural brain MRI data, potential bias with respect to sex and age has not yet been addressed. Here, we examine a state-of-the-art AD classifier for potential sex and age bias even in the case of balanced training data.

Methods: Based on an age- and sex-balanced cohort of 432 subjects (306 healthy controls, 126 subjects with AD) extracted from the ADNI data base, we trained a convolutional neural network to detect AD in MRI brain scans and performed ten different random training-validation-test splits to increase robustness of the results.

Machine learning-based prediction of clinical outcomes after first-ever ischemic stroke.

Background: Accurate prediction of clinical outcomes in individual patients following acute stroke is vital for healthcare providers to optimize treatment strategies and plan further patient care. Here, we use advanced machine learning (ML) techniques to systematically compare the prediction of functional recovery, cognitive function, depression, and mortality of first-ever ischemic stroke patients and to identify the leading prognostic factors.

Methods: We predicted clinical outcomes for 307 patients (151 females, 156 males; 68 ± 14 years) from the PROSpective Cohort with Incident Stroke Berlin study using 43 baseline features.

Predicting psychotic relapse following randomised discontinuation of paliperidone in individuals with schizophrenia or schizoaffective disorder: an individual participant data analysis.

Background: Predicting relapse for individuals with psychotic disorders is not well established, especially after discontinuation of antipsychotic treatment. We aimed to identify general prognostic factors of relapse for all participants (irrespective of treatment continuation or discontinuation) and specific predictors of relapse for treatment discontinuation, using machine learning.

Methods: For this individual participant data analysis, we searched the Yale University Open Data Access Project's database for placebo-controlled, randomised antipsychotic discontinuation trials with participants with schizophrenia or schizoaffective disorder (aged ≥18 years).

Deep neural network heatmaps capture Alzheimer's disease patterns reported in a large meta-analysis of neuroimaging studies.

Deep neural networks currently provide the most advanced and accurate machine learning models to distinguish between structural MRI scans of subjects with Alzheimer's disease and healthy controls. Unfortunately, the subtle brain alterations captured by these models are difficult to interpret because of the complexity of these multi-layer and non-linear models. Several heatmap methods have been proposed to address this issue and analyze the imaging patterns extracted from the deep neural networks, but no quantitative comparison between these methods has been carried out so far.

Prediction of high and low disease activity in early MS patients using multiple kernel learning identifies importance of lateral ventricle intensity.

Background: Lack of easy-to-interpret disease activity prediction methods in early MS can lead to worse patient prognosis.

Objectives: Using machine learning (multiple kernel learning - MKL) models, we assessed the prognostic value of various clinical and MRI measures for disease activity.

Methods: Early MS patients ( = 148) with at least two associated clinical and MRI visits were investigated.

Structural differences in adolescent brains can predict alcohol misuse.

Alcohol misuse during adolescence (AAM) has been associated with disruptive development of adolescent brains. In this longitudinal machine learning (ML) study, we could predict AAM significantly from brain structure (T1-weighted imaging and DTI) with accuracies of 73 -78% in the IMAGEN dataset (n∼1182). Our results not only show that structural differences in brain can predict AAM, but also suggests that such differences might precede AAM behavior in the data.

Generating 3D TOF-MRA volumes and segmentation labels using generative adversarial networks.

Deep learning requires large labeled datasets that are difficult to gather in medical imaging due to data privacy issues and time-consuming manual labeling. Generative Adversarial Networks (GANs) can alleviate these challenges enabling synthesis of shareable data. While 2D GANs have been used to generate 2D images with their corresponding labels, they cannot capture the volumetric information of 3D medical imaging.

Patch individual filter layers in CNNs to harness the spatial homogeneity of neuroimaging data.

Convolutional neural networks (CNNs)-as a type of deep learning-have been specifically designed for highly heterogeneous data, such as natural images. Neuroimaging data, however, is comparably homogeneous due to (1) the uniform structure of the brain and (2) additional efforts to spatially normalize the data to a standard template using linear and non-linear transformations. To harness spatial homogeneity of neuroimaging data, we suggest here a new CNN architecture that combines the idea of hierarchical abstraction in CNNs with a prior on the spatial homogeneity of neuroimaging data.

Nucleus basalis of Meynert predicts cognition after deep brain stimulation in Parkinson's disease.

Introduction: Subthalamic DBS in Parkinson's disease has been associated with cognitive decline in few cases. Volume reduction of the nucleus basalis of Meynert (NBM) seems to precede cognitive impairment in Parkinson's disease. In this retrospective study, we evaluated NBM volume as a predictor of cognitive outcome 1 year after subthalamic DBS.

The Collaborative Research Center FONDA.

Today's scientific data analysis very often requires complex Data Analysis Workflows (DAWs) executed over distributed computational infrastructures, e.g., clusters.