Estimating uncertainty in read-out patterns: Application to controls-based denoising and voxel-based morphometry patterns in neurodegenerative and neuropsychiatric diseases.

Quantifying pathology-related patterns in patient data with pattern expression score (PES) is a standard approach in medical image analysis. In order to estimate the PES error, we here propose to express the uncertainty contained in read-out patterns in terms of the expected squared Euclidean distance between the read-out pattern and the unknown "true" pattern (squared standard error of the read-out pattern, SE ). Using SE , we predicted and optimized the net benefit (NBe) of the recently suggested method controls-based denoising (CODE) by weighting patterns of nonpathological variance (NPV).

Uncertainty estimation and explainability in deep learning-based age estimation of the human brain: Results from the German National Cohort MRI study.

Brain ageing is a complex neurobiological process associated with morphological changes that can be assessed on MRI scans. Recently, Deep learning (DL)-based approaches have been proposed for the prediction of chronological brain age from MR images yielding high accuracy. These approaches, however, usually do not address quantification of uncertainty and, therefore, intrinsic physiological variability.

Central Insulin Modulates Dopamine Signaling in the Human Striatum.

Objective: Activity in the dopaminergic pathways of the brain is highly sensitive to body weight and metabolic states. Animal studies show that dopamine neurons are important targets for the metabolic hormone insulin with abolished effects in the insulin-resistant state, leading to increases in body weight and food intake. In humans, the influence of central acting insulin on dopamine and effects of their interplay are still elusive.

Controls-based denoising, a new approach for medical image analysis, improves prediction of conversion to Alzheimer's disease with FDG-PET.

Objective: The pattern expression score (PES), i.e., the degree to which a pathology-related pattern is present, is frequently used in FDG-brain-PET analysis and has been shown to be a powerful predictor of conversion to Alzheimer's disease (AD) in mild cognitive impairment (MCI).

Hypermetabolism in the cerebellum and brainstem and cortical hypometabolism are independently associated with cognitive impairment in Parkinson's disease.

Purpose: Cognitive impairment (CI) in Parkinson's disease (PD) is associated with a widespread reduction in cortical glucose metabolism and relative increases in the cerebellum and brainstem as measured using F-fluorodesoxyglucose (FDG) PET. We separately analysed CI-related hypermetabolism and hypometabolism in comparison with neuropsychological test performance and investigated whether increased FDG uptake is a true feature of the disease or a normalization effect.

Methods: The study included 29 subjects (12 patients with PD, 10 patients with PD dementia and 7 healthy controls") who underwent FDG PET and comprehensive neuropsychological testing.

Home-Based Physical Behavior in Late Stage Parkinson Disease Dementia: Differences between Cognitive Subtypes.

Background: For the early diagnosis of Parkinson disease dementia (PDD), objective home-based tools are needed to quantify even mild stages of dysfunction of the activities of daily living (ADL).

Objectives: In this pilot study, home-based physical behavior was assessed to examine whether it is possible to distinguish mild cognitive impairment (PD-MCI) from PDD.

Methods: Fifty-five patients with mild to severe Parkinson disease (PD) participated in this cross-sectional study.