Background Striatal dopamine deficiency and metabolic changes are well‐known phenomena in dementia with Lewy bodies and can be quantified in vivo by 123I‐Ioflupane brain single‐photon emission computed tomography of dopamine transporter and 18F‐fluorodesoxyglucose PET. However, the linkage between both biomarkers is ill‐understood. Objective We used the hitherto largest study cohort of combined imaging from the European consortium to elucidate the role of both biomarkers in the pathophysiological course of dementia with Lewy bodies. Methods We compared striatal dopamine deficiency and glucose metabolism of 84 dementia with Lewy body patients and comparable healthy controls. After normalization of data, we tested their correlation by region‐of‐interest–based and voxel‐based methods, controlled for study center, age, sex, education, and current cognitive impairment. Metabolic connectivity was analyzed by inter‐region coefficients stratified by dopamine deficiency and compared to healthy controls. Results There was an inverse relationship between striatal dopamine availability and relative glucose hypermetabolism, pronounced in the basal ganglia and in limbic regions. With increasing dopamine deficiency, metabolic connectivity showed strong deteriorations in distinct brain regions implicated in disease symptoms, with greatest disruptions in the basal ganglia and limbic system, coincident with the pattern of relative hypermetabolism. Conclusions Relative glucose hypermetabolism and disturbed metabolic connectivity of limbic and basal ganglia circuits are metabolic correlates of dopamine deficiency in dementia with Lewy bodies. Identification of specific metabolic network alterations in patients with early dopamine deficiency may serve as an additional supporting biomarker for timely diagnosis of dementia with Lewy bodies. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

While some individuals age without pathological memory impairments, others develop age‐associated cognitive diseases. Since changes in cognitive function develop slowly over time in these patients, they are often diagnosed at an advanced stage of molecular pathology, a time point when causative treatments fail. Thus, there is great need for the identification of inexpensive and minimal invasive approaches that could be used for screening with the aim to identify individuals at risk for cognitive decline that can then undergo further diagnostics and eventually stratified therapies. In this study, we use an integrative approach combining the analysis of human data and mechanistic studies in model systems to identify a circulating 3‐microRNA signature that reflects key processes linked to neural homeostasis and inform about cognitive status. We furthermore provide evidence that expression changes in this signature represent multiple mechanisms deregulated in the aging and diseased brain and are a suitable target for RNA therapeutics.

Schizophrenia is a severe psychiatric disorder with a lifetime prevalence of about 1%. People with schizophrenia have a 4-fold higher prevalence of metabolic syndrome than the general population, mainly because of antipsychotic treatment but perhaps also because of decreased physical activity. Metabolic syndrome is a risk factor for cardiovascular diseases, and the risk of these diseases is 2- to 3-fold higher in schizophrenia patients than in the general population. The suicide risk is also higher in schizophrenia, partly as a result of depression, positive, and cognitive symptoms of the disease. The higher suicide rate and higher rate of cardiac mortality, a consequence of the increased prevalance of cardiovascular diseases, contribute to the reduced life expectancy, which is up to 20 years lower than in the general population. Regular physical activity, especially in combination with psychosocial and dietary interventions, can improve parameters of the metabolic syndrome and cardiorespiratory fitness. Furthermore, aerobic exercise has been shown to improve cognitive deficits; total symptom severity, including positive and negative symptoms; depression; quality of life; and global functioning. High-intensity interval endurance training is a feasible and effective way to improve cardiorespiratory fitness and metabolic parameters and has been established as such in somatic disorders. It may have more beneficial effects on the metabolic state than more moderate and continuous endurance training methods, but to date it has not been investigated in schizophrenia patients in controlled, randomized trials. This review discusses physical training methods to improve cardiorespiratory fitness and reduce metabolic syndrome risk factors and symptoms in schizophrenia patients. The results of studies and future high-quality clinical trials are expected to lead to the development of an evidence-based physical training program for patients that includes practical recommendations, such as the optimal length and type of aerobic exercise programs and the ideal combination of exercise, psychoeducation, and individual weight management sessions.

Background Although convolutional neural networks (CNNs) achieve high diagnostic accuracy for detecting Alzheimer’s disease (AD) dementia based on magnetic resonance imaging (MRI) scans, they are not yet applied in clinical routine. One important reason for this is a lack of model comprehensibility. Recently developed visualization methods for deriving CNN relevance maps may help to fill this gap as they allow the visualization of key input image features that drive the decision of the model. We investigated whether models with higher accuracy also rely more on discriminative brain regions predefined by prior knowledge. Methods We trained a CNN for the detection of AD in N = 663 T1-weighted MRI scans of patients with dementia and amnestic mild cognitive impairment (MCI) and verified the accuracy of the models via cross-validation and in three independent samples including in total N = 1655 cases. We evaluated the association of relevance scores and hippocampus volume to validate the clinical utility of this approach. To improve model comprehensibility, we implemented an interactive visualization of 3D CNN relevance maps, thereby allowing intuitive model inspection. Results Across the three independent datasets, group separation showed high accuracy for AD dementia versus controls (AUC ≥ 0.91) and moderate accuracy for amnestic MCI versus controls (AUC ≈ 0.74). Relevance maps indicated that hippocampal atrophy was considered the most informative factor for AD detection, with additional contributions from atrophy in other cortical and subcortical regions. Relevance scores within the hippocampus were highly correlated with hippocampal volumes (Pearson’s r ≈ −0.86, p < 0.001). Conclusion The relevance maps highlighted atrophy in regions that we had hypothesized a priori. This strengthens the comprehensibility of the CNN models, which were trained in a purely data-driven manner based on the scans and diagnosis labels. The high hippocampus relevance scores as well as the high performance achieved in independent samples support the validity of the CNN models in the detection of AD-related MRI abnormalities. The presented data-driven and hypothesis-free CNN modeling approach might provide a useful tool to automatically derive discriminative features for complex diagnostic tasks where clear clinical criteria are still missing, for instance for the differential diagnosis between various types of dementia.

Introduction It is uncertain whether subjective cognitive decline (SCD) in individuals who seek medical help serves the identification of the initial symptomatic stage 2 of the Alzheimer's disease (AD) continuum. Methods Cross‐sectional and longitudinal data from the multicenter, memory clinic–based DELCODE study. Results The SCD group showed slightly worse cognition as well as more subtle functional and behavioral symptoms than the control group (CO). SCD–A+ cases (39.3% of all SCD) showed greater hippocampal atrophy, lower cognitive and functional performance, and more behavioral symptoms than CO–A+. Amyloid concentration in the CSF had a greater effect on longitudinal cognitive decline in SCD than in the CO group. Discussion Our data suggests that SCD serves the identification of stage 2 of the AD continuum and that stage 2, operationalized as SCD‐A+, is associated with subtle, but extended impact of AD pathology in terms of neurodegeneration, symptoms and clinical progression.