Measuring cortical connectivity in Alzheimer's disease as a brain neural network pathology: Toward clinical applications

Objectives: The objective was to review the literature on diffusion tensor imaging as well as resting-state functional magnetic resonance imaging and electroencephalography (EEG) to unveil neuroanatomical and neurophysiological substrates of Alzheimer’s disease (AD) as a brain neural network pathology affecting structural and functional cortical connectivity underlying human cognition. Methods: We reviewed papers registered in PubMed and other scientific repositories on the use of these techniques in amnesic mild cognitive impairment (MCI) and clinically mild AD dementia patients compared to cognitively intact elderly individuals (Controls). Results: Hundreds of peer-reviewed (cross-sectional and longitudinal) papers have shown in patients with MCI and mild AD compared to Controls (1) impairment of callosal (splenium), thalamic, and anterior–posterior white matter bundles; (2) reduced correlation of resting state blood oxygen level-dependent activity across several intrinsic brain circuits including default mode and attention-related networks; and (3) abnormal power and functional coupling of resting state cortical EEG rhythms. Clinical applications of these measures are still limited. Conclusions: Structural and functional (in vivo) cortical connectivity measures represent a reliable marker of cerebral reserve capacity and should be used to predict and monitor the evolution of AD and its relative impact on cognitive domains in pre-clinical, prodromal, and dementia stages of AD. (JINS, 2016, 22, 138–163

Longitudinal trajectories of cognitive reserve in hypometabolic subtypes of Alzheimer's disease

Previous studies have demonstrated resilience to AD-related neuropathology in a form of cognitive reserve (CR). In this study we investigated a relationship between CR and hypometabolic subtypes of AD, specifically the typical and the limbic-predominant subtypes. We analyzed data from 59 A beta-positive cognitively normal (CN), 221 prodromal Alzheimer's disease (AD) and 174 AD dementia participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) from ADNI and ADNIGO/2 phases. For replication, we analyzed data from 5 A beta positive CN, 89 prodromal AD and 43 AD dementia participants from ADNI3. CR was estimated as standardized residuals in a model predicting cognition from temporoparietal grey matter volumes and covariates. Higher CR estimates predicted slower cognitive decline. Typical and limbic-predominant hypometabolic subtypes demonstrated similar baseline CR, but the results suggested a faster decline of CR in the typical subtype. These findings support the relationship between subtypes and CR, specifically longitudinal trajectories of CR. Results also underline the importance of longitudinal analyses in research on CR

Bridging the Gap Between (AI-) Services and Their Application in Research and Clinical Settings Through Interoperability: the OMI-Protocol

Artificial Intelligence (AI) in research and clinical contexts is transforming the areas of medical and life sciences permanently. Aspects like findability, accessibility, interoperability, and reusability are often neglected for AI-based inference services. The Open Medical Inference (OMI) protocol aims to support remote inference by addressing the aforementioned aspects. Key component of the proposed protocol is an interoperable registry for remote inference services, which addresses the issue of findability for algorithms. It is complemented by information on how to invoke services remotely. Together, these components lay the basis for the implementation of distributed inference services beyond organizational borders. The OMI protocol considers prior work for aspects like data representation and transmission standards wherever possible. Based on Business Process Modeling of prototypical use cases for the service registry and common inference processes, a generic information model for remote services was inferred. Based on this model, FHIR resources were identified to represent AI-based services. The OMI protocol is first introduced using AI-services in radiology but is designed to be generalizable to other application domains as well. It provides an accessible, open specification as blueprint for the introduction and implementation of remote inference services.Anwendungen der Künstliche Intelligenz (KI) im Forschungs- und klinischen Bereich werden die Medizin- und Biowissenschaften nachhaltig verändern. Aspekte wie Auffindbarkeit, Zugänglichkeit, Interoperabilität und Wiederverwendbarkeit werden bei KI-basierten Inferenzdiensten derzeit jedoch oft vernachlässigt. Das Open Medical Inference (OMI) Protokoll zielt darauf ab KI-Algorithmen als Service über institutionelle Grenzen hinweg verfügbar zu machen, indem es die o.g. Aspekte adressiert. Schlüsselkomponente des Protokolls ist ein interoperables Register für Inferenzdienste, welches die Auffindbarkeit von Algorithmen erleichtert. Enthalten sind Informationen, wie Dienste aus der Ferne aufgerufen werden können. Zusammen bilden diese Komponenten die Grundlage für den Aufbau und die Umsetzung von verteilten Inferenzdiensten. Das OMI-Protokoll berücksichtigt aktive Initiativen und Standards für Aspekte wie Datentransport und Datendarstellung. Basierend auf Geschäftsprozessmodellen für Anwendungsfälle innerhalb der Service Registry und Inferenzprozessen wurde ein generisches Informationsmodell abgeleitet. Auf der Grundlage des Informationsmodells wurden FHIR-Ressourcen identifiziert, um KI-Dienste zu repräsentieren. Diese Ressourcen werden profiliert, um erwartete Ein- und Ausgabedatentypen und -formate zu definieren. Das OMI-Protokoll wird zunächst anhand von Anwendungsfällen in der Radiologie beispielhaft abgebildet, ist aber generisch ausgelegt, sodass auch andere Anwendungsdomänen unterstützt werden. Es bietet eine zugängliche, offene Spezifikation als Grundgerüst für die Einführung und Umsetzung von Fern-Inferenz

Applicability of in vivo staging of regional amyloid burden in a cognitively normal cohort with subjective memory complaints: the INSIGHT-preAD study.

BACKGROUND:Current methods of amyloid PET interpretation based on the binary classification of global amyloid signal fail to identify early phases of amyloid deposition. A recent analysis of 18F-florbetapir PET data from the Alzheimer's disease Neuroimaging Initiative cohort suggested a hierarchical four-stage model of regional amyloid deposition that resembles neuropathologic estimates and can be used to stage an individual's amyloid burden in vivo. Here, we evaluated the validity of this in vivo amyloid staging model in an independent cohort of older people with subjective memory complaints (SMC). We further examined its potential association with subtle cognitive impairments in this population at elevated risk for Alzheimer's disease (AD). METHODS:The monocentric INSIGHT-preAD cohort includes 318 cognitively intact older individuals with SMC. All individuals underwent 18F-florbetapir PET scanning and extensive neuropsychological testing. We projected the regional amyloid uptake signal into the previously proposed hierarchical staging model of in vivo amyloid progression. We determined the adherence to this model across all cases and tested the association between increasing in vivo amyloid stage and cognitive performance using ANCOVA models. RESULTS:In total, 156 participants (49%) showed evidence of regional amyloid deposition, and all but 2 of these (99%) adhered to the hierarchical regional pattern implied by the in vivo amyloid progression model. According to a conventional binary classification based on global signal (SUVRCereb = 1.10), individuals in stages III and IV were classified as amyloid-positive (except one in stage III), but 99% of individuals in stage I and even 28% of individuals in stage II were classified as amyloid-negative. Neither in vivo amyloid stage nor conventional binary amyloid status was significantly associated with cognitive performance in this preclinical cohort. CONCLUSIONS:The proposed hierarchical staging scheme of PET-evidenced amyloid deposition generalizes well to data from an independent cohort of older people at elevated risk for AD. Future studies will determine the prognostic value of the staging approach for predicting longitudinal cognitive decline in older individuals at increased risk for AD

Neonicotinoids and bees: A large scale field study investigating residues and effects on honeybees, bumblebees and solitary bees in oilseed rape grown from clothianidin-treated seed

In 2013, the European Food Safety Authority (EFSA) has highlighted several data gaps regarding the exposure and risk of pesticides to honeybees, bumblebees and solitary bees, including the risks from exposure to contaminated nectar and pollen. This study aims to contribute data, results and conclusions to obtain more information on exposure and risks of flowering oilseed rape seed treated with the neonicotinoid clothianidin, to pollinators. Semi-field and field trials were conducted at five different locations across Germany, using the Western honeybee (Apis mellifera L.), the buff-tailed bumblebee (Bombus terrestris L.) and the red mason bee (Osmia bicornis L.) as study organisms.Highest amounts of clothianidin residues were measured in single samples of mud cell walls (7.2 μg kg-1) and pollen (5.9 μg kg-1) from solitary bee nests. Residues in nectar from honey sacs, honeybee combs and bumblebee nests (2.2, 2.9, and 3.0 μg kg-1 respectively) showed no clear differences in the amount of residues, neither did residues in pollen (1.5, 1.8, and 1.3 μg kg-1 respectively). These results suggest differences in the risk profiles of those three bee species. Keywords: clothianidin, residues, honeybees, bumblebees, solitary bees, field, semi-fiel