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IntroductionEfforts to develop cost-effective approaches for detecting amyloid pathology in Alzheimer's disease (AD) have gained significant momentum with a focus on biomarker classification. Recent research has explored non-invasive and readily accessible biomarkers, including magnetic resonance imaging (MRI) biomarkers and some AD risk factors.MethodsIn this comprehensive study, we leveraged a diverse dataset, encompassing participants with varying cognitive statuses from multiple sources, including cohorts from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and our in-house Dementia Disease Initiation (DDI) cohort. As brain amyloid plaques have been proposed as sufficient for AD diagnosis, our primary aim was to assess the effectiveness of multimodal biomarkers in identifying amyloid plaques, using deep machine learning methodologies.ResultsOur findings underscore the robustness of the utilized methods in detecting amyloid beta positivity across multiple cohorts. Additionally, we investigated the potential of demographic data to enhance MRI-based amyloid detection. Notably, the inclusion of demographic risk factors significantly improved our models' ability to detect amyloid-beta positivity, particularly in early-stage cases, exemplified by an average area under the ROC curve of 0.836 in the unimpaired DDI cohort.DiscussionThese promising, non-invasive, and cost-effective predictors of MRI biomarkers and demographic variables hold the potential for further refinement through considerations like APOE genotype and plasma markers.

IntroductionRadiological assessment is necessary to diagnose spontaneous intracerebral hemorrhage (ICH) and traumatic brain injury intracranial hemorrhage (TBI-bleed). Artificial intelligence (AI) deep learning tools provide a means for decision support. This study evaluates the hemorrhage segmentations produced from three-dimensional deep learning AI model that was developed using non-contrast computed tomography (CT) imaging data external to the current study.MethodsNon-contrast CT imaging data from 1263 patients were accessed across seven data sources (referred to as sites) in Norway and Sweden. Patients were included based on ICH, TBI-bleed, or mild TBI diagnosis. Initial non-contrast CT images were available for all participants. Hemorrhage location frequency maps were generated. The number of estimated haematoma clusters was correlated with the total haematoma volume. Ground truth expert annotations were available for one ICH site; hence, a comparison was made with the estimated haematoma volumes. Segmentation volume estimates were used in a receiver operator characteristics (ROC) analysis for all samples (i.e., bleed detected) and then specifically for one site with few TBI-bleed cases.ResultsThe hemorrhage frequency maps showed spatial patterns of estimated lesions consistent with ICH or TBI-bleed presentations. There was a positive correlation between the estimated number of clusters and total haematoma volume for each site (correlation range: 0.45–0.74; each p-value < 0.01) and evidence of ICH between-site differences. Relative to hand-drawn annotations for one ICH site, the VIOLA-AI segmentation mask achieved a median Dice Similarity Coefficient of 0.82 (interquartile range: 0.78 and 0.83), resulting in a small overestimate in the haematoma volume by a median of 0.47 mL (interquartile range: 0.04 and 1.75 mL). The bleed detection ROC analysis for the whole sample gave a high area-under-the-curve (AUC) of 0.92 (with sensitivity and specificity of 83.28% and 95.41%); however, when considering only the mild head injury site, the TBI-bleed detection gave an AUC of 0.70.DiscussionAn open-source segmentation tool was used to visualize hemorrhage locations across multiple data sources and revealed quantitative hemorrhage site differences. The automated total hemorrhage volume estimate correlated with a per-participant hemorrhage cluster count. ROC results were moderate-to-high. The VIOLA-AI tool had promising results and might be useful for various types of intracranial hemorrhage.

BackgroundCurrently, there are no disease-modifying pharmacological treatment options for dementia with Lewy bodies (DLB). The hallmark of DLB is pathological alpha-synuclein (aS) deposition. There are growing amounts of data suggesting that reduced aS clearance is caused by failure in endolysosomal and authophagic pathways, as well as and glucocerebrosidase (GCase) dysfunction and mutations in the GCase gene (GBA). The population’s studies demonstrated that the incidence of GBA mutations is higher among Parkinson’s disease (PD) patients, and carriers of such mutations have a higher risk of developing PD. The incidence of GBA mutations is even higher in DLB and a genome-wide association study (GWAS) confirmed the correlation between GBA mutations and DLB. In vivo experiments have shown that ambroxol (ABX) may increase GCase activity and GCase levels and therefore enhance aS autophagy-lysosome degradation pathways. Moreover, there is an emerging hypothesis that ABX may have an effect as a DLB modifying drug. The aims of the study “Ambroxol in new and early Dementia with Lewy Bodies (ANeED) are to investigate the tolerability, safety and effects of ABX in patients with DLB.MethodsThis is a multicentre, phase IIa, double-blinded, randomised and placebo-controlled clinical trial, using a parallel arm design for 18 months’ follow-up. The allocation ratio is 1:1 (treatment:placebo).DiscussionThe ANeED study is an ongoing clinical drug trial with ABX. The unique, but not fully understood mechanism of ABX on the enhancement of lysosomal aS clearance may be promising as a possible modifying treatment in DLB.Trial RegistrationThe clinical trial is registered in the international trials register – clinicaltrials.com (NCT0458825) and nationally at the Current Research Information System in Norway (CRISTIN 2235504).

High blood pressure is a well-established risk factor of dementia. However, the timing of the risk remains controversial. The aim of the present study was to compare trajectories of systolic blood pressure (SBP) over a 35-year follow-up period in the Health Survey in Trøndelag (HUNT) from study wave 1 to 4 in people with and without a dementia diagnosis at wave 4 (HUNT4). This is a retrospective cohort study of participants aged ≥ 70 years in HUNT4, where 9,720 participants were assessed for dementia. In the HUNT study all residents aged ≥ 20 years have been invited to four surveys: HUNT1 1984–86, HUNT2 1995–97, HUNT3 2006–08 and HUNT4 2017–19. The study sample was aged 70–102 years (mean 77.6, SD 6.0) at HUNT4, 54% were women and 15.5% had dementia, 8.8% had Alzheimer’s disease (AD), 1.6% had vascular dementia (VaD) and 5.1% had other types of dementia. Compared to those without dementia at HUNT4, those with dementia at HUNT4 had higher SBP at HUNT1 and HUNT2, but lower SBP at HUNT4. These differences at HUNT1 and 2 were especially pronounced among women. Results did not differ across birth cohorts. For dementia subtypes at HUNT4, the VaD group had a higher SBP than the AD group at HUNT2 and 3. Age trajectories in SBP showed that the dementia group experienced a steady increase in SBP until 65 years of age and a decrease from 70 to 90 years. SBP in the no- dementia group increased until 80 years before it leveled off from 80 to 90 years. The present study confirms findings of higher midlife SBP and lower late-life SBP in people with dementia. This pattern may have several explanations and it highlights the need for close monitoring of BP treatment in older adults, with frequent reappraisal of treatment needs.

The focus of the current paper is on a design of responsible governance of food consumer science e-infrastructure using the case study Determinants and Intake Data Platform (DI Data Platform). One of the key challenges for implementation of the DI Data Platform is how to develop responsible governance that observes the ethical and legal frameworks of big data research and innovation, whilst simultaneously capitalizing on huge opportunities offered by open science and the use of big data in food consumer science research. We address this challenge with a specific focus on four key governance considerations: data type and technology; data ownership and intellectual property; data privacy and security; and institutional arrangements for ethical governance. The paper concludes with a set of responsible research governance principles that can inform the implementation of DI Data Platform, and in particular: consider both individual and group privacy; monitor the power and control (e.g., between the scientist and the research participant) in the process of research; question the veracity of new knowledge based on big data analytics; understand the diverse interpretations of scientists' responsibility across different jurisdictions.

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder and cause of dementia, and is characterized by amyloid plaques and neurofibrillary tangles. AD has traditionally been considered to primarily affect grey matter, but multiple lines of evidence also indicate white matter (WM) pathology and associated small-vessel cerebrovascular disease (CVD). WM glucose delivery and metabolism may have implications for local tissue integrity, and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) may be helpful to assess neuroglial and axonal function in WM. Hypothesizing that affection of oligodendroglia will be associated with loss of glucose uptake, we aimed to investigate glucose metabolism in MRI white matter hyperintensities (WMHs) and normal-appearing white matter in patients with and without evidence of amyloid plaques. Subjects with mild cognitive impairment or subjective cognitive decline were included and dichotomized according to pathological (Aβ+) or normal (Aβ-) concentrations of cerebrospinal fluid amyloid-β 1-42. A total of fifty subjects were included, of whom thirty subjects were classified as Aβ(+) and twenty subjects as Aβ(-). All subjects were assessed with MRI and FDG-PET. FDG-PET images were corrected for effects of partial voluming and normalized to cerebellar WM, before determining WMH FDG uptake. Although there were no significant differences between the groups in terms of age, WMH volume, number of individual WMHs or WMH distribution, we found significantly lower (p = 0.021) FDG uptake in WMHs in Aβ(+) subjects (mean = 0,662, SD = 0.113) compared to Aβ(-) subjects (mean = 0.596, SD = 0.073). There were no significant group differences in the FDG uptake in normal-appearing white matter. Similar results were obtained without correction for effects of partial voluming. Our findings add to the evidence for a link between Aβ dysmetabolism and white matter pathology in AD.