Explore the vast range of titles available.

In 2018, the US National Institute on Aging and the Alzheimer's Association proposed a purely biological definition of Alzheimer's disease that relies on biomarkers. Although the intended use of this framework was for research purposes, it has engendered debate and challenges regarding its use in everyday clinical practice. For instance, cognitively unimpaired individuals can have biomarker evidence of both amyloid β and tau pathology but will often not develop clinical manifestations in their lifetime. Furthermore, a positive Alzheimer's disease pattern of biomarkers can be observed in other brain diseases in which Alzheimer's disease pathology is present as a comorbidity. In this Personal View, the International Working Group presents what we consider to be the current limitations of biomarkers in the diagnosis of Alzheimer's disease and, on the basis of this evidence, we propose recommendations for how biomarkers should and should not be used for diagnosing Alzheimer's disease in a clinical setting. We recommend that Alzheimer's disease diagnosis be restricted to people who have positive biomarkers together with specific Alzheimer's disease phenotypes, whereas biomarker-positive cognitively unimpaired individuals should be considered only at-risk for progression to Alzheimer's disease.

A large number of papers have introduced novel machine learning and feature extraction methods for automatic classification of Alzheimer's disease (AD). However, while the vast majority of these works use the public dataset ADNI for evaluation, they are difficult to reproduce because different key components of the validation are often not readily available. These components include selected participants and input data, image preprocessing and cross-validation procedures. The performance of the different approaches is also difficult to compare objectively. In particular, it is often difficult to assess which part of the method (e.g. preprocessing, feature extraction or classification algorithms) provides a real improvement, if any. In the present paper, we propose a framework for reproducible and objective classification experiments in AD using three publicly available datasets (ADNI, AIBL and OASIS). The framework comprises: i) automatic conversion of the three datasets into a standard format (BIDS); ii) a modular set of preprocessing pipelines, feature extraction and classification methods, together with an evaluation framework, that provide a baseline for benchmarking the different components. We demonstrate the use of the framework for a large-scale evaluation on 1960 participants using T1 MRI and FDG PET data. In this evaluation, we assess the influence of different modalities, preprocessing, feature types (regional or voxel-based features), classifiers, training set sizes and datasets. Performances were in line with the state-of-the-art. FDG PET outperformed T1 MRI for all classification tasks. No difference in performance was found for the use of different atlases, image smoothing, partial volume correction of FDG PET images, or feature type. Linear SVM and L2-logistic regression resulted in similar performance and both outperformed random forests. The classification performance increased along with the number of subjects used for training. Classifiers trained on ADNI generalized well to AIBL and OASIS. All the code of the framework and the experiments is publicly available: general-purpose tools have been integrated into the Clinica software (www.clinica.run) and the paper-specific code is available at: https://gitlab.icm-institute.org/aramislab/AD-ML.

In the past 8 years, both the International Working Group (IWG) and the US National Institute on Aging–Alzheimer's Association have contributed criteria for the diagnosis of Alzheimer's disease (AD) that better define clinical phenotypes and integrate biomarkers into the diagnostic process, covering the full staging of the disease. This Position Paper considers the strengths and limitations of the IWG research diagnostic criteria and proposes advances to improve the diagnostic framework. On the basis of these refinements, the diagnosis of AD can be simplified, requiring the presence of an appropriate clinical AD phenotype (typical or atypical) and a pathophysiological biomarker consistent with the presence of Alzheimer's pathology. We propose that downstream topographical biomarkers of the disease, such as volumetric MRI and fluorodeoxyglucose PET, might better serve in the measurement and monitoring of the course of disease. This paper also elaborates on the specific diagnostic criteria for atypical forms of AD, for mixed AD, and for the preclinical states of AD.

We present Clinica ( www.clinica.run ), an open-source software platform designed to make clinical neuroscience studies easier and more reproducible. Clinica aims for researchers to (i) spend less time on data management and processing, (ii) perform reproducible evaluations of their methods, and (iii) easily share data and results within their institution and with external collaborators. The core of Clinica is a set of automatic pipelines for processing and analysis of multimodal neuroimaging data (currently, T1-weighted MRI, diffusion MRI, and PET data), as well as tools for statistics, machine learning, and deep learning. It relies on the brain imaging data structure (BIDS) for the organization of raw neuroimaging datasets and on established tools written by the community to build its pipelines. It also provides converters of public neuroimaging datasets to BIDS (currently ADNI, AIBL, OASIS, and NIFD). Processed data include image-valued scalar fields (e.g., tissue probability maps), meshes, surface-based scalar fields (e.g., cortical thickness maps), or scalar outputs (e.g., regional averages). These data follow the ClinicA Processed Structure (CAPS) format which shares the same philosophy as BIDS. Consistent organization of raw and processed neuroimaging files facilitates the execution of single pipelines and of sequences of pipelines, as well as the integration of processed data into statistics or machine learning frameworks. The target audience of Clinica is neuroscientists or clinicians conducting clinical neuroscience studies involving multimodal imaging, and researchers developing advanced machine learning algorithms applied to neuroimaging data.

Sleep changes have been associated with increased risks of developing cognitive disturbances and Alzheimer's disease (AD). A bidirectional relation is underlined between amyloid-beta (Aß) and sleep disruptions. The sleep profile in participants at risk to develop AD is not fully deciphered. We aim to investigate sleep-wake changes with objective sleep measurements in elderly participants without cognitive impairment depending on their brain amyloid status, positive (Aß+) or negative (Aß-) based on standard absorption ratios (SUVr) positron emission tomography-florbetapir imaging. Sixty-eight participants without cognitive impairment who have accepted to be involved in the sleep ancillary study from the InveStIGation of Alzheimer's Predictors in Subjective Memory Complainers (INSIGHT-pre AD) cohort, aiming to record sleep profile based on the analyses of an ambulatory accelerometer-based assessment (seven consecutive 24-hour periods). Neuropsychological tests were performed and sleep parameters have been individualized by actigraph. Participants also underwent a magnetic resonance imaging scan to assess their hippocampal volume. Based on SUVr PET-florbetapir imaging, two groups Aß+ and Aß- were compared. Participants were divided into two groups: Aß+ (n = 24) and Aß- (n = 44). Except for the SUVr, the two subgroups were comparable. When looking to sleep parameters, increased sleep latency, sleep fragmentation (wake after sleep onset [WASO] score and awakenings) and worst sleep efficiency were associated with cortical brain amyloid load. Actigraphic sleep parameters were associated with cortical brain amyloid load in participants at risk to develop AD. The detection of sleep abnormalities in those participants may be of interest to propose some preventive strategies.

Background Temporary disruption of the blood-brain barrier (BBB) using pulsed ultrasound leads to the clearance of both amyloid and tau from the brain, increased neurogenesis, and mitigation of cognitive decline in pre-clinical models of Alzheimer’s disease (AD) while also increasing BBB penetration of therapeutic antibodies. The goal of this pilot clinical trial was to investigate the safety and efficacy of this approach in patients with mild AD using an implantable ultrasound device. Methods An implantable, 1-MHz ultrasound device (SonoCloud-1) was implanted under local anesthesia in the skull (extradural) of 10 mild AD patients to target the left supra-marginal gyrus. Over 3.5 months, seven ultrasound sessions in combination with intravenous infusion of microbubbles were performed twice per month to temporarily disrupt the BBB. 18 F-florbetapir and 18 F-fluorodeoxyglucose positron emission tomography (PET) imaging were performed on a combined PET/MRI scanner at inclusion and at 4 and 8 months after the initiation of sonications to monitor the brain metabolism and amyloid levels along with cognitive evaluations. The evolution of cognitive and neuroimaging features was compared to that of a matched sample of control participants taken from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Results A total of 63 BBB opening procedures were performed in nine subjects. The procedure was well-tolerated. A non-significant decrease in amyloid accumulation at 4 months of − 6.6% (SD = 7.2%) on 18 F-florbetapir PET imaging in the sonicated gray matter targeted by the ultrasound transducer was observed compared to baseline in six subjects that completed treatments and who had evaluable imaging scans. No differences in the longitudinal change in the glucose metabolism were observed compared to the neighboring or contralateral regions or to the change observed in the same region in ADNI participants. No significant effect on cognition evolution was observed in comparison with the ADNI participants as expected due to the small sample size and duration of the trial. Conclusions These results demonstrate the safety of ultrasound-based BBB disruption and the potential of this technology to be used as a therapy for AD patients. Research of this technique in a larger clinical trial with a device designed to sonicate larger volumes of tissue and in combination with disease-modifying drugs may further enhance the effects observed. Trial registration ClinicalTrials.gov, NCT03119961

We previously showed that the injected activity could be reduced to 1 MBq/kg without significantly degrading image quality for the exploration of neurocognitive disorders in 18F-FDG-PET/MRI. We now hypothesized that injected activity could be reduced ten-fold. We simulated a 18F-FDG-PET/MRI ultra-low-dose protocol (0.2 MBq/Kg, PET ULD ) and compared it to our reference protocol (2 MBq/Kg, PET STD ) in 50 patients with cognitive impairment. We tested the reproducibility between PET ULD and PET STD using SUVratios measurements. We also assessed the impact of PET ULD for between-group comparisons and for visual analysis performed by three physicians. The intra-operator agreement between visual assessment of PET STD and PET ULD in patients with severe anomalies was substantial to almost perfect (kappa > 0.79). For patients with normal metabolism or moderate hypometabolism however, it was only moderate to substantial (kappa > 0.53). SUV ratios were strongly reproducible (SUVratio difference ± SD = 0.09 ± 0.08). Between-group comparisons yielded very similar results using either PET ULD or PET STD . 18F-FDG activity may be reduced to 0.2 MBq/Kg without compromising quantitative measurements. The visual interpretation was reproducible between ultra-low-dose and standard protocol for patients with severe hypometabolism, but less so for those with moderate hypometabolism. These results suggest that a low-dose protocol (1 MBq/Kg) should be preferred in the context of neurodegenerative disease diagnosis.

ObjectiveOne of the main challenges of integrated PET/MR is to achieve an accurate PET attenuation correction (AC), especially in brain acquisition. Here, we evaluated an AC method based on zero echo time (ZTE) MRI, comparing it with the single-atlas AC method and CT-based AC, set as reference.MethodsFifty patients (70 ± 11 years old, 28 men) underwent FDG-PET/MR examination (SIGNA PET/MR 3.0 T, GE Healthcare) as part of the investigation of suspected dementia. They all had brain computed tomography (CT), 2-point LAVA-flex MRI (for atlas-based AC), and ZTE-MRI. Two AC methods were compared with CT-based AC (CTAC): one based on a single atlas, one based on ZTE segmentation. Impact on brain metabolism was evaluated using voxel and volumes of interest–based analyses. The impact of AC was also evaluated through comparisons between two subgroups of patients extracted from the whole population: 15 patients with mild cognitive impairment and normal metabolic pattern, and 22 others with metabolic pattern suggestive of Alzheimer disease, using SPM12 software.ResultsZTE-AC yielded a lower bias (3.6 ± 3.2%) than the atlas method (4.5 ± 6.1%) and lowest interindividual (4.6% versus 6.8%) and inter-regional (1.4% versus 2.6%) variabilities. Atlas-AC resulted in metabolism overestimation in cortical regions near the vertex and cerebellum underestimation. ZTE-AC yielded a moderate metabolic underestimation mainly in the occipital cortex and cerebellum. Voxel-wise comparison between the two subgroups of patients showed that significant difference clusters had a slightly smaller size but similar locations with PET images corrected with ZTE-AC compared with those corrected with CT, whereas atlas-AC images showed a notable reduction of significant voxels.ConclusionZTE-AC performed better than atlas-AC in detecting pathologic areas in suspected neurodegenerative dementia.Key Points• The ZTE-based AC improved the accuracy of the metabolism quantification in PET compared with the atlas-AC method.• The overall uptake bias was 21% lower when using ZTE-based AC compared with the atlas-AC method.• ZTE-AC performed better than atlas-AC in detecting pathologic areas in suspected neurodegenerative dementia.

INTRODUCTION We investigated whether older adults with subjective memory complaints (SMC) and amyloid‐β accumulation may show clinical progression over 2 years, as measured by resting‐state electroencephalographic (rsEEG), structural magnetic resonance imaging (sMRI), and cognitive variables, depending on educational attainment. METHODS We analyzed these markers in 84 SMC participants from INSIGHT‐Pre‐AD study, grouped by amyloid‐β deposition (18F‐florbetapir positron emission tomography) and educational attainment. RESULTS In amyloid‐negative individuals, higher educational attainment was linked to greater posterior rsEEG alpha activity, possibly reflecting neuroprotective effects. Conversely, amyloid‐positive individuals with higher educational attainment showed reduced posterior rsEEG alpha rhythms and lower parietal cortical thickness, potentially indicating compensatory mechanisms counteracting early amyloidosis and neurodegeneration. No longitudinal changes were found in either group over 2 years. DISCUSSION Education had a stable influence on rsEEG, sMRI, and cognitive markers over 2 years in SMC individuals. Longer follow‐up periods should be used to monitor brain status with those markers. Highlights Education, subjective memory complaint (SMC), and brain amyloid‐β deposition. Stable influence of education on resting‐state electroencephalographic (rsEEG), structural magnetic resonance imaging (sMRI), and cognitive markers over 2 years. Compensatory mechanism of education against early amyloidosis and neurodegeneration. Longer follow‐up periods to monitor brain status in SMC older adults with those markers.

IntroductionPrimary Central Nervous System Lymphoma (PCNSL) is a rare disease with different therapeutic implications than systemic lymphoma. In this study, we evaluated whole-body 18FDG-PET/CT for pre-chemotherapy imaging of suspected PCNSL.MethodsOne hundred and thirty consecutive immunocompetent patients were retrospectively included. The results of initial 18FDG-PET/CT, contrast-enhanced CT (CeCT) and bone marrow biopsy (BMB) when available were compared to a gold standard based on pathological diagnosis or follow-up.ResultsCNS lesion pathology showed large B-cell lymphoma in 95% of patients, including 11 patients with primary vitro-retinal lymphoma. Ten patients (8%) where ultimately diagnosed with systemic lymphoma involvement, including five pathologically confirmed cases, all of which were detected by 18FDG-PET/CT. 18FDG-PET/CT showed incidental systemic findings unrelated to lymphoma in 14% of patients. An SUVmax threshold of nine enabled good discrimination between systemic lymphoma and other lesions (sensitivity 92% and specificity 89%). CeCT and BMB performed in 108 and 77 patients respectively revealed systemic lesions in only three patients.Conclusion18FDG-PET/CT detected concomitant occult systemic involvement in a non-negligible proportion of suspected PCNSL cases (8%). In this setting its sensitivity is higher than that of CeCT. All of our patients ultimately diagnosed with concomitant systemic involvement had positive 18FDG-PET/CT. We believe it constitutes a safe one-stop shop evaluation for the systemic pre-treatment imaging of suspected PCNSL.