The Dutch Parelsnoer Institute - Neurodegenerative diseases; methods, design and baseline results

Background: The is a collaboration between 8 Dutch University Medical Centers in which clinical data and biomaterials from patients suffering from chronic diseases (so called "Pearls") are collected according to harmonized protocols. The Pearl Neurodegenerative Diseases focuses on the role of biomarkers in the early diagnosis, differential diagnosis and in monitoring the course of neurodegenerative diseases, in particular Alzheimer's disease. Methods: The Pearl Neurodegenerative Diseases is a 3-year follow-up study of patients referred to a memory clinic with cognitive complaints. At baseline, all patients are subjected to a standardized examination, including clinical data and biobank materials, e.g. blood samples, MRI and cerebrospinal fluid. At present, in total more than 1000 patients have been included, of which cerebrospinal fluid and DNA samples are available of 211 and 661 patients, respectively. First descriptives of a subsample of the data (n = 665) shows that patients are diagnosed with dementia (45%), mild cognitive impairment (31%), and subjective memory complaints (24%). Discussion: The Pearl Neurodegenerative Diseases is an ongoing large network collecting clinical data and biomaterials of more than 1000 patients with cognitive impairments. The project has started with data analyses of the baseline characteristics and biomarkers, which will be the starting point of future specific research questions that can be answered by this unique dataset

Alzheimer's disease pathology:pathways between central norepinephrine activity, memory, and neuropsychiatric symptoms

The locus coeruleus (LC) supplies norepinephrine to the brain, is one of the first sites of tau deposition in Alzheimer's disease (AD) and modulates a variety of behaviors and cognitive functions. Transgenic mouse models showed that norepinephrine dysregulation after LC lesions exacerbates inflammatory responses, blood-brain barrier leakage (BBB), and cognitive deficits. Here, we investigated relationships between central norepinephrine metabolism, tau and beta-amyloid (Aβ), inflammation, BBB-dysfunction, neuropsychiatric problems, and memory in-vivo in a memory clinic population (total n = 111, 60 subjective cognitive decline, 36 mild cognitively impaired, and 19 AD dementia). Cerebrospinal fluid (CSF) and blood samples were collected and analyzed for 3-methoxy-4-hydroxyphenylethyleneglycol (MHPG), CSF/plasma albumin ratio (Q-alb), Aβ, phosphorylated tau, and interleukins. The verbal word learning task and the neuropsychiatric inventory assessed memory functioning and neuropsychiatric symptoms. Structural equation models tested the relationships between all fluid markers, cognition and behavior, corrected for age, education, sex, and clinical dementia rating score. Our results showed that neuropsychiatric symptoms show strong links to both MHPG and p-tau, whereas memory deficits are linked to MHPG via a combination of p-tau and inflammation-driven amyloidosis (30-35% indirect effect contribution). These results suggest that the LC-norepinephrine may be pivotal to understand links between AD pathology and behavioral and cognitive deficits in AD

Multilingual Learning for Mild Cognitive Impairment Screening from a Clinical Speech Task

The Semantic Verbal Fluency Task (SVF) is an efficient and minimally invasive speech-based screening tool for Mild Cognitive Impairment (MCI). In the SVF, testees have to produce as many words for a given semantic category as possible within 60 seconds. State-of-the-art approaches for automatic evaluation of the SVF employ word embeddings to analyze semantic similarities in these word sequences. While these approaches have proven promising in a variety of test languages, the small amount of data available for any given language limits the performance. In this paper, we for the first time investigate multilingual learning approaches for MCI classification from the SVF in order to combat data scarcity. To allow for cross-language generalisation, these approaches either rely on translation to a shared language, or make use of several distinct word embeddings. In evaluations on a multilingual corpus of older French, Dutch, and German participants (Controls=66, MCI=66), we show that our multilingual approaches clearly improve over single-language baselines

Association of sleep quality and diffusion MRI derived interstitial fluid content – insights in cerebral waste clearance

Background: Disruptions in sleep are associated with Alzheimer’s disease (AD) progression. As cerebral waste clearance is most active during sleep, these reductions in sleep quality could induce impaired clearance function. Waste products are washed through the parenchyma in the interstitial fluid (ISF), its volume being regulated by the sleep-wake cycle. A smaller ISF-volume is found in awake states and after sleep deprivation. IntraVoxel Incoherent Motion (IVIM) MRI can detect the diffusion of cerebral ISF (Dint) and its volume fraction (fint). Using IVIM, the current exploratory study investigates whether alterations in a proxy of ISF-volume (fint) are related to hours of sleep or self-reported sleep quality. Method: Twenty neurotypical elderly subjects (Table 1) underwent ultra-high field MRI (7T research system, Siemens Healthcare GmbH, Erlangen, Germany) (Table 2). The intermediate diffusion components (Dint) in the range 1.5*10-3&lt;Diffusivity&lt;4.0*10-3 mm2/s were calculated using spectral analysis (Figure 1). The relative signal contribution of Dint was quantified by the ISF-fraction (fint). Median fint values were extracted from the white matter (WM) and gray matter (GM). Sleep quality was assessed with the Pittsburgh Sleep Quality Index (PSQI). In addition to the clinically used total PSQI (sum of all 7 components), the PSQI1 (Subjective sleep quality) and PSQI3 (Sleep duration) components were considered. Pearson correlations were computed between the PSQI scores and fint for WM and GM. To check for potential confounding influences, partial correlations were performed adjusting for age and time of MRI-acquisition. Result: The sample characteristics and descriptive statistics are summarized in Table 1. Worse reported sleep quality (PSQI1) significantly relates to lower WM fint (Table 3 and Figure 2). After adjusting for potential confounding influences, a trend towards significance remained (R=-0.437, p=0.061). Conclusion: The current explorative study identified a lower diffusion MRI-derived proxy of ISF-volume in the WM of subjects who reported a subjective feeling of long-term sleep deprivation. A reduction in ISF-volume may leave less space for waste products, such as soluble Amyloid-beta, to be cleared from in-between the cells. Thereby, this study puts forward a potential method for future studies to investigate impaired clearance function in relation to sleep in patients with AD pathology.</p

On the origin of a potential clearance marker:The contribution of enlarged perivascular fluid diffusion to an MRI derived proxy of interstitial fluid

Background: IntraVoxel Incoherent Motion (IVIM) MRI can detect the diffusion of cerebral interstitial fluid (ISF) (D int) and its volume fraction (f int), which has the potential as a proxy of cerebral waste clearance. fint has been suggested to both be driven by fluid diffusivity within enlarged perivascular spaces (ePVS) - transporting fluid from the subarachnoid space into the parenchyma - and the ISF between the parenchymal cells. Both factors play a crucial role in soluble protein clearance, e.g., Amyloid-Beta (Aβ). This study simultaneously aims to verify the contribution of ePVS fluid diffusivity to fint and highlights the ability to specifically assess ePVS fluid diffusion in the cerebrum using a non-invasive MR method. Method: Twenty neurotypical elderly subjects (Table 1) underwent ultra-high field MRI (7T research system, Siemens Healthcare GmbH, Erlangen, Germany) (Table 2). The intermediate diffusion components (Dint) in the range 1.5*10-3&lt; Diffusivity&lt;4.0*10-3 mm2/s were calculated using spectral analysis (Fig.1). The relative signal contribution of Dint was quantified by fint. ePVS were segmented from the enhanced perivascular contrast images (Fig.2). Median fint values were extracted from ePVS and non-ePVS voxels within the basal ganglia (BG). fint and Dint values from within the ePVS mask were compared with values from surrounding non-ePVS voxels in the BG using Wilcoxon Signed Ranks Tests. Result: Figure 3 contains a graphic summary of the statistical comparison of Dint and fint values from the ePVS and non-ePVS voxels in the BG. Higher Dint and fint values are observed in the ePVS compared to the non-ePVS tissue. Conclusion: The current study identified a higher IVIM-derived proxy of ISF within the ePVS compared to surrounding non-ePVS tissue, confirming that ePVS indeed contribute to the waste clearance marker fint. ePVS are fluid-filled spaces surrounding cerebral blood vessels, in which ISF diffusivity is less restricted. This explains the higher Dint values in ePVS compared to non-ePVS. Furthermore, this study suggests a potential method to investigate diffusivity within the microscopic ePVS, independent of the ISF diffusivity between parenchymal cells. In patients with Alzheimer’s disease, this method could be applied to investigate alterations in ISF diffusivity which could be representative of potential Aβ blockages in ePVS.</p

Validation of the Remote Automated ki:e Speech Biomarker for Cognition in Mild Cognitive Impairment:Verification and Validation following DiME V3 Framework

INTRODUCTION: Progressive cognitive decline is the cardinal behavioral symptom in most dementia-causing diseases such as Alzheimer's disease. While most well-established measures for cognition might not fit tomorrow's decentralized remote clinical trials, digital cognitive assessments will gain importance. We present the evaluation of a novel digital speech biomarker for cognition (SB-C) following the Digital Medicine Society's V3 framework: verification, analytical validation, and clinical validation. METHODS: Evaluation was done in two independent clinical samples: the Dutch DeepSpA (N = 69 subjective cognitive impairment [SCI], N = 52 mild cognitive impairment [MCI], and N = 13 dementia) and the Scottish SPeAk datasets (N = 25, healthy controls). For validation, two anchor scores were used: the Mini-Mental State Examination (MMSE) and the Clinical Dementia Rating (CDR) scale. RESULTS: Verification: The SB-C could be reliably extracted for both languages using an automatic speech processing pipeline. Analytical Validation: In both languages, the SB-C was strongly correlated with MMSE scores. Clinical Validation: The SB-C significantly differed between clinical groups (including MCI and dementia), was strongly correlated with the CDR, and could track the clinically meaningful decline. CONCLUSION: Our results suggest that the ki:e SB-C is an objective, scalable, and reliable indicator of cognitive decline, fit for purpose as a remote assessment in clinical early dementia trials

Functional Brain Activation in Mild Cognitive Impairment With Defined Small Vessel Disease Burden

BackgroundVascular dysregulations and changes in functional brain network integrity play a fundamental role in the pathogenesis of dementia. While often identified in individuals with dementia, the role of small vessel disease (SVD) in the development of dementia is not completely understood yet. Previously, structural and functional brain connectivity was shown to be different between individuals with and without cerebral SVD, but a comprehensive measure of SVD has not been used consistently. We aim to analyze functional brain activation differences in mild cognitive impairment (MCI) individuals with defined SVD burden.MethodFunctional brain activation differences were analyzed in MCI individuals with absent or low (n = 34) or high (n = 34) SVD burden using data from the Parelsnoer Institute, a multicenter study involving eight Dutch medical centers. SVD burden was characterized using an ordinal scale ranging from 0 to 4 (Klarenbeek et al., 2013) considering the following markers: lacunes; microbleeds; perivascular spaces in the basal ganglia; white matter hyperintensities based on the Fazekas score. Two groups were identified: the absent or low SVD burden group with a score of 0 or 1 and the high SVD group with a score between 2 and 4. Activation differences were calculated using resting state fMRI data acquired using 3Tesla scanners and analyzed with group-independent component analysis using the CONN toolbox.ResultActivation of two clusters in the high SVD burden group was lower than in the absent or low SVD group: the cerebellum (p-FDR &gt;. 001, F = 1.49) and the brainstem (p-FDR &gt;. 001, F = 2.79). The cerebellar cluster (4541 voxels, +20 -74 -38) consisted of the left cerebellum crus II, right cerebellum crus II, and left cerebellum lobule VIII. The brainstem cluster (218 voxels, +22 -28 +20) involved the right thalamus and the right caudate nucleus.ConclusionBrain activation differences between groups of individuals with MCI and absent or low or high SVD burden were found in the cerebellum and brainstem. These brain areas are mainly responsible for motor, attentional and executive functions, domains which were found in previous studies to be mostly associated with SVD markers.</p

Small vessel disease burden and functional brain connectivity in mild cognitive impairment

Background: The role of small vessel disease in the development of dementia is not yet completely understood. Functional brain connectivity has been shown to differ between individuals with and without cerebral small vessel disease. However, a comprehensive measure of small vessel disease quantifying the overall damage on the brain is not consistently used and studies using such measure in mild cognitive impairment individuals are missing.Method: Functional brain connectivity differences were analyzed between mild cognitive impairment individuals with absent or low (n = 34) and high (n = 34) small vessel disease burden using data from the Parelsnoer Institute, a Dutch multicenter study. Small vessel disease was characterized using an ordinal scale considering: lacunes, microbleeds, perivascular spaces in the basal ganglia, and white matter hyperintensities. Resting state functional MRI data using 3 Tesla scanners was analyzed with group-independent component analysis using the CONN toolbox.Results: Functional connectivity between areas of the cerebellum and between the cerebellum and the thalamus and caudate nucleus was higher in the absent or low small vessel disease group compared to the high small vessel disease group.Conclusion: These findings might suggest that functional connectivity of mild cognitive impairment individuals with low or absent small vessel disease burden is more intact than in mild cognitive impairment individuals with high small vessel disease. These brain areas are mainly responsible for motor, attentional and executive functions, domains which in previous studies were found to be mostly associated with small vessel disease markers. Our results support findings on the involvement of the cerebellum in cognitive functioning

An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease

Machine learning methods have shown large potential for the automatic early diagnosis of Alzheimer's Disease (AD). However, some machine learning methods based on imaging data have poor interpretability because it is usually unclear how they make their decisions. Explainable Boosting Machines (EBMs) are interpretable machine learning models based on the statistical framework of generalized additive modeling, but have so far only been used for tabular data. Therefore, we propose a framework that combines the strength of EBM with high-dimensional imaging data using deep learning-based feature extraction. The proposed framework is interpretable because it provides the importance of each feature. We validated the proposed framework on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, achieving accuracy of 0.883 and area-under-the-curve (AUC) of 0.970 on AD and control classification. Furthermore, we validated the proposed framework on an external testing set, achieving accuracy of 0.778 and AUC of 0.887 on AD and subjective cognitive decline (SCD) classification. The proposed framework significantly outperformed an EBM model using volume biomarkers instead of deep learning-based features, as well as an end-to-end convolutional neural network (CNN) with optimized architecture.Comment: 11 pages, 5 figure