bluuebunny/crossref_metadata_2025_split huggingface dataset:
Associations between longitudinal neuropsychiatric symptoms and biomarkers of beta‐amyloid, tau, neurodegeneration, and cognitive decline
Authors: Johansson Maurits , Stomrud Erik , Johansson Per , Svenningsson Anna L , Palmqvist Sebastian , Janelidze Shorena , van Westen Danielle , Mattsson‐Carlgren Niklas , Hansson Oskar 
Published: December 2021

AbstractBackgroundA better understanding of drivers behind neuropsychiatric symptoms (NPS) in neurocognitive diseases, such as Alzheimer’s disease, can potentially guide drug development. Cross‐sectional studies have related apathy, depression and anxiety to beta‐amyloid‐ (Aβ) and tau‐pathology, neurodegeneration as well as cognitive deficits already at preclinical stages. However, longitudinal studies are scarce and often small. We investigated effects of neuropathology and cognition on the longitudinal trajectories of NPS in a large group of cognitively unimpaired subjects (CU).MethodThe sample originated from the Swedish BioFINDER study. CU subjects with at least one NPS assessment during the biennial follow‐up of maximum 8 years were included (n=356). The Apathy Evaluation Scale (AES) and the Hospital Depression and Anxiety Scale (HADS) assessed apathy as well as anxious and depressive symptoms, respectively. The Mini Mental State Examination (MMSE) and a modified Preclinical Alzheimer Cognitive Composite (mPACC) measured cognition longitudinally. Plasma P‐tau217, CSF Aβ42/Aβ40 ratio and CSF neurofilament light (NfL) were quantified at baseline using immunoassays. Magnetic Resonance Imaging quantified white matter lesions (WML). Subject‐specific cognitive change per year (slope) was obtained by linear regression models. Linear mixed effect models were used to test associations between longitudinal NPS and other measures (including longitudinal cognition).ResultsSubjects with lower levels of CSF Aβ42/40 (β=‐0.060, p=0.01) or higher plasma P‐tau217 (β=0.253, p=0.015) at study start demonstrated accelerated levels of apathy over time. On a trend level, a high load of WML predicted over time high levels of apathy (β=0.183, p=0.053). Increased levels of longitudinal anxiety were predicted by the interaction between time and lower CSF Aβ42/40 (β=‐0.283, p=0.005) or higher CSF NfL (β=0.054, p=0.024). Furthermore, more rapid decline in mPACC was associated with more rapid progression of apathy (β=‐0.478, p=<0.001) and anxiety (β=‐0.066, p=<0.023). Similar results were obtained for longitudinal associations between MMSE and apathy (β=‐0.673, p=<0.001).ConclusionThese results suggests that the longitudinal trajectories of apathy and anxiety already in CU subjects are related to key hallmarks of Alzheimer´s disease, including Aβ‐ or tau‐pathology at baseline, and further are parallel with cognitive decline over time.
The auditory mismatch negativity reflects accelerated aging in adults with Down’s syndrome
Authors: Avancini Chiara ,   Cambridge Intellectual and Developmental Disabilities Research Group
Published: December 2021

AbstractBackgroundAlzheimer’s disease (AD) affects nearly the entirety of the Down’s Syndrome population (DS) with a prevalence of 75% by the time they reach their 60s. The early symptoms of dementia in DS may reflect frontal lobe vulnerability to amyloid deposition. The Mismatch Negativity (MMN) is a frontocentral event‐related potential (ERP) component elicited by auditory violations. Predictive coding postulates that the brain creates neural predictions about future events by compiling statistical regularities from incoming stimuli. Within the MMN network, the MMN results from the failure of higher frontotemporal nodes to predict the incoming sensory input, which results in changes of coupling between the sensory and the frontotemporal cortices. In the typically developing (TD) population, the MMN response has been found to decrease with age. With a cross‐sectional and longitudinal study, we assessed whether the MMN reflects premature neurological aging in DS and whether it predicts cognitive decline one year later.MethodThe MMN ERP and the MMN Global‐Field Power (GFP) were elicited with an auditory global‐local task and recorded with high‐density EEG in 36 DS adults and in 39 age‐matched TD controls. Cognitive impairment was measured in DS with the neuropsychological battery CAMCOG‐DS. One year later, 34 adults with DS underwent a follow‐up administration of the CAMCOG‐DS. Linear models tested whether the MMN predicted age in the two groups. Linear and curvilinear models tested whether the MMN obtained at the first time‐point was predictive of cognitive decline one year later as measured by changes in CAMCOG‐DS scores.ResultIn the cross sectional phase, an interaction between the MMN amplitude, both expressed in ERP and GFP, and group was found. In particular, MMN amplitude predicted age in DS but not in TD. On the other hand, neither amplitude nor latency of the MMN predicted cognitive decline one year later.ConclusionOur study shows that in our cohort the MMN amplitude reflects accelerated aging in DS but not in TD, suggesting that MMN amplitude may be a sensitive marker of early onset AD in the DS population. On the other hand, neither MMN amplitude or latency predicts cognitive decline one year later.
Evaluating digital device technology in Alzheimer’s disease via artificial intelligence
Authors: Sood Meemansa , Harms Robbert , Bügler Maximilian , Tarnanas Ioannis , Coello Neva , Fröhlich Holger 
Published: December 2021

AbstractBackgroundDigital Measures (DMs) derived from mobile devices and smartphone applications have received a strongly increasing attention during the last years, because they could allow for an accurate, quantitative monitoring of disease symptoms, even outside clinics. In addition, DMs may help to diagnose Alzheimer’s Disease (AD) in a pre‐symptomatic stage and thus increase the success chances of therapeutic interventions. However, before any use in clinical routine, DMs have to be evaluated carefully by assessing their relationship to established clinical scores and understanding their diagnostic benefit. In this regard the IMI project RADAR‐AD (www.radar‐ad.org) has the ambition to evaluate a broad panel of digital technologies with respect to their potential for early disease diagnosis while focusing on functional activities of daily living.MethodAn example of a panel of digital technology RADAR‐AD uses is a smartphone based virtual reality game resulting into an assessment of cognitive impairment. In our work we analyzed connections between digital readouts and cognitive features like MMSE (Mini Mental State Examination) via one of our recently developed Artificial Intelligence (AI) approaches called Variational Autoencoder Modular Bayesian Networks (VAMBN). Going one step further we also tested the possibility to accurately predict MMSE scores from DMs and vice versa via machine learning. Based on this finding we then simulated DMs within the ADNI cohort and re‐ran VAMBN.ResultApplication of VAMBN on the data from virtual reality game resulted into a network comprising DMs, MMSE sub‐item scores and demographic features (Figure 1). It thus allowed to disentangle and quantify the relationship between DMs and established clinical scores. The simulation of DM’s and application of VAMBN in the ADNI cohort allowed us to further predict connections of DMs with FAQ (Functional Activity Questionnaire) and even molecular mechanisms.ConclusionOur results indicate that there is a significant dependency between digital readouts and clinical scores such as MMSE and FAQ. Therefore, DM’s may have the potential to act as a vital measure in the diagnosis of AD in a pre‐symptomatic stage. Our next steps will focus on evaluating the diagnostic benefit of DMs compared to the questionnaire‐based FAQ.
Cost of illness of apathy in Alzheimer’s disease
Authors: Kruse Christopher , Maier Franziska , Bürger Katharina , Dodel Richard , Fellgiebel Andreas , Frölich Lutz , Klöppel Stefan , Kornhuber Johannes , Laske Christoph , Peters Oliver , Priller Josef , Schneider Anja , Spottke Annika , Teipel Stefan J , Arnim Christine , Wiltfang Jens , Jessen Frank 
Published: December 2021

AbstractBackgroundWithin a 12‐week multicentre randomised controlled trial, standardised data on resource use of patients and their caregivers was collected using the Resource Utilization in Dementia (RUD) instrument. The aim of this study is to evaluate the insufficiently known influence of apathy on resource utilisation in Alzheimer's patients and their caregivers.Method107 patients were included in the study. Costs were collected from a societal perspective over a four‐week period and examined depending on the Apathy Evaluation Scale‐Clinician Version (AES‐C). Using the Mann‐Whitney U and Kruskal‐Wallis tests, potential cost drivers were identified in a univariate analysis. Afterwards potential cost drivers for total costs, patient costs and caregiver costs were analysed using a generalised linear model (GLM) with a γ‐distributed dependent variable and log link function.ResultThe mean total costs were €3,069. The mean patient costs were €359 and were mainly related to professional services, hospitalisation and medication. The mean caregiver costs were €2,711 and were largely caused by informal care with a share of €2,509. Within the sensitivity analysis, informal care costs decreased to €1,742 and €903, respectively. The multiple regression analysis showed that Apathy, represented by the AES‐C, is a cost driving factor for total costs and caregiver costs, however not for patient costs.ConclusionPrevious studies have often presented costs as a function of cognitive impairment. The present data show that apathy also has a significant influence on resource utilisation. At the same time, however, it was also shown that this influence was mainly caused by the increased resource use of the caregivers and that the resource use of the patients did not show significant changes.
Cerebrospinal fluid neurogranin in non‐amnestic and amnestic Alzheimer’s disease
Authors: Cousins Katheryn A.Q. , Burke Sarah E. , Ballinger Samantha , Irwin David J. , Shaw Les M. , Wolk David A. , Trojanowski John Q. , Lee Eddie B. , Grossman Murray 
Published: December 2021

AbstractBackgroundNeurogranin (Ng) is a postsynaptic protein involved in long‐term potentiation and is largely expressed in temporal, parietal, and limbic regions (Lista et al., 2017; Kvartsberg et al., 2019). Increased Ng in cerebrospinal fluid (CSF) follows synaptic dysfunction, and is thought to be a marker specific to Alzheimer’s disease (AD) pathology (Portelius et al., 2018; Clarke et al., 2019). Even so, non‐amnestic variants of AD (naAD) show altered CSF biomarker levels compared to amnestic AD (aAD) (Wellington et al., 2018; Cousins et al., 2020). Here, we compared CSF Ng across aAD, naAD, and frontotemporal dementia (FTD). Neuroimaging tested if Ng differences across naAD and aAD are in part due to regional differences in atrophy.MethodPatient inclusion criteria were available CSF Ng and Aβ1‐42, and either a non‐amnestic (naAD, FTD) or amnestic (aAD) clinical diagnosis. naAD patients (n=34) had likely AD pathology, determined by autopsy (n=3) or CSF Aβ1‐42≤192 pg/mL (Shaw et al., 2009). FTD patients (n=60) had likely non‐AD pathology, determined by autopsy (n=21) or CSF Aβ1‐42>192 pg/mL. aAD patients (n=62) had autopsy‐confirmed AD pathology and an amnestic phenotype. Neuroimaging was available for 29 aAD, 24 naAD, and 50 FTD. Mindboggle Atlas defined regions (Klein and Tourville, 2012) and grey matter volume (GMV) estimated atrophic severity. Regressions 1.) identified regions associated with Ng and 2.) compared atrophy across aAD and naAD; age at MRI, time between MRI and CSF, and sex were covariates. We report regions that were significant in both analyses (p<0.01).ResultAn ANCOVA (covarying for age at CSF and sex) showed that CSF Ng differed by group (p=4.5x106). Post‐hoc pairwise comparisons showed aAD had higher Ng than naAD (p=0.016) and FTD (p=2.6x106), with no difference between naAD and FTD (p=0.23). Neuroimaging showed increased Ng correlated with lower GMV in left fusiform (p=0.0062), precuneus (p=0.0076), and superior parietal lobule (p=0.0073), and naAD had higher GMV than aAD in these three regions (all p<0.0007).ConclusionDespite likely AD pathology, naAD patients had significantly lower Ng than aAD, which may be explained in part by regional expression of Ng and differences in disease distribution between naAD and aAD.
Template impact in nucleus basalis segmentation
Authors: Conklin Chris , Scott David , Bracoud Luc , Vuksanovic Vesna , Wischik Claude M , Suhy Joyce , Staff Roger ,   the Alzheimer's Disease Neuroimaging Initiative
Published: December 2021

AbstractBackgroundThe Nucleus Basalis of Meynert (NB), located within the basal forebrain sublenticular region, is a cholinergic center with neurons providing cholinergic afferents to the entire neocortex. As posterior NB provides cholinergic innervation to the temporal pole and superior temporal cortex, this correlates well with memory loss and language impairment in AD.NB volume can serve as a potential emerging biomarker using volumetric MRI. One method consists in using DARTEL normalization to register images to a basal forebrain atlas. Our aim is to study the influence of template content and determine what is sufficient in practice for accurate evaluation, in the context of an on‐going multi‐center trial where the final dataset would not be available until study end.Methods96 good quality 3D T1‐weighted datasets were randomly chosen among screening scans of an on‐going AD trial (TRx‐237‐039/NCT03446001), with equal distribution of datasets from GE, Philips and Siemens, 1.5T/3T scanners (16 each). Separate DARTEL templates were created from the full list of 96 subjects, from subsets of 24, 48 and 72 subjects, and from independent sets of 20 and 96 random ADNI AD subjects to assess comparability.Processing followed a standard SPM12 DARTEL approach. 3DT1 images were initially segmented to create gray/white matter tissue maps and subsequently registered to create a template image. Derived flow fields were applied to warp and smooth all subjects to MNI space while preserving regional signal. Final subject maps were resliced to match a basal forebrain ROI.Volumes were compared with two‐sided paired t‐tests. Correlations across volumes were assessed with Pearson’s coefficient. Mean absolute percent difference to full 96‐template was calculated.ResultsVolumes obtained from either template were all highly and significantly correlated to each other (r>0.99, p<0.001). T‐tests showed significant differences (p<0.001), respectively resulting in mean absolute differences to the full template of 2.3% for the 24‐template, 0.8% for the ADNI‐20‐template, 1.6% for the ADNI‐96‐template and 0.3% for all others.ConclusionTemplate choice does impact measurements significantly. Choosing a sufficiently large sample is important when building the template, to avoid biasing overall results. A common template is essential for proper comparison of separate cohorts with this method.
An MRI multi‐scanner neuroimaging data harmonization study using RAVEL and ComBat
Authors: Torbati Mahbaneh Eshaghzadeh , Minhas Davneet S , Ahmad Ghasan E , O'Connor Erin E , John Muschelli , Laymon Charles M , Zixi Yang , Cohen Ann D , Aizenstein Howard J , Klunk William E , Christian Bradley T , Crainiceanu Ciprian , Hwang Seong Jae , Tudorascu Dana L 
Published: December 2021

AbstractBackgroundLarge‐scale multi‐site neuroimaging studies provide higher power for statistical analyses. However, these aggregated datasets are susceptible to unwanted variability resulting from scanner and acquisition protocol differences. To address this problem, a group of intensity normalization and harmonization methods have been developed recently. This study used a paired 1.5T‐3T MRI dataset to evaluate an intensity normalization procedure, RAVEL (Fortin et al., 2016), a data harmonization method, ComBat (Johnson et al., 2007), and a pipeline combining the two.MethodSixteen healthy elderly participants (median 77.5 years (range=70‐79), 25% (n=4) males) underwent 1.5T and 3T T1‐weighted MRI, three months apart. These images (RAW) were used as input for RAVEL (an intensity normalization technique) and/or ComBat (a harmonization procedure) to characterize the cross‐scanner harmonization of FreeSurfer‐extracted summary measures (cortical thickness and/or volume) relevant to Alzheimer’s disease (Zhang et al., 2001). In setting‐1 (RAVEL), measures were extracted from RAVEL‐normalized images. In setting‐2 (ComBat), measures were extracted from RAW images and then harmonized using ComBat. In setting‐3 (RAVEL‐ComBat), ComBat was applied to the measures resulted from setting‐1. Paired t‐tests were used to evaluate cross‐scanner differences of measures in each of these sets. The measures with statistically significant differences were considered with potential scanner differences and decreasing such differences to lose significance, was considered to achieve harmonization.ResultTable 1 shows the means of the differences and their 95% CI for statistical significance, indicating 14 measures with potential scanner differences in RAW (blue/yellow under RAW). Out of these 14 measures, RAVEL‐Combat harmonized 13 measures (green under RAVEL‐ComBat) compared to 8 and 9 harmonized measures by RAVEL and ComBat, respectively. For example, for the cortical thickness of the Inferior Temporal Left, the mean of differences for RAW was 0.25mm, 95%CI:(0.16mm, 0.34mm), indicating a statistically significant difference (blue). This difference has been reduced to 0.08mm, 95%CI (‐0.05mm, 0.21mm) using RAVEL‐ComBat (green) and no longer statistically significant. We observed that, both, RAVEL‐ComBat and RAVEL slightly increased the variability of the differences.ConclusionA combination of intensity normalization (RAVEL) and data harmonization (ComBat) performs best in terms of harmonizing data across 1.5T and 3T MRI within the same cohort.
Neuroticism is the best predictor of lower emotional resilience during the COVID‒19‐related confinement periods
Authors: Gonneaud Julie , Paly Léo , Delarue Marion , Mézenge Florence , Fauvel Séverine , Lefranc Valérie , Cognet Aurélia , de Flores Robin , Touron Edelweiss , Marchant Natalie L , Poisnel Géraldine , Chetelat Gaël ,   the Medit‐Ageing Research group
Published: December 2021

AbstractBackgroundThe COVID‐19 pandemic and the associated distancing measures dramatically affect psychoaffective health, and this is accentuated in older adults who are more vulnerable to the situation. In this study, we are interested in the predictors of emotional resilience in healthy older adults, and also on how the repetition of confinement periods could influence this resilience and its predictors.Method102 cognitively unimpaired older adults from the Age‐Well cohort were included. They all completed the Depression Anxiety Stress Scales (DASS‐42) during each period of national confinement (April and November 2020), used here as a measure of emotional resilience (lower scores indicating greater resilience). Baseline measures from the Age‐Well study, all acquired before the pandemic, were used as predictors, including demographics (age, sex, education), personality (Big Five Inventory), psychological (anxiety [STAI‐B], depression [GDS]), lifestyle (Lifetime of experience questionnaire), global cognitive functioning (Mattis‐DRS) and neuroimaging data (hippocampal volume, brain perfusion, amyloid burden). We ran stepwise regressions to predict emotional resilience during the first confinement. Then, we used paired t‐test to assess the evolution of emotional resilience between the two confinements. Finally, we replicated the stepwise regressions to predict changes in resilience over time ('second‐minus‐first' confinement).ResultNeuroticism was the only significant predictor of the DASS‐42 score during the first confinement (β=.48; p<.001; Figure‐1), lower neuroticism being associated with better resilience. We found the DASS‐42 score to increase significantly from the first to the second confinement, indicating decreased emotional resilience over time. Moreover, higher neuroticism (β=.53; p<.001) and higher agreeableness (β=.20; p=.03) were associated with higher DASS‐42 increase over time (Figure‐2).ConclusionOur study suggests that lower neuroticism is the main predictor of greater emotional resilience to the COVID‐19‐related confinement. Importantly, the repetition of the confinement situation is associated with a decrease in emotional resilience in older adults, especially if they are prone to experience distress (neuroticism) or have a particularly social personality (agreeableness). Overall, these results underline the increasing need, as the crisis persists, for a psychoaffective support of older adult.
A remote digital tool for diagnosis and monitoring of Alzheimer’s disease
Authors: Modarres Mohammad Hadi , Kalafatis Chris , Apostolou Panos , Marefat Haniye , Khanbagi Mahdiyeh , Karimi Hamed , Vahabi Zahra , Aarsland Dag , Khaligh‐Razavi Seyed‐Mahdi 
Published: December 2021

AbstractBackgroundEarly detection and monitoring of mild cognitive impairment (MCI) and Alzheimer’s Disease (AD) patients are key to tackling dementia and providing benefits to patients, caregivers, healthcare providers and society.MethodWe developed the Integrated Cognitive Assessment (ICA); a 5 minute computerised cognitive test employs Artificial Intelligence (AI) to improve its accuracy in detecting cognitive impairment. ICA presents a series of rapidly changing images on a mobile device to measure cognitive impairment via a person's accuracy and response time in categorising those images.We studied the ICA in a total of 230 participants. 95 healthy volunteers, 80 MCI, and 55 mild AD participants completed the ICA, the Montreal Cognitive Assessment (MoCA) and Addenbrooke’s Cognitive Examination (ACE) cognitive tests.ResultThe ICA demonstrated convergent validity with MoCA (r=0.58) and ACE (r=0.62). The ICA AI model was able to detect cognitive impairment with an area under the curve of 81% for MCI patients (MoCA 77%), and 88% for mild AD patients (MoCA 89%). The AI classifier, based on an explainable logistic regression model, demonstrated improved performance with increased training data. Furthermore it showed generalisability in performance from one population to another. The ICA was able to detect cognitive impairment with high accuracy when trained with one cohort and tested in an independent cohort with different cultural and demographic characteristics, a prerequisite for large population deployment.In a monitoring study, 12 healthy participants self‐administered 78 ICA tests remotely over a period of 3 months (936 tests in total). The ICA demonstrated no significant practice effect observed over the duration of the study.ConclusionThe ICA can support clinicians by aiding accurate diagnosis of MCI and AD and is appropriate for large‐scale screening of cognitive impairment. The ICA is unbiased by differences in language, culture and education and has additional advantages over standard of care tests because of its shorter duration, automatic scoring and potential for medical record or research database integration.The pandemic has presented a challenge for face‐face assessments. A digital tool such as the ICA allows us to adapt to these changes by administering assessments remotely and monitoring disease progression.