bluuebunny/crossref_metadata_2025_split huggingface dataset:
Home monitoring of vital signs and generation of alerts in a cohort of people living with dementia
Authors: David Michael , Barnaghi Payam , Nilforooshan Ramin , Rostill Helen , Soreq Eyal , Sharp David J , Scott Gregory 
Published: February 2022

AbstractBackgroundPeople with dementia (PwD) are at increased risk of adverse medical events (e.g. infections and falls). These often cause clinical deterioration, and potentially preventable admissions. Remote home monitoring of vital signs using internet‐of‐things technology can identify risk factors for these events – something particular pertinent during the COVID‐19 pandemic. We present data from an on‐going UK Dementia Research Institute and Technology Integrated Health Management (DRI‐TIHM) project. We aim to define algorithms that generate automated alerts, like the hospital‐based NEWS system (Morgan, 1997, Clin Intensive Car), and provide more proactive care for PwD.MethodPwD recorded their systolic/diastolic blood pressure (SBP/DBP), heart rate (HR), temperature (BTM), bodyweight (BW) and oxygen saturation (Sats) daily (figure 1) and data were collected centrally. A ‘monitoring team’ followed algorithms in response to alerts and advised medical attention if required. Events such as infections, were logged and correlated with the data. The dataset was then used to calculate the number of alerts that would have been raised if different thresholds were used.Result52 PwD living at home were included. Patients had a range of dementia diagnoses, most commonly Alzheimer’s disease. In total, 89,894 measurements were collected over 556 days (figure 2). During the pandemic, a sub‐group were given Sats probes and these produced a significant number of false positive results (figure 4f). On sub‐group analysis, PwD with Parkinson’s disease dementia had significantly lower SBP and DBP (p=0.004 and p=0.01 respectively, Mann‐Whitney U) (figure 3a), one of whom suffered from repeated falls (figure 3b).Pilot data were used to set alert thresholds. Average values and number of alerts for each domain are shown (table 1). We then modelled the effect of different thresholds, based on NEWS, to the number of alerts generated (figure 4, table 2). This is informative in optimising sensitivity and specificity of alerts whilst considering the burden on the monitoring service.ConclusionRemote monitoring can be implemented for PwD. These data can be used to generate clinical alerts that may signify adverse medical outcomes. The thresholds need to be adjusted to optimise efficiency in the context of varying reliability of home monitoring devices.
Apolipoprotein L1 is increased in frontotemporal lobar degeneration postmortem brain tissue but not in cerebrospinal fluid
Authors: Hok‐A‐Hin Yanaika S. , Dijkstra Anke A. , Rabano Alberto , Hoozemans Jeroen J. , Castillo Lucia , Pijnenburg Yolande A.L. , Teunissen Charlotte E. , Del Campo Marta 
Published: February 2022

AbstractBackgroundFrontotemporal Dementia (FTD) is a spectrum of heterogeneous disorders caused by frontal‐temporal lobar degeneration (FTLD). FTLD is characterized by brain protein aggregates of tau (FTLD‐Tau) or TDP43 (FTLD‐TDP) mainly. However, the clinicopathological heterogeneity across the FTLD spectrum makes ante‐mortem diagnosis of these FTLD pathological subtypes extremely challenging. Our recent proteomic analysis revealed apolipoprotein L1 (APOL1), involved in lipid metabolism and transport, as a potential cerebrospinal fluid (CSF) biomarker for FTLD. However, the association of APOL1 with the subtype of FTLD pathology is not known. Here, we aimed to characterize APOL1 expression in FTLD post‐mortem brain tissue with Tau or TDP pathology and validate its potential as a novel CSF biomarker for FTLD pathological subtypes.MethodAPOL1 immunoreactivity was analyzed in paraffin‐embedded post‐mortem frontal cortex of 18 FTLD cases (FTLD‐Tau: 12 and FTLD‐TDP: 6) and 9 non‐demented controls. APOL1 levels were measured in post‐mortem frontal cortex (44 FTLD cases (FTLD‐Tau: 21 and FTLD‐TDP: 23) and 9 controls) and in ante‐mortem CSF (27 FTD patients (FTLD‐Tau: 12 and FTLD‐TDP: 15) and 15 controls) by western blot analysis and a validated in‐house APOL1 ELISA. APOL1 levels between groups were evaluated either by analysis of covariance (corrected for center) or Kruskal‐Wallis test.ResultHigher APOL1 immunoreactivity associated with neuronal and glia cells was observed in the frontal cortices of FTLD cases compared to controls (p<0.001, figure 1A). However, no differences were observed between the FTLD‐Tau and FTLD‐TDP subtypes. Western blot and ELISA analyses showed that APOL1 levels were increased in lysates from FTLD frontal cortex compared to controls (WB: p<0.001, figure 1B and ELISA: p<0.05, figure 1C), but were similar between the FTLD pathological subtypes. In CSF, we observed no changes in APOL1 between FTD patients and controls, nor between FTLD pathological subtypes.ConclusionIn this study, we were not able to validate APOL1 as a CSF biomarker for FTD using the current immunoassay. However, we do show a strong involvement of APOL1 in FTLD pathology irrespective of FTLD subtype. Future research should explore the specific role of APOL1 in the pathogenesis of FTLD.
Enhancing dementia care during the Corona pandemic: Zooming from home
Authors: Brown Nancy L , Tziraki Chariklia , Cedar Tzippi 
Published: February 2022

AbstractBackgroundThe Covid‐19 pandemic has created social isolation across all age groups. Literature reports that persons with dementia experience significant social isolation resulting in stress and anxiety for their caregivers as well. Interactive communication technologies like Zoom have been employed to reduce the impact of the pandemic. However, we find no reports using this technology with persons with dementia. Our study used the Zoom platform to maintain social connectivity with 16 day center clients who represent an immigrant community identified with mild to moderate cognitive impairment. We hypothesize that by using technology which allows facial recognition and communication and by focusing on the meaning of home, these persons and their carers expressed positive emotional feelings and remained engaged with their social world despite what was going on around them.MethodOnline psychodrama sessions were conducted for 7 weeks, 1 hour each week during the COVID‐19 lockdown. Carers were instructed on using the tablet and preparations needed for the Zoom session. The session was video recorded following the ethics guidelines of the day center MELABEV and manually transcribed. NVivo software was used in the qualitative analysis.ResultFive main themes emerged from this analysis: Emotions, Home/Family, Social Connectivity, Reminiscence with Objects, and Carers Participation. Clients demonstrated significant social interaction without inhibition in their participation and displayed emotional expressions of what home means to them. In contrast to recent literature, neither clients nor their caregivers exhibited anxiety, stress, depression, or security issues despite their social isolation. Despite cognitive limitations, the clients were engaged, connected, interacted, and communicated with one another in very personal ways.ConclusionResults support the notion that technology like Zoom offers an option for preventing social isolation and mitigating the negative impact of supporting someone with dementia during this pandemic and other crises. From a societal perspective, particularly during COVID‐19, the importance of making available technology to persons with dementia and seeing them socially connect and engage through a medium not part of their generation has far‐reaching implications not only for future research but also for policy considerations, mitigating social isolation and caregiver burden among a vulnerable population.
Exercise and carbohydrate‐restricted diet associates with improved cerebral blood flow
Authors: Heston Margo B. , Gaitán Julian M. , Ma Yue , Oh Jennifer M. , Jen Mu‐lan , Derynda Brittany R , Lose Sarah , Kozuch Michael P. , Johnson Kevin M. , Okonkwo Ozioma C. , Gretebeck Kimberlee A. , Gretebeck Randall J. , Bendlin Barbara B. 
Published: February 2022

AbstractBackgroundInsulin resistance (IR) is linked to decreased cerebral blood flow (CBF), an Alzheimer’s disease risk factor. Previous findings of the Blood Flow Improvement Trial (BFiT, clinicaltrials.gov NCT03117829) demonstrate that reversing midlife IR associated with improved cognition, but it is unknown whether this also improves CBF. BFiT tested an exercise and carbohydrate‐restricted diet (CRD) to determine whether improving IR resulted in higher CBF.MethodTwenty‐nine pre‐diabetic, cognitively unimpaired participants from the Wisconsin Alzheimer’s Disease Research Center were recruited in four phases (ns = 9, 7, 5, 8) into a stepped wedge cluster randomized trial. Participants engaged in 12 weeks of supervised aerobic exercise three times weekly, unsupervised exercise twice weekly, and behavioral classes once weekly for program adherence. Post‐intervention, participants were instructed to maintain the regimen for six additional months. At baseline (T1), 12 weeks (T2), and six months post‐intervention (T3), arterial spin labeling (ASL) images were obtained. CBF was calculated globally and in frontal and temporal regions of interest.Intervention effects on regional CBF were tested through pairwise comparison of the three visits using random intercept three‐level mixed models, with visits nested within participants, and participants nested within groups. Age and sex were adjusted as covariates for each region, and models were tested with and without influential outliers. Tukey‐Kramer adjustment was applied to control for the inflation of type I error rate associated with multiple comparison.ResultOf the 29 participants (Table 1) enrolled in the study, 23 completed the intervention, and 21 completed all measurements. Between T1 and T3, CBF showed significant bilateral increases in the middle temporal gyri and temporal poles (superior and middle areas), middle frontal gyri (orbital), superior frontal gyri (medial orbital), and precentral gyri (Figures 1, 2). No regions showed significant CBF increases (p<.05) between T1 and T2, or T2 and T3.ConclusionIn a cognitively unimpaired cohort, an exercise and carbohydrate‐restricting intervention significantly improved CBF over the trial duration. This suggests that sustaining a CRD and exercise may improve CBF in people with impaired glucose regulation. Ongoing analysis of trial data will assess whether improved cerebral blood flow and cardiorespiratory fitness mediate observed enhancements in cognitive performance.
Early bio‐maker detection through novel data equilibration methods
Authors: Pal Prasanta , Brewer Judson A 
Published: February 2022

AbstractBackgroundA novel adaptive universal set of algorithms are developed to map and differentiate normal and alzheimer patient MRI images into orthogonal complementary image spaces. This adaptive method greatly enhances the visual clarity of both (normal and alzheimer) images while providing with additional metric space of outlier image for further differentiation of features in the data sets. This enables us with extra handle to compare baseline and alzheimer MRI images with unprecedented clarity and robustnessMethodTo observe multi‐scale features and its differences between baseline and disease condition MRI data sets (publicly available), we introduce a set of length scale kernels coupled with threshold parameters to transform the original image space to parametrically controlled equilibrium and out of equilibrium (or outlier) data sets. This forms the foundation of the new method of comparison through a novel method of outlier based orthogonal decomposition of image information space.ResultWhile our new comparison method confirms the findings of published results, it adds additional layers of visual clarity for further discovery of the novel never before seen features. For the first time in the literature, we introduce the concept of equilibrium (and nonequilibrium) states from raw data acquired through sensors and demonstrate the efficacy of it in the context of early detection of AD biomarkers.ConclusionWhile historical methods of image difference works well for detecting crude biomarkers, there is a potential for a information loss and low contrast resolution. Our method overcomes those limitations by appropriately re‐mapping the raw data to equilibrium and nonequilibrium states. This makes the comparison between baseline and AD images much visually clear in those respective metric spaces.
Neuroanatomical mapping of artificial intelligence‐based classification of language in PPA
Authors: Rezaii Neguine , Carvalho Nicole , Brickhouse Michael , Loyer Emmaleigh , Wolff Phillip , Touroutoglou Alexandra , Wong Bonnie , Quimby Megan , Dickerson Brad C. 
Published: February 2022

AbstractBackgroundPatients with Primary Progressive Aphasia (PPA) are usually subtyped into one of the three canonical subtypes (nfvPPA, svPPA, lvPPA) based on a neurological and cognitive assessment including their language characteristics typically measured from a battery of confrontational tests. While widely used, this classification system has been criticized, and also the approach makes assumptions about the features of language that are important to measure. Here we use methods from Artificial Intelligence to measure features of speech from naturalistic connected speech samples with the goal of determining how well this data‐driven approach matches independent clinical subtypes and how its results relate to neuroanatomical abnormalities measured from MRI.MethodLanguage data was obtained from 78 PPA patients (28 nfvPPA, 26 lvPPA, 24 svPPA) describing the WAB Picnic Scene. A transformer model, RoBERTa, was used to measure similarities in language features, IVIS to perform dimensionality reduction, and nested k‐means to cluster language samples. We then examined the cortical atrophy patterns of these clusters of PPA patients versus healthy controls (N=25).ResultSeven PPA clusters were identified with 88% agreement with the classic classification system. Individuals in Clusters 1 and 2 (mainly nfvPPA) exhibited speech dysfluency and reduced clausal complexity with atrophy in the left pars opercularis, superior and caudal middle frontal gyri. Those in Clusters 3 and 4 (predominantly lvPPA) exhibited difficulties in subject‐verb agreement, demonstratives and tense and shared atrophy in the superior and middle temporal and inferior parietal gyri. Individuals in Clusters 5‐7 (mainly svPPA) exhibited deficits in nouns/verbs access with atrophy in the left temporal pole and inferior and middle temporal gyri.ConclusionData‐driven Artificial Intelligence methods applied to naturalistic speech samples from PPA patients identify clusters of patients that match well to clinical subtypes, and that exhibit cortical atrophy patterns typical of those subtypes. This suggests that PPA subtypes are natural kinds and that computational analysis of simple speech samples can be used to identify them.
Supervised machine learning method tailored for five brain MRI events is predictive of cognitive status
Authors: Thakar Darshit , Patel Raj , Ravindranath Vijayalakshmi , Tiwari Vivek 
Published: February 2022

AbstractBackgroundDespite the large neuroanatomic measurements obtained from brain MRI segmentation, we do not have yet a definitive neuroanatomic or vascular feature/even that can precisely delineate a cognitively normal subject from cognitively impaired and Alzheimer’s disease (AD). Increasing lifespan of population accompanied with increased prevalence of cardiovascular risks across globe demands for precise early detection of AD and cognitive status (impaired or normal) in the aging individuals. Here, we have employed machine learning (ML) method on neuroanatomic volume and white matter hyperintensity (WMH) volume obtained from MRI analyses of data from National Alzheimer’s Coordinating Center (NACC) to identify participants as cognitively normal (CN), cognitively impaired with etiological diagnosis as AD and cognitively impaired (CI) subjects.MethodBrain segmentation outputs for neuroanatomic‐volume and White matter hyperintensity (WMH) was obtained from NACC for 1290 unique subjects with total of 3684 longitudinal‐MRI. Supervised ML was performed using the MRI parameters for identifying the subjects as AD, CI, and CN. 50% of the MRI were used as training set while another 50% was used as test. Various combinations of neuroanatomic volume with WMH quantity were tested for accuracy of delineating the AD, CI and CN groups using random forest method. Furthermore, various ML methods such as XGB classifiers, classification tree, bagging classifier and simple classification were tested for accuracy using the optimized neuroanatomic volume and WMH.ResultRandom forest method converged with a combination of 5‐MRI features, intracranial, gray‐matter, cerebral CSF, hippocampus, and White‐matter hyperintensity volume with highest accuracy (83.3%) in identifying AD, CI, and CN subjects (Fig. 1). Inclusion of normal appearing white‐matter volume during the ML optimization of MRI features led to significant reduction in the accuracy. Simple random forest ML method showed highest accuracy using the optimized MRI features (Fig. 2).ConclusionAll the neuroanatomic events obtained from brain MRI may not be specific in providing diagnostic features for evaluating brain health. Here we show that a combination of 5 MRI features that includes microvascular pathology and neuroanatomic volume may provide a specific and efficient non‐invasive imaging biomarker to evaluate brain health in aging population.
Regional amyloid accumulation predicts memory decline in initially cognitively unimpaired individuals
Authors: Collij Lyduine E. , Mastenbroek Sophie E , Salvadó Gemma , Wink Alle Meije , Visser Pieter Jelle , Barkhof Frederik , Van Berckel Bart N.M. , Alves Isadora Lopes 
Published: February 2022

AbstractBackgroundThe current shift towards (early) secondary intervention AD trial designs warrants the need for detection of subtle changes of both amyloid‐β (Aβ) burden and cognitive functioning. This study investigated the longitudinal trajectory of global and regional Aβ measurements and their value in predicting cognition in initially cognitively unimpaired (CU) subjects.MethodWe included 133 CU subjects with available longitudinal cognition (MMSE: 2‐13 assessments, 7.9±2.5 years; neuropsychological memory tasks: 2‐5 assessments, 4.0±1.9 years) and [11C]PiB PET imaging (2‐5 scans, 4.4±1.9 years) from the OASIS‐3 cohort. Baseline and annualized Aβ measures (DVR) were computed for a global composite and 4 clusters of regions, reflecting different stages of the amyloid accumulation process. Linear mixed effect models were used to examine the predictive value of Aβ measurements (i.e. Aβbaseline, Aβslope, and Aβbaseline*slope) on global cognition (MMSE) and several aspects of memory performance. All analyses were controlled for age, sex, years of education, and time between baseline PET and first cognitive assessment.ResultGlobal baseline amyloid burden and slope were non‐linearly related (). This was also apparent in stages 1, 2, and 3 clusters, but less in stage 4 (). MMSE performance over time could only be predicted by Aβ burden and accumulation in cluster 2 (precuneus, lateral orbitofrontal (LOFC), and insula; β
baseline=‐0.37, p<.001; β
interaction=14.16, p<.05). Focusing on the precuneus ROI by itself significantly improved the model (βbaseline
=‐0.29, p<.001; βslope
=‐9.71, p<.05; βinteraction
=8.71, p<.01), where fast‐decliners were those at the plateau phase of accumulation (). Similarly, precuneal slope and the interaction term were both predictive of changes in immediate (βslope
=‐60.95, p<.05; βinteraction
=53.39, p<.05) and delayed episodic memory (βslope
=‐65.45, p<.05; βinteraction
=53.14, p<.05). In the Aβ‐ at baseline group (N=93), LOFC slope and the interaction term predicted changes in working (βslope
=‐172.0, p<.05; βinteraction
=‐147.2, p<.05) and semantic memory performance (βslope
=‐439,4, p<.01; βinteraction
=393.1, p<.01).ConclusionQuantifying longitudinal and regional changes in Aβ can improve the prediction of future cognitive functioning in initially CU individuals. For individuals with established amyloid pathology, longitudinal Aβ measures help to identify those subjects most likely to show cognitive decline within the short timelines of clinical trials.
Data‐driven evidence for three distinct patterns of amyloid‐β accumulation
Authors: Collij Lyduine E. , Salvadó Gemma , Wottschel Viktor , Schoenmakers Pierre , Mutti Martina , Aksman Leon M , Wink Alle Meije , van der Flier Wiesje M , Scheltens Philip , Visser Pieter Jelle , Van Berckel Bart N.M. , Barkhof Frederik , Haller Sven , Gispert Juan Domingo , Alves Isadora Lopes 
Published: February 2022

AbstractBackgroundAmyloid burden staging models assume a single spatial‐temporal progression of amyloid accumulation across subjects, while disease heterogeneity suggests there might be subtypes with distinct trajectories. Here, we apply a data‐driven model which simultaneously resolves subtypes and stages (SuStaIn)1 to amyloid PET data to assess evidence for Aβ accumulation subtypes.MethodAmyloid PET data of 3010 subjects () were pooled from ALFA+, EMIF‐AD, ABIDE, OASIS, and ADNI. SUVr was extracted for 17 regions. Next, GMM was applied to regional SUVr of cognitively unimpaired subjects per cohort, per tracer, to derive z‐scores. Then, we applied SuStaIn to baseline data to determine the optimal number of subtypes and the most probable subtype/stage per scan. Subtypes and stages were related to the proportion of APOE‐ε4 carriers and diagnostic groups, mean age, and Centiloid. The final model was also applied to longitudinal scans (>4 years after baseline) from ADNI and OASIS (N=519) for validation, where we assessed subtype stability and stage progression.ResultSuStaIn identified three subtypes, referred to as Frontal, Parietal and Occipital based on the first regions to show abnormality (). This 3‐subtype model was preferred to a traditional one‐trajectory model () based on the Cross‐Validation Information Criteria. Most subjects displayed no amyloid (stage 0: N=1810, 60.1%) or abnormal amyloid levels across all regions (stage 17: N=282, 9.4%), challenging subtyping. Of the N=835 (27.7%) with strong subtype assignment (>50% probability), the majority was assigned to Frontal (55.0%), followed by Parietal (24.1%), and Occipital subtypes (21.0%). Statistically significant differences included lower age for the Parietal subtype (Frontal:71.9±8.3; Parietal:69.3±9.6; Occipital:72.1±8.1), and distinct proportions of APOE‐ε4 carriership (Frontal:61.8%, Parietal:57.1%, Occipital:49.4%) and subjects with dementia across subtypes (Frontal:19.7%, Parietal:19.1%, Occipital:31.0%). Older age, APOE‐ε4 carriership, and Centiloid burden were positively associated with stage independent of subtype. At follow‐up, most subjects (77.9%) maintained baseline subtype assignment, and 65.3% progressed to a later stage within their subtype.ConclusionWhile a one‐trajectory model recovers the established pattern of amyloid accumulation, SuStaIn identified three subtypes with distinct associations to AD risk factors. These initial results suggest subtype classification may have clinical relevance and/or improve risk assessment compared to previous staging models.