bluuebunny/crossref_metadata_2025_split huggingface dataset:
Validation of QyScore’s® fully automated quantitative image segmentation tools against expert manual gold‐standards
Authors: Gordon Elizabeth , Borrot Mathilde , Villa Luca , Jubault Thomas , Guizard Nicolas ,   Alzheimer’s Disease Neuroimaging Initiative (ADNI),   Organization for the Advancement of Structured Information Stand (OASIS)
Published: December 2023

AbstractBackgroundQuantitative imaging provides valuable information for early detection, disease progression and treatment response monitoring. Manual segmentation is the current gold‐standard, but is prohibitively labour‐intensive for large scale use, such as in clinical routine, and suffers from individual variability. Thus, there is a substantial unmet need for validation of automatic segmentation techniques that perform as accurately as this labour‐intensive manual gold‐standard.MethodThe validation cohort consisted of 50 individuals with multiple diagnoses, wide age range (18‐86 yrs), balanced for sex and acquired on multiple scanners for improved generalizability of quantification results (Table 1). Three expert neuroradiologists each manually segmented the caudate, putamen, globus pallidus, thalamus, cerebellum, brainstem, and lateral ventricles on 3DT1 images using itk‐SNAP (http://www.itksnap.org/) software. A consensus expert segmentation was then derived using the STAPLE (Simultaneous Truth and Performance Level Estimation) algorithm (Wakefield et al., (2004)) for each region and compared with the fully automated segmentations produced using QyScore®, a CE‐marked and FDA‐cleared neuroimaging medical device. Performance was investigated using the Dice Similarity Coefficient (DSC) and concordance assessed with plotted linear regression.ResultMean, standard deviation and confidence intervals of the DSC demonstrated a high degree of agreement between the consensus manual gold‐standard and QyScore®’s automated segmentations (Table 2). Importantly, this agreement remained consistent following stratification by field strength, demonstrating generalizability to most clinical imaging centres. For 1.5T scanners the mean DSC was 0.81, 0.89, 0.79, 0.86, 0.94, 0.93 and 0.91 for the caudate, putamen, globus pallidus, thalamus, cerebellum, brainstem, and lateral ventricles respectively. For the 3T scanners, this was equivalent or marginally better at 0.84, 0.89, 0.82, 0.86, 0.94, 0.94, and 0.93 respectively. In addition, strong concordance was demonstrated between the volumes, expressed as a percentage of intracranial volume (%ICV), obtained by the automated medical device QyScore® and the consensus of the manual gold‐standard segmentation (Figure 1: Table 2).ConclusionQyScore® produces fast reliable automated segmentations with comparable accuracy to expert neuroradiologists. These findings support the implementation of QyScore® in clinical trials and in clinical routine to provide quantitative image analysis in support of diagnosis and monitoring of disease progression and treatment response.
Differential dementia diagnosis using event‐based modelling: Alzheimer’s disease versus Dementia with Lewy Bodies
Authors: Leal Gonzalo Castro , Whitfield Tim , Walker Zuzana , Oxtoby Neil P 
Published: December 2023

AbstractBackgroundDifferential diagnosis of dementias in the clinic is challenging, especially in the prodromal phase where early symptoms overlap. Here we prototype a differential diagnosis system based on event‐based modeling of MRI atrophy in two dementias.MethodPublic data from ADNI, NACC, PPMI, PDBP (N = 1978). Inclusion criteria: baseline T1w‐MRI successfully processed by FreeSurfer 7.1.1, CN/AD/DLB/MCI diagnosis. A nearest‐neighbour approach matched disease‐specific control groups to AD and DLB by Age/Sex/scanner (field strength), and Desikan‐Killiany volumes were adjusted for normal trends in Age/Sex/field‐strength/intracranial volume. The final analysis dataset consisted of 91 DLB, 298 AD, and 786 MCI (N = 1175), with matched control groups for each. One event‐based model (EBM) of regional brain atrophy was fit per disease, using Kernel Density Estimation mixture models to quantify biomarker abnormality. Input features were brain volumes having disease signal in both AD and DLB. The EBM patient‐staging tool was adapted to perform differential diagnosis. The tool converts individual data into a probability distribution over stages — stage likelihood — for each model. We define EBM diagnosis as the model having the maximum stage likelihood.ResultFigure 1 shows the event‐based model posterior (positional variance diagram) for AD (left) and DLB (right). AD atrophy starts in a subset of limbic regions before progressing to the temporal cortex and beyond. The DLB atrophy pattern diverges considerably (off‐diagonal density in Figure 1), including involvement of additional limbic regions early on. Figure 2 shows patient staging results for each model, with MCI individuals earlier than patients as expected. Differential diagnosis accuracy was 63%, which is comparable to previous studies.ConclusionWe successfully adapted event‐based modeling of MRI atrophy for differential diagnosis of AD vs DLB. More data for other dementias, especially in the prodromal phase, is needed to test and deploy such a tool in memory clinics.
Mapping Medial Temporal Lobe Longitudinal Change in Preclinical Alzheimer’s Disease
Authors: Yushkevich Paul A. , Xie Long , Wisse Laura EM , Dong Mengjin , Ravikumar Sadhana , Ittyerah Ranjit , de Flores Robin , Das Sandhitsu R. , Wolk David A. 
Published: December 2023

AbstractBackgroundThe medial temporal lobe (MTL) is a site of early tau pathology and neurodegeneration in AD. In recent work [1], we reported that rates of change in MTL subregion volumes on longitudinal structural T1‐MRI differ significantly between cognitively unimpaired individuals with and without amyloid pathology (A+CU vs A‐CU), suggesting that these measures are effective biomarkers for tracking disease progression in preclinical AD. Here, we develop a specialized pipeline that quantifies pointwise longitudinal change across the MTL, revealing more informative spatial patterns of early neurodegeneration than summary volumetric longitudinal measures.MethodOur pipeline, SkelDBM, combines skeletonization and deformation‐based morphometry to generate maps of annualized rate of atrophy on the skeleton of an MTL template (Figure 1). SkelDBM samples subjects’ atrophy maps along the skeleton of an MTL template, increasing robustness to subject‐to‐template registration errors. We evaluate SkelDBM using a subset of ADNI data studied in [1] (Table 1). Two preclinical AD groups (A+CU and tau‐positive A+CU) are compared to a control group (A‐CU) using general linear models (GLM) with age as nuisance covariate; permutation testing is used to derive statistically significant clusters.ResultThe full A+CU vs A‐CU comparison shows significant differences in rates of change in right anterior hippocampus, right entorhinal cortex, right transentorhinal cortex, and left posterior hippocampus (Figure 2). Limiting the A+CU group to tau‐positive individuals yields more widespread differences, including most of the right anterior MTL cortex and both hippocampi. These patterns align with volumetric longitudinal data reported in [1] but provide a richer characterization of spatial atrophy patterns.ConclusionSkelDBM is sensitive to longitudinal atrophy occurring over relatively short periods (∼2 years) in preclinical AD and characterizes patterns of atrophy more comprehensively than summary volumetric measures. Summary measures are also subject to biases, since larger structures are typically associated with less measurement error and hence may show exaggerated statistical effects compared to smaller structures even when the underlying neurodegeneration rates are the same. Regional maps of longitudinal change may be more robust to this bias and may help discovery of “signature regions” associated with specific pathological drivers of neurodegeneration. [1] Xie et al., Human Brain Mapping, 2020.
High expression of Alzheimer’s disease‐related metabolic brain pattern is predictive of conversion to mild cognitive impairment or dementia and faster cognitive decline
Authors: Perovnik Matej , Jamšek Jan , Trošt Maja 
Published: December 2023

AbstractBackgroundAlzheimer’s disease‐related pattern (ADRP) is a metabolic brain biomarker of Alzheimer’s disease (AD), which was previously identified and validated. Its utility for differential diagnosis is known. Here, we aimed to explore its predictive value for conversion from cognitively normal (CN) to mild cognitive impairment (MCI), from MCI to dementia and for the rate of cognitive decline.MethodWe analysed 609 participants followed for up to 15 years and their FDG PET scans from ADNI database (CN = 133, MCI = 352, dementia due to AD = 124) and 56 participants from University Medical centre Ljubljana (MCI = 22, AD = 34). ADRP expression was calculated from pre‐processed FDG PET scans. CN and MCI participants were stratified according to their conversion status and cerebrospinal fluid biomarkers. Amyloid positivity (A+) was defined as Aß42 < 880 pg/mL and tau positivity (T+) as pTau > 21.8 pg/mL. AD patients were stratified into slow (< 2 points/per year decline on mini mental state examination (MMSE)) and fast (> 2 points/per year decline on MMSE) progressors.ResultA+T+ cCN had higher baseline ADRP expression than A–T– (p<0.001), A+T– (p = 0.003) and A+T+ (p = 0.01) stable CN corrected for age, while the groups did not differ in gender, education or baseline MMSE scores. A+T+ cMCI had higher baseline ADRP expression than A–T– (p<0.001), A+T– (p = 0.001) and A+T+ (p<0.001) stable MCI, corrected for baseline age and MMSE. A+T– stable MCI had higher baseline ADRP expression than A–T– stable MCI (p = 0.02). ADRP was a predictor of conversion to dementia in survival analysis (HR = 4.5[2.4‐8.7]). Similarly, in the UMCL dataset A+T+ cMCI had higher baseline ADRP expression than A–T– stable MCI (p = 0.03). Fast progressing AD patients had higher baseline ADRP expression than slowly progressing AD patients (p<0.001). Similarly, in the UMCL dataset fast progressing AD patients had higher baseline ADRP expression than slowly progressing AD patients (p = 0.02). In neither dataset, AD groups differed in age, sex or baseline MMSE scores.ConclusionADRP expression levels can be used to predict conversion from CN to MCI and from MCI to dementia in A+T+ individuals. AD patients with high ADRP expression are at an elevated risk for faster progression of cognitive decline.
Concordance between visual interpretation and quantitative analysis of [18F]flortaucipir pet images
Authors: Moscoso Alexis , Heeman Fiona , Raghavan Sheelakumari , Sparrman Kohl Johnson , Costoya‐Sánchez Alejandro , Camacho Valle , van Essen Martijn , Grothe Michel J. , Mainta Isminni , Ribaldi Federica , Devous Michael D , Pontecorvo Michael J. , Frisoni Giovanni B , Garibotto Valentina , Petersen Ronald C. , Jack Clifford R. , Vemuri Prashanthi , Schöll Michael 
Published: December 2023

AbstractBackgroundPositron emission tomography (PET) imaging with [18F]flortaucipir allows for in‐vivo visualization of aggregated tau in Alzheimer’s disease (AD). The FDA‐approved label for [18F]flortaucipir PET provides a standardized, clinically applicable definition of tau‐PET positivity by visual interpretation. Here, we studied the concordance between this visual interpretation and quantitative approaches employed in research.MethodWe included 2692 participants (cognitively unimpaired [CU] and impaired [CI, MCI or AD dementia]) from four cohorts (Mayo Clinic Study of Aging and ADRC, ADNI, A4, and A05) with available [18F]flortaucipir PET (mean age: 70.2 y, 50.0% females). Three trained readers assessed each [18F]flortaucipir PET scan according to the FDA‐approved method. Visual reads were compared to an established approach for defining tau‐PET positivity based on SUVR values in a temporal meta‐ROI (Jack, et al. Alzheimers Dement. 2017). Previously defined “lenient” (SUVR = 1.22) and “conservative” (SUVR = 1.30) cut‐points were explored. In addition, visual reads were compared to tau‐PET positivity as defined by a scheme based on the expected spatial progression of tau pathology from the medial temporal lobe (MTL) to the temporal neocortex (NEO) (Ossenkoppele, et al. Nat Med. 2022; cut‐points: SUVRMTL = 1.30, SUVRNEO = 1.37).ResultConcordance between visual and quantitative approaches was limited (Figure 1): the “lenient” and MTL‐NEO methods yielded high rates of quantitative‐positive, visual‐negative scans (73‐52% and 26‐18% for CU and CI, respectively), while the “conservative” cut‐point failed to detect 45% and 13% of the CU and CI visual‐positive scans, respectively. The prevalence of tau‐PET positivity in Aß‐negative individuals was highest with the “lenient” cut‐point (∼24%) and lowest with visual interpretation (∼3%) (Figure 2). Visual‐positive, quantitative‐negative individuals were more frequently Aß‐positive than visual‐negative, quantitative‐positive individuals (“lenient”: 86% vs 50%, p<0.001; “conservative”: 87% vs 58%, p<0.001; MTL‐NEO: 85% vs 70%, p = 0.005). Among visually‐positive participants, 164 (28%) showed a tau deposition pattern that deviated from the expected MTL to NEO progression (Figure 3).ConclusionVisual and quantitative methods for [18F]flortaucipir PET are non‐exchangeable. Tau‐PET positivity based on visual interpretation aligns better with Aß‐pathology. A significant number of visually‐positive participants showed a pattern of tau accumulation that deviated from the expected stereotypical spatial progression and was not detected by standard quantitative methods.
Variation in estimates of in‐home sleep architecture in diverse population with and without cognitive impairment
Authors: Leng Yue , Peltz Carrie , Redline Susan , O'Bryant Sid E. , Yaffe Kristine 
Published: December 2023

AbstractBackgroundSleep disturbances are highly prevalent in patients with cognitive impairment and have been increasingly recognized as an important risk factor for dementia. However, in‐lab sleep studies are costly and often impractical at a population level, particularly with diverse populations. There are limited data on measures of sleep continuity and architecture in the home setting among Black, Mexican American (MA) and non‐Hispanic White (NHW) adults, with and without cognitive impairment.MethodTo date, the ongoing Health and Aging Brain Study‐Health Disparities (HABS‐HD)‐Dormir study has enrolled 732 participants using a community‐based participatory research approach. In‐home sleep testing was assessed using WatchPAT (WatchPAT‐200) and the zzzPAT software (5.2.79.7p), which provides estimates of sleep duration, sleep efficiency, and rapid eye movement (REM) and non‐REM (NREM) sleep duration using a proprietary algorithm. Dementia, mild cognitive impairment (MCI) or normal cognition (NC) were determined based on a comprehensive clinical evaluation.ResultOn average, participants were aged 66.7±8.4 (50‐92) years, including 64.2% women; 34.4% MA, 23.8% Black and 41.8% NHW adults; 22.1% classified as MCI or dementia. After adjustment for age, sex, race, body mass index and comorbidities, participants with MCI or dementia compared to those with NC had shorter adjusted mean sleep duration (6.3 vs. 6.7 hours, p = 0.002), lower sleep efficiency (77.2% vs. 82.1%, p<0.001), longer sleep latency (32.3 vs. 25.7 minutes, p = 0.006) and longer REM sleep latency (116.5 vs. 95.4 minutes, p = 0.002); had lower REM sleep percentage (20.1% vs. 23.1%, p<0.001), and higher NREM sleep percentage (80.0% vs. 76.9%, p<0.001). The association between sleep staging (REM or NREM sleep percentage) and cognitive diagnoses was only significant among Black participants (p for interaction = 0.02, Figure 1).ConclusionIndividuals with MCI/dementia differed from those with NC in key measures of sleep architecture assessed by a home sleep test, suggesting both the utility of in‐home assessments to estimate sleep in middle‐aged and older diverse samples and the need to understand the race differences underlying associations between sleep architecture and cognition in community‐based studies.
Predictors of subjective and objective cognitive performance examined in an online sample of adults with and without prior COVID‐19 infection
Authors: Butler Joe , Lawson Rachael A , Ling Jonathon , Paddick Stella‐Maria , Parra‐Rodriguez Mario A , Hodgetts Sophie , Taylor John‐Paul , Watermeyer Tamlyn J 
Published: December 2023

AbstractBackgroundCOVID‐19 is still a major public health concern. COVID‐19 is known to affect the central nervous system (CNS), however, the extent of the cognitive effects of infection are unknown. We developed an online battery for community testing, to learn more about the cognitive effects of infection.MethodParticipants were recruited between October 2020 and December 2021 from social media, and completed objective cognitive‐tasks and online health questionnaires using Psytoolkit (Stoet, 2017). Subjective questionnaire measures included fatigue, sleepiness, mood, and cognitive complaints. Objective cognitive tests measured sustained attention; spatial span; and visual short‐term memory. Participants self‐reported their comorbidities. Prior COVID‐19 infection was identified by self‐reported positive PCR or lateral flow test. Data was split into age/infection status groups of i) 18‐34y positive infection (n = 69, mean‐age: 21.4 [3.6]); ii) 18‐34y negative infection (n = 189, mean‐age: 22.65 [4.3]); iii) positive infection (n = 26, mean‐age: 46.9 [7.1]); and iv) 35y+ negative infection (n = 68, mean‐age: 45.44 [7.56]).ResultOf 359 participants recruited, 95 (26.4%) reported a prior positive COVID‐19 test. We found no differences in demographic variables and any variables of interest between Covid‐positive and Covid‐negative groups overall. However, within the age/infection status groups, we found: The 35+ Covid positive group reported higher fatigue (d = 0.69) and performed poorer (d = 0.66) on a visual short‐memory task relative to the 35+ Covid‐negative individuals. Within the overall Covid‐positive sample, younger age ß = ‐0.18, p = 0.04); higher fatigue (ß<0.001) and poorer mood (ß = ‐0.18, p = 0.05), were associated with greater perceived cognitive complaints [R2 = 0.63 (adjusted R2 = 0.6), F(6,75) = 21.1, p <0.001]. Furthermore, a higher number of co‐morbidities (ß = ‐0.31, p = 0.01) was associated with poorer spatial span [R2 = 0.25 (adjusted R2 = 0.19), F(6, 75) = 4.23, p = 0.001]. Within the overall Covid‐negative sample, sleepiness (ß = 0.32, p<0.001) and higher fatigue (ß = 0.54, p<0.001) were associated with greater perceived cognitive complaints [R2 = 0.66 (adjusted R2 = 0.63), F(6, 65) = 20.9, p<0.001]. There were no significant associations with objective cognitive performance within this sample.ConclusionCOVID‐19 infection status was associated with subjective and objective cognitive complaints. Adults aged 35‐63 years with prior COVID‐19 infection appear to be differentially affected. These data add to the body of evidence indicating there are cognitive consequences from COVID‐19 infection.
Remote App‐Based Assessment of Memory and Executive Functioning in Aging and Pre‐Clinical Alzheimer’s Disease in a Diverse Sample of Older Adults
Authors: Mechanic‐Hamilton Dawn , Halberstadter Kimberly , Rovere Rachel , Liebenberg Erin , Wolk David A. 
Published: December 2023

AbstractBackgroundMobile, valid and engaging cognitive assessments are essential for detecting and tracking change in research participants and patients at risk for Alzheimer’s Disease and Related Dementias (ADRDs). This pilot study aims to determine the feasibility and generalizability of at‐home, app‐based cognitive assessments included in the mobile cognitive app performance platform (mCAPP), to detect cognitive changes associated with aging and preclinical AD.MethodThe mCAPP includes three gamified tasks (Figure 1): (1) a “concentration” memory task that includes learning and matching hidden card pairs with increasing memory load, pattern separation features (lure vs. non‐lure), and spatial memory (2) a stroop‐like task (“brick drop”) with speeded word and color identification and response inhibition components and (3) a digit‐symbol coding‐like task (“space imposters”) with increasing pairs and incidental learning components. Participants completed the NACC UDS3 and additional paper and pencil tests. Participants used the mCAPP at home for two weeks. Participants included sixty older adults (73% female; age = 71.9±4.6, education = 16.6±2.4; 50% White, 48% Black/African American, 2% Multiracial) without cognitive impairment enrolled in the Penn ADRC cohort.ResultParticipants played 12±5.1 sessions over two weeks for 11.5±2.8 min/session, with 68% playing more than the assigned sessions. Almost all participants (98%) used a smartphone and 62% played games on their phone. Usability rating was 6.3±0.8 (1‐7 scale) and most participants reported task difficulty was just right (70%‐95%). 68% reported preferring mobile device based cognitive assessment to standard in‐person cognitive batteries. All tasks showed lower performance with increasing cognitive load (p’s<.05). Age and education correlated with both mCAPP and traditional cognitive measures. Concentration performance correlated with UDS3 memory measures and the PACC overall (p’s<.05), however when examined by self‐identified racial group, relationships remained significant in White participants, but not in Black/African American participants (Figure 2). Brick drop performance correlated with the Stroop task (p<.05) and space imposters performance correlated with the Digit Symbol Substitution Test (p<.001) within all groups.ConclusionThis pilot study shows app usability for at‐home use in a diverse cohort of older adults. Performance across measures indicate initial reliability and validity of mCAPP with attention needed to differences in performance across participants with diverse sociodemographic backgrounds.