bluuebunny/crossref_metadata_2025 huggingface dataset:
Containerizing neuroimaging workflows for scalable and reproducible analyses
Authors: Keefe Sarah J. , McKay Nicole S. , LaMontagne Pamela J. , Flores Shaney , Gordon Brian A. , Benzinger Tammie L.S.
Published: December 2023

Background

Large multi‐center, longitudinal neuroimaging projects aiming to understand neurodegenerative disorders, such as Alzheimer disease (AD), frequently generate large datasets for use by external researchers. However, maintaining a replicable data processing workflow in an on‐going study can be difficult due to changes in personnel, computer or software upgrades, and disparate computing resources across collaborating institutions. Replicating workflows from prior studies is also important for validating and building scientific knowledge but can present similar challenges. Containerization is an increasingly popular solution to these issues. This process allows users to package their code, software, and any dependencies together, ensuring stable and consistent output across computing platforms. The main goal of this project is to guide researchers through the steps of custom container development for their own needs.

Method

We demonstrate the process of containerization using a neuroimaging workflow that quantifies edge density (ED), an emerging metric of white matter integrity. To calculate ED, structural and diffusion magnetic resonance images were processed using standard FreeSurfer (5.3) and FSL (6.0) procedures. Whole‐brain probabilistic tractography, ED metrics, and diffusion tensor indices were computed. A template for building a container image for neuroimaging use was developed. Scripts used for analyses were incorporated into the template and compiled as reusable container images using Docker and Apptainer. Additional scripts and documentation were released to facilitate custom implementation.

Result

Containerized tractography, ED metrics, and tensor indices were identical across multiple computing systems and in‐line with prior research showing white matter integrity begins to decline prior to outward cognitive symptoms. The provided code and documentation outlines the containerization process for our workflow (Figure 1) and describes how to run the resulting container image using data from the recently updated Open Access Series of Imaging Studies 3 dataset (OASIS‐3) as an example.

Conclusion

Containerizing a neuroimaging processing workflow ensures stable results across the lifespan of a study and facilitates replication of prior workflows in new datasets. The containerized ED pipeline provided enables other researchers to replicate or extend upon our analyses in this cohort. The provided documentation and templates can also be adapted for use with other datasets that use common neuroimaging file formats.
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

Background

Quantitative 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.

Method

The 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.

Result

Mean, 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).

Conclusion

QyScore® 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

Background

Differential 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.

Method

Public 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.

Result

Figure 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.

Conclusion

We 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

Background

The 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.

Method

Our 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.

Result

The 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.

Conclusion

SkelDBM 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

Background

Alzheimer’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.

Method

We 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.

Result

A+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.

Conclusion

ADRP 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

Background

Positron 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.

Method

We 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).

Result

Concordance 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).

Conclusion

Visual 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

Background

Sleep 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.

Method

To 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.

Result

On 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).

Conclusion

Individuals 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

Background

COVID‐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.

Method

Participants 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]).

Result

Of 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.

Conclusion

COVID‐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

Background

Mobile, 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.

Method

The 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.

Result

Participants 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.

Conclusion

This 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.
Plasma neurofilament light chain predicts Alzheimer’s disease in patients with subjective cognitive decline and mild cognitive impairment: a longitudinal study
Authors: Mazzeo Salvatore , Bagnoli Silvia , Ingannato Assunta , Padiglioni Sonia , Giacomucci Giulia , Manganelli Alberto , Cavaliere Arianna , Moschini Valentina , Balestrini Juri , Morinelli Carmen , Galdo Giulia , Emiliani Filippo , Polito Cristina , Piazzesi Diletta , Crucitti Chiara , Frigerio Daniele , Berti Valentina , Sorbi Sandro , Bessi Valentina
Published: December 2023

Background

Subjective cognitive decline (SCD) and mild cognitive impairment (MCI) represent a target population for early detection of Alzheimer’s Disease (AD). Nevertheless, both MCI and SCD are common and heterogeneous conditions. We aim to evaluate the accuracy of plasma neurofilament light chain (NfL) in predicting AD and the progression of cognitive decline in patients with SCD and MCI.

Method

140 patients (45 SCD, 73 MCI, and 22 AD dementia [AD‐D]) underwent plasma NfL and CSF biomarker measurement at baseline. A subgroup of patients also underwent amyloid‐PET and 18F‐FDG‐PET brain scans. They were rated according to the A/T/N system and followed up for a mean time of 2.72±0.95 years. Forty‐eight (19 SCD, 29 MCI) had plasma NfL measurement also after two years from baseline.

Result

NfL levels were higher in AD‐D than MCI and in MCI than SCD. In each group, patients with ATN profiles consistent with AD (A+/T+/N+ or A+/T+/N‐ [defined as ATN+]) had higher NfL levels (F[5,103] = 13.50, p<0.001, η2 = 0.396) than patients classified as ATN‐ (including patients with normal AD biomarkers, isolate Aβ pathology [A+] and non‐AD pathologic changes). NfL can distinguish ATN‐ and ATN+ patients with high accuracy (AUC = 0.82) and cut‐off values of 19.45 pg/mL in SCD and 20.45 pg/mL in MCI provide the highest Youden’s index. During the follow‐up, nine (30%) SCD patients progressed to MCI (p‐SCD) and 14 (29.79%) MCI patients developed AD‐D (p‐MCI). The previously defined cut‐off values identified p‐SCD with an 80.00% [95%C.I. = 65.69:94.31] accuracy. The rate of NfL change per year was higher in p‐MCI (3.52±4.06 pg/mL) as compared to np‐SCD (0.81±1.25 pg/mL) and np‐MCI (‐0.13±3.24 pg/mL) and np‐MCI (‐0.13±3.24 pg/mL) patients and in ATN+ (1.53±3.60 pg/mL) as compared to patients in the A+ group (‐0.97±1.66 pg/mL). A rate of change lower than 1.64 pg/mL can exclude progression to AD with a 92.86% (95%C.I. = 83.32:100) accuracy.

Conclusion

NfL concentration and change over time are increased in SCD and MCI patients with a biological demonstration of AD and can predict the progression of cognitive decline. Despite being considered a non‐specific neurodegeneration biomarker, if applied to selected populations, NfL may be a reliable, non‐invasive tool to detect the early stages of AD.
Navigational Decision‐Making Among Older Drivers: An Analysis of Real‐World Driving Patterns
Authors: Derafshi Reihaneh , Babulal Ganesh M. , Bayat Sayeh
Published: December 2023

Background

Driving is an important aspect of independence for older adults. However, cognitive decline may impair driving abilities and safety. A deeper understanding of the impact of cognitive impairment on driving behaviours in older adults can inform public health strategies to support maintaining their ability to drive safely. This study investigates how older adults with and without cognitive impairment make navigational decisions (e.g., route choice) while driving in real‐world settings.

Method

Participants were enrolled in longitudinal driving studies at Washington University School of Medicine. GPS dataloggers tracked the daily driving patterns of 246 older drivers (aged 65+), including 230 cognitively normal older adults (CTL; Clinical Dementia Rating [CDR] = 0) and 16 older adults with cognitive impairment (DEM; CDR>0). Spatial clustering and hashing algorithms were employed to analyze each participant’s destinations and route choices. Trips with hard‐braking, sudden‐acceleration, and over‐speeding were considered “adverse behavior.” The Mann‐Whitney U‐test was performed to identify significant differences between the two groups.

Result

The CTL group had an average age of 68.2 ± 5.7 years and was composed of 46% females, while the DEM group had an average age of 69.2 ± 6.8 years and had 36% females. Additionally, the CTL group had a mean of 16.5 ± 2.3 years of education, while the DEM group had a mean of 16.0 ± 2.3 years of education. The results indicated that drivers with cognitive impairment had fewer full‐cycle trips (i.e., trips with the same start point and destination; p = 0.0165), visited fewer new destinations (p = 0.0210), used fewer distinct routes to reach their most common destination (p = 0.0174), and took fewer distinct routes to reach their most common destination when normalized by the average monthly frequency of this trip (p = 0.0062). No significant differences were observed in the other driving measures.

Conclusion

This study reveals significant differences in driving patterns between cognitively normal older adults and those with cognitive impairment. Older adults with cognitive impairment exhibited more conservative driving behaviors, such as taking fewer full‐cycle trips and having less variety in their daily driving routes. These findings have important implications for designing interventions to improve mobility for older adults.
A neuropsychological investigation of social‐semantic knowledge in frontotemporal dementia
Authors: Rouse Matthew A , Halai Ajay D , Ramanan Siddharth , Patterson Karalyn , Rowe James B. , Ralph Matthew A Lambon
Published: December 2023

Background

The loss of semantic memory is a key feature of frontotemporal dementia (FTD), and is associated with atrophy of the anterior temporal lobes (ATLs). The role of the left and right ATLs in supporting social‐semantic knowledge is unclear. To address this, we developed a social‐semantic battery comprising exemplars of social concepts such as person‐specific knowledge, abstract social concepts, and understanding of social norms. Performance on this battery was directly compared with non‐social semantic knowledge, measured using the Cambridge Semantic Test Battery.

Method

48 people with FTD (behavioural‐variant FTD = 26, semantic dementia = 22), and 19 age‐matched healthy controls were recruited. Participants completed both semantic batteries, alongside tests of general cognition and executive function. Participants also underwent a 3T T1‐weighted structural MRI scan. All neuropsychological tasks were entered into a principal component analysis (with varimax rotation) to explore graded variations in cognitive performance in FTD. Voxel‐based morphometry was conducted to explore grey matter correlates of social and non‐social semantic knowledge. The relative contributions of the left and right ATL to social‐semantic knowledge were tested by including ATL volume and asymmetry indices as predictors in a multiple regression analysis.

Result

People with SD and bvFTD were impaired in both social‐and non‐social knowledge tasks. The principal component analysis indicated separate components for semantic memory and executive function. Both social and non‐social semantic tasks loaded onto the semantic memory component (Figure 1). People with SD scored significantly worse than bvFTD on the semantic memory component (t = 5.18, p<0.0001), while people with bvFTD scored worse than SD on the executive function component (t = 3.97, p<0.001) (Figure 2). Semantic memory factor scores were correlated with grey matter in the bilateral ATLs (Figure 3). There were no significant clusters associated with scores on the executive function component. Multiple regression revealed that performance on the semantic memory component was predicted by the magnitude but not the asymmetry of ATL atrophy.

Conclusion

Both social and non‐social forms of semantic knowledge are impaired by FTD, and this degradation is associated with bilateral ATL atrophy. These results suggest a shared representation for all types of semantic memory across the ATLs.
Contribution of clinical information to the predictive performance of plasma Aβ levels for Aβ PET positivity
Authors: Chun Min Young , Jang Hyemin , Kim Hee Jin , Kim Jun Pyo , Gallacher John , Allué Jose A , Sarasa Leticia , Castillo Sergio , Pascual‐Lucas María , Na Duk L , Seo Sang Won ,   DPUK
Published: December 2023

Background

Early detection of β‐amyloid (Aβ) accumulation, a major biomarker for Alzheimer’s disease (AD), has become important. While Aβ deposition can be detected by positron emission tomography (PET), practical fluid Aβ biomarker is also being developed recently. In the present study, we aimed to determine whether APOE genotypes, age, and cognitive status increase the predictive performance of fluid Aβ levels for amyloid PET positivity.

Methods

We recruited 488 participants who underwent both plasma Aβ and Aβ PET studies (Cohort 1) and 217 participants who underwent both cerebrospinal fluid (CSF) Aβ and Aβ PET studies (Cohort 2). Plasma and CSF samples were analyzed using ABtest‐MS, an antibody‐free liquid chromatography‐differential mobility spectrometry‐triple quadrupole mass spectrometry method and INNOTEST enzyme‐linked immunosorbent assay kits, respectively. To evaluate the predictive performance of plasma Aβ and CSF Aβ, respectively, logistic regression and receiver operating characteristic analyses were performed.

Results

When predicting Aβ PET status, both plasma Aβ42/40 ratio and CSF Aβ42 showed high accuracy (plasma Aβ area under the curve (AUC) 0.814; CSF Aβ AUC 0.848). In the plasma Aβ models, the AUC values were higher than plasma Aβ alone model, when the models were combined with either cognitive stage (p < 0.001) or APOE genotype (p = 0.011). On the other hand, there was no difference between the CSF Aβ models, when these variables were added.

Conclusions

Plasma Aβ might be a useful predictor of Aβ PET status as much as CSF Aβ, particularly when considered with clinical information.
Association of novel CSF biomarker candidates with cortical thickness in genetic frontotemporal dementia
Authors: Ullgren Abbe , Bergström Sofia , Rydell Melissa T. , Öijerstedt Linn , Remnestål Julia , Olofsson Jennie , Seelaar Harro , van Swieten John C. , Synofzik Matthis , Sanchez‐Valle Raquel , Moreno Fermin , Finger Elizabeth , Masellis Mario , Tartaglia Carmela , Vandenberghe Rik , Galimberti Daniela , Borroni Barbara , Butler Christopher , Ber Isabelle Le , Gerhard Alexander , Ducharme Simon , Laforce Robert , Rohrer Jonathan D. , Rodriguez‐Vieitez Elena , Månberg Anna , Nilsson Peter , Graff Caroline ,   the Genetic Frontotemporal Dementia Initiative (GENFI)
Published: December 2023

Background

A novel panel of 14 proteins measured in the CSF could separate individuals with genetic frontotemporal dementia (FTD) from controls, with most significant findings observed for neurofilament medium (NEFM), neuronal pentraxin 2 (NPTX2), neurosecretory protein VGF (VGF) and aquaporin 4 (AQP4) [1]. However, it is currently unknown whether these altered protein levels in the CSF reflect neurodegenerative changes in the brain. The aim of this study was to explore the cross‐sectional associations between the previously identified CSF biomarker candidates and cortical thickness in presymptomatic and symptomatic mutation carriers, and whether those associations differ by FTD mutation type.

Method

We have analyzed T1 MRI scans alongside concurrent CSF samples from 202 individuals from the GENFI cohort, belonging to families that carry FTD mutations in either C9orf72, GRN or MAPT genes. The study sample included symptomatic mutation carriers, presymptomatic mutation carriers and non‐carrier controls. Cortical thickness was estimated with FreeSurfer and CSF protein levels were measured via a multiplexed antibody‐based suspension bead array. The correlations between regional cortical thickness and protein levels were calculated via linear regression.

Result

Altered levels of NEFM, AQP4, APOA1, PTPRN2, CTSS, SERPINA3, C4, AMPH and CD14 were all correlated with increased atrophy of at least one cortical region. Some effects were mutation specific, but NEFM, AQP4 and APOA1 correlated with atrophy in all mutation groups. We also observed mutation specific effects for 10 of the proteins. CTSS levels were only correlated with cortical atrophy in C9orf72 mutation carriers while NPTX2, VGF and PTPRN2 correlated with atrophy in GRN mutation carriers. In MAPT mutation carriers, 6 different proteins correlated with atrophy in the right temporal pole.

Conclusion

The proposed fluid biomarker candidates continue to show promise and further longitudinal studies will contribute to elucidate their relationship to cortical atrophy and their prognostic value in genetic FTD.

[1] Bergström et al. Mol Neurodegener. 2021; 16(1):79
Psychopharmacological Investigation to Explore the Neuroprotective Mechanism of Quercetin and Naringenin in an Experimental Model of Insulin Resistance and Related Cognitive Dysfunction
Authors: Kaur Sukhmanpreet
Published: December 2023

Background

Alzheimer’s disease is a brain ailment that causes cell degradation and is the leading cause of dementia, which is defined by a deterioration in thinking and independence in routine everyday activities. Insulin possesses neuroprotective and memory‐enhancing effects indicating a permissive function for insulin in cognition. Defective insulin signaling promotes Aβ aggregation and accelerates Aβ formation in the brain. Based on this evidence, Alzheimer’s is also defined as diabetes of the brain or Type 3 Diabetes.

Method

Diabetes was induced in mice by Alloxan (100mg/kg) and sucrose 35% solution. Glucose level was monitored, and test drugs were given accordingly. Behavioral models employed in the current study were elevated plus maze, morris water maze, actophotometer, and novel object recognition test. Biochemicals performed were all mitochondrial complexes, glutathione level, protein carbonyl level, lipid peroxidase, and acetylcholinesterase activity. Histopathological evaluation of the hippocampal region was carried out under a bright field microscope.

Result

Alloxan administration produced significant hyperglycemia characterized by a significant increase in serum glucose level as compared to the naïve. During the acquisition test (learning), alloxan‐treated mice significantly delayed the escape latency period to reach the submerged platform as compared to the naïve group. However, quercetin and naringenin administration significantly improved memory performance. In novel object recognition, alloxan treated spent more time near the familiar object, and the treatment group spent more time near the novel object. Alloxan treatment also disrupted the mitochondrial levels and lowered the antioxidant enzyme levels, whereas the treatment group significantly reversed the changes.

Conclusion

In the present study, alloxan administration caused significant impairment in cognitive behavior as evidenced by the Morris water maze (MWM) test, increased fasting blood glucose oxidative stress [through alteration of endogenous antioxidant markers i.e. superoxide dismutase (SOD), and catalase antioxidant enzyme activity and reduced glutathione (GSH) levels and oxidative stress markers i.e. reactive oxygen species (ROS) and protein carbonyls levels] and increased acetylcholinesterase activity in different areas of the brain (frontal cortex and hippocampus). Administration of quercetin and naringenin in different doses significantly reversed alloxan administration‐induced behavioral, biochemical, and cellular alteration in both the frontal cortex as well as in hippocampus.
Managing Money: A Caregiver’s Guide to Finances: A Pragmatic and Scalable Evidence‐Based Program for Caregivers
Authors: Judge Katherine
Published: December 2023

Background

Few programs have been developed that address the financial needs of caregivers. The following presentation discusses the development of an innovative 60‐minute interactive online program led by community educators.

Method

Content includes a) Introduction to the Costs of Caregiving, b) Implications of Alzheimer’s disease, c) Benefits of Early Planning, d) Avoiding Financial Abuse and Fraud, e) Conversations about Finances, Assessing Financial and Legal Needs, and f) Finding Financial Support. A randomized controlled trial was used to examine efficacy with n = 164 participants assigned to the program condition and n = 158 participants assigned to the waitlist condition. Surveys were completed at a) Time 1 (baseline); b) Time 2 (after program completion); c) Time 3 (30 days after program completion); d) Time 4 (60 days after program completion); and e) Time 5 (90 days after program completion). Program participants also completed acceptability questions.

Result

Participants on average were 54 years old (SD = 15.34) with the majority female (80.4%), Caucasian (73.3%), and married (68.9%). Most participants worked part‐ or full‐time (54.9%), had a bachelor’s or post/professional degree (71.8%), and had yearly incomes of $50,000 or more (55.9%). Participants primarily provided care to a parent/in‐law (45%) or a spouse/partner (34.2%). Significant group differences between the program and the control conditions were found for the following outcomes: Perceived Knowledge (F(1, 174) = 4.54, p = .04); Financial Unmet Needs (F(1, 176) = 16.16, p = .01); Financial Unmet Needs Distress (F(1, 176) = 15.42, p = .01); Financial Self‐Efficacy (F(1, 174) = 6.19, p = .01); Financial Behavioral Intentions (F(1, 172) = 8.02, p = .01); and Financial Behavioral Actions (F(1, 172) = 13.05, p = .01). Post‐hoc analyses indicated short‐ and long‐term impacts that will be further discussed. In terms of acceptability, participants rated the program as highly acceptable, with 82.8% to 91.9% of participants rating each statement as ‘agree’ or ‘strongly agree’ and 91.9% of participants indicated they would ‘very likely’ recommend the program.

Conclusion

Discussion highlights key features thought to contribute to the successful evidence‐based program including how to develop pragmatic and scalable programs and alignment of measures with program content and dose.
Evaluating Regional Importance for Tau Spatial Spread in Predicting Cognitive Impairment with Machine Learning
Authors: Doering Stephanie , McCullough Austin A. , Gordon Brian A. , Chen Charles D. , McKay Nicole S. , Hobbs Diana A. , Keefe Sarah J. , Flores Shaney , Hornbeck Russ C. , Xiong Chengjie , Hassenstab Jason J. , Bateman Randall J. , Ances Beau , Morris John C , Benzinger Tammie L.S. ,   the Dominantly Inherited Alzheimer Network (DIAN) Investigators
Published: December 2023

Background

Cognitive decline in Alzheimer Disease (AD) is strongly correlated to the spatiotemporal pattern of tau‐protein neurofibrillary tangles (NFTs). Evidence suggests tau seeding spreads transneuronally in a prion‐like manner, a biological process distinct from NFT aggregation in previously‐affected regions. We previously proposed a method for quantifying global tau spatial spread (TSS) using tau positron emission tomography (PET) which found TSS is particularly informative in preclinical AD. The spread of tau pathology into specific regions may therefore be key to the onset of cognitive decline.

Methods

445 older adult and 21 younger control participants were recruited. Cognition was assessed with the Knight ADRC‐PACC and Tau‐PET images were parcellated using the Schaeffer400 atlas. For each region, TSS was calculated as the proportion of voxels with significant levels of tau. Abnormal voxels were identified by z‐scoring tau‐PET voxel‐wise relative to corresponding voxels in younger controls and then thresholded at z>1.96 for significance. This study predicted participant PACC from regional TSS using random forest regression (trained with 100 repeated 10‐folds). Feature importance (FI) was calculated with Mean Decrease in Impurity to identify regions in which tau spread is most significant to cognitive impairment.

Results

Regional TSS demonstrated highly saturated regions in the limbic and temporal lobes with progressively lower levels in peripheral regions, largely sparing the frontal lobe (Figure 1). However, the importance of regional spread in predicting cognition did not reflect the same spatial pattern (Figure 2). Four regions emerged with the highest FI in the limbic and default networks, indicating the spread of tau within these brain regions is key to predicting cognitive impairment (Table 1).

Conclusion

Our results suggest that tau pathology has spread extensively within medial and temporal regions early in AD. Regional TSS can be used to identify peripheral regions in which tau pathology is currently spreading into. As expected, limbic regions in the temporal lobe were critical to predicting participant cognitive impairment. However, regions in the temporal and parietal lobes belonging to the default network were additionally informative. Spread of tau pathology into new peripheral brain regions should therefore be evaluated when predicting future AD‐related cognitive decline.