bluuebunny/crossref_metadata_2025_split huggingface dataset:
Alzheimer’s Disease Detection with a 3D Convolutional Neural Network using Gray Matter Maps from T1‐weighted Brain MRI
Authors: Dhinagar Nikhil J , Thomopoulos Sophia I , Owens‐Walton Conor , Stripelis Dimitris , Ambite Jose Luis , Steeg Greg Ver , Thompson Paul M 
Published: December 2022

AbstractBackgroundA practical screening tool to detect Alzheimer’s disease (AD) based on brain MRI would be valuable. Here we tested a deep learning method for subject‐wise AD classification; as gray matter (GM) is preferentially affected by AD, we also performed MRI tissue classification on the input data, to test the added value of these input features. We set out to compare different types of imaging data types1 as inputs to a 3D Convolutional Neural Network (CNN) for the AD classification task.MethodWe analyzed T1‐weighted brain MRI scans from 1123 subjects (596M/527F, 55.2 ‐ 95.8 years) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). After registering the T1‐w MRI scans to a common brain template, GM was segmented as shown in Figure 1. We subdivided the dataset into training (3,302 scans/853 subjects), validation (413 scans/100 subjects) and test (170 scans/170 subjects) data for the CNN2. We also trained the CNN on different proportions (10%, 20%, 50% and 100%) of the overall ADNI training data. We further validated our results independently on 232 scans/232 subjects (78M/154F, 33‐96 years) from the Open Access Series of Imaging Studies (OASIS) dataset.ResultWe summarize our results in Table 1 in terms of the receiver‐operator characteristic curve‐area under the curve (ROC‐AUC). Results were aggregated over two separate runs to demonstrate the model’s stability. The CNN with GM segmented T1‐w MRI as input achieved an Average ROC‐AUC of 0.864, compared to 0.859 with the complete T1‐w MRI. The CNN performs better with gray matter segmented input; this performance boost was more pronounced with smaller training sets, i.e., 10% (330 scans) or 20% (660 scans) as seen in Figure 2. The models trained on gray matter maps from ADNI also yielded better performance on the OASIS test set.ConclusionIn this work, we found that using GM extracted from T1‐w MRI scans improves deep learning‐based AD diagnosis. Feature selection is improved by regulating the data input into the CNN.[1] Lu, B., et al., “A Practical Alzheimer’s Disease Classifier… on 85,721 Samples,” bioRxiv Prepr. (2021).[2] Dhinagar, N. J., et al., “3D CNNs for Classification of Alzheimer’s … with T1‐Weighted Brain MRI,” SIPAIM (2021).
Evaluating Simple Objective Metrics for the Remote Measurement of Physical Activity: Preliminary Results from the RADAR‐AD Study
Authors: Muurling Marijn , Hinds Chris , Wu Yuhao , Conde Pauline , Doherty Aiden , Curcic Jelena , de Boer Casper , Aarsland Dag , Narayan Vaibhav A , Visser Pieter Jelle ,   the RADAR‐AD consortium
Published: December 2022

AbstractBackgroundHigher physical activity is associated with better global cognition and a lower risk of dementia (Rojer et al., 2021). Using questionnaires to assess physical activity is difficult in an Alzheimer’s disease (AD) population, since questionnaires rely on retrospective subjective recall. Wearables measure physical activity objectively and continuously. A variety exists, with some delivering raw high‐frequency research‐grade movement data, others providing summary variables only. The aim of this study is to compare simple activity metrics from two activity trackers monitoring physical activity in AD, and relate these to standard questionnaires.MethodParticipants in the RADAR‐AD study (Remote Assessment of Disease and Relapse – Alzheimer’s Disease) were asked to wear two activity trackers (dominant hand: Axivity AX3, non‐dominant hand: Fitbit Charge 3) for 8 weeks. Features calculated from the Axivity’s raw accelerometer data were: acceleration magnitude, time spent in sedentary, light, moderate‐to‐vigorous activity, and sleeping. Fitbit features included: step count and heart rate. Self‐reported activity levels (Godin Leisure Time Questionnaire), depression (Geriatric Depression Scale), social functioning (Social Functioning Scale), global cognition (Mini‐Mental State Examination), partner‐reported function (ADCS‐ADL) and neuropsychiatric symptoms (Neuropsychiatric Inventory) were assessed and related to activity levels using regression models.ResultWe included amyloid negative controls (n = 36) and amyloid positive preclinical (n = 13), prodromal (n = 15) and mild‐to‐moderate (n = 10) AD participants (Table 1). Expected associations were found between sedentary activity time and age, and between self‐reported activity and both moderate‐to‐vigorous activity time and step count (Table 2, Figure 1). Small group sizes currently make further comparisons of exploratory value only.ConclusionWearable devices have the potential to accurately capture clinically relevant aspects of daily activity and function of interest in AD population. On‐going research in the RADAR‐AD study will focus on developing disease‐specific methods of feature extraction based on raw movement data, and combining features from multiple devices, to explore this further.This work has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking (grant No 806999). www.imi.europa.eu. This communication reflects the views of the RADAR-AD consortium and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein.
FDG‐PET is a sensitive imaging biomarker of the neurodegenerative changes that accompany early neurofibrillary tangle pathology
Authors: Grothe Michel J. , Silva‐Rodríguez Jesús , Moscoso Alexis , Schöll Michael 
Published: December 2022

AbstractBackgroundNeurofibrillary tangle pathology in Alzheimer’s disease (AD) is closely related to neurodegeneration, which can be measured in‐vivo using structural MRI or FDG‐PET. However, previous imaging‐pathologic association studies found that gray matter atrophy on structural MRI is relatively insensitive to early stages of neurofibrillary tangle pathology as assessed by the Braak staging scheme. Here we assessed associations between post‐mortem neuropathological Braak staging and ante‐mortem glucose hypometabolism on FDG‐PET, which may be a more sensitive neuroimaging marker of early neurodegenerative changes preceding macrostructural gray matter atrophy.MethodThe study included 64 individuals from the Alzheimer’s Disease Neuroimaging Initiative autopsy cohort who had Braak staging performed at neuropathological examination and had an FDG‐PET scan acquired before death (FDG‐PET to death interval: 3.4±2.3 years). Distribution of Braak stages was N=1/6/9/3/2/33/10 for 0/I/II/III/IV/V/VI. Associations between Braak stages and regional FDG‐PET standard uptake value ratios (SUVR) were assessed in an exploratory brain‐wide Spearman correlation analysis across 52 cortical and subcortical gray matter regions. For brain regions showing a significant association after multiple comparison correction (p<0.05, FDR‐corrected), we then performed pair‐wise comparisons between grouped Braak stages 0/I (N=7), II‐IV (N=14), and V/VI (N=43). For comparison, we conducted analogous analyses for regional gray matter volume assessed on structural MRIs from the same study visit.ResultHigher Braak stages were significantly associated with lower FDG‐PET SUVR in several temporo‐parietal cortical regions typically affected by AD (Fig. 1A). When compared to the Braak 0/I reference group, most of these regions showed significant and pronounced (Cohen’s d>0.9) hypometabolism in grouped Braak stages II‐IV, and severity of hypometabolism further increased in Braak stages V/VI. Although gray matter volume on MRI showed a similar regional association with Braak stages (Fig. 1B), effect sizes were considerably lower and differences to the Braak 0/I reference group were only significant for advanced Braak stages V/VI.ConclusionGlucose hypometabolism as measured by FDG‐PET is a sensitive neuroimaging marker of the neurodegenerative changes that accompany progressive stages of neurofibrillary tangle pathology. Earliest neurodegenerative changes captured by FDG‐PET hypometabolism may precede macrostructural gray matter atrophy as measured by MRI.
Characterisation of pupillary responses to alternating blue and orange light exposures in a fMRI protocol
Authors: Beckers Elise , Campbell Islay , Paparella Ilenia , Sharifpour Roya , Ekaterina Koshmanova , Berger Alexandre , Sherif Siya , Jacobs Heidi I.L. , Gilles Vandewalle 
Published: December 2022

AbstractBackgroundThe non‐image forming (NIF) functions of light are diverse and include stimulation of cognition and alertness, sleep regulation and pupil diameter modulation. Previous work indicated that exposure to blue‐enriched light as well as pupil size variations are associated with higher locus coeruleus (LC) activity. Given the central role of LC structure and function in the earliest stages of Alzheimer’s disease neuropathology, light exposure may constitute a promising stimulation technique to delay disease progression. Prior to addressing light impact on the LC, here, we aimed at characterising the pupil responses to light during fMRI protocol, to determine its quality in assessing NIF effects of light.MethodPupil diameter was continuously recorded in 11 healthy subjects (20‐30yr, 7 women) with an infrared eyetracker device while they were administered a 15‐min auditory oddball task in a 7T MRI scanner. Participants were concomitantly exposed to pseudo‐randomly alternating 30s blocks of blue‐enriched (4,000K; 200µW/cm2) and orange (589nm; 6x1013photons/cm2/s) lights. Statistics consisted in generalized linear mixed models seeking effects of light condition on pupil response, with subjects as random intercept and controlling for age, sex and BMI.ResultBlue light condition was associated with a significantly smaller pupil diameter as compared to the orange condition, (main effect of light condition: F(2,270)=571.22;p<.0001; Fig1). Moreover, the impact of the light condition on pupil size did not significantly change over the 15min‐protocol (light condition x block repetition interaction: F(12,240)=1.21 ; p=.28), suggesting that time‐in‐protocol and prior light blocks did not influence pupil constriction.ConclusionWe report preliminary results confirming that blue‐enriched light triggers a stronger pupil response than longer wavelength light and showing that the protocol induces a stable response in time even when using short alternating light exposures. These findings support the validity of the protocol to assess NIF responses to light in fMRI in healthy participants, including over the LC. Being able to stimulate the LC holds important implications for possibly delaying Alzheimer’s disease progression. We now aim to include more participants, including older individuals, and to extend to other cognitive tasks to assess the generalization of the present findings to other cognitive domains and age‐ranges.
Comparison of Algorithms for Dementia Classification in the Survey of Health, Ageing and Retirement in Europe
Authors: Klee Matthias , Langa Kenneth M , Leist Anja K 
Published: December 2022

AbstractBackgroundLow dementia rates, reflecting underdiagnosis in representative cohort studies, limit statistical power of etiological and preventative research. Although several algorithms for automated classification of presence or absence of dementia have been validated in the Health and Retirement Study (HRS), no such algorithm has yet been applied to the Survey of Health, Ageing and Retirement in Europe (SHARE).MethodThe Langa‐Weir classification (LW) was adapted to readily available indicators in SHARE, including immediate and delayed recall. Adapted algorithms additionally included instrumental activities of daily living (IADL) and used cut‐offs defined by either sample‐ or population‐level distributions. Performance was compared to logistic and bayesian‐logistic regression models and a gradient boosting machine (XGBoost) with the same indicators, adjusting for age groups, gender and educational level. The bayesian‐logistic regression used priors for sociodemographic indicators and global dementia incidence. Accuracy, specificity and sensitivity were compared with a train‐test split approach in SHARE wave 7 (2017).ResultIn total, N = 72,329 participants (57% female) above age 50 had no missing data on self‐reported dementia diagnosis, immediate or delayed recall and IADLs. LW based on immediate and delayed recall with a score cutoff based on dementia population‐incidence performed best overall (Accuracy = .92, Balanced Accuracy = .75, Sensitivity = .58, Specificity = .92), and showed greatest similarities to participants with self‐reported dementia diagnosis regarding risk factors and comorbidities (i.e., gripstrength, numerical performance, verbal fluency). Results from XGBoost suggested comparable performance however with risk of overfitting.ConclusionLW adaptations outperformed regression models regarding sensitivity. Comparisons of risk factor and comorbidity distributions suggest meaningful differences in comorbidities and risk factors in participants classified with and without dementia. With a lack of proxy assessments in SHARE, a suspected healthy volunteer bias and the absence of standardized cognitive assessments, probable dementia detection in SHARE necessarily comes with less confidence compared to algorithms tested in HRS. Nonetheless, performance of LW adaptations in SHARE is in line with previous validation studies in HRS. Future research should validate the algorithms through more extensive cognitive assessments once available.
Role of astrogliosis in the relationship between cerebrovascular burden and Alzheimer’s disease pathology
Authors: Ottoy Julie , Kang Min Su , Benedet Andréa Lessa , Bezgin Gleb , Lussier Firoza Z , Ruthirakuhan Myuri , McSweeney Melissa , Pascoal Tharick A , Ferrari‐Souza João Pedro , Blennow Kaj , Zetterberg Henrik , Ashton Nicholas J. , Gauthier Serge , Black Sandra E. , Rosa‐Neto Pedro , Goubran Maged 
Published: December 2022

AbstractBackgroundCerebrovascular disease represents a common co‐pathology of Alzheimer's disease (AD) contributing to cognitive decline. While both vascular injury and AD pathology were previously linked to reactive astrocytes (astrogliosis), the interrelationship between these biomarkers remains unclear in AD. Here, we investigated potential effects of plasma glial fibrillary acidic protein (GFAP), an assumed marker of astrogliosis, on the relationship between vascular burden and AD pathology.MethodWe enrolled 210 participants across the AD continuum, including 111 cognitively normal amyloid‐negative (CN Aβ‐), 37 CN Aβ+, and 62 MCI/AD Aβ+ (TRIAD). Amyloid‐β pathology was quantified through 18F‐NAV4694‐PET standardized uptake value ratio (SUVR) in the AD‐signature regions, while tau pathology was quantified through 18F‐MK6240‐PET SUVR in regions corresponding to Braak stages. Vascular burden was quantified as the log‐transform of white matter hyperintensity volume by intracranial volume (WMH). GFAP levels were log‐transformed. At baseline, we employed linear regression models to investigate the effects of WMH and GFAP on tau pathology. Longitudinally, we performed linear mixed effect modeling to investigate the effects of baseline WMH and GFAP on tau accumulation over time (613±252 days). All analyses were adjusted for baseline age, sex, APOE‐ε4, and cortical Aβ. Last, we investigated a potential mediating effect of GFAP in the relationship between WMH and tau accumulation over time, using bias‐corrected bootstrapping with 5,000 replications.ResultAt baseline, GFAP (P=0.001), but not WMH, was positively associated with tau in the lower Braak I‐II regions. Conversely, both GFAP and WMH were positively associated with tau in the higher Braak regions (P<0.001), as was their interaction (WMH*GFAP; Braak III‐IV β=0.14±0.06, P=0.014; Braak V‐VI β=0.19±0.06, P=0.001). Longitudinally, baseline Aβ but not WMH or GFAP predicted tau accumulation (P<0.001). Serial mediation analysis revealed a significant indirect effect of WMH→GFAP→cortical Aβ→Tau increases [95%CI 0.03,0.29].ConclusionWe observed an interaction effect between higher WMH volumes and plasma GFAP levels on tau pathology across the AD continuum. Furthermore, we observed an indirect effect of WMH volumes on tau accumulation over time through amyloidosis and plasma GFAP. These results may indicate that reactive astrogliosis, potentially secondary to vascular injury, may be a factor contributing to AD progression.
Engaging cognitive domains of people with dementia during tablet game play
Authors: Sachdev Sukriti , Acenas Maria , Dove Erica , Astell Arlene J. 
Published: December 2022

AbstractBackgroundKeeping cognitively active is important for people living with dementia or MCI but finding engaging activities can be challenging. Playing digital games has been shown to engage cognitive processes such as visual attention, working memory, and visuomotor skills, in children and adults. This study explored whether these cognitive domains are engaged when people living with dementia or MCI play digital games.MethodParticipants (n = 32) with dementia or MCI, mean MoCA 13.9, were recruited from adult day programs to play four mainstream games on touchscreen tablets. Each session was recorded and a video coding scheme was developed using behavioral analysis software to map observable behaviors, including eye gaze, hand movements, and game strategy, to five DSM cognitive domains : complex attention, executive function, learning and memory, language, and perceptual motor (DSM‐V, 2013). Five minutes from each initial gameplay session were analyzed to explore the engagement of cognitive domains.ResultsAll five cognitive domains were engaged but varied across the games. For example, a word search games engaged language much more than a jigsaw puzzle game which was higher on perceptual‐motor skills. The most engaged cognitive domain was ‘complex attention’ and the least engaged cognitive domain was ‘language’. All participants demonstrated cognitive engagement whilst playing the games.ConclusionPlaying digital games can provide cognitive engagement for people living with dementia or MCI. Using mainstream games increases accessibility of opportunities for cognitive engagement, in a way which is also immersing and rewarding. Future research incorporating eye tracking will increase understanding of how people living with dementia learn to play digital games and respond to prompts. This study presents huge potential for low cost cognitive activity for people with dementia or MCI.
Psychosocial and Physical Functioning Predictors of Everyday Functioning in The Kaiser Healthy Aging and Diverse Life Experience (KHANDLE) Study
Authors: Chan Michelle L , Zhou Kevin , Eng Chloe W. , Farias Sarah , Gilsanz Paola , Whitmer Rachel A. 
Published: December 2022

AbstractBackgroundWhile Alzheimer’s disease and associated cognitive impairments are major contributors to functional decline, other variables including physical functioning and psychosocial variables may also impact functional status. Our study examined how psychosocial factors (depression, loneliness, and emotional support) and physical functioning (gait speed and grip strength) may independently contribute to instrumental activities of daily living (IADL) functioning among a diverse ethnoracial cohort.MethodThe Kaiser Healthy Aging and Diverse Life Experience (KHANDLE) study is a diverse cohort of long‐term members of an integrated healthcare delivery system ages ≥65, without a dementia diagnosis at time of recruitment. At enrollment, participants completed self‐report IADL measure, neuropsychological testing, physical functioning assessment (grip strength and walk speed), and self‐report psychosocial inventories assessing depression, loneliness, and perceived emotional support through the NIH Toolbox. Analytic sample included 1,243 participants (395 Caucasian (32%), 331 African American (27%), 292 Asian (23%), and 225 Latino (18%)). Cumulative logit models adjusting for cognition and demographics were used to determine significant predictors of IADL functioning, both in separate and combined models. Possible effect modification by race/ethnicity were assessed through interaction terms.ResultAverage baseline age was 75.5 and 59% were female. In separate models, gait speed, grip strength, loneliness, and emotional support were significant predictors of IADL functioning after controlling for cognition and demographics. Depression was not a significant predictor. When adding all significant variables into a single model as independent predictors, slower gait and greater loneliness were associated with more IADL difficulty, while stronger grip strength and more emotional support was associated with less IADL difficulty. Emotional support was no longer a significant predictor of IADL functioning. Interaction terms between independent variables and race were not significant.ConclusionVery few studies have simultaneously examined psychosocial factors and physical functioning as predictors of everyday functioning, particularly among a diverse ethnoracial sample. Our results indicate that independent of cognition, slower gait and greater loneliness were independently associated with worse IADL functioning, while stronger grip strength was associated with better IADL functioning. Such findings have important implications for identifying older adults at risk for disability and for treatments that could prevent excess disability.