bluuebunny/crossref_metadata_2025_split huggingface dataset:
Gamma Sensory Stimulation at 40Hz for a 6‐month Period Reduces White Matter Atrophy in Alzheimer’s Disease Patients
Authors: Da Xiao , Hempel Evan , Mrozak Holly , Malchano Zach , Vaughan Brent , Megerian Jonathan Thomas , Hajos Mihaly , Cimenser Aylin 
Published: December 2022

AbstractBackgroundAlthough white matter atrophy is observed in normal aging, it is more pronounced in Alzheimer’s Disease (AD) patients. Recently, advanced magnetic resonance imaging (MRI) provided more detailed information which linked changes in white matter to elevated risk and progression of AD. White matter degeneration, myelin loss, and oligodendrocyte damage in AD patients suggest that white matter can be a mechanistically important target in AD patients. Recently, clinical benefits of 40Hz gamma sensory stimulation have been demonstrated in AD patients. Here, we studied the effects of 40Hz gamma sensory stimulation on white matter in patients with clinical presentation of AD spectrum and compared our results to historical data.MethodThe data presented here is from Overture study (NCT03556280) and the Alzheimer's Disease Neuroimaging Initiative (ADNI1) database. Overture study data included treatment group participants who received 1‐hour daily 40Hz gamma auditory‐visual stimulation at home for a 6‐month period and placebo group. Analysis between the groups suggested a difference in white matter atrophy favoring the treatment group. To compare our treatment group with a larger control group, we studied ADNI1. We included a subset of each population so that baseline distributions (age, MMSE, white matter volume) are matched and had Month 6 MRI data. The resulting populations contained N=16 participants from Overture study and N=64 participants from ADNI1. Overture study structural MRI were acquired using 1.5 Tesla MRI and cerebral white matter segmentation was performed using FreeSurfer as it was done in ADNI1. Age, MMSE, white matter volume adjusted least‐square means method is used to assess the percentage changes in white matter volume within 6‐month period.ResultThe Overture study treatment group participants exhibited 0.38±0.82 percentage increase (volume in % of total intracranial volume) and ADNI1 study participants exhibited ‐2.45±0.40 percentage decrease in white matter volume after 6‐month period (p<0.004).ConclusionOur results suggest that 40Hz gamma sensory stimulation therapy for 6‐month period may reduce white matter atrophy, protect axons, and possibly prevent myelin and oligodendrocyte damage in AD patients. Given the crucial role of white matter connecting expansive brain regions, preventing or reducing its atrophy may diminish AD disease progression.
Intergenerational educational attainment among Latino and White men and women and incident risk for dementia and cognitive impairment without dementia
Authors: Meza Erika , Chen Yea‐Hung , Allen Isabel Elaine , González Hector M , Glymour M. Maria , Torres Jacqueline M 
Published: December 2022

AbstractBackgroundLate life cognitive outcomes may depend on an individuals’ and their parents’ educational attainment levels. We evaluate the association of intergenerational education on dementia/Cognitive impairment without dementia (CIND) risk for Latino compared to White men and women.MethodWe analyzed data from 26,643 US Health and Retirement Study (HRS) Latino and non‐Hispanic white participants from 1998‐2016 (new entry cohorts enrolled every 6 years). Age‐eligible (50+ years at enrollment) respondents without probable dementia/CIND at baseline were included in the analyses with follow‐up waves every 2 years. Exposure was a 4‐category variable based on participants’ and parents’ (highest of mother’s or father’s) education in years: 1) first‐generation high‐school graduate (participant≥12; parents’ education<12); 2) multi‐generation (both≥12: reference); 3) only parents graduated high school but not respondent (participant<12; parent(s)≥12); and 4) neither parents nor participant completed high school (both<12). Probable dementia/CIND cases were classified using the Langa‐Weir 27‐point scale. We used Cox proportional hazard models to evaluate the association between education and dementia/CIND risk and included interactions to evaluate effect modification by gender and race/ethnicity.ResultOur sample was 9% Latino, 46% men and 31% were classified with probable dementia/CIND during follow‐up from 2000‐2016 (mean follow‐up: 9 years). Compared to multi‐gen, first‐gen graduates showed higher dementia/CIND risk (HR:1.49 [95% CI: 1.40, 1.60]) independent of demographic factors. The magnitude of increased risk was smaller for men compared to women (interaction term:0.78 [0.70, 0.89]) and did not vary for Latinos compared to whites. Compared to multi‐gen, only parent graduate showed higher dementia/CIND risk (HR:2.21 [2.05, 2.37]), and the magnitude of risk was greatest for Latino compared to white men (interaction term:1.31 [0.77, 2.23]). Lastly, compared to multi‐gen, neither‐graduates showed higher dementia/CIND risk (HR:2.37 [2.14, 2.64]); association did not vary by gender or race/ethnicity.ConclusionWhile all groups showed increased risk for dementia/CIND compared to multi‐gen high school graduates, the most disadvantaged were downwardly mobile Latino men. Our findings suggest declines in dementia risk potentially attributable to growing educational attainment may persist in the future and be especially marked for racial/ethnic groups historically excluded from educational policies rendering high school completion.
Blood plasma profiles of neuropsychiatric symptoms in older people
Authors: Rabl Miriam , Clark Christopher , Dayon Loic , Bowman Gene L. , Popp Julius 
Published: December 2022

AbstractBackgroundNeuropsychiatric symptoms (NPS) in older people worsen the patients’ and caregivers’ quality of life and are associated with cognitive decline. A better understanding of the biology underlying NPS may lead to novel ways for early detection and more targeted treatment. This study tested the hypothesis that NPS are associated with altered protein levels in blood plasma and these proteins improve prediction of NPS in a memory clinic setting. Additionally, we explored the association of the protein patterns with persistent NPS and cognitive decline.MethodWe performed a cross‐sectional and longitudinal study in older people with and without cognitive impairment. NPS were recorded through the Neuropsychiatric Inventory Questionnaire (NPI‐Q) while cognition was assessed through a comprehensive neuropsychological test battery collected at baseline and follow‐up visits. Shotgun proteomic analysis based on liquid chromatography‐mass spectrometry identifying 420 proteins were conducted in blood plasma samples of all participants. Multivariate linear regression and correlation analysis were used.ResultEighty‐five subjects with a mean age of 70 (± 7.4) years, 65% female and 54% with mild cognitive impairment or mild dementia were divided into groups with (NPI‐Q score > 0) and without NPS. We found 15 plasma proteins with altered baseline levels in participants with NPS. The model which best predicted the occurrence of NPS at baseline contained clinical data (age, sex, education years, cognitive impairment) and additionally the 15 altered proteins. Comparing this model with a reference model containing just the clinical data, the prediction of NPS significantly improved (from receiver operating characteristic area under the curve (AUC) 0.72 to AUC 0.89, p = 0.001) with a specificity of 87 % and a sensitivity of 72% (Fig. 1). Preliminary results indicate that five out of the 15 proteins were associated with persisting NPS and six with cognitive decline at follow‐up (mean: 3.4 ± 1.3 years).ConclusionUsing a proteomic approach, we identified a panel of blood proteins associated with NPS in older people. These findings showed the potential of this approach to identify protein biomarkers of pathological changes underlying NPS and to detect novel treatment targets to reduce NPS and related cognitive decline.
Recommending exercise to boost mental sharpness in your aging patients? What clinicians need to know to prescribe right, measure better, and increase buy‐in
Authors: Gomes‐Osman Joyce Rios 
Published: December 2022

AbstractBackgroundAs our population ages, many individuals are at risk of developing age‐related cognitive decline. A wealth of scientific evidence supports that being physically active may prevent or delay the onset of cognitive impairment and dementia and related functional impairments. However, key barriers remain: 1) optimal exercise parameters for cognitive brain health have not been fully elucidated; 2) while most clinicians recommend physical activity, and older adults recognize its health benefits, the overwhelming majority of older adults fail to regularly participate in regular physical activity. Key preliminary results from two studies will be presented and discussed.MethodFifty‐one sedentary aging participants at risk for cognitive decline (age 62.9±6.8, 76.9% female) participated in Study 1. All participants completed an eight‐week moderate to high intensity exercise protocol and underwent assessments of neurocognitive and cardiorespiratory performance. In Study 2, a separate sample of fifteen individuals (68.1±6.5 years, 76.8% female) participated in a focus group to discuss factors influencing their exercise engagement.ResultStudy 1: All participants combined demonstrated significant improvements in cognition (measured by the Repeatable Battery for the Assessment of Neuropsychological Status [RBANS]) and cardiorespiratory fitness assessed by pre‐to‐post improvements in heart rate recovery (HRR) after eight weeks of exercise (p<0.05). Additionally, multiple regressions revealed a relationship between cognition (RBANS list learning [ß = .22] and RBANS list recognition [ß = 1.86] and fitness (p<0.05).
Study 2: The focus group revealed that participants felt providers’ physical activity recommendations were too general, and did not consider their physical and contextual limitations, leading them to feel less likely to follow through with them.ConclusionOur data suggests that committing to a regular exercise regimen, even for a brief two‐month period, can promote improvements in cardiorespiratory fitness and cognition. We discuss the potential utility of heart rate recovery, an easy clinical measure that takes just a few minutes, to estimate cardiorespiratory fitness that appears to be relevant for exercise‐induced improvements in cognition. We discuss simple strategies to effectively translate current guidelines into the needs of the individual, considering the challenges and demands of the clinical setting.
Rate of memory decline is associated with neuronal nuclear protein (NeuN) positive cells count in middle temporal gyrus in the Adult Changes in Thought (ACT) autopsy cohort
Authors: Mukherjee Shubhabrata , Ariza Jeanelle , Gibbons Laura E , Rachleff Victoria M , Travaglini Kyle J , Latimer Caitlin S , Melief Erica J , Campos John S , Gabitto Mariano , Miller Jeremy A , Ding Yi , Levi Boaz , Long Brian , Kaplan Eitan S , Hodge Rebecca D , Larson Eric B , Crane Paul K. , Lein Ed S , Keene C. Dirk ,   Seattle AD Brain Cell Atlas (SEA‐AD)
Published: December 2022

AbstractBackgroundThe trace of memory has not been well studied in middle temporal gyrus (MTG). Although neuronal loss is minimal in most cortical regions of the aging brain, changes in synaptic physiology of ageing neurons may contribute to altered connectivity. In the Adult Changes in Thought (ACT) autopsy cohort, we explored if memory decline is associated with NeuN (neuronal nuclear protein), a biomarker for mature neurons, in MTG.MethodACT study participants were 5,763 older adults randomly sampled from an integrated healthcare delivery system in King County, Washington. We standardized composite memory scores to N(0,1) at baseline. We calculated slopes of memory decline over time using mixed‐effects models, where time was parameterized as years before death.Among 69 individuals who underwent brain autopsy, we measured NeuN positive cell count from whole slide image analysis using HALO in five tissue layers (molecular, external granular, external pyramidal, internal granular, internal pyramidal + multiform) from brain tissue in MTG. In each layer, we normalized NeuN positive cell count over the tissue area. We derived an average NeuN positive cell count (ANPCC) by averaging the values across the layers (Figure 1).We regressed ANPCC on memory slope, adjusting for age at death and sex. In secondary models, we added disease severity (Braak staging) and presence of APOE ε4 alleles separately. We also analyzed layer‐specific ANPCC.Result64 individuals were included for analysis (Table 1).Memory decline in this sample was 0.10 units per year, similar to the entire cohort. Slower decline in memory (β = 0.10; p‐value = 0.002) was significantly associated with increased average ANPCC (Table 2). Adjusting for Braak staging and APOE ε4 gave similar results. Findings were statistically significant across all but the molecular layer.ConclusionANPCC is associated with slower memory decline in multiple neocortical layers in MTG, irrespective of disease staging or APOE genotype. Since Alzheimer’s disease afflicts adult neuronal populations, survival mechanisms in mature neurons are likely to be either reversed or circumvented during neurodegeneration. Further analyses are needed to understand the functional range of NeuN and processes that lead to loss of NeuN immunoreactivity in cells.
Towards the development of a reference measurement procedure for the quantification of neurofilament light chain
Authors: Coppens Salomé , Drinkwater Giles , Mussell Chris , Hirtz Christophe , Quaglia Milena 
Published: December 2022

AbstractBackgroundNeurofilament‐light chain (Nf‐L) is an early marker monitoring axonal damage which occur in many forms of dementia. During neurodegeneration, Nf‐L is released into biological fluid and is quantifiable in cerebrospinal fluid (CSF) and blood. Currently, its quantification is performed by immunoassays. While those ones are sensitive and precise, the development of a reference measurement procedure (RMP) using mass spectrometry (MS) approaches traceable to the International System of Units (SI) is essential to achieve standardisation.Following the characterisation of three commercially available proteins as potential primary calibrator, one has been chosen and used for the method development. Here, we present the purity assessment and quantification traceable to the SI of the primary calibrator and preliminary data on the development of Nf‐L quantification in CSF by LC‐MS.MethodA recombinant Nf‐L protein from Promise was characterized, SI traceably quantified via amino acid analysis and used as primary calibrator for the development of a LC‐MS method for Nf‐L. A triple quadrupole (QQQ) mass spectrometer coupled to a capillary liquid chromatography (LC) was used to set up a multiple reaction monitoring (MRM) method to monitor Nf‐L tryptic peptides and assess the potential of developing a RMP for Nf‐L.ResultThe homogeneity of Nf‐L from Promise was assessed on 3 aliquots prepared at 1.7 nmol/g. The mass fraction content in each vial was estimated at 1nmol/g ± 0.02 nmol/g.A LC‐MRM method was developed to monitor Nf‐L tryptic peptides and assess their suitability for the development of a RMP. Fifteen peptides were selected. The limit of detection in solution is 1 ng/g for 6 peptides. The sample clean‐up was developed using Nf‐L spiked in artificial and real CSF. Several solid‐phase extraction cartridges were assessed in term of recovery and volume load. The use of protein precipitation was also assessed to increase the sensitivity. Preliminary experiments show a sensitivity of 100ng/g. Immunocapture will be used to achieve the required sensitivity.ConclusionA LC‐MRM based method and a sample clean‐up procedure were developed for the quantification of Nf‐L in CSF. A first assessment of sensitivity was carried out and the utilisation of immunocapture is on‐going to reach the required sensitivity.
Towards Fair ML‐based Language Assessment Methods for Detecting Alzheimer’s Disease
Authors: Parsapoor Mahboobeh 
Published: December 2022

AbstractBackgroundAlzheimer's disease (AD) tends to affect the parietal lobe, which is responsible for language functions in the patient's brain. Thus, one significant impairment associated with AD is language impairment which is the cause that patients with AD have deficits at the word level (vocabulary size in speech can be an early sign of cognitive impairment [1]), sentence‐level, and discourse‐level of their languages. Thus, language disorders can be considered as markers to diagnose AD in a patient at its earliest stage [2]. The recent progress in the machine learning (ML) domain has revolutionized the early detection of AD in particular developing and deploying ML‐based language assessment (MLLA) methods for detecting AD at its mild cognitive impairment stage [3]. The ML community needs to develop fair MLLA methods for detecting individuals with AD.MethodTo develop a fair MLLA first we should consider how we can formulate the fairness for developing a MLLA method and how potential bias in language data can be identified and quantified, how text preprocessing, data augmentation and word embedding techniques can be applied without adding biases to MLLA, how can we identify protected and unprotected linguistic features? and finally how can we protect underserved groups in the deployment process of MLLA methods?ResultWe suggest a fair ML pipeline includes 1) preprocessing (i.e., eliminating sources of bias in data collection and data sharing, text augmentation and text preprocessing, defining unknown sensitive features including linguistic diversity, which are highly correlated with sensitive attributes such as races and genders. Note that sensitive attributes can be used to alleviate the bias). 2) In‐processing (i.e., adjusting machine learning process, e.g., using a regularization approach). 3) Post‐processing (i.e., adjusting trained model).ConclusionThe development of fair MLLA methods is crucial to ensure all groups are treated fairly and there are no results that unfairly harm any subgroups of our population. Using our suggested pipeline, we can foster the confidence of clinical services to use MLLA methods and motivate patients to accept the results.
Transfer Learning based Deep Encoder Decoder Network for Amyloid PET Harmonization with Small Datasets
Authors: Shah Jay , Chen Kewei , Reiman Eric M. , Li Baoxin , Wu Teresa , Su Yi 
Published: December 2022

AbstractBackgroundThe existence of multiple amyloid tracers with varied characteristics poses a significant challenge to standardized interpretation and quantification of amyloid PET data. We previously demonstrated that a deep learning model using a residual inception encoder‐decoder network (RIED‐Net) architecture improved harmonization of florbetapir (FBP) and PiB PET. However, scarcity of head‐to‐head comparison datasets of other amyloid PET tracer may limit the ability to generalize this approach to the other amyloid PET tracers. In this research, we investigate the performance of RIED‐Net model trained on smaller datasets and explore transfer learning (TL) approaches which can leverage multiple different datasets and address the data scarcity issue.MethodWe previously trained our RIED‐Net model using PiB‐FBP data from Open Access Series of Imaging Studies (OASIS) (N=92) and tested the performance in the independent Global Alzheimer’s Association Interactive Network (GAAIN) (N=46) dataset. We now use the same procedure to train the RIED‐Net model using the smaller GAAIN dataset and evaluate the model performance. We then examine the feasibility of a TL approach by comparing the model performance between RIED‐Net model directly trained on the PiB‐florbetaben (FBB) from GAAIN (N=35) and the TL approach in which we started with the optimal RIED‐Net model for PiB‐FBP data and fine‐tuned the network parameters using the GAAIN PiB‐FBB data. A cross‐validation (CV) approach was used to evaluate model performance based on difference before and after harmonization in terms of correlation between mean cortical SUVR (mcSUVR) measurements from different tracers.ResultRIED‐Net model trained using the smaller GAAIN PiB‐FBP data improved the agreement of mcSUVR measurements as measured by Pearson correlation from r=0.93 to r=0.96 (p<0.01) on test‐set. For PiB‐FBB harmonization, the TL approach provided numerically better agreement in mcSUVR from r=0.96 (direct train) to r=0.97 (with TL), demonstrating the potential benefits of applying TL based technique to address the issue of smaller sample size.ConclusionWe demonstrate our RIED‐Net model can achieve improved harmonization even when trained on a relatively small training set. We also demonstrate the potential of transferring knowledge learned from one tracer pairs to improve the harmonization model of other related tracer pairs.
The Learning of Memory Support System in Individuals with Mild Cognitive Impairment
Authors: Koay Jun Min , Locke Dona E. , Shandera‐Ochsner Anne , Chandler Melanie 
Published: December 2022

AbstractBackgroundThe syndrome of mild cognitive impairment (MCI) is classified into different subtypes– amnestic MCI (aMCI) and non‐amnestic MCI based on cognitive domains impacted, while different underlying disease etiologies cause the impairments. The Memory Support System (MSS), a memory‐support tool which utilizes a calendar/planner, has been shown to have positive impact on daily functioning in MCI (Chandler et al., 2017). We have previously shown that cognitive impairment severity and personality traits are associated with MSS learning (De Wit et al., 2021; Khayoun et al., 2021). In this study, we hypothesized that 1) participants with single domain aMCI (SD‐aMCI) will have higher MSS learning scores than multi‐domain aMCI (MD‐aMCI), and 2) participants with suspected Alzheimer’s disease (AD) etiology will have lower MSS learning scores than other etiologies.MethodParticipants (mean age = 75.2) underwent daily MSS training between 2017 and 2021 as part of the 10‐day Mayo Clinic HABIT program. The MSS learning success score (LSS) was determined by how far participants progressed in three learning phases of increasing difficulty: Acquisition (0‐2 points), Application (3‐4 points), and Adaptation (5‐6 points; Greenway et al., 2008). Ordinal logistic regression analysis was used to predict MSS learning scores between SD‐aMCI (n = 142) versus MD‐aMCI (n = 119), and AD etiology (n = 213) versus other neurodegenerative etiologies (n = 57). Dementia Rating Scale‐2 scores (Jurica, 2001) were included to control for cognitive severity.ResultCompared to participants with SD‐aMCI, those with MD‐aMCI had 54.1% lower odds of having higher LSS at training end (p = .004), after controlling for cognitive impairment severity. AD versus other etiology’s impact on LSS was nonsignificant.ConclusionParticipants with MD‐aMCI showed poorer MSS learning efficiency than participants with SD‐aMCI. Etiology did not impact MSS learning. Results suggest having more than just memory impairment (e.g., additional language or executive dysfunction) reduces MSS learning, regardless of overall impairment level. Additional MSS training may be needed to facilitate learning for these individuals.