bluuebunny/crossref_metadata_2025_split huggingface dataset:
Clinical consequences of the Alzheimer’s blood‐based biomarker panel: a prospective evaluation in memory clinics ‐ the CANTATE project
Authors: Vigneswaran Sinthujah , Verberk Inge M.W. , Boonkamp Lynn , Rousset Rebecca Z. , Claessen Thomas , Zwan Marissa D. , Ossenkoppele Rik , Muller Majon , Wilson David H , Lemstra Afina Willemina , Pijnenburg Yolande A.L. , van der Flier Wiesje M. , Teunissen Charlotte , van Harten Argonde C. 
Published: January 2025

AbstractBackgroundBlood‐based biomarkers (BBM) are emerging as minimally invasive, scalable and relatively low‐cost options for discriminating different neurodegenerative diseases. Before implementation in clinical practice can take place, it is important to determine their real‐world clinical validity in patients presenting at a memory clinic. Therefore, we prospectively evaluated changes in diagnosis and diagnostic confidence resulting from the use of a BBM panel tailored to common differential diagnostic considerations (Verberk et al., Mastermind of the Alzheimer’s blood‐based biomarkers: development of cut‐offs and a visualization tool for use in clinical practice. https://doi.org/10.1002/alz.078748).MethodsConsecutive patients who visited the Alzheimer Center Amsterdam or the Center Of Geriatrics Amsterdam (COGA) for a standardized diagnostic workup between September‐December 2023 and provided informed consent were included. Diagnoses were determined at a multidisciplinary meeting. BBM panel with pre‐specified cut‐off values (plasma pTau181, GFAP and NfL, Quanterix Corporation, USA) were disclosed after the clinical work‐up, neuropsychological tests and MRI results were presented on a weekly basis reflecting real world clinical practice. We evaluated syndrome diagnosis, suspected aetiology and confidence in the etiological diagnosis before and after disclosing BBM results.ResultsA total of 107 patients with a mean age of 68 ± 9 years and 42 females (39%) were included. The blood biomarker panel led to change in syndrome diagnoses in 10 patients (9%) and change in suspected aetiology in 9 patients (8%) (Table 1). Among those for whom both syndrome diagnosis and suspected aetiology remained unchanged (n=92, 86%), diagnostic certainty changed in 89 patients (83%) after showing the BBM panel. In these patients, the diagnostic confidence changed from 70% [IQR = 60 ‐ 80] to 90% [IQR = 80 ‐ 90]; P < .001) after showing the BBM panel results (Figure 1). The diagnostic confidence increased in 69 (64%) patients and decreased in 20 (19%) patients.ConclusionA BBM panel specific to individual differential diagnostic considerations generally led to an increase in diagnostic confidence and rarely resulted in a different diagnosis. These results suggest that BBM have added value in daily clinical practice. This is an ongoing study with inclusion rate of 15 persons per week, updated results will be presented at AAIC2024.
Data‐driven analysis of Aβ peptide ratios in iPSC models of familial Alzheimer's disease for diagnosis and prediction of symptom onset
Authors: Saguer Isaac Llorente , Gabriele Rebecca , Wray Selina , Oxtoby Neil P , Arber Charlie 
Published: January 2025

AbstractBackgroundFamilial Alzheimer's disease (fAD), arising from mutations in amyloid‐precursor‐protein (APP) and presenilin (PSEN1/2) genes, leads to the production of longer, aggregation‐prone amyloid‐beta (Aβ) peptides—a hallmark of Alzheimer's disease. Age‐at‐onset (AAO) varies among carriers of different mutations. Recent evidence challenges the Aβ42:40 ratio as the leading and predictor of AAO between different pathogenic variants, prompting exploration of peptide combinations as potential biomarkers for these tasks.MethodData consisted of patient‐derived in‐vitro iPSC models of fAD and Ab measurements via enzyme‐linked immunosorbent assay (ELISA). With novel data of our own (N=39) and published data from Liu et al. 2022 (N=134) (Table 1), we performed a data‐driven analysis of Aβ37/38/40/42/43 combinations, employing area under the curve (AUC) and coefficient of determination metrics for classification (WT vs AD) and AAO regression. A linear model optimized for these metrics was also explored.Each peptide was divided by the average of the control group of the same assay. Then, we weighted each peptide in accordance with the average profile of the control population in Liu.ResultData transformation using controls proved necessary for to produce comparable ratios (Figure 1).Table 2 presents diagnostic and predictive performance for each peptide‐ratio biomarker relative to biomarkers from the literature (top 3 rows). Our new data‐driven biomarkers improved upon the literature in both diagnostic AUC (not statistically significant: DeLong’s test p>0.47) and predicting AAO (Spearman’s R^2 significant differences in UCL data; Hittner’s p<0.005; p>0.5 in Liu’s).ConclusionOur results do not recapitulate Liu et al. findings, as Aβ37:42 did not outperform Aβ42:40 in our data; one explanation could be that our lines represent a physiological model of Ab production that is free from plaque deposition.While no significant differences emerged in AD classification, specific peptide ratios demonstrated varying correlations with AAO: short‐to‐long ratio > Aβ42:40, and Aβ42:40 > Aβ37:42, but other ratios were found to perform better in each set, suggesting mutation‐specific profiles.Data‐driven linear combination can improve the results, and notably, both optimizing for AUC or AAO found the same ratio of short/long, albeit with different weights. Standardization through control‐based data was essential for robust measures.
Using New Science to reduce bias of Old Science: AI‐driven Customization of Culturally Biased Tests
Authors: Devine Sherral A. , Lee Melissa , Gurnani Ashita S. , Sunderaraman Preeti , Kourtis Lampros , Pratap Abhishek , Low Spencer , Ho Kristi , Gifford Katherine A. , Au Rhoda 
Published: January 2025

AbstractBackgroundWidely used neuropsychological test instruments are notoriously biased across the demographics of age, sex/gender, education, language and culture. This includes verbal memory tests that elicit speech such as the paragraph recall or list‐learning memory tests. Language tests are similarly biased, including the Boston Diagnostic Aphasia Examination Cookie Theft Test (CTT) that has been used to elicit both written and spoken responses for decades. Biased tests generate biased results, and this problem is exacerbated by the widespread practice of repurposing tests generated for those in largely high income, Westernized countries for use in other countries that are demographically and culturally distinct. We tested the use of large language model (LLM) tools to automatically generate verbal test stimuli that were more locally relevant.MethodsStarting with the CTT, that involves describing a picture that contains 14 core content units, we used ChatGPT to aid in generating customized pictures with the same number of core content units that were more universally relevant, (e.g., open park area, beach, etc.) by suggesting parameters and integrating resulting features into new pictures. We also tested capabilities to generate pictorial scenes that are more representative of regional settings, such as the Asia Pacific or Middle East. We are testing LLM capacity to generate similarly regionally customized list learning stimuli that maintain the original list learning test word frequency and difficulty across languages.ResultsUsing an iterative approach, we were able to use automated artificial intelligence (AI)‐driven tools to rapidly generate regionally relevant test stimuli that retained the original test characteristics to preserve generation of comparable results. Descriptive comparisons found that aided by automated AI tool, we could generate new neuropsychological test stimuli in a matter of minutes compared to months long manual methods.ConclusionThe rapid emergence of AI has generated significant concerns about its appropriate use for health and health care applications. Given that AI is here to stay, we are testing the capacity of this technological advance to address long‐standing, persistent problems in AD research and usher in a new era of precision brain health that has global relevance in a rapidly aging world population.
Machine learning for privacy‐protected voice analysis in dementia assessment
Authors: Ahangaran Meysam , Dawalatabad Nauman , Karjadi Cody , Glass James , Au Rhoda , Kolachalama Vijaya B. 
Published: January 2025

AbstractBackgroundThe prevalence of cognitive impairments, such as mild cognitive impairment (MCI) and Alzheimer's disease (AD), has surged, necessitating rapid, cost‐effective, and non‐invasive diagnostic tools. Speech, as a rich source of cognitive indices, offers a promising avenue for distinguishing between healthy controls, MCI, and AD groups. However, the utilization of voice data poses privacy challenges, as speaker identities can be discerned through automatic speaker verification systems.MethodWe developed a machine learning framework for dementia assessment, utilizing acoustic features obtained through advanced speaker anonymization techniques. Our work leveraged a wealth of digital voice recordings from the Framingham Heart Study, which, since 2005, has captured extensive spoken responses to neuropsychological tests. These recordings were instrumental in refining our privacy‐conscious algorithmic approach to dementia assessment.ResultThe developed tool demonstrated efficacy in de‐identifying speakers while maintaining the integrity of cognitive features in speech. This retention allows for the continued development of vocal biomarkers that correlate with neuropsychological assessments and cognitive impairment diagnoses.ConclusionOur approach unites the need for privacy with the utility of voice data in dementia assessment. It offers a scalable, accessible solution that could revolutionize early cognitive decline detection, while also aligning with the increasing emphasis on patient privacy in medical research.
Differential Privacy Preserving Voice Conversion for Audio Health Data
Authors: Dawalatabad Nauman , Ahangaran Meysam , Karjadi Cody , Kourtis Lampros , Joung Philip , Kolachalama Vijaya B. , Au Rhoda , Glass James 
Published: January 2025

AbstractBackgroundSpeech is a predominant mode of human communication. Speech digital recordings are inexpensive to record and contain rich health related information. Deep learning, a key method, excels in detecting intricate patterns, however, it requires substantial training data. Laboratories have invested significantly to gather extensive digital voice datasets for health insights. The challenge lies in securely sharing this data while protecting the speaker's privacy.MethodWe applied a Generative Adversarial Network (GAN) approach. GANs can generate a voice that closely resembles a real voice and is composed of four key components: (i) Generator, (ii) Formant Extractor, (iii) Speaker Embedding Extractor, and (iv) Discriminator. Model training involves leveraging adversarial loss, wherein the generator strives to produce a voice that convincingly mimics reality to deceive the discriminator. Simultaneously, the discriminator endeavors to discern whether the generated voice is authentic or synthetic. Eventually the generator produces a voice that resembles closely to the real voice.ResultWe performed zero‐shot voice conversion using an emotion preserving GAN. The model preserves the fundamental frequency trajectories. This is one of the most important features in dementia classification.ConclusionBy successfully altering voice prints while preserving sound quality, our initial findings suggest a way for sharing raw digital voice recordings. Future efforts will extend beyond preserving intonation patterns, focusing on preserving additional dementia related markers in the original signal.
Prevalence of Hypothyroidism and its Impact on Cognitive Function in Older Adults: A Cross Sectional Study
Authors: Jessy S Angeline , G Sandhya , S Monisha , Issac Thomas Gregor 
Published: January 2025

AbstractBackgroundThyroid disorders is one of the most common endocrine disorders. It is estimated that 42 million people suffer from thyroid disorders in India. The imbalance in thyroid hormone levels can significantly impact cognitive health of older population. This study aims at emphasizing the prevalence of hypothyroidism and its association with cognition.MethodA cross‐sectional design, the study enrolled 1,181 participants from the TLSA cohort, focusing on individuals with complete data for sociodemographic characteristics and thyroid‐related blood parameters. Comprehensive clinical assessments, including medical history, physical examinations, and cognitive tests namely COGNITO battery (Computerized Assessment of Adult information processing), HMSE (Hindi Mental State Examination) and ACE‐III (Addenbrooke’s Cognition Examination) were conducted. Biochemical tests quantified plasma levels of thyroid‐stimulating hormones (TSH), triiodothyronine (T3), and thyroxine (T4). Participants were classified based on medication history and thyroid hormone levels.ResultThe study identified a 17.69% prevalence of hypothyroidism, among which 6.22% overt and 93.78% subclinical cases. Females exhibited a significantly higher prevalence (p=0.043). People with hypothyroidism are more frequently diagnosed with mild cognitive impairment than the people with euthyroid (p=0.008). Patients managed suboptimally performed poorer in auditory attention (p<0.001) compared to those on adequate treatment. In the domain of visual attention, the inadequately treated group performed significantly worse than the adequately treated group (p=0.018)ConclusionPrevious studies suggest a direct association between hypothyroidism and cognitive impairment. This study concludes that hypothyroidism has a significant relationship with cognitive functioning in the attention domain in older adults. Hence, hypothyroidism should be treated as a priority to maintain cognition in an ageing population.
Is FDG‐PET metabolism associated with cognitive performance in long‐Covid patients?
Authors: Hernández Micaela A , Machado Luiza Santos , Bastiani Marco De , Schlickmann Thomas Hugentobler , Ferrari‐Souza João Pedro , Carello Agostina , Helou Belén , Crivelli Lucia , Sanz Emilia Osa , Bérgamo Yanina , Vazquez Silvia , Sevlever Gustavo , Allegri Ricardo , Zimmer Eduardo R. , Calandri Ismael Luis 
Published: January 2025

AbstractBackgroundCurrent evidence indicates that COVID‐19 infection can lead to neurological complications that persist beyond 12 weeks of infection (long‐COVID), often associated with cognitive decline. However, the underlying mechanisms remain unclear. Our aim is to study the brain metabolism of patients with long‐COVID and its association with cognitive performance.MethodIndividuals with cognitive complaints for at least a month after COVID‐19 infection from an Argentine cohort of long‐COVID. Their brain glucose metabolism was assessed by FDG‐PET imaging, and the cognitive symptoms were monitored using cognitive evaluation. The FDG‐PET images were normalized using the individuals’ global mean value, and regions of interest mean signal was extracted using ICBM152 atlas. After applying Principal Components Analysis (PCA) to reduce dimensionality, we conducted clustering using K‐means with the two primary components extracted to create groups with similar metabolism patterns. With the neuropsychological data, we generated seven composites covering cognitive domains based on z‐scored data relative to the normal Argentine population. We conducted pairwise T‐tests to compare cognitive performance of metabolic clusters.ResultForty‐one subjects were recruited, 27 were female. Mean age of 55 years (±12) with an average of 15 years of education (±2.3). In PC1, key contributing regions included the right medial temporal lobe, right hippocampus and bilateral amygdala, exhibiting hypometabolism, and bilateral frontal lobe, displaying preserved metabolism (Figure 1). PC2 was characterized by hypometabolism in bilateral frontal lobe and right occipital lobe (Figure 2). Three distinct clusters were identified: Cluster 1 and 2, differentiated by PC1 and Cluster 3, distinguished from Cluster 1 and 2 by PC2 (Figure 3). Regarding cognitive assessments, we observed statistically significant differences between Cluster 1 and 2 in executive composite (p=0.049) and global composite (p=0.025).ConclusionOur study identified three distinct clusters based on brain metabolism, with differences in executive functions between the two of them. We consider that FDG‐PET only partially explains cognitive performance, mood and neural networks are probably relevant contributing factors to cognition.
Building consensus on the utility of central and peripheral physiological markers for neuropsychiatric symptoms of dementia: a Delphi Study
Authors: Lai Ka Sing Paris , Choudhury Samira , Kumar Sanjeev , Burhan Amer M. 
Published: January 2025

AbstractBackgroundNeuropsychiatric symptoms (NPS) of dementia are a heterogenous group of non‐cognitive symptoms and behaviors that occur in up to 90% of individuals with the condition. Characterizing NPS is a major issue and current methods are unreliable as they rely on subjective observations. Automatic identification of behaviors using central and peripheral physiological markers may be helpful to detect behaviors, allow for early intervention, and prevent critical incidents in patients with dementia.MethodsThis study will use a modified Delphi methodology to develop a guide for the use of central and peripheral markers that can be used for the detection, assessment, and monitoring of treatment response for persons with dementia exhibiting NPS in institutions. Experts including clinicians, educators, and researchers will be recruited internationally to participate as expert panellists. This study will review the current literature regarding the use of central and peripheral markers and NPS (e.g. wearable devices, mobile tracking devices, heart rate variability, skin conductance, and electroencephalography). A list of survey items will be developed and presented to the expert panellists for review and feedback. Survey items will also be refined accordingly based on panellist feedback. It is anticipated that there will be a total of 3 rounds of the modified Delphi process.ImplicationsNPS can cause distress to patients and caregivers and increase the risk of injuries to the patients themselves and those in their vicinity. It is difficult for caregivers in care facilities or at home to continuously monitor the persons with dementia. If expert recommendations can be achieved about the use of markers for the detection, assessment, and monitoring of treatment response for NPS, clinicians will be able to better provide early intervention and deliver personalized treatment plans for persons with dementia exhibiting NPS.
Sharing Roles in Home Care: Family Care Partners and Paid Caregivers
Authors: Fabius Chanee D , Gallo Joseph J , Wolff Jennifer L 
Published: January 2025

AbstractBackgroundLittle is known about the prevalence and nature of role‐sharing among family care partners and paid caregivers.MethodWe studied 440 participants in the 2015 National Health and Aging Trends Study (NHATS) receiving paid help with self‐care, mobility, or medical care. Guided by Litwak’s task specificity model, we focus on frequency of receiving paid help only, help from care partners only, and role‐sharing by task. We examine whether role sharing and reliance on paid help only differs by older adults’ Medicaid‐enrollment and dementia status.ResultOlder adults whose helping arrangements did (versus did not) involve role‐sharing were more often living with dementia (48.7% vs. 25.6%, p<0.001) but less often Medicaid‐enrolled (32.1% vs. 49.4%, p<0.01). Those living with (versus without) dementia more often experienced role‐sharing in eating (OR 3.9 [95% CI 1.20, 8.50]), bathing (OR 2.7, [95% CI 1.50, 4.96]), dressing (OR 2.1 [95% CI 1.14, 3.86]), toileting (OR 2.9 [95% CI 1.23, 6.74]), and indoor mobility (OR 2.8 [95% CI 1.42, 5.56]), and less often received help solely from paid helpers with medication administration (OR 0.24, [95% CI 0.12, 0.46]). Those who were (versus not) enrolled in Medicaid more often received paid help only in dressing (OR 2.0 [95% CI 1.12, 3.74]), getting around outside (OR 2.4 [95% CI 1.28, 4.36]), and inside (OR 4.3 [95% CI 2.41, 7.62]), as well as with doctor visits (OR 2.8 [95% CI 1.29, 5.94]).ConclusionFindings highlight heterogeneity and the high prevalence of role‐sharing in paid care, particularly for older adults living with dementia and not enrolled in Medicaid. Findings lay the foundation for interventions that align with national recommendations to increase understanding of and strengthen relationships between family caregivers and paid caregivers.
Substantia nigra iron deposition in Lewy body disease: an MRI quantitative susceptibility mapping and neuropathology study
Authors: Diaz‐Galvan Patricia , Przybelski Scott A. , Lesnick Timothy G. , Nguyen Aivi T. , Murray Melissa E. , Reichard Ross R. , Dickson Dennis W. , Ono Daisuke , Schwarz Christopher G. , Senjem Matthew L. , Gunter Jeffrey L. , Lowe Val J. , Forsberg Leah K. , Fields Julie A. , Savica Rodolfo , Graff‐Radford Jonathan , Ramanan Vijay K. , Jones David T. , Botha Hugo , St. Louis Erik K , Knopman David S. , Day Gregory S , Graff‐Radford Neill R , Petersen Ronald C. , Jack Clifford R. , Ferman Tanis J , Boeve Brad F. , Kantarci Kejal 
Published: January 2025

AbstractBackgroundParkinson’s disease and dementia with Lewy bodies are characterized by abnormal iron deposition in the substantia nigra, which can be measured with quantitative susceptibility mapping (QSM) on MRI. However, neuropathologic validation of the increased QSM in the substantia nigra associated with Lewy body disease (LBD) is lacking. We compared substantia nigra QSM on antemortem MRI between patients with and without Lewy‐related pathology which was confirmed by autopsy in all cases.MethodWe studied cases with antemortem QSM who underwent autopsy from the Mayo Clinic Alzheimer’s Disease Research Center and Mayo Clinic Study of Aging (n=54). Immunohistochemistry was performed with rabbit NACP antibody against the amino acids 98‐115 of the α‐synuclein protein to detect Lewy‐related pathology. After neuropathologic evaluation, cases were classified as LBD‐present (n=13) if they had Lewy‐related pathology and LBD‐absent (n=42) if they did not have Lewy‐related pathology. Alzheimer’s disease co‐pathology was present in 9 of the LBD‐present cohort (70%). QSM was calculated for the whole substantia nigra and the two sub‐regions: pars compacta and pars reticulata. Non‐parametric Wilcoxon rank‐sum test was used to compare substantia nigra QSM values between LBD‐present and LBD‐absent cases. Area under the curve (AUC) analyses tested the accuracy of QSM to distinguish the two groups.ResultsLBD‐present group had higher QSM values in the substantia nigra than LBD‐absent group. Furthermore, sub‐regional analyses showed that the LBD‐present group had higher QSM in the substantia nigra pars compacta (p=0.008) but not in the pars reticulata than the LBD‐absent group (Figure 1). QSM of the substantia nigra pars compacta distinguished LBD‐present and LBD‐absent cases with good accuracy (AUC=0.74; p=0.006; Figure 2).ConclusionThis study provides neuropathologic insight into the relevance of QSM of substantia nigra pars compacta as an in‐vivo biomarker of autopsy‐confirmed LBD. These findings indicate that imaging evidence of abnormal iron deposition in the substantia nigra may serve as a biomarker of Lewy‐related pathology in patients with primary LBD (i.e., Parkinson disease, dementia with Lewy bodies) and patients with potential LBD co‐pathology (e.g., patients with Alzheimer disease).