bluuebunny/crossref_metadata_2025_split huggingface dataset:
Discrimination of Alzheimer’s disease and non‐Alzheimer’s disease neurocognitive‐disordered patients by assessing CSF biomarkers in Thai population
Authors: Nanthasi Witsarut , Rattanabannakit Chatchawan , Wongkom Natthamon , Dujada Pathitta , Raksthaput Atthapon , Chaichanettee Sunisa , Phoyoo Paphawadee , Wachirutmangur Lertchai , Senanarong Vorapun 
Published: January 2025

AbstractBackgroundCSF biomarkers including β‐amyloid (Aβ) (1‐42), phosphorylated tau (p‐tau) 181 and total tau (t‐tau) are used as ante‐mortem biomarkers to detect Alzheimer’s disease (AD) pathology in cognitively impaired patients. Decreased level of Aβ(1‐42), elevated level of p‐tau 181 and t‐tau are well established in AD. Base on CSF analysis technique and population, cut‐points are varied to distinguish between AD and non‐AD neurocognitive‐disordered patients. This study aimed to evaluate the local cut‐points of CSF biomarkers as diagnostic tools to discriminate AD and non‐AD neurocognitive‐disordered patients in Thai population.MethodsWe enrolled 74 cognitively impaired patients in memory clinic at Siriraj hospital, Thailand. Participants underwent a standardized diagnostic dementia evaluation, including medical history, physical examination, neuropsychological tests using Thai Mental State Examination (TMSE), Montreal Cognitive Assessment (MoCA) and Clinical Dementia Rating Sum of Boxes score (CDR‐SB), and brain imaging. Patients were clinically diagnosed with each subtype of neurocognitive disorder without knowledge of CSF biomarker results. CSF Aβ(1‐42), p‐tau 181 and t‐tau were measure by ELISA technique. Youden index–based cut‐points of CSF biomarkers were defined using receiver operating characteristic (ROC) to distinguish between AD and non‐AD neurocognitive‐disordered patients.Results45 (60.8%) patients were diagnosed with AD dementia and 29 (39.2%) patients were diagnosed with non‐AD neurocognitive disorders. Median values of CSF Aβ(1‐42) were significantly lower in AD group than in non‐AD group (371.0 vs. 666.0 pg/mL, p <0.001*). Median values of CSF p‐tau 181 and ratios of CSF p‐tau 181/Aβ(1‐42) were also significantly higher in AD group than in non‐AD group (p‐tau 181; 70.0 vs. 44.0 pg/mL, p = 0.002*) (p‐tau 181/Aβ(1‐42) ratio; 0.180 vs. 0.059, p <0.001*). Whereas t‐tau levels were not statistically different between groups (359.0 vs. 227.0 pg/mL, p = 0.053). Local cut‐points of CSF biomarkers to determine AD dementia were proposed as CSF Aβ(1‐42) ≤538 pg/mL (AUC 0.96, p <0.001*), CSF p‐tau 181 >56 pg/mL (AUC 0.72, p <0.001*) and p‐tau 181/Aβ(1‐42) ratio >0.094 (AUC 0.90, p <0.001*).ConclusionThe utility of CSF Aβ(1‐42) and p‐tau 181/Aβ(1‐42) ratio was notably higher in effectively distinguishing Alzheimer's disease from non‐Alzheimer's disease neurocognitive disorder within Thai population.
Do patients with early‐stage cognitive impairment have an increased risk for developing frailty syndrome? A cross‐sectional study
Authors: Lopes Pedro de Castro , Machado João Carlos Barbosa , Barbosa Maira Tonidandel , Aguiar Bianca Pessoa , de Souza Júlia Caroline Barbosa , Ferreira João Pedro Neres Antunes , Garcia Luana Rodrigues , Leopoldino Amanda Aparecida Oliveira 
Published: January 2025

AbstractBackgroundFrailty syndrome is a prevalent condition associated with high‐impact outcomes for the elderly, such as falls, hospitalization, functional loss and mortality. It can be triggered by several clinical conditions and dementia is one of them. More studies are needed to deeply understand the relationship between these two conditions. Frailty is prevalent in patients with advanced dementia, but this study was developed with the objective to understand if patients with mild cognitive impairment have increased risk for developing frailty too.MethodThis is a cross‐sectional study in which 87 community‐dwelling elderly people were included and evaluated in relation to the presence of cognitive impairment and frailty. Patients with severe cognitive impairment, assessed by a score less than 6 in the CS‐10 (Point Cognitive Screening) were excluded. Individuals with a score between 6 and 7 on the CS‐ 10 were considered to have mild or moderate cognitive impairment and participants with a score greater than 7 on this test were considered to have no cognitive impairment. The individuals were evaluated for the presence of frailty based on the criteria proposed by Fried. Individuals with a score greater than or equal to 3 were considered frail, with score between 0 and 2 were considered non‐frail. In order to evaluate the correlation and association between the characteristics, Pearson’s chi‐square test was calculated. If the p‐value was lower than the significance level of 0.05, it is possible to conclude the association between the development of frailty and a worse score in CS‐10.ResultIn this sample, 50.6% were frail. The mean score of the patients in CS 10 was 8.2. 69.2% of frail patients had a CS10 score between 6 and 7 points. At the highest score range above 10 points, we have that 72% are from non‐frail patients. Thus, there is an association between the risk of frailty and the CS10 score (p‐value 0.008). The Kruskal Wallis test, which evaluates the difference between the medians, also indicates statistically significant association between them (p‐value 0.002).ConclusionPatients with mild cognitive impairment have an increased risk of developing frailty syndrome.
Dysphagia in primary progressive aphasia: clinical predictors and neuroanatomical basis
Authors: Mazzeo Salvatore , Mulroy Eoin , Jiang Jessica , Lassi Michael , Johnson Jeremy , Hardy Chris JD , Rohrer Jonathan D. , Warren Jason D , Volkmer Anna 
Published: January 2025

AbstractBackgroundDysphagia is an important feature of neurodegenerative diseases and potentially life‐threatening in primary progressive aphasia (PPA), but remains poorly characterised in these syndromes. We hypothesised that dysphagia would be more prevalent in nonfluent/agrammatic variant (nfv)PPA than other PPA syndromes, predicted by accompanying motor features and associated with atrophy affecting regions implicated in swallowing control.MethodsIn a retrospective case‐control study at our tertiary referral centre, we recruited 56 patients with PPA (21 nfvPPA, 22 semantic variant (sv)PPA, 13 logopenic variant (lv)PPA). Using a proforma based on caregiver surveys and clinical records we documented dysphagia (present/absent) and associated, potentially predictive clinical, cognitive and behavioural features. These were used to train a machine learning model. Patients’ brain MRI scans were assessed using voxel‐based morphometry and region‐of‐interest analyses comparing differential atrophy profiles associated with dysphagia presence/absence.ResultsDysphagia was significantly more prevalent in nfvPPA (43% vs 5% svPPA and no lvPPA). The machine learning model revealed a hierarchy of features predicting dysphagia in the nfvPPA group, with excellent classification accuracy (90.5% [95% confidence interval 77.9:100]): the strongest predictor was orofacial apraxia, followed by older age, parkinsonism, more severe behavioural disturbance and more severe cognitive impairment. Significant grey matter atrophy correlates of dysphagia in nfvPPA were identified in left middle frontal, right superior frontal and right supramarginal gyri, and right caudate.ConclusionDysphagia is a common feature of nfvPPA, linked to underlying cortico‐subcortical network dysfunction. Clinicians should anticipate this symptom particularly in the context of other motor features and more severe disease.
Detecting amyloid positivity from MRI scans using comprehensible convolutional neural networks
Authors: Levin Fedor , Li Qingyue , Singh Devesh , Buerger Katharina , Fliessbach Klaus , Heneka Michael T. , Kleineidam Luca , Laske Christoph , Perneczky Robert , Peters Oliver , Priller Josef , Ramirez Alfredo , Schneider Anja , Spottke Annika , Wagner Michael , Wiltfang Jens , Düzel Emrah , Jessen Frank , Teipel Stefan , Dyrba Martin ,   the DELCODE Study Group
Published: January 2025

AbstractBackgroundAnalysis of neuroimaging data based on convolutional neural networks (CNNs) can improve detection of clinically relevant characteristics of patients with Alzheimer’s disease (AD). Previously, our group developed a CNN‐based approach for detecting AD via magnetic resonance imaging (MRI) scans and for identifying features that are relevant to the decision of the network. In the current study, we aimed to evaluate the potential utility of applying this approach to MRI scans to assist in the identification of individuals at high risk for amyloid positivity to aid in the selection of study samples and case finding for treatment.MethodIn the current analysis, we have trained a CNN to detect amyloid positivity using MRI scans from 1461 Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants (498 cognitively normal participants, 103 participants with significant memory concern, 640 participants with mild cognitive impairment, and 220 participants with AD dementia). Amyloid positivity was assessed via amyloid PET scans obtained with [18F]florbetapir or [18F]florbetaben and quantified on a Centiloid scale. A threshold of 24.1 CL categorized 46% of participants as amyloid‐positive. The modeling approach was evaluated using 10‐fold cross‐validation, the number of epochs in training was set to 10.ResultFor each of 10 cross‐validation folds, we selected a model state corresponding to an epoch showing best performance in the validation partition. Balanced accuracy across these models ranged from 0.62 to 0.72 with an average of 0.68 (SD = 0.03).ConclusionWe used a previously established approach to train CNNs for detecting amyloid positivity using MRI scans. Such models, particularly when tuned to have low rates of false negatives, have a potential to enhance identification of patients who would benefit from more in‐depth assessments, which could then inform antibody treatment. We are conducting ongoing work to improve and characterize the modeling approach, including evaluation of relevance maps which indicate importance of brain regions for detecting amyloid positivity. Future work will evaluate the role of amyloid positivity threshold selection. Planned analyses also include validation in independent data such as the German DZNE ‐ Longitudinal Cognitive Impairment and Dementia Study (DELCODE) dataset.
Association between digital biomarkers and anxiety: a systematic review and meta‐analysis
Authors: Lau Yolanda , Chemas Natalia , Gokani Heema Ajeet , Morrell Rachel , Walker Zuzana , Demnitz‐King Harriet , Marchant Natalie L 
Published: January 2025

AbstractBackgroundAnxiety, both generalised anxiety disorder and anxiety symptoms, has been recognised as a risk factor and prodromal symptom of dementia. Digital biomarkers are gaining interest as proxy markers for mental health because they enable passive and continuous data collection, allowing for early detection. However, the association between digital biomarkers and anxiety remains unknown. This systematic review and meta‐analysis aimed to examine the association between digital biomarkers obtained from wrist‐worn wearables and anxiety symptoms in adults.MethodSystematic literature searches were conducted across six databases, including unpublished grey literature. Studies investigating the association between digital biomarkers from wrist‐worn wearables and anxiety were eligible for this review. Effect sizes were combined across studies, for each digital biomarker separately, using random‐effects meta‐analyses whenever possible. Sensitivity analyses were performed to assess whether results differed according to anxiety type (state, trait), age group (young, middle, older), and sex.ResultTwenty‐two articles were eligible. Meta‐analyses were conducted for four sleep metrics: sleep efficiency, wake after sleep onset, total sleep time, and sleep onset latency. Analyses revealed that sleep efficiency (8 studies, Fisher’s z = ‐0.08, 95% confidence interval [CI] = ‐0.15 to ‐0.01, p = 0.0263) and wake after sleep onset (6 studies, Fisher’s z = 0.13, 95% CI = 0.01 to 0.24, p = 0.0291) were associated with anxiety symptoms. In sensitivity analyses, associations persisted older adults (for sleep efficiency only) and trait anxiety. Meta‐analyses could not be conducted for physical activity metrics, however, a qualitative synthesis of the limited number of studies (five studies) revealed inconsistent results.ConclusionWorse sleep efficiency and longer wake after sleep onset were associated with greater anxiety symptoms. However, due to the limited number of studies, the association with physical activity remains unclear, warranting further research. Further, it became apparent that machine learning studies in this area are limited and of variable quality. Given anxiety is a prodromal symptom of dementia, future research focusing on older adults is essential to explore the use of digital biomarkers in the context of dementia risk.
Dynamic Changes in Quantitative MRI‐based Biomarkers and Its Association with Pathology of Alzheimer’s Disease – A Longitudinal Study
Authors: Fan Xiang , Cai Yuan , Liu Wanting , Zhao Lei , Luo Yishan , Shi Lin , Mok Vincent Chung Tong 
Published: January 2025

AbstractBackgroundAlzheimer’s disease (AD) resemblance atrophy index (AD‐RAI) is a novel MRI‐based machine‐learning derived biomarker for AD that is valid in the detection of early AD. We aimed to investigate the association between the serial change of AD‐RAI and cognitive progression overtime among cognitively unimpaired (CU) and mild cognitive impairment (MCI) subjects with and without A+T+ (i.e., Aβ+ and Tau+) and to compare it with other conventional MRI biomarkers (i.e., hippocampal volume (HV), hippocampal fraction (HF), brain parenchymal volume (BPV) and fraction (BPF)), which have been used in the past as surrogate markers for AD.MethodWe selected participants from the ADNI databases and included those aged 55 to 90 years, diagnosed with CU and MCI at baseline with high‐quality volumetric MRI data and CSF at baseline, and at least once matched T1W scans and CSF during follow‐up. We define A+T+ based on the threshold value of Aβ1‐42 and ptau in CSF. A total of 168 participants were recruited with serial MRI scans and corresponding CSF in total. We assessed longitudinal changes of the MRI biomarkers (i.e., AD‐RAI, HV, HF, BPV, BPF) in correlation with changes in time and conversion status with and without A+T+. We used a linear mixed‐effects model (LMM) in SPSS 26.0 to explore the serial changes of the imaging biomarkers and interaction effects with and without A+T+ over time, and further explore the association between dynamic CSF Aβ1–42 and dynamic changes in the imaging biomarkers’ interaction effects over time.ResultAD‐RAI with A+T+ increased significantly faster than non‐A+T+ over time (P < 0.05), while HV and HF with A+T+ decreased significantly faster than non‐A+T+ over time (P < 0.05, respectively), which performed better than BPV/BPF. Serial AD‐RAI was significantly associated with changes in CSF Aβ1–42 over time, which outperforms other imaging biomarkers.ConclusionSerial AD‐RAI outperforms other conventional biomarkers in correlating with AD pathologies. It may be used as a surrogate marker for monitoring disease progression or treatment response in AD.
Improving Wellbeing in At‐Risk Familial frontotemporal dementia (IWARF): a feasibility study of a blended digital/human online intervention
Authors: Harding Emma , Hayes Oliver S , Brotherhood Emilie V , Greaves Caroline V , Goldsmith Sophie E , Rohrer Jonathan D. , Stott Joshua 
Published: January 2025

AbstractBackgroundFamilial frontotemporal dementia is an autosomal dominant heritable form of frontotemporal dementia, a form of dementia characterised by changes in personality, behaviour and communication which typically onsets in mid‐life. Children of an affected parent are at 50% risk of inheriting the responsible genetic mutation and developing frontotemporal dementia themselves. Individuals living at‐risk have high psychological morbidity, for example they report struggling with guilt and anxiety about risk to themselves and their children, decisions about whether to get tested, uncertainty about onset of symptoms, and see their risk as a barrier in life. Despite this high psychological morbidity there are no tailored interventions available to support those living at‐risk of familial frontotemporal dementia.MethodsGuided by Medical Research Council frameworks we have co‐developed a brief blended Acceptance and Commitment Therapy‐informed person/digital intervention to support individuals at‐risk of familial frontotemporal dementia. The intervention was developed in consultation with experts‐by‐experience and clinical neurology, psychology and genetic counselling experts. It is designed to be self‐paced over 6‐8 weeks, and involves a number of online modules including informational content, videos and independent activities, interspersed with three one‐hour check‐in sessions with a facilitator. We are conducting a mixed‐methods feasibility study (n = 10), with participants with a range of genetic statuses (known mutation carrier, known non‐carrier, unknown). Participants will complete pre‐ and post‐ measures of mood, adaptation to genetic information and psychological flexibility and a qualitative interview to capture their experience of the intervention and its impact on wellbeing.ResultsWe will report on adherence, attrition and acceptability of the intervention, any identified barriers and facilitators to engaging with the intervention, and plans for adaptation and refinement.ConclusionFindings will inform the refinement of the intervention and determine primary and secondary outcome measures for a full randomised controlled‐trial starting in Spring 2024 to test the efficacy of this first‐of‐its‐kind blended digital/human online intervention to improve wellbeing for those living at‐risk of familial frontotemporal dementia. Findings will also inform adaptation to other autosomal dominant conditions (e.g. familial Alzheimer’s disease), in which there are some disease‐specific differences in symptomatology alongside similar unmet psychological needs for those living at‐risk.
Unlocking Tau PET Accessibility: a Machine Learning‐Based Prediction of Tau Pathology from Plasma, MRI and Clinical Variables
Authors: Karlsson Linda , Vogel Jacob W. , Strandberg Olof , Arvidsson Ida , Åström Kalle , Seidlitz Jakob , Bethlehem Richard A.I. , Stomrud Erik , Ossenkoppele Rik , Ashton Nicholas J. , Blennow Kaj , Palmqvist Sebastian , Smith Ruben , Janelidze Shorena , Binette Alexa Pichet , Mattsson‐Carlgren Niklas , Hansson Oskar 
Published: January 2025

AbstractBackgroundA key characteristic of Alzheimer’s disease (AD) is cerebral aggregation of tau. These aggregates can be quantified and localized with positron emission tomography (PET), which improves the diagnostic and prognostic work‐up of AD. However, tau‐PET is expensive and not available in clinical settings globally. With emerging disease‐modifying therapies for AD, comprehensive and accessible ways to estimate the load and distribution of tau pathology are becoming crucial. Here, we address this problem with machine learning (ML) models trained to predict tau‐PET outcomes from accessible features only (clinical variables, plasma biomarkers and MRI).MethodThe study included 1195 participants from the BioFINDER‐2 (BF2) cohort. BF2 was split into 80% train and 20% test sets. We created a rigorous ML pipeline that, with 10‐fold cross‐validation, evaluated different feature selection steps and estimators in a combined grid‐search and Bayesian optimization set‐up (Figure 1). The three variable blocks (clinical, plasma and MRI) were used as features (together and separately) to predict two tau‐PET characteristics: 1) tau load in the temporal cortex 2) hemispheric asymmetry of temporal tau load in tau positive participants (as an example of clinically relevant spatial information). External validation was performed in the BF2 test set, BioFINDER‐1 (BF1, n=147), ADNI (n=136), OASIS (n=46) and A4 (n=45). Note that ADNI, OASIS and A4 did not include plasma biomarkers.ResultFeature combinations including plasma (particularly plasma p‐tau217) resulted in the best prediction of tau‐PET load (Figure 2). The best performing model was a combination of plasma and MRI variables in a CatBoost regressor, which achieved SUVR mean absolute error (MAE) = 0.155 and R‐squared = 0.700 on unseen data. For prediction of hemispheric asymmetry, models using MRI features performed best, and regions in the temporal lobe had the greatest contribution to the models (Figure 3). Top performance on unseen data was laterality index MAE = 6.74 and R‐squared = 0.423 in a SVR model including MRI and clinical variables.ConclusionGeneralizable machine learning models reveal the potential in predicting tau PET from more accessible variables (particularly plasma biomarkers and MRI), which can unlock global accessibility to one of the most informative in vivo diagnostic techniques of AD.
Online awareness: A key element of functional cognition
Authors: Marks Timothy S , Edwards Dorothy F. 
Published: January 2025

AbstractBackgroundIntact awareness of impaired task performance can facilitate the adoption of cognitive strategies to support performance. However, most studies have not examined awareness of task performance (an element of metacognition) relative to complex everyday activities. The Weekly Calendar Planning Activity (WCPA) is a performance‐based assessment of functional cognition that includes indicators of awareness during performance of a simulated complex instrumental activity of daily living (IADL). This study examined if community‐dwelling adults’ Accuracy (primary outcome) and Strategy scores on the WCPA differ according to the individual’s awareness of task performance. Additionally, we examined whether functioning on the Performance Assessment of Self‐care Skills Checkbook Balancing and Shopping Task (PCST) as indicated by the Total Cues was associated with awareness on the WCPA.MethodUsing data collected as part of a larger study we performed a cross‐sectional analysis of 274 community‐dwelling adults aged 55 to 93 years. Online awareness was evaluated on the 17‐item version of the WCPA (WCPA‐17) using participant subjective ratings of performance compared to actual observed performance. Participant data were classified into two groups: those who were aware of their performance versus those who over‐rated performance. A one‐way Analysis of Covariance was used to test three dependent variables, Accuracy scores on the WCPA‐17, number of Strategies used on the WCPA‐17, and Total Cues on the PCST, against the bimodal awareness state while controlling for age, education, and number of chronic health conditions as covariates.ResultIndividuals categorized as aware of their performance on the WCPA‐17 had significantly better Accuracy scores on the WCPA‐17 (F(4,269) = 65.80, p < .001), used more Strategies on the WCPA‐17 (F(4,269) = 4.30, p = .039), and needed fewer Total Cues on the PCST (F(4,269) = 9.15, p = .003).ConclusionOverall, the group categorized as aware on the WCPA‐17 performed better on all functional cognition performance scores. Intact online awareness is critical to performance on complex IADLs and can be evaluated with functional cognition assessments. The WCPA‐17 can be used to evaluate awareness in the context of complex IADL and may be useful for assessment and intervention planning.