bluuebunny/crossref_metadata_2025_split huggingface dataset:
Generative AI improves MRI‐based Detection of Alzheimer’s Disease by using Latent Diffusion Models and Convolutional Neural Networks
Authors: Dhinagar Nikhil J , Thomopoulos Sophia I , Thompson Paul M. 
Published: January 2025

AbstractBackgroundAs new treatments (such as the anti‐amyloid vaccine, lecanamab) emerge for Alzheimer’s disease (AD) and other dementias, approaches are required to rapidly diagnose AD at the earliest possible stage, and to assess disease progression and prognosis. In January 2024, the FDA approved the first AI tool to predict AD progression based on magnetic resonance imaging (MRI) [1]. Here we train a generative AI approach based on latent diffusion models ‐ to encode disease effects on brain structures. We show how AI‐based generation of synthetic scans can complement existing datasets for AD‐related diagnostic tasks.MethodWe analyzed 4,098 3D T1‐weighted brain MRI scans (556M/632F, 55.7 ‐ 92.8 years) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We trained a latent diffusion model (LDM) [2] as our generative AI framework. The model was conditioned on the disease status of AD versus healthy controls to generate synthetic brain MRI scans. For validation, the generated synthetic MRI scans was used to initialize a 3D DenseNet‐121 CNN (convolutional neural network) [3], and then further fine‐tuned for AD classification.ResultThe table below summarizes the AD classification performance of a 3D CNN on a test set of 1,219 scans from ADNI. The synthetic data improved performance by over 2%, i.e., receiver‐operator characteristic curve‐area under the curve (ROC‐AUC) from 0.8656 to 0.8869.ConclusionIn this work, we trained a latent diffusion model to generate brain MRI scans. When these scans were used to train a deep neural network to detect AD, performance increased. Future work will test these generative AI models for interpretable disease detection via counterfactual image generation, and to discover factors that affect AD onset and progression via meta‐data encoding in the generative model.References:[1]. J. Shugart, FDA Approves BrainSee, AI Software That Purportedly Predicts AD, AlzForum News, available at: https://www.alzforum.org/news/research‐news/fda‐approves‐brainsee‐ai‐software‐purportedly‐predicts‐ad, 2024.[2] W. H. L. Pinaya et al., “Brain Imaging Generation with Latent Diffusion Models,” MICCAI Workshop on Deep Generative Models (DGM4MICCAI) 2022, pp 117‐126.[3] N. Dhinagar et al., “Video and Synthetic MRI Pre‐training of 3D Vision Architectures for Neuroimage Analysis,” SPIE Medical Imaging 2024.
Multi‐dimensional Feature Extraction Technology of Spontaneous Speech Based on Machine Learning: Early Identification of Alzheimer's Disease
Authors: Chen Quan , Li Yatian , Wang Ying , Li Yiming , Gao Jingyi , Chen Nan , Wu Jingnan , Li Ran 
Published: January 2025

AbstractBackgroundSpeech variations appear at early stages of Alzheimer's disease (AD) and are potential early indicators. However, the commonly used neuropsychological assessment scales as screening tools for cognitive impairments have limitations due to possible subjective bias of the assessors and the lack of sensitivity in voice detection. We propose a solution based on machine learning for multi‐dimensional feature extraction and data fusion of spontaneous speech signals.MethodParticipates with gold standard diagnosis including cognitively normal, mild cognitive impairment (MCI), and mild AD underwent a speech‐based cognitive assessment (Shanghai Cognitive Screening, SCS), including picture learning and recall tasks. Their voice data were generated and created into semantic datasets and audio datasets, respectively. Then, to obtain semantic and acoustic features, the two datasets were trained in multiple machine learning models and tested separately. Finally, to separating cognitively normal and impaired (MCI+AD) participants, multi‐dimensional features of the speech signal are combined through data fusion using voting style.ResultThe accuracy of this model is 91.5 ± 0.8%, with a specificity of 97.0%. In addition, there are significant differences between cognitively impaired patients and normal controls regarding instant recall as opposed to delayed recall tasks, with the most notable difference being the duration of silence in delayed recall tasks.ConclusionThis method maximizes the exploration of feature information in speech signals, greatly improving the accuracy of identification for Alzheimer's disease.
Collaboration introduces an innovative practice for incontinence monitoring to provide efficiencies and higher quality of care
Authors: Chidester Amy S 
Published: January 2025

AbstractIncontinence Management has long been overlooked as a normal part of aging and the national standard of care has become changing individuals every 2 hours. While this was effective for decades, it is no longer providing results and even more challenging for those suffering from Dementia. Over 50% of nursing home residents are incontinent with over 57% of each shift of a certified nursing aide being attributed to incontinence care and management.1Studies show that incontinence is often the root cause of wounds, infections, falls, odor, depression, and behaviors.2 Senior care has long provided care in a systematic, uniform way, not allowing for an individualized experience. Instead of making briefs more absorbent, we need to focus on real‐time changes to help mitigate health risks.Data and science behind predicting when someone will void and helping them not void in an incontinence product is the key. This is the practice of developing and implementing voiding schedules from data that are individualized to each person and providing them with the help they need and deserve. Going further into this concept, the data around patterns of voiding is also extremely powerful for care teams as over time care teams can identify outliers that can indicate to providers a brewing event or condition.Our practice innovation is a collaboration between an assisted living memory care provider and a digital health sensor platform providing a smart brief that is disposable and remotely “talks” through readers, disguised as art to alert staff to when an individual has had an incontinent episode. Data is collected and reported via a dashboard that provides real‐time alerts for the care team to make decisions on care plans and quality of care can be elevated in ways not available before.
Right temporal variant frontotemporal dementia: a multimodal MRI analysis
Authors: Gallingani Chiara , Carbone Chiara , Salvatori Davide , Tondelli Manuela , Vinceti Giulia , Chiari Annalisa , Zamboni Giovanna 
Published: January 2025

AbstractBackgroundRight temporal variant frontotemporal dementia (rtvFTD), a new recognized entity among the FTD‐spectrum, is characterized by right anterior temporal lobe (rATL) atrophy and a peculiar clinical presentation, involving face and emotions recognition, memory, and naming deficits and behavioral disturbances. Clinical diagnosis is challenging, since rtvFTD shares features with both the behavioral variant FTD (bvFTD) and the semantic variant primary progressive aphasia (svPPA), and there is no consensus yet on its designation and characterization. Although rATL neurodegeneration is a hallmark of this syndrome, only a few studies investigated patterns of gray matter (GM) atrophy. Even less is known about white matter (WM) involvement. We conducted a preliminary study on the rtvFTD neuroimaging features using multimodal magnetic resonance imaging (MRI).MethodWe compared GM volume and WM microstructural integrity in rtvFTD (n=3) and svPPA (n=3) patients, and healthy controls (HC, n=27), using voxel‐based morphometry (VBM) and tract‐based spatial statistics (TBSS). Age and disease duration were considered as covariates of no interest.ResultSvPPA patients showed GM atrophy in the left temporal structures relative to HC. rtvFTD patients compared to HC showed not only GM atrophy in the right frontal and temporal structures, but also in the insula bilaterally, and in the left temporal and orbitofrontal cortices. Direct comparison between rtvFTD and svPPA showed that rtvFTD were more atrophic than svPPA patients in the bilateral temporal and frontal regions. Moreover, rtvFTD patients had less WM integrity than controls in the corpus callosum and the right inferior fronto‐occipital, inferior longitudinal, and uncinate fasciculi, while svPPA patients had decreased WM integrity in the corpus callosum and the left inferior fronto‐occipital fasciculus.ConclusionWe found that rtvFTD patients show greater atrophy compared to svPPA patients both in the right and left hemisphere, independently of disease duration. This suggests that rtvFTD and svPPA do not mirror each other in GM loss, and that a neurodegenerative process starting in the right hemisphere must be more widespread to become clinically evident. We also demonstrate that rtvFTD patients show WM disruption in fasciculi which have been implicated in face recognition, emotion processing, and language functions, in line with the clinical picture.
Modeling the temporal evolution of mass spectrometry plasma biomarkers
Authors: Cogswell Petrice M , Lundt Emily S. , Therneau Terry M. , Griswold Michael E. , Graff‐Radford Jonathan , Algeciras‐Schimnich Alicia , Lowe Val J. , Schwarz Christopher G. , Senjem Matthew L. , Gunter Jeffrey L. , Knopman David S. , Vemuri Prashanthi , Petersen Ronald C. , Jack Clifford R. 
Published: January 2025

AbstractBackgroundPlasma Aβ42/40 and p‐tau217 can predict amyloid positivity in cross‐sectional studies. However, it is unclear how plasma biomarkers perform longitudinally, which is important to inform their utility in tracking disease progression. The goal of this study is to describe temporal evolutions of plasma Aβ42/40 and ptau217 ratio (p‐tau217r) measured via mass spectrometry, p‐tau217 measured via an immunoassay, and amyloid PET.MethodsWe included participants in the Mayo Clinic Study of Aging with amyloid PET (PiB) and plasma Aβ42/40 + ptau217r (mass spectrometry, C2N) and/or p‐tau217 (Lilly, MSD). We fit a joint non‐linear mixed effects model with Aβ42/40, p‐tau217r, p‐tau217 (Lilly), and amyloid PET as outcomes and age, sex, APOE ε4 carriership and education as covariates. The model included a per‐participant adjustment (time‐shift) for each biomarker indicating how much earlier or later in time the biomarker was estimated to progress relative to the average participant, a correlation (R) between adjustments for each pair of biomarkers, and the impact of covariates on each biomarker’s timing.ResultsThe study included 1459 participants, age 77 (10) years, 44% female, 82% cognitively unimpaired. Mean (SD) years of followup was 5.6(3.0) years for mass spectrometry plasma measures, 2.5(0.7) years for p‐tau217 (Lilly), and 4.6(3.3) for amyloid PET. Model fits are shown for each biomarker, with greater variability in serial measurements for plasma than PET (Figure 1). Individual adjustments between each of the plasma biomarkers and amyloid PET were highly correlated: p‐tau217r, R=0.73 (95%CI: 0.67, 0.78); p‐tau217 (Lilly), R=0.66 (0.62, 0.7); Aβ42/40, R=0.6 (0.52, 0.68) (Figure 2). The p‐tau217r and Aβ42/40 individual adjustments were modestly correlated, R=0.39 (0.28, 0.49). APOE ε4 carriership had the strongest covariate effect on all biomarkers, e.g. p‐tau217r progression 9.4 (6.7, 12) years earlier (Figure 3).ConclusionsPlasma Aβ42/40 and p‐tau217 measured with currently available assays may both be amenable to longitudinal modeling, although the degree of variability in Aβ42/40 over time may pose challenges. Findings suggest that both plasma Aβ42/40 and p‐tau217r may provide information regarding an individual’s timing of progression on amyloid PET; lower correlations with each other than amyloid PET suggests that each analyte may provide complimentary information.
Automated analysis of story recall tasks produced by AD and MCI patients
Authors: Kim Jin‐Seo , Nevler Naomi , Irwin David J , Liberman Mark Y , Cho Sunghye 
Published: January 2025

AbstractBackgroundStory recall tasks are often employed in clinical settings to measure verbal episodic memory in individuals with mild cognitive impairment (MCI) and Alzheimer’s disease (AD). Traditional analyses (e.g. total number of recalled words) provide useful but limited information compared to detailed analyses of lexical performance. In this study, we analyzed story recall data produced by patients with AD or MCI using an automated method.MethodWe analyzed digitized recordings of the Craft story recall task produced by 15 individuals with MCI (age=65.8±10.14, females=40%, education=16.07±2.94), 27 individuals with AD (age=65.19±6.82, females=37%, education=15.42±3.01) and 14 healthy age‐matched individuals (HC; age=58.86±16.16, females=50%, education=17.29±2.16). The groups did not differ in any demographic characteristics. Our automated pipeline identified the number of verbatim and paraphrase recalls, calculated semantic similarities between the original story and recalled immediate and delayed stories using word embeddings and rated several lexical characteristics, including word frequency, concreteness, and word length. Two‐way ANOVA tests examined the effect of Group (HC vs. MCI vs. AD), Task (immediate vs. delayed), and their interactions. As the interactions of Group and Task were not significant, post‐hoc analyses examined significant pairwise group differences in immediate and delayed recalls, separately.ResultParticipants with AD produced the fewest verbatim and total recalled words, followed by MCI patients and HC in both immediate and delayed recalls (Figure 1A–B, Table 1). AD patients produced fewer paraphrase recalls compared to the other groups in the delayed recall only (Figure 1C). AD group’s recalled story was also the least similar to the original story, followed by MCI and HC (Figure 1D). Also, individuals with AD produced words that were more frequent and less concrete during immediate and delayed recalls compared to MCI patients and HC (Figure 1E–F). Words produced by AD patients were also shorter than those produced by the MCI and HC groups during the immediate recall (Figure 1G).ConclusionOur method highlights that digitized language‐based tests of verbal episodic memory can be automatically analyzed. These capture traditional language features that cannot be extracted manually without massive effort and promise to provide novel, granular, digitized biomarkers that traditional assessment methods of neuropsychological tests cannot show.
Management of Neuropsychiatric and Sleep Symptoms in LBD
Authors: Onyike Chiadi U. 
Published: January 2025

AbstractThe presentation of neuropsychiatric and sleep features in Lewy body dementia (LBD) are challenging to identify and manage. The US Based Diamond Lewy Neuropsychiatric Symptoms management guidelines provide expert consensus recommendations for the assessment and treatment of the neuropsychiatric features of LBD. These guidelines address the management of delusions, hallucinations, agitation and aggression, apathy, depression and anxiety. Pharmacologic interventions will be discussed in detail, in order to describe recommended agents, prescribing strategies, and potential adverse effects and safety aspects. The parallel Diamond Lewy Sleep Disturbances management expert guidelines will also be discussed, to bring to light best practices in the assessment and management of excessive daytime sleepiness, restless legs syndrome (RLS), motor‐related sleep disturbances, sleep apnea, REM sleep behavior disorder, insomnia and sleep fragmentation.
Bayesian changepoint models for amyloid‐centric synchronisation of biomarker trajectories in AIBL
Authors: Saint‐Jalmes Martin , Beck Daniel , Bourgeat Pierrick , Masters Colin L , Goudey Benjamin 
Published: January 2025

AbstractBackgroundThe modelling of biomarker dynamics in Alzheimer’s Disease from cohort studies faces challenges due to the lack of clear temporal points of references and the natural variability across individuals. Mixed‐effects models are often used to account for individual differences, but a disease timescale can enable better population‐level modelling than age or time since enrolment. Previous literature explored the temporal synchronisation of patients through observed time of conversion to MCI or AD, amyloid positivity, or aligning cognitive trajectories. We propose an amyloid‐centric synchronisation approach that estimates patient‐specific time‐shifts to best align them on a population‐level piecewise linear trajectory.MethodWe rely on 521 patients of the AIBL study with at least 3 measurements of PET amyloid, and model a 3‐segment, piecewise linear trajectory of centiloid over time. Our Bayesian model assumes that observed amyloid values are noisy realisations of the piecewise linear trajectory, and that the time of measurements is shifted by some individual‐specific lag. The first segment is constrained to have a zero slope, and individuals who were not observed to reach the first changepoint were excluded from subsequent analysis, as they may never develop Alzheimer’s Disease.ResultWe identified a changepoint in the amyloid trajectory at a median of (89% credible interval: [14.45, 23.64]) after initial accumulation in shifted time. Patients with measurements falling within this credible interval have a median of 18.9 centiloid (range of 2.0 to 30.1). On average, this changepoint describes an acceleration in amyloid accumulation, switching from a median 0.75 centiloid/year [0.65, 0.87] rate to 6.38 centiloid/year [6.20, 6.56] (Figure 1).ConclusionSynchronisation approaches allow for better group‐level assessments (such as the relative timing of biomarkers) rather than attributing variability to patient‐specific effects. Our approach was able to realign individuals on a common trajectory and amyloid‐centric timescale. Markov chain Monte Carlo sampling allows us to estimate uncertainties for all parameters: slopes and intercepts, changepoints, time‐shifts and the fit residual. Further work could explore the timing of other biomarker changes in this amyloid‐synchronised time.
Head‐to‐Head Comparison of Four Plasma Phosphorylated Tau 217 Biomarkers
Authors: Warmenhoven Noëlle , Salvadó Gemma , Janelidze Shorena , Mattsson‐Carlgren Niklas , Kolb Hartmuth C. , Triana‐Baltzer Gallen , Barthélemy Nicolas R. , Bateman Randall J. , Binette Alexa Pichet , Hansson Oskar 
Published: January 2025

AbstractBackgroundWe assessed the efficacy of four plasma phospho‐tau217 (p‐tau217) biomarkers in a head‐to‐head comparison, and against two clinically available CSF biomarkers for Alzheimer’s disease (AD).MethodSamples were analyzed from 1009 individuals from the Swedish BioFINDER‐2 cohort (Table 1). We included the following biomarkers: %p‐tau217WashU, p‐tau217WashU (both mass‐spectrometry), p‐tau217Lilly, p‐tau217Janssen (both immunoassays), CSF p‐tau181 and p‐tau181/Aβ42 (Elecsys). Biomarker correlations were assessed using linear regression models. Their discriminative accuracy for global Aβ‐ and temporal meta‐ROI tau‐PET status was evaluated with receiver operating characteristic (ROC) curves. Area under the curve (AUC) values from two ROC curves were compared with DeLong tests. Linear regression models with continuous Aβ‐ and tau‐PET measures were performed. Participants were grouped into PET‐positive quartiles, which were compared with t‐tests. Effect sizes (Cohen’s D (CD)) were calculated between PET‐positive/negative groups, and between neighboring quantiles.ResultAll plasma biomarkers were correlated (0.62≥Radj2≥0.92, Figure 1). %p‐tau217WashU showed the significantly largest effect size for both Aβ‐PET status and tau‐PET status (CDAβ‐PET=1.635; CDTau‐PET=1.828) compared to the other biomarkers (all pFDR <0.05). p‐tau217Janssen had a lower plasma effect size (CDAβ‐PET=1.313; CDTau‐PET=1.590), but not significantly different from p‐tau217Lilly. Although all plasma biomarkers showed high AUCs (0.90‐0.95) for Aβ‐PET positivity, %p‐tau217WashU was the highest, performing significantly better than all other biomarkers including CSF p‐tau181/Aβ42Elecsys (all pFDR<0.01) (Figure 2A). A similar pattern was observed for tau‐PET where %p‐tau217WashU also performed significantly better than all other biomarkers except for p‐tau217WashU (all pFDR<0.01) (Figure 2A). With continuous PET measures, %p‐tau217WashU showed the highest Radj2 compared to the other biomarkers for Aβ‐PET and tau‐PET (Figure 2B). In this context, all plasma ptau217 markers performed better that CSF ptau181Elecsys. Compared to CSF p‐tau181/Aβ42Elecsys, p‐tau217Lilly and p‐tau217WashU performed similarly whereas %p‐tau217WashU performed significantly better. Quantile grouping revealed that all biomarkers showed significant differences when distinguishing between negatives and early‐stage positives for both Aβ‐PET and tau‐PET, with %p‐tau217WashU consistently having the significantly largest effect size (Figure 2C). For tau‐PET, plasma biomarkers distinguished better between disease stages compared to CSF.ConclusionWhen predicting Aβ‐ and tau‐PET load, both mass‐spectrometry and immunoassay methods detecting plasma p‐tau217 perform similarly to an FDA‐approved CSF test, with %p‐tau217WashU performing even better.
Associations between cognitive function and long‐term cognitive enhancement therapy: Insights from a 5‐year follow‐up, case study
Authors: Zarouna Stalo , Tziannarou Antonia , Chrysostomou Savvina , Moza Sotiria 
Published: January 2025

AbstractBackgroundA 69‐year‐old retired businessman, born in 1954, with 12 years of education, had been participating in cognitive enhancement sessions for the past 5 years. His medical history included two ischemic strokes, left hemiplegia, as well as disturbances in the left visual field. This study aimed to examine the individual’s cognitive performance over the course of these 5 years, including the COVID‐19 pandemic period.MethodsBi‐weekly, 45‐minute cognitive remediation sessions were performed for 1.5 years, transitioning to once weekly later on. Exercises included orientation (e.g., use of calendar, news, articles), language (e.g., repetition, fluency, fill the gaps), visuospatial (e.g., pattern completion, object localisation, tracking of moving objects), memory (e.g., stories/words/images, learning, consolidation and recall of information, reminiscence and old songs), executive (e.g., dual tasks, categorisation) and motor tasks (e.g., ball games).ResultsAt baseline (July 2018), the person scored 11/30 in the Montreal Cognitive Assessment (MoCA) with significant visuospatial, memory recall, and orientation difficulties. In October 2019 (after 59 sessions), the MoCA score improved to 15/30, with better performance in visuospatial tasks, calculation, delayed recall, and orientation. However, a re‐evaluation in May 2020, following the first wave of COVID‐19, during which sessions were paused, resulted in a score of 11/30. The lowest points were noted in orientation and delayed recall. Sessions continued at a reduced frequency due to COVID‐19 restrictions and medical complications (flu, serious respiratory and urinary tract infections, and hospitalisation) which also affected the fine motor skills of the right hand. Last evaluation, in September 2023, yielded a score of 12/30 points, showing a regain of orientation and calculation scores, but losses in visuospatial skills, naming and delayed recall compared to the 2019 evaluation.DiscussionOur findings suggest a connection between cognitive functionality and cognitive enhancement therapy, particularly in orientation, memory recall, and calculation tasks. Achieving impact appears to require sustained and long‐term engagement in personalised enhancement sessions, which can be significantly hindered by external obstacles, such as COVID‐19 restrictions and other health issues.