bluuebunny/crossref_metadata_2025_split huggingface dataset:
Enhanced Machine Learning Classification of Cognitive Impairment with Multimodal Digital Biomarkers
Authors: Banks Russell E , Higgins Connor , Bates David , Gomes‐Osman Joyce Rios , Pascual‐Leone Alvaro , Tobyne Sean E 
Published: December 2022

AbstractBackgroundLinus Health’s DCTclock™ is an FDA‐listed cognitive assessment solution capable of detecting early signs of cognitive decline. DCTclock digitizes the long‐standing pen‐and‐paper Clock Drawing Test (CDT) and captures a wide variety of cognitive and motor metrics during the entire drawing process. Multiple studies have validated the DCTclock’s ability to detect early cognitive impairment and improve on existing assessment methods (e.g., Souillard‐Mandar et al. 2015 & 2021, Rentz et al. 2021). Linus subsequently developed the Digital Clock and Recall (DCR™) assessment, which incorporates voice and delayed verbal recall alongside the DCTclock. The analysis below evaluated the added benefit of the DCR and the relationship between voice analytics and diagnosis.MethodThe DCR is being administered as part of the BioHermes‐Gap study. Data from 495 participants were used to correlate DCR or DCT with MMSE. A repeated‐measures ANOVA model was used to analyze speech and determine within‐subject differences between sessions (immediate repeat and delayed recall) on 40+ acoustic features extracted from 3‐word recall tasks of 664 older adults. Results were used to build Multilayer Perceptron Neural Networks (ANN) to assess the accuracy of combined acoustic features to classify cognitive status.ResultDCR is more highly correlated with MMSE than the DCTclock (r=0.43 vs. 0.38). ANN results indicate that combining acoustic features with cognitive assessments achieves greater classification accuracy for healthy (AUC=0.95), MCI (0.93), and AD (0.97) groups than models including either acoustic features or cognitive assessments alone.ConclusionA multimodal machine learning investigation of cognitive state, DCR, which includes DCTclock and voice/speech features from delayed recall, increases the accuracy (sensitivity and specificity) of cognitive status classification.
What's in a name?
Authors: Doody Rachelle S. 
Published: December 2022

AbstractBackgroundThere is precedent in the AD field for confusion and dissonance due to the naming of clinical phenomena. Early on, Alzheimer's disease (AD) or Alzheimer's dementia could mean anyone with progressive dementia, and did not require the application of standardised criteria. As standardisation improved at the stage of dementia, the terms Mild Cognitive Impairment or Minimal Cognitive Impairment came into usage and the clinical conditions encompassed by these terms were variably named and defined in different nomenclature systems (eg., AAN, DSM, ICD) and required different criteria depending upon the definitions used by different academic working groups. A similar controversy now exists regarding the naming of individuals who have biological markers consistent with AD, but do not have any clinical symptoms or functional decline from baseline.MethodOther speakers in this symposium will review the history and advocate for particular approaches to describing individuals who have AD biological markers bur are cognitively unimpaired. This presentation will respond to those arguments and bring in additional external perspectives from experience as a clinician treating patients, as a researcher performing non‐interventional trials, and a drug developer.ResultThe implications of naming the condition preclinical AD versus individuals at risk for AD will be explicated for clinical practice, general research, and clinical trials.ConclusionTBD.
Correlations and associations between fluid‐based and morphometric imaging biomarkers through interrogation and integration of data and analysis across two data platforms
Authors: Toga Arthur W. , Henscheid Nicholas , Pappas Ioannis , Cabeen Ryan P , Podichetty Jagdeep , Hobel Sam , Weber Christopher J. , Sivakumaran Sudhir , Romero Klaus 
Published: December 2022

AbstractBackgroundThe Global Alzheimer’s Association Interactive Network (GAAIN) and the Critical Path for Alzheimer’s Disease (CPAD) Consortium have undertaken a collaborative effort to establish a mechanism that can help evaluate the relationship between fluid‐based, morphometric imaging biomarkers and clinical trajectories of disease progression, across datasets and analyses pipelines housed by CPAD and GAAIN. Additionally, this collaboration will demonstrate the feasibility of bidirectional integration of data management models and will guide development of a federated image analysis pipeline model across these two platforms.MethodTo enable interoperability, GAAIN’s original architecture is expanded to accommodate the LONI brain structural imaging pipeline. Under this new framework, GAAIN researchers can initiate brain imaging‐related analyses on their servers directly from GAAIN’s graphical interface with the results integrating back into GAAIN for further analyses. Feasibility was demonstrated for the determination of correlations for CPAD’s biomarker and imaging volumetric data. GAAIN’s brain imaging pipeline was executed on CPAD data, allowing for quantification of volumetric images. In turn, blood and volumetric data were inserted in GAAIN allowing further evaluation and analyses.ResultBased on the results, we provide guidelines for a future framework for a federated analysis model that can be utilized from GAAIN and CPAD to establish relationships between blood and brain markers. In this model, for a cohort of interest, analyses of relevant brain marker data can be remotely done in CPAD servers via GAAINs’ federated platform after which results can be inserted back to GAAIN alongside CPAD’s blood marker data. The full capabilities of this federated model will be explored in subsequent work.ConclusionAs data sharing becomes increasingly prominent and mature, the partnership between GAAIN and CPAD will yield ever greater benefits and knowledge and will continue to grow and evolve in response to the changing Alzheimer’s disease landscape. As such, it represents a non‐exclusive door for researchers to make use of the data sets and advanced analytical tools and is intended to leverage the unique competencies of these organizations and lead to the continued evolution of bringing meaningful and high‐impact advances in Alzheimer’s disease.
From identification to implementation: state of the art in biomarkers for immune function in CSF and blood
Authors: Teunissen Charlotte E. , Boiten Walter , Hok‐A‐Hin Yanaika S. 
Published: December 2022

AbstractBackgroundBrain immune activation, i.e. activation of microglia and astrocytes, is one of the earliest pathological features of Alzheimer’s disease (AD) pathology. It is likely a consequence of pathological aggregation of amyloid, and can have both detrimental and beneficial effects on multiple aspects of AD pathogenesis. Moreover, recent results show that peripheral immune activation may affect AD pathology and the progression of clinical symptoms as well. Accordingly, biomarkers of innate and adaptive immune activation, as measured in CSF and in blood, have been associated with pathologic status, cognitive decline, as well as increased risk of developing AD. Modulation of central and peripheral immune function offers a potentially promising therapeutic approach for treating AD. As such therapies continue to be developed, there will be an increasing need to noninvasively monitor the neuro‐immune response. If we want to target the brain immune‐cells, microglia or astrocytes, it is essential that we have biomarkers specifically tracing their activation and the concomitant pharmacological response. Developing blood based biomarkers for monitoring neuro‐immune activation is especially challenging given the abundance of immune proteins in blood which are often be indistinguishable from brain‐derived immune proteins.MethodIn this introductory presentation I will discuss recent developments in research to develop immune‐related fluid biomarkers.ResultsRecent proteomics studies and hypothesis‐driven approaches have detected promising changes in immune‐related biomarkers, reflecting either microglia (e.g. complement factors, sTREM2, sAXL) or astrocyte activation (GFAP, YKL‐40), in the CSF of AD patients, even in the earliest disease stages. For example, GFAP levels are increased in cognitively unimpaired persons with AD pathology and have prognostic value for cognitive decline. Using a proteomics approach, we identified clusters of patients with an increased CSF immune biomarker profile, indicating that these would benefit most from immune‐modulating therapy. In addition, recent RNA‐sequencing studies have shown disease related activation and senescence of microglia in both mice and man. We related these expression patterns to CSF proteomic datasets and revealed several potential biomarkers for disease‐associated microglia.ConclusionThere are several interesting advancements in biomarker development for immune function that will be addressed in this FRS.
Suboptimal sleep efficiency and duration predicts rate of accumulation of Aβ‐ Amyloid in cognitively normal older adults
Authors: Pivac Louise N , Brown Belinda M , Sewell Kelsey R , Doecke James D , Villemagne Victor L , Dore Vincent , Weinborn Michael , Sohrabi Hamid R , Gardener Samantha L , Bucks Romola S , Masters Colin L , Rowe Christopher , Martins Ralph N , Rainey‐Smith Stephanie 
Published: December 2022

AbstractBackgroundThere is increasing evidence of a bidirectional relationship between suboptimal sleep and brain Aβ‐amyloid (Aβ) accumulation. Suboptimal sleep is suggested to both result from and contribute to the accumulation of brain Aβ. This study tested whether aspects of self‐reported sleep quality predict the longitudinal accumulation of brain Aβ.MethodLinear mixed effect model analyses were conducted on 192 cognitively normal older adults (M = 73.8 years, SD = 5.8, 51.6% female). These individuals, drawn from the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study of Ageing, had self‐reported sleep characteristics assessed at baseline and positron emission tomography‐determined brain Aβ burden measured over a minimum of three AIBL timepoints (range 33.3‐73.9 months). Analyses included random slopes and intercepts, interaction for apolipoprotein E (APOE) ε4 allele status and time, adjusting for sex and baseline age.ResultSleep duration of <5 hours and sleep efficiency of <65% both significantly forecast the longitudinal trajectory of Aβ accumulation in the whole cohort (sleep duration <5 hrs β = 4.70 ± 1.58, p = 0.003; sleep efficiency <65% β = 2.94 ± 0.91, p = 0.001) and in APOE ε4 non‐carriers (sleep duration <5 hrs β = 4.70 ± 1.53, p = 0.003; sleep efficiency <65% β = 2.90 ± 0.87, p = 0.001) but not in APOE ε4 carriers.ConclusionThese findings indicate a role for self‐reported suboptimal sleep efficiency and duration in the accumulation of Alzheimer's disease neuropathology in cognitively normal individuals. Individuals with suboptimal sleep may benefit from an intervention to improve these sleep parameters to lessen future Alzheimer's risk. Our findings also suggest the potential utility of self‐report screening for sleep efficiency and duration as a predictor of risk of Aβ accumulation.
Impact of off‐target signal on longitudinal FTP quantification
Authors: Baker Suzanne L. , He Mark , Dominguez Pablo Aguilar , Landau Susan M. , Harrison Theresa M. , La Joie Renaud , Ward Tyler J. , Zhuang Kailin , Rabinovici Gil D. , Jagust William J. 
Published: December 2022

AbstractBackgroundDespite off‐target signal, Flortaucipir (FTP) PET is useful for measuring tau in vivo. We previously showed in a cross‐sectional cohort of cognitively unimpaired (CU) amyloid negative (Aβ‐) individuals that the global cortical SUVR is highly correlated to putamen and thalamus SUVR, suggesting that there is substantial off‐target signal in the cortex. Here we explore the impact on off‐target signal on longitudinal FTP quantification.MethodsA group of CUAβ‐ subjects scanned through ADNI and Lawrence Berkeley National Laboratory (LBNL) were used to define corrections for off‐target signal (derivation cohort) which were then applied to a separate longitudinal cohort of CUAβ‐, CUAβ+, and cognitively impaired (CI) Aβ+ subjects (Table 1). SUVRs were calculated using two possible reference regions (inferior cerebellar gray and supratentorial white matter) with or without partial volume correction (PVC). The relationship of off‐target signal between thalamus and cortex was estimated as the regression between thalamus and Braak IV‐VI in CUAβ‐ (figure 1) and then applied in the longitiduinal cohort to temporal meta and Braak ROIs to regress out the influence of the thalamic signal (figure 2). Hedges’ effect size was calculated between diagnosis groups with and without regressing out thalamic signal in the LBNL longitudinal cohort to examine effects in a smaller cohort.ResultsThere was a significant correlation in cross‐sectional CUAβ‐ subjects between thalamus and Braak IV‐VI regardless of reference region and PVC (Figure 1). This correlation increased when comparing annual change between thalamus and Braak IV‐VI in longitudinal CUAβ‐ subjects (Figure 3). There was no significant difference across diagnosis groups in the longitudinal cohort between either baseline thalamus or annual change in thalamus. Correlation between annual change in thalamus and entorhinal cortex or meta temporal ROI were often significant (12/12 for inferior cerebellar gray reference region, 8/12 for white matter reference region, Figure 4). Effect size between diagnosis groups increased as a result of regressing out thalamic signal for all temporal meta ROI comparisons and for CUAβ+>CUAβ‐ and CIAβ+>CUAβ‐ in the entorhinal cortex (Figure 5).ConclusionWhen taking into account off‐target signal, it is possible to improve effect size of longitudinal FTP by ∼12%.
From FINGER to MET‐FINGER: metformin and lifestyle intervention for multimodal precision prevention of dementia
Authors: Kivipelto Miia , Barbera Mariagnese , de Jager Celeste A , Lehtisalo Jenni , Levak Nicholas , Middleton Lefkos T , Price Geraint J , Rydström Anders , Thunborg Charlotta , Mangialasche Francesca , Ngandu Tiia , Solomon Alina 
Published: December 2022

AbstractBackgroundThe positive results of the landmark Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability ‐ FINGER, 2‐year lifestyle‐based intervention and up to 11 years follow‐up ‐, followed by the Multimodal Preventive Trial for Alzheimer’s Disease ‐ MIND‐ADmini, 6‐month lifestyle‐based + medical food, up to 1‐year follow‐up ‐, provided the foundation to advance multimodal trials for dementia prevention within the World‐Wide FINGERS (WW‐FINGERS) network. Innovative trials should involve tailored approaches, combining non‐pharmacological and pharmacological interventions, to optimize interventions´ adherence and efficacy.MethodThrough a drug‐repurposing approach, metformin has been identified as a potential disease‐modifying drug for Alzheimer´s disease (AD) and dementia in subjects without type‐2 diabetes, with a robust pharmacological safety profile. Analysis on genetic factors (APOE, AD Polygenic‐Risk‐Score) and intervention effects from the FINGER trial indicated clear cognitive benefits in participants with genetic susceptibility to AD and dementia. Feasibility data (adherence, retention rate) from FINGER and MIND‐ADmini, including extended follow‐up data analysis, have informed the upgrade of the lifestyle‐based intervention implemented in MET‐FINGER.ResultAn update on the trial status will be presented. The MET‐FINGER phase‐IIb trial population includes an APOE‐enriched population of 600 subjects aged 60‐79 years, with increased risk of dementia due to modifiable risk factors. Participants are recruited in three countries (Finland, Sweden, United Kingdom), and randomized to either an upgraded FINGER lifestyle‐based intervention, alone or in association with metformin (up to 2000mg/day), or the reference group, which follows a self‐guided lifestyle program. MET‐FINGER trial outcomes are harmonized with WW‐FINGERS guidelines, and include cognition and biological measures to detect disease‐modifying effects of metformin.ConclusionMET‐FINGER pioneering trial leverages and expands on successful experiences from WW‐FINGERS core trials, embodying the next generation of clinical trials for dementia risk reduction and prevention within the framework of precision prevention. The MET‐FINGER trial protocol is the first in its kind, providing the basis for forthcoming trials combining non‐pharmacological and pharmacological interventions. MET‐FINGER results can inform AD drug development and reduce intervention failure rate of new trials.
Age‐at‐Onset and APOE Related Heterogeneity in the Clinical, Plasma Biomarker, and Neuropathologic Presentation of Alzheimer’s Disease (AD)
Authors: Smirnov Denis S. , Salmon David P. , Galasko Douglas R. , Goodwill Vanessa OF , Hansen Lawrence A , Edland Steven D. , Hiniker Annie OF 
Published: December 2022

AbstractBackgroundPatients with sporadic early‐onset AD (EOAD) show less pronounced memory impairment, faster cognitive decline, and greater likelihood of focal cortical syndromes than those with later onset (LOAD), while often lacking the APOE ε4 allele classically associated with younger onset. This heterogeneity could arise from differences in the distribution of AD pathology or presence of concomitant pathologies.MethodWe examined clinical and cognitive heterogeneity in relation to age‐at‐onset, APOE genotype, and concomitant neuropathology (Lewy body, TDP‐43, and vascular) in patients with autopsy‐confirmed severe AD from the National Alzheimer’s Coordinating Center database (N=1750). In patients from the UCSD Alzheimer’s Disease Research Center (N=121) we examined how age‐related heterogeneity relates to differences in distribution of neurofibrillary tangle (NFT) pathology and plasma biomarkers of AD.ResultAge‐at‐onset distribution in APOE ε4‐ patients was bimodal with best separation at age 63. Patients with EOAD (age ≤63) had faster cognitive and functional decline than LOAD regardless of APOE genotype; and were more likely to present with nonmemory cognitive complaints (OR=5.56, 99%CI: 3.32‐9.39), show greater executive dysfunction (β=2.59 SD, 99%CI: 2.10‐3.09), and receive a non‐AD clinical diagnosis (OR=1.54, 99%CI: 1.19‐1.97), particularly for ε4‐ EOAD (p’s<0.01, APOE × age‐at‐onset interactions). Despite this atypicality, groups did not differ in their high rates of AD biomarker positivity, and ε4‐ EOAD patients were most likely to have pure AD without concomitant pathology. TDP‐43 (OR=0.37; 99%CI: 0.18–0.72) and microvascular (OR=0.65; 99%CI: 0.47–0.88) pathology were less common in EOAD, while Lewy pathology was more common in ε4+ (OR=1.48; 99% CI, 1.02–2.17). Midfrontal NFT density was higher in EOAD than LOAD (β= 0.87 SD, 99%CI: 0.44‐1.29). Greater hippocampal NFT density was associated with APOE ε4+ (β=0.49 SD, 99%CI: 0.02‐0.96). Mediation analyses suggest that differences in midfrontal/hippocampal NFT ratio, rather than concomitant neuropathology, mediate the atypical cognitive profiles and decline of EOAD (p<0.01).ConclusionA relatively more neocortical NFT distribution, rather than concomitant non‐AD pathology, accounts for the atypical, non‐memory dominant clinical presentation and rapid progression in EOAD. This suggests that selective cortical vulnerability may explain the earlier‐than‐expected onset and atypical profile in these individuals without the APOE ε4 risk allele.
Verbal Fluency Performance in Older Adults on a Novel Computerized Test Battery
Authors: Baldo Juliana , Chok Jas M. , Lwi Sandy J. , Schendel Krista , Herron Tim , Curran Brian , Hall Kathleen , Blank Michael , Williams Garrett , Geraci Kristin , Pebler Peter , Sucich Gabriel , Woods David L. 
Published: December 2022

AbstractBackgroundPrevious studies have suggested that impaired verbal fluency is a sensitive measure of semantic impairment in dementia that appears several years prior to diagnosis. This early impairment has been associated with neural changes and altered activation in key regions of bilateral temporal and frontal cortex.MethodIn the current study, we report verbal fluency findings from a large group of healthy older adults (n = 270, ages 56–89) on a new computerized neuropsychological battery that utilizes advanced automatic speech recognition and transcription software. In an ongoing longitudinal study, we are testing healthy older participants on the California Cognitive Assessment Battery (CCAB) every six months for three years, in order to track changes in cognition associated with aging, including evolution of mild cognitive impairment (MCI) and dementia. The CCAB includes six semantic categories: Animals, vegetables, countries, sports, furniture, and fruit. Participants are given one minute per category to generate as many exemplars as possible. Online speech‐to‐text transcription was implemented using consensus automatic speech recognition (CASR) software, which utilizes six different speech recognition systems and chooses the most common transcription.ResultOverall verbal fluency performance was significantly predicted by age. However, this effect depended on the semantic category. For example, females and more educated participants generated more exemplars for the category “countries.”ConclusionFindings from the current study enhance understanding of aging effects on verbal fluency and serve as a critical baseline against which to monitor alterations in performance that coincide with the development of MCI and dementia in older adults.