bluuebunny/crossref_metadata_2025_split huggingface dataset:
Measures of Voice Quality as Indicators of Mild Cognitive Impairment
Authors: Chen Liu , Dodge Hiroko H , Asgari Meysam 
Published: December 2022

AbstractBackgroundNoticeable decline in communication skills of patients with Alzheimer’s disease are often observed in the early stages of the disease, years before the dementia emerges. Prior literature has identified language abnormalities such as decline of linguistic ability or syntactic deficits influenced by the cognitive impairment. While language‐related measures are able to characterize spoken words of speech samples, they are inherently incapable of quantifying the voice‐related properties such as emotion or voice quality. Focusing on automatically derived voice‐based biomarkers that reflect the voicing quality of speakers, our objective is to explore the usefulness of these measures at detecting mild cognitive impairment (MCI) as early indicators of MCI in machine learning models. We hypothesize that cognitive impairment influences the individual’s voice quality resulting in an abnormal periodicity in speech often known as vocal dysperiodicity.MethodWe analyzed acoustic properties of audio‐recordings of Craft Story test administered to 107 subjects (50 MCI and 57 healthy controls, mean age 81 yrs) from a recently completed randomized control trial aimed to enhance cognitive reserve by increasing social interactions using video‐chats (www.I-conect.org). In the Craft Story test, the examiner reads a short story to the participant who is asked to retell the story first immediately after hearing the story and next retell it after a 20‐minute delay. Using cepstral analysis method, we extracted cepstrum from both immediate recall (IR) and delayed recall (DR) tests, and utilized statistical measures to represent their acoustic characteristics. We also used recursive feature elimination to only select informative measures and utilized the support vector machine for building classification models.ResultCepstrum‐based Measures derived from the IR test achieved higher predictive ability than those derived from the DR test. Combining the measures derived from both IR and DR tests increased the classification results and achieved ROC AUC of 98.83% and 97.09% on gender‐balanced models trained on males and females, respectively.ConclusionOur results indicate that voice‐based measures are capable of detecting MCI from audio samples of the Craft Story capitalizing their potential use in automatic screening and telemonitoring of the disorder in research, clinical trials and ultimately clinical practice.
Late‐life behavioral disorder: when is it FTD?
Authors: de Boer Sterre C.M. , Gossink Flora T , Krudop Welmoed A. , Vijverberg Everard G.B. , Pijnenburg Yolande A.L. , Dols Annemiek 
Published: December 2022

AbstractBackgroundDistinguishing the behavioural variant of Frontotemporal Dementia (bvFTD) from late‐onset primary psychiatric diseases (PPD) remains challenging with the lack of robust biomarkers for either disorder. Optimal duration of follow‐up of potential FTD cases remains elusive. Our aim was to determine whether a follow up duration of 5 years has added value compared to 2 years in the late‐onset frontal lobe syndrome. Second, we defined which clinical characteristics are valuable for diagnostic precision.MethodClinical outcomes of participants of the Late‐Onset frontal lobe (LOF) study were retrieved from 137 patients at least 5 years after baseline visit (T5). Endpoint diagnosis of bvFTD, PPD or Other Neurological Disorders (OND) were obtained. Baseline demographics and measures were compared between the T5 diagnosis groups. Clinical records of cases that switched diagnosis during follow‐up were assessed.ResultIn the 137 patients that were included in the study, the final diagnoses at T5 were bvFTD in 24.1% (n = 33, n = 29 probable bvFTD and n = 4 definite bvFTD), PPD 39.4% (n = 54), OND 33.6% (n = 46) and unknown 2.9% (n = 4). Between T2 and T5, 8 patients switched diagnosis compared to 29 patients during the first 2 years after inclusion (figure 1). Patients with a T2 diagnosis of probable bvFTD with an abnormal FDG‐PET whilst having normal structural imaging results (MRI) were prone to a diagnostic switch between T2 and T5.ConclusionA follow up duration of two years is sufficient for patients with a late‐onset frontal lobe syndrome to distinguish bvFTD from PPD on the condition that in cases with non‐supporting structural imaging results after two years further diagnostic work‐up is done.
Understanding the impact of PET amyloid cutpoints on prognostic modelling for cognitively normal individuals
Authors: Goudey Benjamin , Fedyashov Victor , Fripp Jurgen , Rowe Christopher , Maruff Paul , Masters Colin L 
Published: December 2022

AbstractBackgroundAmyloid beta (Aβ), measured using PET imaging, is a key biomarker for Alzheimer’s disease (AD) with the consequences of abnormally high Aβ levels (Aβ+) well‐established from prospective research cohorts. A critical question is whether the prognostic capabilities of Aβ can be improved further, for example by refinement of optimal criteria for abnormality.To date, existing studies have explored such issues using association analyses, which may not reflect performance in prognostic settings due to potential overfitting. Here, the impact of different Aβ cut‐points is determined in a cross‐validation framework, providing performance estimates on data from individuals that were not used for model construction, which better reflects realworld prognostic application. Using data for cognitively normal individuals (CN) from ADNI and AIBL, we estimate time to i) MCI or AD diagnosis and ii) cognitive deficit, defined as MMSE≤26.MethodWe analyse measurements from 344 and 748 CN from ADNI and AIBL respectively who have available PET Aβ scans. PET Aβ SUVRs were transformed to the centiloid scale (CL). For each task, the Aβ cut‐point is varied from ‐10 to 65CL and Cox models are constructed within 10 repeats of 10‐fold cross‐validation. From the resulting 100 models, performance is quantified as the median concordance index (i.e. Harrell’s C).ResultDetails of the two cohorts are shown in Table 1. Across both AIBL and ADNI, a PET only model shows robust performance for cut‐points within a wide range (5 and 50CL) for predicting either time to diagnosis cognitive deficit (Figure 1), with performance dropping rapidly outside this range. When additional covariates are included 2, we see maximal performance for lower cutpoints (5‐20CL) for diagnosis in ADNI and cognitive deficit in ADNI, while remaining tasks show improved performance with higher cut‐point ranges (20‐50CL). Trends in cut‐point are consistent regardless of covariates. Leaving Aβ as a continuous variable yields near‐optimal performance across all tasks.ConclusionOur results suggest that within a range (5 and 50CL), prognostic performance is robust to the choice of cut‐point for Aβ, suggesting further refinement of a single cut‐point within this range may not yield substantial improvements for prognostic tasks for CN individuals.
Comparison of Plasma‐based Biomarkers and Cognitive Assessments in predicting progression of MCI to Dementia
Authors: Nallapu Bhargav Teja , Petersen Kellen K. , Lipton Richard B. , Harvey Danielle J. , Ezzati Ali 
Published: December 2022

AbstractBackgroundWe evaluated the predictive value of phospho‐tau181 (ptau181) and neurofilament light (NfL) in predicting disease progression from amnestic mild cognitive impairment stage (aMCI) to Dementia in models including demographic data and neuropsychological test results.MethodParticipants were 248 Cognitively Normal (CN), 325 MCI, and 91 individuals with dementia due to AD confirmed with amyloid PET from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) who had at least one follow‐up visit, and measures of plasma biomarkers (ptau181 and NfL) were included. We examined the separate and joint predictive value of baseline plasma biomarkers and neuropsychological tests (RAVLT: Rey Auditory Verbal Learning Test; ADAS‐Cog: Alzheimer’s Disease Assessment Scale–Cognitive Subscale; TRA‐B: Trail Making Test – Part B; FAQ: Functional Activity Questionnaire) in estimating disease progression from MCI to Dementia. Random Forests machine learning models were used for the classification. Data from CN and AD participants were used for training the models and trained models were used in MCI group to identify those more likely to progress to Dementia. Predictive performances of these models were assessed using longitudinal outcomes at 2 years.ResultParticipants were on average 71.9 yr. old (SD=6.9) and 49.6% female (Table 1). Addition of plasma biomarkers to the model with demographics and APOE4 (base model) improved predictive performance of models in prediction of conversion from MCI to dementia at 2 years (Sensitivity=53.7%, Specificity=81.5%, AUC=0.68). Models that included cognitive tests in addition to the measures in the base model had higher performance in comparison with that only included biomarkers (Sensitivity=55.6%, Specificity=91.5%, AUC=0.74). Best predictive performance was achieved when cognitive scores and plasma biomarkers were both included in the models (Sensitivity=63.0%, Specificity=91.5%, AUC=0.77, Table 2). In the MCI‐subgroup who were amyloid positive (Aβ+) at baseline, similar pattern of results was observed (Table 3).ConclusionIn persons with MCI, plasma biomarkers of ptau181and NFL can improve our ability to predict disease progression. Concurrent use of plasma biomarkers and cognitive scores improve this predictive ability. This work needs to be extended to various samples and a broader set of plasma biomarkers and neuropsychological tests.
TAR DNA‐binding protein 43 (TDP‐43) contributes to the mismatch between medial temporal volumes and flortaucipir uptake in elderly patients with Alzheimer’s disease neuropathologic changes
Authors: Carlos Arenn F. , Tosakulwong Nirubol , Weigand Stephen D , Senjem Matthew L. , Schwarz Christopher G. , Knopman David S. , Boeve Bradley F. , Petersen Ronald C. , Nguyen Aivi T. , Reichard Ross R. , Dickson Dennis W. , Jack Clifford R. , Lowe Val J. , Whitwell Jennifer L , Josephs Keith A 
Published: December 2022

AbstractBackgroundWe previously demonstrated that Alzheimer’s disease (AD) patients ≥70 y/o tend to have smaller medial temporal volumes in the presence of relatively less medial temporal 18F‐flortaucipir‐PET uptake compared to young AD patients. TAR DNA‐binding protein 43 (TDP‐43) is frequently found in the brains of AD patients and is associated with old age and medial temporal atrophy. In this study, we aimed to determine whether TDP‐43 could be contributing to the mismatch between medial temporal volumes and flortaucipir‐PET uptake in older AD patients.MethodWe identified 77 participants (median age at death 79 years [range: 51‐99]) who had been enrolled into the Mayo Clinic Alzheimer’s Disease Research Center or Neurodegenerative Research Group (NRG), had undergone flortaucipir‐PET and volumetric MRI scans close to death, and autopsy confirmed AD neuropathological changes, as well as TDP‐43 assessments. Grey matter volumes and flortaucipir standard uptake value ratios (SUVRs) were calculated for hippocampus, amygdala, entorhinal, inferior temporal, and midfrontal cortices. We used bivariate‐response linear regression modelling, adjusting for Braak neurofibrillary tangle stage, to estimate the effect of TDP‐43 and age on gray matter volume and flortaucipir uptake, and tested whether TDP‐43 effect sizes varied according to age.ResultAfter accounting for Braak stage, older individuals tended to have significantly lower flortaucipir uptake across regions and lower hippocampal and entorhinal cortex (ERC) volumes. TDP‐43 was associated with reduced hippocampus and ERC volumes but was unrelated to flortaucipir SUVRs (Figure 1). Notably, the TDP‐43 effect size for the hippocampus and ERC was largely consistent across age, i.e., at any age, TDP‐43‐positive participants had significantly smaller hippocampal (∼12% decrease) and ERC (13% decrease) volumes than TDP‐43‐negative participants. However, at older ages, the mean volumes in the whole cohort and, more evidently, in those with dementia moved closer towards the mean of those with TDP‐43 (Figure 1), likely attributable to the increasing frequency of TDP‐43 with age.ConclusionTDP‐43 is a major contributor to the mismatch between flortaucipir uptake and medial temporal lobe volumes in older AD patients where lower medial temporal regional volumes are observed in the presence of less flortaucipir uptake.
Successful Recruitment of Vietnamese Americans into the Asian Cohort for Alzheimer’s Disease (ACAD) Pilot Study at UC San Diego
Authors: Lu Anna T. , Park Van Ta , Peavy Guerry M. , Evans Carol , Vo Nam Khue , Tzuang Marian , Revta Carolyn , Nguyen Diep , Nguyen Luan , Nam Percival , Nguyen Khai H. , Feldman Howard H. ,   ACAD Consortium
Published: December 2022

AbstractBackgroundVietnamese Americans represent the 4th largest Asian American population in the United States with about 2.3 million people nationwide, yet research and literature on the recruitment of Vietnamese Americans in dementia research is limited. In addition to the cultural/linguistic barriers, the lack of Vietnamese language recruitment materials and cognitive assessments has made it difficult to enroll this underserved population into Alzheimer’s disease (AD) research.MethodTo facilitate the recruitment of Vietnamese Americans aged 60 and older into the Asian Cohort for Alzheimer’s Disease (ACAD) pilot study, recruitment methods were established to address physical, technological, linguistic, experiential (protection of identifiable data), and cultural challenges (war‐related trauma, dementia stigma). Localized data collection forms and outreach materials were developed and appropriately translated into Vietnamese with socio‐cultural/linguistic sensitivity. Trusted community advisors/partners and bilingual/bicultural Vietnamese‐English research staff were central to the successful recruitment of Vietnamese American participants. Remote assessments were conducted using a customized electronic tablet platform (Claris Companion
®
) via Zoom that allowed consent, cognitive interviews, and medical exams. Saliva samples were collected remotely with prepaid envelopes to mail‐in samples.ResultRemote assessments were well accepted by participants. This was critical in conducting this study given the technological, safety (during COVID‐19 pandemic), and socio‐economic limitations. Although time consuming on the front‐end, our methods resulted in the successful recruitment of the target sample of 25 participants, with no dropouts. ACAD community advisory board members referred 56% of participants, enrolled study participants referred 40%, and other sources, 4%. Mean age of participants was 73 years (median: 76; range: 60‐91), 52% female, and 100% foreign‐born. Most (80%) were interviewed in Vietnamese. Successful recruitment strategies included: (1) development of culturally/linguistically appropriate recruitment materials, (2) community connected bilingual/bicultural (Vietnamese‐English) research staff, (3) responsiveness to identified physical, technological, and language/cultural recruitment challenges, (4) community partnerships and participant referrals, and (5) appropriate technology/device for remote assessments.ConclusionTo facilitate successful research recruitment and participation in the ACAD pilot study and AD‐related research in the Vietnamese American community, recruitment methods and strategies require a thorough understanding of the specific linguistic, cultural, access, and historical/experiential challenges experienced in this population.
Amyloid burden is not associated with brain atrophy in the oldest‐old
Authors: Woodworth Davis C. , Faramarzi Sina , Greenia Dana E. , Kawas Claudia H. , Sajjadi S. Ahmad 
Published: December 2022

AbstractBackgroundAlzheimer’s disease (AD) is an important contributor to brain atrophy in older individuals. However, in the oldest‐old (≥85 years), where multiple pathologies are common and contribute to brain atrophy, the relationship between AD and brain atrophy may be attenuated. Here we examined associations between amyloid burden and brain volumes in oldest‐old participants and compared this to younger‐old.MethodWe used data from participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and The 90+ Study who had both MRI and florbetapir amyloid PET scans. We examined the association between volumes of brain structures (from FreeSurfer‐v7) and amyloid standardized uptake value ratios (SUVr) using multiple linear regressions adjusted for age, sex, and education. We performed two different analyses, with and without adjusting for cognitive status. For ADNI we used the summary cortical region SUVr normalized to composite white‐matter/brainstem/cerebellum reference, and for The 90+ Study we used a precuneus/posterior‐cingulate SUVr normalized to eroded white matter. Oldest‐old (≥85 years) data were obtained from The 90+ Study (N=206) and ADNI (N=178) whereas younger‐old (55 to 85 years) data (N=1134) came exclusively from the ADNI cohort. We display semi‐partial correlations (sr) as measures of effect size, thresholded at Puncorrected<0.05.ResultTable‐1 summarizes participant characteristics for ADNI and The 90+ Study. In younger‐old participants amyloid was related to atrophy of multiple regions (sr∼‐0.1), but in the oldest‐old the opposite was true, where amyloid was weakly associated with increased volume in some regions for both ADNI and The 90+ Study (Figure‐1). In the model without adjusting for cognitive status, the atrophic effect of amyloid on brain volumes was more pronounced in the younger‐old (sr∼‐0.3), while amyloid remained unrelated to brain atrophy in the oldest‐old (Figure‐2). Lastly, we further illustrate this effect by plotting volumes for two regions, the hippocampus and inferior temporal, against SUVrs for the different groups (Figure‐3).ConclusionWe found that in the oldest‐old amyloid burden was not related to brain atrophy. This finding underscores the importance of non‐AD pathologies and/or general aging processes compared with AD pathology in the oldest‐old.
Qualification of classical biomarkers for Alzheimer’s Disease in a multiplex format in blood using a new technology
Authors: Vanderstichele Hugo Marcel , Yu Heng , Juang Alice , Davis Pepe , Galasko Douglas R. , Carbonell Luis , Verdooner Steven R , Anderson Leyla 
Published: December 2022

AbstractBackgroundProtein biomarker profiling in biological fluids will soon become an established procedure in clinical diagnosis of neurodegenerative disease subtypes. Observed differences between technology platforms in performances can be attributed to technology principles and their development status, selection and characterization of antibody combinations, reproducibility of test results, and pre‐analytical factors and processing of samples. Combination of advantages of emerging technologies, together with availability of precision‐qualified assay formats and innovative sample processing procedures, will create additional possibilities for individual patient management using clinically validated protein biomarker panels.MethodThe Giant Magneto Resistance (GMR) technology platform was used to develop multiplex immunoassays for different biomarker proteoforms in blood (e.g., Aß, (phospho)Tau, NF‐L, and GFAP).ResultWe have developed and optimized performance of multiplex assays using a Design of Experiment (DOE) approach to generate reproducible results. The selectivity and specificity of each antibody combination was confirmed with multiplex assay designs. We have used clinical samples instead of calibrators in buffer to shorten the development time. Process optimization through DOE was done with biotinylated peptides. After optimization, reproducibility was improved by approximately 4‐fold to < 5% CV for each analyte. We have included 3D6 as a capture antibody on the printed circuit boards (PCBs), while analyzing Aß1‐42 & Aß1‐40 using 21F12 and 2G3 as capture antibodies, respectively, to identify the presence of Aß oligomers. PCB selection, printing conditions including environmental factors, buffer selections, and conjugation process were optimized in a stepwise manner while frequently checking biomarker levels in clinical samples. Sample processing is part of our roadmap to generate clinically valuable results. The diagnostic potential for each assay format to identify amyloidopathy in an early phase of the disease will be presented using blood samples from cognitively normal, MCI, and AD subjects, characterized by CSF biomarker profiles and/or amyloid PET status.ConclusionThe GMR technology allows screening of performance of antibodies in multiplex formats, shortening the assay development time. The biomarker panels and novel methods for analysis are uniquely positioned to help the healthcare community. This study was funded by Alzheimer’s Drug Discovery Foundation's Diagnostics Accelerator (DxA) Program.
Differential associations of amyloid and tau (from PET) with brain volumes (from MRI) by cognitive status
Authors: Woodworth Davis C. , Corrada Maria M. , Kawas Claudia H. , Sajjadi S. Ahmad 
Published: December 2022

AbstractBackgroundAlzheimer’s disease pathology (AD) is an important cause of neurodegeneration and brain atrophy. Non‐AD mechanisms can also cause neurodegeneration alone or in conjunction with AD. Harboring combination of AD and non‐AD pathologies leads to greater cognitive impairment and dementia. Here, we examined the hypothesis that the association of amyloid and tau burden from positron emission tomography (PET) with brain volumes from magnetic resonance imaging (MRI) is weaker in those with dementia compared to those with mild cognitive impairment (MCI) or normal cognition.MethodWe used data from participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) who had either florbetapir amyloid PET (N=1320) of flortaucipir tau PET (N=833, 535 of whom had both). We examined the association between volumes of brain structures (FreeSurfer‐v7) and standardized uptake value ratios (SUVr) for amyloid (summary cortical region, normalized to whole cerebellum) and tau (meta‐temporal region, normalized to inferior cerebellar gray matter) separately, using linear regressions adjusting for age, sex, and education. We report results in overall groups and by cognitive status (normal, MCI, dementia). We display semi‐partial correlations (sr, thresholded at Puncorrected<0.05), as measures of effect size.Result
Table‐1 summarizes participant characteristics. The effect of amyloid on brain volumes was modest in the overall cohort, with associations strongest for inferior/middle temporal, inferior parietal, entorhinal, hippocampus, and amygdala regions (sr∼‐0.2, P<0.001 all). In those with dementia, however, there were no distinct associations between amyloid and brain atrophy (Figure‐1). For tau, associations in the overall cohort were similarly strongest for inferior/middle temporal, inferior parietal, entorhinal, hippocampus, and amygdala regions (sr∼‐0.3, P<0.001 all). In those with dementia, associations remained strong for inferior/middle temporal regions (sr∼‐0.3, P<0.001) but tau was not associated with hippocampus volumes (left, sr=‐0.05, P=0.6; right, sr=‐0.12, P=0.14, Figure‐2); this divergence is further highlighted in select regions (inferior temporal and hippocampus) in Figure‐3.ConclusionWe found that the relationship between amyloid and tau burden and atrophy was stronger in the overall cohort than in participants with dementia. This was particularly the case for medial temporal lobe structures. The effect of non‐AD pathologies on medial temporal lobe atrophy is a potential explanation for the observed results.