bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluating NODDI‐based biomarkers of Alzheimer’s disease
Authors: Reina Julio E Villalon , Nir Talia M , Thomopoulos Sophia I , Salminen Lauren , Jahanshad Neda , Thompson Paul 
Published: December 2020

AbstractBackgroundMulti‐compartment (MC) diffusion MRI (dMRI) models are increasingly used to evaluate brain microstructure beyond the classic diffusion tensor model (DTI). The ability to separate the neural tissue into distinct compartments, i.e., intracellular volume fraction (ICVF), extracellular volume fraction (ECVF) and free‐water (FW), offers novel biomarkers for early detection of cognitive decline and Alzheimer’s disease. The most commonly used MC model used is Neurite Orientation Dispersion and Density Imaging (NODDI). Recent evidence shows that the voxel‐wise volume fractions compartment can be wrongly estimated if the same T2 relaxation time is assumed for the gray matter (GM), white matter (WM) and corticospinal fluid (CSF). To more accurately estimate the tissue compartments, here we use a novel method recently proposed by the DMIPY package (https://github.com/AthenaEPI/dmipy), that initially estimates a response function for each brain region (GM, WM and CSF) and uses these to better estimate the ICVF and FW compartments.MethodWe analyzed ADNI‐3 multi‐shell diffusion MRI (dMRI) data to estimate NODDI measures and to assess their ability to predict mild cognitive impairment (MCI). For each measure, we used regularized logistic regression to find cohesive clusters of brain tissue that contribute to correct classification. Data included 34 participants with MCI (mean age: 75.6±7.8yrs, 22M/9F) and 74 cognitively normal individuals (CN; mean age= 74.1±7.1yrs; 25M/39F). Siemens Prisma 3T multi‐shell dMRI data included 13 b0 images, 6 b=500, 48 b=1000, and 60 b=2000 s/mm2 diffusion weighted 32 images (voxel size=2mm, TE=71ms, TR=3300ms, δ=13.6ms, Δ=35ms). We fit three types of NODDI: the classic NODDI model, the NODDI model with a GM tissue response function, and with a WM response function.ResultThe NODDI model with the GM response function increased the accuracy of the logistic regression classification (see Table 1). The highest recall was achieved by the neurite dispersion index (ODI) in all three models.ConclusionAdding specific response functions for the GM and WM in NODDI improved classification accuracies and recall of MCI subjects. The distribution of the classifying regions was different across the three types of NODDI (Figures 1, 2 and 3).
Relationship between visual alterations and neuropsychiatric symptoms in posterior cortical atrophy
Authors: Lipa‐Pari Karol Melissa , Ibarrola Mariana Longoria , Sosa‐Ortiz Ana Luisa , Acosta‐Castillo Gilberto Isaac 
Published: December 2020

AbstractBackgroundPosterior cortical atrophy (PCA) is a cognitive behavioral syndrome characterized by visual and cognitive alterations that is associated with neurodegeneration of the posterior cerebral cortex (occipital, parietal and posterior temporal). 95% of these cases also present neuropsychiatric symptoms (NPS), as anxiety, depression and irritability being the most reported. Due to the early presence of visual disturbances and the dysfunction that they generate, we considered that they could influence the presentation of NPS. The objective of the study was: Describe visual disturbances, neuropsychiatric symptoms and their association in patients with PCA.MethodObservational, descriptive and cross‐sectional study. Information was obtained from the clinical, neuro‐ophthalmological and neuropsychological evaluation of clinical records of patients with PCA, treated at the Cognitive Aging and Dementia Clinic of the National Institute of Neurology and Neurosurgery (Mexico), from January 2015 to September 2019. Sociodemographic and clinical variables of interest (visual and non‐visual alterations, NPS and baseline clinimetric measures) were analyzed. The association between visual disturbances and NPS was estimated by a bivariate analysis.ResultThe most frequent visual disturbances were difficulty reading (100%) and the visual agnosia (84.6%). The non‐visual acalculia (92.3%) and agraphia (76.9) and the NPS: apathy (76.9%) and irritability (69.2%) All patients had at least one NPS. In the bivariate analysis, a finding with clinical interest was that all patients with prosopagnosia presented anxiety, but only showed a tendency (p=0.06). It was also found that patients with visual agnosia and achromatopsia had less insomnia (p=0.03 and 0.04, respectively)ConclusionIt was found that the presence of visual agnosia and achromatopsia were associated with the lower presence of insomnia, however, we found no pathophysiological explanation for this relationship. In accordance with our results, an association between prosopagnosia and chronic anxiety has been described, however, in our analysis, this association was marginal, perhaps due to the small number of the sample.
Quantitative neuroimaging of volume loss in persons with Alzheimer’s dementia
Authors: Meysami Somayeh , Raji Cyrus A. , Merrill David A. , Porter Verna R. , Mendez Mario F. 
Published: December 2020

AbstractBackgroundAutomated brain volumetric software programs are FDA‐cleared for clinical use. However, these programs have not yet been tested in their diagnostic utility, specifically in the clinical setting of AD.MethodClinically acquired volumetric MPRAGE brain MRI scans of 79 persons with a clinical diagnosis of AD (age 67.2 ± 11 years) were retrospectively reviewed and then analyzed using Neuroreader, an FDA‐cleared software program. In this sample, 54% were women. The volumes of 45 brain structures were calculated and statistically adjusted for head size, age, and gender. Regional volumes were then compared to a normative database to compute Z‐scores and percentiles for evaluating regionally specific atrophy.ResultPersons with clinically diagnosed AD demonstrated a pattern of diffuse, global atrophy with a temporal and mesial temporal predilection. Despite exhibiting the greatest magnitude of volume loss, regions such as the hippocampus, amygdala, putamen, ventral diencephalon, left parietal lobe, and temporal lobe were in the 30th‐40th percentile compared to the normative database. These values are higher than the vendor recommended cutoff of 25th percentile for determining abnormally low brain volume.ConclusionData driven quantitative thresholds for volumetrically evaluated brain atrophy on MRI scans do not currently exist in clinical practice. In our study, atrophy in patients with clinically diagnosed AD may be detected with automated brain volumetric software programs. In this study sample, regions demonstrating atrophy were in the 30th‐40th percentile as compared to patients in a healthy normative database. As such, these regions would not have been considered “abnormal” in standard cut‐offs at the 25th percentile. We suggest that regions in the 26th‐40th percentile may therefore be atrophic and future studies with larger samples should further refine these thresholds.
Alzheimer‐associated circular RNA circHOMER1 regulates synaptic gene expression and cognition
Authors: Dube Umber , Harari Oscar , Zimmerman Amber , Hafez Alexander , Brigman Jonathan , Cruchaga Carlos , Mellios Nikolaos , Benitez Bruno A. 
Published: December 2020

AbstractBackgroundCircular RNAs (circRNAs) are an unexplored and novel type of non‐coding RNA generated by backsplicing and covalent joining of exons and introns, which are rarely translated into protein. Recent studies have demonstrated that circRNAs are enriched in the brain and differentially altered during aging. However, the mechanism of action of most brain‐expressed circRNAs remains elusive and very little is known about their relevance in neurological disorders. Here, we conducted a circular‐transcriptome‐wide analysis of the differential expression of circRNA in AD cases and their correlation with clinical and neuropathological AD severity measures.MethodsWe generated paired‐end 151‐nt, paired‐end, rRNA‐depleted, and RNA sequencing data from frozen parietal cortex tissue from 83 AD patients and 13 controls. We aligned this data to the human reference genome (GRCh38) using STAR and called circRNAs using DCC software. circRNA differential expression (DE) analysis was performed using DESeq2 software. The superior temporal cortex RNA sequencing data from the Mount Sinai Brain Bank (195 individuals) was used as replication cohort. We investigated the association of human cortical circRNA expression with AD case–control status, Clinical dementia rating (CDR) and Braak score.ResultsA total of 3,146 well‐supported circRNAs common to both the discovery and the replication datasets were analyzed. Overall, 164 circRNAs were significantly associated with at least one AD trait in the meta‐analyses. Among them, three circRNAs, circHOMER1, circKCNN2 and circMAN2A1, were significantly associated with CDR score, Braak score and case‐control status. CircHOMER1, a neuronal‐enriched circRNA abundantly expressed in the brain explained 14.3% and 5.8% of the CDR variation in both cohorts. In vivo knockdown of circHomer1 in the mouse frontal cortex alters synaptic gene expression and disrupts cognitive flexibility. Moreover, circHomer1 can bind to an RNA‐binding protein that can increase its synaptic localization. Lastly, ongoing work is aimed at understanding how circHomer1 can affect synaptic gene expression and synaptic transmission.ConclusionMultiple circRNAs are differentially expressed in AD brain and are associated with clinical and neuropathological traits. At least one AD‐associated circRNA regulates synaptic gene expression and cognition. circRNAs may yield novel biomarkers or therapeutic targets for AD.
Different effects of metabolic syndrome according to dementia type
Authors: Kim Yeo Jin 
Published: December 2020

AbstractBackgroundMetabolic syndrome is a cluster of components that reflect overnutrition, which including hypertension, diabetes, dyslipidemia and obesity. Although metabolic syndrome is a risk factor of cardiovascular disease and type 2 diabetes mellitus, the association between metabolic syndrome and dementia has remained controversial. Using nationwide population cohort data, we investigated the association between metabolic syndrome and dementia in Gangwon province in South Korea.MethodWe analyzed data of Gangwon province for 127,666 individuals aged more than 60 years in the (Korean) National Health Insurance Service (NHIS) cohort who were enrolled between January 1, 2009 and December 31, 2009. Then we investigated the presence of dementia in 2017. We classified type of dementia as Alzheimer’s disease dementia (ADD) and vascular dementia (VD). Dementia including ADD and VD was defined according to criteria in the International Classification of Disease, Tenth Revision, Clinical Modification (ICD‐10‐CM) codes.ResultMetabolic syndrome was associated with ADD, while it was not associated with VD. Previous stroke was associated with both ADD and VD. Rather than metabolic syndrome, VD was associated with smoking. All five components of metabolic syndrome associated with ADD, independently. However, among components of metabolic syndrome, only high glucose associated with VD. In ADD, it was most relevant when there were three of the five components of metabolic syndrome, while in VD, it was only relevant when there were all five components of metabolic syndrome.ConclusionMetabolic syndrome was associated with ADD, while was not associated with VD. VD was associated only with several risk factors that could affect the vascular state rather than metabolic syndrome. We suggested that the effect of metabolic syndrome on dementia would be different depending on the type of dementia.
Decision support models for Alzheimer’s disease and mild cognitive impairment classification based on features extracted from MRI and FDG PET
Authors: Luo Weidong , Ulug Aziz M , Magda Sebastian , Le Linh , Haxton Robert K. , Melton Robert , Miao Yu , Airriess Chris 
Published: December 2020

AbstractBackgroundAccurate diagnosis in early Alzheimers’ disease (AD) and Mild Cognitive Impairment (MCI) is critical for patients to start treatments. In AD and MCI patients, there is a high correlation between brain atrophy and metabolism and physical and cognitive impairments. Here we evaluate the performance of machine learning models for identifying AD and MCI subjects based on features extracted from MR and FDG PET imaging.MethodThe ADNI database was used in this study. 201 AD subjects: 80f(58‐88yo), 121m(56‐90yo); 427 MCI subjects: 163f(56‐90yo), 263m(59‐93yo); and 326 NL subjects: 169f(60‐95yo), 157m(66‐91yo) were included. T1‐weighted MRI and matched F18‐FDG PET scans were processed by PETQuant (CorTechs Labs, San Diego, USA). Segmented brain volumes normalized by intracranial volume and corresponding PET ROI SUV ratios (SUVRs) were calculated. Random Forests algorithm was used in creating machine learning models. One‐third of randomly selected data were used for testing while the rest were used for training. The performance of the models and importance of their features were evaluated.ResultThree models’ performances were evaluated. The first one used both PET SUVRs and MRI‐based volumes, and average model accuracy, recall for identifying AD, MCI and normal were 0.77 and 0.77. The top three features were right isthmus cingulate SUVR, left amygdala volume, and left hippocampus volume. The second model only used MRI, and average accuracy and recall for identifying AD,MCI and normal were 0.77 and 0.76. The top three features were left amygdala volume, left hippocampus volume, and right inferior temporal volume. The third model used PET SUVRs only, and average accuracy and recall were 0.72 and 0.72. The top three features were SUVRs from right isthmus cingulate, left medial‐parietal, and right entorhinal cortex.ConclusionThese models showed a good classification performance among AD, MCI and normal subjects. The model using MRI and PET showed the best performance and was slightly better than using MRI data alone. The MRI model performed better than using the PET data alone. This suggests MRI volumetric measures provide a better classification power than SUVRs extracted from FDG‐PET images.
Associations of sleep characteristics with mild cognitive impairment and dementia among Chinese elderly people living in a rural area: A population‐based study
Authors: Liu Rui , Tang Shi , Wang Yongxiang , Dong Yi , Hou Tingting , Ren Yifei , Qin Yu , Cong Lin , Qiu Chengxuan , Du Yifeng 
Published: December 2020

AbstractBackgroundSleep patterns might be changed in the early stage of the dementing process. Thus, sleep characteristics may be useful markers for identifying elderly persons at risk of mild cognitive impairment (MCI) and dementia. However, previous studies that investigated the associations of sleep characteristics and cognitive function have been scarcely conducted among Chinese older adults. We aim to determine the associations of various sleep characteristics with MCI and dementia among rural‐dwelling Chinese elderly people.MethodThis population‐based cross‐sectional study included 4680 participants who were aged ≥65 years and living in the rural communities in Yanggu County, Shandong Province. In March‐September 2018, we collected data through interviews, clinical examinations, cognitive testing, and laboratory tests. We used Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS) to evaluate sleep characteristics such as sleep duration, sleep efficiency, and excessive daytime sleepiness (EDS). Cognitive function was assessed with a neuropsychological test battery. Dementia was diagnosed based on the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, criteria, and MCI was defined according to the modified Petersen’s criteria. Multiple multinomial logistic regression models were used to examine the associations of sleep characteristics with MCI or dementia.ResultOf the 4680 participants, 195 were diagnosed with dementia and 691 with MCI. Compare to the sleep duration of 4‐8 hours/night, the multivariable‐adjusted odds ratio (95% confidence interval) of MCI and dementia was 1.34 (1.01, 1.77) and 1.97 (1.25, 3.12) for very short sleep duration (≤4 hours/night), and 1.35 (1.06, 1.72) and 1.74 (1.11, 2.74) for long sleep duration (>8 hours/night). The EDS was significantly associated with the adjusted odds ratio of 1.36 (1.05, 1.78) for MCI and 1.80 (1.15, 2.84) for dementia. Moreover, low sleep efficiency was associated with an adjusted odds ratio of 1.65 (1.18, 2.30) for dementia and 1.10 (0.91, 1.33) for MCI.ConclusionLong and very short sleep duration and EDS were associated with an elevated likelihood of MCI and dementia among rural‐dwelling Chinese older adults. In addition, low sleep efficiency was associated with a high likelihood of dementia but not MCI.
Medium‐to‐high physical activity specifically reduces the risk of memory impairment among older adults: The Shanghai Aging Study
Authors: Xiao Zhenxu , Wu Wanqing , Zhao Qianhua , Ding Ding 
Published: December 2020

AbstractBackgroundIt has been reported that physical activity (PA) could reduce the risk of incident dementia. This study aimed to investigate the relationship between PA intensity and domain‐specific cognitive function among Chinese elderly.MethodWe prospectively followed up 1,373 non‐demented community dwellers aged ≥60 years in the Shanghai Aging Study. At baseline (2009‐2011), a questionnaire was designed to collect their demographic characteristics and PA condition, which was further transformed into metabolic equivalent value (MET). PA was categorized into low (<10.5 METs/week) and medium‐to‐high level (≥10.5 METs/week), based on the tertile of the METs distribution. Participants were administered a battery of neuropsychological tests including 1) Mini‐Mental State Examination (MMSE); 2) Conflicting Instructions Task(CIT) (Go/No Go Task); 3) Stick Test (ST); 4) Modified Common Objects Sorting Test (COST); 5)Auditory Verbal Learning Test (AVLT); 6) Trail‐making Tests A and B;7) Modified Common Objects Sorting Test (HOMT); and 8) Renminbi (currency of China) Test. Participants with <6 years of education were given tests 1 to 4, 7 and 8. Participants with ≥6 years of education were given tests 1 to 6. At the follow‐up (2014‐ 2016), same neuropsychological battery was used to evaluated the domain‐specific cognitive functions. Impairment of each domain was defined as having test score >1Standard Deviation (SD) worse than that of baseline. Cox regression model was used to estimate hazard ratio (HR) and 95% confidence interval (CI) for cognitive impairment of each domain in two PA levels.ResultThere is significant difference of incident cognitive domain impairment between low and medium‐to‐high PA groups in MMSE (p<0.001), Go/No‐Go correct tapping (p=0.029) of CIT, imitating (p=0.003) of ST, delayed recall (p=0.034) and recognition (p=0.005) of AVLT/HOMT. After adjusted for age, sex, education year, APOE ε4, smoking, drinking, hypertension, diabetes and body mass index, participants with medium‐to‐high baseline PA had a lower risk of impairment in MMSE (HR 0.45, 95%, CI 0.31‐0.66), delayed recall (HR 0.54, 95% CI 0.34‐0.87) and recognition (HR 0.58, 95% CI 0.34‐0.98) of AVLT/ HOMT.ConclusionMedium‐to‐high level of PA was independently associated with reduced risk of global cognition and memory impairment in older adults.
Effect of berries and fruits on cognitive change during a 2‐year multi‐domain lifestyle intervention: The Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER)
Authors: Eskelinen Marjo H , Solomon Alina , Ngandu Tiia , Antikainen Riitta , Hänninen Tuomo , Laatikainen Tiina , Lindstrom Jaana , Paajanen Teemu , Soininen Hilkka , Strandberg Timo , Tuomilehto Jaakko , Kivipelto Miia , Lehtisalo Jenni ,   FINGER Study Group
Published: December 2020

AbstractBackgroundDietary components such as berries and fruits are part of the recommended Nordic diet, and are considered having health‐enhancing characteristics. A randomized multi‐center and multi‐domain lifestyle intervention trial FINGER has shown benefits on cognition with dietary guidance, exercise, cognitive training and vascular risk management. Here, we investigate the association between berry and fruit intake at baseline and cognitive function over 2 years.MethodsIndividuals aged 60‐77 years were randomly assigned in a 1:1 ratio to intervention (n=631, nutritional guidance, exercise, cognitive training, and management of metabolic and vascular risk factors) or control group (n=629, general health advice). A modified neuropsychological test battery and 3‐day food records were administered annually. Mixed‐model repeated‐measures analyses with maximum likelihood estimation were used to analyse change in cognitive scores as a function of randomization group, time, berry consumption, total energy intake, berry consumption x time interaction, group x time interaction, energy intake x time interaction, age x time interaction, age, sex, education, study site, C‐reactive protein, systolic blood pressure, and low‐density lipoprotein cholesterol. A similar model was used to investigate effects of fruit consumption.ResultsHigh berry consumption at baseline compared with no berry consumption was associated with more improvement in all investigated cognitive domains. Berry consumption x time interaction coefficient (95% CI) was for global cognition 0.058 (0.034‐0.082), p<0.001; for executive functioning 0.050 (0.018‐0.081), p=0.002; for memory 0.069 (0.028‐0.109), p=0.001; and for processing speed 0.051 (0.018‐0.085), p=0.003 over two years. When berry consumption was combined with fruit consumption, the results remained similar. However, fruit consumption alone did not show any association with the cognitive domains.ConclusionsBerries are an important component of the Nordic diet, and higher consumption of berries seems to have a beneficial impact on cognitive functioning. Several healthy components such as flavonoids in berries might be behind this association, e.g. by influencing cerebrovascular blood flow and synaptic plasticity of the brain. Acknowledgements: The presenting author wish to acknowledge The Juho Vainio Foundation; Yrjö Jahnsson Foundation; Päivikki and Sakari Sohlberg Foundation.