bluuebunny/crossref_metadata_2025_split huggingface dataset:
Polysomnography derived sleep metrics and cognition in the Sleep and Dementia Consortium (SDC): a study of 5 population‐based cohorts
Authors: Pase Matthew P. , Harrison Stephanie , Misialek Jeffrey , Kline Christopher , Cavuoto Marina G. , Baril Andree‐Ann , Yiallourou Stephanie , Bisson Alycia , Himali Dibya , Leng Yue , Yang Qiong , Seshadri Sudha , Beiser Alexa S. , Gottesman Rebecca F. , Redline Susan , Lopez Oscar L. , Lutsey Pamela L. , Yaffe Kristine , Stone Katie L , Purcell Shaun , Himali Jayandra J 
Published: December 2023

AbstractBackgroundGood sleep is essential for health, yet the role of sleep in dementia risk is incompletely understood. The Sleep and Dementia Consortium (SDC) was established to study associations between polysomnography (PSG)‐derived sleep metrics and the risk of dementia and related cognitive and brain MRI endophenotypes. This study presents the associations of sleep architecture and obstructive sleep apnea (OSA) with cognitive function across participating cohorts.MethodThe SDC curates data from five population‐based cohorts with methodologically consistent, overnight, home‐based PSG and neuropsychological assessments over 5 years of follow‐up. Cohorts include the Atherosclerosis Risk in Communities (ARIC) study, Cardiovascular Health Study (CHS), Framingham Heart Study (FHS), Osteoporotic Fractures in Men Study (MrOS), and Study of Osteoporotic Fractures (SOF). Global cognitive composite scores were derived from principal component analysis as the primary outcome. All sleep metrics were harmonized centrally and then distributed to the participating cohorts for cohort‐specific analysis using linear regression; study‐level estimates were pooled in random effects meta‐analyses. Results are adjusted for demographic variables, the time interval between the PSG and neuropsychological assessment (0‐5 years), body mass index, antidepressant use, and sedative medication use.ResultThe mean age of the cohorts ranged from 58 to 89 years (Figure 1) with a pooled sample of 5,946 participants. As shown in Figure 2, across cohorts, higher REM sleep percentage (pooled β±SE = 0.49±0.18 per % increase; p = 0.008) and Sleep Maintenance Efficiency (pooled β±SE = 0.08±0.03 per % increase; p = 0.01) were associated with better global cognition whereas OSA (Apnea‐hypopnea index [AHI] ≥ 5) was associated with worse global cognition (pooled β±SE = ‐0.06±0.02 vs. AHI<5; p = 0.03). Differences in N3 sleep were not associated with cognition.ConclusionBetter sleep consolidation and higher REM sleep percentage were associated with better cognition whereas OSA was associated with worse cognition over 5‐years follow‐up. The role of interventions to improve sleep for maintaining cognitive function and potentially reduce dementia risk requires investigation.
Causal effects of Alzheimer’s Disease on snoring may be driven by Body Mass Index: A bidirectional Mendelian Randomization Study
Authors: Gao Yaqing , Andrews Shea J , Yaffe Kristine , Leng Yue 
Published: December 2023

AbstractBackgroundLoud snoring is a symptom commonly associated with obesity and obstructive sleep apnea (OSA), which have both been suggested as risk factors for Alzheimer’s disease (AD). However, it remains unclear whether snoring might be causally linked to AD and if body mass index (BMI) may play a role in this relationship. Here we use bidirectional Mendelian randomization (MR) analysis to investigate the causal relationship between snoring and AD.MethodWe performed two sample MR using summary statistics for genome‐wide association studies of AD (n = 94,437; cases = 35,274) and snoring (n∼408,000; snorers∼152,000). Genetic instruments were obtained by selecting independent genome‐wide significant SNPs (p<5e‐8, r2 = 0.001, window = 10Mb) for each exposure and harmonizing their effects with each outcome. We used fixed‐effects inverse‐variance weighted (IVW) meta‐analysis as the primary method, and MR‐Egger, Weighted mode, Weighted median, radial‐MR, estimators as sensitivity analyses. LHC‐MR was used to investigate bidirectional effects accounting for potential heritable confounders. Heterogeneity was assessed using Cohcrans Q test, and pleiotropy using the MR‐Egger intercept. We used multivariable MR to estimate direct effects of AD on snoring independent from BMI.ResultNo causal effect of snoring (OR [95%CI] = 0.842 [0.391, 1.798], p = 0.653) on AD was observed. In the reverse direction, we did not observe significant effects of AD on snoring using IVW (OR [95%CI] = 0.991 [0.979, 1.002], p = 0.114) or LHC‐MR (OR [95%CI] = 1.022 [0.805, 1.298], p = 0.850). However, suggestive causal effects were observed in the MR‐Egger (OR [95%CI] = 0.974 [0.958, 0.990], p = 0.004), Weighted mode (OR [95%CI] = 0.981 [0.965, 0.997], p = 0.031), and Weighted Median (OR [95%CI] = 0.979 [0.963, 0.996], p = 0.013) analyses. There was no evidence of heterogeneity, but some evidence of pleiotropic effects (p = 0.009). Multivariable MR indicated that the effect of AD on snoring was likely driven by BMI.ConclusionGenetic liability to AD reduced snoring, possibly through lower BMI due to AD. Further analyses are needed to determine if OSA is causally related to AD and the role of BMI in this relationship.
Improvement of Cognitive Functions And Neurotrophic Effects through by Α5‐GABAA Receptor Positive Allosteric Modulation in Animal Models of Risk for Alzheimer’s Disease
Authors: Prevot Thomas D , Bernardo Ashley D , Marcotte Michael D , Wong Kayla D , Mian Md Yeunus , Sharmin Dishary , Mondal Prithu , Cook James M , Sibille Etienne 
Published: December 2023

AbstractBackgroundReduced GABA/somatostatin (SST) signaling is reported in psychiatric, stress‐related and neurodegenerative disorders. SST+ interneurons from cortical layers and the hippocampus inhibit the dendrites of excitatory neurons, largely through α5‐containing GABAA receptors (α5‐GABAAR). Recently, we showed that an α5‐positive allosteric modulator (α5‐PAM) alleviates working memory deficits and reverses neuronal atrophy in old mice. We then started to investigate the behavioral and neurotrophic effects of this α5‐PAM in animal models of aging, chronic stress, and β‐amyloid load, as risk factor for developing Alzheimer’s disease.MethodThree studies are presented, with ∼12 mice/group, 50% female: 1) Young C57BL6 subjected to unpredictable chronic mild stress (UCMS) to induce cognitive deficits. 2) 22 month‐old C57BL6 developing an age‐related cognitive decline. 3) 5xFAD transgenic mice with progressive amyloid‐related cognitive decline. In all studies, efficacy of chronic administration of GL‐II‐73 (30mg/kg, p.o, for 4 weeks) at rescuing cognitive deficits across 3 domains was assessed. Working memory was assessed in an alternation task, spatial memory in the water maze, and cognitive flexibility in a set‐shifting assay. Brains were then stained using Golgi‐Cox technique (n = 4brain/group; 8cell/brain) for quantification of dendritic length and spine density in the prefrontal cortex and hippocampus (NeuroLucida).ResultChronic treatment in all models reversed cognitive deficits across domains (ps<0.01), with a strong effect on working memory. Chronic treatment also significantly reversed UCMS‐, age‐, or amyloid‐induced dendritic shrinkage and reduction of spine density at apical and basal dendrites (p<0.001 in PFC and CA1).ConclusionTogether, results support that selective α5 targeting of GABAA receptors overcomes chronic stress‐, aging‐, or amyloid‐related cognitive deficits and detriments in neuronal morphology. This represents the first intervention targeting the GABAergic system to have a symptomatic and disease‐modifying therapeutic potential in a transdiagnostic manner. This could represent a major avenue for clinical development for patients suffering from cognitive deficits across brain disorders.
Proportion of Dementia Cases in the Oldest‐old Attributable to 8 Neuropathological Changes: The 90+ Study
Authors: Corrada María M. M. , Jiang Luohua , Sajjadi S. Ahmad , Montine Thomas J. , Bukhari Syed A. , Cholerton Brenna , Boyle Patricia A , Brookmeyer Ron , Kawas Claudia H. 
Published: December 2023

AbstractBackgroundPeople aged 90 and older have the highest rates of dementia. At advanced ages, dementia is due to a variety of diseases but the relative contribution of each is unknown. We estimated the proportion of dementia cases attributable to 8 different neuropathological indices in a cohort of individuals aged 90 and older.MethodsParticipants (n = 425) are from The 90+ Autopsy Study, a community‐based longitudinal study of aging and cognition. Most participants (62.1%) were initially part of the Leisure World Cohort Study and the remaining were non‐cohort community volunteers (37.9%). The 8 neuropathologic features examined were Alzheimer’s disease neuropathologic change (ADNC), limbic predominant age‐related tdp‐43 encephalopathy (LATE‐NC), hippocampal sclerosis (HS), Lewy body disease (LBD), microvascular lesions (MVL), cerebral amyloid angiopathy (CAA), atherosclerosis, and arteriolosclerosis. We examined the association of the 8 neuropathologic features with all‐cause dementia using multivariable logistic regression adjusting for age, gender education, and cohort affiliation. We then estimated the proportion of dementia cases that could be attributed to each neuropathologic change.ResultsParticipants were on average 97.6 years at time of death, most were women (68%), and about half (43%) had dementia at time of death (Table 1). ADNC, LBD, LATE‐NC, arteriolosclerosis and atherosclerosis were significantly associated with dementia, whereas HS, MVLs, and CAA trended toward a significant association with dementia (Table 2). For the individual neuropathologic features, ADNC (26%) and arteriolosclerosis (24%) had the highest and similar attributable fraction of dementia cases, followed by LATE‐NC (17%) and CAA (10%) (Figure). When combinations of neuropathologic features were considered, vascular pathologic changes had the highest attributable fraction of dementia cases (38%), non‐AD changes (LBD, LATE‐NC, HS) had an attributable fraction of 28%, and LATE‐NC and HS combined had an attributable fraction of 20% (Figure). Overall, 78% of dementia cases could be attributed to these 8 neuropathologic features.ConclusionsVascular pathologic changes, as a group, account for the largest fraction of dementia cases in this oldest‐old cohort. Furthermore, when considered together, non‐AD neuropathologic changes are as important contributors to dementia as ADNC. Therefore, interventions targeting a variety of diseases will be needed to significantly reduce the burden of dementia in the oldest‐old.
Screening of Mild Cognitive Impairment Subtypes Through the Training of 1D Convolutional Neural Network with QEEG Features
Authors: Ko Juhee , Choi Boeun , Park Ukeob , Baik Kyoungwon , Ye Byoung Seok , Kang Seung Wan 
Published: December 2023

AbstractBackgroundThe pre‐clinical stage of dementia, mild cognitive impairment (MCI) carries various pathological pathways and respective prognoses. The two foremost causes of MCI and dementia that have been identified through autopsy diagnoses are Amyloid plaques and Lewy body, commonly referred to as Alzheimer’s disease (AD) and Lewy body disease (LBD) respectively. Although correct identification of pathological pathways plays a crucial role in effective designing of treatment methods, MCI subtypes are underexploited in the current diagnostic paradigm due to multiple reasons, mainly cost‐effectiveness. Hence, we utilized quantitative electroencephalography (QEEG) that are cheap and convenient to record in the distinguishment of MCI subtypes.MethodA total of 151 MCI patients’ EEG data were aggregated into 3 groups, pure AD (n = 29), pure LBD (n = 88), and mixed (n = 34). The mixed type was further subdivided into main AD (n = 31) and main LBD (n = 3) groups, in accordance with AD/LBD tendency exhibited by the patients. The clinical labelling was brought by the experienced experts of Yonsei Severance hospital, South Korea.1D SE‐ResNet‐based classification model was established for quantitative investigation of AD and LBD propensity. The power spectrum density (PSD) in dB/Hz scale were computed from the EEG data and were used to train the model. Due to the small number of data, augmentation was applied to the training data. The final dataset was split into 8 to 1 to 1 ratio (Train n = 7092: 2274 pure AD + main AD; 3694 pure LBD + main LBD, Validation n = 14: 6 pure AD + main AD; 8 Pure LBD + main LBD, Test n = 15: 6 pure AD + main AD; 9 pure LBD + main LBD).ResultThe classification model showed validation results of 85.7% accuracy, 83.3% AD sensitivity and 87.5% LBD sensitivity. Test results were at 92.8% accuracy, 83.3% AD sensitivity and 88.9% LBD sensitivity.ConclusionQEEG‐based deep learning classifier developed in this study successfully distinguished the two main causes of cognitive degeneration in MCI patients. Our model can help identify subtype‐specific spectral trends, which could also make contributions in the establishment of effective treatment methods for MCI.
Application of Cognitive Enhancement Protocol Based on ICT Program to Improve Cognitive Level of Older Adults Residents in Small‐sized City Community: A Pilot Study
Authors: Yun Sohyeon , Lee Hamin , Kim Mi Kyeong , Kim Inhye , An Hyun Seo , Park Hae Yean 
Published: December 2023

AbstractBackgroundA home‐based ICT program to elderly people aged 65 years or older to confirm the effect of the cognitive enhancement program and explore the possibility of remote rehabilitation.MethodThis study from August to October 2022, three subjects were selected and the intervention was conducted for about two months. This intervention was conducted using the Korean version of Mini‐Mental State Examination(MMSE‐K), the Korean version of Montreal Cognitive Assessment(MoCA‐K), the Computer Cognitive Senior Assessment System(CoSAS), and the two shorter form of the Center for Epidemiologic Studies Depression scale(CESD‐10‐D) to evaluated cognitive improvement before and after the program. The therapist remotely set the difficulty lever of cognitive training suitable fore the level of the subject through weekly feedback, and during the program. Interviews were conducted for additional opinions on the cognitive enhancement program.ResultAfter the intervention, all subjects showed improved scores in most items of the Korean version of the Montreal Cognitive Assessment conducted before and after the intervention. In addition, among the items of CoTras, upper cognition, language ability, attention, visual perception, and memory were improved. In addition, subjective satisfaction with the cognitive improvement program was high.ConclusionCognitive rehabilitation training using a home‐based ICT program not only prevented dementia but also made it habitual. Through this study, it was confirmed that remote rehabilitation for the elderly could be possible.
Deep learning based automatic detection algorithm of Frank’s sign and its application
Authors: Jo Sungman , Kim Ki Woong 
Published: December 2023

AbstractBackgroundFrank’s sign (FS) is a diagonal crease in the ear lobe extending from the tragus across the lobule to the rear edge of the auricle. Many of previous research have studied that FS is associated with the risk of cardiovascular, cerebrovascular diseases, cognitive impairment and aging. However, existing studies have lack of work in the application of FS. Meanwhile, cutting edge deep learning segmentation techniques allow to build large‐scale trainable models that have the capacity to learn the optimal features with great speed and accuracy. This study is the first to analyze 3D Nifti FS dataset by applying a deep learning approach and developing the automatic detection algorithm of FS (ADAFS).MethodWe used 700 brain MRI dataset and manually segmented FS masks of 550 older adults with FS. 400 out of 550 datasets were used into the developing ADAFS. Rest of them and 150 older adults without FS served to validate the ADAFS. The preprocessed 3D datasets were fed into the candidate models including 3D U‐net, 3D Attention U‐net, 3D Nested U‐net, and 3D USE‐net in order to train ADAFS (figure 1). We adapted 5‐fold cross‐validation test which can estimate the general effectiveness of an algorithm on an independent dataset. Furthermore, we classified the FS‐detected mask in 4 cases; left lateral, right lateral, bi‐lateral and no FS.ResultOn development of ADAFS, Nested U‐net out of 4 different models showed the highest average dice similar coefficient (DSC; 0.714 ± 0.160) across test folds. Given validating the developed ADAFS, the multi‐label classification and segmentation results of receiver operating characteristic curve (ROC) analysis were below: accuracy = 0.907, average area under ROC curve = 0.940, and DSC = 0.736 ± 0.172.ConclusionADAFS may support the faster decisions of FS presence, and considerably similar FS segmentation performance. This may serve a variety of FS shape descriptors and further study including an association of cognitive aspects and them.
Alpha Asymmetry as biomarker for Mild Cognitive Impairment
Authors: Shin Jaekang , Park Sanguk , Jeong Taegyun , Park Ukeob , Kim Daekeun , Youn Young Chul , Kang Do‐Young , Park Kyung Won , Kim Sangjin , Park Hyuntae , Lee Young‐Min , Seo Chang‐Sung , Kang Seung Wan 
Published: December 2023

AbstractBackgroundEarly screening of dementia at its pre‐clinical stage is crucial, seeing as how deteriorated cognitive functions can be recovered through appropriate medicinal practices. The pre‐clinical stage, which we refer to as mild cognitive impairment (MCI) is known to affect the alpha rhythm of the brain activity. Hence, our study aims to establish quantitative EEG (qEEG)‐based alpha asymmetry biomarker that aids the diagnoses of MCI.MethodThe qEEG dataset were composed of age‐ and sex‐ matched subjects (N = 634, 317 healthy control (HC), 317 MCI), acquired in accordance with the 10‐20 system and under resting state with their eyes being closed. Subjects were recruited by Chung‐Ang University (153 HC, 111 MCI), iMediSync(164 HC, 76 MCI), Dong‐a Medical Center(84 MCI) and Pusan Medical Center(46 MCI).The features were classified into four types: the absolute and relative region alpha power (AAP/RAP) of six regions (Prefrontal, Frontal, Temporal, Central, Parietal, Occipital); alpha power asymmetry (APA) between regions that align in anterior‐posterior and lateral planes; occipital alpha peak frequency (OPF) that analyzes peak frequencies.In order to highlight the alpha variability, EEG signals were bandpass filtered (6.5‐12Hz). Filtered signal at each channel was categorized into slow (6.5‐8Hz), middle (8‐10Hz) and fast alpha (10‐12Hz). The mean and standard deviation of their FFT were computed through moving time window. Henceforth, the feature set were used to train machine learning algorithms. Data were split into 8(training N = 507) to 2(test N = 127) ratio. 101 subjects were randomly selected and used as validation set. The algorithm that exhibited the best classification performance were determined through 5‐fold cross validation (CV) results.ResultSupport vector machine (SVM) showed the best 5‐fold CV results – 84.61% average accuracy, 74.02% sensitivity, 95.25% specificity, 94.15% precision and F1‐Score of 0.8273. The RAP decreased for all regions and OPF were slower in MCI group when compared to HC. APA patterns of the right intra‐hemisphere exhibited most significant differences.ConclusionThis study illustrates the potential of APA as a biomarker for MCI. Current applications of APA are confined to emotional‐related studies, such as depressive disorder. The reported cases of MCI‐associated depression also uphold the validity of APA as a biomarker for MCI.
Comparing informant‐based measures of cognitive functioning between the US and Mexico
Authors: Cantu Phillip A , Zhang Yuan S , Saenz Joseph 
Published: December 2023

AbstractBackgroundInformant reports are important for understanding cognitive impairment and dementia in older populations. Recently, several population‐based aging studies have included harmonized direct and informant assessments to assess the prevalence of dementia globally as part of the international Harmonized Cognitive Assessment Protocol (HCAP). However, the social and cultural contexts of nations, sociodemographic characteristics of informants, and the participant‐informant relationship may affect the accuracy of informant ratings of cognitive decline and functioning. This study examines the extent to which informant characteristics and the participant‐informant relationships are associated with informant‐reported cognitive function in the United States and Mexico.MethodsData are from the 2016 US Health and Retirement Study (HRS) HCAP (N = 2,918) and the Ancillary Study of Cognitive Aging in Mexico (Mex‐Cog) (N = 1,750). Informant‐reported cognitive function was measured by the informant Community Screening Instrument for Dementia (CSI‐D). Participants’ cognitive function was measured using the HCAP. We compared the characteristics of informants, informant ratings, and directly‐assessed cognitive functioning in both nations, and used regression to assess the association of informant characteristics and the participant‐informant relationship with the informant CSI‐D score.ResultsWe find older Americans have worse informant‐reported cognitive functioning but higher directly‐assessed cognitive functioning than Mexicans. Nearly 80% of informants lived with the subject in Mexico, compared to less than half in the US. In both countries, older informants, children and other family members (compared to spouse) report less cognitive impairment. In Mexico, female informants reported more impairment. In the US, coresident informants reported more impairment.ConclusionThis research shows how social environments influence informant reporting. Understanding heterogeneity in informants is vital when examining informant measures of cognitive function in cross‐countries studies.
Serum FOXO3A: A potent marker for early diagnosis of Alzheimer’s disease
Authors: Pradhan Rashmita 
Published: December 2023

AbstractBackgroundAlzheimer’s disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline. In any ageing society it’s a major health concern and a public health challenge. Diagnosis of Alzheimer’s disease (AD) is often difficult because of distinct and subjective clinical features, especially in the early stage. FOXO3a protein present in the cognitive centre of brain in inferior temporal region and parahippocampus. FOXO3a can be a potential novel target against AD.MethodAD, Mild Cognitive impairment (MCI) and Geriatric Control (GC) were recruited after diagnosis by clinical assessment, MRI, Tau PET and FDG PET. We have quantified serum FOXO3a by surface plasmon resonance (SPR) and compare with Tau PET between of AD, MCI patients and GC.ResultSerum FOXO3A was significantly lower in AD (1.42 ± 0.09 ng/μl) compare to MCI (1.61 ± 0.14 ng/μl) and GC (1.89 ± 0.07 ng/μl). ROC curve for FOXO3A protein level was plotted and showed a reasonably good sensitivity and specificity at the cut‐off value for detecting cognitively impaired patients (AD and MCI) in compared to control older subjects. For Tau PET in inferior temporal region, the AUC for predicting AD from GC was 0.72 and AD from MCI was 0.71.ConclusionSerum FOXO3A could significantly differentiate AD vs MCI, MCI vs GC and AD vs GC. It may serve as novel blood marker for early detection for AD and target for therapeutic intervention.