bluuebunny/crossref_metadata_2025_split huggingface dataset:
An End‐to‐End Model for Visualization and Highlight of Alzheimer's Disease Progression
Authors: Zhang Juexin , Weng Ying , Xiao Weizhong 
Published: January 2025

AbstractBackgroundSubtle cognitive decline in the prodromal stages of Alzheimer's disease (AD), encompassing both significant memory concerns (SMC) and mild cognitive impairment (MCI), presents a crucial diagnostic hurdle. This challenge stems from the inherent difficulty in both detecting the nuanced neurocognitive aberrations and visualizing the corresponding brain alterations, particularly within the hippocampus, on conventional magnetic resonance imaging (MRI). Consequently, current clinical diagnostic procedures remain arduous and necessitate substantial clinical expertise. Considering these limitations, the development of automated deep learning‐based methods for visualizing early‐stage AD‐related brain changes holds immense promise.MethodWe propose a generative model capable to synthesize high quality MRIs for both retroactive and predictive visualization of a given MRI. Furthermore, our model also indicates the morphological changes of a given MRI. Our model is comprised of three interconnected components: a classifier, a generative model, and a morphological feature identifier. Initially, a convolutional neural network (CNN) classifier categorizes the input MRI into its corresponding stage within the disease progression spectrum. Subsequently, the generative model, constructed to capture the nuances of disease‐related morphological evolution, synthesizes MRIs that simulate the brain's structural configuration at other potential stages of the disease. Finally, the morphological feature identifier, through a process of automated segmentation, delineates areas exhibiting significant morphological deviations and highlights those regions deemed most pertinent for further clinical evaluation.ResultWe employed T1‐weighted MRIs from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) public dataset to test our model. Our synthetic MR images achieved high visual fidelity, as well as indistinguishable from real images for human observers, and yielded high peak signal‐to‐noise ratio (PSNR) and structural similarity index measure (SSIM) scores. Moreover, our model can precisely highlight morphological deviations.ConclusionTo facilitate diagnosis and intervention in early AD, we put forward a generative model to synthesize other stages of a given MRI. This allows clinicians to visualize potential disease progression pathways and clearly observe morphological changes in vulnerable brain regions. Our model can also highlight key anatomical alterations to help AD analysis and offer valuable insights into promise for early AD management.
The Effect of Social Isolation on Cognitive Performance and Brain Morphology among the Urban Elderly Indian Cohort: A Cross‐Sectional Analysis
Authors: Lingegodwa Abhishek Mensegere , Singh Sadhana , Stezin Albert , Partha Ajith , N M Divya , C M Amitha , HS Deva Kumar , Narayanasamy Rajitha , R Meghana , Menon Meenakshi , vishwanath Vindhya , HS Sunitha , Velavarajan Goutham , Arvind Prathima , Agrawal Deepashri , Malo Palash K , Mondal Banashree , Hameed Shafeeq K Shahul , Sundarakumar Jonas S. , Issac Thomas Gregor 
Published: January 2025

AbstractBackgroundSocial isolation among the elderly is increasing and its detrimental effect on cognition is not fully understood. We aimed to study the effects of social isolation on cognitive performance and brain morphology among the urban elderly.MethodIn this cross‐sectional analysis of 1484 participants from the Tata Longitudinal Study of Aging (TLSA) based in urban Bangalore, we administered the social network index questionnaire to identify participants experiencing social isolation. Cognitive performance was assessed using the Addenbrooke Cognitive Examination III (ACE III) battery and brain cortical volumes were obtained after subcortical volumetry was performed on T1 images (Siemens 3T Magnetom Prisma Fit) using the Freesurfer software (v 7.2.0). Multiple linear regression analysis was performed with social network diversity score (low scores indicating less involvement in social roles) as the independent variable and, ACE III total, subdomains, and brain volumes as dependent variables controlling for age, gender, education, blood pressure, cholesterol, physical activity, and depression were used as covariates.ResultIn this study 727 (49%) were males, the mean age (SD) was 62.6 (9.6), the majority of them belonged to upper and middle socio‐economic strata (96.3%) and the mean (SD) education was 14 (4.2) years. People who have poor social network diversity scores performed poorly in ACE total (β=1.056, p<0.001), attention (β=0.119, p=0.006), memory (β=0.437, p<0.001), fluency (β=0.169, p=0.005), language (β=0.161, p=0.001), and visuospatial domain (β=0.169, p=0.001) (Table 1). Those with lower social network scores had lower total grey matter volume (β=9474.99, p<0.001), and higher white matter hyperintensities (β=‐0.469, p<0.001).ConclusionSocial isolation negatively affects global cognition and its subdomains and shows significant changes in brain volumes. Addressing social isolation among the elderly using policy measures should be mandated to decrease the load of cognitive impairment in the population.
Factor Structure and Internal Consistency of the Alzheimer's Disease Center's Neuropsychological Test Battery in the Uniform Data Set Version 3: The Nigeria Sample
Authors: Ucheagwu Valentine A , Ugokwe‐Joseph Rita Nonyelum , Ossai Jesse , Odilora Chiamaka , Giordani Bruno 
Published: January 2025

AbstractBackgroundHarmonized neuropsychological battery for research in Alzheimer’s disease and related dementia (ADRD) in sub‐Sahara Africa is very important if we are to understand ADRD phenotype and mechanisms in the region and how they relate and differ from other regions. No study in Nigeria has examined factor structure (construct validity) at both exploratory and confirmatory levels of harmonized neuropsychological battery for research in Nigeria. This study was a move in this direction using already established neuropsychological test battery the Uniform Data Set Neuropsychological Battery version 3 (UDSNB 3.0) of the Alzheimer’s Disease Center United States.MethodUDSNB 3.0 fully completed by 349 older adults (220 females) between the 65 – 85 years were used to test for construct validity. Exploratory and confirmatory factor analyses as well as Cronbach Alpha internal consistencies were analyzed.ResultPrincipal axis factor analysis (PA) method with orthogonal rotation (varimax with Keiser normalization) and eigenvalue greater than 1 was used for exploratory factor analysis (EFA). Four factors were extracted: first – Executive function/language (Phonemic and category fluencies, number span tasks – forward and backward and multilingual naming test) accounted for 17.23% of the variance, second – Memory (Craft Story immediate and recall) accounted for 16.30%, third factor – Visual spatial (Benson Complex Figure Immediate, recall and recognition) accounting 11.59% and fourth factor – Processing speed accounting 5.64%. Confirmatory factor analysis (CFA) did not fit the four factor exploratory model: CMIN/DF = 4.23; CFI= 0.75; RMSEA=0.12. However, a good model fit was established: CMIN/DF= 2.13; CFI=0.94; RMSEA=0.07, when executive function was unbundled into working memory (Number Span – forward and backward) and language (phonemic and category fluencies) and recognition task of the Benson Complex Figure removed from the model. Moderate internal consistencies were found among the subtests.ConclusionUDSNB 3.0 showed good model fit on a five factor structure : Working memory, language, memory, visual spatial and processing speed. These domains could be measured in Nigeria older adults using UDSNB 3.0 cognitive tasks.
Sex differences in the relationship between cognition and brain structure in midlife individuals at risk for future dementia
Authors: Qi Qing , Deng Feng , Dounavi Maria‐Eleni , Muniz‐Terrera Graciela , Koychev Ivan , Malhotra Paresh , O'Brien John T , Ritchie Craig W , Lawlor Brian , Naci Lorina 
Published: January 2025

AbstractBackgroundTwo‐thirds of Alzheimer’s Disease (AD) cases occur in women. Compared to men, women exhibit more rapid cognitive decline and brain atrophy in the presence of AD‐related neuropathology (Gamache et al., 2020). It is now acknowledged that AD processes are present decades before the onset of clinical symptoms (Jack et al., 2013). However, whether there are sex differences in cognition‐brain structure coupling and how AD risk affects their relationships in midlife remain unclear. In this study, we investigated associations between sex, AD risk, brain structure and cognition.MethodStructural Magnetic Resonance Imaging and detailed neuropsychological assessments were obtained for 614 cognitively healthy individuals (40‐59 years, 233 M/ 381 F) from the PREVENT‐Dementia study. Dementia risk factors were assessed by Apolipoprotein E [APOE] ε4 allele status, and the Cardiovascular Risk Factors Aging and Dementia (CAIDE) score. Multiple linear regression model was used to investigate the associations between sex, AD risk, brain structure and cognition.ResultCAIDE was negatively associated with global cortical thickness, and negatively associated with episodic and relational memory (Figure 1). We didn’t find a significant association between global cortical thickness and cognition (Figure 2a) but found a sex‐specific coupling of global cortical thickness and episodic and relational memory (Figure 2b). Such sex‐specific coupling between global cortical thickness and cognition was absent in APOE ε4 carriers, and only shown in APOE ε4 non‐carriers (Figure 2c, 2d). Furthermore, among the selected AD signature regions, cognition was decoupled from the precuneus cortical thickness in APOE ε4 carriers, and only coupled with cortical thickness of precuneus in APOE ε4 non‐carriers (Figure 2e, 2f).ConclusionWe found inherent sex‐specific differences in the coupling between brain structure and cognition. Our results suggest that these sex‐specific differences are being eroded by APOE ε4 carriership in mid‐life. Longitudinal follow‐up in this cohort will shed light on the long‐term sex‐specific impact of APOE genotype on brain structure and cognition in preclinical populations with risk for AD.
Changes in plasma biomarker levels are predictive of amyloid pathology and memory decline in cognitively unimpaired individuals
Authors: den Braber Anouk , Rousset Rebecca Z. , Verberk Inge M.W. , Boonkamp Lynn , Tomassen Jori , van der Landen Sophie M. , Collij Lyduine E. , Barkhof Frederik , van de Giessen Elsmarieke , Wilson David H , Miller Mike , Pijnenburg Yolande A.L. , van der Flier Wiesje M. , de Geus Eco J.C. , Visser Pieter Jelle , Teunissen Charlotte 
Published: January 2025

AbstractBackgroundPlasma levels of amyloid‐β1‐42/1‐40, and glial fibrillary acidic protein (GFAP) have demonstrated predictive potential for amyloid pathology in the early stages of Alzheimer’s disease (AD) development. Utilizing baseline and up to 6‐year follow‐up plasma, positron emission tomography (PET) and cognitive data from cognitively unimpaired individuals, we here aim to test whether early changes in plasma biomarker levels associate with change in amyloid status and cognitive decline.MethodsFrom the EMIF‐AD PreclinAD study we selected individuals with normal cognition and longitudinal plasma, PET and cognitive data available (nbaseline=200, table 1). Amyloid‐status was determined using visual assessment of dynamic [18F]flutemetamol‐PET scans. Plasma levels of amyloid‐β1‐40, amyloid‐β1‐42, neurofilament light (NfL), and GFAP were measured using the neurology 4‐plex E Simoa assay (Quanterix). Linear mixed models were applied to examine longitudinal changes in plasma biomarker levels, and their association with longitudinal changes in amyloid‐status and cognition.ResultsAt baseline 13.3%, and at 4‐year follow‐up 25.9% of participants were amyloid‐positive. Twenty‐one participants converted from a negative to positive amyloid‐status. At baseline, GFAP levels were higher and amyloid‐β1‐42/1‐40 lower in amyloid‐positive compared to negative individuals (β(SE)=0.43(0.14), β(SE)=‐0.75(0.21), p<0.005, respectively). In the total cohort, plasma levels of GFAP and NfL increased over time, whereas amyloid‐β1‐42/1‐40 remained stable (Figure 1). Although overall stable, plasma amyloid‐β1‐42/1‐40 decreased in participants that converted from a negative to positive amyloid‐status over time (β(SE)=‐0.04(0.02), p=0.02). Alongside, an increase in GFAP levels was observed in participants that converted from a negative to positive amyloid‐status compared to stable negative participants (Figure 1). No group differences were observed for NfL. A longitudinal increase in GFAP levels was associated with memory decline in stable amyloid positive individuals (GFAP*time*amyloid‐group status: β(SE)=‐0.09(0.02), p<0.001, Figure 2).ConclusionsEarly changes in plasma GFAP and amyloid‐β1‐42/1‐40 are associated with changes in amyloid‐status in cognitively unimpaired individuals. Further, increases in plasma GFAP levels over time are predictive of future memory decline in amyloid‐positive individuals. These results support the potential of plasma GFAP and, to a lesser extent, amyloid‐β1‐42/1‐40 as diagnostic and prognostic markers for AD in early stages of disease development.
Association of microstructural changes with vascular disease and Alzheimer’s Disease
Authors: Sun Pan , He Zhengbo , Li Anqi , Zhu Yalin , Cai Yue ,   ADNI, Guo Tengfei 
Published: January 2025

AbstractBackgroundNon‐invasive diffusion‐weighted imaging (DWI) can be used to assess cortical microstructure through cortical mean diffusivity (cMD). However, it is still unclear about the dynamic changes of cMD, as well as how it relates to vascular disease and Alzheimer’s disease (AD).MethodWe identified 318 cognitively unimpaired (CU) and 305 cognitively impaired (CI) participants with DWI image, β‐amyloid (Aβ) positron emission tomography (PET) image from the Alzheimer’s Disease Neuroimaging Initiative cohort. The 623 participants were separated into A‐CU, A‐CI, A+CU, and A+CI groups. Microstructural changes reflected by cMD were processed with a surface‐based diffusion tensor imaging (DTI) approach. Aβ PET was processed using cortical summary composite standardized uptake value ratio (SUVR) and converted to the Centiloid following the standard equation from the ADNI website. Tau PET SUVR was extracted in the composite temporal‐metaROI. T2‐flair‐based magnetic resonance imaging (MRI) was utilized for white matter hyperintensity (WMH) total number calculation with an in‐house developed algorithm and toolbox. T1 MRI image was processed using Freesurfer, and the cMD value in the 68 Desikan‐Killiany atlas regions of interest was extracted. Linear mixed effect (LME) models were used to calculate slopes of cMD for all participants with longitudinal DWI and WMH data. Generalized linear models (GLM) were used to compare baseline and slope of cMD among subgroups, adjusting for age, sex, APOE‐ε4, and education.ResultCompared to the commonly used macrostructure indicators of cortical thickness and gray matter volume, the micro‐structural indicator cMD exhibited high sensitivity of group‐level alteration crosse‐sectionally and longitudinally for both vascular disease and AD (Figure 1). cMD was negatively correlated with Aβ in the A‐CI group but positively related to tau in the A+CI group. Tau PET was associated with faster increases of cMD only in the A+CI group (Figure 2). In contrast, cMD and WMH were linked with each other in both cross‐sectional and longitudinal datasets in Aβ negative individuals (Figure 3).ConclusionThe findings suggest that cortical microstructure alternation reflected by cMD may be a promising biomarker for neurodegenerative disease compared to the cortical thickness and gray matter volume, particularly for vascular disease.
The association between cannabis use and neuroimaging measures in older adults: Findings from the UK Biobank
Authors: Vered Shiraz , Sznitman Sharon , Weinstein Galit 
Published: January 2025

AbstractBackgroundCannabis use has increased substantially in recent years, for both recreational and medicinal purposes. While the deleterious effects of cannabis use during adolescence on brain health are well acknowledged, long‐term implications of cannabis use on brains of older adults remain unknown. We explored the associations between past and current cannabis use and volumetric brain MRI measures in older participants of the UK biobank.MethodThis cross‐sectional and longitudinal study (with repeated MRI measures) included dementia‐free UK Biobank participants aged ≥60 years who had data on cannabis use and brain MRI outcomes. Linear regression models were used to assess the relationship of self‐reported cannabis use and patterns of use (i.e., frequency, duration, age of onset) with volumetric brain MRI measures, adjusting for sociodemographic factors and health behaviors. To evaluate change between two MRI examinations, we used the second MRI measures as dependent variables while controlling for the first MRI measures and other covariates.ResultThe sample included 19,932 participants (mean age 68±5 years, 48% men), of which 3,800 (19%) reported lifetime use of cannabis. The cross‐sectional analyses revealed that cannabis users had significantly smaller total brain, white matter, grey matter and cortical grey matter volumes compared to non‐users (b = ‐7110±1181; p<0.001, b = ‐4923±780; p<0.001, b = ‐2031±705; p = 0.004 and b = ‐2334±617; p<0.001, respectively). Stronger associations with brain MRI measures were observed among current vs. former users, and in those who used frequently, started using at younger ages and used for longer time periods (Table 1). In contrast, longitudinal analyses showed that cannabis use may be associated with a slower decline in total brain and cortical grey matter volumes (b = 3315±1582; p = 0.036, 0.22% between‐group difference and b = 2320±910; p = 0.011, 0.38% between‐group difference, respectively) (Figure 1).ConclusionOur study suggests that cannabis use and patterns of use during the life‐course may be related to detrimental brain health effects (e.g. smaller grey and white matter volumes) in middle‐age and older adults. On the other hand, our findings further suggest a possible protective effect (e.g. slower decline in brain volumes) of cannabis use among cannabis users compared to non‐users, which needs to be validated in future research.
Plasma and neuroimaging biomarkers of small vessel disease and Alzheimer’s disease in a diverse cohort
Authors: Lockhart Samuel N. , Sutphen Courtney L. , Tanley Jordan E. , Gonzalez‐Ortiz Fernando , Kac Przemyslaw Radoslaw , Habes Mohamad , Heckbert Susan R. , Ashton Nicholas J. , Mielke Michelle M. , Whitlow Christopher T , Hiatt Kevin , Craft Suzanne , Register Thomas C. , Hayden Kathleen M. , Rapp Stephen R. , Zetterberg Henrik , Blennow Kaj , Karikari Thomas K , Hughes Timothy M. 
Published: January 2025

AbstractBackgroundLittle is known about how plasma Alzheimer’s disease (AD) biomarkers relate to neuroimaging biomarkers of cerebral small vessel disease (cSVD) in the context of neurodegeneration and AD pathology in late life.MethodThis cross‐sectional study included 251 Multi‐Ethnic Study of Atherosclerosis (MESA) Exam 6 participants with plasma AD biomarkers (Aβ42/Aβ40, GFAP, NfL, p‐tau181, p‐tau217, p‐tau231; Quanterix SIMOA), MRI (neurodegeneration and cSVD), PiB (amyloid) PET, and UDSv3‐based adjudicated cognitive status (69% cognitively normal, 27% MCI, 4% probable dementia) data at the Wake Forest site. Multivariable models examined relationships among cognitive status, plasma, and neuroimaging biomarkers (covariates: age, education, race, gender, smoking status, kidney function [eGFR], APOE‐ε4, BMI; significance at p<.05 uncorrected).ResultCognitive status associated with imaging biomarkers and with plasma biomarkers of Aβ42/Aβ40, GFAP, p‐tau217 and p‐tau231. No NfL or p‐tau181 group differences were observed (Table 1). Lower plasma Aβ42/Aβ40 (Table 2) was associated with lower gray matter (GM) volume (GMV) and hippocampal volume (HCV); higher GFAP levels were associated with lower HCV. Higher NfL levels were associated with lower GMV and HCV, and with higher GM Free Water (FW). Higher p‐tau217 levels were associated with lower GMV. Plasma p‐tau231 was not associated with neurodegeneration imaging biomarkers. Higher NfL levels were associated with higher white matter (WM) hyperintensity (WMH) volume and WM FW. All plasma tau measurements were positively associated with WM FW but not other cSVD biomarkers. Lower Aβ42/Aβ40 ratio was significantly associated with greater prevalence of microbleeds. Higher GFAP was associated with higher WMH volume. As anticipated, lower plasma Aβ42/Aβ40 ratio and higher p‐tau217 and p‐tau231 were associated with higher Aβ deposition and odds of Aβ positivity (Table 3); a doubling in the level of p‐tau217 or p‐tau231 was associated with a 2‐3 fold increase in the odds of Aβ PET positivity.ConclusionWe found that plasma GFAP, NfL, p‐tau181, p‐tau217, and to a lesser extent p‐tau231 are associated with cognitive status, vascular comorbidities and imaging biomarkers of cSVD, in addition to measures of AD‐related pathology. Importantly, we demonstrated novel associations between plasma AD biomarkers of p‐tau and NfL with cSVD measures of WM structural health.
Cyclomatic Complexity: A Novel Approach for Analyzing Movement with Machine Learning to Predict Mild Cognitive Impairment
Authors: Johnson Brian B , Bolstad Britni A 
Published: January 2025

AbstractBackgroundPatterns of human movement evolve as an individual experiences the progression from cognitively intact (CIN) to mild cognitive impairment (MCI) and finally dementia and Alzheimer’s Disease (AD). Quantification of movement with the goal of identifying MCI though step counts alone do not take into consideration the floor layout of a home. Cyclomatic complexity is an approach to normalizing differences in the individual’s living space to better predict the transition from CIN to MCI. This approach could be especially beneficial for organizations that manage large populations of individuals that prefer to age‐in‐place and hospital‐at‐home environments.MethodCyclomatic complexity is the sequence of movements through rooms rather than room transitions or step counts alone. For example, if two rooms are in a straight line (north to south) this has less cyclomatic complexity than two rooms that are diagonal (north to southwest). Cyclomatic complexity is represented as M = E − N + 2P where E is the amount of walking paths, N is the number of rooms, P is the total number of connected rooms (Khan & Jacobs). Movement entropy considers the decrease in the sequence rather than for separate rooms. Monitoring these variables with ambient sensors could provide groundbreaking insights on the quality of life of individuals as they age.ResultEarly studies have shown cyclomatic complexity to be a statically significant predictor of the progression of CI to MCI six months before diagnosis according to “Prediction of Mild Cognitive Impairment Using Movement Complexity” by Dr. Taha Khan and Dr. Peter G. Jacobs.ConclusionAn estimation of movement to correlate the progression of MCI would benefit from using both sensor data on total step count in addition to cyclomatic complexity. Room transitions alone are unable to produce p < 0.05. Cyclomatic complexity adds potentially higher predictive value for MCI because it measures sequences of movement instead of quantity which varies depending on the individual’s home. Additional research is required to understand the technology requirements and data science to use cyclomatic complexity in the standard of care.