bluuebunny/crossref_metadata_2025_split huggingface dataset:
Intelligent dementia friendly environment: A Preliminary Study on the Feasibility of Integrating Assistive Humanoid Robots, Wearable Sensors, and Spatial Technology for maintaining quality of life
Authors: Khan Arshia A , Das Rupak Kumar , Imtiaz Rana Z 
Published: January 2025

AbstractBackgroundIntegrating humanoid robots, wearable sensors, and spatial technology into an intelligent dementia‐friendly living space is crucial for tailoring personalized and supportive environments, thereby addressing the unique needs of individuals affected by dementia and maintaining quality of life.[1‐10].MethodsWe programmed Pepper, a humanoid robot, for independent verbal communication to interact, tell jokes, and offer medications. To validate our assistive robot integrated with a sensor‐based system, we built a specifically designed living space optimized for sensor functionality and conducted a 90‐minute study involving 32 healthy participants. The participants performed various activities after consenting, filling pre and post‐experiment surveys and pre, during, and post‐sensor readings. The activities comprised of administration of a placebo sugar pill by the robot, treadmill exercises following the Bruce submaximal treadmill protocol, engagement in cognitively stimulating activities, wandering detection using spatial sensors, media presentation to induce higher arousal state, performance of some daily tasks such as preparing short meals, folding towels, cleaning the floor, and an interaction with the robot. These activities aimed to assess sensor functionality, simulate real‐world scenarios, and monitor physiological responses during various tasks, contributing to the evaluation of the integrated sensor system.Various physiological data, including Electrodermal Activity (EDA), blood pressure, and Electrocardiogram (EKG), were gathered using distinct wearable sensors throughout the experiment. To gauge the subjective experiences of participants, two surveys were employed: the Presentation survey, conducted during the International Affective Digital Sound (IADS) presentation, and the Post‐Study survey, distributed on paper after the study’s completion.ResultsBy incorporating pressure sensors in chairs and the robot’s emotion detection system, participants' moods were accurately assessed, aligning with self‐reported moods in 95% of cases. Furthermore, door sensors consistently identified exiting behavior, and wall‐mounted PIR sensors successfully recognized wandering behavior. Wearable sensors, such as EDA and blood pressure sensors, in conjunction with robot interactions, detected physiological changes during diverse activities, with the most notable impact observed during interactions with the robot.ConclusionThe findings suggest that an intelligent living space specifically designed for dementia care can effectively preserve the quality of life for affected individuals, particularly through robot interactions, showcasing promising effectiveness.
Identifying Five Dominant Dimensions of Neurodegeneration in Brain Aging through Deep Learning: Correlations with Clinical and Genetic Measures
Authors: Yang Zhijian , Wen Junhao , Erus Guray , Davatzikos Christos ,   the iSTAGING consortium
Published: January 2025

AbstractBackgroundBrain aging is a complex process influenced by various genetic, lifestyle, and environmental factors, as well as by age‐related and often co‐existing pathologies. MRI and AI methods have been instrumental in understanding neuroanatomical changes that occur during aging in large and diverse populations.MethodWe leveraged Surreal‐GAN, a state‐of‐the‐art deep representation learning method, to elucidate the heterogeneity of brain aging by deriving dominant dimensions (patterns) of brain changes and examining their associations with various clinical and genetic factors. Our analysis utilized structural MRIs, clinical variables, and imputed genotype data from a diverse cohort of 49,482 individuals across 11 studies.ResultSurreal‐GAN identifies five dominant dimensions of neurodegeneration in brain aging: R1, subcortical atrophy; R2, MTL atrophy; R3, parieto‐temporal atrophy; R4: diffuse cortical atrophy; and R5: perisylvian atrophy (Figure 1a). Individuals express varying but non‐exclusive levels of these patterns, quantified by respective R‐indices. The five R‐indices demonstrate positive correlations with each other, with R5 showing the strongest associations with white matter lesion volumes (WMH) and chronological age (Figure 1bc). Clinically, MCI/dementia participants exhibit significantly advanced R‐indices, particularly in R2, R3, and R5. Schizophrenia and Parkinson’s disease participants demonstrate severe aging effects in R3. R5 is associated with neuropsychiatric diseases and other chronic conditions (Figure 2a). Among the dementia‐associated dimensions, R2 displays stronger associations with memory impairment, while R3 is more closely associated with executive dysfunction (Figure 2b). In contrast to R2 and R3, R5 reveals weaker associations with amyloid and tau, but stronger associations with various other CSF biomarkers related to hemostatic and inflammatory mechanisms (Figure 2c). Genetically, GWAS identifies 73 genomic loci significantly associated with R1‐5 (Figure 3a). Previously identified SNPs correlate with various relevant clinical traits, including R5 loci that show associations with cardiovascular and neuropsychiatric conditions and WMH (Figure 3b). Additionally, the R1 dimension correlates with a gene set linked to the response to cortisol.ConclusionThis study characterized the neuroanatomical heterogeneity related to the aging process by offering a 5‐dimensional representation system with five distinct brain signatures. It elucidates connections to demographics, pathology, and genetics, offering potential for personalized diagnostics, improved patient management, and enhanced precision in clinical trials.
The first year of amyloid immunotherapy in an academic dementia specialty practice
Authors: Paczynski Madeline , Schindler Suzanne E. , Musiek Erik S. , Holtzman David M. , Benzinger Tammie L.S. , Dow Alan , Namazie‐Kummer Sheyda J , Posey Zachary J , Woodfin Carson , Ellington Dawn , Morris John C. , Snider B. Joy 
Published: January 2025

AbstractImplementation of amyloid‐lowering treatments in clinical care for early symptomatic Alzheimer disease (AD) raises many challenges. The Memory Diagnostic Center (MDC), the dementia specialty practice associated with Barnes‐Jewish Hospital/Washington University School of Medicine (BJH/WUSM), has 16 clinicians (12 physicians and 4 advanced practice providers) who see over 2,000 patients with memory disorders per year. BJH is the academic flagship of BJC HealthCare (BJC), an integrated health system in St. Louis, Missouri. Leadership of BJC/WUSM and MDC staff met regularly for two years in anticipation of amyloid‐lowering treatments becoming available. BJC/WUSM decided not to offer aducanumab (Aduhelm®) but did approve use of lecanemab (Leqembi®) after it received FDA approval in July 2023. Providing amyloid‐lowering treatments required coordination with BJC administration, neuroradiology, infusion centers and the clinical practice. Patients meeting appropriate use criteria were identified prior to approval of lecanemab. The requirement for AD biomarkers led to an increase in referrals to our lumbar puncture clinic prior to ∼30/month; there was less increase in referrals for AD blood tests or amyloid PET scans. The first patient received lecanemab within 6 weeks of FDA approval. Barriers to treatment included limited availability of appropriate MRI imaging, lack of reimbursement for AD blood tests and amyloid PET, limited capacity of infusion centers, and issues with private insurers. The MDC developed an on‐call system for response to infusion reactions and other adverse events and a tracking system for infusions and monitoring MRIs. We are developing registries and mechanisms in the electronic medical record to alert Emergency Department providers that patients on lecanemab being evaluated for stroke could be at increased risk of bleeding from thrombolytic therapies. Increasing capacity for patient assessments, biomarker testing, infusions, and follow‐up appointments has required adding advanced practice providers and office staff and presents challenges for reimbursement. As of January 2024, 80 MDC patients had received at least one lecanemab infusion and about 30 are undergoing evaluation to initiate treatment. Ongoing challenges include extending amyloid immunotherapy beyond the academic medical center, which will require training neurologists throughout the BJC system and developing sustainable staffing and billing models for management of amyloid‐lowering treatments.
Detection of Amyloid Status and Preclinical Alzheimer's Disease Using Cognivue Clarity, An Adaptive Psychophysics Computerized Cognitive Battery in The Bio‐Hermes Study
Authors: Galvin James E , Kleiman Michael J , Estes Paul W. , Harris Heather M. ,   Cognivue, Inc.
Published: January 2025

AbstractBackgroundAn easy and reliable method for detection of Alzheimer's Disease (AD) and mild cognitive impairment (MCI) is critical for clinical trial enrollment. In the era of amyloid‐lowering therapies, there is a need to identify individuals likely to have amyloid to enrich recruitment and lower costs related to amyloid PET. In addition, a subset of cognitively normal individuals have amyloid deposition (Preclinical AD) but to date there is no cognitive assessment or screening method that can detect these individuals in the absence of expensive biomarkers. Cognivue Clarity is an adaptive psychophysics computerized cognitive assessment generating a global score and 10 subtest scores.MethodsAs part of the Bio‐Hermes study, sponsored by the Global Alzheimer’s Platform Foundation, Cognivue Clarity was administered to 964 individuals who also had amyloid PET, plasma amyloid and tau measures, ApoE genotyping, MMSE, Functional Activities Questionnaire (FAQ), and Rey Auditory Verbal Learning Task (RAVLT). Cognivue Clarity performance was compared between (1) clinically‐defined, (2) biomarker‐defined, and (3) clinicopathological‐defined groups.ResultsThe sample had a mean age of 72.0 ± 6.7y, 15.5 ± 2.7y education, 55.9% females and 23.0% individuals from underrepresented groups. Clinical groups included 42.8% Healthy, 31.6% MCI, and 25.5% Probable AD. Amyloid PET was positive in 62.7%. Clinicopathological groups included 33.5% Healthy, 10.3% Preclinical AD, 27.2% MCI/AD, and 29.1% non‐AD. While Cognivue, MMSE, FAQ, and RAVLT scores were different between impaired vs unimpaired and amyloid positive vs amyloid negative individuals, only Cognivue Clarity was different between Healthy vs Preclinical AD (p=.009). Three subtests [Shape Discrimination (p=.002), Visual Salience (p=.005), Adaptive Motor Control (p=.004)] and their mean (p<.001) differentiated True Controls from Preclinical AD with an area under the curve of 0.634 (95%CI:0.570‐0.698, p<.001) and were moderately correlated with Amyloid PET Centiloid (r=‐.308) and pTau217 (r=‐.315). ApoE carriers had lower scores than non‐carriers (p=.02).ConclusionsCognivue Clarity, a 10‐minute computerized cognitive battery, can detect individuals with cognitive impairment and identify individuals likely to have amyloid positivity. Cognivue Clarity is the first test able to identify individuals with Preclinical AD. This has great potential as an enrichment strategy for AD clinical trials testing amyloid‐lowering therapies and AD prevention.
Progression of functional brain complexity in longitudinal Alzheimer's diseases
Authors: Zhang Ru , Aksman Leon M. , Wijesinghe Dilmini , Ringman John M , Wang Danny JJ , Jann Kay 
Published: January 2025

AbstractBackgroundReduced complexity of resting‐state fMRI has been associated with mild cognitive impairment (MCI) and Alzheimer's diseases (AD) in cross‐sectional cohorts. However, the trajectory of complexity in AD progression remains unknown. We conducted complexity analyses in a longitudinal AD dataset.MethodWe used demographic, clinical, T1 structural, and resting‐state fMRI (rsfMRI) data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The final sample included 210 subjects in Group CN (remaining cognitively normal), 27 subjects in Group CNtoMCI (converted from CN to MCI), and 32 subjects in Group MCItoAD (converted from MCI to AD). The three groups matched in terms of sex, education, and age of their first rsfMRI scan (Table 1).Standard image preprocessing was performed in the CONN toolbox. Multiscale entropy (MSE) for number of temporal scales a = 6 (0.33‐0.05Hz) was computed for each of fourteen meta regions of interest (meta‐ROIs). The area under a curve across all scales was calculated to reflect complexity for each meta‐ROI.A linear mixed effects (LME) model was implemented to evaluate group differences in complexity and altered rates of progression of complexity across groups.ResultThe LME model revealed significantly lower rsfMRI‐complexity in Group MCItoAD as compared to Group CN in the prefrontal, cingulate, and insula (t = ‐3.019 to ‐2.669, p = 0.003 to 0.008, Benjamini‐Hochberg (BH) corrected with false discovery rate < 0.05, Figure 1(A), 1(C), & Table 2). Additionally, rsfMRI‐complexity decayed significantly faster in Group CNtoMCI than in Group CN in the prefrontal, superior frontal, inferior frontal, lateral temporal, and medial temporal lobe (t = ‐2.417 to ‐2.093, p < 0.05, uncorrected, Figure 1(B), 1(D), & Table 2). By contrast, complexity decayed significantly faster in Group MCItoAD relative to Group CN only in the prefrontal (t = ‐2.338, p < 0.05, uncorrected, Figure 1(B), 1(D), & Table 2).ConclusionThe affected regions were mainly the frontal and temporal cortices which was consistent with the hypothesis of decline in executive functions and memory in AD. It demonstrates the potential of complexity analysis as an early AD biomarker.
Predicting cognitive function from multimodal mesoscale neuroimaging data
Authors: Duchesne Simon , Potvin Olivier , Dieumegarde Louis 
Published: January 2025

AbstractBackgroundOur objective was to assess individual and joint relationships between various mesoscale indicators of brain health (e.g., neuronal, metabolic, and vascular integrity) and cognitive function.MethodWe used 7224 timepoint measures originating from 1825 participants of the ADNI study who were cognitively healthy or with mild cognitive impairment at initial assessment. Cognitive function outcomes were test results on the Alzheimer’s Disease Assessment Scale–Cognitive Subscale (ADAS‐Cog), the Rey auditory Verbal Learning Test immediate recall (RAVLTi), the Rey auditory Verbal Learning Test delayed recall (RAVLTd), the Trail Making Test A (Trail a), and the Trail Making Test B (Trail b) tasks. Four families of markers were used. First were three families of mesoscale neuroimaging assessed by MRI for 88 brain regions: 1. Neuronal integrity was estimated by FreeSurfer 6.0 produced volumes extracted from T1‐weighted (T1w) images; 2. Neurite integrity was estimated through T1w/T2‐weighted MRI ratios; 3. Cerebral metabolism integrity was measured by synthetic fluorodeoxyglucose‐positron emission tomography, generated by a model based on a latent‐space regularized generative adversarial net from T1w MRIs. Secondly, 4. Systemic markers of vascular health‐related factors were included in the model (history of cardiovascular diseases, alcohol abuse, smoking history, systolic and diastolic blood pressure, history of hypertension, type 2 diabetes, Hachinski score).Cross‐sectional and future cognitive outcomes were all predicted in a single machine learning model using all combinations of predictor families at baseline, time interval between outcomes and baseline as long as sex and education. Three types of regression models were tested (Ridge, Partial Least Squares and Support Vector).ResultIndividually, neuronal and metabolism integrity were the best predictors of cognitive function (Figure 1; R2: .15‐.35 and MAE in Z score: .08‐.23). The combination of two or more mesoscale brain health markers improved predictions, with the best combination being neuronal integrity with metabolism (Figures 2‐3; mean R2: .28; mean MAE: .12). However, increasing the number of markers led to similar results.ConclusionWhile the prediction of cognition improved with a combination of brain health mesoscale markers, a plateau seems to be reached after combining two families.
PTPRS is a novel marker for early tau pathology and synaptic integrity in Alzheimer’s disease
Authors: Poirier Alexandre , Picard Cynthia , Labonté Anne , Aubry Isabelle , Auld Daniel L. , Zetterberg Henrik , Blennow Kaj , Tremblay Michel L. , Poirier Judes 
Published: January 2025

AbstractBackgroundSynaptic dysfunction is a central pathologic feature of Alzheimer’s disease (AD), with synaptic loss even preceding neuronal loss in specific brain regions. In healthy individuals, synaptic function and plasticity are orchestrated through the complex integration of signaling inputs generated by cell surface receptors.MethodsIn this study, we investigate the role of one such receptor, protein tyrosine phosphatase receptor sigma (PTPRS), in the context of Alzheimer’s disease. Publicly available datasets (BRAINEAC, ROSMAP, ADC1) and a cohort of asymptomatic but “at risk” individuals (PREVENT‐AD) were used to explore the relationship between PTPRS and various Alzheimer’s disease biomarkers.ResultsWe identified that PTPRS rs10415488 variant C shows features of neuroprotection against early phospho (181)‐tau pathology (p<0.005) and synaptic degeneration (GAP‐43, p<0.001) in Alzheimer’s disease. In brain tissues, PTPRS protein abundance was significantly correlated with the quantity of two markers of synaptic integrity: SNAP25(R2=0.20, p < 0.0001) and SYT‐1(R2=0.12, p < 0.0001). We also found the presence of sexual dimorphism for PTPRS, with higher CSF concentrations in males (p<0.001) than females. Male carriers for variant C exhibit a 10‐month delay in the onset of AD (p <0.05). One molecular function of PTPRS we chose to explore is macrophagy, given the implication of this biological process in the response against neurodegeneration. Macrophagy‐associated LC3 immunoreactivity occurs in most dystrophic neurites in AD and co‐localized with abnormal P‐tau in most neurofibrillary tangles. In the ROSMAP subjects, LC3 cortical protein levels were found to be significant reduced in C carriersConclusionsPTPRS acts as a soluble, secreted neuroprotective receptor, found in the extracellular space in Alzheimer’s disease. These findings also highlight the functional importance of synaptic plasticity in the protection against neurodegenerative diseases.
Metabolic Dysfunction and Alzheimer’s Disease Risks in African Americans
Authors: Mondal Tanmoy , Johnson Jheannelle , Sur Tapas Kumar , Loffredo Christopher A. , Cotin Sharleine T. , Sahota Jasneet , Korbe Brent E. , Nunlee‐Blnad Gail , Ghosh Somiranjan Ghosh 
Published: January 2025

AbstractBackgroundMetabolic diseases like chronic Type 2 Diabetes Mellitus (T2DM) are now a serious global health concern In the United States. African Americans (AA) are being affected at a disproportionate rate with the condition compared to other ethnic groups, yet there are relatively few studies that have specifically focused on this group. Our previous findings have suggested that AA patients with T2DM had gene expression signals associated with Alzheimer’s Disease (AD). The current pilot study aimed to examine the amyloidogenic processing pathways (APP) and cholesterol biosynthesis‐related marker genes in the circulation of clinically identified T2DM AA patients in the Washington DC area.MethodA total of 12 confirmed T2DM AA patients and matched healthy controls were recruited from a regional hospital's outpatient departments, with sociodemographic and medical details obtained for analysis. Whole blood was collected in DNA/RNA Shield™ Blood Collection Tube for RNA extraction and isolation. The APP and cholesterol biosynthesis‐related gene expression patterns were examined using profiler array gene expression (qRT‐PCR). The affected pathways were identified using Ingenuity Pathway Analysis (IPA).ResultWe identified Amyloid Processing, Neuroinflammation Signaling, ERB4 Signaling, Interleukin signaling, and nNos signaling as the top canonical pathways. The top diseases and their functions were Metabolic disease, Neurological disease, Organismal Injury and Abnormalities, Psychological disorders, and cardiovascular diseases. APP, CDK5R1, PSENEN, and MAPT were the key genes that were differentially expressed (≥2‐fold change at p <0.05) in APP pathways, with PHLPP2, IL33, APP, CRP, & TAC1 as the top upstream regulators.ConclusionThe results suggest that T2DM may influence the risk of developing AD in AA patients. Further studies on larger multi‐ethnic populations would be of interest to validate and extend these findings. Information from such biochemical and genetic studies will be crucial to the development of therapeutic targets to assess future AD risks.
Inflammatory Architecture of Frailty in the Realm of Alzheimer's Disease and Related Dementias
Authors: Chen Jiachen , Doyle Margaret F , Cao Yumeng , Ragab Ahmed , Murabito Joanne M , Lunetta Kathryn L. 
Published: January 2025

AbstractBackgroundAs a risk factor for Alzheimer's disease and related dementias (ADRD) in older adults, inflammatory mechanisms underlying physical frailty remain incompletely elucidated. This study aimed to characterize the inflammatory architecture of frailty and explore predictive implications of inflammatory signatures of frailty on ADRD.MethodThe study included 741 Framingham Heart Study Offspring cohort participants (52% female, mean 60 years range 40 to 85), dementia‐free at Exam 7 (1998‐2001), followed for incident dementia over 15.8 years on average. The sample was randomly split 50/50 into discovery and validation sets. A total of 209 circulating inflammatory markers including peripheral immune cells and inflammatory biomarkers were measured at Exam 7, with Fried frailty index assessed at Exam 8 (2005‐2008). Using frailty and its 5 components as outcomes, LASSO was conducted on inflammatory markers adjusted for covariates in discovery set. Inflammatory signatures were generated by summing the product of regression coefficients and marker levels for each participant. Pairwise associations between 6 signatures and incident dementia/AD were conducted by Cox models in validation set, with FDR ≤0.05 declaring significance.ResultA total of 48 inflammatory markers were involved in signatures: 9 protein biomarkers (4E‐BP1, ADA, CCL3, CD40, CDCP1, HGF, IL‐10RB, IL‐12B, and IL‐17C) and 4 immune cells (CD4 T helper cells, CD4+FoxP3+, CD8+FoxP3+, and TREM2+ NCM) contributed to at least two signatures. In the validation set a handgrip‐strength‐specific signature was significantly associated with incident dementia (HR=0.50, 95%CI: (0.32, 0.76), FDR=0.01) and incident AD (HR=0.44, 95%CI: (0.25, 0.76), FDR=0.01), a walking speed‐specific signature was associated with incident dementia (HR=14.30, 95%CI: (3.67, 55.73), FDR=<0.01) and incident AD (HR=13.95, 95%CI: (2.65, 73.49), FDR=0.01). An unintended weight loss‐specific signature was significantly associated with incident dementia (HR=3.45, 95%CI: (1.09, 10.94), FDR=0.05), and exhaustion‐specific signature was associated with incident dementia (HR=4.12, 95%CI: (1.59, 10.69), FDR=0.01) and incident AD (HR=5.73, 95%CI: (1.84, 17.90), FDR=0.01), and overall frailty signature was associated with incident dementia (HR=2.33, 95%CI: (1.13, 4.84), FDR=0.04) and incident AD (HR=2.82, 95%CI: (1.08, 7.37), FDR=0.05).ConclusionFrailty‐implicated signatures unveil inflammatory markers related to the biology of frailty and ADRD. Future replication studies are needed to inform ADRD prediction signatures.