bluuebunny/crossref_metadata_2025_split huggingface dataset:
How value‐based healthcare can improve the work of memory clinics
Authors: Schönberger Anna R. , Steinmetz Annika , Schild Ann‐Katrin , Hausner Lucrezia , Spielmann‐Benson Gloria , Frölich Lutz , Jessen Frank 
Published: January 2025

AbstractBackgroundValue‐based healthcare (VBHC) is a novel concept derived from economic research which is recently implemented in various medical departments and facilities. Additionally to an improvement of patient care, it postulates a reduction of expenses by providing patients with what they really need. Value in this context is defined as outcomes achieved for patients relative to the required costs. In VBHC collecting patient‐reported outcome measures (PROM) and patient‐reported experience measures (PREM) is essential to clarify patients’ needs. According to the VBHC approach, a modification of patient care based on PROM and PREM achieves a higher value and more efficient treatment for patients.We have implemented the VBHC approach at two German memory clinics to test its applicability in this novel setting. We expect to gain concrete aspects for improving the diagnostic process for patients, relatives, and employees.MethodsIn each memory clinic we interviewed 25 patient‐relative dyads with regard to PREM and PROM. In addition, we conducted an employee survey about current problems and potential suggestions for improvement. Based on these results, respective modifications of the diagnostic process took place in both memory clinics. Thereafter a second evaluation, interviewing another set of 25 dyads was carried out.ResultsIn the first evaluation the majority of patients of both memory clinics quoted the length of the diagnostic process as main negative experience. The duration from the first visit to the final diagnosis took several months which was experienced as highly stressful for patients and their relatives. Hence, the diagnostic process was shortened to a duration of approx. 3‐4 weeks. In the second evaluation preliminary data indicate a significantly better experience of patients and family members with the diagnostic process (PREM) after modification. Other aspects reported by patients, relatives as well as employees offer additional perspectives for improvement.ConclusionsThis is the first study implementing the VBHC approach in a memory clinic setting. We found that VBHC can indeed improve patient care and may contribute in establishing a standardized and optimized procedure in the diagnostic processes.
Diabetes‐Related Disruption of White Matter Microstructural Integrity is Associated with Faster Late‐Life Cognitive Decline
Authors: Tong Han , Capuano Ana W. , Biswas Roshni , Zhang Shengwei , Bennett David A. , Arfanakis Konstantinos , Arvanitakis Zoe 
Published: January 2025

AbstractBackgroundType‐2 diabetes mellitus is associated with both altered white matter microstructural integrity and increased risk of dementia. However, whether there is a link between the white matter changes in older adults with diabetes and their cognitive decline remains unclear. The objective of this study was to examine the association of diabetes‐related changes in white‐matter microstructural integrity with longitudinally assessed late‐life cognitive function.MethodA total of 674 older adults were studied (mean age at time of scan: 78.4 years; 125 males; 104 with diabetes), among participants of an ongoing, community‐based cohort study of aging at the Rush Alzheimer’s Disease Center. Annual clinical evaluations included detailed neuropsychological testing from which a global and five domain‐specific cognitive summary measures were derived (mean follow‐up duration: 3.0 years). High‐quality (3T) brain magnetic resonance imaging scans were acquired, and quality‐assured diffusion tensor imaging data were available for analyses. Tract‐based statistical analysis (TBSS) was used to project each fractional anisotropy (FA) image to the IIT white matter skeleton. Voxel‐wise generalized linear regression models were fitted using Permutation Analysis of Linear Models in FSL, with FA and mean diffusivity (MD) as the outcome and diabetes as predictor, and co‐variates of age, sex, education, scanner type, and white matter hyperintensities. Using linear mixed‐effects models and controlling for age, sex, education, and diabetes, we examined the association between median FA of all significant voxels and longitudinally assessed cognitive function data.ResultDiabetes was associated with lower FA in the rostral body of the corpus callosum (p<0.05, threshold‐free cluster enhancement, family‐wise error corrected). A lower median FA of these voxels was associated with a faster rate of decline in global cognition (estimate=0.121 [SE=0.056], p=0.030) and three cognitive domains including semantic memory (estimate=0.225 [SE=0.090], p=0.013), working memory (estimate=0.205 [SE=0.081], p=0.012) and visuospatial abilities (estimate=0.193 [SE=0.090], p=0.032), but not episodic memory and perceptual speed. No difference was found in MD between older adults with diabetes and those without.ConclusionDiabetes‐related disruption of white matter microstructural integrity in the rostral body of corpus callosum in older adults may be associated with faster late‐life cognitive decline.
Cerebrovascular reactivity (CVR) MRI as a biomarker for cerebral small vessel disease (SVD) related cognitive decline: Multi‐site validation in the MarkVCID Consortium
Authors: Liu Peiying , Lin Zixuan , Hazel Kaisha , Pottanat George , Xu Cuimei , Jiang Dengrong , Pillai Jay J , Lucke Emma , Bauer Christopher E. , Gold Brian T , Greenberg Steven M. , Helmer Karl G , Jann Kay , Jicha Gregory A , Kramer Joel H. , Maillard Pauline , Mulavelil Rachel M. , Satizabal Claudia L , Schwab Kristin , Seshadri Sudha , Singh Herpreet , Velarde Angel G , Wang Danny JJ , Kalyani Rita , Moghekar Abhay , Rosenberg Paul B. , Yasar Sevil , Albert Marilyn S. , Lu Hanzhang ,   MarkVCID Consortium
Published: January 2025

AbstractBackgroundCerebral small vessel disease (SVD) related vascular contributions represent a major factor contributing to cognitive decline and dementia (VCID) in older adults. However, there has not been a validated biomarker for the diagnosis and treatment monitoring of this condition. Recently, the US National Institute on Neurological Disorders and Stroke (NINDS) funded the MarkVCID Consortium to identify and validate clinical‐trial‐ready biomarkers for VCID. Cerebrovascular reactivity (CVR) MRI is one of the selected biomarkers that underwent multi‐site testing in the Consortium. The present study aimed to report the relationship between CVR and cognitive function at independent sites, based on a pre‐specified hypothesis.MethodCVR, the ability of cerebral small vessels to dilate upon stimulus, is thought to directly reflect physiological function of the brain microvasculature. Based on previous single‐site findings, the pre‐defined hypothesis was that CVR will be associated with the global cognitive function measured by Montreal Cognitive Assessment (MoCA) after adjusting for age, sex, and education, and this association will be observed in data collected and analyzed at each individual site.A total of 264 older participants from three sites were included (Table 1). Each site performed an identical CVR MRI procedure using 5% CO2 inhalation, and a standardized clinical and cognitive evaluation. Multi‐linear regression analysis was conducted on a site‐by‐site basis to examine the pre‐defined hypothesis.ResultGray‐matter CVR showed a positive association with MoCA score after adjustment for age, sex, and education, in which participants with higher CVR had higher MoCA scores. This relationship was reproduced at each site (Figure 1, p<0.05 for each), confirming our pre‐specified hypothesis.In the secondary analysis of all data together, higher gray‐matter CVR was found to be significantly associated with better executive function measure of item response theory (IRT) score (β=2.87, p=0.003).ConclusionThe present study evaluated the relationship between CVR and cognition in a multi‐center setting. CVR was found to be positively associated with global cognitive function measured by the MoCA, which was shown to be reproducible across different sites with diverse cohorts. These findings support the utility of CVR as a biomarker in future clinical trials of SVD and VCID.
Total and Structural Carotid Artery Stiffness are related to Cognitive Decline and ADRD Pathology: The Multi‐Ethnic Study of Atherosclerosis (MESA)
Authors: DeConne Theodore M , Williams Jeremy R , Pewowaruk Ryan , Korcarz Claudia E. , Tanley Jordan E. , Carlsson Cynthia M. , Heckbert Susan R. , Habes Mohamad , Nasrallah Ilya M. , Lockhart Samuel N. , Luchsinger José A. , Ding Jingzhong , Liu Yongmei , Hayden Kathleen M. , Gepner Adam D , Hughes Timothy M. 
Published: January 2025

AbstractBackgroundStiffening of the large elastic arteries is an emerging age‐related risk factor for Alzheimer’s disease (AD) and related dementia (ADRD). Arterial stiffness is associated with pathological changes underlying AD/ADRD, and total arterial stiffness (T‐PWV) can be subdivided into two main mechanisms. Structural stiffening (S‐PWV) is due to intrinsic remodeling of the artery wall, and load‐dependent stiffening (LD‐PWV) is due to increased blood pressure without intrinsic changes to the artery wall. We hypothesized that greater structural stiffness (S‐PWV rather than LD‐PWV) would be associated with faster cognitive decline, and with increased white matter hyperintensity volume (WMHv) and decreased white matter fractional anisotropy (WMFA). Lower WMFA is interpreted as indicating poor WM integrity.MethodMulti‐Ethnic Study of Atherosclerosis (MESA) participants included in this analysis, represented four racial/ethnic groups that completed carotid arterial stiffness by duplex ultrasound (2010‐2012) from which pulse wave velocity was calculated, repeated cognitive assessments with the Cognitive Abilities Screening Instrument (CASI), Digit Symbol Coding (DSC), and Digit Span (DS) (2010‐12, 2016‐2019, 2019‐2021, Table 1), and 3T MRI (2016‐2021, Table 2). 3T MRI were acquired; T1, FLAIR and diffusion imaging sequences were acquired and processed to generate global WMHv and WMFA. Multivariable linear mixed and regression models related standardized arterial stiffness components to cognitive decline and neuroimaging and were adjusted for age, race and ethnicity, gender, education, site, and language.ResultGreater T‐PWV was significantly associated with faster declines in DSC and DS (Table 1), greater WMHv, and lower WMFA (Table 2), but not CASI. S‐PWV was significantly associated with faster declines in DSC and DS (Table 1) over a decade. Greater S‐PWV was also significantly associated with greater WMHv, but no WMFA differences (Table 2). Interestingly, LD‐PWV was not associated with changes in any measure of cognitive decline, and was associated with lower WMFA (Table 2).ConclusionHigher structural stiffness of the carotid artery drives associations between total arterial stiffness with faster declines in cognitive function and with brain MRI findings. Interventions targeting arterial remodeling rather than blood pressure alone may be the next step in delaying age‐related cognitive decline and structural changes in the brain underlying ADRD.
Identification of CSF proteins in Alzheimer’s Disease highly correlated with [18F]GTP1
Authors: Lee Julie 
Published: January 2025

AbstractBackgroundNeurofibrillary tangles (NFTs) are neuropathological hallmarks of Alzheimer’s Disease (AD) and consist of insoluble aggregates of tau protein (MAPT). Tau pathology spreads in the brain with increasing disease severity. Tau is commonly measured in AD trials as it is diagnostic and prognostic of clinical progression, and also a pharmacodynamic biomarker to quantify treatment effects of emerging tau‐targeting therapeutics. Tau PET tracers, like [18F]Genentech Tau Probe 1 ([18F]GTP1), selectively bind to NFTs to enable imaging of tau pathology in AD patients. Tau PET imaging requires special infrastructure at clinical sites and has high costs limiting its broad applicability in trials. The goal of this analysis is to identify CSF proteins highly correlated with [18F]GTP1 to develop fluid surrogate biomarkers of tau PET that may allow greater accessibility to pathological tau measurements in clinical settings.MethodAD CSF samples were from participants enrolled in Tauriel or Lauriet, two semorinemab PhII trials. Baseline protein levels were measured by analyzing samples using data‐independent acquisition mass spectrometry (DIA‐MS). After data processing and filtering, 3197 unique protein intensities were included in this analysis. Trial participants also completed [18F]GTP1 imaging. Analysis was completed by Invicro where standardized uptake value ratios (SUVR) were reported using inferior cerebellar gray matter as the reference area. The five regions evaluated in this analysis are whole cortical gray matter, temporal, Braak stages I‐II, Braak stages III‐IV, and Braak stages V‐VI. [18F]GTP1 SUVR values for the selected regions were compared against CSF protein intensity measurements to calculate Spearman correlation coefficients and FDR‐adjusted p‐values to identify highly correlated proteins.ResultCSF proteomic measurements and [18F]GTP1 SUVR values were available from 128 Tauriel or Lauriet participants at baseline. The correlation analysis demonstrated proteins MAPT, SYN1, PAK1, YWHAE, YWHAB, and YWHAZ are highly correlated with [18F]GTP1 across more than one brain region. Proteins with absolute correlation values greater than 0.4 with tau PET are functionally related to the synapse.ConclusionSix CSF proteins highly correlated with tau PET were identified. Validation of these results by replicating the analysis on a secondary dataset with PET and CSF protein measurements may establish further confidence in these findings.
Brain age correlates with plasma NfL in amyloid positive individuals
Authors: Sihag Saurabh , Ribeiro Alejandro 
Published: January 2025

AbstractBackgroundBrain age prediction using machine learning (ML) models provides novel opportunities to characterize neurobiology of accelerated aging. Brain age gap, i.e., the gap between the predicted age and the chronological age for an individual, is a meaningful metric to assess accelerated or premature aging. Plasma neurofilament light (NfL) is a well‐recognized biomarker that can reflect the severity of structural atrophy and the loss of white matter integrity in Alzheimer's disease. Here, we leveraged an anatomically interpretable ML pipeline for brain age prediction (see ref. [1]) to investigate the correlations between brain age and plasma neurofilament light chain (NfL) in older adults in the ADNI cohort.MethodBrain age was derived from the cortical thickness measures (Desikan‐Killiany atlas, available online as part of the ADNI database) for 206 healthy individuals (HC), 372 individuals with MCI diagnosis, and 118 individuals with dementia diagnosis (AD). Further, contributions from individual brain regions to the predicted brain age were characterized for each individual. The amyloid status was available for all individuals and plasma NfL (within 30 days of the MRI scan acquisition) was available for a subset of individuals (Table 1). The ML model used to derive brain age had been trained on the healthy individuals from the OASIS‐3 dataset. We assessed the associations between brain age and plasma NfL for HC and the combined MCI and AD cohorts.ResultBrain age was significantly elevated in AD cohort with respect to HC cohort (Table 1, Figure 1). No significant differences were found between the brain age gap for amyloid positive and amyloid negative individuals (Figure 1). For HC cohort, brain age was positively correlated with plasma NfL in amyloid positive individuals (with brain age contributions from entorhinal region exhibiting the most significant positive correlation (ρ=0.72, p=2.45e‐6) with plasma NfL), but not amyloid negative individuals (Figure 2). In the combined MCI and AD cohort, brain age was correlated with plasma NfL in only amyloid positive individuals.ConclusionOur results suggest that amyloid status may mediate the association between plasma NfL and brain age in older individuals.[1] Sihag et al., Explainable Brain Age Prediction using coVariance Neural Networks, NeurIPS, 2023.
Prospective Associations of Interleukin‐6 and APOE 4 Allele with Cognitive Decline in Biracial Community‐dwelling Older Adults: the Chicago Health and Aging Project (CHAP)
Authors: Ng Ted K.S. , Beck Todd , Desai Pankaja , Dhana Klodian , Wilson Robert S. , Evans Denis A , Rajan Kumar B 
Published: January 2025

AbstractBackgroundIt is unclear whether inflammation, i.e., high interleukin‐6 (IL‐6) levels, and genetic risk, i.e., APOE ε4 allele, have a compounding effect on cognitive decline (CD).MethodWe analyzed a subset of participants from the longitudinal cohort study, Chicago Health and Aging Project, comprising 1,120 biracial community‐dwelling older adults (60% African American & 62% women), and mean follow‐up=6.4 years. We ran adjusted mixed‐effects models of longitudinal cognitive decline and explored potential racial differences.ResultIn ε4 carriers, higher serum IL‐6 was not associated with the rate of CD (β=‐0.0091 (SD=0.0165, p=0.5800). Conversely, in non‐ε4 carriers, compared to the lower tertile, those with the upper tertile of serum IL‐6 levels experienced significantly accelerated CD [β=‐0.0257 (SD=0.0084 p=,0.0023)]. These results did not differ between races.ConclusionEven without the largest genetic risk factor for late‐onset AD/ADRD, elevated serum IL‐6 still accelerate the rate of CD. Hence, interventions ameliorating inflammation may prevent AD/ADRD. In contrast to the ε4 allele, as serum IL‐6 is a modifiable risk factor, these findings could implicate precision medicine, including AD/ADRD preventions and therapies to prevent AD/ADRD.
Quantifying Deep Learning Interpretability in Alzheimer's Disease Research: Validating Deep Learning Heatmaps through Meta‐Analysis
Authors: Wang Di , Honnorat Nicolas , Nirmala Anoop Benet , Fox Peter T , Seshadri Sudha , Habes Mohamad 
Published: January 2025

AbstractBackgroundDeep learning has shown promising results regarding Alzheimer’s disease (AD) studies. In the meantime, heatmap methods have emerged as a popular tool to visualize deep learning models, enhancing the transparency and explainability of deep learning. However, when no ground truth is available, the heatmap methods are particularly difficult to trust. In the neuroimaging field, meta‐analysis is often conducted to reduce heterogeneity and reveal the true positive. Thus, in this work, we aim at validating heatmap methods by quantifying the overlap of a ground truth map provided by a large meta‐analysis and heatmaps derived by three methods, the Layer‐wise Relevance Propagation (LRP) method, the Integrated Gradients (IG) method, and the Guided grad‐CAM (GGC) method from convolutional neural network achieving state‐of‐the‐art classification performance for AD classification on a sample of MRI scans from ADNI.Method502 ADNI participants were included in this study with 250 AD participants and 252 healthy controls. Those brain scans were trained by five different 3D CNN architectures. Three CNN heatmap methods were applied on trained models to produce a heatmap indicating which voxels were the most important when classifying AD and control participants. The structural MRI meta‐analysis map used as the ground truth summarized 77 neuroimaging studies reporting 773 locations in the brain affected by AD.ResultThe best CNN 5‐fold cross validated accuracy reached 87.25% for classifying AD and CN. The best Dice overlap measured for LRP, IG, and GGC, using this model was 0.502 for LRP heatmap, 0.55 for the IG heatmap, and 0.54 for the GGC heatmap. SVM activation patterns achieved a dice of 0.363.ConclusionWe found that the three heatmap methods capture brain regions that overlap well with the meta‐analysis map, and we observed the best overlap for the IG method. All three heatmap methods outperformed linear SVM models suggesting that the deep feature learnt by the most recent deep neural networks can produce more meaningful representations than linear and shallow models.
Comparing neural activity of older adults with and without Parkinson’s disease using mobile electroencephalography in different environments
Authors: Marshall Samantha , Jeyarajan Gianna , Al‐Shaikh Jennifer Hanna , Gabiazon Raphael , Seleem Tia , Nagamatsu Lindsay S 
Published: January 2025

AbstractBackgroundParkinson’s disease is a progressive neurodegenerative disorder that is becoming more prevalent as the population ages. Mobile neuroimaging has made it possible to observe cortical activity in this population outside of standard laboratory environments. Yet, few studies have explored the portability of these devices in a true real‐world environment without a specific task imposed on participants (e.g., dual task, motor demands).MethodMobile electroencephalography (EEG) was utilized to examine and compare cortical activity during sitting in different environments across older adults with and without Parkinson’s disease. We used the Muse S Generation 2 Brain‐Sensing Headband to record brain function. The first condition involved participants sitting in a standard laboratory environment while their brain activity was recorded. In the second condition, participants sat in an indoor space that included a living green wall, natural light, and other people while their brain activity was recorded. Fast Fournier Transform (FFT) analysis was performed on the raw EEG data to obtain corresponding waves for each electrode to examine power (uV^2) at each frequency (Hz).ResultPreliminary findings demonstrate significant differences in mean cortical activations across older adults with and without Parkinson’s disease. Differences in cortical activations were also observed when comparing the laboratory and real‐world environment.ConclusionThese findings suggest that elicited brain activity may differ across older adults with and without Parkinson’s disease and across different environments. These findings expand current knowledge on Parkinson’s disease, brain function, and cognition using real‐world methods and technology, which may inform interventions to increase quality of life among this population. As our findings suggest that environmental factors may modulate cortical activity, we also highlight the potential and importance for real‐world methods to supplement standard research practices to increase the ecological validity of studies conducted across the scientific community.