bluuebunny/crossref_metadata_2025_split huggingface dataset:
Understanding cognitive deficits in memory clinic patients with mixed disease by combining plasma markers and advanced diffusion MRI
Authors: Dewenter Anna , Dehsarvi Amir , Steward Anna , Biel Davina , Pescoller Julia , Zhu Zeyu , Wagner Fabian , Gross Mattes , Nuscher Brigitte , Roemer Sebastian Niclas , Buerger Katharina , Janowitz Daniel , Ewers Michael , Tiedt Steffen , Dichgans Martin , Brendel Matthias , Franzmeier Nicolai 
Published: January 2025

AbstractBackgroundMemory clinic patients typically present with Alzheimer’s disease (AD) and cerebral small vessel disease (SVD) to varying degrees. Therefore, it is crucial to determine the etiology of cognitive deficits for facilitating patient‐centered treatment in memory clinics. Plasma biomarkers (ptau217, Glial Fibrillary Acidic Protein [GFAP], Neurofilament light chain [NfL]) and fixel‐based advanced diffusion MRI markers (fiber density, fiber‐bundle cross‐section) show potential towards disentangling AD‐ and SVD‐related brain changes (Dewenter et al., Brain, 2023). However, their predictive power in understanding heterogeneous and SVD/AD‐specific cognitive deficits in memory clinic patients remains incomplete. We assessed i) how plasma‐based and fixel markers explain AD‐typical and SVD‐typical cognitive deficits and ii) their interrelation to uncover disease‐specific mechanisms.MethodWe included n = 76 in‐house memory clinic patients with Simoa‐based plasma ptau217, GFAP and NfL assessments, comprehensive neuropsychological testing (CERAD‐plus battery) and 3T MRI. Global white matter hyperintensity (WMH) volume and average skeletonized mean diffusivity (MD) were included as well‐established SVD markers. AD severity was probed through plasma ptau217 and cortical thickness of the pre‐established AD signature ROI. Advanced diffusion MRI was used to assess fiber density and fiber bundle cross‐section of key white matter tracts (Fig. 1A).ResultUsing linear regression, increased ptau217, reduced cortical thickness in AD signature ROI and fiber‐bundle cross‐section were highly associated with impaired episodic memory, i.e. a typical AD‐related symptom (Fig.1B, Fig.2A). In contrast, GFAP and NfL increases and reductions in MSMD and fiber density were associated with executive dysfunction, a typical sign of SVD‐related cognitive impairment (Fig.1B, Fig.2A). Additionally, fiber density was associated with GFAP and NfL levels, while fiber bundle‐cross section, a macroscopic marker of tract atrophy, was not associated with any of the plasma markers (Fig.2B).ConclusionPtau217 displayed high sensitivity and specificity for AD‐typical memory impairment, whereas GFAP and NfL were associated with SVD‐typical processing speed and executive impairment. Additionally, fiber density, a pre‐established imaging marker for SVD, was associated with GFAP and NfL. This highlights the effectiveness of these markers in distinguishing and characterizing SVD and AD in memory clinic patients and emphasizes the importance of considering concomitant SVD in patients with elevated GFAP and NfL levels.
Type 2 diabetes and Alzheimer's disease share common cortical neurodegenerative patterns
Authors: Motaghi Mahboubeh , Potvin Olivier , Duchesne Simon 
Published: January 2025

AbstractBackgroundType 2 diabetes (T2D) is a prevalent health condition associated with cognitive impairment and dementia. T2D induces adverse effects not only on the pancreas but also on the liver, kidneys, muscles, fat cells, and, notably, the brain. Both T2D and Alzheimer's disease (AD) exhibit associations with neurodegeneration, yet the extent of their shared patterns of brain atrophy remains poorly understood, potentially indicating common pathways. The primary objectives were to assess the similarity between neural degeneration patterns in T2D and AD and to identify commonalities between these two diseases.MethodWe employed linear models to compare neurodegeneration patterns linked to T2D and AD. A total of 43,172 participants from the United Kingdom Biobank (UKBB) (3,479 with T2D) and 8,378 from ADNI (871 with AD) were included in our study. We selected Free Surfer‐derived cortical thickness measures extracted from T1‐weighted MRI, age, sex, and blood biomarkers of T2D. The age range for UKBB was 40‐70 years, while for ADNI, it was 55‐97 years. We filtered data for individuals aged 55 and older, normalized features, and created age‐ and sex‐matched groups. We then created cortical atrophy maps using linear regression models for each group. Comparison of the AD and T2D maps was performed using permutation‐based tests accounting for spatial autocorrelation.ResultOur findings revealed similar patterns of neural degeneration in T2D and AD brain maps, particularly in the frontal, parietal, temporal, and occipital lobes, suggesting shared neuroanatomical alterations. Statistically significant overlap was observed in atrophy patterns between AD and T2D patients. T2D individuals exhibited greater atrophy in brain regions affected in AD, such as the hippocampus. Permutation analysis indicated no significant difference between the impact of T2D and AD on brain regions, supporting the notion that T2D is a substantial risk factor for AD.ConclusionThis study enhances our understanding of the similarities in how T2D and AD impact the brain, highlighting the potential connection between these conditions. Further research is warranted to explore underlying mechanisms and implications for clinical interventions.
Feasibility of detecting Amyloid burden in preclinical Alzheimer's disease with quantitative magnetization transfer MRI
Authors: Mao Andrew , Flassbeck Sebastian , Marchetto Elisa , Rusinek Henry , Assländer Jakob 
Published: January 2025

AbstractBackgroundMagnetization transfer (MT) MRI is sensitive to the presence of macromolecules, including amyloid‐beta, and previous work suggests that it may be useful for discriminating patients with Alzheimer's disease (AD) from healthy controls. In this study, we investigated if quantitative MT (qMT) is capable of detecting the amyloid concentration in a preclinical cohort.MethodWe recruited 14 subjects with a clinical dementia rating of 0 from NYU's ADRC cohort (7 male, mean age 74, 6 amyloid‐negative). An experimental 12 min qMT technique with an effective 1.24 mm isotropic resolution was used to obtain 6 parameter maps describing an unconstrained 2‐pool qMT model (Figure 1). MPRAGE and FLAIR images were acquired at 1 mm isotropic, rigid‐body registered to the qMT maps and used to perform Freesurfer‐based cortical parcellation. [18F]Florbetaben (FBB) images acquired within the preceding 24 months were rigid‐body registered to the qMT maps and used to compute average measurements across each cortical lobe for each hemisphere at 50% of the cortical thickness. Pearson's correlation coefficients were computed for all 6 qMT parameters with respect to FBB SUVR values. Amyloid‐positive and negative groups were compared using one‐way ANOVA tests. 5 Desikan‐Killiany ROIs containing substantial artifacts in the qMT maps were excluded from all analyses, though some imaging artifacts remain.ResultThe exchange rate between the two spin pools and the macromolecular pool's longitudinal relaxation rate were found to be both negatively correlated with amyloid concentration and significantly decreased in amyloid‐positive subjects most prominently within the temporal lobe (Figs.2–3). The former is consistent with previous reports in more advanced AD stages, but the latter has not previously been reported as its value is fixed in most qMT methods. However, we observed no significant changes in the macromolecular pool size—which encompasses both myelin and amyloid‐beta—with increasing amyloid burden.ConclusionMT MRI may be a promising tool for monitoring amyloid accumulation without the need for contrast agents or radiotracers. A larger cohort and a further optimized qMT sequence are needed to validate these preliminary findings.
Associations Between Choroid Plexus Integrity and Cognitive Decline in Aging: Insights from Advanced MRI Analysis
Authors: Gong Zhaoyuan , Bilgel Murat , Faulkner Mary E , Bae Jonghyun , Laporte John P , Guo Alex , Resnick Susan M. , Egan Josephine M. , Bouhrara Mustapha 
Published: January 2025

AbstractBackgroundThe choroid plexus (CP), a vital component in the brain’s ventricles, is crucial for cerebrospinal fluid (CSF) production and maintenance of the brain’s physiological environment. It plays a key role in regulating neuroinflammatory responses, clearing harmful substances, producing neurotrophic factors and signaling molecules, and forming blood‐CSF barrier. Consequently, changes to the CP’s structural integrity could disrupt brain homeostasis and lead to cognitive impairment. Indeed, recent research has highlighted CP alterations in aging as well as in various cognitive disorders including Alzheimer’s and Parkinson’s diseases. This study investigates the correlation between the macrostructural and microstructural integrity of the CP and longitudinal cognitive changes in cognitively unimpaired individuals.Method116 cognitively unimpaired participants from the BLSA and GESTALT underwent advanced MRI, including relaxometry (T1 and T2) and diffusion tensor imaging (DTI), enabling the quantification of CP volume, fractional anisotropy (FA), and mean diffusivity (MD). Participants received cognitive assessments at visits preceding and concurrent with MRI scans (320 visits total) to measure memory, attention, executive function, verbal fluency, and processing speed (Table 1). Linear mixed‐effects models were used to analyze the association between MRI metrics and longitudinal cognitive changes, adjusting for age, sex, education, and race.ResultOur study found that larger CP volume, elevated T1 and T2 values, and higher MD values, all indicative of compromised macrostructural or microstructural integrity, were associated with faster declines in various cognitive domains (Table 2). Particularly, the association was found in memory, verbal fluency, and processing speed for CP volume; in attention, executive function, and processing speed for T1 and T2 (Figure 1); and in attention, executive function, and processing speed for MD. Conversely, lower FA values, representing deteriorated microstructural integrity, correlated with accelerated decline in memory, attention, verbal fluency, and processing speed.ConclusionThis study highlights a significant association between CP characteristics and cognitive decline in cognitively unimpaired individuals. These findings underscore the importance of the CP in cognitive health and emphasize the need for further research into the role of the CP in healthy aging, potentially opening new pathways for early detection and intervention in cognitive decline.
Non‐invasive quantification of [18F]‐PI‐2620 binding to tau deposits in the brain using an image‐derived input function
Authors: Bhattarai Anjan , Holy Emily Nicole , Wang Yiran , Spencer Benjamin A. , Wang Guobao , DeCarli Charles S. , Fan Audrey P. 
Published: January 2025

AbstractBackgroundThis study quantified tau binding in the brain with 18F‐PI2620 PET using a non‐invasive Image‐Derived Input function(IDIF), derived using a new total‐body EXPLORER PET/CT scanner (Spencer et al.,2021). Additionally, we explored how PET scan duration influences the quantification of kinetic parameters across brain regions of interest(ROIs) that are vulnerable in Alzheimer’s Disease.MethodThe study cohort included 15 individuals(10 cognitively unimpaired, 3 mild cognitive impairment, and 2 AD; ages = 68‐92 years; all tau negative) from the UC Davis ADRC. Dynamic total‐body 18F‐PI‐2620 PET images were acquired over 90 minutes using the uEXPLORER (resolution = 2.344 mm isotropic voxels, framing protocol:30×2s,12×10s,7×60s,16×300s). Brain cropped PET images were motion corrected (Jenkinson et al.,2002), and linearly registered to individual T1‐weighted(T1W) MRIs (Jenkinson & Smith, 2001). Grey matter ROIs, including the medial temporal(MT), posterior cingulate(PC), and lateral parietal(LP) cortices, were obtained parcellating T1W images(Desikan et al.,2006). Dynamic time activity curves for each brain ROI were fitted to a reversible two‐tissue compartmental model(2TCM), accounting for delay, using a subject‐specific IDIF(plasma and metabolite‐corrected) derived from the descending aorta of the same images (Figure 1). ROI‐specific micro and macro kinetic parameters were estimated. Logan graphical analysis (Logan,2000) was also used to estimate total distribution volume(VT).Result2TCM with IDIF demonstrated high‐quality fits of 18F‐PI2620 binding across grey matter ROIs(Figure1). Perfusion (K1) was observed to be reduced in MT compared to PC and LP(Figure2A). All kinetic parameters remained relatively stable after the 60‐minute scan window across all ROIs. Notably, K1 showed stability after 30 minutes of scan duration across all ROIs(Figure2 and 3). A strong correlation was observed between VT estimated using 2TCM and Logan plot analysis, across all ROIs LP(r = 1.00,p<0.001), MT(r = 0.96,p<0.001), PC(r = 1.00,p<0.001). Additionally, significant negative correlation was observed between binding potential and perfusion across all ROIs (r = ‐0.40,p = 0.007).ConclusionThis study highlights the utility of a non‐invasive descending aorta IDIF derived using a total‐body EXPLORER PET/CT scanner to quantify 18F‐PI‐2620 grey matter kinetics. Preliminary findings suggest that a 60‐minute scan window may be sufficient for reliable quantification of kinetic parameters, while a 30‐minute scan time may suffice for perfusion alone. These findings warrant validation in a cohort involving tau positive individuals.
Multimodal neuroimaging and plasma biomarker evidence of white matter macrostructure loss in Parkinson's dementia
Authors: Zarkali Angeliki , Hannaway Naomi , McColgan Peter , Heslegrave Amanda J , Veleva Elena , Laban Rhiannon , Zetterberg Henrik , Lees Andrew , Fox Nick C , Weil Rimona S 
Published: January 2025

AbstractBackgroundParkinson’s (PD) is common and debilitating with over half of patients progressing to postural instability, dementia or death within 10 years. However, onset and rate of progression is highly variable, reflecting heterogeneity in underlying pathology, and biomarker studies to‐date have been limited to a single modality or assessed patients with established cognitive impairment.MethodWe assessed multimodal neuroimaging and plasma biomarkers in 98 PD patients (mean disease duration at baseline 4.33 years) and 28 age‐matched controls followed‐up over 3 years. Of PD patients, 31 (31.6%) had a poor outcome (PD poor outcomes), defined as mild cognitive impairment, dementia, frailty, or death during follow‐up. We compared biomarkers of gray matter (cortical thickness), white matter macrostructure (fibre cross‐section) and microstructure (fibre density) at whole‐brain and tract level, structural and functional connectivity, and plasma levels of neurofilament light chain (NFL) and phosphorylated tau 181 (p‐tau) between PD who progress to poor outcomes versus those with good outcomes.ResultWe found extensive white matter macrostructural changes in PD who progress to poor outcomes already at baseline: with 19% reduction in fibre cross‐section and a subnetwork of reduced structural connectivity (105 nodes, 215 edges, p=0.017), particularly involving interhemispheric connections. Gray matter and functional connectivity were preserved. NFL but not p‐tau was increased in PD poor outcomes and correlated with white matter loss.ConclusionPD patients who progress to poor outcomes, show white matter macrostructural loss already at baseline, evident both from imaging and fluid biomarkers. Our findings highlight white matter imaging and plasma NFL as potential biomarkers of poor outcomes in Parkinson’s, informing the design of future clinical trials.
Quantitative Analysis of Choroid Plexus Enlargement in Alzheimer’s Dementia: A Study of Automated Volumetric Technique
Authors: Kim Dong‐Hee , Suh Chong Hyun , Jung Wooseok , Lee Seung Hyun , Kim Jinyoung , Cho Hyeonwoo , Lee Yeha , Kim Sang Joon , Moon Won‐Jin 
Published: January 2025

AbstractBackgroundChoroid plexus enlargement is implicated in the exacerbation of cognitive impairments characteristic of Alzheimer’s dementia (AD). Manual volumetric assessment by radiologists, while accurate, is impractical for large‐scale application due to its resource‐intensive nature. We examine the use of automated brain volumetry software as an efficient and cost‐effective alternative for quantifying choroid plexus volume.MethodWe collected 3D T1 brain MRI images from 293 patients aged between 44 and 91, with a median age of 73, at Konkuk University Hospital (n = 95), Asan Medical Center (n = 99), and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (n = 99). The study comprised 76 normal control (NC), 89 mild cognitive impairment (MCI), and 128 AD cases, with a female majority of 58.4%. Using VUNO‐Med DeepBrain software, we obtained choroid plexus segmentation masks and volumes from both radiologists and neural networks trained with deep learning. All choroid plexus volumes were normalized by intracranial volume (ICV) multiplied by 1000. Additionally, to assess DeepBrain’s accuracy, we calculated the Intraclass Correlation Coefficient (ICC(2, k)) between the manually labeled volumes and those labeled by DeepBrain.ResultThe median choroid plexus volumes measured by radiologists were 1.328 for NC, 1.354 for MCI, and 1.495 for AD. In contrast, measurements by DeepBrain were 1.163 (NC), 1.284 (MCI), and 1.428 (AD). A one‐way ANOVA analysis (NC vs. MCI vs. AD) revealed that DeepBrain‐based measurements (p < 0.001) may be more significant than manual measurements (p = 0.17) in elucidating the relationship between choroid plexus enlargement and cognitive impairments in AD. Additionally, the Intraclass Correlation Coefficient (ICC(2, k)) between manual and DeepBrain volumes indicated a moderate correlation with a score of 0.69 (p < 0.001).ConclusionOur results suggest that automated brain volumetry is a reliable method for assessing choroid plexus volume, correlating with the cognitive impairment continuum in AD. Its implementation could significantly streamline the diagnostic process, offering a scalable and objective approach for clinicians and researchers.
Longitudinal interplay between cerebral glucose metabolism, subclinical atherosclerosis and cardiovascular risk factors in midlife: the PESA study
Authors: Tristão‐Pereira Catarina , Fuster Valentin , Garcia‐Lunar Ines , Schöll Michael , Suarez‐Calvet Marc , Moro Maria Angeles , Garcia‐Alvarez Ana , Sanchez‐Gonzalez Javier , Zetterberg Henrik , Blennow Kaj , Ibañez Borja , Gispert Juan Domingo , Cortes‐Canteli Marta 
Published: January 2025

AbstractBackgroundCardiovascular disease and dementia often co‐exist at advanced stages. Yet, midlife longitudinal studies examining the interplay between atherosclerosis and its risk factors on brain health are scarce. We aimed to determine the longitudinal associations between cerebral glucose metabolism, subclinical atherosclerosis and cardiovascular risk factors in middle‐aged asymptomatic individuals.MethodThe Progression of Early Subclinical Atherosclerosis (PESA) is a longitudinal observational cohort study of 4184 asymptomatic individuals aged 40‐54. Participants with subclinical atherosclerosis underwent longitudinal cerebral [18F]fluorodeoxyglucose (FDG)‐PET (outcomes). Cardiovascular risk was quantified with SCORE2 and subclinical atherosclerosis with 3D vascular ultrasound (exposures). Multivariate regression and linear mixed effects models were used to assess outcome‐exposure associations through region‐of‐interest and voxelwise approaches. Additionally, blood‐based biomarkers of neuropathology were quantified and mediation analyses performed.ResultThis longitudinal study included 370 participants (median age: 49·8 [IQR=46·1‐52·2] years; 84% men; follow‐up: 4·7 [4·2‐5·2] years). Persistent high‐risk of cardiovascular disease was associated with an accelerated decline of cortical FDG uptake compared to low‐risk (ß=‐0·008 [95% CI ‐0·013 to ‐0·002]; pFDR=0·040), with plasma neurofilament light chain, a marker of neurodegeneration, mediating this association by 20% (ß=0·198 [0·008 to 0·740]; pFDR=0·050) (Figure 1). Moreover, progression of subclinical carotid atherosclerosis was associated with an additional decline in FDG uptake in Alzheimer’s disease brain regions, not explained by cardiovascular risk (ß=‐0·269 [95% CI ‐0·509 to ‐0·027]; p=0·029) (Figure 2).ConclusionMiddle‐aged asymptomatic individuals with sustained cardiovascular risk and subclinical carotid atherosclerosis already present brain metabolic decline, suggesting that maintaining cardiovascular health during midlife may be key to reducing neurodegenerative disease burden later in life.
Subject‐level Detection of Focal Neurodegeneration Using Spatiotemporal Connectomics: Towards Atrophy Characterization in Preclinical Alzheimer's Disease
Authors: Sánchez Cristina , Diez Ibai , Bueichekú Elisenda , Kim Chan‐Mi , Grothe Michel J. , Sanchez‐Juan Pascual , Sepulcre Jorge 
Published: January 2025

AbstractBackgroundBrain atrophy is a normal part of healthy aging, but it is aggravated by several neurodegenerative diseases. Previous studies have described a large heterogeneity in individual neurodegeneration patterns, but the underlying brain mechanisms are currently not fully understood. From a graph theory‐based framework, the estimation of subject‐specific focal or multifocal brain atrophy in healthy aging and in the preclinical stage of different neurodegenerative diseases, such as Alzheimer's disease (AD), will help to better understand individual atrophy networks and likely improve prediction of phenotypic heterogeneity in disease trajectories. This study aimed to develop a novel spatiotemporal connectomic method based on graph theory applied to serial MRI measurements to identify neurodegeneration focality (i.e., unifocal or multifocal atrophy patterns) in healthy aging at the single‐subject level.MethodThe study included a unique sample of 79 older cognitively normal participants from the the Vallecas project who underwent longitudinal T1 MRI scanning with 8 follow‐up timepoints over 9,46±1,97 years. Voxel‐based morphometry was used to define the brain atrophy topology of each subject, and uni‐ or multifocal atrophy patterns were identified using a graph theory approach based on structural similarity between each voxel and the rest of the brain across serial gray matter measurements (Figure 1).ResultWe identified individualized atrophy phenotypes characterized by different graph morphologies (Figure 2), which could be classified into three main groups based on their connectivity behavior. One group of subjects was characterized by atrophic voxels with a coordinated behavior, another with a predominance towards divergent or discoordinated behavior, and a third group that laid between these two extremes.ConclusionWe present a novel analytical tool for characterizing individualized atrophy phenotypes in healthy subjects based on graph theory and structural similarity analyses of longitudinal MRI data. This method may help to describe the first structural events in preclinical AD and other neurodegenerative diseases and, therefore, could be crucial for predicting differences in disease phenotype and progression in single subjects.