bluuebunny/crossref_metadata_2025_split huggingface dataset:
Age‐related Modularity Changes of the Structural Brain Network: The Framingham Study
Authors: Seiler Stephan , Fletcher Evan , Beiser Alexa S. , Himali Jayandra Jung , Satizabal Claudia L. , Seshadri Sudha , Maillard Pauline , Decarli Charles 
Published: December 2023

AbstractBackgroundWhile studies have consistently shown that brain network efficiency decreases with age, data on age‐related reconfiguration of network modules are scarce. We assessed structural brain networks from healthy adults of the Framingham Study to explore age‐related modularity differences.MethodStructural brain networks of 2,903 healthy adults (mean age 55, SD 0.96, range 25‐95, 53% female) were obtained from DTI probabilistic tractography. Connectivity matrices were constructed from 72 cortical and subcortical regions. First, we calculated efficiency to check the validity of our data. Second, we constructed a multilayer network with each layer representing one age decile and calculated modularity for each layer using a robust implementation of the Louvain community‐detection algorithm (Blondel et al., 2008). Third, we compared modular structures between each layer and a reference group (youngest) using the variation of information (VI), an index measure of distance between two modular patterns (e.g. modularity at different ages). Finally, we calculated the participation coefficient, a measure of modular node integration, and the node flexibility, a measure of module allegiance change and assessed their relationships with age.ResultEfficiency decreased with age (b = ‐0.34, SE 0.02, p<0.001), being stable until approximately 50 and declining linearly thereafter (see Figure 1, panel a). Modularity showed a u‐shaped trajectory with most integration at the fifth decile of age (see Figure1, panel b). VI index revealed that modular reconfiguration was greatest between the youngest and the oldest age decile (0.43). Participation coefficient was highest at the fifth decile (see Figure2, panel c) and in frontal nodes (see Figure2, panel a), where it also exhibited high variability. Frontal variability followed a complex pattern (Figure2, panel b). Conversely, central and temporal‐occipital nodes showed less participation and variability (see Figure2, panels a and b), and the lowest flexibility (see Figure3).ConclusionBrain network modules reconfigure with aging. This reconfiguration is non‐uniform. While midline‐near modules, including the cingulate and parieto‐occipital cortices, show relative stability, frontal modules seemed to reconfigure considerably, as seen by increased average flexibility (change in modular allegiance) and less integration with advancing age, as seen by declining participation coefficients.
Regional correlation analysis between neuronal metabolism and synaptic density using [18F]FDG and [11C]UCB‐J
Authors: DiFilippo Alexandra H , McLachlan Max , Jonaitis Erin M. , Ennis Gilda E. , McVea Andrew K , Bettcher Brecca , Pasquesi Mary‐Elizabeth , Thor Yer , Davenport Nancy J , Grover Ethan , Hudson Sarah , Barnhart Todd E , Betthauser Tobey J , Engle Jonathan W , Johnson Sterling C , Bendlin Barbara B , Christian Bradley T. 
Published: December 2023

AbstractBackgroundNeuronal hypometabolism measured with [18F]FDG PET scans can be used to identify neurodegeneration as part of the AT(N) (amyloid, tau, neurodegeneration) framework in AD. PET imaging of synaptic vesicle glycoprotein SV2A using [11C]UCB‐J provides an index of synaptic density, which has been shown to decrease during neurodegeneration. The purpose of this study was to examine the regional relationship between neuronal metabolism and synaptic density in participants across the continuum of AD‐related pathology.MethodParticipants were recruited from the Wisconsin ADRC and the Wisconsin Registry for Alzheimer’s Prevention (WRAP) study. A subset of 13 participants (Table 1) were selected who had completed cognitive testing, structural MR imaging, and PET imaging with the following: [11C]PiB centiloid (CL) for amyloid +/‐ status, [18F]MK‐6240 SUVR for tau +/‐ status, [11C]UCB‐J DVR for regional synaptic density burden, and [18F]FDG SUVR for regional glucose metabolism. UCB‐J and FDG analysis used a whole cerebellum reference region; PiB and MK‐6240 analysis used cerebellar GM reference regions. Inter‐tracer (UCB‐J/FDG) correlations were performed in Freesurfer‐defined bilateral Braak‐associated ROIs (Medial Temporal Lobe [MTL]: entorhinal cortex, hippocampus, and amygdala; Ventral Temporal Lobe [VTL]: fusiform, lingual, and inferior temporal gyri), signature regions of hypometabolism in AD (posterior cingulate, parietal, and frontal cortices) and a region with only marginal hypometabolism in AD (occipital cortex). Associations were determined using Pearson correlations (r).ResultAll ROIs demonstrate a positive association between FDG SUVR and UCB‐J DVR, with r > 0.7 and p < 0.01 (Figure 1). The occipital, frontal, and posterior cingulate cortices demonstrate the strongest correlations (r = 0.84, 0.83, and 0.81, respectively).ConclusionIn cognitively impaired and unimpaired participants showing varying degrees of ß‐amyloid and neurofibrillary tau burden, strong positive correlations are observed in regions associated with AD tau pathology and hypometabolism. Correlations are also observed in regions not typically associated with hypometabolism. This suggests that a relationship may exist between synaptic density and neuronal metabolism in the presence of AD neuropathology.
Neuroinflammatory biomarkers in neurodegenerative disease: Insights from the ONDRI Cohort
Authors: Sumra Vishaal , Dilliott Allison Ann , Frank Andrew R , Lang Anthony E , Roberts Angela C , Troyer Angela , Levine Brian , Arnott Stephen R. , Tan Brian , Fischer Corinne E. , Marras Connie , Kwan Donna , Munoz Douglas , Tang‐Wai David F. , Finger Elizabeth , Rogaeva Ekaterina , Orange Joseph B , Ramirez Joel , Sunderland Kelly M , Zinman Lorne , Binns Malcolm , Borrie Michael , Masellis Mario , Freedman Morris , Montero‐Odasso Manuel , Ozzoude Miracle , Bartha Robert , Swartz Richard H. , Tartaglia Carmela 
Published: December 2023

AbstractBackgroundNeuroinflammation (NI) has been implicated in both the pathogenesis of and neuroprotection against neurodegenerative diseases (NDs)(Psenicka et al., 2021). Plasma glial fibrillary acidic protein (GFAP), and Neurofilament light (NFL) are measures of astrogliosis and neurodegeneration, respectively. Amyloid beta (Aß)42/40 ratio (Aß42 concentration to total Aß concentration) below 0.068 is associated with AD pathology (Baldeiras et al., 2018). Neuroimaging‐based inflammatory biomarkers have been proposed, including free‐water diffusion (FWD)(Pasternak et al., 2009). Here we investigated FWD as a candidate biomarker for NI in AD compared to non‐AD dementia using Aß42/40 ratio in a subset of data from the Ontario Neurodegenerative Disease Research Initiative (ONDRI).MethodFWD maps were generated in 370 subjects (126 non‐AD and 244 AD). MRI processing included ICVmapp3r for brain extraction and bias field correction, Synb0, Topup and Eddy for dMRI preprocessing and MATLAB for freewater mapping. Plasma Aß42, Aß40, GFAP and NFL were measured using the Simoa Human Neurology 4‐Plex E assay, and cognition was estimated using the Montreal Cognitive Assessment (MoCA). Linear regression was used to estimate the ability for FWD in the left (LcGM) and right (RcGM) cortical grey matter to predict GFAP, NFL and MoCA score in AD and nonAD based on Aß42/40 threshold of 0.068.ResultFWD correlated with GFAP (LcGM; R = 0.4, p = 0.0013 and RcGM; R = 0.37, p = 0.0069), and MoCA total score (LcGM; R = ‐0.29, p = 0.001 and RcGM; R = ‐0.27, p = 0.001), but not with NFL across the whole group. This relationship was largely driven by the AD group wherein FWD predicted GFAP (LcGM: R = 0.12, p = 0.02, RcGM approaching significance R = 0.1, p = 0.06), and MoCA Total score (LcGM: R = ‐0.33, p<0.0001), RcGM: R = ‐0.25, p<0.0001). The nonAD group did not show this relationship. FWD did not predict NFL in the AD and nonAD group.ConclusionIn patients with Aß42/40<0.068, suggestive of AD, FWD in cGM was more strongly related to GFAP than NFL, and predicted cognition, a pattern that was not observed in nonAD patients. Our results suggest distinct patterns of NI in AD compared with nonAD that can be detected with FWD and with a multi‐modal approach could further understanding of differences in pathophysiology across NDs.
Effects of cerebrovascular disease on tau progression and cognitive decline in Alzheimer’s disease
Authors: Dewenter Anna , Steward Anna , Dehsarvi Amir , Biel Davina , Roemer Sebastian Niclas , Wanger Fabian , Franzmeier Nicolai 
Published: December 2023

AbstractBackgroundRecent post‐mortem studies suggest that cerebrovascular disease contributes to Alzheimer’s disease (AD) pathophysiology and clinical progression. In particular, cerebral amyloid angiopathy is highly common in AD and has been linked to faster cognitive decline. Yet, in‐vivo evidence on the contribution of cerebrovascular disease to pathobiological and/or clinical AD progression is rare. Thus, we assessed in‐vivo, whether i) higher cerebrovascular burden is associated with accelerated progression of primary AD pathology and ii) whether cerebrovascular burden synergistically accelerates AD‐related cognitive decline.MethodWe included n = 256 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) across the AD spectrum with longitudinal 18F‐flortaucipir tau‐PET (follow‐up∼2.30±1.32 years), 18F‐florbetapir or 18F‐florbetaben Aß‐PET transformed to centiloid (A7: n = 85 Cognitively normal (CN); Aß+: n = 83 CN, n = 57 Mild Cognitive Impairment, n = 31 Dementia). We focused on global white matter hyperintensity (WMH) volume adjusted to intracranial volume and microhemorrhage (MCH) count as well‐established cerebrovascular disease markers. Cognition was probed through the AD Assessment Scale Cognition 13‐item scale (ADAS‐13). Subject‐specific annual changes in tau‐PET and cognition were calculated via linear mixed models, including random slope and intercept. To enhance reproducibility of our findings, statistical analyses were pre‐specified at AsPredicted.org. All statistical models were controlled for age, sex and APOE.ResultUsing linear regression, we found no main effect of cerebrovascular disease markers on baseline amyloid‐PET or tau‐PET (ps>0.05). Further, higher cerebrovascular burden was not associated with faster Aß‐related tau‐PET SUVR increase (Fig. 1A+B). In contrast, higher cerebrovascular burden was associated with accelerated cognitive decline. Specifically, interaction models revealed that a higher MCH count was associated with faster cognitive decline at higher tau levels (Fig. 1C), while higher white matter hyperintensity burden was associated with faster cognitive decline at lower tau levels (Fig. 1D).ConclusionWe found no effect of cerebrovascular disease on primary AD pathology. In contrast, cerebrovascular disease was linked to accelerated tau‐associated cognitive decline. This suggests that cerebrovascular disease may not modulate pathobiological AD trajectories but rather add synergistically to AD symptom severity. This has important therapeutic implications, as treating cerebrovascular disease in addition to primary AD pathology (e.g. amyloid) may yield largest clinical efficacy.
Cognitively healthy Indians with higher Framingham Cardiovascular Risk Scores have higher WMH burden with lesser grey matter, white matter and hippocampal volumes
Authors: Sundarakumar Jonas S. , Lingegodwa Abhishek Mensegere , Issac Thomas Gregor 
Published: December 2023

AbstractBackgroundIncreasing evidence indicates the important role of cardiovascular risk factors in dementia risk, particularly in Indians. It is known that cardiovascular risk factors tend to cluster together, and a widely used multivariable risk scoring is the Framingham Cardiovascular Risk Score (FRS). However, very little is known about the association between FRS and structural brain MRI parameters in cognitively healthy Indians.MethodBaseline clinical and brain MRI data of 487 cognitively healthy Indians (Clinical Dementia Rating score of ‘0’), who are part of two ongoing, large, aging cohort studies in India – Srinivaspura Aging, Neuro Senescence and COGnition study (SANSCOG) study (n = 200) and Tata Longitudinal Study of Aging (TLSA, n = 289) were utilized. FRS was calculated using clinical and blood biochemistry data, whereas brain MRI scans were performed on 3T MRI scanners. White matter hyperintensity (WMH) lesion segmentation was performed using Lesion Segmentation Toolbox (LST) and brain volumes (total grey matter, total white matter, 3rd ventricle, right and left hippocampus) were derived using FreeSurfer software and normalized according to total intracranial volumes of the respective subjects. Linear regression analysis was performed with FRS as independent variable and various structural MRI parameters as dependent variables. A p value of <0.05 was considered significant.ResultUrban subjects with higher FRS had significantly higher WMH volume (ß = 0.00011, p<0.001, R2 = 0.130), lower grey matter (ß = ‐0.00285, p<0.001, R2 = 0.206), white matter (ß = ‐0.00180, p<0.001, R2 = 0.106), right hippocampal (ß = ‐0.00004, p<0.001, R2 = 0.180), left hippocampal (ß = ‐0.00003, p<0.001, R2 = 0.156) volumes and higher 3rd ventricle volumes (ß = 0.00003, p<0.001, R2 = 0.150). Rural subjects higher FRS had lower grey matter (ß = ‐0.00166, p<0.001, R2 = 0.118, white matter (ß = ‐0.0011, p<0.001, R2 = 0.060), right hippocampal (ß = ‐0.00002, p<0.001, R2 = 0.087), left hippocampal (ß = ‐0.00002, p<0.001, R2 = 0.084) volumes and higher 3rd ventricle volumes (ß = 0.00004, p<0.001, R2 = 0.264) but no significant association was seen with WMH volume.ConclusionThese findings indicate that cardiovascular risk is associated with structural brain changes in cognitively healthy Indians. Since cardiovascular risk can be reduced through cost‐effective community level strategies, such as lifestyle‐based interventions, promptly addressing this risk in Indians could be an important part of dementia prevention strategies.
Cerebellar‐cortical functional connectivity is associated with Alzheimer’s disease pathology and cerebellar atrophy
Authors: Kim Soyun , Adams Jenna N. , Stith Lea , Taylor Lisa M. , Harris Alyssa L. , McMillan Liv , Yassa Michael A. 
Published: December 2023

AbstractBackgroundBrain atrophy has been associated with Alzheimer’s disease (AD) pathology. Previous research also suggests that functional connectivity (FC) among distributed neural networks is disrupted in preclinical AD. The cerebellum has been thought to play a crucial role in motor control, yet recent evidence points to its additional role in non‐motor domains as well as cognitive decline in AD. Cerebellar volume has been shown to decline along the course of AD progression. Moreover, neuroimaging studies have delineated intrinsic cerebellar‐cortical functional networks that presumably support cognitive functions. Despite the accumulating findings on neurodegeneration or FC, how cerebellar FC and atrophy relate to AD pathology in the early stage has not been extensively investigated.MethodData from 83 cognitively normal older adults (mean 71.0 yrs, 66% females) in the Alzheimer’s Disease Neuroimaging Initiative were analyzed. Volumetric data were obtained from FreeSurfer 6.0 segmentation. Resting state fMRI scans were preprocessed using the CONN Toolbox. Aß and tau standardized uptake value ratio (SUVR) data were obtained from [18F]‐Florbetapir or [18F]‐Florbetaben (Aß) and [18F]‐Flortaucipir (tau) PET scans. For Aß, SUVR values were transformed to the Centiloid scale, and a global summary measure was used. For tau, SUVR values in the temporal meta regions of interest were used. Seed‐based functional connectivity metrics were computed using the 7 cerebellar parcellations (Buckner et al., 2011) that are functionally coupled to different cortical networks (Yeo et al., 2011).ResultCerebellar volume showed no significant relationship with amyloid or tau SUVR. By contrast, hippocampal atrophy was positively associated with amyloid or tau SUVR and showed trending significance (ps < 0.1). Increased Aß burden was associated with increased cerebellar‐cortical FC in the attention, frontoparietal, and default mode networks. Increased tau load was associated with increased cerebellar‐cortical FC in the visual, somatomotor, and attention networks. Cerebellar volume was associated with cerebellar‐cortical FC in the visual, somatomotor, attention networks, but not with the limbic, frontoparietal, or default mode networks.ConclusionOur findings suggest that functional cerebellar‐cortical connectivity varies with amyloid and tau pathology in different networks, and such functional changes may precede cerebellar atrophy in preclinical AD.
Increased regional brain inflammation predicts longitudinal brain atrophy in individuals in the Alzheimer’s disease continuum
Authors: Povala Guilherme , Bellaver Bruna , Ferreira Pamela C.L. , Lussier Firoza Z , Ferrari‐Souza João Pedro , Leffa Douglas Teixeira , Tissot Cécile , Therriault Joseph , Bezgin Gleb , Servaes Stijn , Benedet Andrea Lessa , Stevenson Jenna , Rahmouni Nesrine , Cohen Ann D. , Lopez Oscar L. , Tudorascu Dana , Klunk William E , Villemagne Victor L , Karikari Thomas K , Zimmer Eduardo R , Rosa‐Neto Pedro , Pascoal Tharick Ali 
Published: December 2023

AbstractBackgroundNeuroinflammation typically involves the activation of microglial cells in the brain and has been linked to Alzheimer’s disease (AD) pathology. However, how microglial activation influences brain neurodegeneration in individuals across the AD continuum is still poorly understood. Here, we aimed to investigate the influence of microglial activation in longitudinal brain atrophy in individuals across the AD continuum. We hypothesize that high levels of regional brain inflammation predict widespread brain atrophy.MethodWe assessed 95 individuals from the TRIAD cohort (60 cognitively unimpaired and 35 cognitively impaired) with available [11C]PBR28‐PET, a measure of microglial activation, and a 2‐year longitudinal MRI (mean = 2.07 years). We generated grey matter voxel‐based morphometry (VBM) images using SPM12 and DARTEL, smoothed with a Gaussian kernel of full‐width half maximum of 8mm. We built the uncorrected (p < 0.05) association matrix between the [11C]PBR28‐PET SUVR and longitudinal VBM ROIs (z‐score) with the ß‐estimates from linear regressions accounting for age, sex, and diagnosis. We divided the [11C]PBR28‐PET levels into terciles (low, intermediate, and high) to generate the averages of longitudinal VBM changes.ResultBaseline ROI‐based [11C]PBR28‐PET levels associate with longitudinal brain atrophy not only locally but also in distinct brain regions (Fig 1a). After FDR‐correction, the inferior temporal cortex was the region where [11C]PBR28‐PET levels were better associated with widespread longitudinal brain atrophy (Fig. 1b). Brain atrophy was observed in AD‐related regions, including the amygdala, insula, and the superior temporal cortex, independently of global amyloid load and tau (Fig. 1b). Accordingly, individuals with higher [11C]PBR28‐PET levels in the inferior temporal cortex presented increased longitudinal brain atrophy compared to individuals with lower [11C]PBR28‐PET (Fig. 1c).ConclusionWe identified increased baseline [11C]PBR28‐PET levels in the inferior temporal cortex that were highly associated with longitudinal brain atrophy in individuals across the AD continuum. Our results demonstrated that higher levels of inflammation in key brain regions could predict widespread longitudinal brain atrophy, suggesting that microglial activation has a detrimental impact on AD‐related neurodegeneration progression.
Discovering correlates of off‐target 18F‐MK6240 signal in community‐dwelling adults
Authors: Johnson Aubrey S. , Zou James , Smith Anna C. , Okafor Amarachukwu , Guzmán Diana S. , Kreisl William Charles , Brickman Adam M. , Luchsinger José A. , Lao Patrick J. 
Published: December 2023

AbstractBackground18F‐MK6240 binds hyperphosphorylated tau protein in neurofibrillary tangles, a hallmark of Alzheimer’s disease(AD); however, the tracer may also bind to other similarly structured molecules, known as off‐target binding. Off‐target binding is inconsistent across individuals and can lead to biased quantitative assessment, making it difficult to accurately identify neurofibrillary tangle pathology. We examined variable off‐target binding by performing a correlation analysis with demographic factors, vascular risk, lifestyle factors, and co‐morbidities.MethodNinety‐five participants (65±3yrs; 67% women; 42/58% White/Black; 66% Hispanic; 12±4yrs education) in the Northern Manhattan Study of Metabolism and Mind(NOMEM) underwent 18F‐MK6240(90‐110min) and 18F‐Florbetaben(90‐110min) PET imaging and completed surveys, including age, sex, education, exercise levels, and medical history. Only visually‐determined amyloid‐negative participants were included to increase the likelihood that elevated tau PET signal was off‐target. Tau SUVR (cerebellar gray matter reference region) was calculated for composite Braak stages (I/II, III/IV, V/VI) and two known off‐target regions, the choroid plexus and the extra‐cerebral space around the brain. The extra‐cerebral mask was generated by dilating the gray/CSF boundary of the ANTs probability map and then subtracting the brain. Percent of variance explained (r2) from general linear models are reported between off‐target regions and factors of interest.ResultDescriptively, 18F‐MK6240 off‐target binding in choroid plexus was positively correlated with SUVR in Braak stage regions I/II (r2 = 0.37;p<0.001) and III/IV (r2 = 0.44;p<0.001) to a larger extent than V/VI (r2 = 0.21;p<0.001), while off‐target binding in the extra‐cerebral space was positively correlated with Braak stages I/II (r2 = 0.07;p<0.001) to a lesser extent than III/IV (r2 = 0.13;p<0.001) and V/VI (r2 = 0.18;p<0.001), suggesting that proximity of key AD regions to off‐target regions increases the likelihood of confounded signal. Off‐target binding in the choroid plexus was elevated amongst self‐identified Hispanic participants (r2 = 0.19;p<0.001). Extra‐cerebral off‐target binding was negatively associated with participant weight (r2 = 0.12;p<0.001).ConclusionExtra‐cerebral off‐target binding of 18F‐MK6240 may confound signal in key AD regions in amyloid‐negative participants. Off‐target binding should be accounted for regionally and methods for capturing extra‐cerebral signal are critical. Understanding the role of factors related to tracer distribution, including weight, and sociodemographic factors will enhance the quantification of 18F‐MK6240 for the early detection of neurofibrillary tangle pathology.