bluuebunny/crossref_metadata_2025_split huggingface dataset:
Neurophysiological activity linked to Alzheimer’s disease pathology relates to longitudinal cognition and progression to mild cognitive impairment
Authors: Rudolf Jonathan E Gallego , Wiesman Alex I , Baillet Sylvain , Villeneuve Sylvia 
Published: December 2023

AbstractBackgroundAlzheimer’s Disease (AD) is defined by the pathological accumulation of amyloid‐beta (Aß) and tau in the brain. Regional deposition of these proteins is associated with changes in frequency‐defined neurophysiological activity that can be detected using non‐invasive magnetoencephalography (MEG). Here we examined whether these neurophysiological alterations were associated with participants’ demographics and longitudinal changes in cognition. Additionally, we addressed the clinical utility of MEG to predict progression to mild cognitive impairment (MCI), as compared to other established AD imaging biomarkers.MethodWe used Positron Emission Tomography (PET) to measure the deposition of whole‐brain Aß ([18F]NAV4694) and tau ([18F] Flortaucipir), Magnetic Resonance Imaging (MRI) to measure hippocampal volume and resting‐state MEG to capture neurophysiological activity in a group of clinically unimpaired older adults with family history of AD (PREVENT‐AD cohort; n = 103). We implemented a multivariate partial least squares (PLS) analysis to test the association between imaging markers (i.e., neurophysiological activity and AD proteinopathy) and participants’ clinical profiles (i.e., demographic‐clinical variables and longitudinal cognition data). For the second analysis we used logistic regression models to assess the added value of neurophysiological activity to predict MCI progression compared to established MRI/PET imaging markers (n = 100; 14 MCI progressors).ResultThe PLS analysis identified significant latent variables that linked proteinopathy‐related neurophysiological activity slowing to older age, lower education, positive APOE e4 status and longitudinal deficits in cognition across multiple domains (Figure 1). The initial logistic regression model included demographic and clinical variables and had an AUC = 0.772, which increased to 0.794 after adding the MRI hippocampal volume. Incorporating MEG spectral power from temporal regions where tau accumulates resulted in a considerable increase in accuracy with an AUC = 0.873. The model including Aß and tau PET on top of MRI and MEG markers reached an AUC = 0.905 (Figure 2).ConclusionOur results show that the neurophysiological changes linked to AD pathology are associated with longitudinal cognitive impairment across multiple domains. MEG spectral power from early tau accumulating regions improved the accuracy for predicting MCI progression, contributing information beyond traditional demographic/clinical variables and structural MRI and representing a more accessible and less invasive alternative compared to PET imaging.
Association between glial and synaptic fluid biomarkers and brain [18F]FDG‐PET signal
Authors: Machado Luiza Santos , da Rocha Andreia Silva , De Bastiani Marco Antônio , Bellaver Bruna , Soares Carolina , Lazzarotto Gabriela , Vidor Pedro , Schlickmann Thomas , Rahmouni Nesrine , Siebert Marina , Ferreira Pamela C.L. , Pascoal Tharick A. , Souza Diogo O. , Rosa‐Neto Pedro , Benedet Andrea Lessa , Blennow Kaj , Zetterberg Henrik , Ashton Nicholas J. , Zimmer Eduardo R. 
Published: December 2023

AbstractBackgroundBeyond the hypometabolic signature, indexed by [18F]FDG‐PET imaging, seen in the brain of Alzheimer’s disease (AD) patients, a transient brain glucose hypermetabolism has been recently identified in the early stages of AD. The cellular source of this dual metabolic response remains controversial, with glial cells gaining a lot of attention. In light of this, whether glial and synaptic fluid biomarkers are associated with [18F]FDG‐PET signal in the early stages of AD remains debatable. Thus, we evaluated the association of astrocyte, microglial and synaptic fluid biomarkers with [18F]FDG‐PET signal in an AD rat model.Method[18F]FDG‐PET imaging was conducted in ten‐month‐old (early amyloid stage) APP/PS1 (TgF344‐AD, n = 8‐17) and wild‐type (WT, n = 8‐15) rats. Glial (GFAP and sTREM2), synaptic (neurogranin), and amyloid biomarkers (Aß1‐40 and Aß1‐42) were quantified in the cerebrospinal fluid (CSF) and plasma through a multiplex immunoassay. T‐statistical maps of brain [18F]FDG‐PET and those fluid biomarkers were conducted at the voxel level using RMINC. Differences were considered statistically significant at p<0.05 (t>2).ResultA large hypermetabolic cluster was identified in ten‐month‐old TgF344‐AD (Fig. 1AB, local maxima, t(13) = 4.28). We found positive correlations between CSF TREM2 and brain [18F]FDG‐PET signal (Fig. 2AC, local maxima, t(13) = 4.12). In addition, we identified a positive correlation between plasma GFAP and brain [18F]FDG‐PET signal (Fig. 2BC, local maxima, t(13) = 10.62). Plasma TREM2, CSF GFAP, and plasma neurogranin did not associate with brain glucose metabolism.ConclusionOur findings suggest that CSF TREM2 and plasma GFAP are associated with brain metabolism before amyloid load reaches a plateau. Stronger association of plasma GFAP, rather than CSF GFAP, with brain pathology corroborates cross‐sectional human studies. One could argue that [18F]FDG‐PET hypermetabolism identified is likely a response to astrocyte and microglial changes.
Aβ in the Lens of the Eye: A Noninvasively Detectable Early Biomarker of Alzheimer’s Disease β‐Amyloidopathy in Humans and Transgenic Mouse Model
Authors: Goldstein Lee E , Moncaster Juliet A , Moir Robert D , Minaeva Olga , Alvarez Victor E. , Clark John I , McKee Ann C. , Tanzi Rudolph E. 
Published: December 2023

AbstractBackgroundAmyloid‐ß (Aß) accumulation in brain is an early hallmark of Alzheimer’s disease (AD). We discovered Aß deposition, ß‐amyloidopathy, and colocalizing supranuclear cataracts (SNC) in lenses from people with AD (Goldstein LE et al., Lancet, 2003) and Down syndrome (DS; Moncaster JA et al., PLoS One, 2010), but not other neurodegenerative diseases or normal aging. These findings spurred development of an investigational drug‐device lens Aß eye scanner (Sapphire II, Cognoptix) that combines an Aß‐binding fluorescent ligand (Aftobetin) and specialized laser scanning ophthalmoscope for noninvasive measurement of lens Aß (FDA Breakthrough Device, 2021). Early clinical results indicate that lens Aß is an ideal biomarker for early AD detection. Here, we investigated Aß accumulation and amyloidopathy in lens and brain.MethodTg2576 transgenic (Tg+) mice express Swedish mutant human amyloid precursor protein (hAPP‐Swe), human Aß (hAß), ß‐amyloidopathy, and cognitive deficits (Hsiao et al., Science, 1996). Non‐transgenic (Tg–) littermates were used as controls. Lenses were examined by ex vivo lens stereomicroscopy. Brains and lenses were analyzed by Aß immunohistochemistry, amyloid histopathology, anti‐Aß immunogold electron microscopy, tryptic digest mass spectrometry, anti‐hAß ELISA/immunoblotting. Quasi‐elastic light scattering hAß‐lens protein aggregation analysis.ResultTg2576 Tg+ mice, but not Tg– controls, age‐dependently express hAPP, accumulate hAß, and develop hAß molecular pathology in the lens and exhibit age‐dependent Aß supranuclear opacification that recapitulates lens pathology and SNC phenotype expression in human AD and DS. We detected hAß in conditioned medium from Tg+ but not Tg– lens explant cultures. hAß potently promotes mouse lens protein aggregation in vitro.ConclusionThese results support mechanistic (genotype‐phenotype) linkage between age‐dependent Aß pathology and AD‐related phenotypes in lens and brain. Collectively, our findings identify Aß pathology as the shared molecular etiology of age‐dependent supranuclear cataracts associated with two human diseases (AD, DS) and homologous murine cataracts in the Tg2576 transgenic mouse model of AD. These results represent the first evidence of AD‐related Aß pathology outside the brain and support lens Aß as an optically‐accessible biomarker for early detection and longitudinal monitoring of AD.
Assessing amyloid plaque burden in aging marmosets and genetically engineered PSEN1 mutation carriers using 11C‐PiB PET
Authors: Schaeffer David J , Dubberley Lauren R , Schaeffer Lauren K Hayryren , Park Jung Eun , Homanics Gregg E , Strick Peter L , Carter Gregory W , Rizzo Stacey J Sukoff , Silva Afonso C 
Published: December 2023

AbstractBackgroundThere is an urgent need to develop novel animal models to investigate the underlying cellular and molecular root causes of the pathogenesis and progression of AD. Our group has worked to establish the common marmoset as the first primate‐specific model of AD to reveal the earliest cellular and molecular events of AD processes and allow charting of AD progression from its inception. For this, we established a colony of outbred marmosets and used genetic engineering techniques to generate marmosets carrying the PSEN1‐C410Y and PSEN1‐A426P mutations that cause early‐onset, familial AD. The present study aims to use PET imaging to access and track the progression of Ab plaque burden non‐invasively and longitudinally in our marmoset models of AD.MethodMarmosets anesthetized with isoflurane delivered via a facemask were placed in a preclinical PET/CT scanner customized with marmoset‐specific beds. Respiratory rate, heart rate, arterial O2 saturation, and rectal temperature were monitored and maintained at normal physiological values. Our PET imaging protocol follows the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The amyloid‐binding 11C‐Pittsburgh Compound B (11C‐PiB) was delivered intravenously in doses varying from 18.5 – 185 MBq (0.5 – 5 mCi). A 90‐min dynamic acquisition was performed immediately following the radiotracer injection, followed by a 3D CT scan acquired at 200 µm isotropic resolution. After the PET/CT neuroimaging scan, the marmosets were recovered from anesthesia and returned to their home cages.ResultWe observed positive 11C‐PiB binding and retention in the brains of aging outbred marmosets and in marmosets harboring PSEN1 mutations. Significant 11C‐PiB retention was observed in the cortex and white matter tracts. The areas with the highest 11C‐PiB retention were the lateral frontal cortex, anterior cingulate cortex, and temporal lobe regions.ConclusionThis study demonstrates using 11C‐PiB to track Ab plaque burden in the brains of aging marmosets and marmosets harboring PSEN1 mutations. As observed in human patients of AD, the amount of 11C‐PiB bound (measured in SUV and SUVR units) varied from animal to animal, and we are currently performing group analysis to investigate the dependence of 11C‐PiB retention on age and sex. This research was funded by NIH/NIA grants R24AG073190 and U19AG074866.
Abnormal functional activation during response inhibition in mild cognitive impairment: an fMRI study
Authors: Pommy Jessica , Umfleet Laura Glass , Cohen Alexander D , Ristow Kelly , Mason Lilly , Obarski Shawn , Franczak Malgorzata , Wang Yang 
Published: December 2023

AbstractBackgroundDeclines in response inhibition are observed both typical and atypical aging, i.e., mild cognitive impairment (MCI). Increased functional activation in frontoparietal regions during response inhibition has been interpreted to represent the emergence of a compensatory mechanism in older adulthood. While electrophysiology studies suggest the decline in response inhibition in MCI reflects a similar but amplified process as seen in normal aging this has not been examined thoroughly using functional MRI (fMRI).Method31 cognitively normal older adults (M age = 73.6, 9 male) and 22 adult patients with MCI (M age = 67.2, 14 male) performed a Go/NoGo task during an fMRI scan. Group differences in accuracy and reaction time were examined for Go and NoGo trials. Individual BOLD signal change during task fMRI was examined, then voxel‐wise group comparison was conducted for MCI vs. HC groups, using age, gender, education, and gray matter density as covariates. Permutation‐estimated cluster size at alpha <.05 was defined to correct for errors of multiple comparisons.ResultBehaviorally, the MCI group made significantly more errors during NoGo and Go trials (p<.001, p = .032, respectively), however, average reaction time did not differ significantly between groups. During NoGo trials, greater activation in the anterior default mode network (DMN), right dorsolateral prefrontal cortex (DLPFC), and motor areas was observed for MCI vs. HC, while during go trials, there was greater activation in DMN and right insula (corrected p<0.05). However, when Go vs. NoGo trials were examined, MCI showed more activation in right insula and DLPFC, but less activation in the motor area at trend level (p<0.05, uncorrected). While, there were no significant task performance differences between MCI subtypes (amnestic vs. non‐amnestic), amnestic MCI (aMCI) showed more activation in posterior DMN and dorsal attention area during NoGo trials (corrected p<0.05).ConclusionMCI demonstrated significantly greater activation during both Go and NoGo trials and made significantly more errors in performance during both trial types as well. While the MCI subtypes did not exhibit differences in performance on the task, the aMCI exhibited significantly greater activation in multiple regions. Our findings suggested inefficient DMN suppression during task performance in the MCI cohort, especially in the aMCI subgroup.
The structural integrity of anterior‐temporal and posterior‐medial brain regions in Alzheimer’s disease and Parkinson’s disease: Effects of APOEε4, p‐tau181 and Aβ42
Authors: Coughlan Gillian T , Zhukovsky Peter , Sanchez Erlan , McLaughlin Paula , Grady Cheryl , Black Sandra E. , Munoz Douglas , Buckley Rachel F. , Masellis Mario 
Published: December 2023

AbstractBackgroundAlzheimer’s disease (AD) and Parkinson’s disease (PD) are two distinct neurodegenerative brain conditions associated with varying degrees of cognitive impairment, including an overlapping amnestic phenotype. At autopsy, APOEe4 and AD proteinopathies are associated with dementia severity in both AD and PD. The extent to which in‐vivo AD biomarkers are associated with memory and underlying regional brain volume in AD and PD patients is under‐investigation. Here, we focused on plasma biomarker associations with anterior‐temporal and posterior‐medial regions that are known to accumulate tau and Aß in AD.MethodNeuroimaging, biomarker, genetic and cognitive data were collected from 244 patients through the Ontario Neurodegenerative Disease Research Initiative (MeanAge = 68.96; 41% women; 35%APOE‐e4‐carriers). Based on clinical diagnostic criteria, three groups were formed: i)AD with mild cognitive impairment or dementia(ADMCI/ADD;N = 120), ii)PD with MCI or dementia(PDMCI/PDD;N = 76) and iii)PD with normal cognition(PD‐NC;N = 48). Linear regressions assessed clinical diagnosis differences in the volumetric integrity of a priori regions: anterior‐temporal(ATN; including amygdala, fusiform gyrus, and ITG) and posterior‐medial(PMN; including PCC, parahippocampal cortex and precuneus;Fig1). Differences on related item recognition memory and associative memory was examined via the FaceName Association Task. APOE(e4‐/e4+), p‐tau181‐UGOT and Aß42 associations between ATN/PMN and item recognition/associative memory outcomes were estimated. Diagnostic*APOEe4*ptau181 interactions were tested. Age, sex, education and ICV were covaried.ResultBackground characteristics showed that the ADMCI/ADD group exhibited the oldest age and the highest frequency of female sex and e4‐carriership. ADMCI/ADD and PDMCI/PDD, but not PD‐NC, exhibited elevated ptau181 and Aß42 relative to a healthy control group. Both ADMCI/ADD and PDMCI/PDD groups exhibited PMN and ATN degradation, as well as memory impairment, relative to the PD‐NC(Fig2A‐B). Plasma p‐tau181 was associated with ATN and PMN, as well as associative memory(Fig3A). APOEe4 was associated with PMN only, as well as item recognition(Fig3B). Diagnostic*APOEe4*ptau181 interactions were not significant. Post‐hoc analysis showed significant p‐tau181xAPOEe4 interactions on item recognition and total white matter hyperintensity(WMH;Fig3C). Aß42 was not associated with outcomes.ConclusionVolumes of the ATN and PMN are lower in cognitively impaired AD and PD patients with higher p‐tau and/or APOEe4‐carriership. P‐tau and APOEe4 may work synergistically to promote domain‐specific memory impairment and WMH burden in both AD and PD.
Association of reactive astrogliosis and microglial activation with tau pathology in Alzheimer’s disease
Authors: Ferrari‐Souza João Pedro , Bellaver Bruna , Ferreira Pamela C.L. , Povala Guilherme , Lussier Firoza Z , Leffa Douglas Teixeira , Therriault Joseph , Tissot Cécile , Benedet Andrea Lessa , Soares Carolina , Aguzzoli Cristiano Schaffer , Zalzale Hussein , Rohden Francieli , Wang Yi‐Ting , Chamoun Mira , Servaes Stijn , Macedo Arthur C. , Vermeiren Marie , Bezgin Gleb , Kang Min Su , Stevenson Jenna , Rahmouni Nesrine , Pallen Vanessa , Poltronetti Nina Margherita , Souza Diogo O. , Soucy Jean‐Paul , Gauthier Serge , Triana‐Baltzer Gallen , Saad Ziad S. , Kolb Hartmuth C. , Schilling Lucas Porcello , Karikari Thomas K , Ashton Nicholas J. , Zetterberg Henrik , Blennow Kaj , Rosa‐Neto Pedro , Pascoal Tharick A. , Zimmer Eduardo R 
Published: December 2023

AbstractBackgroundGlial contributions to Alzheimer’s disease (AD) etiology and progression have been increasingly recognized. Experimental and clinical evidence suggests that both microglial activation and astrocyte reactivity have pivotal roles in the progression of tau pathology. However, it remains to be elucidated how microglia and astrocytes complement each other in AD pathogenesis. Here, we tested the associations between reactive astrogliosis and microglial activation with tau phosphorylation and aggregation in individuals across the aging and AD spectrum.MethodWe studied 95 participants (14 cognitively unimpaired [CU] young, 46 CU elderly, and 35 cognitively impaired [CI]) from the Translational Biomarkers in Aging and Dementia (TRIAD) cohort. Individuals had available positron emission tomography (PET) for Aß ([18F]AZD4694), tau tangles ([18F]MK6240), and microglial activation ([11C]PBR28). We further assessed reactive astrogliosis (plasma glial fibrillary acidic protein [GFAP]) and pathological tau phosphorylation (plasma phosphorylated tau [p‐tau] at threonine 231, 181, and 217).ResultDemographic characteristics of the study population are reported in Table 1. Regression analyses revealed that reactive astrogliosis and microglial activation were synergistically associated with higher plasma phosphorylated tau levels independently of Aß pathology in CU but not CI individuals (Figure 1A). On the other hand, no interactive effects of reactive astrogliosis and microglial activation on tau‐PET burden were observed in either CU or CI individuals (Figure 1B). Similar findings were observed in sensitivity analyses excluding CU young participants.ConclusionOur results suggest that reactive astrocytes and activated microglia have Aß‐independent synergistic effects on the early progression of AD pathophysiology by contributing to tau phosphorylation but not tau aggregation. These findings can help to better understand the complementary roles of glial cells in neurodegenerative diseases, as well as provide insights for the development novel therapeutic strategies for AD targeting the interplay between astrocyte and microglial reactivity.
Predicting the chance for being at‐risk of AD based on smartphone data obtained during an everyday wayfinding task in the real world
Authors: Marquardt Jonas , Mohan Priyanka , Spiliopoulou Myra , Glanz Wenzel , Butryn Michaela , Kuehn Esther , Schreiber Stefanie , Maass Anne , Diersch Nadine 
Published: December 2023

AbstractBackgroundPatients with Alzheimer’s disease (AD) often show problems in spatial navigation. However, spatial navigation is rarely investigated in real world settings and in people at early disease stages. Capitalizing on recent advancements in digital technologies, we measured the performance of healthy younger (YA) and older adults (OA), as well as older adults with subjective cognitive decline (SCD) while they solved a smartphone‐based wayfinding task.MethodUsing the novel smartphone application “Explore”, we tracked GPS data and other behavioral indicators in 24 YAs, 25 OAs, and 23 SCDs, while they were asked to find points of interest on the medical campus in Magdeburg. In our analyses, we first quantified similarities between the GPS trajectories and identified wayfinding styles using k‐medoids clustering. Next, we extracted navigation profiles with aggregated performance measures as input features (e.g., wayfinding distance and number of help function calls) in a latent profile analysis. Mixed effect models were fitted to evaluate group differences for each performance measure separately. We then fed the performance measure that showed the largest groups differences into a multinomial logistic regression model to predict age and diagnostic group in unknown users via leave‐one‐out cross‐validation.ResultThe GPS data could be clustered into three wayfinding styles. However, the correspondence to our groups was rather low (Figure 1a). In contrast, navigation profiles obtained from the latent profile analysis showed a higher correspondence (Figure 1b). Particularly the number of brief stops during navigation differed between the groups, all p = .006 (Figure 2), and predicted age group, p = .015, and diagnostic group above chance, p = .004 (Figure 3). The leave‐one‐out cross‐validation confirmed the successful prediction of group membership in unknown participants, accuracy = 0.57 (chance level 0.33).ConclusionHere, we show how information about the cognitive health status of an individual can be inferred from smartphone‐data, obtained during a brief episode of a frequently performed everyday behavior. In particular, the number of short stops during wayfinding could be used to distinguish between YA, OA, and SCDs. Hence, real‐world navigation data, obtained from mobile devices, might help to identify individuals at increased risk for developing AD.