Biology subjects

Povala, G.

Publications and source records attributed to Povala, G..

4 recordsLinked to original sources

bioRxiv2026

Metabolic brain network reorganization precedes clinical conversion in Alzheimer's disease

Structured AbstractO_ST_ABSIntroductionC_ST_ABSBrain glucose hypometabolism is a hallmark of Alzheimers disease (AD), yet conventional [18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET) analyses have limited sensitivity in preclinical stages. Metabolic brain network approaches may better capture early vulnerability preceding clinical conversion. MethodsCognitively unimpaired individuals (n = 127) from the ADNI cohort with baseline FDG-PET and amyloid (A) and tau (T) status were classified as clinically stable or converters over an average longitudinal follow-up of 5.8 years. Baseline brain FDG uptake patterns were analyzed at the regional, voxel, and network levels across AT profiles. Network density was quantified globally and within functional networks. AD biomarkers and cognitive performance were also examined. ResultsConventional FDG-PET SUVr analyses failed to distinguish cognitively stable individuals from clinical converters at baseline, either at the regional or voxel levels. AT(N) biomarkers and neuropsychological performance likewise did not differ significantly between groups. In contrast, clinical converters exhibited hyperconnected metabolic networks at baseline, including within the default-mode network. These effects were consistent across A-T-, A+T-, and A+T+ groups, with network density higher in clinical converters than in cognitively stable individuals. Conversely, network density among stable individuals declined with AT progression, pointing to divergent network trajectories. DiscussionMetabolic network organization analysis revealed early AD-related vulnerability beyond regional hypometabolism, even before detectable amyloid positivity, and may reflect divergent trajectories of resilience and pathological propagation preceding clinical conversion. By leveraging existing FDG-PET datasets, this framework offers a valuable opportunity to identify individuals at risk of clinical progression at scale.

neuroscience↗

bioRxiv2026

Sources of Variability in Normative Cerebral FDG-PET imaging

PurposeQuantitative interpretation of brain [{superscript 1}F]FDG-PET increasingly relies on comparisons with normative datasets. However, normative values may be influenced by technical and biological factors, limiting their generalizability. We investigated the effects of scanner manufacturer, reference region, age, and sex on regional [{superscript 1}F]FDG uptake in cognitively normal (CN) adults and generated covariate-adjusted normative reference data. MethodsA total of 449 CN participants from the Alzheimers Disease Neuroimaging Initiative (ADNI) were included. Regional SUVr were calculated using three reference regions (whole cerebellum, pons, cortical gray matter) and converted to Z-scores. Linear regression models were used to estimate standardized regression coefficients ({beta}), and 10-fold cross-validation was performed to quantify the out- of-sample predictive contribution of each covariate using incremental explained variance ({Delta}R{superscript 2}). ResultsScanner manufacturer introduced large, spatially structured biases. Compared with Siemens systems, GE and Philips scanners yielded lower Z-scores in frontal and medial temporal regions, with effect sizes approaching one standard deviation in selected regions ({beta} up to -0.85). Age showed region- specific associations with subcortical nuclei, medial temporal structures, and the posterior cingulate cortex, and was the strongest biological predictor in cross-validation ({Delta}R{superscript 2}{approx}0.11). Sex effects were negligible ({Delta}R{superscript 2}<0.001). Cortical gray matter normalization minimized biological and technical confounding, and the AD meta-ROI demonstrated high robustness across manufacturers and normalization strategies. ConclusionScanner manufacturer and age are the major sources of variance in brain [{superscript 1}F]FDG-PET quantification in CN subjects. Cortical gray matter provides the most stable reference region and supports harmonized, covariate-adjusted normative datasets for clinical and research applications.

neuroscience↗

bioRxiv2026

Evaluation of harmonization methods to mitigate assay and cohort effects in plasma p-tau217

INTRODUCTIONThe growing number of assay platforms measuring blood-based biomarkers (BBMs) for Alzheimers disease (AD) has introduced challenges in interpretability and comparability across assays. Differences across studies also limit comparability of data. To address these challenges, a systematic evaluation of harmonization methods is needed to support BBM data integration within or across studies. METHODSTwo multisite studies, Alzheimers Disease Neuroimaging Initiative (ADNI, n = 219) and Human Connectome Project (HCP, n = 111), were used to evaluate harmonization methods for mitigating assay and cohort effects in plasma p-tau217 measurements. Methods includes various normalization, regression, and standardization approaches, including the recently developed CentiMarker. Assay effects were evaluated using repeated-measures data across assay platforms within each cohort, whereas cohort effects were assessed using pooled ADNI and HCP data. Harmonization performance was evaluated using distributional statistics and downstream modeling of p-tau217. RESULTSQuantile normalization and quantile mapping methods were most effective for mitigating assay effects, whereas conditional quantile mapping performed best for pooled multi-cohort data. These methods also preserved biological variability. In contrast, simple means adjustment and reference-based z-score standardization were least effective for mitigating assay effects, while simple means adjustment, z-score standardization, and quantile normalization were least effective for mitigating cohort effects. CentiMarker had minimal impact on assay or cohort effects. DISCUSSIONBased on our evaluation, we recommend (conditional) quantile mapping for p-tau217 studies integrating data across multiple assays or cohorts. In contrast, we caution against using CentiMarker and z-score-based methods, as they limit comparability and do not effectively mitigate technical variability.

neuroscience↗

bioRxiv2022

Hippocampal GFAP-positive astrocyte responses to amyloid and tau pathologies

IntroductionIn Alzheimers disease clinical research, glial fibrillary acidic protein (GFAP) released into the cerebrospinal fluid and blood is widely measured and perceived as a biomarker of reactive astrogliosis. However, it was demonstrated that GFAP levels differ in individuals presenting with amyloid-{beta} (A{beta}) or tau pathology. The molecular underpinnings behind this specificity are unexplored. Here we investigated biomarker and transcriptomic associations of GFAP-positive astrocytes with A{beta} and tau pathologies in humans and mouse models. MethodsWe studied 90 individuals with plasma GFAP, A{beta}- and Tau-PET to investigate the association between biomarkers. Then, transcriptomic analysis in hippocampal GFAP-positive astrocytes isolated from mouse models presenting A{beta} (PS2APP) or tau (P301S) pathologies was applied to explore differentially expressed genes (DEGs), Gene Ontology processes, and protein-protein interaction networks associated with each phenotype. ResultsIn humans, we found that plasma GFAP associates with A{beta} but not tau pathology. Unveiling the unique nature of GFAP-positive astrocytic responses to A{beta} or tau pathology, mouse transcriptomics showed scarce overlap of DEGs between the A{beta} and tau mouse models, While A{beta} GFAP-positive astrocytes were overrepresented with genes associated with proteostasis and exocytosis-related processes, tau hippocampal GFAP-positive astrocytes presented greater abnormalities in functions related to DNA/RNA processing and cytoskeleton dynamics. ConclusionOur results offer insights into A{beta}- and tau-driven specific signatures in GFAP-positive astrocytes. Characterizing how different underlying pathologies distinctly influence astrocyte responses is critical for the biological interpretation of astrocyte-related biomarker and suggests the need to develop context-specific astrocyte targets to study AD. FundingThis study was supported by Instituto Serrapilheira, Alzheimers Association, CAPES, CNPq and FAPERGS.

neuroscience↗