Biology subjects

Carbonell, F.

Publications and source records attributed to Carbonell, F..

8 recordsLinked to original sources

bioRxiv2024

A novel method for harmonization of PET image spatial resolution without phantoms

The most common approach for estimating the spatial resolution of PET images in multi-center studies typically uses Hoffman phantom data as a surrogate. Specifically, the phantom-based matching resolution approach assumes that scanned phantom PET images are well approximated by a ground truth, noise-free digital phantom convolved with a Gaussian kernel of unknown size. The size of the kernel is then estimated by an exhaustive search on the amount of blurring needed to match the smoothed digital phantom to a particular scanned phantom image. Unfortunately, Hoffman phantom images may not always be readily available, and phantom-based approaches may yield sub-optimal results. We propose a new, computational approach that allows estimation of spatial resolution directly from the PET image itself. We generalized the so-called logarithmic intensity plots method to the 3D case to perform a spatial resolution estimation in both axial and in-plane directions of the PET images. The proposed approach was applied to two different cohorts. The first cohort consisted of [18F]florbetapir amyloid PET images and matching phantoms coming from a Phase II clinical trial and includes different scanner models and/or orientation and grid reconstructions. The second cohort included {beta}-amyloid, FDG and tau PET images from the Alzheimers Disease Neuroimaging Initiative (ADNI) study. We obtained in-plane and axial resolution estimators that vary between 3.5 mm and 8.5 mm for both PET and matching phantom images. In both cases, we obtained small across-subject variability in groups of images sharing the same PET scanner model and reconstruction parameters. For human PET images, we also obtained a strong cross-tracer and longitudinal consistency in the spatial resolution estimators. Our novel approach does not only eliminate the need for surrogate brain phantom data, but also provides a general framework that can be applied to a wide range of tracers and other image modalities, such as SPECT.

neuroscience↗

bioRxiv2024

Sex and APOE4-specific links between cardiometabolic risk factors and white matter alterations in individuals with a family history of Alzheimer's disease

INTRODUCTIONWhite matter (WM) alterations are among the earliest changes in Alzheimers disease (AD), yet limited work has comprehensively characterized the effects of AD risk factors on WM. METHODSIn older adults with a family history of AD, we investigated the sex-specific and APOE genotype-related relationships between WM microstructure and risk factors. Multiple MRI-derived metrics were integrated using a multivariate approach based on the Mahalanobis distance (D2). The links between WM D2 and cognition were also explored. RESULTSWM D2 in several regions was associated with high systolic blood pressure, BMI, and glycated hemoglobin, and low cholesterol, in both males and females. APOE4+ displayed a distinct risk pattern, with LDL-cholesterol having a detrimental effect only in carriers, and this pattern was linked to immediate memory performance. Myelination was the main mechanism underlying WM alterations. DISCUSSIONOur findings reveal that combined exposure to multiple cardiometabolic risk factors negatively impacts microstructural health, which may subsequently affect cognition. Notably, APOE4 carriers exhibited a different risk pattern, especially in the role of LDL, suggesting distinct underlying mechanisms in this group.

neuroscience↗

bioRxiv2024

Microstructural Correlates of Cognitive and Motor Functioning Revealed via Multimodal Multivariate Analysis

Recent advances in cognitive neuroscience emphasise the importance of healthy white matter (WM) for optimal behavioural functioning. It is now widely accepted that brain connectivity via WM contributes to the emergence of behaviour. However, the association between the microstructure of WM fibres and behaviour is poorly understood due to the indirect and overlapping nature of methods used to assess microstructure. Here, we used the Mahalanobis Distance (D2) to integrate 10 metrics of WM derived from multimodal neuroimaging that have strong ties to microstructure. The D2 metric was chosen because it accounts for metrics' covariance as it measures the voxelwise distance between every subject and the average; thus providing a robust multiparametric assessment of microstructure. We used multivariate correlation to examine voxelwise WM-behaviour associations with two cognitive and two motor tasks, which allowed us to compare within and across behavioural domains. We observed that behaviour is organised in cognitive, motor, and integrative components that have widespread associations with WM, from frontal to parietal regions. Notably, the decomposition of these factors shows that tasks traditionally labelled as cognitive also contain motor components that map onto motor-related WM patterns, and that motor tasks likewise contain non-motor components linked to distinct WM microstructural features. Our results highlight the complex nature of the links between microstructure and behaviour, and support the relevance of multivariate modelling when examining brain-behaviour associations.

neuroscience↗

bioRxiv2024

MVComp toolbox: MultiVariate Comparisons of brain MRI features accounting for common information across metrics

Multivariate approaches have recently gained in popularity to address the physiological unspecificity of neuroimaging metrics and to better characterize the complexity of biological processes underlying behavior. However, commonly used approaches are biased by the intrinsic associations between variables, or they are computationally expensive and may be more complicated to implement than standard univariate approaches. Here, we propose using the Mahalanobis distance (D2), an individual-level measure of deviation relative to a reference distribution that accounts for covariance between metrics. To facilitate its use, we introduce an open-source python-based tool for computing D2 relative to a reference group or within a single individual: the MultiVariate Comparison (MVComp) toolbox. The toolbox allows different levels of analysis (i.e., group-or subject-level), resolutions (e.g., voxel-wise, ROI-wise) and dimensions considered (e.g., combining MRI metrics or WM tracts). Several example cases are presented to showcase the wide range of possible applications of MVComp and to demonstrate the functionality of the toolbox. The D2 framework was applied to the assessment of white matter (WM) microstructure at 1) the group-level, where D2 can be computed between a subject and a reference group to yield an individualized measure of deviation. We observed that clustering applied to D2 in the corpus callosum yields parcellations that highly resemble known topography based on neuroanatomy, suggesting that D2 provides an integrative index that meaningfully reflects the underlying microstructure. 2) At the subject level, D2 was computed between voxels to obtain a measure of (dis)similarity. The loadings of each MRI metric (i.e., its relative contribution to D2) were then extracted in voxels of interest to showcase a useful option of the MVComp toolbox. These relative contributions can provide important insights into the physiological underpinnings of differences observed. Integrative multivariate models are crucial to expand our understanding of the complex brain-behavior relationships and the multiple factors underlying disease development and progression. Our toolbox facilitates the implementation of a useful multivariate method, making it more widely accessible.

neuroscience↗

bioRxiv2024

Tau-related reduction of glucose metabolism in mild cognitive impairment occurs independently of APOE ε4 genotype and is gradually modulated by β amyloid

BackgroundPET imaging studies have shown that spatially distributed measurements of {beta}-amyloid are significantly correlated with glucose metabolism in Mild Cognitive Impairment (MCI) independently of the APOE {varepsilon}4 genotype. In contrast, the relationship between tau and glucose metabolism at different stages of Alzheimers Disease (AD) has not been fully understood. ObjectiveWe hypothesize that spatially distributed scores of tau PET are associated with an even stronger reduction of glucose metabolism, independent of the APOE {varepsilon}4 genotype and gradually modulated by {beta}-amyloid. MethodsWe applied a cross-sectional statistical analysis to concurrent [18F]flortaucipir PET, [18F]florbetapir PET, and 2-[18F]fluoro-2-deoxyglucose (FDG) PET images from the Alzheimers Disease Neuroimaging Initiative (ADNI) study. We employed a Singular Value Decomposition (SVD) approach to the cross-correlation matrix between tau and the FDG images, as well as between tau and {beta}-amyloid PET images. The resulting SVD-based tau scores are associated with cortical regions where a reduced glucose metabolism is maximally correlated with distributed patterns of tau, accounting for the effect of spatially distributed {beta}-amyloid. ResultsFrom a population of MCI subjects, we found that the SVD-based tau scores had their maximal spatial representation within the entorhinal cortex and the lateral inferior temporal gyrus, and were significantly correlated with glucose metabolism in several cortical regions, independently from the confounding effect of the {beta}-amyloid scores and APOE {varepsilon}4. Moreover, {beta}-amyloid gradually modulated the association between tau and glucose metabolism. ConclusionsOur approach uncovered spatially distributed patterns of the tau-glucose metabolism relationship after accounting for the {beta}-amyloid effects. We showed that the SVD-based tau scores have a strong relationship with decreasing glucose metabolism. By highlighting the more significant role of tau, rather than {beta}-amyloid, on the reduction of glucose metabolism, our results could have important consequences in the therapeutic treatment of AD.

neuroscience↗

bioRxiv2023

Spatial association between distributed β-amyloid and tau varies with cognition

Several PET studies have explored the relationship between {beta}-amyloid load and tau uptake at the early stages of Alzheimers disease (AD) progression. Most of these studies have focused on the linear relationship between {beta}-amyloid and tau at the local level and their synergistic effect on different AD biomarkers. We hypothesize that patterns of spatial association between {beta}-amyloid and tau might be uncovered using alternative association metrics that account for linear as well as more complex, possible nonlinear dependencies. In the present study, we propose a new Canonical Distance Correlation Analysis (CDCA) to generate distinctive spatial patterns of the cross-correlation structure between tau, as measured by [18F]flortaucipir PET, and {beta}-amyloid, as measured by [18F]florbetapir PET, from the Alzheimers Disease Neuroimaging Initiative (ADNI) study. We found that the CDCA-based {beta}-amyloid scores were not only maximally distance-correlated to tau in cognitively normal (CN) controls and mild cognitive impairment (MCI), but also differentiated between low and high levels of {beta}-amyloid uptake. The most distinctive spatial association pattern was characterized by a spread of {beta}-amyloid covering large areas of the cortex and localized tau in the entorhinal cortex. More importantly, this spatial dependency varies according to cognition, which cannot be explained by the uptake differences in {beta}-amyloid or tau between CN and MCI subjects. Hence, the CDCA-based scores might be more accurate than the amyloid or tau SUVR for the enrollment in clinical trials of those individuals on the path of cognitive deterioration.

neuroscience↗

bioRxiv2023

Transcriptomic signatures of Abeta- and tau-induced neuronal dysfunction reveal inflammatory processes at the core of Alzheimer's disease pathophysiology

Molecular mechanisms enabling pathology-induced neuronal dysfunction in Alzheimers disease (AD) remain elusive. Here, we use mechanistic computational models to infer the combined influence of PET-measured A{beta} and tau burdens on fMRI-derived neuronal activity and to subsequently identify the transcriptomic spatial correlates of AD pathophysiology. Our results reveal overrepresented genes and biological processes that participate in synaptic degeneration and interact with A{beta} and tau deposits. Furthermore, we confirmed the central role of the immune system and neuroinflammatory pathways within AD pathogenesis; microglia were significantly enriched in the gene set associated with A{beta} and tau synergistic influences on neuronal activity. Lastly, our computational approach unveiled drug candidates with the potential to halt or reduce the observed pathological effects on neuronal activity, including existing medication for cancer, immune disorders, and cardiovascular diseases, many currently under clinical evaluation in AD. Overall, these findings support the notion that the AD brain experiences functional changes intricately associated with a diverse spectrum of molecular processes.

neuroscience↗

bioRxiv2023

Revealing the combined roles of Abeta and tau in Alzheimer's disease via a pathophysiological activity decoder

Neuronal dysfunction and cognitive deterioration in Alzheimers disease (AD) are likely caused by multiple pathophysiological factors. However, evidence in humans remains scarce, necessitating improved non-invasive techniques and integrative mechanistic models. Here, we introduce personalized brain activity models incorporating functional MRI, amyloid-{beta} (A{beta}) and tau-PET from AD-related participants (N=132). Within the model assumptions, electrophysiological activity is mediated by toxic protein deposition. Our integrative subject-specific approach uncovers key patho-mechanistic interactions, including synergistic A{beta} and tau effects on cognitive impairment and neuronal excitability increases with disease progression. The data-derived neuronal excitability values strongly predict clinically relevant AD plasma biomarker concentrations (p-tau217, p-tau231, p-tau181, GFAP). Furthermore, our results reproduce hallmark AD electrophysiological alterations (theta band activity enhancement and alpha reductions) which occur with A{beta}-positivity and after limbic tau involvement. Microglial activation influences on neuronal activity are less definitive, potentially due to neuroimaging limitations in mapping neuroprotective vs detrimental phenotypes. Mechanistic brain activity models can further clarify intricate neurodegenerative processes and accelerate preventive/treatment interventions.

neuroscience↗