Modeling of dynamic cerebrovascular reactivity to spontaneous and externally induced CO2 fluctuations in the human brain using BOLD-fMRI

In this work, we investigate the regional characteristics of the dynamic interactions between arterial CO2 and BOLD (dynamic cerebrovascular reactivity - dCVR) during normal breathing and hypercapnic, externally induced step CO2 challenges. To obtain dCVR curves at each voxel, we use a custom set of basis functions based on the Laguerre and gamma basis sets. This allows us to obtain robust dCVR estimates both in larger regions of interest (ROIs), as well as in individual voxels. We also implement classification schemes to identify brain regions with similar dCVR characteristics. Our results reveal considerable variability of dCVR across different brain regions, as well as during different experimental conditions (normal breathing and hypercapnic challenges), suggesting a differential response of cerebral vasculature to spontaneous CO2 fluctuations and larger, externally induced CO2 changes that are possibly associated with the underlying differences in mean arterial CO2 levels. The clustering results suggest that anatomically distinct brain regions are characterized by different dCVR curves that in some cases do not exhibit the standard, positive valued curves that have been previously reported. They also reveal a consistent set of dCVR cluster shapes for resting and forcing conditions, which exhibit different distribution patterns across brain voxels

Modeling the dynamics of cerebrovascular reactivity to carbon dioxide in fMRI under task and resting-state conditions

Conventionally, cerebrovascular reactivity (CVR) is estimated as the amplitude of the hemodynamic response to vascular stimuli, most commonly carbon dioxide (CO(2)). While the CVR amplitude has established clinical utility, the temporal characteristics of CVR (dCVR) have been increasingly explored and may yield even more pathology-sensitive parameters. This work is motivated by the current need to evaluate the feasibility of dCVR modeling in various experimental conditions. In this work, we present a comparison of several recently published/utilized model-based deconvolution (response estimation) approaches for estimating the CO(2) response function h(t), including maximum a posteriori likelihood (MAP), inverse logit (IL), canonical correlation analysis (CCA), and basis expansion (using Gamma and Laguerre basis sets). To aid the comparison, we devised a novel simulation framework that incorporates a wide range of SNRs, ranging from 10 to -7 dB, representative of both task and resting-state CO(2) changes. In addition, we built ground-truth h(t) into our simulation framework, overcoming the conventional limitation that the true h(t) is unknown. Moreover, to best represent realistic noise found in fMRI scans, we extracted noise from in-vivo resting-state scans. Furthermore, we introduce a simple optimization of the CCA method (CCA(opt)) and compare its performance to these existing methods. Our findings suggest that model-based methods can accurately estimate dCVR even amidst high noise (i.e. resting-state), and in a manner that is largely independent of the underlying model assumptions for each method. We also provide a quantitative basis for making methodological choices, based on the desired dCVR parameters, the estimation accuracy and computation time. The BEL method provided the highest accuracy and robustness, followed by the CCA(opt) and IL methods. Of the three, the CCA(opt) method has the lowest computational requirements. These findings lay the foundation for wider adoption of dCVR estimation in CVR mapping

Magnetic resonance imaging of resting cerebral oxygen metabolism : applications in Alzheimer’s disease

The BOLD contrast employed in functional MRI studies is an ambiguous signal composed of changes in blood flow, blood volume and oxidative metabolism. In situations where the vasculature and metabolism may have been affected, such as in aging and in certain diseases, the dissociation of the more physiologically-specific components from the BOLD signal becomes crucial. The latest generation of calibrated functional MRI methods allows the estimation of both resting blood flow and absolute oxygen metabolism. The work presented here is based on one such proof-of-concept approach, dubbed QUO2, whereby taking into account, within a generalized model, both arbitrary changes in blood flow and blood O2 content during a combination of hypercapnia and hyperoxia breathing manipulations, yields voxel-wise estimates of resting oxygen extraction fraction and oxidative metabolism. In the first part of this thesis, the QUO2 acquisition protocol and data analysis were revisited in order to enhance the temporal stability of individual blood flow and BOLD responses, consequently improving reliability of the model-derived estimates. Thereafter, an assessment of the within and between-subject variability of the optimized QUO2 measurements was performed on a group of healthy volunteers. In parallel, an analysis was performed of the sensitivity of the model to different sources of random and systematic errors, respectively due to errors in measurements and choice of assumed parameters values. Moreover, the various impacts of the oxygen concentration administered during the hyperoxia manipulation were evaluated through a simulation and experimentally, indicating that a mild hyperoxia was beneficial. Finally, the influence of Alzheimer’s disease in vascular and metabolic changes was explored for the first time by applying the QUO2 approach in a cohort of probable Alzheimer’s disease patients and age-matched control group. Voxel-wise and region-wise differences in resting blood flow, oxygen extraction fraction, oxidative metabolism, transverse relaxation rate constant R2* and R2* changes during hypercapnia were identified. A series of limitations along with recommended solutions was given with regards to the delayed transit time, the susceptibility artifacts and the challenge of performing a hypercapnia manipulation in cohorts of elderly and Alzheimer’s patients.Le contraste BOLD employé dans les études d’imagerie par résonance magnétique fonctionnelle (IRMf) provient d’une combinaison ambigüe de changements du flux sanguin cérébral, du volume sanguin ainsi que du métabolisme oxydatif. Dans un contexte où les fonctions vasculaires ou métaboliques du cerveau ont pu être affectées, tel qu’avec l’âge ou certaines maladies, il est crucial d’effectuer une décomposition du signal BOLD en composantes physiologiquement plus spécifiques. La dernière génération de méthodes d’IRMf calibrée permet d’estimer à la fois le flux sanguin cérébral et le métabolisme oxydatif au repos. Le présent travail est basé sur une telle technique, appelée QUantitative O2 (QUO2), qui, via un model généralisé, prend en considération les changements du flux sanguin ainsi que ceux en concentrations sanguine d’O2 durant des périodes d’hypercapnie et d’hyperoxie, afin d’estimer, à chaque voxel, la fraction d’extraction d’oxygène et le métabolisme oxydatif au repos. Dans la première partie de cette thèse, le protocole d’acquisition ainsi que la stratégie d’analyse de l’approche QUO2 ont été revus afin d’améliorer la stabilité temporelle des réponses BOLD et du flux sanguin, conséquemment, afin d’accroître la fiabilité des paramètres estimés. Par la suite, une évaluation de la variabilité intra- et inter-sujet des différentes mesures QUO2 a été effectuée auprès d’un groupe de participants sains. En parallèle, une analyse de la sensibilité du model à différentes sources d’erreurs aléatoires (issues des mesures acquises) et systématiques (dues aux assomptions du model) a été réalisée. De plus, les impacts du niveau d’oxygène administré durant les périodes d’hyperoxie ont été évalués via une simulation puis expérimentalement, indiquant qu’une hyperoxie moyenne était bénéfique. Finalement, l’influence de la maladie d’Alzheimer sur les changements vasculaires et métaboliques a été explorée pour la première fois en appliquant le protocole QUO2 à une cohorte de patients Alzheimer et à un groupe témoin du même âge. Des différences en terme de flux sanguin, fraction d’oxygène extraite, métabolisme oxydatif, et taux de relaxation transverse R2* au repos comme en réponse à l’hypercapnie, ont été identifiées au niveau du voxel, ainsi qu’au niveau de régions cérébrales vulnérables à la maladie d’Alzheimer. Une liste de limitations accompagnées de recommandations a été dressée en ce qui a trait au temps de transit différé, aux artéfacts de susceptibilité magnétique, de même qu’au défi que représente l’hypercapnie chez les personnes âgées ou atteintes de la maladie d’Alzheimer

ICA-based denoising strategies in breath-hold induced cerebrovascular reactivity mapping with multi echo BOLD fMRI

Available online 6 March 2021.Performing a BOLD functional MRI (fMRI) acquisition during breath-hold (BH) tasks is a non-invasive, robust method to estimate cerebrovascular reactivity (CVR). However, movement and breathing-related artefacts caused by the BH can substantially hinder CVR estimates due to their high temporal collinearity with the effect of interest, and attention has to be paid when choosing which analysis model should be applied to the data. In this study, we evaluate the performance of multiple analysis strategies based on lagged general linear models applied on multi- echo BOLD fMRI data, acquired in ten subjects performing a BH task during ten sessions, to obtain subject-specific CVR and haemodynamic lag estimates. The evaluated approaches range from conventional regression models, i.e. including drifts and motion timecourses as nuisance regressors, applied on single-echo or optimally-combined data, to more complex models including regressors obtained from multi-echo independent component analysis with different grades of orthogonalization in order to preserve the effect of interest, i.e. the CVR. We compare these models in terms of their ability to make signal intensity changes independent from motion, as well as the reliability as measured by voxelwise intraclass correlation coefficients of both CVR and lag maps over time. Our results reveal that a conservative independent component analysis model applied on the optimally-combined multi-echo fMRI signal offers the largest reduction of motion-related effects in the signal, while yielding reliable CVR amplitude and lag estimates, although a conventional regression model applied on the optimally-combined data results in similar estimates. This work demonstrates the usefulness of multi-echo based fMRI acquisitions and independent component analysis denoising for precision mapping of CVR in single subjects based on BH paradigms, fostering its potential as a clinically-viable neuroimaging tool for individual patients. It also proves that the way in which data-driven regressors should be incorporated in the analysis model is not straight-forward due to their complex interaction with the BH-induced BOLD response.This research was supported by the European Union’s Horizon 2020 research and innovation program ( Marie Sk ł odowska-Curie grant agreement No. 713673 ), a fellowship from La Caixa Foundation (ID 100010434 , fellowship code LCF/BQ/IN17/11620063 ), the Spanish Ministry of Economy and Competitiveness ( Ramon y Cajal Fellowship, RYC-2017- 21845 ), the Spanish State Research Agency (BCBL “Severo Ochoa ”excellence accreditation, SEV- 2015-490 ), the Basque Govern- ment ( BERC 2018-2021 and PIBA_2019_104 ), the Spanish Ministry of Science, Innovation and Universities (MICINN; PID2019-105520GB-100 and FJCI-2017-31814 ), and the Eunice Kennedy Shriver National Insti- tute of Child Health and Human Development of the National Institutes of Health under award number K12HD073945

Fuzzy approximate entropy analysis of resting state fMRI signal complexity across the adult life span

Acknowledgment The authors would like to acknowledge the work of the International Consortium for Brain Mapping (ICBM) fMRI community in creating the resting state database and making it publicly available within the framework of the 1000 Functional Connectomes project (https://www.nitrc.org/projects/fcon_1000/). M.O. Sokunbi was supported by an MRC grant G1100629.Peer reviewedPreprin

Assessing the repeatability of absolute CMRO 2 , OEF and haemodynamic measurements from calibrated fMRI

As energy metabolism in the brain is largely oxidative, the measurement of cerebral metabolic rate of oxygen consumption (CMRO2) is a desirable biomarker for quantifying brain activity and tissue viability. Currently, PET techniques based on oxygen isotopes are the gold standard for obtaining whole brain CMRO2 maps. Among MRI techniques that have been developed as an alternative are dual calibrated fMRI (dcFMRI) methods, which exploit simultaneous measurements of BOLD and ASL signals during a hypercapnic-hyperoxic experiment to modulate brain blood flow and oxygenation. In this study we quantified the repeatability of a dcFMRI approach developed in our lab, evaluating its limits and informing its application in studies aimed at characterising the metabolic state of human brain tissue over time. Our analysis focussed on the estimates of oxygen extraction fraction (OEF), cerebral blood flow (CBF), CBF-related cerebrovascular reactivity (CVR) and CMRO2 based on a forward model that describes analytically the acquired dual echo GRE signal. Indices of within- and between-session repeatability are calculated from two different datasets both at a bulk grey matter and at a voxel-wise resolution and finally compared with similar indices obtained from previous MRI and PET measurements. Within- and between-session values of intra-subject coefficient of variation (CVintra) calculated from bulk grey matter estimates 6.7 ± 6.6% (mean ± std.) and 10.5 ± 9.7% for OEF, 6.9 ± 6% and 5.5 ± 4.7% for CBF, 12 ± 9.7% and 12.3 ± 10% for CMRO2. Coefficient of variation (CV) and intraclass correlation coefficient (ICC) maps showed the spatial distribution of the repeatability metrics, informing on the feasibility limits of the method. In conclusion, results show an overall consistency of the estimated physiological parameters with literature reports and a satisfactory level of repeatability considering the higher spatial sensitivity compared to other MRI methods, with varied performance depending on the specific parameter under analysis, on the spatial resolution considered and on the study design

BOLD signal physiology: Models and applications

The BOLD contrast mechanism has a complex relationship with functional brain activity, oxygen metabolism, and neurovascular factors. Accurate interpretation of the BOLD signal for neuroscience and clinical applications necessitates a clear understanding of the sources of BOLD contrast and its relationship to underlying physiology. This review describes the physiological components that contribute to the BOLD signal and the steady-state calibrated BOLD models that enable quantification of functional changes with a separate challenge paradigm. The principles derived from these biophysical models are then used to interpret BOLD measurements in different neurological disorders in the presence of confounding vascular factors related to disease

A dual-center validation of the PIRAMD scoring system for assessing the severity of ischemic Moyamoya disease

Prior Infarcts, Reactivity, and Angiography in Moyamoya Disease (PIRAMD) is a recently proposed imaging-based scoring system that incorporates the severity of disease and its impact on parenchymal hemodynamics in order to better support clinical management and evaluate response to intervention. In particular, PIRAMD may have merit in identifying symptomatic patients that may benefit most from revascularization. Our aim was to validate the PIRAMD scoring system

Depth-Dependent Physiological Modulators of the BOLD Response in the Human Motor Cortex

This dissertation proposes a set of methods for improving spatial localization of cerebral metabolic changes using functional magnetic resonance imaging (fMRI). Blood oxygen level dependent (BOLD) fMRI estabilished itself as the most frequently used technique for mapping brain activity in humans. It is non-invasive and allows to obtain information about brain oxygenation changes in a few minutes. It was discovered in 1990 and, since then, it contributed enormously to the developments in neuroscientific research. Nevertheless, the BOLD contrast suffers from inherent limitations. This comes from the fact that the observed response is the result of a complex interplay between cerebral blood flow (CBF), cerebral blood volume (CBV) and cerebral metabolic rate of oxygen consumption (CMRO2) and has a strong dependency on baseline blood volume and oxygenation. Therefore, the observed response is mislocalized from the site where the metabolic activity takes place and it is subject to high variability across experiments due to normal brain physiology. Since the peak of BOLD changes can be as much as 4 mm apart from the site of metabolic changes, the problem of spatial mislocalization is particularly constraining at submillimeter resolution. Three methods are proposed in this work in order to overcome this limitation and make data more comparable. The first method involves a modification of an estabilished model for calibration of BOLD responses (the dilution model), in order to render it applicable at higher resolutions. The second method proposes a model-free scaling of the BOLD response, based on spatial normalization by a purely vascular response pattern. The third method takes into account the hypothesis that the cortical vasculature could act as a low-pass filter for BOLD fluctuations as the blood is carried downstream, and investigates differences in frequency composition of cortical laminae. All methods are described and tested on a depth-dependent scale in the human motor cortex