Felix Carbonell

Age- and Gender-Related Differences in the Cortical Anatomical Network

Neuroanatomical differences attributable to aging and gender have been well documented, and these differences may be associated with differences in behaviors and cognitive performance. However, little is known about the dynamic organization of anatomical connectivity within the cerebral cortex, which may underlie population differences in brain function. In this study, we investigated age and sex effects on the anatomical connectivity patterns of 95 normal subjects ranging in age from 19 to 85 years. Using the connectivity probability derived from diffusion magnetic resonance imaging tractography, we characterized the cerebral cortex as a weighted network of connected regions. This approach captures the underlying organization of anatomical connectivity for each subject at a regional level. Advanced graph theoretical analysis revealed that the resulting cortical networks exhibited “small-world” character (i.e., efficient information transfer both at local and global scale). In particular, the precuneus and posterior cingulate gyrus were consistently observed as centrally connected regions, independent of age and sex. Additional analysis revealed a reduction in overall cortical connectivity with age. There were also changes in the underlying network organization that resulted in decreased local efficiency, and also a shift of regional efficiency from the parietal and occipital to frontal and temporal neocortex in older brains. In addition, women showed greater overall cortical connectivity and the underlying organization of their cortical networks was more efficient, both locally and globally. There were also distributed regional differences in efficiency between sexes. Our results provide new insights into the substrates that underlie behavioral and cognitive differences in aging and sex.

Model driven EEG/fMRI fusion of brain oscillations

This article reviews progress and challenges in model driven EEG/fMRI fusion with a focus on brain oscillations. Fusion is the combination of both imaging modalities based on a cascade of forward models from ensemble of post‐synaptic potentials (ePSP) to net primary current densities (nPCD) to EEG; and from ePSP to vasomotor feed forward signal (VFFSS) to BOLD. In absence of a model, data driven fusion creates maps of correlations between EEG and BOLD or between estimates of nPCD and VFFS. A consistent finding has been that of positive correlations between EEG alpha power and BOLD in both frontal cortices and thalamus and of negative ones for the occipital region. For model driven fusion we formulate a neural mass EEG/fMRI model coupled to a metabolic hemodynamic model. For exploratory simulations we show that the Local Linearization (LL) method for integrating stochastic differential equations is appropriate for highly nonlinear dynamics. It has been successfully applied to small and medium sized networks, reproducing the described EEG/BOLD correlations. A new LL‐algebraic method allows simulations with hundreds of thousands of neural populations, with connectivities and conduction delays estimated from diffusion weighted MRI. For parameter and state estimation, Kalman filtering combined with the LL method estimates the innovations or prediction errors. From these the likelihood of models given data are obtained. The LL‐innovation estimation method has been already applied to small and medium scale models. With improved Bayesian computations the practical estimation of very large scale EEG/fMRI models shall soon be possible. Hum Brain Mapp, 2009.

Realistically Coupled Neural Mass Models Can Generate EEG Rhythms

We study the generation of EEG rhythms by means of realistically coupled neural mass models. Previous neural mass models were used to model cortical voxels and the thalamus. Interactions between voxels of the same and other cortical areas and with the thalamus were taken into account. Voxels within the same cortical area were coupled (short-range connections) with both excitatory and inhibitory connections, while coupling between areas (long-range connections) was considered to be excitatory only. Short-range connection strengths were modeled by using a connectivity function depending on the distance between voxels. Coupling strength parameters between areas were defined from empirical anatomical data employing the information obtained from probabilistic paths, which were tracked by water diffusion imaging techniques and used to quantify white matter tracts in the brain. Each cortical voxel was then described by a set of 16 random differential equations, while the thalamus was described by a set of 12 random differential equations. Thus, for analyzing the neuronal dynamics emerging from the interaction of several areas, a large system of differential equations needs to be solved. The sparseness of the estimated anatomical connectivity matrix reduces the number of connection parameters substantially, making the solution of this system faster. Simulations of human brain rhythms were carried out in order to test the model. Physiologically plausible results were obtained based on this anatomically constrained neural mass model.

Developmental Changes in Organization of Structural Brain Networks

Recent findings from developmental neuroimaging studies suggest that the enhancement of cognitive processes during development may be the result of a fine-tuning of the structural and functional organization of brain with maturation. However, the details regarding the developmental trajectory of large-scale structural brain networks are not yet understood. Here, we used graph theory to examine developmental changes in the organization of structural brain networks in 203 normally growing children and adolescents. Structural brain networks were constructed using interregional correlations in cortical thickness for 4 age groups (early childhood: 4.8-8.4 year; late childhood: 8.5-11.3 year; early adolescence: 11.4-14.7 year; late adolescence: 14.8-18.3 year). Late childhood showed prominent changes in topological properties, specifically a significant reduction in local efficiency, modularity, and increased global efficiency, suggesting a shift of topological organization toward a more random configuration. An increase in number and span of distribution of connector hubs was found in this age group. Finally, inter-regional connectivity analysis and graph-theoretic measures indicated early maturation of primary sensorimotor regions and protracted development of higher order association and paralimbic regions. Our finding reveals a time window of plasticity occurring during late childhood which may accommodate crucial changes during puberty and the new developmental tasks that an adolescent faces.

Global and System-Specific Resting-State fMRI Fluctuations Are Uncorrelated: Principal Component Analysis Reveals Anti-Correlated Networks

The influence of the global average signal (GAS) on functional-magnetic resonance imaging (fMRI)-based resting-state functional connectivity is a matter of ongoing debate. The global average fluctuations increase the correlation between functional systems beyond the correlation that reflects their specific functional connectivity. Hence, removal of the GAS is a common practice for facilitating the observation of network-specific functional connectivity. This strategy relies on the implicit assumption of a linear-additive model according to which global fluctuations, irrespective of their origin, and network-specific fluctuations are super-positioned. However, removal of the GAS introduces spurious negative correlations between functional systems, bringing into question the validity of previous findings of negative correlations between fluctuations in the default-mode and the task-positive networks. Here we present an alternative method for estimating global fluctuations, immune to the complications associated with the GAS. Principal components analysis was applied to resting-state fMRI time-series. A global-signal effect estimator was defined as the principal component (PC) that correlated best with the GAS. The mean correlation coefficient between our proposed PC-based global effect estimator and the GAS was 0.97±0.05, demonstrating that our estimator successfully approximated the GAS. In 66 out of 68 runs, the PC that showed the highest correlation with the GAS was the first PC. Since PCs are orthogonal, our method provides an estimator of the global fluctuations, which is uncorrelated to the remaining, network-specific fluctuations. Moreover, unlike the regression of the GAS, the regression of the PC-based global effect estimator does not introduce spurious anti-correlations beyond the decrease in seed-based correlation values allowed by the assumed additive model. After regressing this PC-based estimator out of the original time-series, we observed robust anti-correlations between resting-state fluctuations in the default-mode and the task-positive networks. We conclude that resting-state global fluctuations and network-specific fluctuations are uncorrelated, supporting a Resting-State Linear-Additive Model. In addition, we conclude that the network-specific resting-state fluctuations of the default-mode and task-positive networks show artifact-free anti-correlations.

Quantification of the impact of a confounding variable on functional connectivity confirms anti-correlated networks in the resting-state

The effect of regressing out the global average signal (GAS) in resting state fMRI data has become a concern for interpreting functional connectivity analyses. It is not clear whether the reported anti-correlations between the Default Mode and the Dorsal Attention Networks are intrinsic to the brain, or are artificially created by regressing out the GAS. Here we introduce a concept, Impact of the Global Average on Functional Connectivity (IGAFC), for quantifying the sensitivity of seed-based correlation analyses to the regression of the GAS. This voxel-wise IGAFC index is defined as the product of two correlation coefficients: the correlation between the GAS and the fMRI time course of a voxel, times the correlation between the GAS and the seed time course. This definition enables the calculation of a threshold at which the impact of regressing-out the GAS would be large enough to introduce spurious negative correlations. It also yields a post-hoc impact correction procedure via thresholding, which eliminates spurious correlations introduced by regressing out the GAS. In addition, we introduce an Artificial Negative Correlation Index (ANCI), defined as the absolute difference between the IGAFC index and the impact threshold. The ANCI allows a graded confidence scale for ranking voxels according to their likelihood of showing artificial correlations. By applying this method, we observed regions in the Default Mode and Dorsal Attention Networks that were anti-correlated. These findings confirm that the previously reported negative correlations between the Dorsal Attention and Default Mode Networks are intrinsic to the brain and not the result of statistical manipulations. Our proposed quantification of the impact that a confound may have on functional connectivity can be generalized to global effect estimators other than the GAS. It can be readily applied to other confounds, such as systemic physiological or head movement interferences, in order to quantify their impact on functional connectivity in the resting state.

Rate of convergence of local linearization schemes for initial-value problems

There is a large variety of Local Linearization (LL) schemes for the numerical integration of initial-value problems, which differ with respect to the algorithm that is used in the numerical implementation of the Local Linear Discretization. However, in contrast with the LL Discretization, the order of convergence of the LL schemes have not been studied so far. In this paper, a general result about that matter is given. In addition, a brief survey of the main implementations of the LL method is also presented.

Early cortical thickness changes predict β-amyloid deposition in a mouse model of Alzheimer's disease.

Magnetic resonance imaging (MRI) studies have identified aberrant cortical structure in Alzheimers disease (AD). The association between MRI-derived cortical morphometry measures and β-amyloid, however, remains poorly understood. In this study, we explored the potential relationship between early alterations in cortical thickness and later stage β-amyloid deposition, using a novel approach, in a transgenic AD mouse model. We acquired longitudinal anatomical MRI scans from mutant amyloid precursor protein (APP) transgenic mice and age-matched wild-type mice at 1 and 3.5months-of-age, and employed fully-automated image processing methods to derive objective, quantitative measures of cortical thickness on a region-of-interest basis. We also generated 3D quantitative immunohistochemistry (qIHC) volumes of deposited β-amyloid burden from 18month-old transgenic mice using an automated, production-level process. These studies revealed thinner cortex in most regions in the 1month-old transgenic mice relative to age-matched wild-types, with the exception of the frontal, perirhinal/entorhinal, posterior cingulate, and retrosplenial cortical regions. Between 1 and 3.5months-of-age, the transgenic mice demonstrated stable or increasing cortical thickness, while the wild-type mice showed cortical thinning. Based on data from co-registered 3D MRI and qIHC volumes, we identified an association between abnormal, early, regional cortical thickness change over 2.5months and later β-amyloid deposition. These observations suggest that the spatio-temporal pattern of early (pre-plaque) alterations in cerebral cortical structure is indicative of regional predisposition to later β-amyloid pathology in a transgenic AD mouse model.

Identification and comparison of stochastic metabolic/hemodynamic models (sMHM) for the generation of the BOLD signal.

This paper extends a previously formulated deterministic metabolic/hemodynamic model for the generation of blood oxygenated level dependent (BOLD) responses to include both physiological and observation stochastic components (sMHM). This adds a degree of flexibility when fitting the model to actual data by accounting for un-modelled activity. We then show how the innovation method can be used to estimate unobserved metabolic/hemodynamic as well as vascular variables of the sMHM, from simulated and actual BOLD data. The proposed estimation method allowed for doing model comparison by calculating the model’s AIC and BIC. This methodology was then used to select between different neurovascular coupling assumptions underlying sMHM. The proposed framework was first validated on simulations and then applied to BOLD data from a motor task experiment. The models under comparison in the analysis of the actual data considered that vascular response was coupled to: (I) inhibition, (II) excitation, (III) both excitation and inhibition. Data was best described by model II, although model III was also supported.

Multifactorial causal model of brain (dis)organization and therapeutic intervention: Application to Alzheimer’s disease

ABSTRACT Generative models focused on multifactorial causal mechanisms in brain disorders are scarce and generally based on limited data. Despite the biological importance of the multiple interacting processes, their effects remain poorly characterized from an integrative analytic perspective. Here, we propose a spatiotemporal multifactorial causal model (MCM) of brain (dis)organization and therapeutic intervention that accounts for local causal interactions, effects propagation via physical brain networks, cognitive alterations, and identification of optimum therapeutic interventions. In this article, we focus on describing the model and applying it at the population‐based level for studying late onset Alzheimer’s disease (LOAD). By interrelating six different neuroimaging modalities and cognitive measurements, this model accurately predicts spatiotemporal alterations in brain amyloid‐&bgr; (A&bgr;) burden, glucose metabolism, vascular flow, resting state functional activity, structural properties, and cognitive integrity. The results suggest that a vascular dysregulation may be the most‐likely initial pathologic event leading to LOAD. Nevertheless, they also suggest that LOAD it is not caused by a unique dominant biological factor (e.g. vascular or A&bgr;) but by the complex interplay among multiple relevant direct interactions. Furthermore, using theoretical control analysis of the identified population‐based multifactorial causal network, we show the crucial advantage of using combinatorial over single‐target treatments, explain why one‐target A&bgr; based therapies might fail to improve clinical outcomes, and propose an efficiency ranking of possible LOAD interventions. Although still requiring further validation at the individual level, this work presents the first analytic framework for dynamic multifactorial brain (dis)organization that may explain both the pathologic evolution of progressive neurological disorders and operationalize the influence of multiple interventional strategies. Graphical abstract Symbol Symbol. No Caption available. HighlightsA multifactorial causal model (MCM) of brain (dis)organization and therapeutic intervention is proposed.Prediction of complex multifactorial alterations in Alzheimer’s disease, using six different neuroimaging modalities and the MCM.Identification of potential triggering pathological events and associated “epicenter” brain regions.Identification and characterization of direct multifactorial interactions, vulnerability, and influence level of each biological factor.Identification of optimum therapeutic strategies to stop/reverse disease.