Novel 4-D Algorithm for Functional MRI Image Regularization using Partial Differential Equations

International audienceState-of-the-art techniques for denoising functional MRI (fMRI) images consider the problems of spatial and temporal regularization as decoupled tasks. In this work we propose a partial differential equations (PDEs)-based algorithm that acts directly on the 4-D fMRI image. Our approach is based on the idea that large image variations should be preserved as they occur during brain activation, but small variations should be smoothed to remove noise. Starting from this principle, by means of PDEs we were able to smooth the fMRI image with an anisotropic regularization, thus recovering the location of the brain activations in space and their timing and duration

Deconvolution of fMRI Data using a Paradigm Free Iterative Approach based on Partial Differential Equations

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A Paradigm Free Regularization Approach to Recover Brain Activations: Validation on Task fMRI

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Non-invasive inference of information flow using diffusion MRI, functional MRI, and MEG

International audienceObjective: To infer information flow in the white matter of the brain and recover cortical activity using functional MRI, diffusion MRI, and MEG without a manual selection of the white matter connections of interest. Approach: A Bayesian network which encodes the priors knowledge of possible brain states is built from imaging data. Diffusion MRI is used to enumerate all possible connections between cortical regions. Functional MRI is used to prune connections without manual intervention and increase the likelihood of specific regions being active. MEG data is used as evidence into this network to obtain a posterior distribution on cortical regions and connections. Main results: We show that our proposed method is able to identify connections associated with the a sensory-motor task. This allows us to build the Bayesian network with no manual selection of connections of interest. Using sensory-motor MEG evoked response as evidence into this network, our method identified areas known to be involved in a visuomo-tor task. In addition, information flow along white matter fiber bundles connecting those regions was also recovered. Significance: Current methods to estimate white matter information flow are extremely invasive, therefore limiting our understanding of the interaction between cortical regions. The proposed method makes use of functional MRI, diffusion MRI, and M/EEG to infer communication between cortical regions, therefore opening the door to the non-invasive exploration of information flow in the white matter

Exploring resting-state functional connectivity invariants across the lifespan in healthy people by means of a recently proposed graph theoretical model

In this paper we investigate the changes in the functional connectivity intensity, and some related properties, in healthy people, across the life span and at resting state. For the explicit computation of the functional connectivity we exploit a recently proposed model, that bases not only on the correlations data provided by the acquisition equipment, but also on different parameters, such as the anatomical distances between nodes and their degrees. The leading purpose of the paper is to show that the proposed approach is able to recover the main aspects of resting state condition known from the available literature, as well as to suggest new insights, perspectives and speculations from a neurobiological point of view. Our study involves 133 subjects, both males and females of different ages, with no evidence of neurological diseases or systemic disorders. First, we show how the model applies to the sample, where the subjects are grouped into 28 different groups (14 of males and 14 of females), according to their age. This leads to the construction of two graphs (one for males and one for females), that can be realistically interpreted as representative of the neural network during the resting state. Second, following the idea that the brain network is better understood by focusing on specific nodes having a kind of centrality, we refine the two output graphs by introducing a new metric that favours the selection of nodes having higher degrees. As a third step, we extensively comment and discuss the obtained results. In particular, it is remarkable that, despite a great overlapping exists between the outcomes concerning males and females, some intriguing differences appear. This motivates a deeper local investigation, which represents the fourth part of the paper, carried out through a thorough statistical analysis. As a result, we are enabled to support that, for two special age groups, a few links contribute in differentiating the behaviour of males and females. In addition, we performed an average-based comparison between the proposed model and the traditional statistical correlation-based approach, then discussing and commenting the main outlined discrepancies

Resolving the crossing/kissing fiber ambiguity using Functionally Informed COMMIT

International audienceThe architecture of the white matter is endowed with kissing and crossing bundles configurations. When these white matter tracts are reconstructed using diffusion MRI tractography, this systematically induces the reconstruction of many fiber tracts that are not coherent with the structure of the brain. The question on how to discriminate between true positive connections and false positive connections is the one addressed in this work. State-of-the-art techniques provide a partial solution to this problem by considering anatomical priors in the false pos-itives detection process. We propose a novel model that tackles the same issue but takes into account both structural and functional information by combining them in a convex optimization problem. We validate it on two toy phantoms that reproduce the kissing and the crossing bundles configurations, showing that through this approach we are able to correctly distinguish true positives and false positives

fMRI Deconvolution via Temporal Regularization using a LASSO model and the LARS algorithm

International audienceIn the context of functional MRI (fMRI), methods based on the deconvolution of the blood oxygenated level dependent (BOLD) signal have been developed to investigate the brain activity, without a need of a priori knowledge about activations occurrence [2]. In this work, we propose a novel temporal regularized deconvolution of the BOLD signal using the Least Absolute Shrinkage and Selection Operator (LASSO) model, solved by means of the Least-Angle Regression (LARS) algorithm. In this way, we were able to recover the underlying neurons activations and their dynamics

L1-Norm Regularized Deconvolution of Functional MRI BOLD Signal

International audienceDeconvolution methods are used to denoise the blood oxygen level-dependent (BOLD) response, the signal that forms the basis of functional MRI (fMRI). In this work we propose a novel approach based on a temporal regularized deconvolution of the BOLD fMRI signal with the least absolute shrinkage and selection operator (LASSO) model, solved using the angle regression algorithm (LARS). In this way we were able to recover the underlying neurons activations and their dynamic

Deconvolution of fMRI BOLD signal in time-domain using an exponential operator and Lasso optimization

International audienceMany techniques have been explored so far in the study of neural activations using the blood oxygenated level dependent (BOLD) signal. Among them, deconvolution methods have been developed in order to explore spontaneous brain activity when the brain is in resting-state. These techniques are powerful since they do not require a priori knowledge about timing and duration of activations [2]. In this work, we propose a regularized deconvolution technique which uses an exponential operator, whose shape and performance can be adjusted by tuning a parameter α, and the Least-Angle Regression (LARS) algorithm, by using the least absolute shrinkage and selection operator (LASSO) model

Reducing false positive connection in tractograms using joint structure-function filtering

International audienceDue to its ill-posed nature, tractography generates a significant number of false positive connections between brain regions [3]. To reduce the number of false positives, Daducci et al. [1] proposed the COMMIT framework, which has the goal of re-establishing the link between tractography and tissue microstructure. In this framework, the diffusion MRI signal is modeled as a linear combination of local models associated with streamlines where the weights are identified by solving a convex optimization problem. Streamlines with a weight of zero do not contribute to the diffusion MRI data and are assumed to be false positives. Removing these false positives yields a subset of streamlines supporting the anatomical data. However, COMMIT does not make use of the link between structure and function and thus weights all bundles equally. In this work, we propose a new strategy that enhances the COMMIT framework by injecting the functional information provided by functional MRI. The result is an enhanced tractogram filtering strategy that considers both functional and structural data