Multi-slice passband bSSFP for human and rodent fMRI at ultra-high field

Balanced steady-state free precession (bSSFP) can be used as an alternative to gradient-echo (GE) EPI for BOLD functional MRI when image distortions and signal drop-outs are severe such as at ultra-high field. However, 3D-bSSFP acquisitions have distinct drawbacks on either human or animal MR systems. On clinical scanners, 3D imaging is suboptimal for localized fMRI applications. It can also display distortions when acceleration methods such as spiral read-outs are used, and, compared to multi-slice acquisitions, suffers from increased sensitivity to motion or physiological noise which further results in blurring. On pre-clinical systems, 3D acquisitions have low temporal resolution due to limited acceleration options, while single slice often results in insufficient coverage. The aim of the present study was to implement a multi-slice bSSFP acquisition with Cartesian read-out to obtain non-distorted BOLD fMRI activation maps in the human and rat brain at ultra-high field. We show that, when using a new pseudo-steady-state, the bSSFP signal characteristics are preserved. In the human brain at 7 T, we demonstrate that both task- and resting-state fMRI can be performed with multi-slice bSSFP, with a temporal SNR that matches that of 3D-bSSFP, resulting in - at least - equal performance. In the rat brain at 14 T, we show that the multi-slice bSSFP protocol has similar sensitivity to gradient-echo EPI for task fMRI, while benefitting from much reduced distortions and drop-outs. The advantages of passband bSSFP at 14 T in comparison with GE-EPI are expected to be even more marked for mouse brain.

Sparsity-Promoting Spatiotemporal Regularization for Data Mining in Functional Magnetic Resonance Imaging

Fikret Isik Karahanoglu

Magnetic resonance imaging (MRI) has opened unprecedented avenues to observe the human brain non-invasively. In particular, for about two decades, functional MRI (fMRI) has enabled to monitor brain function using the blood-oxygen-level-dependent (BOLD) contrast as a proxy for neuronal activity. The impact of fMRI on neurosciences, medicine, and psychology is ever increasing and has been mainly focussing on (1) understanding brain organization in terms of segregation (i.e., localized processing) and integration (i.e., distributed processing), specifically, related to sensory processing and cognition; (2) exploring temporal characteristics of brain processes. FMRI provides large spatiotemporal datasets, typically one whole-brain volume with spatial resolution of 1–3 mm in each dimension, every 1–4 seconds during several minutes. The structure of neurophysiological contributions in these data is complex and therefore requires advanced data processing. Conventional fMRI analysis is exploiting timing properties of a stimulation or task paradigm designed by the experimenter; i.e., evidence is sought for the presence of a hypothetical BOLD response. More recently, the community has shown increasing interest in spontaneous brain activity acquired during resting-state fMRI (RS-fMRI). In the absence of any task, data-driven or exploratory methods have found great use. In particular, blind source separation such as independent component analysis (ICA) has been widely applied to RS-fMRI data. One limitation of current data-driven methods is the lack of incorporating knowledge about the hemodynamic system, which governs any activity-related signal component in the fMRI measurements. In this dissertation, we build upon the latest advances in convex optimization and propose a novel framework that can reveal activity-inducing signals at the fMRI timescale. In particular, our regularization strategy, termed “total activation” (TA), allows deconvolving the fMRI signal to remove hemodynamic blur and to improve spatial contrast of activation patterns by incorporating knowledge about meaningful brain regions. The contribution of our method lies in adapting and tailoring state-of-the-art signal processing techniques with specific domain knowledge from fMRI and neurosciences. First, we extend “total variation” (TV), which is a well-recognized method in image processing for edge-preserving regularization. TV favors signals that are piecewise constant, and, therefore, whose derivatives are sparse. We generalize this concept for signals of which the derivative of an additional linear differential operator is sparse, and build a variational formulation for the denoising problem. The recovered signal can be also studied after applying the regularizing operators; e.g., applying the differential operator will lead to the piecewise constant driving signal, while applying an additional derivative reveals a sparse “innovation” signal. Fast and efficient schemes from convex optimization are deployed to solve the variational problem at hand. Second, we apply TA for fMRI data analysis to explore the underlying “deblurred” activity-inducing signals. The temporal regularization is based on generalization of TV where the differential operator is chosen to invert the (linearized) hemodynamic system. Consequently, this will favor block-type activity-inducing signals without restrictions on timing nor duration. The spatial regularization uses a mixed-norm regularization to favor coherent activity-inducing signals in brain regions chosen from an anatomical brain atlas. After demonstrating the feasibility of the proposed method using simulated data, we show results on experimental fMRI data consisting of long resting-state periods and a few unanticipated visual stimuli. The method is able to readily recover plausible activation patterns for the visual stimuli without any prior knowledge about their timing. More interestingly, we also recovered complex spatiotemporal patterns of spontaneous activity that were organized in “resting-state networks”, as such providing a new approach to study non-stationary dynamics of RS-fMRI, a research direction that will be of major interest in the coming years. Finally, we include convincing results for localizing epileptogenic brain regions in patients using simultaneous EEG-fMRI recordings.

EPFL2013

Traces Of Human Functional Activity: Moment-To-Moment Fluctuations In Fmri Data

Luca Dodero, Djalel Eddine Meskaldji, Dimitri Nestor Alice Van De Ville

Dynamic functional connectivity (dFC) measured by functional magnetic resonance imaging (fMRI) shows evidence of large-scale networks with highly dynamic (re) configurations. We propose a novel approach to extract traces of human brain function by the construction of a trajectory in a meaningful low-dimensional space. This allows studying dFC in more detail and identify possible meaningful brain states from the moment-to-moment fluctuations of the brain signals during resting state or naturalistic conditions such as passive movie watching. Specifically, we explored dynamic organization of sub-networks derived from the time-dependent graph Laplacian in combination with Riemannian manifold distance to measure dissimilarity over time of dFC and to subsequently build the trajectory of brain activity. As a proof-of-principle, we show results for an fMRI dataset containing both rest and movie epochs in 15 healthy participants. The movie condition varied (i.e., fearful, joyful, and neutral movie excerpts) and clearly influenced the subsequent resting-state period in terms of FC brain state.

IEEE2016

TbCAPs: A toolbox for co-activation pattern analysis

Eva Blondiaux, Thomas William Arthur Bolton, Farnaz Delavari, Herberto Dhanis Pedro De Barros Camacho, Baptiste Gauthier, Julian Gaviria, Elenor Morgenroth, Gwladys Rey, Lukasz Dawid Smigielski, Constantin Tuleasca, Dimitri Nestor Alice Van De Ville

Functional magnetic resonance imaging provides rich spatio-temporal data of human brain activity during task and rest. Many recent efforts have focussed on characterising dynamics of brain activity. One notable instance is co-activation pattern (CAP) analysis, a frame-wise analytical approach that disentangles the different functional brain networks interacting with a user-defined seed region. While promising applications in various clinical settings have been demonstrated, there is not yet any centralised, publicly accessible resource to facilitate the deployment of the technique. Here, we release a working version of TbCAPs, a new toolbox for CAP analysis, which includes all steps of the analytical pipeline, introduces new methodological developments that build on already existing concepts, and enables a facilitated inspection of CAPs and resulting metrics of brain dynamics. The toolbox is available on a public academic repository at http://c4science.ch/source/CAP_Toolbox.git. In addition, to illustrate the feasibility and usefulness of our pipeline, we describe an application to the study of human cognition. CAPs are constructed from resting-state fMRI using as seed the right dorsolateral prefrontal cortex, and, in a separate sample, we successfully predict a behavioural measure of continuous attentional performance from the metrics of CAP dynamics (R = 0.59).

ACADEMIC PRESS INC ELSEVIER SCIENCE2020

Sparsity-Promoting Spatiotemporal Regularization for Data Mining in Functional Magnetic Resonance Imaging

Fikret Isik Karahanoglu

EPFL2013