A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data

A great improvement to the insight on brain function that we can get from fMRI data can come from effective connectivity analysis, in which the flow of information between even remote brain regions is inferred by the parameters of a predictive dynamical model. As opposed to biologically inspired models, some techniques as Granger causality (GC) are purely data-driven and rely on statistical prediction and temporal precedence. While powerful and widely applicable, this approach could suffer from two main limitations when applied to BOLD fMRI data: confounding effect of hemodynamic response function (HRF) and conditioning to a large number of variables in presence of short time series. For task-related fMRI, neural population dynamics can be captured by modeling signal dynamics with explicit exogenous inputs; for resting-state fMRI on the other hand, the absence of explicit inputs makes this task more difficult, unless relying on some specific prior physiological hypothesis. In order to overcome these issues and to allow a more general approach, here we present a simple and novel blind-deconvolution technique for BOLD-fMRI signal. Coming to the second limitation, a fully multivariate conditioning with short and noisy data leads to computational problems due to overfitting. Furthermore, conceptual issues arise in presence of redundancy. We thus apply partial conditioning to a limited subset of variables in the framework of information theory, as recently proposed. Mixing these two improvements we compare the differences between BOLD and deconvolved BOLD level effective networks and draw some conclusions

Statistical Analysis of fMRI Time-Series: A Critical Review of the GLM Approach.

Functional magnetic resonance imaging (fMRI) is one of the most widely used tools to study the neural underpinnings of human cognition. Standard analysis of fMRI data relies on a general linear model (GLM) approach to separate stimulus induced signals from noise. Crucially, this approach relies on a number of assumptions about the data which, for inferences to be valid, must be met. The current paper reviews the GLM approach to analysis of fMRI time-series, focusing in particular on the degree to which such data abides by the assumptions of the GLM framework, and on the methods that have been developed to correct for any violation of those assumptions. Rather than biasing estimates of effect size, the major consequence of non-conformity to the assumptions is to introduce bias into estimates of the variance, thus affecting test statistics, power, and false positive rates. Furthermore, this bias can have pervasive effects on both individual subject and group-level statistics, potentially yielding qualitatively different results across replications, especially after the thresholding procedures commonly used for inference-making

A practical modification to a resting state fMRI protocol for improved characterization of cerebrovascular function

Available online 24 June 2021.Cerebrovascular reactivity (CVR), defined here as the Blood Oxygenation Level Dependent (BOLD) response to a CO 2 pressure change, is a useful metric of cerebrovascular function. Both the amplitude and the timing (hemo- dynamic lag) of the CVR response can bring insight into the nature of a cerebrovascular pathology and aid in understanding noise confounds when using functional Magnetic Resonance Imaging (fMRI) to study neural ac- tivity. This research assessed a practical modification to a typical resting-state fMRI protocol, to improve the characterization of cerebrovascular function. In 9 healthy subjects, we modelled CVR and lag in three resting- state data segments, and in data segments which added a 2–3 minute breathing task to the start of a resting-state segment. Two different breathing tasks were used to induce fluctuations in arterial CO 2 pressure: a breath-hold task to induce hypercapnia (CO 2 increase) and a cued deep breathing task to induce hypocapnia (CO 2 decrease). Our analysis produced voxel-wise estimates of the amplitude (CVR) and timing (lag) of the BOLD-fMRI response to CO 2 by systematically shifting the CO 2 regressor in time to optimize the model fit. This optimization inher- ently increases gray matter CVR values and fit statistics. The inclusion of a simple breathing task, compared to a resting-state scan only, increases the number of voxels in the brain that have a significant relationship between CO 2 and BOLD-fMRI signals, and improves our confidence in the plausibility of voxel-wise CVR and hemody- namic lag estimates. We demonstrate the clinical utility and feasibility of this protocol in an incidental finding of Moyamoya disease, and explore the possibilities and challenges of using this protocol in younger populations. This hybrid protocol has direct applications for CVR mapping in both research and clinical settings and wider applications for fMRI denoising and interpretation.This research was supported by the Eunice Kennedy Shriver Na- tional Institute of Child Health and Human Development of the Na- tional Institutes of Health under award number K12HD073945. The pediatric dataset and cerebral palsy dataset were collected with sup- port of National Institutes of Health award R03 HD094615–01A1. The authors would like to acknowledge Marie Wasielewski and Carson Ingo for their support in acquiring these data. K.Z. was supported by an NIH-funded training program (T32EB025766). S.M. was supported by the European Union’s Horizon 2020 research and innovation pro- gram (Marie Sk ł odowska-Curie grant agreement No. 713673), a fel- lowship from La Caixa Foundation (ID 100010434, fellowship code LCF/BQ/IN17/11620063) and C.C.G was supported by the Spanish Ministry of Economy and Competitiveness (Ramon y Cajal Fellowship, RYC-2017- 21845), the Basque Government (BERC 2018–2021 and PIBA_2019_104) and the Spanish Ministry of Science, Innovation and Universities (MICINN; PID2019–105520GB-100)

Estimating Neural Signal Dynamics in the Human Brain

Although brain imaging methods are highly effective for localizing the effects of neural activation throughout the human brain in terms of the blood oxygenation level dependent (BOLD) response, there is currently no way to estimate the underlying neural signal dynamics in generating the BOLD response in each local activation region (except for processes slower than the BOLD time course). Knowledge of the neural signal is critical if spatial mapping is to progress to the analysis of dynamic information flow through the cortical networks as the brain performs its tasks. We introduce an analytic approach that provides a new level of conceptualization and specificity in the study of brain processing by non-invasive methods. This technique allows us to use brain imaging methods to determine the dynamics of local neural population responses to their native temporal resolution throughout the human brain, with relatively narrow confidence intervals on many response properties. The ability to characterize local neural dynamics in the human brain represents a significant enhancement of brain imaging capabilities, with potential applications ranging from general cognitive studies to assessment of neuropathologies