Reconstructing anatomy from electro-physiological data.

Here we show how it is possible to make estimates of brain structure based on MEG data. We do this by reconstructing functional estimates onto distorted cortical manifolds parameterised in terms of their spherical harmonics. We demonstrate that both empirical and simulated MEG data give rise to consistent and plausible anatomical estimates. Importantly, the estimation of structure from MEG data can be quantified in terms of millimetres from the true brain structure. We show, for simulated data, that the functional assumptions which are closer to the functional ground-truth give rise to anatomical estimates that are closer to the true anatomy.

Sensor space group analysis for fNIRS data.

BACKGROUND: Functional near-infrared spectroscopy (fNIRS) is a method for monitoring hemoglobin responses using optical probes placed on the scalp. fNIRS spatial resolution is limited by the distance between channels defined as a pair of source and detector, and channel positions are often inconsistent across subjects. These challenges can lead to less accurate estimate of group level effects from channel-specific measurements. NEW METHOD: This paper addresses this shortcoming by applying random-effects analysis using summary statistics to interpolated fNIRS topographic images. Specifically, we generate individual contrast images containing the experimental effects of interest in a canonical scalp surface. Random-effects analysis then allows for making inference about the regionally specific effects induced by (potentially) multiple experimental factors in a population. RESULTS: We illustrate the approach using experimental data acquired during a colour-word matching Stroop task, and show that left frontopolar regions are significantly activated in a population during Stroop effects. This result agrees with previous neuroimaging findings. COMPARED WITH EXISTING METHODS: The proposed methods (i) address potential misalignment of sensor locations between subjects using spatial interpolation; (ii) produce experimental effects of interest either on a 2D regular grid or on a 3D triangular mesh, both representations of a canonical scalp surface; and (iii) enables one to infer population effects from fNIRS data using a computationally efficient summary statistic approach (random-effects analysis). Significance of regional effects is assessed using random field theory. CONCLUSIONS: In this paper, we have shown how fNIRS data from multiple subjects can be analysed in sensor space using random-effects analysis.

Learning and generalization under ambiguity: an fMRI study.

Adaptive behavior often exploits generalizations from past experience by applying them judiciously in new situations. This requires a means of quantifying the relative importance of prior experience and current information, so they can be balanced optimally. In this study, we ask whether the brain generalizes in an optimal way. Specifically, we used Bayesian learning theory and fMRI to test whether neuronal responses reflect context-sensitive changes in ambiguity or uncertainty about experience-dependent beliefs. We found that the hippocampus expresses clear ambiguity-dependent responses that are associated with an augmented rate of learning. These findings suggest candidate neuronal systems that may be involved in aberrations of generalization, such as over-confidence.

Probing the cortical network underlying the psychological refractory period: a combined EEG-fMRI study.

Human performance exhibits strong multi-tasking limitations in simple response time tasks. In the psychological refractory period (PRP) paradigm, where two tasks have to be performed in brief succession, central processing of the second task is delayed when the two tasks are performed at short time intervals. Here, we aimed to probe the cortical network underlying this postponement of central processing by simultaneously recording electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data while 12 subjects performed two simple number-comparison tasks. Behavioral data showed a significant slowing of response times to the second target stimulus at short stimulus-onset asynchronies, together with significant correlations between response times to the first and second target stimulus, i.e., the hallmarks of the PRP effect. The analysis of EEG data showed a significant delay of the post-perceptual P3 component evoked by the second target, which was of similar magnitude as the effect on response times. fMRI data revealed an involvement of parietal and prefrontal regions in dual-task processing. The combined analysis of fMRI and EEG data-based on the trial-by-trial variability of the P3-revealed that BOLD signals in two bilateral regions in the inferior parietal lobe and precentral gyrus significantly covaried with P3 related activity. Our results show that combining neuroimaging methods of high spatial and temporal resolutions can help to identify cortical regions underlying the central bottleneck of information processing, and strengthen the conclusion that fronto-parietal cortical regions participate in a distributed "global neuronal workspace" system that underlies the generation of the P3 component and may be one of the key cerebral underpinnings of the PRP bottleneck.

Rewarding feedback after correct visual discriminations has both general and specific influences on visual cortex.

Reward can influence visual performance, but the neural basis of this effect remains poorly understood. Here we used functional magnetic resonance imaging to investigate how rewarding feedback affected activity in distinct areas of human visual cortex, separating rewarding feedback events after correct performance from preceding visual events. Participants discriminated oriented gratings in either hemifield, receiving auditory feedback at trial end that signaled financial reward after correct performance. Greater rewards improved performance for all but the most difficult trials. Rewarding feedback increased blood-oxygen-level-dependent (BOLD) signals in striatum and orbitofrontal cortex. It also increased BOLD signals in visual areas beyond retinotopic cortex, but not in primary visual cortex representing the judged stimuli. These modulations were seen at a time point in which no visual stimuli were presented or expected, demonstrating a novel type of activity change in visual cortex that cannot reflect modulation of response to incoming or anticipated visual stimuli. Rewarded trials led on the next trial to improved performance and enhanced visual activity contralateral to the judged stimulus, for retinotopic representations of the judged visual stimuli in V1. Our findings distinguish general effects in nonretinotopic visual cortex when receiving rewarding feedback after correct performance from consequences of reward for spatially specific responses in V1.

Multinomial inference on distributed responses in SPM.

In this work, we propose statistical methods to perform inference on the spatial distribution of topological features (e.g. maxima or clusters) in statistical parametric maps (SPMs). This contrasts with local inference on the features per se (e.g., height or extent), which is well-studied (e.g. Friston et al., 1991, 1994; Worsley et al., 1992, 2003, 2004). We present a Bayesian approach to detecting experimentally-induced patterns of distributed responses in SPMs with anisotropic, non-stationary noise and arbitrary geometry. We extend the framework to accommodate fixed- and random-effects analyses at the within and between-subject levels respectively. We illustrate the method by characterising the anatomy of language at different scales of functional segregation.

Topological FDR for neuroimaging.

In this technical note, we describe and validate a topological false discovery rate (FDR) procedure for statistical parametric mapping. This procedure is designed to deal with signal that is continuous and has, in principle, unbounded spatial support. We therefore infer on topological features of the signal, such as the existence of local maxima or peaks above some threshold. Using results from random field theory, we assign a p-value to each maximum in an SPM and identify an adaptive threshold that controls false discovery rate, using the Benjamini and Hochberg (BH) procedure (1995). This provides a natural complement to conventional family wise error (FWE) control on local maxima. We use simulations to contrast these procedures; both in terms of their relative number of discoveries and their spatial accuracy (via the distribution of the Euclidian distance between true and discovered activations). We also assessed two other procedures: cluster-wise and voxel-wise FDR procedures. Our results suggest that (a) FDR control of maxima or peaks is more sensitive than FWE control of peaks with minimal cost in terms of false-positives, (b) voxel-wise FDR is substantially less accurate than topological FWE or FDR control. Finally, we present an illustrative application using an fMRI study of visual attention.

Graph-partitioned spatial priors for functional magnetic resonance images.

Spatial models of functional magnetic resonance imaging (fMRI) data allow one to estimate the spatial smoothness of general linear model (GLM) parameters and eschew pre-process smoothing of data entailed by conventional mass-univariate analyses. Recently diffusion-based spatial priors [Harrison, L.M., Penny, W., Daunizeau, J., and Friston, K.J. (2008). Diffusion-based spatial priors for functional magnetic resonance images. NeuroImage.] were proposed, which provide a way to formulate an adaptive spatial basis, where the diffusion kernel of a weighted graph-Laplacian (WGL) is used as the prior covariance matrix over GLM parameters. An advantage of these is that they can be used to relax the assumption of isotropy and stationarity implicit in smoothing data with a fixed Gaussian kernel. The limitation of diffusion-based models is purely computational, due to the large number of voxels in a brain volume. One solution is to partition a brain volume into slices, using a spatial model for each slice. This reduces computational burden by approximating the full WGL with a block diagonal form, where each block can be analysed separately. While fMRI data are collected in slices, the functional structures exhibiting spatial coherence and continuity are generally three-dimensional, calling for a more informed partition. We address this using the graph-Laplacian to divide a brain volume into sub-graphs, whose shape can be arbitrary. Their shape depends crucially on edge weights of the graph, which can be based on the Euclidean distance between voxels (isotropic) or on GLM parameters (anisotropic) encoding functional responses. The result is an approximation the full WGL that retains its 3D form and also has potential for parallelism. We applied the method to high-resolution (1 mm(3)) fMRI data and compared models where a volume was divided into either slices or graph-partitions. Models were optimized using Expectation-Maximization and the approximate log-evidence computed to compare these different ways to partition a spatial prior. The high-resolution fMRI data presented here had greatest evidence for the graph partitioned anisotropic model, which was best able to preserve fine functional detail.

Anatomically informed interpolation of fMRI data on the cortical surface.

Analyzing functional magnetic resonance imaging (fMRI) data restricted to the cortical surface is of particular interest for two reasons: (1) to increase detection sensitivity using anatomical constraints and (2) to compare or use fMRI results in the context of source localization from magneto/electro-encephalography (MEEG) data, which requires data to be projected on the same spatial support. Designing an optimal scheme to interpolate fMRI raw data or resulting activation maps on the cortical surface relies on a trade-off between choosing large enough interpolation kernels, because of the distributed nature of the hemodynamic response, and avoiding mixing data issued from different anatomical structures. We propose an original method that automatically adjusts the level of such a trade-off, by defining interpolation kernels around each vertex of the cortical surface using a geodesic Voronoï diagram. This Voronoï-based interpolation method was evaluated using simulated fMRI activation maps, manually generated on an anatomical MRI, and compared with a more standard approach where interpolation kernels were defined as local spheres of radius r=3 or 5 mm. Several validation parameters were considered: the spatial resolution of the simulated activation map, the spatial resolution of the cortical mesh, the level of anatomical/functional data misregistration and the location of the vertices within the gray matter ribbon. Using an activation map at the spatial resolution of standard fMRI data, robustness to misregistration errors was observed for both methods, whereas only the Voronoï-based approach was insensitive to the position of the vertices within the gray matter ribbon.