Despite decades of research, discovering instantaneous causal relationships from observational brain imaging data, such as spontaneous MEG energies or fMRI, remains a difficult problem. Popular methods, such as Granger Causality and Non-Gaussian Structural Equation Models (SEM), either are unable to handle instantaneous effects or do not work because the data are not non-Gaussian enough. Here, we propose a model with instantaneous causality for temporally dependent variables; these are both very common properties in neuroimaging data. Then, we propose a method to estimate the causal directions based on likelihood ratios, which are related to mutual information between the residual and data variables. We thus construct a simple decision criterion that allows for instantaneous causal discovery in time-dependent data. The proposed method is computationally and conceptually very simple, and we show with simulated data that it performs well even in the case of limited sample sizes, presumably due to the general optimality properties of likelihood. We further apply it to an MEG dataset from the Cam-CAN repository, for which the method gives consistent causal directionalities of energies both intra-subject and inter-subject, as measured by split-half tests. It also gives better performance than Granger causality and non-Gaussian SEM methods in a brain age prediction task. The results also demonstrate that our method might be useful in analyzing causal brain connectomes in functional brain-imaging data.

Magnetoencephalography (MEG) is a functional brain imaging modality, which measures the weak magnetic field arising from neuronal activity. The source amplitudes and locations are estimated from the sensor data by solving an ill-posed inverse problem. Commonly used solutions for these problems operate on data from individual subjects. Combining the measurements of multiple subjects has been suggested to increase the spatial resolution of MEG by leveraging the intersubject differences for increased information. In this article, we compare 3 multisubject analysis methods on a retinotopic mapping dataset recorded from 20 subjects. The compared methods are eLORETA with source-space averaging, minimum Wasserstein estimates (MWE), and MWE with source-space averaging. The results were quantified by the geodesic distances between early (60–100 ms) MEG peak activations and fMRI-based retinotopic target points in the primary visual cortex (V1). By increasing the subject count from 1 to 10, the median distances decreased by 6.6–9.4 mm (33–46%) compared with the single-subject median distances of around 20 mm. The observed peak activation locations with multisubject analysis also comply better with the established retinotopic maps of the primary visual cortex. Our results suggest that higher spatial accuracy can be achieved by pooling data from multiple subjects. The strength of MWE lies in individualized and sparse source estimates, but in our data, averaging eLORETA estimates across individuals in source space outperformed MWE in spatial accuracy.

Reliable individual-level measures of neural activity are essential for capturing interindividual variability in brain activity recorded by magnetoencephalography (MEG). While conventional group-level analyses highlight shared features in the data, individual-level specificity is often lost. Current methods for assessing reproducibility of brain responses focus on group-level statistics and neglect subject-specific temporal and spatial characteristics. This study proposes a combined ICA algorithm (comICA), aimed at extracting within-individual consistent MEG evoked responses. The proposed hypotheses behind comICA are based on the temporal profiles of the evoked responses, the corresponding spatial information, as well as independence and linearity. ComICA is presented and tested against simulated data and test–retest recordings of a high-level cognitive task (picture naming). The results show high reliability in extracting the shared activations in the simulations (success rate >93%) and the ability to successfully reproduce group-level results on reproducibility for the test–retest MEG recordings. Our model offers means for noise reduction, targeted extraction of specific activation components in experimental designs, and potential integration across different recordings.

Discriminating patterns of brain activity corresponding to multiple hand movements are a challenging problem at the limit of the spatial resolution of magnetoencephalography (MEG). Here, we use the combination of MEG, a novel experimental paradigm, and a recently developed convolutional-neural-network-based classifier to demonstrate that four goal-directed real and imaginary movements—all performed by the same hand—can be detected from the MEG signal with high accuracy: > 70 % for real movements and > 60 % for imaginary movements. Additional experiments were used to control for possible confounds and to establish the empirical chance level. Investigation of the patterns informing the classification indicated the primary contribution of signals in the alpha (8–12 Hz) and beta (13–30 Hz) frequency range in the contralateral motor areas for the real movements, and more posterior parieto–occipital sources for the imagined movements. The obtained high accuracy can be exploited in practical applications, for example, in brain–computer interface-based motor rehabilitation.