Electrophysiological source imaging (ESI) aims at reconstructing the precise origin of brain activity from measurements of the electric field on the scalp. Across laboratories/research centers/hospitals, ESI is performed with different methods, partly due to the ill-posedness of the underlying mathematical problem. However, it is difficult to find systematic comparisons involving a wide variety of methods. Further, existing comparisons rarely take into account the variability of the results with respect to the input parameters. Finally, comparisons are typically performed using either synthetic data, or in-vivo data where the ground-truth is only roughly known. We use an in-vivo high-density EEG dataset recorded during intracranial single pulse electrical stimulation, in which the true sources are substantially dipolar and their locations are precisely known. We compare ten different ESI methods, using their implementation in the MNE-Python package: MNE, dSPM, LORETA, sLORETA, eLORETA, LCMV beamformers, irMxNE, Gamma Map, SESAME and dipole fitting. We perform comparisons under multiple choices of input parameters, to assess the accuracy of the best reconstruction, as well as the impact of such parameters on the localization performance. Best reconstructions often fall within 1 cm from the true source, with most accurate methods hitting an average localization error of 1.2 cm and outperforming least accurate ones erring by 2.5 cm. As expected, dipolar and sparsity-promoting methods tend to outperform distributed methods. For several distributed methods, the best regularization parameter turned out to be the one in principle associated with low SNR, despite the high SNR of the available dataset. Depth weighting played no role for two out of the six methods implementing it. Sensitivity to input parameters varied widely between methods. While one would expect high variability being associated with low localization error at the best solution, this is not always the case, with some methods producing highly variable results and high localization error, and other methods producing stable results with low localization error. In particular, recent dipolar and sparsity-promoting methods provide significantly better results than older distributed methods. As we repeated the tests with "conventional" (32 channels) and dense (64, 128, 256 channels) EEG recordings, we observed little impact of the number of channels on localization accuracy; however, for distributed methods denser montages provide smaller spatial dispersion. Overall findings confirm that EEG is a reliable technique for localization of point sources and therefore reinforce the importance that ESI may have in the clinical context, especially when applied to identify the surgical target in potential candidates for epilepsy surgery.

Electrical source imaging (ESI) aims at reconstructing the electrical brain activity from measurements of the electric field on the scalp. Even though the localization of single focal sources should be relatively straightforward, different methods provide diverse solutions due to the different underlying assumptions. Furthermore, their input parameter(s) further affects the solution provided by each method, making localization even more challenging. In addition, validations and comparisons are typically performed either on synthetic data or through post-operative outcomes, in both cases with considerable limitations. We use an in-vivo high-density EEG dataset recorded during intracranial single pulse electrical stimulation, in which the true sources are substantially dipolar and their locations are known. We compare ten different ESI methods under multiple choices of input parameters, to assess the accuracy of the best reconstruction, as well as the impact of the parameters on the localization performance. Best reconstructions often fall within 1 cm from the true source, with more accurate methods outperforming less accurate ones by 1 cm, on average. Expectedly, dipolar methods tend to outperform distributed methods. Sensitivity to input parameters varies widely between methods. Depth weighting played no role for three out of six methods implementing it. In terms of regularization parameters, for several distributed methods SNR=1 unexpectedly turned out to be the best choice among the tested ones. Our data show similar levels of accuracy of ESI techniques when applied to "conventional" (32 channels) and dense (64, 128, 256 channels) EEG recordings. Overall findings reinforce the importance that ESI may have in the clinical context, especially when applied to identify the surgical target in potential candidates for epilepsy surgery.

Electrical source imaging (ESI) aims at reconstructing the electrical brain activity from measurements of the electric field on the scalp. ESI is a key element in the analysis of EEG data, in both research and clinical settings. In the last twenty years several algorithms have been applied for solving the ill- posed EEG inverse problem. Most of these popular methods can be derived within a Bayesian statistical framework in which all variables can be modelled as random variables with associated probability density functions (pdf) and the solution of the inverse problem is the posterior pdf for the unknown primary current distribution conditioned on the measurements. The different methods mainly differ from each other by the quality and quantity of a priori information they use in order to solve the EEG inverse problem. In this study [1] we validate and compare ten different ESI methods (wMNE, dSPM, sLORETA, eLORETA, LCMV, dipole fitting, RAP-MUSIC, MxNE, gamma map and Sesame) "in vivo", by exploiting a recently published EEG dataset [2] for which the ground truth is known. We compare the different inverse methods under multiple choices of input parameters, to assess the accuracy of the best reconstruction, as well as the impact of the parameters on the localization performance

It is generally accepted that visual perception results from the activation of a feed-forward hierarchy of areas, leading to increasingly complex representations. Here we present evidence for a fundamental role of backward projections to the occipito-temporal region for understanding conceptual object properties. The evidence is based on two studies. In the first study, using high-density EEG, we showed that during the observation of how objects are used there is an early activation of occipital and temporal areas, subsequently reaching the pole of the temporal lobe, and a late reactivation of the visual areas. In the second study, using transcranial magnetic stimulation over the occipital lobe, we showed a clear impairment in the accuracy of recognition of how objects are used during both early activation and, most importantly, late occipital reactivation. These findings represent strong neurophysiological evidence that a top-down mechanism is fundamental for understanding conceptual object properties, and suggest that a similar mechanism might be also present for other higher-order cognitive functions.

The cerebral cortex of primates is endowed with neurons specifi- cally tuned for biological actions. These neurons are located in a network of areas comprising the visual areas of the region of the superior temporal sulcus (STS) and the visuomotor areas of the inferior parietal lobule and premotor cortex. It is generally assumed that action understanding depends on a serial recruitment of these areas. The observed actions, following an initial processing in striate and extrastriate visual areas, are encoded in STS. Subsequently, they are transformed into a motor format in the parietal and premotor areas. This transformation is done via the mirror mechanism. Here we present evidence for a fundamental role in action perception of backward projections to the occipital lobe. The evidence is based on two studies. In the first one, using high-density EEG, we showed that, during hand- action observation, following an early activation of occipital, parietal and premotor areas, late waves occur in the occipital lobe; in the second study, using TMS, we showed a clear impairment of action perception following occipital stimulation at the time of the late occipital waves. We conclude that, backward projections from motor cortex ‘bind’ the understanding of the goal of an action with the pictorial descriptions of the same action. This binding allows the full perception of the observed actions as a joint function of visual and motor areas and overcomes the traditional functional separation between the two systems