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Objective. fMRI-based neurofeedback (NF) interventions represent the method of choice for the neuromodulation of localized brain areas. Although we have already validated an fMRI-NF protocol targeting the facial expressions processing network (FEPN), its dissemination is hampered by the economical and logistical constraints of fMRI-NF interventions, which may be however surpassed by transferring it to EEG setups, due to their low cost and portability. One of the major challenges of this procedure is then to reconstruct the BOLD-fMRI signal measured at the FEPN using only EEG signals. Because these types of approaches have been poorly explored so far, here we systematically investigated the extent at which the BOLD-fMRI signal recorded from the FEPN during a fMRI-NF protocol could be reconstructed from the simultaneously recorded EEG signal. Approach. Several features from both scalp and source spaces (the latter estimated using continuous EEG source imaging) were extracted and used as predictors in a regression problem using random forests. Furthermore, three different approaches to deal with the hemodynamic delay of the BOLD signal were tested. The resulting models were compared with the only approach already proposed in the literature that uses spectral features and considers different time delays. Main results. The combination of linear and non-linear features (particularly the largest Lyapunov exponent and entropy measures) projected into the source space, spatially filtered by independent component analysis (ICA) and convolved with multiple HRF functions peaking at different latencies, increases significantly the reconstruction accuracy (defined as the correlation between the measured and approximated BOLD signal) from 20% (direct comparison with the method used in the current literature) to 56%. Significance. With this pipeline, a more accurate reconstruction of the BOLD signal can be obtained, which will positively impact the transfer of fMRI-based neurofeedback interventions to EEG setups, and more importantly, their dissemination and efficacy in modulating the activity of the desired brain areas.

Visual adaptation describes the processes by which the visual system alters its operating properties in response to changes in the environment. It is one of the mechanisms controlling visual perceptual bistability - when two perceptual solutions are available - by controlling the duration of each percept. Moving plaids are an example of such ambiguity. They can be perceived as two surfaces sliding incoherently over each other or as a single coherent surface. Here, we investigated, using fMRI, whether activity in the human motion complex (hMT+), a region tightly related to the perceptual integration of visual motion, is modulated by distinct forms of visual adaptation to coherent or incoherent perception of moving plaids. Our hypothesis is that exposure to global coherent or incoherent moving stimuli leads to different levels of measurable adaptation, reflected in hMT+ activity. We found that the strength of the measured visual adaptation effect depended on whether subjects integrated (coherent percept) or segregated (incoherent percept) surface motion signals. Visual motion adaptation was significant both for coherent motion and globally incoherent surface motion. Although not as strong as to the coherent percept, visual adaptation due to the incoherent percept also affects hMT+. This shows that adaptation can contribute to regulate percept duration during visual bistability, with distinct weights, depending on the type of percept. Our findings suggest a link between bistability and adaptation mechanisms, both due to coherent and incoherent motion percepts, but in an asymmetric manner. These asymmetric adaptation weights have strong implications in models of perceptual decision and may explain asymmetry of perceptual interpretation periods.

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