Neurofeedback training of executive function in autism spectrum disorder: distinct effects on brain activity levels and compensatory connectivity changes.

BACKGROUND: Deficits in executive function (EF) are consistently reported in autism spectrum disorders (ASD). Tailored cognitive training tools, such as neurofeedback, focused on executive function enhancement might have a significant impact on the daily life functioning of individuals with ASD. We report the first real-time fMRI neurofeedback (rt-fMRI NF) study targeting the left dorsolateral prefrontal cortex (DLPFC) in ASD. METHODS: Thirteen individuals with autism without intellectual disability and seventeen neurotypical individuals completed a rt-fMRI working memory NF paradigm, consisting of subvocal backward recitation of self-generated numeric sequences. We performed a region-of-interest analysis of the DLPFC, whole-brain comparisons between groups and, DLPFC-based functional connectivity. RESULTS: The ASD and control groups were able to modulate DLPFC activity in 84% and 98% of the runs. Activity in the target region was persistently lower in the ASD group, particularly in runs without neurofeedback. Moreover, the ASD group showed lower activity in premotor/motor areas during pre-neurofeedback run than controls, but not in transfer runs, where it was seemingly balanced by higher connectivity between the DLPFC and the motor cortex. Group comparison in the transfer run also showed significant differences in DLPFC-based connectivity between groups, including higher connectivity with areas integrated into the multidemand network (MDN) and the visual cortex. CONCLUSIONS: Neurofeedback seems to induce a higher between-group similarity of the whole-brain activity levels (including the target ROI) which might be promoted by changes in connectivity between the DLPFC and both high and low-level areas, including motor, visual and MDN regions.

Training the social brain: Clinical and neural effects of an 8-week real-time functional magnetic resonance imaging neurofeedback Phase IIa Clinical Trial in Autism.

LAY ABSTRACT: Neurofeedback is an emerging therapeutic approach in neuropsychiatric disorders. Its potential application in autism spectrum disorder remains to be tested. Here, we demonstrate the feasibility of real-time functional magnetic resonance imaging volitional neurofeedback in targeting social brain regions in autism spectrum disorder. In this clinical trial, autism spectrum disorder patients were enrolled in a program with five training sessions of neurofeedback. Participants were able to control their own brain activity in this social brain region, with positive clinical and neural effects. Larger, controlled, and blinded clinical studies will be required to confirm the benefits.

How much of the BOLD-fMRI signal can be approximated from simultaneous EEG data: relevance for the transfer and dissemination of neurofeedback interventions.

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.

Self-Modulation of Premotor Cortex Interhemispheric Connectivity in a Real-Time Functional Magnetic Resonance Imaging Neurofeedback Study Using an Adaptive Approach.

Recent studies have reported on the feasibility of real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback (NF) training. Although modulation of blood oxygenation level-dependent signal of single brain regions in rt-fMRI NF is a well established technique, the same does not hold true for modulation of connectivity. Self-modulation of interregional connectivity is a potential alternative in clinical neuroscience applications, since long-range functional dysconnectivity is being increasingly recognized as a mechanism underlying neuropsychiatric disorders. In this study, a framework was designed to train participants to self-regulate, in real time, interhemispheric functional connectivity between bilateral premotor cortices. To this end, participants use a novel adaptive motor imagery task, with gradual frequency variation preventing activity plateaus and subsequent decreases in correlation of activity (three NF runs). Participants were able to upregulate and maintain interhemispheric connectivity using such adaptive approach, as measured by correlation analysis. Modulation was achieved by simultaneous volitional control of activity in premotor areas. Activation patterns in the downregulation condition led to significantly lower correlation values than those observed in the upregulation condition, in the first two NF runs. Comparison between runs with and without feedback showed enhanced activation in key reward, executive function, and cognitive control regions, suggesting NF promotes reward and the development of goal-directed behavior. This proof-of-principle study suggests that functional connectivity feedback can be used for volitional self-modulation of neuronal connectivity. Functional connectivity-based NF could serve as a possible therapeutic tool in diseases related to the impairment of interhemispheric connectivity, particularly in the context to motor training after stroke.

Targeting dynamic facial processing mechanisms in superior temporal sulcus using a novel fMRI neurofeedback target.

The superior temporal sulcus (STS) encompasses a complex set of regions involved in a wide range of cognitive functions. To understand its functional properties, neuromodulation approaches such brain stimulation or neurofeedback can be used. We investigated whether the posterior STS (pSTS), a core region in the face perception and imagery network, could be specifically identified based on the presence of dynamic facial expressions (and not just on simple motion or static face signals), and probed with neurofeedback. Recognition of facial expressions is critically impaired in autism spectrum disorder, making this region a relevant target for future clinical neurofeedback studies. We used a stringent localizer approach based on the contrast of dynamic facial expressions against static neutral faces plus moving dots. The target region had to be specifically responsive to dynamic facial expressions instead of mere motion and/or the presence of a static face. The localizer was successful in selecting this region across subjects. Neurofeedback was then performed, using this region as a target, with two novel feedback rules (mean or derivative-based, using visual or auditory interfaces). Our results provide evidence that a facial expression-selective cluster in pSTS can be identified and may represent a suitable target for neurofeedback approaches, aiming at social and emotional cognition. These findings highlight the presence of a highly selective region in STS encoding dynamic aspects of facial expressions. Future studies should elucidate its role as a mechanistic target for neurofeedback strategies in clinical disorders of social cognition such as autism.

Control of Brain Activity in hMT+/V5 at Three Response Levels Using fMRI-Based Neurofeedback/BCI.

A major challenge in brain-computer interface (BCI) research is to increase the number of command classes and levels of control. BCI studies often use binary control level approaches (level 0 and 1 of brain activation for each class of control). Different classes may often be achieved but not different levels of activation for the same class. The increase in the number of levels of control in BCI applications may allow for larger efficiency in neurofeedback applications. In this work we test the hypothesis whether more than two modulation levels can be achieved in a single brain region, the hMT+/V5 complex. Participants performed three distinct imagery tasks during neurofeedback training: imagery of a stationary dot, imagery of a dot with two opposing motions in the vertical axis and imagery of a dot with four opposing motions in vertical or horizontal axes (imagery of 2 or 4 motion directions). The larger the number of motion alternations, the higher the expected hMT+/V5 response. A substantial number (17 of 20) of participants achieved successful binary level of control and 12 were able to reach even 3 significant levels of control within the same session, confirming the whole group effects at the individual level. With this simple approach we suggest that it is possible to design a parametric system of control based on activity modulation of a specific brain region with at least 3 different levels. Furthermore, we show that particular imagery task instructions, based on different number of motion alternations, provide feasible achievement of different control levels in BCI and/or neurofeedback applications.

Feature analysis for correlation studies of simultaneous EEG-fMRI data: A proof of concept for neurofeedback approaches.

The identification and interpretation of facial expressions is an important feature of social cognition. This characteristic is often impaired in various neurodevelopmental disorders. Recent therapeutic approaches to intervene in social communication impairments include neurofeedback (NF). In this study, we present a NF real-time functional Magnetic Resonance Imaging (rt-fMRI), combined with electroencephalography (EEG) to train social communication skills. In this sense, we defined the right Superior Temporal Sulcus as our target region-of-interest. To analyze the correlation between the fMRI regions of interest and the EEG data, we transposed the sources located at the nearest cortical location to the target region. We extracted a set of 75 features from EEG segments and performed a correlation analysis with the brain activations extracted from rt-fMRI in the right pSTS region. The finding of significant correlations of simultaneously measured signals in distinct modalities (EEG and fMRI) is promising. Future studies should address whether the observed correlation levels between local brain activity and scalp measures are sufficient to implement NF approaches.