Effective Connectivity of the Bilateral Amygdala, Dorsomedial Prefrontal, and Subgenual Anterior Cingulate Cortices: Feasibility of Positive Social Emotion Regulation Models for Real-Time Functional Magnetic Resonance Imaging.

Effective connectivity based on functional magnetic resonance imaging (fMRI) allows assessing directions of interaction between brain regions. For real-time fMRI, we compared models of positive social emotion regulation based on a network involving the bilateral amygdala, dorsomedial prefrontal, and subgenual anterior cingulate cortex. The top-down regulation model implied modulation of the dorsomedial prefrontal cortex exerted onto other regions, while the bottom-up model implied the inverse modulation. The validity of model calculations was tested using the data from three healthy volunteers who imagined positive interactions with people in presented photos (stimuli). We confirmed the dominance of the top-down model and evaluated the number and duration of iterations required for model estimations. The study shows the applicability of the four-node effective connectivity models for regulation of positive social emotions using real-time fMRI, e.g., for neurofeedback applications.

No time for drifting: Comparing performance and applicability of signal detrending algorithms for real-time fMRI.

As a consequence of recent technological advances in the field of functional magnetic resonance imaging (fMRI), results can now be made available in real-time. This allows for novel applications such as online quality assurance of the acquisition, intra-operative fMRI, brain-computer-interfaces, and neurofeedback. To that aim, signal processing algorithms for real-time fMRI must reliably correct signal contaminations due to physiological noise, head motion, and scanner drift. The aim of this study was to compare performance of the commonly used online detrending algorithms exponential moving average (EMA), incremental general linear model (iGLM) and sliding window iGLM (iGLMwindow). For comparison, we also included offline detrending algorithms (i.e., MATLAB's and SPM8's native detrending functions). Additionally, we optimized the EMA control parameter, by assessing the algorithm's performance on a simulated data set with an exhaustive set of realistic experimental design parameters. First, we optimized the free parameters of the online and offline detrending algorithms. Next, using simulated data, we systematically compared the performance of the algorithms with respect to varying levels of Gaussian and colored noise, linear and non-linear drifts, spikes, and step function artifacts. Additionally, using in vivo data from an actual rt-fMRI experiment, we validated our results in a post hoc offline comparison of the different detrending algorithms. Quantitative measures show that all algorithms perform well, even though they are differently affected by the different artifact types. The iGLM approach outperforms the other online algorithms and achieves online detrending performance that is as good as that of offline procedures. These results may guide developers and users of real-time fMRI analyses tools to best account for the problem of signal drifts in real-time fMRI.

Upregulation of the rostral anterior cingulate cortex can alter the perception of emotions: fMRI-based neurofeedback at 3 and 7 T.

Recent advances in real-time functional magnetic resonance imaging (rt-fMRI) techniques enable online feedback about momentary brain activity from a localized region of interest. The anterior cingulate cortex (ACC) as a central hub for cognitive and emotional networks and its modulation has been suggested to elicit mood changes. In the presented real-time fMRI neurofeedback experiment at a 3 and a 7 T scanner we enabled participants to regulate ACC activity within one training session. The session consisted of three training runs of 8.5 min where subjects received online feedback about their current ACC activity. Before and after each run we presented emotional prosody. Subjects rated these stimuli according to their emotional valence and arousal, which served as an implicit mood measure. We found increases in ACC activation at 3 T (n = 15) and at 7 T (n = 9) with a higher activation success for the 3 T group. FMRI signal control of the rostral ACC depended on signal quality and predicted a valence bias in the rating of emotional prosody. Real-time fMRI neurofeedback of the ACC is feasible at different magnetic field strengths and can modulate localized ACC activity and emotion perception. It promises non-invasive therapeutic approaches for different psychiatric disorders characterized by impaired self-regulation.

Self-regulation of inter-hemispheric visual cortex balance through real-time fMRI neurofeedback training.

Recent advances in neurofeedback based on real-time functional magnetic resonance imaging (fMRI) allow for learning to control spatially localized brain activity in the range of millimeters across the entire brain. Real-time fMRI neurofeedback studies have demonstrated the feasibility of self-regulating activation in specific areas that are involved in a variety of functions, such as perception, motor control, language, and emotional processing. In most of these previous studies, participants trained to control activity within one region of interest (ROI). In the present study, we extended the neurofeedback approach by now training healthy participants to control the interhemispheric balance between their left and right visual cortices. This was accomplished by providing feedback based on the difference in activity between a target visual ROI and the corresponding homologue region in the opposite hemisphere. Eight out of 14 participants learned to control the differential feedback signal over the course of 3 neurofeedback training sessions spread over 3 days, i.e., they produced consistent increases in the visual target ROI relative to the opposite visual cortex. Those who learned to control the differential feedback signal were subsequently also able to exert that control in the absence of neurofeedback. Such learning to voluntarily control the balance between cortical areas of the two hemispheres might offer promising rehabilitation approaches for neurological or psychiatric conditions associated with pathological asymmetries in brain activity patterns, such as hemispatial neglect, dyslexia, or mood disorders.