Data_Sheet_1_Probing fMRI brain connectivity and activity changes during emotion regulation by EEG neurofeedback.PDF

Despite the existence of several emotion regulation studies using neurofeedback, interactions among a small number of regions were evaluated, and therefore, further investigation is needed to understand the interactions of the brain regions involved in emotion regulation. We implemented electroencephalography (EEG) neurofeedback with simultaneous functional magnetic resonance imaging (fMRI) using a modified happiness-inducing task through autobiographical memories to upregulate positive emotion. Then, an explorative analysis of whole brain regions was done to understand the effect of neurofeedback on brain activity and the interaction of whole brain regions involved in emotion regulation. The participants in the control and experimental groups were asked to do emotion regulation while viewing positive images of autobiographical memories and getting sham or real (based on alpha asymmetry) EEG neurofeedback, respectively. The proposed multimodal approach quantified the effects of EEG neurofeedback in changing EEG alpha power, fMRI blood oxygenation level-dependent (BOLD) activity of prefrontal, occipital, parietal, and limbic regions (up to 1.9% increase), and functional connectivity in/between prefrontal, parietal, limbic system, and insula in the experimental group. New connectivity links were identified by comparing the brain functional connectivity between experimental conditions (Upregulation and View blocks) and also by comparing the brain connectivity of the experimental and control groups. Psychometric assessments confirmed significant changes in positive and negative mood states in the experimental group by neurofeedback. Based on the exploratory analysis of activity and connectivity among all brain regions involved in emotion regions, we found significant BOLD and functional connectivity increases due to EEG neurofeedback in the experimental group, but no learning effect was observed in the control group. The results reveal several new connections among brain regions as a result of EEG neurofeedback which can be justified according to emotion regulation models and the role of those regions in emotion regulation and recalling positive autobiographical memories.</p

Multidimensional scaling in observation space and likelihood space.

<p>A multidimensional scaling technique is used to illustrate the capability of the likelihood space in increasing the separability of the clusters. (A) The distance measurement and multidimensional scaling results for pairs of spike trains from the human face and car stimuli in the observation space. (B) The distance measurement and multidimensional scaling results for the same spike trains after projection onto the likelihood space.</p

Recording areas and the average firing rate's response of the neuronal population.

<p>Recording positions were evenly distributed at anterior 14–20 mm over the ventral bank of the superior temporal sulcus and the ventral convexity up to the medial bank of the anterior middle temporal sulcus with 1-mm track intervals.</p

Extending the likelihood space for populations of neurons.

<p>The likelihood space generation for populations of neurons based on projecting the spiking activity of the population recorded from 100 neurons in IT cortex. These recordings were taken while the human face, dogface, and car images were presented to the monkey. The marked point processes theory was used for developing the probability model for the population.</p

Dynamic between-stimulus distance measure.

<p>(A) Dynamic distance measurement between pairs of stimuli from two different categories in a 100-ms sliding time window, with 10-ms sliding step based on correlation distance. (B) Dynamic distance measurement for the same stimulus pair with 100-ms sliding time window and 10-ms sliding step based on stimulus distance in the likelihood space.</p

Projection onto likelihood space.

<p>The repeated trial observation of neuronal spiking activity was used to estimate the probability model of the spike train. This enabled us to transfer any spike train into likelihood space and represent it as a single point. The coordinate components of this point are equal to the probability of spike train generated from a specific stimulus. (A) Reconstruction of likelihood space for the neural activity of a single neuron in IT cortex, while the human face and car pictures were presented. Since we reconstructed the space with respect only two stimuli, the projected space has only two dimensions. (B) The likelihood space was generated for the same neuron while spike trains from presenting human face, dog face, and car images were projected.</p

Projection of spike train onto likelihood space.

<p>Sample response of a single neuron to face stimulus presentation in raster plot format. This data is for the repeated trials, where each row is the spike train recorded for any individual trial. The transformation of the spike train for the single trial, from the observation space into a likelihood space, is illustrated. Based on previous observations and estimated stimuli conditional probability distribution, each point in the new space is generated by the projection of the binary vector of spike train.</p

Model parameter estimation.

<p>Sample responses of a neuron from IT cortex of a macaque monkey while performing the passive fixation task. The spike trains in repeated trials, in the form of a raster plot and the estimated conditional intensity function are shown for (A) a human face presentation with 95% goodness-of-fit criteria and (B) a car presentation. For face stimulus the raster plot is used for fitting the point process model on the neuronal responses with the conditional intensity estimation. The goodness-of-fit criterion is used to compare the point process model with conventional peristimulus time histogram.</p

Three different cases of neighborhood between Gd-enhanced area and necrosis.

<p>a) Gd-enhanced area is not fully adjacent to necrosis (Patient ID: 969). b) Gd-enhanced area contains necrosis and there is complete adjacency (Patient ID: 852). c) There is no necrosis (Patient ID: 178).</p

Tumor volumes and treatment effects on the tumors and time lengths between acquisitions as well as survival length.

<p>Tumor volumes before bevacizumab treatment as well as relative changes in the Gd-enhancement, edema, and necrosis, between baseline MRI and the one acquired about 2–3 months after the treatment, calculated by (<i>V</i>₁−<i>V</i>₂)/<i>V</i>₁, length of time between two image acquisitions, and survival times of the patients.</p><p>(*:tumors without necrosis or those with minimal adjacency of Gd-enhanced and necrotic areas).</p