Resting frontal alpha asymmetry was measured with an electroencephalogram in 28 volunteers to predict the evaluation of emotions in music. Sixteen music excerpts either expressing happiness, sadness, anger or fear were rated by the participants with regard to conveyed mood, pleasantness and arousal. In addition, various variables of music background were collected. The experiment started with the assessment of current mood, followed by the evaluation of the music excerpts, and finished with the assessment of the participants’ approach and withdrawal behaviour. The results showed that each music excerpt was specic for the intended mood except for music of the category anger which obtained also high ratings for fear. These music excerpts were also the only ones for which a difference in ratings between relatively more left-active and right-active participants could be observed. Partly against expectations, left-dominant volunteers perceived music excerpts of the category anger to express more fear and anger than right-active participants. Results are interpreted within the behavioural inhibitionand approach model of anterior brain asymmetry.

Emotional intelligence (EI) is defined as an individual's capacity to accurately perceive, understand, reason about, and regulate emotions, and to apply that information to facilitate thought and achieve goals. Although EI plays an important role in mental health and success in academic, professional and social realms, the neurocircuitry underlying this capacity remains poorly characterized, and no study to date has yet examined the relationship between EI and intrinsic neural network function. Here, in a sample of 54 healthy individuals (28 women, 26 men), we apply independent components analysis (ICA) with dual regression to functional magnetic resonance imaging (fMRI) data acquired while subjects were resting in the scanner to investigate brain circuits (intrinsic resting state networks) whose activity is associated with greater self-reported (i.e. Trait) and objectively measured (i.e. Ability) EI. We show that higher Ability EI, but not Trait EI, is associated with stronger negatively correlated spontaneous fMRI signals between the basal ganglia/limbic network (BGN) and posterior default mode network (DMN), and regions involved in emotional processing and regulation. Importantly, these findings suggest that the functional connectivity within and between intrinsic networks associated with mentation, affective regulation, emotion processing, and reward are strongly related to ability EI.

Sensory processing challenges are common and often difficult for children on the autism spectrum and can affect some neurotypical children. Furthermore, sensory processing atypicalities are associated with autistic traits and other co-occurring behaviors associated with autism, such as intolerance of uncertainty. As such, traits common to autism may vary continuously across diagnostic boundaries (i.e., Broad Autism Phenotype). Working to uncover behavioral and neurophysiologic correlates of sensory differences could positively impact clinical support of children with and without a diagnosis of autism. Therefore, this study examined relationships between sensory processing, intolerance of uncertainty (a possible measure of prediction), autistic traits, and associated resting state brain connectivity, in autistic (n=30) and neurotypical (NT; n=26) children ages 6-11. To this end, we calculated the relationships between behavioral scores on measures related to sensory processing, intolerance of uncertainty, and autistic traits. Also, we carried out independent component network functional connectivity analysis to investigate associations between cortical and cerebellar networks and behavioral results. Autistic participants presented with significant correlations of sensory processing with autistic traits and sensory processing with intolerance of uncertainty. Neurotypical participants presented with significant correlations of autistic traits with sensory processing and autistic traits with intolerance of uncertainty. Between groups correlations demonstrated sensory processing and intolerance of uncertainty scores overlapping and spanning the groups. Brain (rs-fMRI)--behavioral relationships regarding the above were also examined revealing strong associations between sensory and cerebellar networks and behavioral scores. Overall, our findings suggest that sensory differences may be related to altered prediction abilities and, in NT children, autistic traits. Neurophysiologic data pointed to abnormal functional connectivity between sensory cortices and the cerebellum in autistic children. These findings provide evidence for the notion of the BAP and suggest a role of prediction in sensory processing and its behavioral correlates.

Magnetic resonance imaging of neuronal activity is one of the most promising techniques in modern psychiatric research. While clear functional links with phenotypic variables have been established and detailed networks of activity robustly identified, fMRI scans have not yet yielded the robust biomarkers of psychiatric diseases, such as schizophrenia, which would allow for their use as a clinical diagnostic tool. One possible explanation for the lack of such results is that neural activity is highly non- stationary, whereas most analysis techniques assume that signal properties remain relatively static over time. Time-frequency analysis is a family of analytic techniques which do not assume that data is stationary, and thus is well suited to the analysis of neural time series. Resting state fMRI scans from a publicly available dataset were decomposed using the Wavelet transform and Hilbert Huang Transform, techniques from time-frequency analysis. The results of these processes were then used as the basis for calculating several properties of the fMRI signal within each voxel. The wavelet transform, a simpler technique, generated measures which showed broad differences between patients with schizophrenia and healthy controls but failed to reach statistical significance in the vast majority of situations. The Hilbert Huang transform, in contrast, showed significant increases in certain measures throughout areas associated with sensory processing, dysfunction in which is a symptom of schizophrenia. These results support the use of analysis techniques able to capture the nonstationarities in neural data and encourages the use of such techniques to explore the nature of the neural differences in psychiatric disorders.

Major depressive disorder (MDD) is one of the most common psychiatric illnesses and causes significant disturbances in daily functioning. Research on heart-rate variability (HRV) biofeedback training suggests that HRV is an efficacious adjunct to psychotherapy in reducing depressive symptoms. The purpose of this study was to examine neurological changes in depressed individuals who were randomized to either a psychotherapy plus HRV biofeedback training or to a treatment as usual group. A control group with no history of depression was also studied. We collected psychological, physiological, and imaging data from 30 participants (10 in an experimental group, 10 in a treatment as usual group, and the other 10 in a healthy control group) at baseline and follow-up. Regions of interest (ROIs) included anterior cingulate cortex, hippocampus, and amygdala. Participants from the experimental group went through 5 weekly HRV trainings in conjunction with traditional psychotherapy approaches. The treatment as usual group only received psychotherapy. The healthy controls did not receive any HRV training or therapy services. Overall, we found significant improvements in the experimental group's depression score, overall distress level, and HRV measurements relative to the TAU and control groups. However, we did not find significant HRV and resting-state connectivity group differences among experimental group relative to healthy controls. Together, results suggest that HRV training helps to reduce depressed participants' overall distress level and depressive symptoms. However, findings do not show any changes in participants' imaging data. These findings serve as pilot data on literature related to HRV biofeedback training in a depressed population.

Reappraisal — reinterpreting a situation to change emotional response — is an effective emotion regulation strategy that relies on cognitive control network activity, including engagement of the dorsolateral prefrontal cortex (dlPFC), to attenuate amygdala activity. Greater dlPFC-Amygdala functional connectivity predicts instructed reappraisal task success, and daily use of reappraisal for younger adults (Pico-Perez et al.., 2018) but not older adults (Opitz et al., 2012), while the connectivity of the vmPFC is predictive of physiological markers of ER success for all ages (Sakaki et al., 2016 & Urry et al., 2006). However, the relationship between Resting-State Functional Connectivity (RSFC) and reappraisal task success across the lifespan has yet to be investigated. Participants in the Cambridge Center for Aging Neuroscience study (N=299) completed an 8-minute resting-state fMRI scan. In each trial of an emotion regulation task, participants either viewed or reappraised a negative film and reported post-regulation positive affect. RSFC across bilateral amygdala and the mPFC, the left and the right dlPFC were calculated with Matlab’s CONN Toolbox. The hypothesis is that the strength of the amygdala-mPFC RSFC will predict lower negative and higher positive affect scores after reappraising, however, this study data failed to find evidence to support this hypothesis. The association between the amygdala-dlPFC RSFC and post-reappraisal negative affect scores was moderated by age. Positive affect was higher when there was a stronger negative RSFC in young and middle-aged adults, and this relationship was not significant at older ages (~72). Our results suggest that dlPFC-amygdala activity at rest may be a predictor of emotion regulation in younger and midlife adults but that dlPFC-amygdala activity may be less predictive of emotion regulation outcomes in later life.

The rapid advancements in the field of machine learning and artificial intelligence has led to the emergence of technologies like the Brain Computer Interface (BCI), which has revolutionized rehabilitation protocols. However, given the neural basis of BCIs and the dependence of its performance on cognitive factors, BCIs may be used to characterize the functional capacity of the user. A resting state segment can also be considered for characterization of the functional network integrity, creating a two part framework that probes the functional networks and their cognitive manifestations. This thesis explores such a two part framework using a simultaneous EEG-fMRI setup on a healthy population. The BCI accuracies for all subjects increased over the course of the scan and is thought to be due to learning processes on the subject's part. Since such learning processes require cognitive faculties such as attention and working memory, these factors might modulate the BCI performance profile, making it a potential metric for the integrity of such cognitive factors. The resting state analysis identified four EEG Microstates that have been previously found to be associated with verbal, visual, saliency and attention reorientation tasks. The proportion of each microstate that composed the corresponding fMRI resting state networks (RSN) were identified, opening up the potential for predicting fMRI-based RSN information, from EEG microstates alone. The developed protocol can be used to diagnose potential conditions that negatively affect the functional capacity of the user by using the results from this study as healthy control data. This is the first known BCI based system for characterization of the user's functional integrity, opening up the possibility of using BCIs as a metric for diagnosing a neuropathology. / Thesis / Master of Applied Science (MASc)

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Social Anxiety Disorder (SAD) is characterized by excessive concern or fear of negative evaluation in one or more social situations and ranks as one of the most common psychiatric disorders. SAD has also been characterized by significant deficits in social motivation and a lack of reactivity to pleasurable stimuli (i.e., positive affect; [PA]), particularly within social contexts. Recent neuroimaging work has shifted towards examining positively-valenced motivational systems in SAD focused on reward responses to social and nonsocial stimuli. These studies have revealed aberrant reward processing during social reward tasks in individuals with SAD. However, not all individuals with SAD exhibit reward circuitry dysfunction. Therefore, the current study aimed to examine if functional patterns of connectivity in the brain underlie heterogeneity in PA differences in individuals with SAD. Results revealed several functional connectivity strength differences between SAD and control groups within reward regions. Additionally, associations between regions of interest (ROIs)-couplings (i.e., OFC and insula, OFC and subgenual cingulate, insula and cingulate, and cingulate and subgenual cingulate) and diminished PA were present in individuals with SAD, but not controls. Lastly, results demonstrated that individuals with SAD had higher variability in their reward connectivity strength presentations and reports of PA as compared to controls. These results hold significance for the development of interventions for SAD that focus on the enhancement of PA to bolster social reward responsivity. / M.S. / Social Anxiety Disorder (SAD) is a common disorder where individuals experience persistent excessive fear of one or more social situations. Individuals with SAD also tend to show lower social motivation and a lack of reactivity to pleasurable activities/events (referred to broadly as positive affect; [PA]), particularly within social situations. Current work has focused on areas within the brain that are responsible for reward responses, and have indicated that individuals with SAD show different types of reward processing during social reward situations. However, not all individuals with SAD show these same patterns. Therefore, the current study aimed to examine if connections between reward regions in the brain underlie differences in PA differences in individuals with SAD. Results showed several differences between SAD and control groups within reward regions of the brain. Additionally, specific associations between brain regions of interest and low PA were present in individuals with SAD, but not controls. Lastly, results demonstrated that individuals with SAD had higher variability in their connections between reward regions and reports of PA as compared to controls. These results can help inform the development of treatments for SAD that focus on the improving PA in an attempt to increase responsiveness to social rewards.