Monitoring the functional connectivity between brain networks is becoming increasingly important in elucidating brain functionality in normal and disease states. Current methods of detecting networks in the recorded EEG such as correlation and coherence are limited by the fact that they assume stationarity of the relationship between channels, and rely on linear dependencies. Here we utilize mutual information (MI) as the metric for determining nonlinear statistical dependencies between electroencephalographic (EEG) channels. Previous work investigating MI between EEG channels in subjects with widespread diseases of the cerebral cortex had subjects simply rest quietly with their eyes closed. In motor disorders such as Parkinson’s disease (PD), abnormalities are only expected during performance of motor tasks, but this makes the assumption of stationarity of relationships between EEG channels untenable. We therefore propose a novel EEG segmentation method based on the temporal dynamics of the cross-spectrogram of the computed Independent Components (ICs). After suitable thresholding of the MI values between channels in the temporally segmented EEG, graphical theoretical analysis approaches are applied to the derived networks. The method was applied to EEG data recorded from six normal subjects and seven PD subjects on and off medication performing a motor task involving either their right hand only or both hands simultaneously. One-way analysis of variance (ANOVA) tests demonstrated statistically significant difference between subject groups. This proposed segmentation/MI network method appears to be a promising approach for EEG analysis.

Monitoring the functional connectivity between brain networks is becoming increasingly important in elucidating brain functionality in normal and disease states. Current methods of detecting networks in the recorded EEG such as correlation and coherence are limited by the fact that they assume stationarity of the relationship between channels, and rely on linear dependencies. Here we utilize mutual information (MI) as the metric for determining nonlinear statistical dependencies between electroencephalographic (EEG) channels. Previous work investigating MI between EEG channels in subjects with widespread diseases of the cerebral cortex had subjects simply rest quietly with their eyes closed. In motor disorders such as Parkinson’s disease (PD), abnormalities are only expected during performance of motor tasks, but this makes the assumption of stationarity of relationships between EEG channels untenable. We therefore propose a novel EEG segmentation method based on the temporal dynamics of the cross-spectrogram of the computed Independent Components (ICs). After suitable thresholding of the MI values between channels in the temporally segmented EEG, graphical theoretical analysis approaches are applied to the derived networks. The method was applied to EEG data recorded from six normal subjects and seven PD subjects on and off medication performing a motor task involving either their right hand only or both hands simultaneously. One-way analysis of variance (ANOVA) tests demonstrated statistically significant difference between subject groups. This proposed segmentation/MI network method appears to be a promising approach for EEG analysis.

Monitoring the functional connectivity between brain networks is becoming increasingly important in elucidating brain functionality in normal and disease states. Current methods of detecting networks in the recorded EEG such as correlation and coherence are limited by the fact that they assume stationarity of the relationship between channels, and rely on linear dependencies. Here we utilize mutual information (MI) as the metric for determining nonlinear statistical dependencies between electroencephalographic (EEG) channels. Previous work investigating MI between EEG channels in subjects with widespread diseases of the cerebral cortex had subjects simply rest quietly with their eyes closed. In motor disorders such as Parkinson’s disease (PD), abnormalities are only expected during performance of motor tasks, but this makes the assumption of stationarity of relationships between EEG channels untenable. We therefore propose a novel EEG segmentation method based on the temporal dynamics of the cross-spectrogram of the computed Independent Components (ICs). After suitable thresholding of the MI values between channels in the temporally segmented EEG, graphical theoretical analysis approaches are applied to the derived networks. The method was applied to EEG data recorded from six normal subjects and seven PD subjects on and off medication performing a motor task involving either their right hand only or both hands simultaneously. One-way analysis of variance (ANOVA) tests demonstrated statistically significant difference between subject groups. This proposed segmentation/MI network method appears to be a promising approach for EEG analysis. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

Various topics on functional magnetic resonance imaging (fMRI) analyses have been in study through the last 30 years. This work delineates the pathways required for resting-state functional connectivity analyses, which illuminates the correlations between different rat brain regions and can be presented in a functional connectivity matrix. The matrix is built based on the category nomenclature system of Swanson Rat Atlas 1998. From which a structural connectivity matrix is also built. This work developed the complete functional connectivity counterpart to the physical connections and explored the relationships between the functional and structural connectivity matrices. The functional connectivity matrices developed in this work map the entire rat brain. The results demonstrate that where structural connectivities exist, functional connectivities exist as well. The methodologies used to create the functional and structural analyses were completely independent.

Functional magnetic resonance imaging studies have long demonstrated a high degree of correlated activity between the left and right hemispheres of the brain. Interregional correlations between the time series of each brain voxel or region and its homotopic pair have recently been identified by methods such as homotopic resting-state functional connectivity (H-RSFC). However, little is known about whether interhemispheric regions in patients with Attention-deficit/hyperactivity disorder (ADHD) are functionally abnormal. The aim of this thesis is to examine the association between H-RSFC and medication status, age, sex, and volumetric asymmetry index (AI). In our approach, region-based activity was obtained using three different methods. To test for associations, two linear mixed-effects models were used. Across results, H-RSFC variation was found in subcortical regions and portions of cortical regions. In addition, changes in functional connectivity were found to be linked with structural asymmetry in two cortical regions. More importantly, shifting in homotopic functional activation was found as a result of medication intake in youths with ADHD. These findings demonstrate the utility of homotopic resting-state functional connectivity for measuring differences among pharmacotherapy intake, gender, neurodevelopment, and structural asymmetry.

In everyday settings, spatial attention helps listeners isolate and understand individual sound sources. However, the neural mechanisms of auditory spatial attention (ASpA) are only partially understood. This thesis uses within-subject analysis of functional magnetic resonance imaging (fMRI) data to address fundamental questions regarding cortical mechanisms supporting ASpA by applying novel multi-voxel pattern analysis (MVPA) and resting-state functional connectivity (rsFC) approaches. A series of fMRI studies of ASpA were conducted in which subjects performed a one-back task in which they attended to one of two spatially separated streams. Attention modulated blood oxygenation level-dependent (BOLD) activity in multiple areas in the prefrontal, temporal, and parietal cortex, including non-visuotopic intraparietal sulcus (IPS), but not the visuotopic maps in IPS. No spatial bias was detected in any cortical area using standard univariate analysis; however, MVPA revealed that activation patterns in a number of areas, including the auditory cortex, predicted the attended direction. Furthermore, we explored how cognitive task demands and the sensory modality of the inputs influenced activity with a visual one-back task and a visual multiple object tracking (MOT) task. Activity from the visual and auditory one-back tasks overlapped along the fundus of IPS and lateral prefrontal cortex (lPFC). However, there was minimal overlap of activity in the lPFC between the visual MOT task and the two one-back tasks. Finally, we endeavored to identify visual and auditory networks using rsFC. We identified a dorsal visual attention network reliably within individual subjects using visuotopic seeds. Using auditory seeds, we found a prefrontal area nested between segments of the dorsal visual attention network. These findings mark fundamental progress towards elucidating the cortical network controlling ASpA. Our results suggest that similar lPFC structures support both ASpA and its visual counterpart during a spatial one-back task, but that ASpA does not drive visuotopic IPS in the parietal cortex. Furthermore, rsFC reveals that visual and auditory seed regions are functionally connected with non-overlapping lPFC regions, possibly reflecting spatial and temporal cognitive processing biases, respectively. While we find no evidence for a spatiotopic map, the auditory cortex is sensitive to direction of attention in its patterns of activation.

Thesis advisor: Stefano Anzellotti / Autism Spectrum Disorder (ASD) displays uniquely in every individual, creating disparities in symptom severity, genetics, and functional connectivity. Examining the relationship between genetic and functional connectivity variability could help to better understand individual differences in ASD. From this, improved diagnosis, treatment, and understanding of ASD can be developed. To resolve individual differences in symptom severity and presentation, I generated matrices of subject functional connectivity data and compared this to gene expression maps. Multivariate regression analysis was performed on the data to anticipate ASD symptoms from these correlation matrices and to establish which genes have the largest impact on these predictions. The ANOVAs ran on the data were not significant, but there were several genes implicated in specific aspects of ASD. STX1A, MVP, CDKL5, and RABEP2 were the only genes correlated across more than one subtype of ASD. These results pave the way for future research to investigate the roles of these genes in a larger size of ASD subjects. / Thesis (BS) — Boston College, 2023. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: Departmental Honors. / Discipline: Psychology and Neuroscience.

Expertise and training in fine motor skills has been associated with changes in brain structure, function, and connectivity. Fewer studies have explored the neural effects of athletic activities that do not seem to rely on precise fine motor control (e.g., distance running). Here, we compared resting-state functional connectivity in a sample of adult male collegiate distance runners (n = 11; age = 21.3 +/- 2.5) and a group of healthy age matched non-athlete male controls (n = 11; age = 20.6 +/- 1.1), to test the hypothesis that expertise in sustained aerobic motor behaviors affects resting state functional connectivity in young adults. Although generally considered an automated repetitive task, locomotion, especially at an elite level, likely engages multiple cognitive actions including planning, inhibition, monitoring, attentional switching and multi-tasking, and motor control. Here, we examined connectivity in three resting-state networks that link such executive functions with motor control: the default mode network (DMN), the frontoparietal network (FPN), and the motor network (MN). We found two key patterns of significant between-group differences in connectivity that are consistent with the hypothesized cognitive demands of elite endurance running. First, enhanced connectivity between the FPN and brain regions often associated with aspects of working memory and other executive functions (frontal cortex), suggest endurance running may stress executive cognitive functions in ways that increase connectivity in associated networks. Second, we found significant anti-correlations between the DMN and regions associated with motor control (paracentral area), somatosensory functions (post-central region), and visual association abilities (occipital cortex). DMN deactivation with task-positive regions has been shown to be generally beneficial for cognitive performance, suggesting anti-correlated regions observed here are engaged during running. For all between-group differences, there were significant associations between connectivity, self-reported physical activity, and estimates of maximum aerobic capacity, suggesting a dose-response relationship between engagement in endurance running and connectivity strength. Together these results suggest that differences in experience with endurance running are associated with differences in functional brain connectivity. High intensity aerobic activity that requires sustained, repetitive locomotor and navigational skills may stress cognitive domains in ways that lead to altered brain connectivity, which in turn has implications for understanding the beneficial role of exercise for brain and cognitive function over the lifespan.

Background. Impaired insight, or unawareness of illness, is a common symptom of schizophrenia. Clinical insight is awareness of having a mental disorder; cognitive insight is ability to self-reflect (self­reflectiveness) and certainty in cognitions (self­certainty). In schizophrenia insight is associated with brain function and improving insight is a potential early intervention point. This study investigated whether insight is impaired in youth at ultra high-risk (UHR) for psychosis, and if it is related to major brain networks. Methods. Data from a larger UHR study was used, including 55 UHR adolescents and 55 controls assessed with the Structured Interview of Prodromal Symptoms, MATRICS Consensus Cognitive Battery, Scale to Assess Unawareness of Mental Disorder, and Beck Cognitive Insight Scale, as well as resting state functional MRI scans. UHR and control groups were tested for differences in self-reflectiveness and self-certainty, and correlations between insight dimensions and clinical and cognitive measures. Functional connectivity was calculated for the default mode, the cingulo-opercular, and central executive networks and regressed on participants’ reported clinical and cognitive insight, while covarying for head motion. Results. Self-reflectiveness was higher in the UHR group (d = 1.28), but the groups did not differ in self-certainty (d = 0.28). Among UHR, poorer clinical insight was related to greater symptom severity. Default mode connectivity was negatively correlated with self-reflectiveness (R2 = .091) and clinical insight (R2 = .399) in UHR, but no such correlations were found in controls. Cerebello-prefrontal cortex connectivity was negatively associated with self-certainty in the UHR group (R2 = .089 - .138). Conclusions. Default mode connectivity appears to be associated with the facets of insight concerning self-awareness, whereas cerebello-prefrontal connectivity appears to be associated specifically with self-certainty. This is the first study to relate major brain networks to insight before the onset of psychosis, and is consistent with models proposing that different facets of insight are related to self-awareness and executive functioning through networks associated with these processes.

To study potential hyperactivity and hyperconnectivity based on the latent resource hypothesis, this study assessed functional connectivity in survivors of childhood brain tumors compared to their healthy peers during an attention task using psychophysiological interaction (PPI) analyses and evaluated for a relationship with performance. Twenty-three survivors and 23 healthy controls completed a letter n-back task in the scanner. An empirically-based seed was placed in the parietal lobe, a theoretical seed was placed in the hippocampus, and a control seed was placed in the occipital lobe. Differences in both performance and functional connectivity networks from each seed emerged between groups, with some findings supporting the latent resource hypothesis and other networks showing compensatory function in survivors. Attention networks, phonological networks, and executive function networks were all found to differ between controls and survivors.