Att undersöka områden i hjärnan som är aktiva utan att någon stimulans sker kan ge information om en individs standardnätverks basnivå. Denna basnivå kan användas för att identifiera avvikande spatiala mönster i hjärnan som associeras med sjukdomar och funktionsnedsättningar. Denna uppsats syftar till att undersöka hur skillnaderna ser ut för individspecifika nätverkskartor genom att jämföra tre olika anpassningar av Dual Regression, en frekventistisk och två Bayesianska modeller. Datamaterialet som analyseras i uppsatsen är från Cambridge-Buckner, en del av 1000 Functional Connectomes Project som innehåller fMRI-data. Från datamaterialet har även tillhörande förhandsskattade gruppvisa oberoende komponenter erhållits från 20 utvalda individer vilket sedan används i uppsatsen för att skatta individspecifika nätverkskartor i hjärnan för tre individer från studien. Det anpassas tre olika Dual Regressions-modeller: En frekventistisk modell med homoskedastisk varians, en Bayesiansk modell med heteroskedastisk varians med okorrelade feltermer samt en Bayesiansk modell med heteroskedastisk varians och korrelerade feltermer. För de två Bayesianska modellerna används icke-informativa priorfördelningar. Dessa olika modeller skiljer sig åt då de kan ta hänsyn till olika mängder av information genom att ha olika komplexa kovariansstrukturer. Det observeras att den frekventistiska modellen och den Bayesianska modellen med heteroskedastisk varians och okorrelerade feltermer skattar nätverk som är i stor utsträckning lika varandra. Den Bayesianska modellen med heteroskedastisk varians och korrelerade feltermer tenderar att skatta nätverk som är skild från de andra modellerna, där det ofta förekom skillnader i nätverkens former samt en del amplitudskillnader. I kovariansmatrisen för den Bayesianska modellen med heteroskedatisk varians och korrelerade feltermer observeras ett flertal höga korrelationer mellan feltermerna vilket indikerar på att det bör tas hänsyn till korrelerade feltermer. Det diskuteras även om problem som förekommer hos respektive tillvägagångssätt för att skatta modellen, där frekventistiska tillvägagångssättet inte tar hänsyn till all information i data men är enkel att anpassa. Den Bayesianska modellen med heteroskedastisk varians och okorrelerade feltermer ger liknande resultat som det frekventistiska tillvägagångssättet. Den Bayesianska modellen med heteroskedastisk varians och korrelerade feltermer ger resultat som anpassar data bättre än de andra två modellerna men är mer komplex att beräkna. / Examining regions in the brain that are active without any stimuli gives information about an individual's default brain networks. These default mode networks can be analyzed to identify deviating spatial patterns in the brain that are associated with diseases and disabilities. This thesis aims to analyze the difference in how frequentist and Bayesian Dual Regression estimates subject specific spatial-maps. We received pre-estimated groupwise independent components from 20 individuals based off of fMRI-data from the Cambridge-Buckner dataset which is part of the 1000 Functional Connectomes Project. These are later used to create subject specific spatial-maps for 3 individuals in the study. In this thesis 3 different types of Dual Regression models will be fitted: A frequentist Dual Regression, A Bayesian model with heteroscedastic variance and uncorrelated error terms and a Bayesian model with heteroscedastic variance and correlated error terms. Non-informative prior distributions are used for both Bayesian models. As these 3 models can account for varying amounts of information in the data due to varying complexity of the covariance structure some difference were observed in the subject specific maps. The frequentist Dual Regression and the Bayesian model with heteroscedastic variance and uncorrelated error terms often showed similar results, however the resulting networks from the Bayesian model with heteroscedastic variance and correlated error terms often differed from the other two models. The difference was observed both in network shapes and in activation amplitude. The covariance matrix for the Bayesian model with heteroscedastic variance and correlated error terms contained a number of high correlations between the error terms, indicating that correlation among error terms should be taken into account. Some arguments are made for respective way of fitting the model as each model has its unique advantage and disadvantage; where the frequentist model does not take into account all information from the data it is easy to fit. The Bayesian model with heteroscedastic variance and uncorrelated error terms is also relatively easy to fit and provides similar results to the frequentist model. The Bayesian model with heteroscedastic variance and correlated error terms however does account for more information and yields better results but is more computationally expensive.

The cognitive decline associated with healthy aging begins in early adulthood and is important to understand as a precursor of and relative to mild cognitive impairment and Alzheimer disease. Anatomical atrophy, functional compensation, and network reorganization have been observed in populations of older adults. In the current study, we examine functional network correlates of memory performance on the Wechsler Memory Scale IV and the Mnemonic Discrimination Task (MST). We report a lack of association between global graph theory metrics and age or memory performance. In addition, we observed a positive association between lure discrimination scores from the MST and right hippocampus centrality. Upon further investigation, we confirmed that old subjects with poor memory performance had lower right hippocampus centrality scores than young subjects with high average memory performance. These novel results connect the role of the hippocampus in global brain network information flow to cognitive function and have implications for better characterizing and predicting memory decline in aging.

Indiana University-Purdue University Indianapolis (IUPUI) / The social cognitive ability to identify another’s internal state and social affective ability to share another’s emotional experience, known as empathy, are integral to healthy social functioning. During tasks, neural systems active when adolescents empathize include cognitive (medial prefrontal cortex and posterior cingulate cortex with the dorsolateral prefrontal cortex) and affective (anterior insula and anterior cingulate cortex) regions that are consistent with the adult task-based literature implicating the default mode, salience, and frontoparietal networks. However, task-based studies are limited to examining neural regions probed by the task; thus, do not capture broader patterns of information processing associated with complex processes, such as empathy. Methods of functional connectivity capture broader patterns of information processing at the level of network connectivity. Although it has clear advantages in identifying neural vulnerabilities to disorder, functional connectivity has yet to be used in adolescent investigations of empathy. Via parent- and self-report, deficits in either cognitive or affective processes central to empathy associate with the most widely agreed on classifications of behavioral disorders in adolescents – transdiagnostic symptoms of internalizing and externalizing. However, this evidence relies exclusively on self-report measures and research has yet to examine the neural connectivity underlying transdiagnostic symptoms in relation to cognitive and affective empathy. What has yet to be known is (1) how the social cognitive and affective processes of empathy are functionally connected across a heterogeneous sample of adolescents and (2) the association of cognitive, affective, and imbalanced empathy with transdiagnostic symptoms. Addressing these gaps in knowledge is an important incremental step for specifying vulnerabilities not fully captured via subjective report alone. This information can be used to improve prevention and intervention strategies. The present study will examine the functional connectivity of neural networks underlying empathy in early to mid-adolescents and their association with transdiagnostic symptoms.

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Frontal Alpha Asymmetry (FAA) has been associated with individual differences such as various aspects of personality. However, the nature of the relationship between FAA and personality traits is not yet fully understood. The present study further investigated this relationship by exploring the correlation between resting-state FAA and the Big Five personality traits: openness, agreeableness, conscientiousness, extraversion, and neuroticism. 15 healthy participants completed resting-state EEG recordings three times and the Big Five Personality Inventory (BFI) twice. The results showed only one statistically significant correlation among the 20 correlations examined, between the F4-F3 resting-state FAA and openness scores. Besides, the direction of the relationship was the opposite of what would be expected. The small sample size of this study may have contributed to results, indicating the need for future research with larger samples. Nonetheless, the current findings add to the existing literature and suggest that the relationship between resting-state FAA and personality traits may be more complex than previously thought.

The brain connectivity of resting-state fMRI (rs-fMRI) represents an intrinsic state of brain architecture, and it has been used as a useful neural marker for detecting psychiatric conditions such as autism spectrum disorder, as well as for predicting psychosocial characteristics such as age. However, most studies using brain connectivity have focused more on the strength of functional connectivity over time (static-FC) than temporal features of connectivity changes (connectome variability). The primary goal of the current study was to investigate the effectiveness of using the connectome variability in classifying an individual’s pathological characteristics from others and predicting psychosocial characteristics. In addition, the current study aimed to prove that benefits of the connectome variability are reliable across various analysis procedures. To this end, three open public large rs-fMRI datasets including ABIDE, COBRE, and NKI were used. The static-FC and the connectome variability metrics were calculated with various brain parcellations and parameters and then utilized for subsequent machine learning (ML) classification and prediction. The results demonstrated that including the connectome variability increased the ML performances significantly in most cases of analytical variations. In addition, including the connectome variability prevented ML performance deterioration when excessive components were used. In conclusion, the current finding proved the usefulness of the connectome variability and its reliability. / M.S. / Functional magnetic resonance imaging (fMRI) with functional connectivity (FC) analysis has been widely used to understand the human brain’s system and cognitive processes. Especially, the resting-state fMRI (rs-fMRI) has been regarded as a comprehensive map of the brain’s large-scale functional architecture. Previous seminal findings demonstrated that brain regions show synchronized patterns even without any external stimulus or task (Biswal et al., 1995; Power et al., 2011), and recent studies also demonstrated that functional network architecture during tasks can be formed based on resting-state network architecture primarily suggesting that the resting-state is an intrinsic and fundamental of brain organization functionally. At the early stage of fMRI FC studies, researchers commonly adopted static measure of connectivity (static-FC) such as Pearson correlation. However, the brain has a dynamic nature, thus the static approach does not capture temporal information of the brain. In this context, time-varying or dynamic-FC has been suggested as a promising substitute. The derived dynamic-FC usually has been used to distinguish several dynamic states by identifying repeated spatial dynamic-FC profiles. Another utilization is quantifying moment-to-moment changes of dynamic-FC (connectome variability) which can represent how much dynamic-FC is stable. Interestingly, although its importance of dynamic-FC temporal features, few studies have utilized connectome variability. In addition, only a few studies compared static-FC and connectome variability (Fong et al., 2019; Wang et al., 2018). Therefore, it is necessary to demonstrate the benefits of connectome variability and its reliability across various cognitive domains and analytic procedures. To this aim, this study used three large open fMRI datasets: ABIDE comprised of autism spectrum disorder and typical development, COBRE comprised of schizophrenia and control group, and NKI which is a developmental dataset across the lifespan. In individuals’ resting-state fMRI, brain signal time series was extracted using various parcellation methods including AAL2 atlas (Rolls et al., 2015), bilateralized AAL2 atlas, and LAIRD network atlas (Laird et al., 2011). To calculate static-FC, pairwise Pearson correlation was used. For the dynamic-FC, sliding-window correlation was used with 60 second window size. Additional 90 second and 120 second sliding window sizes were also used to test the reliability of the current study. The additional sliding window sizes showed almost identical results to that of the main sliding window size (60s). The derived dynamic-FC was used to calculate ‘connectome variability’ using mean square successive difference (MSSD). The calculated static-FC and the connectome variability were inputted to support vector machine (SVM) for group classifications or support vector regression (SVR) for predicting individuals’ characteristics. Before machine learning analysis (SVM, SVR), lasso regression was adopted as a feature selection method. The SVM results showed that including connectome variability increased group classification performances in ABIDE and COBRE datasets. Interestingly, including connectome variability improved the robustness of SVM classification when the number of components was controlled. Similarly, the SVR results also demonstrated that including connectome variability increased prediction performances for autism symptom severity score (ADOS), social responsiveness score (SRS), and individuals’ age. These benefits were consistent across three parcellation schemes. In conclusion, the current study demonstrated that the connectome variability is useful to classify different groups and to predict individuals’ characteristics. Such benefits were reliable across multiple cognitive domains and robust to several analytic procedures. These results emphasized that the connectome variability which has been usually overlooked reflects some aspects of functional brain architecture, and future fMRI studies should more attend connectome variability between brain regions.

Extensive research has been conducted on the relationship between brain activity and personality traits, and several theories propose a lateralization of specific personality qualities. A prominent model suggests frontal lateralization of motivational direction, specifically, the behavioral inhibition and activation systems (BIS/BAS), with greater right frontal activity linked to behavioral inhibition and greater left frontal activity linked to behavioral activation. Recent studies have presented contrasting findings in the absence of this correlation. With the present study I aimed to investigate the link between frontal lateralization and the BIS/BAS. I further examined the test-retest reliability of resting-state frontal alpha asymmetry (FAA), and of the BIS/BAS scale. Resting-state frontal EEG asymmetry and participants’ responses to the BIS/BAS scale were collected from University of Skövde students on multiple occasions. FAA were obtained from electrode sites F4-F3, F6-F5, and F8-F7 over three sessions, two weeks apart, along with BIS/BAS scores from the first and third sessions. Within-subject FAA showed variability over time, suggesting FAA to be a less reliable measure of personality traits. Only two out of the four BIS/BAS subscales demonstrated consistent scores, raising doubts about the reliability of using it to assess personality traits. BAS Drive correlated negatively with FAA, contrary to the expected direction, but no other significant correlation was observed between resting-state FAA and BIS/BAS. Verifying FAA as an indicator of BIS and BAS is important for drawing meaningful associations between them. Future research should consider employing a repeated measures design and a larger sample size to enhance the understanding of this relationship.

Children and adolescents with concussion display aberrant functional connectivity in some of the major neurocognitive networks. This includes the Default Mode Network, Central Executive Network and Salience Network. Using resting state fMRI, the purpose of this thesis was to explore the functional connectivity of cognition-related networks in youth experiencing concussion. With a prospective cohort study, the functional connectivity (defined as the temporal coherence between spatially separated brain regions) of children and adolescents ages 10-18 years old was evaluated in relation to a number of demographic and injury-specific factors including recovery length, age at the time of injury, symptom severity, and neurocognitive performance. The results showed two general trends: (1) a reduction in connectivity (i.e., hypoconnectivity) between the regions of the Default Mode Network, and (2) an increase in connectivity (i.e., hyperconnectivity) between additional sensory-related regions like the cerebellum and hippocampus. The Default Mode Network, which processes self-referential information, has a long-protracted development across childhood through adulthood. Given that the participants in this cohort exhibited reduced functional connectivity within the Default Mode Network and between the Default Mode Network and other neurocognitive networks suggests that this is an area of vulnerability in youth in the event of concussion. Increased connectivity between the Central Executive Network and Salience Network, and between cognitive- and sensory-related regions such as the hippocampus and cerebellum might be interpreted as a compensatory mechanism to supplement deficits of the Default Mode Network. This thesis sheds light on important concussion-related regions for future research to investigate further and delves into the possible neural mechanisms contributing to the cognitive, sensory, mood, and sleep disturbances in children and adolescents with concussion. / Dissertation / Doctor of Philosophy (PhD) / Your brain at rest is not resting. In fact, your many brain regions are continuously communicating even during rest to maintain important communication between them. This communication between brain regions is termed functional connectivity. When you receive a blow to the head, face, neck, or another part of your body that senses a biomechanical force to your brain, the functional connectivity (i.e., communication lines) between your brain regions may be altered. A blow of this nature is considered a concussion, also known as a mild traumatic brain injury. With disruptions to the typical functional connectivity between your brain regions following a concussion, you may experience difficulty in managing cognitive tasks, emotions, and body coordination. Among those most vulnerable to the effects of concussion are children and adolescents whose brains have yet to develop fully. The goal of this thesis was to evaluate the functional connectivity between brain regions of children and adolescents to determine how brain communication might be disrupted following concussion. These evaluations were done using functional magnetic resonance imaging (fMRI) of the brains of children and adolescents ages 10-18 years old. It was discovered that the functional connectivity of the frontal lobe is related severity of post-concussion symptoms such that individuals with worse symptoms had reduced functional connectivity in the frontal lobe compared to individuals who reported less severe symptoms. Further, children and adolescents with longer recovery periods have a different level of functional connectivity in the temporal lobe compared to youth with relatively shorter recovery periods. This might suggest that both of these regions could provide prognostic value in determining who might have worse symptoms or a longer recovery time following injury. In comparison to children and adolescents who have not had a concussion, children and adolescents experiencing a concussion are more likely to have abnormal functional connectivity between the hippocampus and cerebellum, which are particularly involved in processing sensory information and navigation. This was interpreted to mean that the brain responded to the concussion by increasing the communication between regions that might help a child with a concussion coordinate their bodies so that they can move from place to place. This was additionally supported by a further investigation which showed that children and adolescents have reduced communication between areas of the brain that might allow them to process information about the self (e.g., memories, sensations, relationships with others, etc.). Overall, the results demonstrated that following a concussion, children and adolescents may have a deficit in the functioning of the frontal lobe in a specific region that allows them to process cognitive and sensory information. This might explain why concussion leads to poor memory, body coordination, sensitivity to light and sounds, and even difficulty sleeping. Their brains might then compensate for the disruption by increasing alternate pathways of communication. Together these findings open gateways for future researchers to look more deeply at the specific regions affected by concussion in youth. It draws attention to the many neurocognitive, emotional, and somatic symptoms a child with a concussion exhibits and their symptoms’ underlying neurological processes.

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No description available.