The physiologic and metabolic effects of chronic consumption of energy below recommended levels on constant-load exercise was studied in women age 19-24 years. Ten controls (average caloric intake ~ 35 kcal/kg body weight) and 10 subjects with suboptimal energy intake (average caloric intake - 25 kcal/kg body weight) were matched for age, exercise caloric expenditure and percentage of HB-predicted RMR. Subjects (n=20) completed a maximal incremental cycle ergometer test and a 30-minute cycle ergometer test at 75 % of peak oxygen consumption. Although both groups presented a significant increase in ventilation (VE) over time of exercise, the rate of change in VE and end-exercise VE value was significantly greater, F (1 J 18) = 5.63, P < .05, for the suboptimal energy intake group than for the controls. Although not statistically significant (p = 0.454), heart rate (HR) tended to be continuously higher for the suboptimal energy intake group than the control group during the constant-load cycle test. Peripheral ratings of perceived exertion (RPE-L) also tended to be higher for the suboptimal energy intake group at the end of constant-load exercise, however, not significantly (p = 0.072). Two subjects in the suboptimal energy intake group failed to complete the 30-minute submaximal constant-load cycle test; one completed 15 minutes and the other 20 minutes. All subjects in the control group completed the entire submaximal constant-load test (30 minutes). These results suggest that women with chronic suboptimal energy intake have increased difficulty tolerating moderate intensity exercise for 30 minutes (an intensity and duration that mimics a typical exercise session). / Master of Science

The objective of this study was to expand on a method previously established in the lab for predicting subcortical structures using functional magnetic resonance imaging (fMRI) data restricted to the cortical surface. Our goal is to enhance the utility of low cost, portable imaging modalities, such as functional near infrared spectroscopy (fNIRS), which is limited in signal penetration depth. Previous work in the lab successfully employed functional connectivity to predict ten resting state networks and six anatomically de fined structures from the outer 10 mm layer of cortex using resting state fMRI data. The novelty of this study was two-fold: we chose to predict the functionally de fined region fusiform face area (FFA), and we utilized the functional connectivity of both resting state and task activation. Right FFA was identi ed for 27 subjects using a general linear model of a functional localizer tasks, and the average time series were extracted from right FFA and used as training and testing labels in support vector regression (SVR) models. Both resting state and task data decoded activity in right FFA above chance, both within and between run types. Our method is not specific to resting state, potentially broadening the scope of research questions depth-limited techniques can address. We observed a similarity in our accuracy cross-validation to previous work in the lab. We characterized this relationship between prediction accuracy and spatial signal-to-noise (SNR). We found that this relationship varied between resting state and task, as well as the functionality of features included in SVR modeling. / Master of Science / We used functional magnetic resonance imaging (fMRI) to predict activity in a deep brain region based on activity along the brain surface. This would increase the type of brain function a person could study using alternative methods that are less costly and easier to use, but can only detect signals along the surface. We were able to use this method to predict the fusiform face are, a region in the brain that responds more strongly to face images than other types of images. We also found a relationship between the quality of spatial information in the brain and the accuracy of predictions. This relationship differed depending on the types of brain regions were used to build the models, as well as whether the subjects were performing a task or rest scan.

In this dissertation, I have addressed the question of how resting-state EEG markers primarily in the alpha frequency range are linked to general cognitive performance and age. In the three studies presented in the work, I show that alpha power, frequency, and temporal dynamics, have distinct contributions to cognitive control functions in different age groups. Moreover, individual alpha peak frequency as well as the slope of 1/f decay of the PSD shows consistent age-related alterations, while alpha power is linked to structural alterations in the white matter. Our research extends further existing literature by specifying relevant neural networks as well as important methodological considerations that should be taken into account when analysing properties of oscillations.

Each year, about 800,000 individuals die by suicide globally, affecting millions more. Mitigating suicide risks by targeting modifiable factors such as the sleep disturbances of insomnia and nightmares, which are prevalent and linked to suicidality is important. This study investigated the structural and functional brain differences related to sleep disturbances and suicidality, with the anterior cingulate (caudal and rostral), insula, middle frontal gyrus, posterior cingulate, thalamus, amygdala, and orbitofrontal cortex as seed regions. Participants had no history of suicidal ideation (NSI; n=43) or suicidal ideation within the past two weeks (SI; n=25). Measures for analyses included the Insomnia Severity Index (ISI), Disturbing Dream and Nightmare Severity Index (DDNSI), and Frequency of Suicidal Ideation Inventory (FSII). The relationships between group (control vs suicidal ideation), structural measurements (cortical surface area, cortical thickness, gray matter volume), insomnia and nightmares across the eight regions in each hemisphere were examined. Functional connectivity-change differences were measured across wake and sleep with the eight regions as seeds. The SI group had smaller cortical surface area and gray matter volumes in the left insula (t= 2.58, p = 0.012; t = 2.44, p = 0.017); however, not after adjusting for multiple comparisons. ISI and FSII total scores correlated with each other and the surface area and gray matter volume of the left insula. In a mediation model, ISI total score was significantly related to insula surface area and FSII total score (p = 0.023; p =0.027), but the insula surface area was not significantly associated with FSII total score (p = 0.075). The indirect effect of ISI on FSII through the left insula surface area was not significant (p =0.161). The SI group had smaller changes from wake to sleep than the NSI group in the functional connectivity of the right thalamus to the left and right superior/middle temporal regions. Other neurological mechanisms could be at play as only the cortical surface area and gray matter volume in the left insula had implied differences between groups and the structural differences did not mediate the relationship between insomnia and suicidality. Smaller functional connectivity-changes differences across wake and sleep for SI compared to NSI, potentially indicate deficits in auditory inhibition.

Resting state functional magnetic resonance imaging (fMRI) is currently at the forefront of research on cognition and the brain’s large-scale organization. Patterns of hemodynamic activity that it records have been strongly linked to certain behaviors and cognitive pathologies. These signals are widely assumed to reflect local neuronal activity but our understanding of the exact relationship between them remains incomplete. Researchers often address this using multimodal approaches, pairing fMRI signals with known measures of neuronal activity such as electroencephalography (EEG). It has long been thought that infraslow (< 0.1 Hz) fMRI signals, which have become so important to the study of brain function, might have a direct electrophysiological counterpart. If true, EEG could be positioned as a low-cost alternative to fMRI when fMRI is impractical and therefore could also become much more influential in the study of functional brain networks. Previous works have produced indirect support for the fMRI-EEG relationship, but until recently the hypothesized link between them had not been tested in resting humans. The objective of this study was to investigate and characterize their relationship by simultaneously recording infraslow fMRI and EEG signals in resting human adults. We present evidence strongly supporting their link by demonstrating significant stationary and dynamic correlations between the two signal types. Moreover, functional brain networks appear to be a fundamental unit of this coupling. We conclude that infraslow electrophysiology is likely playing an important role in the dynamic configuration of the resting state brain networks that are well-known to fMRI research. Our results provide new insights into the neuronal underpinnings of hemodynamic activity and a foundational point on which the use of infraslow EEG in functional connectivity studies can be based.

Diffusion weighted imaging (DWI) and dynamic susceptibility contrast-enhanced (DSC) perfusion-weighted imaging (PWI) are commonly employed in clinical practice and in research to give pathophysiological information for patients with acute ischemic stroke. DWI is thought to roughly reflect the severely damaged infarct core, while DSC-PWI reflects the area of hypoperfusion. The volumetric difference between DWI and DSC-PWI is termed the PWI/DWI-mismatch, and has been suggested as an MRI surrogate of the ischemic penumbra. However, due to the application of a contrast agent, which has potentially severe side-effects (e.g., nephrogenic systemic fibrosis), the DSC-PWI precludes repetitive examinations for monitoring purposes. New approaches are being sought to overcome this shortcoming. BOLD (blood oxygen-level dependent) signal can reflect the metabolism of blood oxygen in the brain and hemodynamics can be assessed with resting-state fMRI. The aim of this thesis was to use resting-state fMRI as a new approach to give similar information as DSC-PWI. This thesis comprises two studies: In the first study (see Chapter 2), two resting-state fMRI methods, local methods which compare low frequency amplitudes between two hemispheres and a k-means clustering approach, were applied to investigate the functional damage of patients with acute ischemic stroke both in the time domain and frequency domain. We found that the lesion areas had lower amplitudes than contralateral homotopic healthy tissues. We also differentiated the lesion areas from healthy tissues using a k-means clustering approach. In the second study (see Chapter 3), time-shift analysis (TSA), which assesses time delays of the spontaneous low frequency fluctuations of the resting-state BOLD signal, was applied to give similar pathophysiological information as DSC-PWI in the acute phase of stroke. We found that areas which showed a pronounced time delay to the respective mean time course were very similar to the hypoperfusion area. In summary, we suggest that the resting-state fMRI methods, especially the time-shift analysis (TSA), may provide comparable information to DSC-PWI and thus serve as a useful diagnostic tool for stroke MRI without the need for the application of a contrast agent.

La complexité de la connaissance humaine est révèlée dans l'organisation spatiale et temporelle de la dynamique du cerveau. Nous pouvons connaître cette organisation grâce à l'analyse des signaux dépendant du niveau d'oxygène sanguin (BOLD), lesquels sont obtenus par l'imagerie par résonance magnétique fonctionnelle (IRMf). Nous observons des dépendances statistiques entre les régions du cerveau dans les données BOLD. Ce phénomène s' appelle connectivité fonctionnelle (CF). Des modèles computationnels sont développés pour reproduire la connectivité fonctionnelle (CF). Comme les études expérimentales précédantes, ces modèles assument que la CF est stationnaire, c'est-à-dire la moyenne et la covariance des séries temporelles BOLD utilisées par la CF sont constantes au fil du temps. Cependant, des nouvelles études expérimentales concernées par la dynamique de la CF à différentes échelles montrent que la CF change dans le temps. Cette caractéristique n'a pas été reproduite dans ces modèles computationnels précédants. Ici on a augmenté la non-linéarité de la dynamique locale dans un modèle computationnel à grande échelle. Ce modèle peut reproduire la grande variabilité de la CF observée dans les études expérimentales. / The complexity of human cognition is revealed in the spatio-temporal organization of brain dynamics. We can gain insight into this organization through the analysis of blood oxygenation-level dependent (BOLD) signals, which are obtained from functional magnetic resonance imaging (fMRI). In BOLD data we can observe statistical dependencies between brain regions. This phenomenon is known as functional connectivity (FC). Computational models are being developed to reproduce the FC of the brain. As in previous empirical studies, these models assume that FC is stationary, i.e. the mean and the covariance of the BOLD time series used for the FC are constant over time. Nevertheless, recent empirical studies focusing on the dynamics of FC at different time scales show that FC is variable in time. This feature is not reproduced in the simulated data generated by some previous computational models. Here we have enhanced the non-linearity of local dynamics in a large-scale computational model. By enhancing this non-linearity, our model is able to reproduce the variability of the FC found in empirical data.

Very little is known about the spatio-temporal ecology of the rusty-spotted genet, Genetta maculata. With this study I aimed to describe the activity patterns, resting site use and spatial ecology of G. maculata in Telperion Nature Reserve. I particularly looked at the activity profile and the activity period. I wanted to determine the spatial distribution of resting sites, the number of sites used per individual as well as the index of resting site reuse. I also calculated the distance between resting sites on consecutive days and tested for differences between sexes and seasons. I determined the size of home ranges, as well as that of core areas and compared space use between sexes and seasons as well as vegetation types. A total of six males and nine females were trapped, radio-collared and tracked during continuous night and daytime sessions between September 2015 and August 2016. Rusty-spotted genets were primarily nocturnal (nocturnality index: 0.84) and therefore made use of the darkness for cover when hunting. Overall, male effective activity duration (586 ± 172 min) was greater than for females (564 ± 175 min) possibly because they search for females to mate with as well as due to their larger body size. Seasonal changes in activity were evident – specifically in winter – and were probably a function of both food availability and temperature. Areas with a denser vegetation structure seemed to be more suitable for rusty-spotted genet resting sites. Neither the number of resting sites nor the reuse rate of these resting sites differed between sexes or seasons. The inter-resting site distance on consecutive days was higher for males (938 ± 848 m) than females (707 ± 661 m). This was possibly caused by males travelling larger distances when searching for females to mate with. The inter-resting site distance was higher during autumn, likely due to the decrease in food availability, which made it necessary for genets to increase their hunting efforts. However, a similar increase in hunting effort was not evident during winter as genets decreased their overall activity, possibly in order to avoid colder temperatures. No sexual or seasonal differences in home range size were found. This was attributed to a well-spread and consistent availability of food sources. Core areas only covered on average 7% of the total individual home range which further supports the hypothesis that food was readily available. Both intra- and intersexual home range overlaps were recorded. This was not unusual for carnivores and due to a combination of reproductive and social actions. Home ranges mainly included bushveld vegetation (78%) rather than grassland as these areas provided better cover and likely more abundant food sources. As this was the first exhaustive study of its kind on this species over a full annual cycle, the information gathered is important for the development of conservation strategies for this species, but also for other Genetta species in the rest of Africa. / College of Agriculture and Environmental Sciences / M. Sc. (Environmental Science)

Orientador: Prof. Dr. João Ricardo Sato / The brain is a complex system organized in structurally segregated and functionally specialized regions. The brain areas are composed of neuronal networks interconnected by axonal pathways that integrate through correlated neural activity. Recent studies on neural connectivity using graph theoretical analysis have revealed that brain networks interact through densely connected regions with high topological value called hubs. Previous studies of Default Mode Network (DMN), one of the most important resting-state networks, have improved the understanding of the intrinsic neuronal activity and the dynamics of the human brain. Spontaneous brain activity and Resting-State Functional Connectivity (RSFC) patterns of Resting-State Network (RSN) are essential for the comprehension of the brain function. Neuroimaging techniques such as functional Near Infrared Spectroscopy (fNIRS) make these studies possible. Thus, the main objective of this study was to investigate the RSFC using Eigenvector Centrality (EVC) measure of graph theory in fNIRS data. This work has demonstrated the effectiveness of the graph analysis for detection of hubs and mcommunities, and identified brain regions associated with rich-club, that integrates highly interconnected hubs and plays a central role in the flow and integration of Information throughout the brain. One can also conclude from the RSFC analysis the existence of functional hubs associated with DMN. / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Engenharia da Informação, 2017. / O cérebro é um sistema complexo organizado em regiões segregadas estruturalmente e especializadas funcionalmente que são compostas por redes neuronais interconectadas por vias axonais que se integram através de atividade neural correlacionada. Estudos recentes sobre conectividade neural usando teoria de grafos revelaram que as redes cerebrais interagem através de regiões densamente conectadas e com alto valor topológico denominadas hubs. Dentre as redes existentes destaca-se, por sua contribuição para a melhor compreensão do funcionamento do cérebro humano, a rede de modo padrão (Default Mode Network, DMN). A atividade espontânea do cérebro e os padrões de conectividade funcional (Resting-State Functional Connectivity, RSFC) das redes cerebrais na condição de repouso (Resting-State Network, RSN) também se tornam essenciais nos estudos que visam compreender a função desse órgão, estudos esses possibilitados graças às técnicas de neuroimagem destacando-se a espectroscopia funcional no infravermelho próximo (functional Near Infrared Spectroscopy, fNIRS). Assim, o objetivo principal deste estudo foi investigar a RSFC usando a medida de centralidade do autovetor (Eigenvector Centrality, EVC), técnica pertencente à teoria de grafos, em dados de fNIRS. Este estudo pode demonstrar a eficácia da metodologia empregada para analisar a RSFC além de revelar a existência de um núcleo estrutural, denominado hub complex, densamente conectado (rich-club), que integra hubs altamente interligados e desempenha papel central no fluxo e integração da informação ao longo do cérebro. Pode-se também concluir a partir da análise da RSFC a existência de hubs funcionais associados à DMN.

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