2 research outputs found
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Spectral Diversity in Default Mode Network Connectivity Reflects Behavioral State.
Default mode network (DMN) functional connectivity is thought to occur primarily in low frequencies (<0.1 Hz), resulting in most studies removing high frequencies during data preprocessing. In contrast, subtractive task analyses include high frequencies, as these are thought to be task relevant. An emerging line of research explores resting fMRI data at higher-frequency bands, examining the possibility that functional connectivity is a multiband phenomenon. Furthermore, recent studies suggest DMN involvement in cognitive processing; however, without a systematic investigation of DMN connectivity during tasks, its functional contribution to cognition cannot be fully understood. We bridged these concurrent lines of research by examining the contribution of high frequencies in the relationship between DMN and dorsal attention network at rest and during task execution. Our findings revealed that the inclusion of high frequencies alters between network connectivity, resulting in reduced anticorrelation and increased positive connectivity between DMN and dorsal attention network. Critically, increased positive connectivity was observed only during tasks, suggesting an important role for high-frequency fluctuations in functional integration. Moreover, within-DMN connectivity during task execution correlated with RT only when high frequencies were included. These results show that DMN does not simply deactivate during task execution and suggest active recruitment while performing cognitively demanding paradigms
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Brain Network Connectivity in Anaesthesia and Disorders of Consciousness
Until recently, understanding the nature of consciousness was considered a philosophical
pursuit. However, technological developments in brain imaging have allowed the study of
consciousness as a natural, neurobiological phenomenon. The neurobiology of
consciousness has been studied using cognitive and behavioural testing in healthy
volunteers and by examining how brain function and connectivity is altered in various
clinical settings. The focus of this thesis is to use two of these clinical settings,
pharmacologically-induced sedation and disorders of consciousness (DOC), as
experimental models for measuring changes in connectivity patterns associated with
alterations in consciousness. Experiment 1 presents a method for improving functional
magnetic resonance imaging (fMRI) data pre-processing to measure brain network
connectivity more accurately. This pre-processing method is then applied to the analyses
in the remainder of the thesis. Experiment 2 focuses on a fMRI dataset in which healthy
volunteers were administered propofol, an anaesthetic drug known to act on inhibitory
GABAergic interneurons. Using a novel multimodal analysis, changes in functional brain
network connectivity in default mode, salience, and frontoparietal control networks were
found to correlate with the cortical distribution of parvalbumin-expressing GABAergic
interneurons. Using the same dataset, Experiment 3 identified a relationship between
structural and functional networks in connections between default mode and salience
networks. Similar results have been reported in non-human primate models, however,
this is the first study to find network-specific structure-function relationships during
sedation in humans. These findings informed the remainder of the thesis, which focused
on developing network-based machine learning methods for examining brain
connectivity in patients with DOC. Experiment 4 developed and validated a graph
convolutional neural network (GCNN) classifier using fMRI data and functional
connectivity from healthy volunteers performing a volitional mental imagery task.
Experiment 5 applied the GCNN to patients with DOC and found frontoparietal control
network connectivity measured at rest to be most important in classifying patients
capable of performing the mental imagery task. Taken together, these results contribute to
the improvement of brain network analysis techniques, the understanding of the neurobiology of propofol-induced sedation, and the development of machine learning
algorithms to identify DOC patients with preserved covert volitional capacity. This work
demonstrates the utility of clinical models in deepening our understanding of the
neurobiology of consciousness