The default mode network (DMN) is a highly cited neural network whose functional roles are not well understood. Until recently, event related fMRI experiments used to study the DMN could only be conducted in an open-loop format. The purpose of this study was to demonstrate the potential statistical advantages of real-time fMRI studies to conduct closed-loop experiments to directly test putative DMN functions. Using both fMRI simulations and large archival datasets, we demonstrate that open-loop designs are less statistically powerful than closed-loop experiments that can trigger stimuli at controlled levels of brain activity. When simulating event scheduling on resting state data, DMN levels were normally distributed, but the event timing proved to be ineffective in capturing the highest and lowest DMN values on average across subjects. Statistical differences in DMN levels collected by the Human Connectome Project-Aging (HCP-A) during a Go/NoGo task were also reported, along with the network's distributional effects across subjects. When examining DMN levels in 136 subjects more prone to commission errors the mean DMN levels were reported to be higher during and prior to incorrect NoGo responses. Exploring DMN levels in these same individuals reacting to a Go task also revealed differing measurement patterns when compared to all 711 subjects in the study. Additionally, the distribution of total DMN levels across all participants, as well as during a Go or NoGo trial, showed a shift in the mean towards deactivation. Furthermore, the peak at this location was greater and revealed that increased sampling occurred at the mean and under sampling at the tails. Overall, the cumulative findings in this study were successful in providing statistical arguments to support propositions for more powerful closed-loop experimentation in fMRI. / Master of Science / Activity in a neural network is observed through the use of functional MRI (fMRI) by tracking higher levels of oxygenated blood to that region when active and lower quantities when inactive. Neural networks vary in their responsibilities, thus fMRI tasks are designed to trigger a response based on the functional role of the network. This can be exemplified by studying the blood flow to default mode network (DMN), a network responsible for mind wandering, during a task that requires focus. Researchers can then correlate moments of high activity, which indicates a greater degree of mind wandering, or low activity to a correct or incorrect response to the task. Unfortunately, the timing in which a task is presented to the participant is predetermined prior to the subject entering the MRI making it difficult to capture a correct or incorrect response at the precise moment of activation or deactivation. This concept is known as open-loop and often collects data at moments of neutral activity, neither high nor low. In contrast, a closed-loop design allows a researcher to monitor the DMN's activation levels in real time and present the task at a desired time. This provides more useful data to the experimenter as all recorded responses to the task correlate with exact moments of high and low activation. This makes claims about the neural network's role statistically more powerful as there is a greater quantity of data at these moments rather than during a neutral activation state. The purpose of this thesis is to provide statistical arguments that support propositions for more powerful closed-loop experimentation in fMRI.

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The right ear advantage (REA) phenomenon has been utilized in clinical and research settings to study auditory processing disorders and linguistic lateralization. Previous research has established that the REA is not reliable in its measures within or between individuals. This is likely due to the influence of other variables, such as neuromaturation and attention. One variable that has not been studied in depth in this context is linguistic complexity. It was hypothesized that stimulus conditions with levels of linguistic complexity would elicit corresponding levels of temporal lobe activity. Understanding and controlling the variables that affect the REA will increase the reliability of the measure. Twenty right handed, neurotypical individuals aged 18-29 participated in a functional magnetic resonance imaging (fMRI) study that identified the regions and the extent of activation involved in listening to dichotic syllables, words, and sentences. Three durations of speech babble corresponding to the mean duration of the syllables, words, and sentences were used as control stimuli. Participants listened to dichotic stimuli and reported the stimulus they heard best during an fMRI scan. Reaction time (RT), ear preference, and fMRI data were recorded simultaneously and analyzed post hoc. Behavioral results showed that words had the shortest RTs and the greatest REA; syllables and sentences were similar to each other for both measures. Significant main effects were found in brain regions known to be involved in cognitive control of attention and linguistic processing. Words were associated with significant activation differences for ear preferences and minimal frontal lobe involvement for right ear preference. Syllables caused the least activity in the frontal lobe regions and less voxel activity in the temporal lobes than syllable-length babble. Sentences had the greatest voxel activity in the frontal and temporal lobe regions. It was concluded that words would best reflect the REA in clinical and experimental designs. Words had minimal involvement of frontal lobe regions indicating minimal cognitive control of attention and the largest discrepancies in activation patterns between right and left ear preferences that showed less cognitive power to process right ear stimuli in a dichotic listening situation.

Motion-selectivity in human visual cortex comprises a number of different cortical loci including V1, V2, V3A, V3B, hV5/MT+ and V6 (Wandell et al., 2007). This thesis sought to investigate the specific functions of V3A and sub-divisions of hV5/MT+ (TO-1 and TO-2) by using transcranial magnetic stimulation (TMS) to transiently disrupt cortical activations within these areas during psychophysical tasks of motion perception. The tasks were chosen to coincide with previous non-human primate and human neuroimaging literature; translational, radial and rotational direction discrimination tasks and identification of the position of a focus of expansion. These results assert that TO-1 and TO-2 are functionally distinct subdivisions of hV5/MT+, as we have shown that both TO-1 and TO-2 are responsible for processing translational motion direction whilst only TO-2 is responsible for processing radial motion direction. In ipsilateral space, it was found that TO-1 and TO-2 both contribute to the processing of ipsilateral translational motion. Taken in a wider context, further results also suggested that these areas may form part of a network of cortical areas contributing to perception of self-motion (heading/egomotion), as TO-2 was not found to be responsible for processing the position of the central focus of expansion (imperative for self-direction). Instead, area V3A has been implicated as functionally responsible for processing this attribute of vision. Overall it is clear that TO-1, TO-2 and V3A have specific, distinct functions that contribute towards both parallel and serial motion processing pathways within the human brain. / Life Science Research

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Arterial spin labeling (ASL) perfusion Magnetic Resonance Imaging (MRI) is a noninvasive technique for measuring quantitative cerebral blood flow (CBF) but subject to an inherently low signal-to-noise-ratio (SNR), resulting in a big challenge for data processing. Traditional post-processing methods have been proposed to reduce artifacts, suppress non-local noise, and remove outliers. However, these methods are based on either implicit or explicit models of the data, which may not be accurate and may change across subjects. Deep learning (DL) is an emerging machine learning technique that can learn a transform function from acquired data without using any explicit hypothesis about that function. Such flexibility may be particularly beneficial for ASL denoising. In this dissertation, three different machine learning-based methods are proposed to improve the image quality of ASL MRI: 1) a learning-from-noise method, which does not require noise-free references for DL training, was proposed for DL-based ASL denoising and BOLD-to-ASL prediction; 2) a novel deep learning neural network that combines dilated convolution and wide activation residual blocks was proposed to improve the image quality of ASL CBF while reducing ASL acquisition time; 3) a prior-guided and slice-wise adaptive outlier cleaning algorithm was developed for ASL MRI. In the first part of this dissertation, a learning-from-noise method is proposed for DL-based method for ASL denoising. The proposed learning-from-noise method shows that DL-based ASL denoising models can be trained using only noisy image pairs, without any deliberate post-processing for obtaining the quasi-noise-free reference during the training process. This learning-from-noise method can also be applied to DL-based ASL perfusion prediction from BOLD fMRI as ASL references are extremely noisy in this BOLD-to-ASL prediction. Experimental results demonstrate that this learning-from-noise method can reliably denoise ASL MRI and predict ASL perfusion from BOLD fMRI, result in improved signal-to-noise-ration (SNR) of ASL MRI. Moreover, by using this method, more training data can be generated, as it requires fewer samples to generate quasi-noise-free references, which is particularly useful when ASL CBF data are limited. In the second part of this dissertation, we propose a novel deep learning neural network, i.e., Dilated Wide Activation Network (DWAN), that is optimized for ASL denoising. Our method presents two novelties: first, we incorporated the wide activation residual blocks with a dilated convolution neural network to achieve improved denoising performance in term of several quantitative and qualitative measurements; second, we evaluated our proposed model given different inputs and references to show that our denoising model can be generalized to input with different levels of SNR and yields images with better quality than other methods. In the final part of this dissertation, a prior-guided and slice-wise adaptive outlier cleaning (PAOCSL) method is proposed to improve the original Adaptive Outlier Cleaning (AOC) method. Prior information guided reference CBF maps are used to avoid bias from extreme outliers in the early iterations of outlier cleaning, ensuring correct identification of the true outliers. Slice-wise outlier rejection is adapted to reserve slices with CBF values in the reasonable range even they are within the outlier volumes. Experimental results show that the proposed outlier cleaning method improves both CBF quantification quality and CBF measurement stability. / Electrical and Computer Engineering

Adolescent depression is a major public health problem, which is associated with educational problems, long-term psychiatric illness and suicide. One major source of stress during adolescence is peer rejection. In this thesis, I investigate the nature of the relationship between peer rejection and adolescent depression. In a review of longitudinal and experimental studies, I describe a bi-directional relationship between peer rejection and depressive symptoms. I then outline how genetic, cognitive and neural vulnerability may modify the effects of peer rejection on adolescent depression. Finally, I introduce five empirical chapters which test these hypotheses using different methodological approaches. The first study is a molecular genetic analysis of a sample of adolescents with and without a diagnosis of mood disorder. I report an interaction between diagnostic group, environmental stress (though not peer rejection specifically) and 5HTTLPR genotype on symptoms of anxiety, which supports the role of genetic factors in modifying the relationship between environmental stress and adolescent mood disorder. The second study is a behavioural study of negative attention biases in a typically developing sample of adolescents. I report a negative attention bias in adolescents with low (versus high) self-esteem. Although the data do not support a causal role for attention biases in adolescent depression, such biased cognitions could also moderate responses to peer rejection, maintaining affective symptoms. A final set of three fMRI datasets investigates how neural circuitry may influence depressed adolescents’ responses to peer rejection at three distinct stages: i) expectation of peer feedback, ii) the receipt of peer rejection, iii) emotion regulation of peer rejection. Data show distinct behavioural and neural differences between depressed patients and healthy controls during expectation and reappraisal of peer rejection, although heightened emotional reactivity immediately following the receipt of peer rejection did not differentiate behavioural or neural responses in adolescents with and without depression.