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<p>The accurate diagnosis of disorders of consciousness presents substantial difficulty because of the reliance on behaviour-based assessment tools. A patient may be covertly aware but unable to indicate their state due to physical impairments. Neuroimaging researchers have begun to seek alternate methods of assessment that rely on brain responses rather than behavioural ones. To this end, mental imagery has been employed as a voluntary cognitive activity that can be measured with fMRI or EEG to indicate awareness. In this dissertation I examine the advantages and limitations of these two imaging techniques and argue that EEG is more suitable for this patient population. I expand upon existing mental imagery research by exploring additional tasks that have not been applied to this problem, in order to address three previously unanswered questions that are central to the development of imagery-based diagnostic tools. First, do individuals differ on which imagery tasks produce the most reliable activation? Second, can the robustness of brain activation during imagery be predicted from familiarity with the imagined activity? Third, do fMRI and EEG provide converging evidence about individual imagery performance? In order to answer these questions, 6 mental imagery tasks were examined using simultaneous EEG and fMRI recordings, in combination with participant ratings. The findings revealed that, of the mental imagery tasks studied, mental arithmetic consistently produced the most robust activation at the single subject level. Additionally, there was no relationship between participants’ familiarity with an activity and the level of brain activation during performance. The key finding demonstrated that EEG and fMRI were in agreement on both of these questions, lending support to the increasing use of EEG over fMRI in disorders of consciousness.</p> / Doctor of Philosophy (PhD)

This dissertation develops new algorithms with different techniques in utilizing spatial and spectral information for hyperspectral image classification. It is necessary to perform spatial and spectral analysis and conduct dimensionality reduction (DR) for effective feature extraction, because hyperspectral imagery consists of a large number of spatial pixels along with hundreds of spectral dimensions. In the first proposed method, it employs spatial-aware collaboration-competition preserving graph embedding by imposing a spatial regularization term along with Tikhonov regularization in the objective function for DR of hyperspectral imagery. Moreover, Collaboration representation (CR) is an efficient classifier but without using spatial information. Thus, structure-aware collaborative representation (SaCRT) is introduced to utilize spatial information for more appropriate data representations. It is demonstrated that better classification performance can be offered by the SaCRT in this work. For DR, collaborative and low-rank representation-based graph for discriminant analysis of hyperspectral imagery is proposed. It can generate a more informative graph by combining collaborative and low-rank representation terms. With the collaborative term, it can incorporate within-class atoms. Meanwhile, it can preserve global data structure by use of the low-rank term. Since it employs a collaborative term in the estimation of representation coefficients, its closed-form solution results in less computational complexity in comparison to sparse representation. The proposed collaborative and low-rank representation-based graph can outperform the existing sparse and low-rank representation-based graph for DR of hyperspectral imagery. The concept of tree-based techniques and deep neural networks can be combined by use of an interpretable canonical deep tabular data learning architecture (TabNet). It uses sequential attention for choosing appropriate features at different decision steps. An efficient TabNet for hyperspectral image classification is developed in this dissertation, in which the performance of TabNet is enhanced by incorporating a 2-D convolution layer inside an attentive transformer. Additionally, better classification performance of TabNet can be obtained by utilizing structure profiles on TabNet.

Several studies have investigated the psychosocial impact of breast cancer, however, there is a paucity of research examining interventions geared towards breast cancer survivors. This study investigated the effects of group music psychotherapy on improving the self-concept of breast cancer survivors. Eleven breast cancer survivors where randomly assigned to one of two treatment groups - group music psychotherapy or a cognitive behavioral based support group. Pre and post intervention comparisons were made using the Body Image After Breast Cancer Scale and the Tennessee Self-Concept Scale on measures of identity, role performance, self-esteem, and body image. After ten weeks, results from the Wilcoxon U test indicated participants in the music psychotherapy group significantly improved on measures on identity, family role relationships, self-esteem, and body image. Furthermore, as compared to participants in the cognitive behavioral support group, participants in group music psychotherapy improved on measures of identity, role relationships, and body image. Implications and suggestions for further research are discussed. / Music Therapy

The purpose of this study was to solicit information from Fellows of the Association for Music and Imagery concerning their use of the method known as Guided Imagery and Music (GIM). Modifications to the individual and group forms of GIM that were created by Helen Bonny in the 1970s have been reported in the literature over the past 30 years, but the prevalence of these practices is unknown. Based on distinctions provided by Bruscia (2002a), an anonymous electronic survey was designed to gather data on the extent to which original and modified forms of GIM found in the literature are currently practiced. In addition, data were gathered on the basic demographics of GIM fellows, as well as characteristics of their clients and their delivery of GIM treatment. Results indicated that the original Bonny Method practices were frequently used by a majority of GIM fellows. All of the modifications included in the survey were also practiced, although less frequently than original Bonny Method practices. T-tests and ANOVAs were computed to identify significant differences between GIM fellows' use of practices, and, their demographics and delivery of GIM treatment. Pearson correlations were computed to identify significant relationships between GIM fellows' use of practices and their clients' characteristics. A number of significant results were found and discussed. Results of factor analyses indicated that the boundary distinctions between the original Bonny Method and modified practices as articulated by Bruscia (2002a) are reflected in GIM fellows' frequency ratings for these practices. It is recommended that the GIM community adopt a system for distinguishing boundaries between practices, conduct research to determine where Bonny Method and modified practices are learned and for what purposes they are used, and to use this information to inform GIM training and practice. / Music Therapy

The aim of the present study was to characterize participants’ abilities to answer binary yes/no questions by mentally manipulating imagery to produce imagined changes in luminance, which would in turn cause reflexive perturbations in pupil diameter. First, a paired association was established with participants, linking “yes” responses with imagining a “sunny sky” and “no” responses with imagining a “dark room”. Participants (N=20) then answered 16 yes/no questions using this response method, in place of providing verbal or gestural (e.g., head nod) answers. Pupil diameters were recorded for a period of 8000 ms following each stimulus question while participants maintained the mental image that corresponded with their answer. We hypothesized that on average, “no” responses would yield a pupil dilation and increased diameter relative to baseline, while “yes” responses would instead result in constrictions and smaller pupil diameters compared to baseline. A 2-factor repeated measures analysis of variance (ANOVA), where time was one factor and response type (i.e., yes or no) was the other, revealed a statistically significant interaction of time and response type, a significant main effect of time, and a trend toward significance for response type in aggregated group data. Item level discrimination consisted of comparing the mean pupil diameter in response to a single item for a single participant (e.g., “yes” response on one trial) to the mean pupil diameter of all contrasting responses for that same participant (e.g., all “no” response trials). This method achieved a 64.5% discrimination accuracy. This investigation affirmed the plausibility of leveraging pupillometry and luminance-based mental imagery in favor of an alternative communication system for individuals who are locked-in, as well as its potential as a screening tool. However, further investigation is warranted prior to its implementation. / Communication Sciences

Guided Imagery and Music (GIM) is a unique method of psychotherapy that uses music and spontaneous imagery while the client is in a non-ordinary state of consciousness (NOSC). Therapists in any therapeutic modality including GIM have a wide range of unanticipated reactions to clients' experiences. Studies have indicated that reactions of the therapist can lead to different interventions and can impact the outcome of the session and perceived effectiveness as a therapist. Because GIM therapists often work with clients who have extensive emotional and physical issues, it is possible that the therapists have strong reactions to clients’ experiences, which can lead to burnout. The purpose of this study, therefore, was to explore therapists’ experience of unanticipated reactions through the within-case and cross-case analysis using phenomenology outlined by Moustakas (1994). Findings suggested that the GIM therapist had certain expectations in GIM and it was normal for the GIM therapist to have unanticipated reactions to clients' experiences when those expectations were not met. There were several factors that affected the decision-making of GIM therapists after unanticipated reactions, including self-mediation, cognitive foundation, self-awareness, therapist's past experiences, trust, surrender, being present, integration, and self-care. Findings also suggested that GIM therapists could deal with unanticipated reactions within the scope of practice as they engage in personal-growth, self-care, and continuing education. Two guidelines were proposed to understand the process of GIM therapists in their moments of unanticipated reactions. Limitations and implications for further studies were discussed. / Music Therapy

Brain-computer interfaces (BCIs) create a new form of communication and control for humans by translating brain activity directly into actions performed by a computer. This new field of research, best known for its breakthroughs in enabling fully paralyzed or locked-in patients to communicate and control simple devices, has resulted in a variety of remarkable technological developments. However, the field is still in its infancy, and facilitating control of a computer application via thought in a broader context involves a number of a challenges that have not yet been met. Advancing BCIs beyond the experimental phase continues to be a struggle. End-users have rarely been reached, except for in the case of a few highly specialized applications which require continual involvement of BCI experts. While these applications are profoundly beneficial for the patients they serve, the potential for BCIs is much broader in scope and powerful in effect. Unfortunately, the current approaches to brain-computer interfacing research have not been able to address the primary limitations in the field: the poor reliability of most BCIs and the highly variable performance across individuals. In addition to this, the modes of control available to users tend to be restrictive and unintuitive (\emph{e.g.}, imagining complex motor activities to answer ``Yes" or ``No" questions). This thesis presents a novel approach that addresses both of these limitations simultaneously. Brain-computer interfacing is currently viewed primarily as a machine learning problem, wherein the computer must learn the patterns of brain activity associated with a user's mental commands. In order to simplify this problem, researchers often restrict mental commands to those which are well characterized and easily distinguishable based on \emph{a priori} knowledge about their corresponding neural correlates. However, this approach does not fully recognize two properties of a BCI which makes it unique to other human-computer interfaces. First, individuals can vary widely with respect to the patterns of activation associated with how their brains generate similar mental activity and with respect to which kinds of mental activity have been most trained due to life experience. Thus, it is not surprising that BCIs based on predefined neural correlates perform inconsistently for different users. Second, for a BCI to perform well, the human and the computer must become a cohesive unit such that the computer can adapt as the user's brain naturally changes over time and while the user learns to make their mental commands more consistent and distinguishable given feedback from the computer. This not only implies that BCI use is a skill that must be developed, honed, and maintained in relation to the computer's algorithms, but that the human is the fundamental component of the system in a way that makes human learning just as important as machine learning. In this thesis it is proposed that, in the long term, a generalized BCI that can discover the appropriate neural correlates of individualized mental commands is preferable to the traditional approach. Generalization across mental strategies allows each individual to make better use of their own experience and cognitive abilities in order to interact with BCIs in a more free and intuitive way. It is further argued that in addition to generalization, it is necessary to develop improved training protocols respecting the potential of the user to learn to effectively modulate their own brain activity for BCI use. It is shown through a series of studies exploring generalized BCI methods, the influence of prior non-BCI training on BCI performance, and novel methods for training individuals to control their own brain activity, that this new approach based on balancing the roles of the user and the computer according to their respective capabilities is a promising avenue for advancing brain-computer interfacing towards a broader array of applications usable by the general population. / Thesis / Doctor of Philosophy (PhD)

Despite the important values forests play in the tropics, sustainable forest management still remains a challenge as manifested through continued forest loss. The objective of this study was to provide information on the dynamics of forest cover and their drivers vital for enhancing sustainable forest management in the Lake Victoria crescent, Uganda. Several methodologies including remote sensing and Geographic Information Systems techniques, analysis of landscape patterns and various social science techniques were integrated in working towards the stated goal. Results showed that the Lake Victoria crescent, Uganda covering an area of about 1,509,228 ha, experienced a decline in forest cover from 9.0% in 1989 to 4.4% in 2009. This was in comparison with non-forest cover which increased from 58.7% in 1989 to 63.5% in 2009 while open water coverage generally remained unchanged averaging 32.3% from 1989 to 2009. Mean annual deforestation rate from 1989 to 2009 decreased with a weighted mean rate of 2.56%. Both deforestation and afforestation declined between 1989 and 2009 although deforestation still exceeded afforestation. In addition to deforestation, the Lake Victoria crescent also experienced forest fragmentation from 1989 to 2009. Forests greater than 100 ha in size were the most vulnerable to forest fragmentation yet they still constituted a big proportion of forest cover in 2009. Deforestation was a consequence of proximate causes which were triggered by a number of underlying drivers acting singly or in combination, with underlying drivers being more influential. In a bid to promote sustainable forest management, there is a need to continue with efforts to curb deforestation and forest fragmentation, especially amongst forests greater than 100 ha. This could be achieved through empowerment of local communities to take a core role in sustainable management of forest resources. / Ph. D.