<p>Emotion is known to influence multiple aspects of memory formation, which may include the initial encoding of the memory trace, its consolidation over time, and the efficacy of its retrieval. However, prior investigations have tended to treat emotional modulation of episodic memory as a unitary construct, thus conflating the contributions of these different stages to emotion-mediated memory enhancements. The present thesis aims to disentangle the component processes of emotional memory formation and retrieval through a series of studies using cognitive behavioral and functional magnetic resonance imaging (fMRI) methods. In the first 2 studies, neural activity was evaluated during the initial viewing of emotionally arousing and neutral scenes and, in the 3rd study, neural activity during this initial viewing period was compared to that during a recognition memory task. The findings are compatible with the proposal that two distinct networks support successful emotional memory formation: an amygdala-medial temporal lobe (MTL) network that modulates the consolidation of memories over time and a prefrontal-MTL network that translates emotion effects on controlled elaboration into superior memory encoding. The superlative quality of emotional memories is furthermore marked by heightened similarity between neural states at encoding and retrieval, suggesting another line of evidence through which emotion effects can be observed. Taken together, the results presented here highlight the heterogeneity of processes that confer mnemonic advantages to emotionally significant information.</p> / Dissertation

Functional magnetic resonance imaging (fMRI) is a prime example of multi-disciplinary research. Without the beautiful physics of MRI, there wouldnot be any images to look at in the first place. To obtain images of goodquality, it is necessary to fully understand the concepts of the frequencydomain. The analysis of fMRI data requires understanding of signal pro-cessing, statistics and knowledge about the anatomy and function of thehuman brain. The resulting brain activity maps are used by physicians,neurologists, psychologists and behaviourists, in order to plan surgery andto increase their understanding of how the brain works. This thesis presents methods for real-time fMRI and non-parametric fMRIanalysis. Real-time fMRI places high demands on the signal processing,as all the calculations have to be made in real-time in complex situations.Real-time fMRI can, for example, be used for interactive brain mapping.Another possibility is to change the stimulus that is given to the subject, inreal-time, such that the brain and the computer can work together to solvea given task, yielding a brain computer interface (BCI). Non-parametricfMRI analysis, for example, concerns the problem of calculating signifi-cance thresholds and p-values for test statistics without a parametric nulldistribution. Two BCIs are presented in this thesis. In the first BCI, the subject wasable to balance a virtual inverted pendulum by thinking of activating theleft or right hand or resting. In the second BCI, the subject in the MRscanner was able to communicate with a person outside the MR scanner,through a virtual keyboard. A graphics processing unit (GPU) implementation of a random permuta-tion test for single subject fMRI analysis is also presented. The randompermutation test is used to calculate significance thresholds and p-values forfMRI analysis by canonical correlation analysis (CCA), and to investigatethe correctness of standard parametric approaches. The random permuta-tion test was verified by using 10 000 noise datasets and 1484 resting statefMRI datasets. The random permutation test is also used for a non-localCCA approach to fMRI analysis.

Regional tissue temperature dynamics in the brain are determined by the balance of the metabolic heat production rate and heat exchange with blood flowing through capillaries embedded in the brain tissue, the surrounding tissues and the environment. Local changes in blood flow and metabolism during functional activity can upset this balance and induce transient temperature changes. Invasive experimental studies in animal models have estab- lished that the brain temperature changes during functional activity are observable and a definitive relationship exists between temperature and brain activity. We present a theoreti- cal framework that links tissue temperature dynamics with hemodynamic activity allowing us to non-invasively estimate brain temperature changes from experimentally measured blood- oxygen level dependent (BOLD) signals. With this unified approach, we are able to pinpoint the mechanisms for hemodynamic activity-related temperature increases and decreases. In addition to these results, the potential uses and limitations of optical measurements are dis- cussed.

In this thesis a simulated fMRI toolbox is developed in order to generate simulated data to compare and benchmark different functional magnetic resonance image analysis methods. This toolbox is capable of loading a high resolution anatomic brain volume, generating 4D fMRI data in the same data space with the anatomic image, and allowing the user to create block and event-related design paradigms. Common fMRI artifacts such as scanner drift, cardiac pulsation, habituation and task related or spontaneous head movement can be incorporated into the 4D fMRI data. Input to the toolbox is possible through MINC 2.0 file format, and output is provided in ANALYZE format. The major contribution of this toolbox is its facilitation of comparison of fMRI analysis methods by generating several different fMRI data under varying noise and experiment parameters.

In this thesis, we conduct functional Magnetic Resonance Imaging (fMRI) data analysis with the aim of grouping the brain voxels depending on their responsiveness to a neural task. We mathematically treat the fMRI signals as the convolution of the neural stimulus with the hemodynamic response function (HRF). We first estimate a time series including HRFs for each of the observed fMRI signals from a given set and we cluster them in order to identify the groups of brain voxels. The HRF estimation problem is studied within the Bayesian framework through a blind deconvolution algorithm using MAP approach under completely unsupervised and model-free settings, i.e, stimulus is assumed to be unknown and also no particular shape is assumed for the HRF. Only using a given fMRI signal together with a weak Gaussian prior distribution imposed on HRF favoring &lsquo / smoothness&rsquo / , our method successfully estimates all the components of our framework: the HRF, the stimulus and the noise process. Then, we propose to use a modified version of Hausdorff distance to detect similarities within the space of HRFs, spectrally transform the data using Laplacian Eigenmaps and finally cluster them through EM clustering. According to our simulations, our method proves to be robust to lag, sampling jitter, quadratic drift and AWGN (Additive White Gaussian Noise). In particular, we obtained 100% sensitivity and specificity in terms of detecting active and passive voxels in our real data experiments. To conclude with, we propose a new framework for a mathematical treatment for voxel-based fMRI data analysis and our findings show that even when the HRF is unpredictable due to variability in cognitive processes, one can still obtain very high quality activation detection through the method proposed in this thesis.

Functional Magnetic Resonance Imaging (fMRI) is one of the most popular neuroimaging methods for investigating the activity of the human brain during cognitive tasks. The main objective of the thesis is to identify this underlying brain activation over time, using fMRI signal by detecting active and passive voxels. We performed two sub goals sequentially in order to realize the main objective. First, by using simple, data-driven Fourier Wavelet Regularized Deconvolution (ForWaRD) method, we extracted hemodynamic response function (HRF) which is the information that shows either a voxel is active or passive from fMRI signal. Second, the extracted HRFs of voxels are classified as active and passive using Laplacian Eigenmaps. By this, the active and passive voxels in the brain are identified, and so are the activation areas. The ForWaRD method is directly applied to fMRI signals for the first time. The extraction method is tested on simulated and real block design fMRI signals, contaminated with noise from a time series of real MR images. The output of ForWaRD contains the HRF for each voxel. After HRF extraction, using Laplacian Eigenmaps algorithm, active and passive voxels are classified according to their HRFs. Also with this study, Laplacian Eigenmaps are used for HRF clustering for the first time. With the parameters used in this thesis, the extraction and clustering methods presented here are found to be robust to changes in signal properties. Performance analyses of the underlying methods are explained in terms of sensitivity and specificity metrics. These measurements prove the strength of our presented methods against different kinds of noises and changing signal properties.

In cases of temporal lobe epilepsy, seizures are often controlled by anterior temporal lobe resection. However, an assessment of the impact of surgery on language is required. Currently-used assessments are either non-specific within regions or use functional magnetic resonance imaging (fMRI), which can suffer signal distortion in the temporal lobes due to the presence of airways. Magnetoencephalography (MEG) shows a complimentary sensitivity, but has not been used for laterality assessment. We present a method that combines fMRI with MEG for optimized sensitivity. MEG activation maps were generated using a beamformer, showing activity in the anterior temporal lobes and lateral occipital cortex. fMRI showed activation in medial temporal lobe regions, the frontal poles and the hippocampus, an area of clinical concern during surgical planning. The present study introduces a method for integrating MEG and fMRI activation to create high-resolution laterality maps in regions of concern for epilepsy.

Impaired attention can hinder information processing at multiple levels and may explain some aspects of the cognitive decline in aging. An inefficient inhibitory system can lead to deficits in focused attention (FA). FA deficits are observed in patients with mild cognitive impairment and Alzheimer’s disease (AD). The Stroop task was applied to functional magnetic resonance imaging (fMRI) to investigate the neural correlates of FA in cognitively normal older adults (NC), patients with amnestic MCI (aMCI) and patients with mild AD. Twenty-one NC, seven aMCI and fifteen mild AD patients performed a verbal Stroop- fMRI paradigm. Both structural and T2*-weighted functional scans were acquired. In Series 0, subjects were presented with colour words printed in black ink and were asked to read the word. In Series 1 and 2, subjects were presented with colour words printed in an incongruent ink colour. Series 1 had four blocks of the ‘Read the word’ condition followed by four blocks of the ‘Say the colour of the ink’ condition. Series 2 had eight blocks of alternating ‘Read the word’ and ‘Say the colour of the ink’ conditions. SPM5 was used to detect anatomical areas with significant signal intensity differences between the two conditions. The NC group performed significantly better in the Stroop-fMRI task than the aMCI and mild AD groups. The percentage of errors on incongruent trials was significantly lower in the NC group (2%) than the aMCI (14%) and mild AD (13%) groups. The ‘Say the colour of the ink’ minus ‘Read the word’ contrast for the NC and mild AD groups yielded common areas of activation in the supplementary motor area, precentral gyrus, and inferior frontal gyrus. aMCI patients also showed activation in the precuneus, temporal and postcentral gyri. Worse performance on the Stroop-fMRI task by the aMCI and mild AD groups suggests deficits in FA. This is the first study to investigate the neural correlates of FA using the Stroop task in aMCI and AD patients. The verbal Stroop-fMRI paradigm employed in the current study provides a means to study the neural correlates of FA in older adult and patient populations. / Thesis (Master, Neuroscience Studies) -- Queen's University, 2009-12-31 11:57:52.374

Functional magnetic resonance imaging (fMRI) has been widely used to study cortical and subcortical mechanisms related to pain. The pathophysiology of burning mouth disorder (BMD) is not clearly understood. Central neuropathic mechanisms are thought to be main players in BMD. This study aimed to compare the location and extension of brain activation following thermal stimulation of the trigeminal nerve with fMRI blood oxygenation level dependent (BOLD) signal. This study included 8 female patients with BMD and 8 matched pain-free volunteers. Qualitative and quantitative differences in brain activation patterns between the two study groups were demonstrated. There were differences in the activation maps regarding the location of activation, with patients displaying greater BOLD signal changes in the right anterior cingulate cortex (ACC BA 32/24) and bilateral precuneus (pandlt;0.005). The control group showed larger BOLD signal changes in the bilateral thalamus, right middle frontal gyrus, right pre-central gyrus, left lingual gyrus and cerebellum (pandlt;0.005). It was also demonstrated that patients had far less volumetric activation throughout the entire brain compared to the control group. These data are discussed in light of recent findings suggesting brain hypofunction as a key player in chronic neuropathic pain conditions.

Machine learning algorithms have been successfully applied to learning classifiers in many domains such as computer vision, fraud detection, and brain image analysis. Typically, classifiers are trained to predict a class value given a set of labeled training data that includes all possible class values, and sometimes additional unlabeled training data. Little research has been performed where the possible values for the class variable include values that have been omitted from the training examples. This is an important problem setting, especially in domains where the class value can take on many values, and the cost of obtaining labeled examples for all values is high. We show that the key to addressing this problem is not predicting the held-out classes directly, but rather by recognizing the semantic properties of the classes such as their physical or functional attributes. We formalize this method as zero-shot learning and show that by utilizing semantic knowledge mined from large text corpora and crowd-sourced humans, we can discriminate classes without explicitly collecting examples of those classes for a training set. As a case study, we consider this problem in the context of thought recognition, where the goal is to classify the pattern of brain activity observed from a non-invasive neural recording device. Specifically, we train classifiers to predict a specific concrete noun that a person is thinking about based on an observed image of that person’s neural activity. We show that by predicting the semantic properties of the nouns such as “is it heavy?” and “is it edible?”, we can discriminate concrete nouns that people are thinking about, even without explicitly collecting examples of those nouns for a training set. Further, this allows discrimination of certain nouns that are within the same category with significantly higher accuracies than previous work. In addition to being an important step forward for neural imaging and braincomputer- interfaces, we show that the zero-shot learning model has important implications for the broader machine learning community by providing a means for learning algorithms to extrapolate beyond their explicit training set.