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No description available.

No description available.

No description available.

No description available.

It has been suggested that the Anterior Cingulate Cortex (ACC) plays an important role in decision-making. Activity in this area reflects processing related to two principles of Reinforcement Learning Theory (RLT): (i) signalling the predicted value of actions at the time they are instructed and (ii) signalling prediction errors at the time of the outcomes of actions. It has been suggested that neurons in the gyrus of the ACC (ACCg) process information about others' decisions and not one's own. An important aim of this thesis is to investigate whether the ACCg processes others' decisions in a manner that conforms to the principles of RLT. Four fMRI experiments investigate activity in the ACCg at the time of cues that signal either the predicted value of others' actions or that signal another's predictions are erroneous. • Experiment 1: Activity in the ACCg occurred when the outcome of another's decision was unexpectedly positive. • Experiment 2: Activity in the ACCg varied parametrically with the discrepancy between another's prediction of an outcome and the actual outcome known by the subject, in a manner that conformed to the computational principles of RLT. • Experiment 3: Activity in the ACCg varied with the predicted value of a reward, discounted by the amount of effort required to obtain it. • Experiment 4: Activity in the ACCg varied with the value of delayed rewards that were discounted in a manner that conformed to a social norm. These results support the hypothesis that the ACCg processes the predicted value of others' actions and also signals when others' predictions about the value of their actions are erroneous, in a manner that conforms to the principles of RLT.

Every day we perceive visual scenes filled with different stimuli. Visual attention allows us to select the information that is most relevant to ongoing behaviour. The aim of this thesis is to explore how top-down modulations of activity in the human visual cortex affect perception and how attention interacts with visual processing in the brain. Specifically, we investigate the role of the modulation that occurs after a cue to attend but before onset of a visual stimulus, referred to in the literature as pre-stimulus attentional modulation, using fMRI methods alongside behavioural measurements. The main focus of the first three experiments is on the interactions between pre-stimulus attentional modulation and modulation by attention of the stimulus-evoked response. Results overall suggest that pre-stimulus activity is correlated with the effects of attention on the stimulus-evoked response and that the two attentional effects may therefore reflect a single process. The aim of the fourth experiment is to study the interaction of spatial and feature-based attention, and the results suggest that when both are engaged together, visual cortical areas do not benefit in an additive way, suggesting either that one dominates or that attentional resources saturate. The fifth experiment investigates the interaction of pre-stimulus activity with the speed and accuracy of saccade movements, and the results suggest no relation between those two processes. Finally the last two experiments focus on the role of pre-stimulus attentional modulation in perceptual learning, and the results strongly suggest that attentional modulation is involved in this process. Based on the results of these experiments, the role of pre- stimulus attentional modulation in visual processing is discussed.

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The goal of this thesis was to investigate the accuracy and precision of estimators of surface area and volimie in the human brain using stereology and magnetic resonance imaging (MRI) and to study the relationship between brain asymmetry and handedness in a novel in-vivo application to monozygotic (MZ) twin pairs discordant for handedness.

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus.