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In this work, we aimed to assess the possibility of applying the SPM method to a smallanimal PET data set including the use of the SPM registration option and the investigation of its applications in translational studies. We also evaluated and determined which image registration technique is best suited for aligning mice brain PET/CT images. Methods: The data were acquired from a cohort of C57BL/6 wild-type mice which underwent PET/CT scanning, producing the matched FDG-PET and CT brain images. Three different registration techniques to align mouse brain PET images were investigated: 1) PET/PET template 2) CT/CT template and 3) the segmented mask brain registration (described below). The automatic registration package within SPM was used to register the mice brain images. Optimal parameter settings for registering mice brain images were examined. Multiple measures of accuracy including: visual inspection, volume overlap, distance error and mutual information values were used to evaluate the image registration techniques. Results: In comparison between the PET-PET and CT-CT methods, there were no statistically significant in the means of the volume overlap measures. However, the mean distance error from the CT-CT method was significantly lower than for the PET/PET method. The qualitative visual inspection results showed that there were three registered images from the PET-PET method, and two from the CT-CT method were misalignment could be detected whereas there was no image misalignment detected from the mask-mask technique. Conclusion: The use of the segmented (mask) brain registration method produced the most successful results. A major benefit of including a mask brain in the registration is that it excludes the non-brain regions which contain unwanted signal (i.e. bright intensity of eyes), ensuring that the registration procedure used specifically the brain area for registering, avoiding the other bright areas of non-brain structures.

The analysis of complex systems has attracted great interest in recent years due to its potential application in medicine. Biological signals are decidedly nonlinear (Bertolaccini, Bussolati & Padovini 1978) and usually exhibit complex behaviour with nonlinear dynamic properties. Examples of these signals are electroencephalogram (EEG) signals, which measure the electrical activity of the brain and the blood oxygen level dependent (BOLD) signal of the brain from functional magnetic resonance imaging (fMRI) acquisition. The changes and differences in the signals produced by biological or physiological systems are mostly undetected by conventional linear signal processing statistics such as mean, standard deviation, variance, Fourier transforms and the like. Nonlinear approaches may be better approach than conventional linear methods in characterizing the complex nature of biological or physiological signals and revealing subtle and important insights into biological processes. The literature study on the application of the nonlinear dynamics theory to analyze physiological signals shows that the complexity of a system's output is an indication of the capacity to adapt to perturbation (Goldberger 1996); (Goldberger, Peng & Lipsitz 2002); (Vaillancourt, Newell 2002). Therefore, the characterization and analysis of the brain's output in terms of its complexity may reveal a better understanding of an individual's health, robustness and adaptive capacity in terms of brain ageing, diseases and in-vivo effect of drugs. Lipsitz (Lipsitz 2004) has suggested that some systems loose complexity in their output with ageing and disease. Vaillancourt and Newell have characterised two types of systems which have either a fixed point or an oscillatory attractor. An attractor is the state to which a system 'wants' to return to after perturbation (Vaillancourt and Newell, 2002). In a fixed-point attractor system they proposed that the complexity of the output reduces with age and disease, while in the oscillatory attractor system the opposite is the case. The study of nonlinear dynamics and concepts of complexity can give the opportunities to develop new approaches that are needed to understand and control the complex system in biology and medicine. The historical developments of the concepts of complexity has centered on measuring regularity using various types of entropy measures. Entropy measures the randomness and predictability of a stochastic process and in general increases with greater randomness. Regularity statistics such as approximate entropy (ApEn} and sample entropy (SampEn) are measures that have evolved from the historical developments, adaptations and interdisciplinary applications of entropy. These types of entropy measures can be used to characterize and analyse the different aspects of complex physiological signals brought about by ageing, diseases and the in-vivo effect of drug. These entropy measures have been applied to analyse 1 dimensional (ID) EEG data of heart rate, nerve activity, arterial pressure and respiratory signals. The main challenge of this project was how to pioneer the implementation of these measures to 4 dimensional (4D) MRI data such as BOLD signal, which has a very poor temporal resolution, with the intension of producing whole brain entropy maps. The aim of this project was to develop a robust and computationally less intensive image processing algorithm for the measurement of entropy with fMRI data. Also, to evaluate and test the fMRI entropy codes by measuring the complexity differences in brain fMRI data and investigating its association with a range of phenomena, such as cognitive performance, normal ageing, anaesthesia, dyslexia and schizophrenia. The implementation of these measures of entropy involved a careful selection of parameters such as the tolerance value, r, number of time points N, the pattern length, m and the time delay, t that are appropriate for fMRI data. Recent studies and my investigation (Appendix A and B) have shown that the recommended range of r (0.1 ~ r~0.2) in the literature is not appropriate for fMRI data (Lu et al. 2008). I found that the appropriate r in an fMRI context was 2 to 3 times that recommended for other modalities. The effect of the number of time points, N on r shows that the appropriate r changed with the number of time points. As a result of these it is necessary to derive an appropriate value of r for each fMRI study. The fMRI entropy codes were developed on a MA TLAB and C platform, the codes were tested on fMRI data acquired while examining resting state normal ageing, dyslexia, cognitive performance, Propofol anaesthesia and Schizophrenia. The results obtained from these analyses show that individuals who were older, had dyslexia, who has lower than expected cognitive performance and who were under the influence of anaesthesia has lower entropy in particular regions than their respective control groups, This is consistent with the GoldbergerlLipsitz model for robustness (Goldberger 1996, Goldberger, Peng & Lipsitz 2002, Goldberger et al. 2002, Goldberger 1997) where complexity decreases with age and disease. The results obtained from applying SampEn to fMRI data of schizophrenia patients indicate an opposite effect. This result is however consistent with other findings in schizophrenia which have consistently demonstrated complex behavioural and physiological output in schizophrenics. This result is also consistent with the second postulate of Vaillancourt and Newell which says that complexity increases with age and diseases (Vaillancourt, Newell 2002) in systems governed by an oscillatory attractor. The results do however suggest that, in this context, that too much complexity is not a good thing and represents abnormal function. This may be a result of impairments in the feedback mechanism of the dopamine system, which is responsible for keeping the system (s) stable. This approach of measuring entropy in fMRI data may find applications in many diverse pathologic and non-pathologic areas of the medical field.

Il est possible d’aborder l'organisation fonctionnelle du cerveau en modélisant le cerveau comme un système dynamique, ce qui permet d'étudier comment l'architecture fonctionnelle dépend du squelette structurel sous-jacent. En combinant approches expérimentales et théoriques chez la souris, nous avons étudié de façon systématique comment le connectome structurel contraint le connectome fonctionnel.Dans une première partie nous avons généralisé à la souris le logiciel open source The Virtual Brain (Sanz-Leon et al., 2013, Melozzi et al., 2017).En utilisant les données d'IRM de diffusion (IRMd) de 19 souris, nous avons virtualisé leur cerveau pour générer un signal BOLD in silico que nous avons comparé aux données d'IRM fonctionnelle enregistrées chez les mêmes souris pendant la veille passive. Nous montrons que les prédictions du modèle basé sur le connectome dépendent strictement de la structure du réseau (Melozzi et al., en révision). Nous démontrons que les variations individuelles définissent une empreinte structurelle spécifique ayant un impact direct sur l'organisation fonctionnelle des cerveaux individuels. Ces résultats démontrent l’existence d’un lien causal entre le connectome structurel et le connectome fonctionnel.Finalement, nous confirmons certaines de nos conclusions en utilisant l’approche inverse: nous avons étudié s’il était possible de déduire le connectome structurel à partir du connectome fonctionnel en utilisant la méthode d'inférence Bayésienne (Melozzi et al., en préparation).Nos résultats aux futures études testant la causalité entre structure et fonction, au niveau du cerveau entier individuel, en conditions physiologique et pathologique / The connectome-based model approach aims to understand the functional organization of the brain by modeling the brain as a dynamical system and then studying how the functional architecture rises from the underlying structural skeleton. In this thesis, taking advantage of mice studies, we investigated the informative content of different structural features in explaining the functional ones.First, we extended the open-source software TVB (Leon et al., 2013), originally designed for humans, to accommodate the connectome-based model approach in mice (Melozzi et al., 2017).Using diffusionMRI (dMRI) data from 19 mice, we virtualised their brains to generate in silico fMRI that we compared to functional MRI data recorded in the same mice during passive wakefulness. We show that the predictions of the connectome-based model strictly depend on the structure of the underlying network (Melozzi et al., under review). We demonstrate that individual variations define a specific structural fingerprint with a direct impact upon the functional organization of individual brains. Comparing the predictive power of the tracer-based and the dMRI-based connectome we identify how the limitations of the dMRI method restrict our comprehension of the structural-functional relation. Together, these results strongly support the existence of a causal link between the structural and the functional connectomes.Finally, we infer the connectome form resting state dynamics by inferring the structural connectome using the Bayesian inference (Melozzi et al., in prep).Our results pave the way to future studies focusing on the causal link between structure and function at the individual brain level.

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Kiwi (Apteryx spp.) have evolved under unique evolutionary pressures and uniquely occupy a nocturnal, ground-dwelling niche. They share few traits with other birds: they have small eyes, an elongated bill, and several features more characteristic of mammals. Early anatomical studies described a number of unique features in the kiwi brain, but their relevance to the behaviour and ecology of the species was not clearly established. This study aims to describe the structure of the primary cranial sensory systems of kiwi and comment on the evolutionary pressures that may have shaped their current form. The external morphology and relatively large size of the brain of kiwi, in particular those of the telencephalon, contrast with those of other Palaeognaths. The relative size of the cerebral hemispheres is rivalled only by a handful of parrots and songbirds. This enlargement results from a differential enlargement of the nidopallium, mesopallium and, to a lesser extent, of the basal ganglia. In other birds these regions are associated with the integration of information, cognition and learning. Kiwi brain centres processing visual information were small, although the retina structure showed an adaptation to dim light. The olfactory and trigeminal systems associated with the bill were hypertrophied. The auditory system shows specialisations associated with an overrepresentation of high frequency coding areas that originates in the cochlea and is preserved throughout the auditory brainstem. In absolute terms, the upper frequency response limit, based on hair cell morphology, is estimated to be about 5 kHz, the lower limit to be about 500 Hz, with a slightly higher frequency range predicted from the morphology of central auditory structures. The organisation of both nucleus angularis (NA) and nucleus laminaris (NL) in kiwi suggest that the central auditory system has retained the ancestral organisation except for the morphological features associated with the overrepresentation of high frequencies. Overall, the brain and sensory structures of kiwi have evolved neural adaptations that accompany the very different behavioural strategies associated with the unique niche the birds occupy. A large telencephalic size and shift away from vision towards an increased reliance on olfactory, tactile and auditory cues constitute a collection of features that make kiwi unique among birds. These findings provide a unique glimpse of the evolutionary history that has led to this unusual design, in particular, and challenge many of our current views about the evolution of brains and encephalisation, in general.

Thesis (M.Soc.Sc.)--University of Hong Kong, 2006. / Title from title page (viewed Apr. 23, 2007) Includes bibliographical references (p. 64-72).

Targeting is a skill that involves the accurate projection of an object to a target; this requires accurate integration of visual information with spatial and motor skills. Targeting tasks demonstrate a consistent male advantage. Contrary to popular belief, this male advantage is not accounted for by participants throwing experience or their size. The factors that mediate or account for the sex difference observed in targeting accuracy have not yet been identified. This dissertation addresses issues following from two prominent theories that attempt to explain this sex difference. The first theory proposes that the male advantage on targeting accuracy is due to the tasks proxemic and/or motoric characteristics, whereas the second theory proposes that the sex difference in targeting accuracy is due to differential exposure to androgenic or estrogenic sex hormone concentrations. The first and second studies in this dissertation follow from the first theory, examining whether changing the motoric or proxemic characteristics of targeting tasks will mediate the sex difference. The third study is related to the second theory; it examines the relations among direct and indirect measures of prenatal and circulating sex hormone concentrations and targeting accuracy within samples of men and women. Collectively the results from studies 1 and 2 indicate that the proxemic and motoric characteristics are related to the sex difference on targeting tasks; specifically, targeting tasks must involve only fine motor movements and be performed in intrapersonal space in order for the male advantage to be negated.<p>The results from study 3 indicate that men who were exposed to relatively high prenatal testosterone concentrations and continue to have relatively high circulating testosterone concentrations perform less accurately on targeting tasks than do all other groups of men. The results from study 3 also indicate that women exposed to relatively high prenatal testosterone concentrations target significantly more accurately than women that were exposed to relatively low prenatal testosterone concentrations. As well, the results showed that women who use oral contraceptives target significantly more accurately when they are not currently taking the exogenous estrogen supplements (menstrual phase) than when they are taking the supplements (midluteal phase). These results are discussed in light of the two prominent theories explaining the sex difference in targeting accuracy. A synthesized theory is proposed, and directions for future research are discussed.