High-resolution functional MRI at 3 T: 3D/2D echo-planar imaging with optimized physiological noise correction.

High-resolution functional MRI (fMRI) offers unique possibilities for studying human functional neuroanatomy. Although high-resolution fMRI has proven its potential at 7 T, most fMRI studies are still performed at rather low spatial resolution at 3 T. We optimized and compared single-shot two-dimensional echo-planar imaging (EPI) and multishot three-dimensional EPI high-resolution fMRI protocols. We extended image-based physiological noise correction from two-dimensional EPI to multishot three-dimensional EPI. The functional sensitivity of both acquisition schemes was assessed in a visual fMRI experiment. The physiological noise correction increased the sensitivity significantly, can be easily applied, and requires simple recordings of pulse and respiration only. The combination of three-dimensional EPI with physiological noise correction provides exceptional sensitivity for 1.5 mm high-resolution fMRI at 3 T, increasing the temporal signal-to-noise ratio by more than 25% compared to two-dimensional EPI

Structured low-rank methods for robust 3D multi-shot EPI

Magnetic resonance imaging (MRI) has inherently slow acquisition speed, and Echo-Planar Imaging (EPI), as an efficient acquisition scheme, has been widely used in functional magnetic resonance imaging (fMRI) where an image series with high temporal resolution is needed to measure neuronal activity. Recently, 3D multi-shot EPI which samples data from an entire 3D volume with repeated shots has been drawing growing interest for fMRI with its high isotropic spatial resolution, particularly at ultra-high fields. However, compared to single-shot EPI, multi-shot EPI is sensitive to any inter-shot instabilities, e.g., subject movement and even physiologically induced field fluctuations. These inter-shot inconsistencies can greatly negate the theoretical benefits of 3D multi-shot EPI over conventional 2D multi-slice acquisitions. Structured low-rank image reconstruction which regularises under-sampled image reconstruction by exploiting the linear dependencies in MRI data has been successfully demonstrated in a variety of applications. In this thesis, a structured low-rank reconstruction method is optimised for 3D multi-shot EPI imaging together with a dedicated sampling pattern termed seg-CAIPI, in order to enhance the robustness to physiological fluctuations and improve the temporal stability of 3D multi-shot EPI for fMRI at 7T. Moreover, a motion compensated structured low-rank reconstruction framework is also presented for robust 3D multi-shot EPI which further takes into account inter-shot instabilities due to bulk motion. Lastly, this thesis also investigates into the improvement of structured low-rank reconstruction from an algorithmic perspective and presents the locally structured low-rank reconstruction scheme

Scaling Multidimensional Inference for Big Structured Data

In information technology, big data is a collection of data sets so large and complex that it becomes difficult to process using traditional data processing applications [151]. In a world of increasing sensor modalities, cheaper storage, and more data oriented questions, we are quickly passing the limits of tractable computations using traditional statistical analysis methods. Methods which often show great results on simple data have difficulties processing complicated multidimensional data. Accuracy alone can no longer justify unwarranted memory use and computational complexity. Improving the scaling properties of these methods for multidimensional data is the only way to make these methods relevant. In this work we explore methods for improving the scaling properties of parametric and nonparametric models. Namely, we focus on the structure of the data to lower the complexity of a specific family of problems. The two types of structures considered in this work are distributive optimization with separable constraints (Chapters 2-3), and scaling Gaussian processes for multidimensional lattice input (Chapters 4-5). By improving the scaling of these methods, we can expand their use to a wide range of applications which were previously intractable open the door to new research questions

Development & optimization of diffusion tensor imaging at high field strengths in translational research

Ever since the inception of Diffusion Tensor Imaging (DTI), unabated advancements in its capabilities and applications have been spearheaded by a vibrant research effort to devise dedicated acquisition sequences, protocols and hardware. In translational research, however, the transition of these innovations into the arenas of biomedical research, and ultimately clinical practice is frequently hampered by practical considerations. These include the availability of appropriate expertise, time and resources for their implementation, and considerations of compatibility with established techniques and results reported in literature. Such concerns provide the impetus to maximize the utility of existing protocols before attempting the development of novel dedicated techniques. In this thesis, three investigations, each targeting a different DTI application, are presented. The strategy implemented throughout involves assessing the suitability of existing sequences for the intended task, and determining any limiting factors, evaluating whether appropriate modifications of the acquisition protocols used are capable of alleviating limitations, and developing novel, dedicated protocols wherever necessary. The value and, importantly, the wide scope of this approach in answering important research questions is exemplified through the breadth of the studies presented. The first study presents, for the first time, a quantitative evaluation of the effects of cardiac pulsation on prevalent DTI metrics acquired with a specific acquisition protocol used routinely in clinical practice. Findings inform the on-going debate on whether the investment in cardiac gating is merited by improvements in data quality. Effects were observed during only 6 % of the cardiac cycle, and not 20 % as previously reported. The impact of cardiac pulsation on selected diffusion Tensor indices was minimal in group studies, but of potential practical relevance in individual cases. Methods to predict which individuals may benefit from gating have also been suggested. Secondly, the feasibility of post-mortem DTI was established through the successful acquisition, also for the first time, of DTI data on a chemically fixed whole human post-mortem brain using a clinical sequence. Previous failed attempts have been attributed to insufficient SNR. In this study scanner stability and distortion are found to be the main limiting factors, and mitigated using appropriate averaging and co-registration strategies. The third study assessed the potential of ultra-high field strength DTI by identifying and optimizing the potential strengths of DTI at 7T. Subsequent to optimization with respect to SNR, the main sources of artefact were found to be B1 inhomogeneity and inadequate fat suppression. Both were alleviated by modification of the available acquisition protocol, resulting in higher SNR and data quality than previously reported. Finally, in developing appropriate data quality measures, the ‘Difference method’, commonly used for the quantification of SNR, was found to be unsuitable for in vivo DTI acquisitions at 7 T, leading to the proposal, and successful implementation and validation of an alternative

Development & optimization of diffusion tensor imaging at high field strengths in translational research

Ever since the inception of Diffusion Tensor Imaging (DTI), unabated advancements in its capabilities and applications have been spearheaded by a vibrant research effort to devise dedicated acquisition sequences, protocols and hardware. In translational research, however, the transition of these innovations into the arenas of biomedical research, and ultimately clinical practice is frequently hampered by practical considerations. These include the availability of appropriate expertise, time and resources for their implementation, and considerations of compatibility with established techniques and results reported in literature. Such concerns provide the impetus to maximize the utility of existing protocols before attempting the development of novel dedicated techniques. In this thesis, three investigations, each targeting a different DTI application, are presented. The strategy implemented throughout involves assessing the suitability of existing sequences for the intended task, and determining any limiting factors, evaluating whether appropriate modifications of the acquisition protocols used are capable of alleviating limitations, and developing novel, dedicated protocols wherever necessary. The value and, importantly, the wide scope of this approach in answering important research questions is exemplified through the breadth of the studies presented. The first study presents, for the first time, a quantitative evaluation of the effects of cardiac pulsation on prevalent DTI metrics acquired with a specific acquisition protocol used routinely in clinical practice. Findings inform the on-going debate on whether the investment in cardiac gating is merited by improvements in data quality. Effects were observed during only 6 % of the cardiac cycle, and not 20 % as previously reported. The impact of cardiac pulsation on selected diffusion Tensor indices was minimal in group studies, but of potential practical relevance in individual cases. Methods to predict which individuals may benefit from gating have also been suggested. Secondly, the feasibility of post-mortem DTI was established through the successful acquisition, also for the first time, of DTI data on a chemically fixed whole human post-mortem brain using a clinical sequence. Previous failed attempts have been attributed to insufficient SNR. In this study scanner stability and distortion are found to be the main limiting factors, and mitigated using appropriate averaging and co-registration strategies. The third study assessed the potential of ultra-high field strength DTI by identifying and optimizing the potential strengths of DTI at 7T. Subsequent to optimization with respect to SNR, the main sources of artefact were found to be B1 inhomogeneity and inadequate fat suppression. Both were alleviated by modification of the available acquisition protocol, resulting in higher SNR and data quality than previously reported. Finally, in developing appropriate data quality measures, the ‘Difference method’, commonly used for the quantification of SNR, was found to be unsuitable for in vivo DTI acquisitions at 7 T, leading to the proposal, and successful implementation and validation of an alternative

Modulation of functional connectivity using neurofeedback and its effects on behaviour

The brain is composed of several functionally specialized areas. Communication between these brain regions serves as the main substrate for complex cognitive processes and behaviours that require a continuous integration of information. The kind of interaction concluding between disparate brain regions both, time-locked to, or independent of external events can be indexed by functional communication. There is abundance of literature suggesting the modulation of the underlying functional communication between different brain regions by manipulating the behaviour i.e. different variations in motor tasks and cognitive tasks. But, the outcome of all these studies only suggests the correlative nature of the task induced functional communication without suggesting the causal relation. In the presented dissertation, we have trained healthy participant to volitionally modulate their functional connectivity between the target brain regions using real-time magnetoencephalography neurofeedback (rt-MEG Neurofeedback) and assessed its effects on behavioural outcome. Together with literature reports, our result hint towards a causal relationship between changes of functional connectivity and changes in perceptual and behavioural performance. In the first study, 30 healthy participants learned to modulate their functional connectivity between primary motor cortices using real-time neurofeedback. Effects of the training on the behavioural outcome was assessed by investigating their motor performance prior and after the training. We conclude from this study that the increase of the functional communication between the two primary motor cortices results in the deterioration of the motor performance in a bimanual finger tapping task. In the second study, 8 healthy participants learned to modulate their fronto-parietal communication using a ViBM paradigm in neurofeedback setup. Effect of the training on the perceptual threshold were assessed. We demonstrated that the modulation of the fronto-parietal communication is feasible and does influence participants’ perceptual thresholds suggesting that the improvement in the fronto-parietal communication does reduce the perceptual threshold measured before (Pre-test) and after (Post-test) the neurofeedback training. This doctoral dissertation provides evidence supporting a causal relation between the modulation of functional connectivity and behaviour and perception and thus provides new insights in the intra-cortical communication and thus in the hierarchical organization of the human brain

Functional magnetic resonance imaging of the mouse brain

Functional magnetic resonance imaging (fMRI) measuring a blood-oxygen-level dependent (BOLD) signal is the most commonly used neuroimaging tool to understand brain function in humans. As mouse models are one of the most commonly used neuroscience experimental models, and with the advent of transgenic mouse models of neurodegenerative pathologies, there has been an increasing push in recent years to apply fMRI techniques to the mouse brain. This thesis focuses on the development and implementation of mouse brain fMRI techniques, in particular to describe the mouse visual system. Multiple studies in the literature have noted several technical challenges in mouse fMRI. In this work I have developed methods which go some way to reducing the impact of these issues, and I record robust and reliable haemodynamic-driven signal responses to visual stimuli in mouse brain regions specific to visual processing. I then developed increasingly complex visual stimuli, approaching the level of complexity used in electrophysiology studies of the mouse visual system, despite the geometric and magnetic field constraints of using a 9.4T pre-clinical MRI scanner. I have also applied a novel technique for measuring high-temporal resolution BOLD responses in the mouse superior colliculus, and I used this data to improve statistical parametric mapping of mouse brain BOLD responses. I also describe the first application of dynamic causal modelling to mouse fMRI data, characterising effective connectivity in the mouse brain visual system. This thesis makes significant contributions to the reverse translation of fMRI to the mouse brain, closing the gap between invasive electrophysiological measurements in the mouse brain and non-invasive fMRI measurements in the human brain

Miniaturized Optical Probes for Near Infrared Spectroscopy

RÉSUMÉ L’étude de la propagation de la lumière dans des milieux hautement diffus tels que les tissus biologiques (imagerie optique diffuse) est très attrayante, car elle offre la possibilité d’explorer de manière non invasive le milieu se trouvant profondément sous la surface, et de retrouver des informations sur l’absorption (liée à la composition chimique) et sur la diffusion (liée à la microstructure). Dans la gamme spectrale 600-1000 nm, également appelée gamme proche infrarouge (NIR en anglais), l'atténuation de la lumière par le tissu biologique (eau, lipides et hémoglobine) est relativement faible, ce qui permet une pénétration de plusieurs centimètres dans le tissu. En spectroscopie proche infrarouge (NIRS en anglais), de photons sont injectés dans les tissus et le signal émis portant des informations sur les constituants tissulaires est mesuré. La mesure de très faibles signaux dans la plage de longueurs d'ondes visibles et proche infrarouge avec une résolution temporelle de l'ordre de la picoseconde s'est révélée une technique efficace pour étudier des tissus biologiques en imagerie cérébrale fonctionnelle, en mammographie optique et en imagerie moléculaire, sans parler de l'imagerie de la durée de vie de fluorescence, la spectroscopie de corrélation de fluorescence, informations quantiques et bien d’autres. NIRS dans le domaine temporel (TD en anglais) utilise une source de lumière pulsée, généralement un laser fournissant des impulsions lumineuses d'une durée de quelques dizaines de picosecondes, ainsi qu'un appareil de détection avec une résolution temporelle inférieure à la nanoseconde. Le point essentiel de ces mesures est la nécessité d’augmenter la sensibilité pour de plus grandes profondeurs d’investigation, en particulier pour l’imagerie cérébrale fonctionnelle, où la peau, le crâne et le liquide céphalo-rachidien (LCR) masquent fortement le signal cérébral. À ce jour, l'adoption plus large de ces techniques optique non invasives de surveillance est surtout entravée par les composants traditionnels volumineux, coûteux, complexes et fragiles qui ont un impact significatif sur le coût et la dimension de l’ensemble du système. Notre objectif est de développer une sonde NIRS compacte et miniaturisée, qui peut être directement mise en contact avec l'échantillon testé pour obtenir une haute efficacité de détection des photons diffusés, sans avoir recours à des fibres et des lentilles encombrantes pour l'injection et la collection de la lumière. Le système proposé est composé de deux parties: i) une unité d’émission de lumière pulsée et ii) un module de détection à photon unique qui peut être activé et désactivé rapidement. L'unité d'émission de lumière utilisera une source laser pulsée à plus de 80 MHz avec une largeur d'impulsion de picoseconde.----------ABSTRACT The study of light propagation into highly diffusive media like biological tissues (Diffuse Optical Imaging) is highly appealing due to the possibility to explore the medium non-invasively, deep beneath the surface and to recover information both on absorption (related to chemical composition) and on scattering (related to microstructure). In the 600–1000 nm spectral range also known as near-infrared (NIR) range, light attenuation by the biological tissue constituents (i.e. water, lipid, and hemoglobin) is relatively low and allows for penetration through several centimeters of tissue. In near-infrared spectroscopy (NIRS), a light signal is injected into the tissues and the emitted signal carrying information on tissue constituents is measured. The measurement of very faint light signals in the visible and near-infrared wavelength range with picosecond timing resolution has proven to be an effective technique to study biological tissues in functional brain imaging, optical mammography and molecular imaging, not to mention fluorescence lifetime imaging, fluorescence correlation spectroscopy, quantum information and many others. Time Domain (TD) NIRS employs a pulsed light source, typically a laser providing light pulses with duration of a few tens of picoseconds, and a detection circuit with temporal resolution in the sub-nanosecond scale. The key point of these measurements is the need to increase the sensitivity to higher penetration depths of investigation, in particular for functional brain imaging, where skin, skull, and cerebrospinal fluid (CSF) heavily mask the brain signal. To date, the widespread adoption of the non-invasive optical monitoring techniques is mainly hampered by the traditional bulky, expensive, complex and fragile components which significantly impact the overall cost and dimension of the system. Our goal is the development of a miniaturized compact NIRS probe, that can be directly put in contact with the sample under test to obtain high diffused photon harvesting efficiency without the need for cumbersome optical fibers and lenses for light injection and collection. The proposed system is composed of two parts namely; i) pulsed light emission unit and ii) gated single-photon detection module. The light emission unit will employ a laser source pulsed at over 80MHz with picosecond pulse width generator embedded into the probe along with the light detection unit which comprises single-photon detectors integrated with other peripheral control circuitry. Short distance source and detector pairing, most preferably on a single chip has the potential to greatly expedites the traditional method of portable brain imaging

Department of Computer Science Activity 1998-2004

This report summarizes much of the research and teaching activity of the Department of Computer Science at Dartmouth College between late 1998 and late 2004. The material for this report was collected as part of the final report for NSF Institutional Infrastructure award EIA-9802068, which funded equipment and technical staff during that six-year period. This equipment and staff supported essentially all of the department\u27s research activity during that period