The possibility to perform virtual, non-invasive, quantitative, in vivo histological assessments might revolutionize entire fields, among which clinical and cognitive neurosciences. Magnetic Resonance Imaging (MRI) is an ideal non-invasive imaging technique to achieve these goals. Tremendous advancements in the last decades have favored the transition of MRI scanners from “imaging devices” to “measurement devices” (Novikov, 2021), thus capable to yield measurements in physical units, which might be further combined to provide quantities describing histological properties of substrates. A central role in this community endeavor has been played by diffusion-weighted MRI (dMRI), which by measuring the dynamics of spin diffusion, allows inferences on geometrical properties of tissues. Yet, conventional dMRI methodologies suffer from poor specificity. In this thesis, techniques aiming at improving the specificity of microstructural descriptions have been explored in dMRI datasets supporting an increasing level of complexity of the dMRI signal representations. Applications in individuals with different age range, in different populations, and for different MRI scanner fields, have been considered. Firstly, tractography has been combined with Diffusion Tensor Imaging (DTI), an along-tract framework, and morphometry, in the study of the microstructure of the optic radiations in different groups of blind individuals. Secondly, DTI has been combined with Free-Water Imaging (FWI) to monitor the effect of proton-irradiation in a pediatric brain tumor case study. Thirdly, FWI and Diffusion Kurtosis Imaging (DKI) have been combined with an advanced thalamic segmentation framework to study the associations between motor performance and thalamic microstructure in a cohort of individuals affected by Parkinson’s disease. Finally, the largest contribution of this thesis is represented by the adaptation of the Correlation Tensor Imaging - a technique increasing the specificity of DKI harnessing Double Diffusion Encoding previously applied only in preclinical settings - for a clinical 3 T scanner. The ensuing investigation revealed new important insights on the sources of diffusional kurtosis, in particular of the microscopic kurtosis (μK), a component so far neglected by contemporary neuroimaging techniques, which might carry an important clinical role (Alves et al., 2022), and can now be accessed by clinical scanners. In conclusion, strategies to increase the specificity of microstructural descriptions in the brain are presented for different datasets, and their strength and limitations are discussed.

published_or_final_version / Diagnostic Radiology / Doctoral / Doctor of Philosophy

Mild traumatic brain injuries (MTBI) are the leading type of head injuries with appreciable risque of sequelae leading to functional and psychological deficits. Although mild traumatic brain injuries are frequently underdiagnosed by conventional imaging modalities, rapidly evolving techniques such as diffusion tensor imaging (DTI) reveal subtle changes in white matter integrity as a result of head trauma and play an important role in refining diagnosis, therapeutic interventions and management of MTBI. In this dissertation we use diffusion tensor imaging to detect the microstructural changes induced by axonal injuries and to monitor their evolution during the recovery process. DTI data were previously acquired from 11 subjects, football players of age 19-23 years (median age 20 years). Three players had suffered a mild traumatic brain injury during the season and underwent scanning within 24 hours after the injury with follow-ups after one and two weeks. A set of diffusion indices, such as fractional anisotropy, axial, radial and mean diffusivity were derived from the diffusion tensor. Changes in diffusion indices in concussed subjects were analyzed based on two different approaches: whole brain analysis, using tract-based spatial statistics (TBSS) and region of interest analysis (ROI). In both approaches we use a voxelwise analysis to examine group differences in diffusion indices between five controls and three concussed subjects for all DTI scans. Additional statistical analysis was performed between control groups consisting of five and three non-injured players. Both analyses demonstrated that the MTBI group reveals increase in fractional anisotropy and decreases in transversal and mean diffusivity in cortical and subcortical areas within 24 hours after the injury. / No changes were detected in TBSS analysis for the follow-up data sets. Furthermore, our ROI approach revealed multiples regions with significantly different voxels, non-uniformly distributed throughout the brain, for all diffusion indices in all three scans. Three of the diffusion indices fractional anisotropy, mean and transversal diffusivity showed higher vulnerability to head trauma in subcortical and cortical areas than in regions in the lower brain. Recovery of white matter pathways occured at different locations in the brain at one and two weeks after head trauma. Strong recovery was observed in mean and transversal diffusivity in subcortical areas that correspond to the corticospinal tract. No recovery was found for fractional anisotropy and axial diffusivity in the same region. Also, decreases in fractional anisotropy and increases in transversal and axial diffusivity were observed in the spleninum of the corpus callosum. As voxelwise analysis performed on DTI data revealed white matter regions, which exhibit changes in diffusion parameters in the concussed group for all three scans, we conclude that diffusion tensor imaging is a powerful technique for early detection of axonal injuries and may serve as an important tool for monitoring microstructural changes during the recovery process. / by Angelica Hotiu. / Thesis (Ph.D.)--Florida Atlantic University, 2010. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2010. Mode of access: World Wide Web.

Although we rarely think about our everyday cognition as skilled cognition --- because it comes naturally and all of us possess it --- we are all experts in mastering our everyday environment. This expertise may be manifested in mundane or everyday tasks like discerning familiar faces from strangers, or for some people, in more complex situations such as determining whether to swing at a 95mph fastball. The athlete's brain offers a good opportunity for studying neuroplasticity and perceptual expertise because athletes participate in long term training and practice, often starting very early in childhood, and continuing throughout their entire careers. The goal of this dissertation is to investigate the effect of expertise on brain network dynamics during perceptual decision making tasks using techniques for multimodal data fusion. Specifically, we design novel stimuli and methods of combining simultaneously collected electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to investigate brain networks involved in the split-second decisions faced by baseball batters. Using single-trial analysis with experts in baseball, we find the neural correlates of expertise in baseball pitch recognition in both the temporal domain (EEG) and spatial domain (fMRI). We find that experts in baseball pitch recognition exhibit larger activations in early visual prediction networks as well as motor planning areas which aid in the experts superior behavioral performance. In this dissertation, we also focus on leveraging the complementary strengths of the two neuroimaging modalities (EEG, fMRI) to create novel fusion techniques that can provide richer network dynamics than by either modality separately. We design a novel encoding model to fuse EEG and fMRI to provide unprecedented spatio-temporal resolution of a perceptual decision in the human brain. On top of the methodologies for EEG-fMRI fusion, we show that motion correction hardware can be implemented to significantly improve signal-to-noise for fMRI acquisition by reducing motion artifacts which highly contaminate simultaneous EEG-fMRI data. These tools taken together provide researchers with another dimension---temporal ordering of brain activations--- to probe behavioral, psychological, or even compromised states during perceptual decision making.

Since the advent of functional magnetic resonance imaging (fMRI) and the rise in popularity of its use for resting state functional connectivity mapping (rs-FCM) to non-invasively detect correlated networks of brain activity in human and animal models, many resting state FCM studies have reported differences in these networks under pathologies such as Alzheimer’s or schizophrenia, highlighting the potential for the method’s diagnostic relevance. A common underlying assumption of this analysis, however, is that the blood oxygen level dependent (BOLD) signal of fMRI is a direct measurement of local neural activity. The BOLD signal is in fact a measurement of the local changes in concentration of deoxy-hemoglobin (HbR). Thus, it is imperative that neurovascular coupling—the relationship between neuronal activity and subsequent hemodynamic activity—be better characterized to enable accurate interpretation of resting state fMRI in the context of clinical usage. This dissertation first describes the development and utility of WFOM paradigm for the robust and easily adaptable imaging of simultaneous neuronal and hemodynamic activity in awake mouse models of health or disease in strains with genetically encoded fluorescent calcium reporters. Subsequent exploration of resting state WFOM data collected in Thy1-GCaMP3 and Thy1-GCaMP6f mouse strains is then presented, namely the characterization of spatiotemporal patterns of neuronal and hemodynamic activity and different modulatory depths of neuronal activity via a toolbox of unsupervised blind source separation (e.g. k-means clustering) and supervised (e.g. non-negative least squares, Pearson correlation) analysis tools. The presence of these different modulatory depths of neuronal activity were then confirmed in another Thy1-jRGECO1a mouse strain using the same imaging scheme. Finally, the dissertation documents the application of the WFOM paradigm and select analysis tools to a novel mouse model of diffusely infiltrating glioma, through which neuronal and hemodynamic activity changes during diffusely infiltrating glioma development which impact temporal coherence of the tumor region activity relative to non-tumor regions activity were recorded and analyzed. The paradigm also allowed for recording of numerous spontaneous occurrences of interictal neuronal activity during which neurovascular coupling is modified in the tumor, as well as occurrences of non-convulsive generalized seizure activity (during which neurovascular is non-linear and cortex eventually suffers hypoxia). The detection of spatiotemporal patterns and different modulatory depths of activity in the awake mouse cortex, as well as observation of changes in functional activity in the context of diffusely infiltrating glioma, provide us with new insights into the possible mechanisms underlying variations in resting state connectivity networks found in resting state fMRI studies comparing health and disease states.

Functional MRI is a method of imaging changes in blood oxygenation that accompany neural activity in the brain. A specific area within fMRI studies investigates what the brain is doing when it is not being stimulated. It is postulated that there are distinctly separate regions of the brain that are connected based upon functional relations and that these connected regions synchronously communicate even during rest. Resting state connectivity has become a tool to investigate neurological disorders in humans without specific knowledge of the mechanisms that correlate neural activity with brain metabolism and blood flow. This work attempts to characterize resting state connectivity in the rat brain to establish a model that will help elucidate the relationship between functional connectivity, as measured with fMRI, and brain function. Four analysis techniques, power spectrum estimation, cross correlation analysis, principle component analysis, and independent component analysis, are employed to examine data acquired during a non-stimulation, single-slice, gradient echo EPI sequence in search of functionally connected, spatially distant regions of the rat brain.

Manganese-Enhanced Magnetic Resonance Imaging (MEMRI) has proven itself to be a beneficial technique in the field of Neuroscience. This thesis applies MEMRI to studies in neuroscience by first establishing the limitations concerning the use of MEMRI in live rats. Experiment 1 used an osmotic pump for manganese (Mn) delivery to the lateral ventricles for acquisition of anatomical images using MEMRI. From my knowledge, this was the first method demonstrating slow infusion of Mn to the lateral ventricles. In Experiment 2, MEMRI was used for volumetric analysis the whole brain and hippocampus of prenatally stressed rats. To my knowledge, this study was the first to investigate the effect of generational prenatal stress on the structure of a rat’s brain using MEMRI and histology. Additionally, Experiment 2 investigated the use of a subcutaneous osmotic pump to deliver Mn for MEMRI. A summary on the use of MEMRI in Neuroscience concludes this thesis, with a discussion on the methods used and related technical considerations. / xi, 84 leaves ; 29 cm

Thesis (MSc.(Anatomy)--Faculty of Health Sciences)-University of Pretoria, 2008. / Includes bibliographical references.

Tuberous sclerosis complex (TSC) is an autosomal-dominant neurocutaneous disease caused by loss of the TSC1 (encoding hamartin) or TSC2 (encoding tuberin) genes. Neurologic symptoms are common and varied in TSC and include epilepsy and behavioral conditions like autism spectrum disorders (ASD). Between 17 and 61% of children with TSC exhibit symptoms of ASD. The purpose of this study was to investigate a potential correlate of poor neurological outcome in TSC by assessing the integrity of brain language pathways and the relationship to ASD. 42 patients with TSC and 42 age-matched control subjects were scanned with advanced diffusion-weighted MRI. White matter language pathways were identified with a validated automatic method and analyzed for microstructural characteristics, including fractional anisotropy (FA) and mean diffusivity (MD). Well-defined white matter pathways in the brain are characterized by high FA and low MD. During normal development, brain white matter pathways increase in FA and decrease in MD. Out of 42 patients with TSC, 12 had ASD (29%). After controlling for age, TSC patients without ASD showed a small decrease in FA of the arcuate fasciculus compared to control subjects, and TSC patients with ASD had much lower FA than both control subjects and TSC patients without ASD. Similarly, while TSC patients without ASD had only a small increase in MD compared to control subjects in the arcuate fasciculus, TSC patients with ASD had much higher MD than control subjects and TSC patients without ASD. A new method for assessing the microstructure of young patients showed similar results with decreased compactness in language pathways of TSC patients with ASD. Another new method designed to better analyze regions with crossing pathways showed modifications in language pathway microstructure that correlated with ASD diagnosis in the TSC patients. Preliminary analysis of neuropsychiatric data also showed a trend toward an association of arcuate fasciculus MD with verbal IQ, although the result was not significant after multiple comparisons correction. It remains unclear why some patients with TSC develop ASD, while others have better language outcomes. Our results suggest that aberrant development of language pathways may act as a marker for poor neurological outcome in TSC patients. The impaired microstructure in language pathways of TSC patients may be responsible for the development of ASD, although prospective studies examining the development of language pathways and subsequent ASD diagnosis in this patient population remain essential. It is also possible that a primary problem with language leads to decreased use and subsequent poor development of language pathways. Early diagnosis of ASD is crucial for improving the outcomes of affected children.

Simultaneous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) have gained increasing popularity in studying human cognition due to their potential to map the brain dynamics with high spatial and temporal fidelity. Such detailed mapping of the brain is crucial for understanding the neural mechanisms by which humans make perceptual decisions. Despite recent advances in data acquisition and analysis of simultaneous EEG-fMRI, the lack of effective computational tools for optimal fusion of the two modalities remains a major challenge. The goal of this dissertation is to provide a recipe of machine learning methods for fusion of simultaneous EEG-fMRI data. Specifically, we investigate three types of fusion approaches and apply them to study the whole-brain spatiotemporal dynamics during a rapid object recognition task where subjects discriminate face, car, and house images under ambiguity. We first use an asymmetric fusion approach capitalizing on temporal single-trial EEG variability to identify early and late neural subsystems selective to categorical choice of faces versus nonfaces. We find that the degree of interaction in these networks accounts for a substantial fraction of our bias to see faces. Based on a computational modeling of behavioral measures, we further dissociate separate neural correlates of the face decision bias modulated by varying levels of stimulus evidence. Secondly, we develop a state-space model based symmetric fusion approach to integrate EEG and fMRI in a probabilistic generative framework. We use a variational Bayesian method to infer the network connectivity among latent neural states shared by EEG and fMRI. Finally, we use a data-driven symmetric fusion approach to compare representations of the EEG and fMRI against those of a deep convolutional neural network (CNN) in a common similarity space. We show a spatiotemporal hierarchical correspondence in visual processing stages between the human brain and the CNN. Collectively, our results show that the spatiotemporal properties of neural circuits revealed by the analysis of simultaneous EEG-fMRI data can reflect the choice behavior of subjects during rapid perceptual decision making.