A Diffusion Tensor Imaging Study of Motor Fibre Path Integrity and Overt Responsiveness in Disorders of Consciousness

This study investigated the relationship between motor thalamo-cortico-cerebellar fibre path integrity and overt responsiveness in patients with disorders of consciousness (DOC). Additionally, we investigated the potential of imaging these motor tracts at ultra-high fields. Study I and II aimed to map the white matter connections of motor execution fibres in DOC patients. Our results showed significant reductions in motor fibre path integrity across DOC diagnostic categories. Study III and IV aimed to develop a 7T MRI Diffusion Tensor Imaging (DTI) sequence. We optimized this sequence to image motor fibre paths in DOC patients. We concluded that, in healthy controls, probabilistic tractography of these tracts at ultra-high fields was superior to tractography at lower magnetic fields. Further investigation is needed to determine the advantages of imaging these motor tracts at ultra-high fields in patients with disorders of consciousness

Quantitative Multimodal Mapping Of Seizure Networks In Drug-Resistant Epilepsy

Over 15 million people worldwide suffer from localization-related drug-resistant epilepsy. These patients are candidates for targeted surgical therapies such as surgical resection, laser thermal ablation, and neurostimulation. While seizure localization is needed prior to surgical intervention, this process is challenging, invasive, and often inconclusive. In this work, I aim to exploit the power of multimodal high-resolution imaging and intracranial electroencephalography (iEEG) data to map seizure networks in drug-resistant epilepsy patients, with a focus on minimizing invasiveness. Given compelling evidence that epilepsy is a disease of distorted brain networks as opposed to well-defined focal lesions, I employ a graph-theoretical approach to map structural and functional brain networks and identify putative targets for removal. The first section focuses on mesial temporal lobe epilepsy (TLE), the most common type of localization-related epilepsy. Using high-resolution structural and functional 7T MRI, I demonstrate that noninvasive neuroimaging-based network properties within the medial temporal lobe can serve as useful biomarkers for TLE cases in which conventional imaging and volumetric analysis are insufficient. The second section expands to all forms of localization-related epilepsy. Using iEEG recordings, I provide a framework for the utility of interictal network synchrony in identifying candidate resection zones, with the goal of reducing the need for prolonged invasive implants. In the third section, I generate a pipeline for integrated analysis of iEEG and MRI networks, paving the way for future large-scale studies that can effectively harness synergy between different modalities. This multimodal approach has the potential to provide fundamental insights into the pathology of an epileptic brain, robustly identify areas of seizure onset and spread, and ultimately inform clinical decision making

Fluctuations of attention network activation during the resting state reflect fluctuations in subjective attentional state.

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Multi-parametric quantification of white matter microstructure in the human brain

To date the majority of MRI studies of white matter (WM) microstructure have used diffusion tensor MRI (DT-MRI), comparing groups on a voxel-by-voxel basis. There are limitations to this approach. Firstly, the analysis approach treats each voxel independently, ignoring the fact that adjacent voxels may come from the same tract (or may come from completely separate tracts). Secondly, DT-MRI is sensitive to both interesting properties of WM (e.g., myelination, axon density), and less interesting properties (e.g., intra-voxel orientational dispersion). In contrast, other imaging approaches, based on different contrast mechanisms, can provide increased specificity and therefore sensitivity to differences in one particular attribute of tissue microstructure (e.g., myelin content or axonal density). Both quantitative magnetization transfer (qMT) imaging and multicomponent relaxometry provide proxy estimates of myelin content while the combined hindered and restricted model of diffusion (CHARMED) provides a proxy estimate of axon density. We present a novel imaging method called tractometry, which permits simultaneous quantitative assessment of these different microstructural attributes along specific pathways.Crucially, the metrics were only weakly correlated, suggesting that tractometry provides complementary WM microstructural information to DT-MRI. In developing the tractometry pipeline, we also performed a detailed examination of the qMT pipeline, identifying and reducing sources of variance to provide optimized results. We also identify a number of issues with the current state-of-the art, including the stability of tract based spatial statistics (TBSS). We show that conducting a structure-function correlation TBSS study may lead to vastly different conclusions, based simply on the participants recruited into the study. We also address microstructural asymmetry, including the degree of partial-volume effects (PVEs) from free water, which impact on WM metrics. The observed spatial heterogeneity of PVEs can potentially confound interpretation in studies where contralateral hemispheres are used as internal controls, and could either exacerbate or possibly negate tissue difference

Dual Tensor Atlas Generation Based on a Cohort of Coregistered non-HARDI Datasets

Abstract. We propose a method to create a dual tensor atlas from multiple coregistered non-HARDI datasets. Increased angular resolution is ensured by random variations of subject positioning in the scanner and different local rotations applied during coregistration resulting in dispersed gradient directions. Simulations incorporating residual coregistration misalignments show that using 10 subjects should already double the angular resolution, even at a relatively low b-value of b = 1000 smm −2. Commisural corpus callosum fibers reconstructed by our method closely approximated those found in a HARDI dataset.