Variability and anatomical specificity of the orbitofrontothalamic fibers of passage in the ventral capsule/ventral striatum (VC/VS): precision care for patient-specific tractography-guided targeting of deep brain stimulation (DBS) in obsessive compulsive disorder (OCD)

Deep Brain Stimulation (DBS) is a neurosurgical procedure that can reduce symptoms in medically intractable obsessive-compulsive disorder (OCD). Conceptually, DBS of the ventral capsule/ventral striatum (VC/VS) region targets reciprocal excitatory connections between the orbitofrontal cortex (OFC) and thalamus, decreasing abnormal reverberant activity within the OFC-caudate-pallidal-thalamic circuit. In this study, we investigated these connections using diffusion magnetic resonance imaging (dMRI) on human connectome datasets of twenty-nine healthy young-adult volunteers with two-tensor unscented Kalman filter based tractography. We studied the morphology of the lateral and medial orbitofrontothalamic connections and estimated their topographic variability within the VC/VS region. Our results showed that the morphology of the individual orbitofrontothalamic fibers of passage in the VC/VS region is complex and inter-individual variability in their topography is high. We applied this method to an example OCD patient case who underwent DBS surgery, formulating an initial proof of concept for a tractography-guided patient-specific approach in DBS for medically intractable OCD. This may improve on current surgical practice, which involves implanting all patients at identical stereotactic coordinates within the VC/VS region

MRI for gray matter: statistical modelling for in-vivo application and histological validation of dMRI

Gray matter (GM) forms the ‘computational engine’ of our brain and plays the key role in brain function. Measures derived from MRI (e.g., structural MRI (sMRI) and diffusion MRI (dMRI)) provide a unique opportunity to non-invasively study GM structure in-vivo and thus can be used to probe GM pathology in development, aging and neuropsychiatric disorders. Investigation of the influence of various factors on MRI measures in GM is critical to facilitate their use for future non-invasive studies in healthy and diseased populations. In this dissertation, GM structure was studied with MRI to understand how it is influenced by genetic and environmental factors. Validation of dMRI- derived measures was conducted by comparing them with histological data from monkeys to better understand the cytoarchitectural features that influence GM measures. First, the influence of genetic and environmental factors was quantified on gray matter macrostructure and microstructure measures using phenotypic modelling of structural and diffusion MRI data obtained from a large twin and sibling population (N = 840). Results of this study showed that in GM, while sMRI measures like cortical thickness and GM volume are mainly affected by genetic factors, advanced dMRI measures of mean squared displacement (MSD) and return to origin probability (RTOP) derived from advanced biexponential model can tap into regionally specific patterns of both genetic and environmental influence in cortical and subcortical GM. Our results thus highlight the potential of these advanced dMRI measures for use in future studies that aim to investigate and follow in healthy and clinical population changes in GM microstructure linked with both genes and environment. Second, using data from a large healthy population (n=550), we investigated changes in sMRI tissue contrast at the gray-white matter boundary with biological development during adolescence to assess how this affects estimation of the developmental trajectory of cortical thickness. Results of this study suggest that increased myelination during brain development contributes to age-related changes in gray-white boundary contrast in sMRI scans causing an apparent shift of the estimated gray-white boundary towards the cortical surface, in turn reducing estimations of cortical thickness and its developmental trajectory. Based on these results, we emphasize the importance of accounting for the effects of myelination on T1 gray-white matter boundary contrast to enable more precise estimation of cortical thickness during neurodevelopment. Finally, we conducted histological validation of dMRI measures in gray matter by comparing dMRI measures derived from two models, conventional Diffusion Tensor Imaging (DTI) model and an advanced biexponential model with histology acquired from the same 4 rhesus monkeys. Results demonstrate differences in the ability of distinct dMRI measures including DTI-derived measures of fractional anisotropy (FA), Trace and advanced Biexponential model-derived measures of MSD and RTOP to capture the biological features of underlying cytoarchitecture and identify the dMRI measures that best reflect underlying gray matter cytoarchitectural properties. Investigation of the contribution of underlying cytoarchitecture (cellular organization) to dMRI measures in gray matter provides validation of dMRI measures of average and regional heterogeneity in MSD & Trace as markers of cytoarchitecture as measured by regional average and heterogeneity in cell area density. This postmortem validation of these dMRI measures makes their use possible for treatment monitoring of various GM pathologies. These studies and their results together demonstrate the utility of imaging measures to investigate the complex relationships between GM cellular organization, brain development, environment and genes

The sensitivity of diffusion MRI to microstructural properties and experimental factors

Diffusion MRI is a non-invasive technique to study brain microstructure. Differences in the microstructural properties of tissue, including size and anisotropy, can be represented in the signal if the appropriate method of acquisition is used. However, to depict the underlying properties, special care must be taken when designing the acquisition protocol as any changes in the procedure might impact on quantitative measurements. This work reviews state-of-the-art methods for studying brain microstructure using diffusion MRI and their sensitivity to microstructural differences and various experimental factors. Microstructural properties of the tissue at a micrometer scale can be linked to the diffusion signal at a millimeter-scale using modeling. In this paper, we first give an introduction to diffusion MRI and different encoding schemes. Then, signal representation-based methods and multi-compartment models are explained briefly. The sensitivity of the diffusion MRI signal to the microstructural components and the effects of curvedness of axonal trajectories on the diffusion signal are reviewed. Factors that impact on the quality (accuracy and precision) of derived metrics are then reviewed, including the impact of random noise, and variations in the acquisition parameters (i.e., number of sampled signals, b-value and number of acquisition shells). Finally, yet importantly, typical approaches to deal with experimental factors are depicted, including unbiased measures and harmonization. We conclude the review with some future directions and recommendations on this topic