Non-negative data-driven mapping of structural connections with application to the neonatal brain.

Mapping connections in the neonatal brain can provide insight into the crucial early stages of neurodevelopment that shape brain organisation and lay the foundations for cognition and behaviour. Diffusion MRI and tractography provide unique opportunities for such explorations, through estimation of white matter bundles and brain connectivity. Atlas-based tractography protocols, i.e. a priori defined sets of masks and logical operations in a template space, have been commonly used in the adult brain to drive such explorations. However, rapid growth and maturation of the brain during early development make it challenging to ensure correspondence and validity of such atlas-based tractography approaches in the developing brain. An alternative can be provided by data-driven methods, which do not depend on predefined regions of interest. Here, we develop a novel data-driven framework to extract white matter bundles and their associated grey matter networks from neonatal tractography data, based on non-negative matrix factorisation that is inherently suited to the non-negative nature of structural connectivity data. We also develop a non-negative dual regression framework to map group-level components to individual subjects. Using in-silico simulations, we evaluate the accuracy of our approach in extracting connectivity components and compare with an alternative data-driven method, independent component analysis. We apply non-negative matrix factorisation to whole-brain connectivity obtained from publicly available datasets from the Developing Human Connectome Project, yielding grey matter components and their corresponding white matter bundles. We assess the validity and interpretability of these components against traditional tractography results and grey matter networks obtained from resting-state fMRI in the same subjects. We subsequently use them to generate a parcellation of the neonatal cortex using data from 323 new-born babies and we assess the robustness and reproducibility of this connectivity-driven parcellation.

Probabilistic non-linear registration with spatially adaptive regularisation.

This paper introduces a novel method for inferring spatially varying regularisation in non-linear registration. This is achieved through full Bayesian inference on a probabilistic registration model, where the prior on the transformation parameters is parameterised as a weighted mixture of spatially localised components. Such an approach has the advantage of allowing the registration to be more flexibly driven by the data than a traditional globally defined regularisation penalty, such as bending energy. The proposed method adaptively determines the influence of the prior in a local region. The strength of the prior may be reduced in areas where the data better support deformations, or can enforce a stronger constraint in less informative areas. Consequently, the use of such a spatially adaptive prior may reduce unwanted impacts of regularisation on the inferred transformation. This is especially important for applications where the deformation field itself is of interest, such as tensor based morphometry. The proposed approach is demonstrated using synthetic images, and with application to tensor based morphometry analysis of subjects with Alzheimer's disease and healthy controls. The results indicate that using the proposed spatially adaptive prior leads to sparser deformations, which provide better localisation of regional volume change. Additionally, the proposed regularisation model leads to more data driven and localised maps of registration uncertainty. This paper also demonstrates for the first time the use of Bayesian model comparison for selecting different types of regularisation.

A Bayesian framework for global tractography.

We readdress the diffusion tractography problem in a global and probabilistic manner. Instead of tracking through local orientations, we parameterise the connexions between brain regions at a global level, and then infer on global and local parameters simultaneously in a Bayesian framework. This approach offers a number of important benefits. The global nature of the tractography reduces sensitivity to local noise and modelling errors. By constraining tractography to ensure a connexion is found, and then inferring on the exact location of the connexion, we increase the robustness of connectivity-based parcellations, allowing parcellations of connexions that were previously invisible to tractography. The Bayesian framework allows a direct comparison of the evidence for connecting and non-connecting models, to test whether the connexion is supported by the data. Crucially, by explicit parameterisation of the connexion between brain regions, we infer on a parameter that is shared with models of functional connectivity. This model is a first step toward the joint inference on functional and anatomical connectivity.

Correction of MR image distortions induced by metallic objects using a 3D cubic B-spline basis set: application to stereotactic surgical planning.

Metallic implants in MRI cause spin-echo (SE) images to be distorted in the slice and frequency-encoding directions. Chang and Fitzpatrick (IEEE Trans Med Imaging 1992;11:319-329) proposed a distortion correction method (termed the CF method) based on the magnitude images from two SE acquisitions that differ only in the polarity of the frequency-encoding and slice-selection gradients. In the present study we solved some problems with the CF method, primarily by modeling the field inhomogeneities as a single 3D displacement field built by 3D cubic B-splines. The 3D displacement field was applied in the actual distortion direction in the slice/frequency-encoding plane. To account for patient head motion, a 3D rigid body motion correction was also incorporated in the model. Experiments on a phantom containing an aneurysm clip showed that the knot spacing between the B-splines is a very important factor in both the final image quality and the processing speed. Depending on the knot spacing and the image volume size, the number of unknowns range from a few thousands to over 100,000, leading to processing times ranging from minutes to days. Optimal knot spacing, a means of increasing the processing speed, and other parameters are investigated and discussed.

Haemodynamic and metabolic disturbances in the acute stage of subarachnoid haemorrhage demonstrated by PET.

OBJECTIVE: To investigate the occurrence of early haemodynamic and metabolic changes in the acute stage of subarachnoid haemorrhage (SAH). MATERIAL AND METHODS: Eleven SAH patients were studied. Regional cerebral blood flow (CBF), cerebral metabolic rate of oxygen (CMRO2) and oxygen extraction ratio (OER) were measured with positron emission tomography (PET) 22-53 h after haemorrhage, utilizing 15O-labelled water bolus and the 15O-inhalation technique. Ten volumes of interest (VOIs) representing vascular territories were outlined in each patient according to a standardized procedure. The occurrence of irreversible ischaemia, penumbra, oligaemia, hyperperfusion and normal haemodynamics according to PET criteria was investigated. These pathophysiological categories were related to final tissue outcome as determined by follow-up computed tomography (CT). RESULTS: All five tissue subtypes were represented in the vascular region VOIs; oligaemia was the predominant pathophysiological pattern. When global changes were analysed, blood flow was reduced in three, oxygen metabolism was reduced in four, and OER was increased in four of seven unsedated patients, respectively. The sedated patients all had markedly reduced CBF and CMRO2 and OER in the high or supranormal range. CONCLUSION: Haemodynamic and metabolic disturbances proved to be common after SAH. These abnormalities probably reflect the primary brain injury caused by the initial haemorrhage. The impact of secondary insults such as acute hydrocephalus, brain oedema, vasospasm, seizures, hypotension and hypoxaemia are likely to be dependent on the degree of primary injury, which can be assessed by PET.