Developmental synchrony of thalamocortical circuits in the neonatal brain

10.1016/j.neuroimage.2015.03.039Neuroimage116168-176GUSTO (Growing up towards Healthy Outcomes

Probabilistic algorithms for white matter fibre tractography and clustering using diffusion MR images

The human brain is certainly the most complex biological system as it contains a network of more than l0 to the power of 11 individual nerve cells and interconnections. Fibre tractography using diffusion MR imaging is a promising non-invasive method for reconstructing the 3D fibre architecture of the human brain white matter in vivo. Despite the great potential, white matter tractography is relatively immature. At the current resolution of diffusion MR images, researchers agree that more than one third of imaging voxels in human brain white matter contain crossing fibre bundles. Generally, conventional diffusion tensor imaging (DTI) fibre tracking approaches have difficulties in crossing regions. Also, noise and other artefacts associated with diffusion MR data lead to uncertainty in the estimates of fib re orientation directions. Furthennore. each fib re tracking method has limitations due to the algorithmic approach that they follow and the assumptions they make. This thesis presents novel probabilistic based fibre tracking algorithms aiming to tackle a number of limitations of existing fibre tracking algorithms. Fibre clustering is a key step towards tract-based, quantitative analysis of white matter. Clustering algorithms analyse a collection of fibre curves in 3D and delineate them into anatomically distinct fibre tracts groups. In this thesis, a probabilistic framework is developed and the framework al lows for the clustering of sets of cunres In curve space. This thesis describes a number of original contributions to the field. First, a novel statistical framework is developed for improved fibre tractography and a quantitative analysis tool is introduced for probabilistic tracking methods using the statistical measures. The goal is to elucidate problems with existing detenninistic and probabilistic algorithms used to process diffusion MR images and propose solutions and methods through a new framework. Subsequently, random-walk and modelbased bootstrapping algorithms are developed using a two-tensor field to quantify the uncertainty of fibre orientation and probabilistic fibre tractography. A further problem tackled here is resolving crossing fibre configurations, a major concern in diffusion MR imaging, using data that can be routinely acquired in a clinical setting. Finally, a new probabilistic clustering algorithm is introduced using regression mixtures and the result of clustering is the probabilistic assignment of the fibre trajectories to each cluster. The tract geometry model is estimated using fitted parameters of the probabilistic clustering algorithm. Local reconstruction, tracking results, segmentation and quantitative analysis are shown on simulated datasets, on a hardware phantom and on multiple human brain datasets.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Probabilistic Clustering and Shape Modelling of White Matter Fibre Bundles using Regression Mixtures

We present a novel approach for probabilistic clustering of white matter fibre pathways using curve-based regression mixture modelling techniques in 3D curve space. The clustering algorithm is based on a principled method for probabilistic modelling of a set of fibre trajectories as individual sequences of points generated from a finite mixture model consisting of multivariate polynomial regression model components. Unsupervised learning is carried out using maximum likelihood principles. Specifically, conditional mixture is used together with expectation-maximisation (EM) algorithm to estimate cluster membership. The result of clustering is the probabilistic assignment of fibre trajectories to each cluster and an estimate of the cluster parameters. A statistical model is calculated for each clustered fibre bundles using fitted parameters of the probabilistic clustering. We illustrate the potential of our clustering approach on synthetic data and real data

Model-Based Bootstrapping on Classified Tensor Morphologies: Estimation of Uncertainty in Fibre Orientation and Probabilistic Tractography.

In this study, fast and clinically feasible model-based bootstrapping algorithms using a geometrically constrained two-tensor diffusion model are employed for estimating uncertainty in fibre-orientation. Voxels are classified based on tensor morphologies before applying single or two-tensor model-based bootstrapping algorithms. Classification of tensor morphologies allows the tensor morphology to be considered when selecting the most appropriate bootstrap procedure. A constrained two-tensor model approach can greatly reduce data acquisition times and computational time for whole bootstrap data volume generation compared to other multi-fibre model techniques, facilitating widespread clinical use. For comparison, we propose a new repetition-bootstrap algorithm based on classified voxels and the constrained two-tensor model. White matter tractography with these bootstrapping algorithms is also developed to estimate the connection probabilities between brain regions, especially regions with complex fibre configurations. Experimental results on a hardware phantom and human brain data demonstrate the superior performance of our algorithms compared to conventional approaches

Stochastic Two-Tensor Fibre Tractography

Diffusion tensor magnetic resonance imaging (DT-MRI) is a non-invasive in-vivo imaging technique that can be used to generate fibre trajectories in brain white matter. Many current tractography methods assume that the fibre direction coincides with the principal eigenvector of a single diffusion tensor. This is, however, not the case for regions with crossing fibres. In addition noise introduces more uncertainty and makes the computation of the fibre direction difficult. Multi-tensor fibre tracking can alleviate the problems when crossing fibres are encountered. Stochastic fibre tracking techniques overcome the uncertainties of deterministic methods by adding a degree of randomness to deterministic tractography. We propose an algorithm using a stochastic fibre tracking approach based on two tensors. The method is verified on a synthetic dataset and an in-viv

Resolving complex fibre configurations using two-tensor random-walk stochastic algorithms

Fibre tractography using diffusion tensor imaging allows the study of anatomical connectivity of the brain, and is an important diagnostic tool for a range of neurological diseases. Deterministic tractography algorithms assume that the fibre direction coincides with the principal eigenvector of a diffusion tensor. This is, however, not the case for regions with crossing fibres. In addition noise introduces uncertainty and makes the computation of fibre directions difficult. Stochastic tractography algorithms have been developed to overcome the uncertainties of deterministic algorithms. However, generally, both parametric and non-parametric stochastic algorithms require longer computational time and large amounts of memory. Multi-tensor fibre tracking methods can alleviate the problems when crossing fibres are encountered. In this study simple and computationally efficient random-walk algorithms are described for estimating anatomical connectivity in white matter. These algorithms are then applied to a two-tensor model to compute the probabilities of connections between regions with complex fibre configurations. We analyze the random-walk models quantitatively using simulated data and estimate the optimal parameter values of the models. The performance of the tracking algorithms is verified using a physical phantom and an in vivo dataset with a wide variety of seed points. The results confirm the effectiveness of the proposed approach, which gives comparable results to other stochastic methods. Our approach is however significantly faster and requires less memory. The results of two-tensor random-walk algorithms demonstrate that our algorithms can accurately identify fibre bundles in complex fibre regions

Dengue Incidence and <i>Aedes</i> Vector Collections in Relation to COVID-19 Population Mobility Restrictions

Contrary to expectation, dengue incidence decreased in many countries during the period when stringent population movement restrictions were imposed to combat COVID-19. Using a seasonal autoregressive integrated moving average model, we previously reported a 74% reduction in the predicted number of dengue cases from March 2020 to April 2021 in the whole of Sri Lanka, with reductions occurring in all 25 districts in the country. The reduction in dengue incidence was accompanied by an 87% reduction in larval collections of Aedes vectors in the northern city of Jaffna. It was proposed that movement restrictions led to reduced human contact and blood feeding by Aedes vectors, accompanied by decreased oviposition and vector densities, which were responsible for diminished dengue transmission. These findings are extended in the present study by investigating the relationship between dengue incidence, population movement restrictions, and vector larval collections between May 2021 and July 2022, when movement restrictions began to be lifted, with their complete removal in November 2021. The new findings further support our previous proposal that population movement restrictions imposed during the COVID-19 pandemic reduced dengue transmission primarily by influencing human–Aedes vector interaction dynamics

Adaptation of brain functional and structural networks in aging

The human brain, especially the prefrontal cortex (PFC), is functionally and anatomically reorganized in order to adapt to neuronal challenges in aging. This study employed structural MRI, resting-state fMRI (rs-fMRI), and high angular resolution diffusion imaging (HARDI), and examined the functional and structural reorganization of the PFC in aging using a Chinese sample of 173 subjects aged from 21 years and above. We found age-related increases in the structural connectivity between the PFC and posterior brain regions. Such findings were partially mediated by age-related increases in the structural connectivity of the occipital lobe within the posterior brain. Based on our findings, it is thought that the PFC reorganization in aging could be partly due to the adaptation to age-related changes in the structural reorganization of the posterior brain. This thus supports the idea derived from task-based fMRI that the PFC reorganization in aging may be adapted to the need of compensation for resolving less distinctive stimulus information from the posterior brain regions. In addition, we found that the structural connectivity of the PFC with the temporal lobe was fully mediated by the temporal cortical thickness, suggesting that the brain morphology plays an important role in the functional and structural reorganization with aging.Published versio