Structural connectivity asymmetry in the neonatal brain

Asymmetry of the neonatal brain is not yet understood at the level of structural connectivity. We utilized DTI deterministic tractography and structural network analysis based on graph theory to determine the pattern of structural connectivity asymmetry in 124 normal neonates. We tracted white matter axonal pathways characterizing interregional connections among brain regions and inferred asymmetry in left and right anatomical network properties. Our findings revealed that in neonates, small-world characteristics were exhibited, but did not differ between the two hemispheres, suggesting that neighboring brain regions connect tightly with each other, and that one region is only a few paths away from any other region within each hemisphere. Moreover, the neonatal brain showed greater structural efficiency in the left hemisphere than that in the right. In neonates, brain regions involved in motor, language, and memory functions play crucial roles in efficient communication in the left hemisphere, while brain regions involved in emotional processes play crucial roles in efficient communication in the right hemisphere. These findings suggest that even at birth, the topology of each cerebral hemisphere is organized in an efficient and compact manner that maps onto asymmetric functional specializations seen in adults, implying lateralized brain functions in infancy.

Abnormalities of cortical thickness, subcortical shapes, and white matter integrity in subcortical vascular cognitive impairment

Subcortical vascular cognitive impairment (sVCI) is caused by lacunar infarcts or extensive and/or diffuse lesions in the white matter that may disrupt the white matter circuitry connecting cortical and subcortical regions and result in the degeneration of neurons in these regions. This study used structural magnetic resonance imaging (MRI) and high angular resolution diffusion imaging (HARDI) techniques to examine cortical thickness, subcortical shapes, and white matter integrity in mild vascular cognitive impairment no dementia (VCIND Mild) and moderate-to-severe VCI (MSVCI). Our study found that compared to controls (n = 25), VCIND Mild (n = 25), and MSVCI (n = 30) showed thinner cortex predominantly in the frontal cortex. The cortex in MSVCI was thinner in the parietal and lateral temporal cortices than that in VCIND Mild. Moreover, compared to controls, VCIND Mild and MSVCI showed smaller shapes (i.e., volume reduction) in the thalamus, putamen, and globus pallidus and ventricular enlargement. Finally, compared to controls, VCIND Mild, and MSVCI showed an increased mean diffusivity in the white matter, while decreased generalized fractional anisotropy was only found in the MSVCI subjects. The major axonal bundles involved in the white matter abnormalities were mainly toward the frontal regions, including the internal capsule/corona radiata, uncinate fasciculus, and anterior section of the inferior fronto-occipital fasciculus, and were anatomically connected to the affected cortical and subcortical structures. Our findings suggest that abnormalities in cortical, subcortical, and white matter morphology in sVCI occur in anatomically connected structures, and that abnormalities progress along a similar trajectory from the mild to moderate and severe conditions.

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.

Developmental synchrony of thalamocortical circuits in the neonatal brain

The thalamus is a deep gray matter structure and consists of axonal fibers projecting to the entire cortex, which provide the anatomical support for its sensorimotor and higher-level cognitive functions. There is limited in vivo evidence on the normal thalamocortical development, especially in early life. In this study, we aimed to investigate the developmental patterns of the cerebral cortex, the thalamic substructures, and their connectivity with the cortex in the first few weeks of the postnatal brain. We hypothesized that there is developmental synchrony of the thalamus, its cortical projections, and corresponding target cortical structures. We employed diffusion tensor imaging (DTI) and divided the thalamus into five substructures respectively connecting to the frontal, precentral, postcentral, temporal, and parietal and occipital cortex. T2-weighted magnetic resonance imaging (MRI) was used to measure cortical thickness. We found age-related increases in cortical thickness of bilateral frontal cortex and left temporal cortex in the early postnatal brain. We also found that the development of the thalamic substructures was synchronized with that of their respective thalamocortical connectivity in the first few weeks of the postnatal life. In particular, the right thalamo-frontal substructure had the fastest growth in the early postnatal brain. Our study suggests that the distinct growth patterns of the thalamic substructures are in synchrony with those of the cortex in early life, which may be critical for the development of the cortical and subcortical functional specialization.

A novel approach for improved tractography and quantitative analysis of probabilistic fibre tracking curves

This paper presents a novel approach for improved diffusion tensor fibre tractography, aiming to tackle a number of the limitations of current fibre tracking algorithms, and describes a quantitative analysis tool for probabilistic tracking algorithms. We consider the sampled random paths generated by a probabilistic tractography algorithm from a seed point as a set of curves, and develop a statistical framework for analysing the curve-set geometrically that finds the average curve and dispersion measures of the curve-set statistically. This study is motivated firstly by the goal of developing a robust fibre tracking algorithm, combining the power of both deterministic and probabilistic tracking methods using average curves. These typical curves produce strong connections to every anatomically distinct fibre tract from a seed point and also convey important information about the underlying probability distribution. These single well-defined trajectories overcome a number of the limitations of deterministic and probabilistic approaches. A new clustering algorithm for branching curves is employed to separate fibres into branches before applying the averaging methods. Secondly, a quantitative analysis tool for probabilistic tracking methods is introduced using statistical measures of curve-sets. Results on phantom and in vivo data confirm the efficiency and effectiveness of the proposed approach for the tracking algorithm and the quantitative analysis of the probabilistic methods.

Neuroimaging Assessment of Spasticity Developed after Acquired Brain Injuries and Multiple Sclerosis

This study evaluated whether different imaging techniques correlate with specific variables routinely used to grade the types and complexities of patient conditions in neurorehabilitation services and their clinical outcomes, and if there are radiological patterns, topography or distribution of the lesions correlated to spasticity. The cohort studied included 75 patients, 38 patients with multiple sclerosis (MS) and 37 patients with acquired brain injuries (ABI) referred to the neurorehabilitation department from April 2009 to March 2010. Data included age, gender, diagnoses, complications, spasticity, length of stay, Rehabilitation Complexity Scale (RCS) and Northwick Park Dependency (NPDS) scores on admission and discharge. Forward stepwise multiple regressions were performed considering Spasticity as dependent and considering NPDS, RCS on admission and discharge, age, gender and length of stay as independent variables. Standardized intra-axial lesions in those with spasticity were fused with standardized axial DTIs from normal controls obtained under 3 T and 1.5 T MRI scanners. Spasticity was present in 36 patients. Regression was found between spasticity and all the other variables with r = 0.42, r2 = 0.17, adjr2 = 0.12r, F(4,66) = 3.53, p < 0.01. Residuals were acceptable. If the same is calculated excluding MS patients the results are higher with: r = 0.59, r2 = 0.35, adjr2 = 0.30, F(2,27) = 7.1885, p < 0.003. In MS subjects, spasticity appeared if lesions affected pyramidal tracts, callosal radiations extensively or brainstem. Those with ABI did develop spasticity if the lesion involved the internal capsules, pericommissural areas, optochiasmatic cistern or brainstem. Findings suggest that NPDSa and discharge and RCSd are higher in those developing spasticity. No obvious correlation between spasticity and RCSa was found. Subjects with MS are more likely to develop spasticity especially if the lesions are numerous and affect the corticospinal tracts, callosal radiations extensively or brainstem. In patients with ABI lesions affecting pyramidal tracts, pericommissural areas and optochiasmatic cisterns seem more likely to develop spasticity.

Brain Network Measures for Groups of Nodes: Application to Normal Aging and Alzheimer's Disease

Background: The nodal brain network measures (e.g., centrality measures) are defined for a single node and the global network measures (e.g., global efficiency) are defined for the whole brain in the literature. But a meaningful group of nodes will be benefited from a formulation that applies to a group of nodes rather than a single node or the whole brain. The question such as "which brain lobe is more structurally central in the older-adult brain?" could be answered to some extent by the application of a centrality measure that applied to the group of nodes from each lobe. In the brain asymmetric studies, path-based global measures were applied to the left and right hemispherical networks separately, considering only intrahemispheric edges. However, for a valid comparison, such global measures should include the interhemispheric edges as well. This problem can be solved by considering both hemispherical nodes as two groups in one network. Methods: Novel definitions for group nodes network measures are presented in this study, to solve a number of such group-context problems in the brain networks analysis. We apply the group measures to the structural connectomes of older adults and Alzheimer's disease (AD) subjects based on the brain lobes and hemispherical groups to demonstrate the effectiveness of the proposed measures. Results: The temporal and parietal lobes are the most central lobes in older adults and AD, but the strength of these lobes has been heavily affected in AD. However, the rewiring of the AD brain preserves the paths for communication between other regions through these lobes. Leftward efficiency revealed in older adults and the asymmetry disappeared in the rewired AD. Conclusion: We prove that the concepts of group network measures have the potential to solve a number of such group-context problems in the brain networks analysis and the group network measures change the way of analyzing brain networks.

Two-tensor model-based bootstrapping on classified tensor morphologies: estimation of uncertainty in fiber orientation and probabilistic tractography

In this manuscript, fast and clinically feasible model-based bootstrapping algorithms using a geometrically constrained two-tensor diffusion model are employed for estimating uncertainty in fiber orientation. A Monte-Carlo-based tensor morphology voxel classification algorithm is initially applied using single-tensor bootstrap samples before the use of a two-tensor model-based bootstrapping algorithm. 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 and computational times for whole bootstrap data volume generation compared to other multifiber 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. Tractography with these bootstrapping algorithms is also developed to estimate the connection probabilities between brain regions, especially regions with complex fiber configurations. Experimental results on synthetic data, a hardware phantom and human brain data demonstrate the superior performance of our algorithms compared to conventional approaches.

Discriminative patterns of white matter changes in Alzheimer's

Changes in structural connectivity of the Alzheimer's brain have not been widely studied utilizing cutting-edge methodologies. This study develops an efficient structural connectome-based convolutional neural network (CNN) to classify the AD and uses explanations of CNNs' choices in classification to pinpoint the discriminative changes in white matter connectivity in AD. A CNN architecture has been developed to classify normal control (NC) and AD subjects from the weighted structural connectome. Then, the CNN classification decision is visually analyzed using gradient-based localization techniques to identify the discriminative changes in white matter connectivity in Alzheimer's. The cortical regions involved in the identified discriminative structural connectivity changes in AD are highly covered in the temporal/subcortical regions. A specific pattern is identified in the discriminative changes in structural connectivity of AD, where the white matter changes are revealed within the temporal/subcortical regions and from the temporal/subcortical regions to the frontal and parietal regions in both left and right hemispheres.  The proposed approach has the potential to comprehensively analyze the discriminative structural connectivity differences in AD, change the way of detecting biomarkers, and help clinicians better understand the structural changes in AD and provide them with more confidence in automated diagnostic systems.