A Method for In-Vivo Mapping of Axonal Diameter Distributions in the Human Brain Using Diffusion-Based Axonal Spectrum Imaging (AxSI).

In this paper we demonstrate a generalized and simplified pipeline called axonal spectrum imaging (AxSI) for in-vivo estimation of axonal characteristics in the human brain. Whole-brain estimation of the axon diameter, in-vivo and non-invasively, across all fiber systems will allow exploring uncharted aspects of brain structure and function relations with emphasis on connectivity and connectome analysis. While axon diameter mapping is important in and of itself, its correlation with conduction velocity will allow, for the first time, the explorations of information transfer mechanisms within the brain. We demonstrate various well-known aspects of axonal morphometry (e.g., the corpus callosum axon diameter variation) as well as other aspects that are less explored (e.g., axon diameter-based separation of the superior longitudinal fasciculus into segments). Moreover, we have created an MNI based mean axon diameter map over the entire brain for a large cohort of subjects providing the reference basis for future studies exploring relation between axon properties, its connectome representation, and other functional and behavioral aspects of the brain.

Resolution considerations in imaging of the cortical layers.

The cortical layers are a finger print of brain development, function, connectivity and pathology. Obviously, the formation of the layers and their composition is essential to cognition and behavior. The layers were traditionally measured by histological means but recent studies utilizing MRI suggested that T1 relaxation imaging consist of enough contrast to separate the layers. Indeed extreme resolution, post mortem, studies demonstrated this phenomenon. Yet, one of the limiting factors of using T1 MRI to visualize the layers in neuroimaging research is partial volume effect. This happen when the image resolution is not high enough and two or more layers resides within the same voxel. In this paper we demonstrate that due to the physical small thickness of the layers it is highly unlikely that high resolution imaging could resolve the layers. By contrast, we suggest that low resolution multi T1 mapping conjugate with composition analysis could provide practical means for measuring the T1 layers. We suggest an acquisition platform that is clinically feasible and could quantify measures of the layers. The key feature of the suggested platform is that separation of the layers is better achieved in the T1 relaxation domain rather than in the spatial image domain.

Clinical benefits of diffusion tensor imaging in hydrocephalus.

OBJECT The object of this study was to use diffusion tensor imaging (DTI) to evaluate and characterize white matter changes in hydrocephalus. METHODS The authors performed a retrospective analysis of DTI in a cohort of patients with hydrocephalus (n = 35), 19 of whom had both pre- and postsurgical imaging studies. These patient's DTI values were compared with values extracted from age-dependent trend lines computed from a healthy subject group (n = 70, age span 14 months-14 years). Several DTI parameters in different regions of interest (ROIs) were evaluated to find the most sensitive parameters for clinical decision making in hydrocephalus. RESULTS Compared with healthy controls, patients with active hydrocephalus had a statistically significant change in all DTI parameters. The most sensitive and specific DTI parameter for predicting hydrocephalus was axial diffusivity (λ1) measured at the level of the corona radiata. Diffusion tensor imaging parameters correlated with several conventional radiological parameters in the assessment of hydrocephalus but were not superior to them. There was no convincing correlation between clinical disease severity and DTI parameters. When examining the pre- and postsurgical effect, it was found that DTI may be a sensitive tool for estimating tissue improvement. CONCLUSIONS This large-cohort study with a multidisciplinary approach combining clinical, neurological, radiological, and multiple DTI parameters revealed the most sensitive DTI parameters for identifying hydrocephalus and suggested that they may serve as an important tool for the disorder's quantitative radiological assessment.

Combinatorial fiber-tracking of the human brain.

This paper presents a novel fiber-tracking algorithm, termed combinatorial tracking, which uses stochastic process modeling and global optimization algorithm for tractography. Combinatorial tracking is a probabilistic tracking algorithm that transforms the brain's white matter into a grid in which each voxel has 26 weighted connections with adjacent voxels. We model the random walk on this graph using a Markov Chain model and suggest two approaches for fiber reconstruction. In the first approach, we find the most probable paths between two voxels with prior connectivity knowledge using a shortest path algorithm. In the second approach, the all-pairs mean first passage time (MFPT) matrix M (or hitting time as referred to in the Spectral Graph theory literature) is calculated analytically. We suggest that M can be interpreted as a global connectivity matrix and use it for fiber reconstruction. We also introduce a simulation framework that can be used to calculate specific elements of the matrix M, and show how it can be employed to select the target of a fiber in a high resolution diffusion tensor imaging (DTI) dataset. Because any source and any target voxel can be connected, combinatorial tracking permits true connectivity analysis, overcoming the limitations of conventional tracking, especially stopping criteria (e.g. low FA). We applied combinatorial tracking to a standard DTI dataset and demonstrated the reconstruction of the cortico-thalamic pathway, the pyramidal decussation, and the medial cerebellar peduncle fibers. While the DTI ellipsoid served as input for the algorithms, any diffusion imaging based orientation density function (ODF) can be used. This framework can potentially turn diffusion imaging tractography into a true connectivity measure.