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.

Selective atrophy of the connected deepest cortical layers following small subcortical infarct.

OBJECTIVE: To explore whether in patients with chronic small subcortical infarct the cortical layers of the connected cortex are differentially affected and whether these differences correlate with clinical symptomatology. METHODS: Twenty patients with a history of chronic small subcortical infarct affecting the corticospinal tracts and 15 healthy controls were included. Connected primary motor cortex was identified with tractography starting from infarct. T1-component probability maps were calculated from T1 relaxation 3T MRI, dividing the cortex into 5 laminar gaussian classes. RESULTS: Focal cortical thinning was observed in the connected cortex and specifically only in its deepest laminar class compared to the nonaffected mirrored cortex (p < 0.001). There was loss of microstructural integrity of the affected corticospinal tract with increased mean diffusivity and decreased fractional anisotropy compared to the contralateral nonaffected tract (p ≤ 0.002). Clinical scores were correlated with microstructural damage of the corticospinal tracts and with thinning of the cortex and specifically only its deepest laminar class (p < 0.001). No differences were found in the laminar thickness pattern of the bilateral primary motor cortices or in the microstructural integrity of the bilateral corticospinal tracts in the healthy controls. CONCLUSION: Our results support the concept of secondary neurodegeneration of connected primary motor cortex after a small subcortical infarct affecting the corticospinal tract, with observations that the main cortical thinning occurs in the deepest cortex and that the clinical symptomatology is correlated with this cortical atrophy pattern. Our findings may contribute to a better understanding of structural reorganization and functional outcomes after stroke.

Motion correction and registration of high b-value diffusion weighted images.

It has been suggested that, high b-value diffusion weighted MRI improves the sensitivity and specificity of these images to tissue microstructure when compared with "clinical" b-value diffusion weighted MRI (b ≈ 1000 s/mm(2)). However, it suffers from poor signal to noise ratio - leading to longer acquisition times and therefore more motion artifacts. Together with the orientational sensitivity of the diffusion weighted MRI signal, the contrast at different b-values and different gradient directions is significantly different. These features of high b-value diffusion images preclude the ability to perform conventional image-registration-based motion/distortion correction. Here, we suggest a framework based on both experimental data (diffusion tensor MRI) and simulations (using the composite hindered and restricted model of diffusion framework) to correct the motion induced misalignments and artifacts of high b-value diffusion weighted MRI. This approach was evaluated using visual assessment of the registered diffusion weighted MRI and the composite hindered and restricted model of diffusion analysis results, as well as residual analysis to assess the quality of the composite hindered and restricted model of diffusion fitting. Both qualitative and quantitative results demonstrate an improvement in fitting the data to the composite hindered and restricted model of diffusion model following the suggested registration framework, thereby, addressing a long-standing problem and making the correction of motion/distortions in data collected at high b-values feasible for the first time.