Background and Objectives . The hypothesized link between extracranial venous abnormalities and some neurological disorders awoke interest in the investigation of the internal jugular veins (IJVs). However, different IJV cross-sectional area (CSA) values are currently reported in literature. In this study, we introduced a semiautomatic method to measure and normalize the CSA and the degree of circularity (Circ) of IJVs along their whole length. Methods . Thirty-six healthy subjects (31.22 ± 9.29 years) were recruited and the 2D time-of-flight magnetic resonance venography was acquired with a 1.5 T Siemens scanner. The IJV were segmented on an axial slice, the contours were propagated in 3D. Then, IJV CSA and Circ were computed between the first and the seventh cervical levels (C1–C7) and normalized among subjects. Inter- and intrarater repeatability were assessed. Results . IJV CSA and Circ were significantly different among cervical levels (p<0.001). A trend for side difference was observed for CSA (larger right IJV,p=0.06), but not for Circ (p=0.5). Excellent inter- and intrarater repeatability was obtained for all the measures. Conclusion . This study proposed a reliable semiautomatic method able to measure the IJV area and shape along C1–C7, and suitable for defining the normality thresholds for future clinical studies.
Resting state (RS) functional magnetic resonance images (rsfMRI) were analyzed by spatial independent component analysis (sICA). Functional connectivity (FC) was further analyzed within the identified RS networks either by high dimension sICA or by local clustering. The latter approach permitted to drive a matched structural connectivity (SC) based on probabilistic tractography between the same clusters. Cortex segmentation tools ad diffusion MRI were used to correlate fiber and cortical damage. Methods and results are here compared concerning the translational fall-outs and the applicability in the evaluation and follow-up of neurodegenerative diseases. Emphasis is given to the integration of image, signal, and data processing methods.
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