[Magnetic resonance imaging postprocessing techniques in the study of brain connectivity]. / Técnicas de análisis de posproceso en resonancia magnetica para el estudio de la conectividad cerebral.

Brain connectivity is a key concept for understanding brain function. Current methods to detect and quantify different types of connectivity with neuroimaging techniques are fundamental for understanding the pathophysiology of many neurologic and psychiatric disorders. This article aims to present a critical review of the magnetic resonance imaging techniques used to measure brain connectivity within the context of the Human Connectome Project. We review techniques used to measure: a) structural connectivity b) functional connectivity (main component analysis, independent component analysis, seed voxel, meta-analysis), and c) effective connectivity (psychophysiological interactions, causal dynamic models, multivariate autoregressive models, and structural equation models). These three approaches make it possible to combine and use different statistical techniques to elaborate mathematical models in the attempt to understand the functioning of the brain. The findings obtained with these techniques must be validated by other techniques for analyzing structural and functional connectivity. This information is integrated in the Human Connectome Project where all these approaches converge to provide a representation of all the different models of connectivity.

Accurate quantification methods to evaluate cervical cord atrophy in multiple sclerosis patients.

OBJECT: Automatic accurate measurement techniques are needed to increase reproducibility in the quantification of cervical cord area (CCA) with magnetic resonance (MR) imaging in the assessment of central nervous system (CNS) atrophy in multiple sclerosis (MS) patients. MATERIALS AND METHODS: Two segmentation methods were implemented: (1) spatial mean brightness level estimation (SMBLE), and (2) partial-volume modeling (PVM). These were evaluated with the inclusion of spinal cord inclination and/or partial-volume-effect corrections. An averaged manually segmented set was considered as reference. Thirty MR studies were used to compare the different methods. A set of 15 MS patients and 15 control subjects within a two-year longitudinal study were used to evaluate cord atrophy with the best method. Statistical evaluation was made by using an intraclass correlation coefficient and Bland-Altman comparisons. RESULTS: Partial-volume modeling with spinal cord inclination correction and partial-volume spinal-cord contour contribution estimation was the most accurate method. The longitudinal test showed a 4% decrease in CCA in MS patients with no significant reduction in control subjects. CONCLUSION: The automatic PVM cord-segmentation approach, taking into consideration the spinal-cord inclination and partial-volume treatment, provides reproducibility and increased accuracy in the evaluation of cord atrophy, allowing the monitoring of changes in MS patients.