AimExamination of central compensatory mechanisms following peripheral vocal nerve injury and recovery is essential to build knowledge about plasticity of the neural network underlying phonation. The objective of this prospective multiple-cases longitudinal study is to describe brain activity in response to unilateral vocal fold paralysis (UVFP) management and to follow central nervous system adaptation over time in three patients with different nervous and vocal recovery profiles.Materials and methodsParticipants were enrolled within 3 months of the onset of UVFP. Within 1 year of the injury, the first patient did not recover voice or vocal fold mobility despite voice therapy, the second patient recovered voice and mobility in absence of treatment and the third patient recovered voice and vocal fold mobility following an injection augmentation with hyaluronic acid in the paralyzed vocal fold. These different evolutions allowed comparison of individual outcomes according to nervous and vocal recovery. All three patients underwent functional magnetic resonance imaging (fMRI task and resting-state) scans at three (patient 1) or four (patients 2 and 3) time points. The fMRI task included three conditions: a condition of phonation and audition of the sustained [a:] vowel for 3 s, an audition condition of this vowel and a resting condition. Acoustic and aerodynamic measures as well as laryngostroboscopic images and laryngeal electromyographic data were collected.Results and conclusionThis study highlighted for the first time two key findings. First, hyperactivation during the fMRI phonation task was observed at the first time point following the onset of UVFP and this hyperactivation was related to an increase in resting-state connectivity between previoulsy described phonatory regions of interest. Second, for the patient who received an augmentation injection in the paralyzed vocal fold, we subsequently observed a bilateral activation of the voice-related nuclei in the brainstem. This new observation, along with the fact that for this patient the resting-state connectivity between the voice motor/sensory brainstem nuclei and other brain regions of interest correlated with an aerodynamic measure of voice, support the idea that there is a need to investigate whether the neural recovery process can be enhanced by promoting the restoration of proprioceptive feedback.

Recent advances in MRI technology have enabled richer multi-shell sequences to be implemented in diffusion MRI, allowing the investigation of both the microscopic and macroscopic organization of the brain white matter and its complex network of neural fibers. The emergence of advanced diffusion models has enabled a more detailed analysis of brain microstructure by estimating the signal received from a voxel as the combination of responses from multiple fiber populations. However, disentangling the individual microstructural properties of different macroscopic white matter tracts where those pathways intersect remains a challenge. Several approaches have been developed to assign microstructural properties to macroscopic streamlines, but often present shortcomings. ROI-based heuristics rely on averages that are not tract-specific. Global methods solve a computationally-intensive global optimization but prevent the use of microstructural properties not included in the model and often require restrictive hypotheses. Other methods use atlases that might not be adequate in population studies where the shape of white matter tracts varies significantly between patients. We introduce UNRAVEL, a framework combining the microscopic and macroscopic scales to unravel multi-fixel microstructure by utilizing tractography. The framework includes commonly-used heuristics as well as a new algorithm, estimating the microstructure of a specific white matter tract with angular weighting. Our framework grants considerable freedom as the inputs required, a set of streamlines defining a tract and a multi-fixel diffusion model estimated in each voxel, can be defined by the user. We validate our approach on synthetic data and in vivo data, including a repeated scan of a subject and a population study of children with dyslexia. In each case, we compare the estimation of microstructural properties obtained with angular weighting to other commonly-used approaches. Our framework provides estimations of the microstructure at the streamline level, volumetric maps for visualization and mean microstructural values for the whole tract. The angular weighting algorithm shows increased accuracy, robustness to uncertainties in its inputs and maintains similar or better reproducibility compared to commonly-used analysis approaches. UNRAVEL will provide researchers with a flexible and open-source tool enabling them to study the microstructure of specific white matter pathways with their diffusion model of choice.