Functional and structural brain connectivity in congenital deafness.

Several studies have been carried out to verify neural plasticity and the language process in deaf individuals. However, further investigations regarding the intrinsic brain organization on functional and structural neural networks derived from congenital deafness are still an open question. The objective of this study was to investigate the main differences in brain organization manifested in congenitally deaf individuals, concerning the resting-state functional patterns, and white matter structuring. Functional and diffusion magnetic resonance imaging modalities were acquired from 18 congenitally deaf individuals and 18 age-sex-matched hearing controls. Compared to the hearing group, the deaf individuals presented higher functional connectivity among the posterior cingulate cortex node of the default mode network with visual and motor networks, lower functional connectivity between salience networks, language networks, and prominence of functional connectivity changes in the right hemisphere, mostly in the frontoparietal and temporal lobes. In terms of structural connectivity, we found changes mainly in the occipital and parietal lobes, involving both classical sign language support regions as well as concentrated networks for focus activity, attention, and cognitive filtering. Our findings demonstrated that the congenital deaf individuals who learned sign language developed significant brain functional and structural reorganization, which provides prominent support for large-scale brain networks associated with attention decision-making, environmental monitoring based on the movement of objects, and on the motor and visual controls.

Brain activation inhomogeneity highlighted by the Isotropic Anomalous Diffusion filter.

The visual appealing nature of the now popular BOLD fMRI may give the false impression of extreme simplicity, as if the the functional maps could be generated with the press of a single button. However, one can only get plausible maps after long and cautious processing, considering that time and noise come into play during acquisition. One of the most popular ways to account for noise and individual variability in fMRI is the use of a Gaussian spatial filter. Although very robust, this filter may introduce excessive blurring, given the strong dependence of results on the central voxel value. Here, we propose the use of the Isotropic Anomalous Diffusion (IAD) approach, aiming to reduce excessive homogeneity while retaining the natural variability of signal across brain space. We found differences between Gaussian and IAD filters in two parameters gathered from Independent Component maps (ICA), identified on brain areas responsible for auditory processing during rest. Analysis of data gathered from 7 control subjects shows that the IAD filter rendered more localized active areas and higher contrast-to-noise ratios, when compared to equivalent Gaussian filtered data (Student t-test, p<0.05). The results seem promising, since the anomalous filter performs satisfactorily in filtering noise with less distortion of individual localized brain responses.

Anisotropic anomalous diffusion filtering applied to relaxation time estimation in magnetic resonance imaging.

Relaxometry mapping is a quantitative modality in magnetic resonance imaging (MRI) widely used in neuroscience studies. Despite its relevance and utility, voxel measurement of relaxation time in relaxometry MRI is compromised by noise that is inherent to MRI modality and acquisition hardware. In order to enhance signal to noise ratio (SNR) and quality of relaxometry mapping we propose application of anisotropic anomalous diffusion (AAD) filter that is consistent with inhomogeneous complex media. Here we evaluated AAD filter in comparison to two usual spatial filters: Gaussian and non local means (NLM) filters applied to real and simulated T2 relaxometry image sequences. The results demonstrate that AAD filter is comparatively more efficient in noise reducing and maintaining the image structural edges. AAD shows to be a robust and reliable spatial filter for brain image relaxometry.