Anatomical and Functional MRI Changes after One Year of Auditory Rehabilitation with Hearing Aids.

Hearing aids (HAs) are an effective strategy for auditory rehabilitation in patients with peripheral hearing deficits. Yet, the neurophysiological mechanisms behind HA use are still unclear. Thus far, most studies have focused on changes in the auditory system, although it is expected that hearing deficits affect a number of cognitive systems, notably speech. In the present study, we used audiometric evaluations in 14 patients with bilateral hearing loss before and after one year of continuous HA use and functional magnetic resonance imaging (fMRI) and cortical thickness analysis in 12 and 10 of them compared with a normal hearing control group. Prior to HA fitting, fMRI activity was found reduced in the auditory and language systems and increased in visual and frontal areas, expanding to multimodal integration cortices, such as the superior temporal gyrus, intraparietal sulcus, and insula. One year after rehabilitation with HA, significant audiometric improvement was observed, especially in free-field Speech Reception Threshold (SRT) test and functional gain, a measure of HA efficiency. HA use increased fMRI activity in the auditory and language cortices and multimodal integration areas. Individual fMRI signal changes from all these areas were positively correlated with individual SRT changes. Before rehabilitation, cortical thickness was increased in parts of the prefrontal cortex, precuneus, fusiform gyrus, and middle temporal gyrus. It was reduced in the insula, supramarginal gyrus, medial temporal gyrus, occipital cortex, posterior cingulate cortex, and claustrum. After HA use, increased cortical thickness was observed in multimodal integration regions, particularly the very caudal end of the superior temporal sulcus, the angular gyrus, and the inferior parietal gyrus/superior temporal gyrus/insula. Our data provide the first evidence that one year of HA use is related to functional and anatomical brain changes, notably in auditory and language systems, extending to multimodal cortices.

Extratemporal damage in temporal lobe epilepsy: magnetization transfer adds information to volumetric MR imaging.

BACKGROUND AND PURPOSE: MTS is characterized by gliosis and atrophy of the hippocampus and related limbic structures. However, the damage is not limited to those structures with atrophy and has been reported in extratemporal regions. Because volumetric studies are nonspecific, the pathophysiology of the brain damage remains to be solved. MTI is an MR imaging technique more sensitive to subtle neuropathologic changes than conventional MR imaging. Here we combined MTI with VBM analysis to evaluate extratemporal damage in patients with TLE. MATERIALS AND METHODS: We studied 23 healthy controls and 21 patients with TLE with mean ages, respectively, of 37.6 ± 10.9 and 38.6 ± 9.02 years. All subjects had a full clinical follow-up and MR imaging. We processed the images with VBM for volumetric analysis of WM and GM, as well as with voxel-based analysis of MTR for macromolecular integrity analysis. RESULTS: In addition to MTR decrease in the temporal lobes, we found a significant decrease in GM and WM volumes. In the WM, the MTR decrease was correlated to volume loss detected by VBM, indicating that brain atrophy may explain part of the MTR decrease. We also found areas in which the MTR decrease was not associated with volume loss, suggesting an additional pathophysiologic process other than neuronal loss and atrophy underlying the MTR changes. CONCLUSIONS: Our results support the hypothesis that there are widespread lesions in the brain, including the corpus callosum and the frontal lobe, affecting both GM and WM.

Brain tissue segmentation using q-entropy in multiple sclerosis magnetic resonance images

The loss of brain volume has been used as a marker of tissue destruction and can be used as an index of the progression of neurodegenerative diseases, such as multiple sclerosis. In the present study, we tested a new method for tissue segmentation based on pixel intensity threshold using generalized Tsallis entropy to determine a statistical segmentation parameter for each single class of brain tissue. We compared the performance of this method using a range of different q parameters and found a different optimal q parameter for white matter, gray matter, and cerebrospinal fluid. Our results support the conclusion that the differences in structural correlations and scale invariant similarities present in each tissue class can be accessed by generalized Tsallis entropy, obtaining the intensity limits for these tissue class separations. In order to test this method, we used it for analysis of brain magnetic resonance images of 43 patients and 10 healthy controls matched for gender and age. The values found for the entropic q index were 0.2 for cerebrospinal fluid, 0.1 for white matter and 1.5 for gray matter. With this algorithm, we could detect an annual loss of 0.98 percent for the patients, in agreement with literature data. Thus, we can conclude that the entropy of Tsallis adds advantages to the process of automatic target segmentation of tissue classes, which had not been demonstrated previously.

Brain tissue segmentation using q-entropy in multiple sclerosis magnetic resonance images.

The loss of brain volume has been used as a marker of tissue destruction and can be used as an index of the progression of neurodegenerative diseases, such as multiple sclerosis. In the present study, we tested a new method for tissue segmentation based on pixel intensity threshold using generalized Tsallis entropy to determine a statistical segmentation parameter for each single class of brain tissue. We compared the performance of this method using a range of different q parameters and found a different optimal q parameter for white matter, gray matter, and cerebrospinal fluid. Our results support the conclusion that the differences in structural correlations and scale invariant similarities present in each tissue class can be accessed by generalized Tsallis entropy, obtaining the intensity limits for these tissue class separations. In order to test this method, we used it for analysis of brain magnetic resonance images of 43 patients and 10 healthy controls matched for gender and age. The values found for the entropic q index were 0.2 for cerebrospinal fluid, 0.1 for white matter and 1.5 for gray matter. With this algorithm, we could detect an annual loss of 0.98% for the patients, in agreement with literature data. Thus, we can conclude that the entropy of Tsallis adds advantages to the process of automatic target segmentation of tissue classes, which had not been demonstrated previously.