White Matter Abnormalities in Patients with Treatment-Resistant Genetic Generalized Epilepsies.

BACKGROUND Genetic generalized epilepsies (GGEs) are associated with microstructural brain abnormalities that can be evaluated with diffusion tensor imaging (DTI). Available studies on GGEs have conflicting results. Our primary goal was to compare the white matter structure in a cohort of patients with video/EEG-confirmed GGEs to healthy controls (HCs). Our secondary goal was to assess the potential effect of age at GGE onset on the white matter structure. MATERIAL AND METHODS A convenience sample of 23 patients with well-characterized treatment-resistant GGEs (13 female) was compared to 23 HCs. All participants received MRI at 3T. DTI indices, including fractional anisotropy (FA) and mean diffusivity (MD), were compared between groups using Tract-Based Spatial Statistics (TBSS). RESULTS After controlling for differences between groups, abnormalities in DTI parameters were observed in patients with GGEs, including decreases in functional anisotropy (FA) in the hemispheric (left>right) and brain stem white matter. The examination of the effect of age at GGE onset on the white matter integrity revealed a significant negative correlation in the left parietal white matter region FA (R=-0.504; p=0.017); similar trends were observed in the white matter underlying left motor cortex (R=-0.357; p=0.103) and left posterior limb of the internal capsule (R=-0.319; p=0.148). CONCLUSIONS Our study confirms the presence of widespread white matter abnormalities in patients with GGEs and provides evidence that the age at GGE onset may have an important effect on white matter integrity

Modulation of the thalamus by microburst vagus nerve stimulation: a feasibility study protocol

Vagus nerve stimulation (VNS) was the first device-based therapy for epilepsy, having launched in 1994 in Europe and 1997 in the United States. Since then, significant advances in the understanding of the mechanism of action of VNS and the central neurocircuitry that VNS modulates have impacted how the therapy is practically implemented. However, there has been little change to VNS stimulation parameters since the late 1990s. Short bursts of high frequency stimulation have been of increasing interest to other neuromodulation targets e.g., the spine, and these high frequency bursts elicit unique effects in the central nervous system, especially when applied to the vagus nerve. In the current study, we describe a protocol design that is aimed to assess the impact of high frequency bursts of stimulation, called “Microburst VNS”, in subjects with refractory focal and generalized epilepsies treated with this novel stimulation pattern in addition to standard anti-seizure medications. This protocol also employed an investigational, fMRI-guided titration protocol that permits personalized dosing of Microburst VNS among the treated population depending on the thalamic blood-oxygen-level-dependent signal. The study was registered on clinicaltrials.gov (NCT03446664). The first subject was enrolled in 2018 and the final results are expected in 2023

Cortical localizations of the 10 task-related independents components: for each component we presented the anatomical location, corresponding Brodmann area(s), and the maximum Z-score with its Talairach coordinates (obtained using the Talairach utility provided in GIFT toolbox on group-ICA components maps).

<p>Cortical localizations of the 10 task-related independents components: for each component we presented the anatomical location, corresponding Brodmann area(s), and the maximum Z-score with its Talairach coordinates (obtained using the Talairach utility provided in GIFT toolbox on group-ICA components maps).</p

Interhemispheric Plasticity following Intermittent Theta Burst Stimulation in Chronic Poststroke Aphasia

The effects of noninvasive neurostimulation on brain structure and function in chronic poststroke aphasia are poorly understood. We investigated the effects of intermittent theta burst stimulation (iTBS) applied to residual language-responsive cortex in chronic patients using functional and anatomical MRI data acquired before and after iTBS. Lateralization index (LI) analyses, along with comparisons of inferior frontal gyrus (IFG) activation and connectivity during covert verb generation, were used to assess changes in cortical language function. Voxel-based morphometry (VBM) was used to assess effects on regional grey matter (GM). LI analyses revealed a leftward shift in IFG activity after treatment. While left IFG activation increased, right IFG activation decreased. Changes in right to left IFG connectivity during covert verb generation also decreased after iTBS. Behavioral correlations revealed a negative relationship between changes in right IFG activation and improvements in fluency. While anatomical analyses did not reveal statistically significant changes in grey matter volume, the fMRI results provide evidence for changes in right and left IFG function after iTBS. The negative relationship between post-iTBS changes in right IFG activity during covert verb generation and improvements in fluency suggests that iTBS applied to residual left-hemispheric language areas may reduce contralateral responses related to language production and facilitate recruitment of residual language areas after stroke

A Model for Visual Memory Encoding - Figure 1

<p>A) Relations and directionality of the information flow between task-related ICs. Details regarding each component are provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107761#pone-0107761-t001" target="_blank">Table 1</a>. Each component was attributed to a particular network (See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107761#s4" target="_blank">discussion</a> section for a precise analysis). Each arrow is indicating a significant (p<0.05, FDR corrected) causal relation between two components. Component representations are in neurological convention (left hemisphere is on the left side of the image). B) Respective timecourse of components depicted in A)</p

Proposed model for visual memory encoding based on results obtained in <b>Figure 1</b>.

<p>A precise description of the model is provided in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107761#s4" target="_blank">Discussion</a> section.</p