Synthetic aperture magnetometry and excess kurtosis mapping of Magnetoencephalography (MEG) is predictive of epilepsy surgical outcome in a large pediatric cohort.

OBJECTIVE: Resective surgery is the most effective treatment option for patients with refractory epilepsy; however identification of patients who will benefit from epilepsy surgery remains challenging. Synthetic aperture magnetometry and excess kurtosis mapping (SAM(g2)) of magnetoencephalography (MEG) is a non-invasive tool that warrants further examination in the pediatric epilepsy population. Here, we examined the utility of MEG with SAM(g2) to determine if MEG epileptiform foci correlates with surgical outcome and to develop a predictive model incorporating MEG information to best assess likelihood of seizure improvement/freedom from resective surgery. METHODS: 564 subjects who had MEG at the Children's Hospital of Philadelphia between 2010-2015 were screened. Clinical epilepsy history and prior electrographic records were extracted and reviewed and correlated with MEG findings. MEG assessments were made by both a neurologist and neuroradiologist. Predictive models were developed to assess the utility of MEG in determining Engel class at one year and five years after resective epilepsy surgery. RESULTS: The number of MEG spike foci was highly associated with Engel class outcome at both one year and five years; however, using MEG data in isolation was not significantly predictive of 5 year surgical outcome. When combined with clinical factors; scalp EEG (single ictal onset zone), MRI (lesional or not), age and sex in a logistic regression model MEG foci was significant for Engel class outcome at both 1 year (p = 0.03) and 5 years (0.02). The percent correctly classified for Engel class at one year was 78.43% and the positive predictive value was 71.43. SIGNIFICANCE: MEG using SAM(g2) analysis in an important non-invasive tool in the identification of those patients who will benefit most from surgery. Integrating MEG data analysis into pre-surgical evaluation can help to predict epilepsy outcome after resective surgery in the pediatric population if utilized with skilled interpretation.

GABA estimation in the brains of children on the autism spectrum: measurement precision and regional cortical variation.

(1)H magnetic resonance spectroscopy ((1)H MRS) and spectral editing methods, such as MEGA-PRESS, allow researchers to investigate metabolite and neurotransmitter concentrations in-vivo. Here we address the utilization of (1)H MRS for the investigation of GABA concentrations in the ASD brain, in three locations; motor, visual and auditory areas. An initial repeatability study (5 subjects, 5 repeated measures separated by ~5days on average) indicated no significant effect of reference metabolite choice on GABA quantitation (p>0.6). Coefficients of variation for GABA+/NAA, GABA+/Cr and GABA+/Glx were all of the order of 9-11%. Based on these findings, we investigated creatine-normalized GABA+ ratios (GABA+/Cr) in a group of (N=17) children with autism spectrum disorder (ASD) and (N=17) typically developing children (TD) for Motor, Auditory and Visual regions of interest (ROIs). Linear regression analysis of gray matter (GM) volume changes (known to occur with development) revealed a significant decrease of GM volume with Age for Motor (F(1,30)=17.92; p<0.001) and Visual F(1,16)=14.41; p<0.005 but not the Auditory ROI (p=0.55). Inspection of GABA+/Cr changes with Age revealed a marginally significant change for the Motor ROI only (F(1,30)=4.11; p=0.054). Subsequent analyses were thus conducted for each ROI separately using Age and GM volume as covariates. No group differences in GABA+/Cr were observed for the Visual ROI between TD vs. ASD children. However, the Motor and Auditory ROI showed significantly reduced GABA+/Cr in ASD (Motor p<0.05; Auditory p<0.01). The mean deficiency in GABA+/Cr from the Motor ROI was approximately 11% and Auditory ROI was approximately 22%. Our novel findings support the model of regional differences in GABA+/Cr in the ASD brain, primarily in Auditory and to a lesser extent Motor but not Visual areas.

Evidence for a motor gamma-band network governing response interference.

The gamma-band response is thought to represent a key neural signature of information processing in the human brain. These brain signals have been associated with a variety of sensory modalities (vision, sensation, and audition) and also following basic motor responses, yet the functional significance of the motor gamma-band response remains unclear. We used the Multi-Source Interference Task (MSIT) to assess the sensitivity of these cortical motor gamma-band rhythms to stimuli producing response interference. We recorded MEG from adult participants (N=24) during MSIT task performance and compared motor gamma-band activity on Control and Interference trials. Reaction time on MSIT Interference trials was significantly longer (~0.2 s) for all subjects. Response interference produced a significant increase in motor gamma-band activity including ~0.5 s sustained increase in gamma-band activity from contralateral primary motor area directly preceding the response. In addition, activation of increased right Inferior Frontal Gyrus (R-IFG) was observed at gamma-band frequencies ~0.2 s prior to the button press response. Post-hoc analysis of R-IFG gamma-band activity was observed to correlate with reaction time increases to response interference. Our study is the first to record MEG during MSIT task performance. We observed novel activity of the motor gamma-band on interference trials which was sustained prior to the response and in novel locations including contralateral (BA6), and R-IFG. Our results support the idea that R-IFG is specialized structure for response control that also functions at gamma-band frequencies. Together, these data provide evidence for a motor gamma-band network for response selection and maintenance of planned behavior.

Unsupervised white matter fiber clustering and tract probability map generation: applications of a Gaussian process framework for white matter fibers.

With the increasing importance of fiber tracking in diffusion tensor images for clinical needs, there has been a growing demand for an objective mathematical framework to perform quantitative analysis of white matter fiber bundles incorporating their underlying physical significance. This article presents such a novel mathematical framework that facilitates mathematical operations between tracts using an inner product between fibres. Such inner product operation, based on Gaussian processes, spans a metric space. This metric facilitates combination of fiber tracts, rendering operations like tract membership to a bundle or bundle similarity simple. Based on this framework, we have designed an automated unsupervised atlas-based clustering method that does not require manual initialization nor an a priori knowledge of the number of clusters. Quantitative analysis can now be performed on the clustered tract volumes across subjects, thereby avoiding the need for point parameterization of these fibers, or the use of medial or envelope representations as in previous work. Experiments on synthetic data demonstrate the mathematical operations. Subsequently, the applicability of the unsupervised clustering framework has been demonstrated on a 21-subject dataset.