Delineating potential epileptogenic areas utilizing resting functional magnetic resonance imaging (fMRI) in epilepsy patients

No change in network connectivity measurements between separate rsfMRI acquisition times

The role of resting state functional MRI (rsfMRI) is increasing in the field of epilepsy surgery because it is possible to interpolate network connectivity patterns across the brain with a high degree of spatial resolution. Prior studies have shown that by rsfMRI with scalp electroencephalography (EEG), an epileptogenic network can be modeled and visualized with characteristic patterns of connectivity that are relevant to both seizure-related and neuropsychological outcomes after surgery. The aim of this study is to show that a 5-min acquisition time provides reproducible results related to the relevant connectivity metrics when compared to a separately acquired 5-min scan. Fourteen separate rsfMRI sessions from ten different patients were used for comparison, comprised of patients with temporal lobe epilepsy both pre- and post-operation. Results showed that there was no significant difference in any of the connectivity metrics when comparing both 5-min scans to each other. These data support the continued use of a 5-min scan for epileptogenic network modeling in future studies because the inter-scan variability is sufficiently low as not to alter the output metrics characterizing the network connectivity.

Preoperative evaluation of dynamic contrast-enhanced MRI-guided lesion identification using morphometric and textural analysis for patients with epilepsy

Diagnosing epilepsy at the early stages is pivotal in the prevention and subsequent treatment of major epileptic events. MRI has been previously demonstrated to be beneficial in optimizing diagnostic efficacy and the subsequent treatment of epilepsy. In the present study, morphometric and textural analysis was performed pre-operatively on dynamic contrast-enhanced (Dce)-magnetic resonance imaging (MRI)-guided lesions in patients with epilepsy. The diagnostic efficacies of MRI and Dce-MRI were evaluated in 280 patients with epilepsy. The performance of Dce-MRI (n=140) was compared with that of classic MRI (n=140) in the analysis of the morphometric and textural features of the lesions, and the accuracy of mapping to regions of the brain that were potentially associated with the region of seizure onset was also investigated. Diagnostic quality was evaluated by comparing the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the hippocampus, the grey-white matter contrast and the morphometric and textural analysis of epileptogenic lesions. Data from the present study demonstrated that the regions of suspected epileptic activity in the brain were mapped more successfully using Dce-MRI compared with MRI. Image quality obtained by Dce-MRI was adequate for the detection of epileptic lesions, including those of focal cortical dysplasia or periventricular nodular heterotopia (PNH). The diagnostic value of Dce-MRI for subtle lesions such as PNH was found to be more efficient compared with that of MRI due to the significantly increased SNR and CNR. In addition, Dce-MRI exhibited higher accuracy compared with MRI for the identification of cortical lesions and for the mapping to the zone of suspected seizure onset. Dce-MRI also exhibited higher sensitivity and specificity in the diagnosis of patients with epilepsy and mapping to the lesions associated with epilepsy compared with MRI. These findings indicate that Dce-MRI is a feasible technique that may be beneficial in the diagnosis and subsequent management of patients with epilepsy.

Using Low-Frequency Oscillations to Detect Temporal Lobe Epilepsy with Machine Learning

The National Institutes of Health-sponsored Epilepsy Connectome Project aims to characterize connectivity changes in temporal lobe epilepsy (TLE) patients. The magnetic resonance imaging protocol follows that used in the Human Connectome Project, and includes 20 min of resting-state functional magnetic resonance imaging acquired at 3T using 8-band multiband imaging. Glasser parcellation atlas was combined with the FreeSurfer subcortical regions to generate resting-state functional connectivity (RSFC), amplitude of low-frequency fluctuations (ALFFs), and fractional ALFF measures. Seven different frequency ranges such as Slow-5 (0.01-0.027 Hz) and Slow-4 (0.027-0.073 Hz) were selected to compute these measures. The goal was to train machine learning classification models to discriminate TLE patients from healthy controls, and to determine which combination of the resting state measure and frequency range produced the best classification model. The samples included age- and gender-matched groups of 60 TLE patients and 59 healthy controls. Three traditional machine learning models were trained: support vector machine, linear discriminant analysis, and naive Bayes classifier. The highest classification accuracy was obtained using RSFC measures in the Slow-4 + 5 band (0.01-0.073 Hz) as features. Leave-one-out cross-validation accuracies were ∼83%, with receiver operating characteristic area-under-the-curve reaching close to 90%. Increased connectivity from right area posterior 9-46v in TLE patients contributed to the high accuracies. With increased sample sizes in the near future, better machine learning models will be trained not only to aid the diagnosis of TLE, but also as a tool to understand this brain disorder.

Estimating cortical column sensory networks in rodents from micro-electrocorticograph (μECoG) recordings

Micro-electrocorticograph (μECoG) arrays offer the flexibility to record local field potentials (LFPs) from the surface of the cortex, using high density electrodes that are sub-mm in diameter. Research to date has not provided conclusive evidence for the underlying signal generation of μECoG recorded LFPs, or if μECoG arrays can capture network activity from the cortex. We studied the pervading view of the LFP signal by exploring the spatial scale at which the LFP can be considered elemental. We investigated the underlying signal generation and ability to capture functional networks by implanting, μECoG arrays to record sensory-evoked potentials in four rats. The organization of the sensory cortex was studied by analyzing the sensory-evoked potentials with two distinct modeling techniques: (1) The volume conduction model, that models the electrode LFPs with an electrostatic representation, generated by a single cortical generator, and (2) the dynamic causal model (DCM), that models the electrode LFPs with a network model, whose activity is generated by multiple interacting cortical sources. The volume conduction approach modeled activity from electrodes separated < 1000 μm, with reasonable accuracy but a network model like DCM was required to accurately capture activity > 1500 μm. The extrinsic network component in DCM was determined to be essential for accurate modeling of observed potentials. These results all point to the presence of a sensory network, and that μECoG arrays are able to capture network activity in the neocortex. The estimated DCM network models the functional organization of the cortex, as signal generators for the μECoG recorded LFPs, and provides hypothesis-testing tools to explore the brain.