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

Pizarro, Ricardo; Nair, Veena A.; Meier, Timothy B.; Holdsworth, Ryan; Tunnell, Evelyn; Rutecki, Paul; Sillay, Karl; Meyerand, M. Elizabeth; Prabhakaran, Vivek

doi:10.1080/13554794.2016.1195845

Cited by 4 publications

(5 citation statements)

References 29 publications

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“…Currently, the most specific clinical method to pinpoint the seizure source consists of using volume conduction to model ECoG potentials recorded over the suspected cortical area [48, 49, 50]. The neurosurgeon first identifies the electrode recording abnormal activity, then proceeds with the removal of the underlying cortical region.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2017

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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.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2017

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“…Measurements from MRI images can be applied for the identification of epileptic regions and size estimation for clinical reference ( 34-36 ). Resting functional MRI data analysis using temporal clustering is an efficient method, which can assist clinical practice in characterizing the epileptogenic network in patients with epilepsy ( 37 ). In the present study, Dce-MRI exhibited higher diagnostic sensitivity and sensitivity for epilepsy compared with MRI.…”

Section: Discussionmentioning

confidence: 99%

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

Du¹,

Guan²,

Wang³

et al. 2020

Exp Ther Med

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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.

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“…These results are promising for future applications in diagnosing and understanding the basic pathophysiology of TLE. Currently TLE diagnosis is made with a combination of EEG, MEG, structural MRI, and/or single-photon emission computed tomography/PET in conjunction with clinical phenomenology, but in many cases, convincing evidence is difficult to capture noninvasively, and more invasive approaches such as subdural grids or depth electrodes are used (Pizarro et al, 2016). Diagnosis of TLE with an rs-fMRI scan could provide valuable adjunctive evidence and might eventually replace some current clinical tests with a more cost-effective method.…”

Section: Fig 4 Fifteen Significantmentioning

confidence: 99%

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

et al. 2019

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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 Free-Surfer 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.

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

Cited by 4 publications

References 29 publications

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

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

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

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

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