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Distributed source modeling of language with magnetoencephalography: application to patients with intractable epilepsy.

ABSTRACT: To examine distributed patterns of language processing in healthy controls and patients with epilepsy using magnetoencephalography (MEG), and to evaluate the concordance between laterality of distributed MEG sources and language laterality as determined by the intracarotid amobarbital procedure (IAP).MEG was performed in 10 healthy controls using an anatomically constrained, noise-normalized distributed source solution (dynamic statistical parametric map, dSPM). Distributed source modeling of language was then applied to eight patients with intractable epilepsy. Average source strengths within temporoparietal and frontal lobe regions of interest (ROIs) were calculated, and the laterality of activity within ROIs during discrete time windows was compared to results from the IAP.In healthy controls, dSPM revealed activity in visual cortex bilaterally from approximately 80 to 120 ms in response to novel words and sensory control stimuli (i.e., false fonts). Activity then spread to fusiform cortex approximately 160-200 ms, and was dominated by left hemisphere activity in response to novel words. From approximately 240 to 450 ms, novel words produced activity that was left-lateralized in frontal and temporal lobe regions, including anterior and inferior temporal, temporal pole, and pars opercularis, as well as bilaterally in posterior superior temporal cortex. Analysis of patient data with dSPM demonstrated that from 350 to 450 ms, laterality of temporoparietal sources agreed with the IAP 75% of the time, whereas laterality of frontal MEG sources agreed with the IAP in all eight patients.Our results reveal that dSPM can unveil the timing and spatial extent of language processes in patients with epilepsy and may enhance knowledge of language lateralization and localization for use in preoperative planning.

SUBMITTER: McDonald CR

PROVIDER: S-EPMC2760088 | biostudies-literature | 2009 Oct

REPOSITORIES: biostudies-literature

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Distributed source modeling of language with magnetoencephalography: application to patients with intractable epilepsy.

McDonald Carrie R CR Thesen Thomas T Hagler Donald J DJ Carlson Chad C Devinksy Orrin O Kuzniecky Rubin R Barr William W Gharapetian Lusineh L Trongnetrpunya Amy A Dale Anders M AM Halgren Eric E

Epilepsia 20090622 10

<h4>Purpose</h4>To examine distributed patterns of language processing in healthy controls and patients with epilepsy using magnetoencephalography (MEG), and to evaluate the concordance between laterality of distributed MEG sources and language laterality as determined by the intracarotid amobarbital procedure (IAP).<h4>Methods</h4>MEG was performed in 10 healthy controls using an anatomically constrained, noise-normalized distributed source solution (dynamic statistical parametric map, dSPM). D ...[more]

PMID: 19552656

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Project description:Objective: Magnetoencephalography source imaging (MSI) of interictal epileptiform discharges (IED) is a useful presurgical tool in the evaluation of drug-resistant frontal lobe epilepsy (FLE) patients. Yet, failures in MSI can arise related to artifacts and to interference of background activity. Independent component analysis (ICA) is a popular denoising procedure but its clinical application remains challenging, as the selection of multiple independent components (IC) is controversial, operator dependent, and time consuming. We evaluated whether selecting only one IC of interest based on its similarity with the average IED field improves MSI in FLE. Methods: MSI was performed with the equivalent current dipole (ECD) technique and two distributed magnetic source imaging (dMSI) approaches: minimum norm estimate (MNE) and coherent Maximum Entropy on the Mean (cMEM). MSI accuracy was evaluated under three conditions: (1) ICA of continuous data (Cont_ICA), (2) ICA at the time of IED (IED_ICA), and (3) without ICA (No_ICA). Localization performance was quantitatively measured as actual distance of the source maximum in relation to the focus (Dmin), and spatial dispersion (SD) for dMSI. Results: After ICA, ECD Dmin did not change significantly (p > 0.200). For both dMSI techniques, ICA application worsened the source localization accuracy. We observed a worsening of both MNE Dmin (p < 0.05, consistently) and MNE SD (p < 0.001, consistently) for both ICA approaches. A similar behaviour was observed for cMEM, for which, however, Cont_ICA seemed less detrimental. Conclusion: We demonstrated that a simplified ICA approach selecting one IC of interest in combination with distributed magnetic source imaging can be detrimental. More complex approaches may provide better results but would be rather difficult to apply in real-world clinical setting. In a broader perspective, caution should be taken in applying ICA for source localization of interictal activity. To ensure optimal and useful results, effort should focus on acquiring good quality data, minimizing artifacts, and determining optimal candidacy for MEG, rather than counting on data cleaning techniques.

Dataset Information

Distributed source modeling of language with magnetoencephalography: application to patients with intractable epilepsy.

Publications

Distributed source modeling of language with magnetoencephalography: application to patients with intractable epilepsy.

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