Whole-brain MRI phenotyping in dysplasia-related frontal lobe epilepsy

Abstract

Objective: To perform whole-brain morphometry in patients with frontal lobe epilepsy and evaluate the utility of group-level patterns for individualized diagnosis and prognosis.Methods: We compared MRI-based cortical thickness and folding complexity between 2 frontal lobe epilepsy cohorts with histologically verified focal cortical dysplasia (FCD) (13 type I; 28 type II) and 41 closely matched controls. Pattern learning algorithms evaluated the utility of group-level findings to predict histologic FCD subtype, the side of the seizure focus, and postsurgical seizure outcome in single individuals. Focal cortical dysplasia (FCD) is a frequent epileptogenic developmental malformation in children and adults undergoing epilepsy surgery. Results1 While FCD type II combining cytological anomalies with varying degrees of dyslamination 2 is generally associated with obvious morphology and signal changes on MRI, 3 the imaging signature of FCD type I, characterized by subtle cortical thinning and dyslamination, 2 remains elusive. Because of the crucial role of MRI in defining the surgical target, imaging studies in FCD have been primarily dedicated to lesion detection in single patients. 4 Whole-brain cohortspecific structural brain anomalies remain largely unknown. 5 In other epilepsy syndromes, group-based designs have provided new insights by unveiling clinically relevant characteristics and helped in formulating hypotheses about disease mechanisms. 6,7 In this study, we aimed to compare whole-brain morphology between frontal lobe epilepsy cohorts with histologically verified FCD type I and II. Our approach was motivated by case reports indicating that FCD type II may present with histologic 8 and MRI anomalies 9,10 in remote cortices resembling those found in the primary lesion and that type I may be associated with subtle multilobar hypoplasia on MRI. 11We hypothesized widespread, yet diverging patterns of anomalies, with cortical thinning in FCD type I and thickening in type II. Our MRI phenotyping combined group-and *These authors have contributed equally to this work. From the Neuroimaging

Cortical thickness asymmetries and surgical outcome in neocortical epilepsy

“…Theoretically, lower values on the affected side (epileptic hemisphere) may occur not only by ipsilateral cortical thinning but also due to higher values (cortical thickening) in the contralateral cortex. However, most previous studies (discussed in detail below) found cortical thinning in various groups with focal epilepsy; contralateral cortical thickening was recently reported in frontal lobe epilepsy associated with type II cortical dysplasia [14], a pathology present only in one of our 21 patients. Therefore, it is unlikely that the observed, prognostic asymmetries were due to contralateral cortical thickening in our patient cohort.…”

“…An earlier study of patients with temporal lobe epilepsy demonstrated that cortical thinning in distinct brain regions, even outside the presumed epileptogenic zone, is associated with poor surgical outcome [11]. In a recent study of adults with frontal lobe epilepsy, frontal cortical thinning was associated with poor surgical outcome in those with type I dysplasia [14]. However, the relationship between cortical thickness and surgical outcome has not been evaluated in patients with pediatric neocortical epilepsy and non-localizing conventional MRI.…”

Change in emotional self‐concept following socio‐cognitive training relates to structural plasticity of the prefrontal cortex

“…In line with the above, the data described in our manuscript using a CFT/CDT of p  < .025 with p  < .05 for family‐wise‐error (FWE) correction (two‐tailed) should have resulted in false‐positive rates no larger than 5%–10%. The applied thresholds furthermore conform to previous research on other (Andrews et al., 2017; Bernhardt et al., 2013; Ecker et al., 2013; Hong et al., 2016) as well as the same data set (Valk et al., 2017), particularly for 20 mm FWHM smoothed surface‐based 2D thickness data where higher smoothing kernels relate to more readily fulfilled assumptions of Gaussian Random Field Theory (Eklund et al., 2016; Flandin & Friston, 2016; Greve & Fischl, 2017). In addition, the effects observed at the above thresholds within the three brain areas of interest are significant for each training cohort separately as evident from post hoc analyses—except for one association at a marginal level of p  = .054.…”

“…Statistical results were corrected for multiple comparisons by means of random field theory using both a typically used (Andrews et al., 2017; Bernhardt et al., 2013; Ecker et al., 2013; Hong, Bernhardt, Schrader, Bernasconi, & Bernasconi, 2016) cluster‐determining threshold (CDT) of p  < .025 and FWE of p  < .05 (two‐tailed) for 20 mm FWHM smoothed surface‐based 2D thickness data where higher smoothing kernels relate to more readily fulfilled assumptions of Gaussian Random Field Theory (Eklund, Nichols, & Knutsson, 2016; Flandin & Friston, 2016; Greve & Fischl, 2017), as well as a more conservative cluster‐determining threshold recently recommended for the analysis of surface‐based anatomical cortical thickness data (Greve & Fischl, 2017) of CDT p  < .01 and FWE p  < .02 (two‐tailed). Moreover, we verified consistency of results across the two separate training cohorts.…”

Impact of advanced parallel or cloud computing technologies for image guided diagnosis and therapy

“…And also been shown to applicable in general epilepsy patients [19]. What's more, specifically, image guidance diagnosis has been used in frontal lobe epilepsy patients [20]. However, image guidance has never been used in a more refined and crucial areas such as the supplementary sensorimotor area (SSMA).…”