To the Editor: As idiopathic generalized epilepsies (IGEs), juvenile myoclonic and absence epilepsies (JME and JAE) typically manifest during adolescence. These disorders share key clinical features like adolescent-onset seizures and high relapse rates after the withdrawal of antiepileptic drug therapy.1 It has been believed that the thalamocortical system participates in the imbalance of cortical excitation and inhibition of IGEs.2 However, less is known about whether these abnormalities in thalamocortical circuits are specific to subsyndromes. Moreover, no research has explored whole-brain functional connectivity (FC) by a graph theory approach in IGEs focused on patients with JME and JAE. Magnetic resonance imaging (MRI) images were obtained by use of standard sequences with a Siemens 3T MRI system (Skyra, Siemens, Erlangen, Germany). The system has a head coil with 12 channels. Foam pads were used for reducing head movement, and earplugs were used for reducing the noise of the scanner. Brain functional images were got using gradient-echo T2*-weighted echo planar imaging: 30 5-mm-thick slices, 205 volumes, a repetition time of 2000 ms, an echo time of 30 ms, a field-of-view of 24 cm × 24 cm, a matrix of 64 × 64, an in-plane resolution of 2 × 2 mm, and a flip angle of 90°. Blood oxygen level-dependent functional MRI (fMRI) data were gathered in consecutive runs of 410 s. In the same session, T1-weighted structural brain images were acquired with a three-dimensional spoiled gradient-recalled sequence: 176 axial slices (a thickness of 1 mm, a repetition time of 1900 ms, a echo time of 2.26 ms, a field-of-view of 256 × 256 mm2, a flip angle of 9°, and a matrix of 320 × 320). During MRI scanning, a 32-channel electroencephalogram (EEG) device (EBNeuro Mizar 40, Florence, Tuscany, Italy) with a sampling rate of 4 kHz was applied to record synchronous EEG. BE-MRI Toolbox (La Sapienza, Rome, Italy) was adopted to filter magnetic resonance (MR) artifacts. Two professional epileptologists would identify interictal generalized spike-and-wave discharges (GSWDs) occurring during scanning. The subsequent analysis gave up entire MRI sessions with interictal GSWDs to avoid the impact of interictal epileptic discharges. In the case of a seizure during fMRI scanning, postictal effects were avoided by discarding the EEG-fMRI scanning of the next session. The same patient was required to be scanned again if seizures or spikes were recorded in a session. Only one session without seizures or spikes for participants was conducted. They were matched for every patient and control. DPABI V3.1 (http://rfmri.org/dpabi) and SPM8 software package (http://www.fil.ion.ucl.ac.uk/spm) were leveraged to preprocess structural and functional images. All participants had a consistent volume of data. The first 10 volumes of fMRI data were cancelled. Next, the steps below were taken: slice timing was corrected. All functional volumes were realigned first, and then functional volumes and the T1-volume were coregistered. Each voxel was realigned with the relevant T1-volume, spatially normalized to the space of the Montreal Neurological Institute (MNI), resampled to 3 × 3 × 3 mm3, smoothed by use of a 4-mm Gaussian kernel, and filtered using a band-pass filter (0.01–0.08 Hz). In addition, the confounding of head movement was reduced using Friston’s 24-parameter model. Multiple linear regression analysis was used for regressing out nuisance covariates like cerebrospinal fluid signals as well as white matter. Subjects whose maximum displacement was greater than 2 mm or rotation was above 2° were not included. This study was exclusive of participants with an average framewise displacement (FD) of over 0.2 mm. To explore whether specific thalamocortical circuit alterations exist between JME and JAE, the FC analysis took the 16 bilateral thalamic subregions drawn from human subcortex atlases3 as seeds. Seed regions of interest (ROIs) are detailed in Supplementary Figure 1, https://links.lww.com/CM9/C751. The seed-based correlation approach was carried out based on DPABI software. Pearson correlation coefficients between all the other voxels in a gray matter mask and the average time series of every seed region were calculated. Later, Fisher’s r-to-z transformation was utilized for converting all correlation maps into z-value ones to raise the normality of correlation distribution. At last, z-FC maps of subjects were obtained for group statistics. A graph theory analysis was conducted through the GRETNA 2.0.0 toolbox (https://www.nitrc.org/projects/gretna/) to investigate whole-brain FC measures. Nodes for whole-brain analysis were defined by parcellating the brain into 160 ROIs in accordance with a functionally defined Dosenbach’s atlas. Pairwise Pearson correlation coefficients among all of these ROIs were calculated, and the results were converted by using Fisher’s Z-transformation. To estimate network metrics, multiple sparsity thresholds from 5% to 40% with an interval of 0.01 were used. Two topological organization parameters, global efficiency and small-worldness, were chosen. They are the most representative of the functional integration of a network. An analysis was made of the area under the curve (AUC) for every network metric with superior sensitivity. One-way analysis of covariance (ANCOVA) was used for comparing age, whereas Kruskal–Wallis analysis of variance (ANOVA) was used for comparing sex among JME, JAE, and healthy control (HC) groups. Disease duration and onset age between JME and JAE were compared using a two-sample t-test. ANCOVA was adopted to examine group effects on FC among epilepsy groups and HCs. Gender, age, and FD values were covariates. Post hoc analysis was performed on significant group effects identified by ANCOVA. The Tukey–Kramer honestly significant difference correction method was used to make multiple comparisons (corrected P 0.05, with FDR correction). A few limitations can be found in this study. Validation in larger cohorts with longitudinal follow-up is warranted due to the modest sample size. It is necessary to consider residual confounding from antiepileptic drugs despite the inclusion of 13 drug-naive patients. While preprocessing mitigated motion effects, some residual influence remains. The focus on thalamocortical circuits and whole-brain topology excluded other potentially relevant networks. Altered thalamic connectivity was not significantly correlated with clinical variables across groups. Notably, the findings from seed-based connectivity analyses showed limited convergence with graph-theoretical approaches. Longitudinal studies are needed to establish clinical correlations. This study attempts to evaluate subtype specific thalamic-cortical circuitry and whole-brain topologic organization in IGE at the seed-based level and the whole-brain scale. Our results support the notion that both JME and JAE are associated with thalamo-cortical network abnormalities but with preserved global functional architecture. Specifically, both the JME and JAE group exhibited decreased FC in thalamic-cerebellar circuits and prefronto-thalamic network. The difference of thalamocortical circuit between JME and JAE was in striatal-thalamic circuit. We highlighted the convergence and divergence of thalamocortical circuit dysfunctions in patients with JME and JAE, which may provide crucial insights into pathophysiological mechanisms of the two IGE subtypes. Conflicts of interest None.
Yang et al. (Thu,) studied this question.