Dongmei An

Surgical failure in temporal lobe epilepsy with hippocampal sclerosis (TLE‐HS) may reflect insufficient disruption of epileptogenic networks. We investigated whether integrating quantitative spatial patterns of regional atrophy and hypometabolism, along with clinical features, could provide complementary prognostic information for postoperative seizure freedom. T1‐weighted MRI, ¹⁸ F‐FDG‐PET, and postoperative CT scans of patients with TLE‐HS were retrospectively analyzed against a validated healthy control cohort to compute age‐ and gender‐adjusted W ‐score maps. Regional relationships of atrophy and hypometabolism were assessed using bivariate correlations and multiple regression analyses. Machine learning models integrating laterality of seizure onset, preoperative (extra‐)temporal atrophy/hypometabolism extent, seizure frequency, focal to bilateral tonic–clonic seizures during the preceding year, and resection volume were developed to predict seizure freedom 1 year after surgery. Model interpretability was assessed via permutation‐based variable importance analysis. The cohort comprised 101 patients with TLE‐HS (48 left, LHS; 53 right, RHS), including 72 patients who underwent anterior temporal lobectomy. Distinct modality‐specific patterns emerged: hypometabolism was significantly greater than local atrophy in left temporo‐limbic cortex in TLE‐LHS, while atrophy exceeded hypometabolism in bilateral occipital regions in TLE‐RHS. Positive correlations between local atrophy and hypometabolism were more robust in ipsilateral temporo‐limbic cortex in TLE‐LHS. The combined model integrating preoperative atrophy and hypometabolism features (AUC: 0.56–0.64) significantly outperformed single‐modality models ( p _fdr < 0.05). Notably, incorporating clinical factors further enhanced predictive performance (AUC: 0.63–0.70) and model calibration. This comprehensive preoperative clinical‐imaging model achieved robust prognostic efficacy that was not significantly improved by the addition of actual postoperative resection volumes. Structural MRI and ¹⁸ F‐FDG‐PET reveal complementary facets of the epileptogenic network in TLE‐HS. While integrating multimodal imaging with clinical metrics demonstrates significant synergistic potential for predicting surgical outcomes, our models remain exploratory. External validation in independent multi‐center cohorts is required before translating these findings into precise clinical tools for surgical planning.

Objective: Temporal lobe epilepsy (TLE) is the most common focal epilepsy but remains highly heterogeneous across hemispheric and structural etiology. This study aimed to characterize microstate‐based network dynamics in TLE and evaluate their diagnostic value for seizure lateralization and structural etiology using machine learning. Methods: Resting‐state electroencephalography (EEG) recordings from 150 patients with unilateral TLE (71 right, 79 left) and 65 healthy controls (HCs) were analyzed. EEG signals were segmented into canonical microstates (A, B, C, D), and microstate‐specific spatial, and temporal dynamic functional connectivity (dFC) variability metrics were extracted using phase lag index analysis. After two‐step feature selection, the appropriate number of features were derived and input into Random Forest, XGBoost, and Support Vector Machine (SVM) classifiers to distinguish: TLE vs HCs, left vs right TLE, and magnetic resonance imaging (MRI)‐negative (MRI‐neg) vs hippocampal sclerosis (HS) TLE subtypes (TLE‐HS). Model performance was evaluated on independent hold‐out validation set using receiver operating characteristic analyses. Results: Compared with HCs, patients with TLE exhibited increased duration and occurrence of microstate D and reduced expression microstate B, reflecting maladaptive attentional overactivation and visual suppression. Spatial variability was globally decreased, most prominently in left TLE. SVM achieved excellent performance for TLE detection (area under the curve [AUC] = .98) and lateralization (AUC = .97), whereas classification between MRI‐neg TLE and TLE‐HS was limited (AUC = .58). Significance: EEG microstate‐derived dFC metrics provide reliable, non‐invasive biomarkers for identifying and lateralizing TLE using short duration resting‐state EEG recordings. This framework advances understanding of TLE heterogeneity and supports the development of individualized electrophysiological tools for precision diagnosis.

This meta-analysis aims to investigate the convergent gray matter alterations observed in idiopathic generalized epilepsy through voxel-based morphometry studies. Voxel-based morphometry studies investigating the whole-brain gray matter alterations between patients with idiopathic generalized epilepsy and healthy controls were systematically searched. A coordinate-based meta-analysis of gray matter alterations was performed utilizing the anisotropic effect size–based signed differential mapping. Subgroup analyses were also conducted to further validate these findings and identify specificities. Twenty-two studies including 913 idiopathic generalized epilepsy patients and 887 healthy controls were enrolled. Patients with idiopathic generalized epilepsy exhibited increased gray matter in the right cuneus cortex, precuneus, superior frontal gyrus, middle frontal gyrus, parahippocampal gyrus, and fusiform gyrus, while exhibiting decreased gray matter in the bilateral thalami, left caudate nucleus, and right insula. Subgroup analyses revealed overlapping gray matter alterations in the bilateral thalami, with specific involvement of frontal lobe and insula in juvenile myoclonic epilepsy, right superior temporal gyrus in generalized tonic–clonic seizures alone, and bilateral paracentral lobules in absence epilepsy. This meta-analysis identified that both cortical and subcortical structural abnormalities especially thalamo-cortical circuit was involved in patients with idiopathic generalized epilepsy, while overlapped and disassociated gray matter alterations were identified across different idiopathic generalized epilepsy subsyndromes.

Objective: To identify the Electroencephalogram (EEG) microstate characteristics that can distinguish between patients with first unprovoked seizure (FUS) and newly diagnosed epilepsy (NDE), providing insight into predicting the progress of FUS to NDE, and to find the predictive biomarkers for the responsiveness to initial antiseizure medication (ASM) therapy.Methods: Fifty‐six NDE patients in a drug naïve state, 26 FUS patients, and 31 healthy controls (HCs) were compared on microstates features of 21‐channel resting‐state EEG without artifact. Four classic EEG microstates (A, B, C and D) were derived. The global explained variance (GEV), mean duration (MD), time coverage (TC), and frequency of occurrence (FO) of each microstate were calculated.Results: NDE and FUS patients exhibited decreased MD, TC, and FO in microstate C compared to the HCs. The FUS patients showed decreased MD, TC, and FO in microstate A compared to the NDE patients. Non‐seizure free (NSF) patients showed longer MD, higher TC, and FO in microstate B compared to the seizure free (SF) patients.Significance: EEG microstate serves as electrophysiological markers that can distinguish between patients with FUS and NDE. Additionally, EEG microstate parameters can also serve as the predictive biomarkers for the responsiveness to initial ASM therapy.

Objective: Temporal lobe epilepsy ( TLE ) has been recognized as a network disorder with widespread gray matter atrophy. However, the role of connectome architecture in shaping morphological alterations and identifying atrophy epicenters remains unclear. Furthermore, individualized modeling of atrophy epicenters and their potential clinical applications have not been well established. This study aims to explore how gray matter atrophy correlates with normal connectome architecture, identify potential atrophy epicenters, and employ individualized modeling approach to evaluate the impact of different epicenter patterns on surgical outcomes in patients with TLE . Methods: This study utilized anatomic MRI data from 126 refractory TLE patients who underwent anterior temporal lobectomy and 60 healthy controls ( HCs ), along with normative functional and structural connectome data, to investigate the relationship between gray matter volume ( GMV ) changes and functional or structural connectivity. Two models were employed to identify atrophy epicenters: a data‐driven approach evaluating nodal and neighbor atrophy rankings, and a network diffusion model ( NDM ) simulating the spread of pathology from different seed regions. K‐means clustering was applied in patient‐tailored modeling to uncover distinct epicenter subtypes. Results: Our findings indicate that the pattern of gray matter atrophy in TLE is constrained primarily by structural connectivity rather than by functional connectivity. Using the structural connectome, we pinpointed the hippocampus and adjacent temporo‐limbic regions as key atrophy epicenters. The patient‐tailored modeling revealed significant variability in epicenter distribution, allowing us to categorize them into two distinct subtypes. Notably, patients in subtype 2, with epicenters localized to the ipsilateral temporal pole and medial temporal lobe, exhibited significantly higher seizure‐free rates compared to patients in subtype 1, whose epicenters situated in frontocentral regions. Significance: These findings highlight the central role of structural connectivity in shaping TLE ‐related morphological changes. Individualized epicenter modeling may enhance surgical decisions and improve prognostic stratification in TLE management.