Hiroatsu Murakami

Objective: Magnetoencephalography (MEG) is used widely in presurgical evaluation of medically intractable epilepsy. Prior research has documented the added value of MEG in refining surgical strategies; however, not all MEG clusters require resection for seizure freedom, suggesting that they represent nodes within a broader epileptic network, with differing importance for epileptogenic zone (EZ) localization. We examined whether MEG cluster characteristics can identify clusters most relevant to EZ. Methods: We studied 101 patients with focal epilepsy who underwent intracranial electroencephalography (ICEEG), resective surgery, and became seizure‐free at 1 year. MEG recordings were obtained with a 306‐channel system and analyzed using single equivalent current dipole (SECD) modeling. Clusters were categorized by anatomic location, tightness, and orientation and defined as “matched” with the EZ if ≥70% of dipoles were included in the resection. A sublobar anatomo‐functional scheme was used to map cluster–resection spatial relationships. Logistic regression and generalized linear mixed models with patient as a random effect evaluated age, sex, epilepsy duration, prior surgery, and cluster characteristics. Match rates were compared across pathology groups. Results: Clusters in frontal and interlobar junctional regions were less likely than temporal clusters to be matched ( p = .003 and p = .005). Clusters from patients with multiple clusters were less likely to be matched ( p < .001). Tight clusters were more likely matched than loose clusters ( p = .013). Cluster–resection mapping identified distinct propagation patterns, including anterior–posterior temporal and ipsilateral–contralateral opercular spread. Among 49 mismatched clusters sampled with ICEEG, 91% showed interictal discharges and 63% were involved in seizure propagation. Match rates did not differ by pathology. Significance: Our findings support incorporating detailed analysis of MEG cluster characteristics into presurgical evaluation to refine EZ localization, especially for invasive evaluation. Cluster–resection mapping patterns may assist surgical hypothesis generation. Matched clusters in mild malformation of cortical development and negative pathology cases further highlight MEG's value in challenging pathological substrates.

Objective: Surgical decision‐making in temporal lobe epilepsy (TLE) faces a critical challenge in determining whether the hippocampus can be safely spared during anterior temporal resection, particularly when surgery involves the language‐dominant hemisphere. We investigated whether presurgical network control metrics derived from magnetoencephalography (MEG) can differentiate patients who achieved seizure freedom with hippocampal resection (HR) from those who achieved seizure freedom with hippocampal sparing (HS). Methods: We analyzed presurgical spike‐free interictal MEG in 25 TLE patients with seizure freedom after anterior temporal resection (19 with hippocampal resection, 6 with sparing). Functional networks constructed from MEG data were partitioned into communities using a Louvain‐based consensus clustering algorithm. Within identified hippocampal‐related communities from each patient, we applied control centrality analysis to quantify the influence of each node on community synchronizability. Group differences were assessed using Fisher’s exact tests with false discovery rate (FDR) correction applied within each frequency band and hemisphere. Leave‐one‐subject‐out (LOSO) sensitivity analysis assessed the robustness of findings to individual subjects. Results: After FDR correction, synchronization of the contralateral anterior superior temporal sulcus (STS) in the HS group at beta band emerged as the primary finding ( p = .001, q = .02). This finding showed 100% stability in LOSO and persisted in a subgroup analysis excluding patients with hippocampal seizure onset ( p = .004). At p < .05, additional region‐frequency differences formed a coherent anatomic pattern: the HR group showed higher synchronization fractions within ipsilateral limbic structures, whereas the HS group showed higher synchronization in the ipsilateral posterior hippocampus and contralateral temporal regions with 100% LOSO stability. Significance: Presurgical MEG‐based network control analysis identified contralateral anterior STS synchronization as a candidate biomarker for hippocampal sparing eligibility in TLE. This proof‐of‐concept finding warrants prospective validation as potential tools to support individualized surgical planning beyond localization‐based approaches.

Objective: Magnetic resonance fingerprinting (MRF) is a novel paradigm for magnetic resonance imaging (MRI) that efficiently generates multiparametric quantitative tissue property maps with a single acquisition. Its quantitative nature offers many advantages over conventional MRI. Although interest in MRF for epilepsy research has grown in recent years, systematic evaluation of its radiological and clinical value as a supplementary imaging modality is limited. Methods: We reviewed the diagnostic utility of MRF imaging for 100 epilepsy patients undergoing presurgical evaluation. MRF quantitative maps (T1, T2, gray matter fraction, and white matter fraction) were generated and reviewed alongside conventional MRI. MRF findings were classified as (1) consistent—MRF findings the same as conventional MRI; (2) additional—MRF revealed findings beyond those seen on conventional MRI; or (3) discrepant—findings on conventional MRI not reproduced on MRF. We further stratified results by radiological diagnosis or pathology if available. Observations were further classified based on whether T1 or T2 MRF maps provided greater diagnostic utility. Results: MRF revealed additional findings in a total of 46 patients (46/100, 46%). In one patient, findings on conventional MRI were not recapitulated on MRF (1/100, 1%). In the remaining 53 patients, MRF findings were consistent with conventional MRI (53/100, 53%). Additional benefits of MRF included the following: improved lesion conspicuity ( n = 16), improved visualization of lesion extent ( n = 11) or asymmetry between bilateral mesial temporal regions ( n = 9), differentiation between multiple lesions ( n = 5), new lesion identified ( n = 2), and visualization of connection between lesion and other areas ( n = 1). Added value from MRF was particularly common in patients with focal cortical dysplasia (FCD) type IIa ( p = .009 vs. FCD IIb) and periventricular nodular heterotopia. Significance: Our findings demonstrate the additional yield from MRF in the identification and definition of the extent of epileptogenic lesions, underscoring its potential as a valuable adjunct in presurgical epilepsy evaluation.

Objective: This study was undertaken to develop a framework for focal cortical dysplasia (FCD) detection using surface‐based morphometric (SBM) analysis and machine learning (ML) applied to three‐dimensional (3D) magnetic resonance fingerprinting (MRF). Methods: We included 114 subjects (44 patients with medically intractable focal epilepsy and FCD, 70 healthy controls [HCs]). All subjects underwent high‐resolution 3‐T MRF scans generating T1 and T2 maps. All patients had clinical T1‐weighted (T1w) images; 35 also had 3D fluid‐attenuated inversion recovery (FLAIR). A 3D region of interest (ROI) was manually created for each lesion. All maps/images and lesion ROIs were registered to T1w images. Surface‐based features were extracted following the Multi‐center Epilepsy Lesion Detection pipeline. Features were normalized using intrasubject, interhemispheric, and intersubject z ‐scoring. A two‐stage ML approach was applied: a vertexwise neural network classifier for lesional versus normal vertices using T1w/MRF/FLAIR features, followed by a clusterwise Random Undersampling Boosting classifier to suppress false positives (FPs) based on cluster size, prediction probabilities, and feature statistics. Leave‐one‐out cross‐validation was performed at both stages. Results: Using T1w features, sensitivity was 70.4% with 11.6 FP clusters/patient and 4.1 in HCs. Adding MRF reduced FPs to 6.6 clusters/patient and 1.5 in HCs, with 68.2% sensitivity. Combining T1w, MRF, and FLAIR achieved 71.4% sensitivity, with 4.7 FPs/patient and 1.1 in HCs. Detection probabilities were significantly higher for true positive clusters than FPs ( p < .001). Type II showed higher detection rates than non‐type II. Magnetic resonance imaging (MRI)‐positive patients showed higher detection rates and fewer FPs than MRI‐negative patients. Seizure‐free patients demonstrated higher detection rates than non‐seizure‐free patients. Subtyping accuracy was 80.8% for non‐type II versus type II, and 68.4% for IIa versus IIb, although limited by small sample size. The transmantle sign was present in 61.5% of IIb and 40% of IIa cases. Significance: We developed an ML framework for FCD detection integrating SBM with clinical MRI and MRF. Advances include improved FP control and enhanced subtyping; selected model outputs may provide indicators of detection confidence and seizure outcome.