OBJECTIVE
To clarify the characteristics of MEG findings in patients with temporal lobe epilepsy with amygdala enlargement (TLE-AE).
METHODS
We retrospectively identified patients who showed unilateral amygdala enlargement (AE) on MRI and ≥ 5 interictal spikes localized to the ipsilateral temporal region on MEG. Patients were classified into an AE-only group with isolated AE and an AE-plus group with AE plus additional abnormalities. The equivalent current dipole (ECD) model was used to estimate the cortical sources of MEG interictal spikes. The mean pairwise angle (MPA) of the ECD orientations was calculated while the ECD locations were examined. To stabilize the estimates, K-sampling with 1,000 iterations was performed. Group differences were assessed using the Mann-Whitney U test.
RESULTS
Seventeen patients (nine males, aged 18-67 years) met the criteria. The AE-only group (n = 9) showed a significantly higher MPA than the AE-plus group (n = 8, p = 0.046), indicating greater orientation dispersion in the AE-only group. In both groups, the estimated ECDs were located in the anterior and mesial temporal lobes.
CONCLUSIONS
Patients with Isolated TLE-AE exhibited more dispersed orientations of MEG interictal spike dipoles than those with concomitant lesions, whereas the dipole locations consistently involved the anterior and mesial temporal lobes.
SIGNIFICANCE
Orientation dispersion of the MEG interictal spike dipoles may reflect differences in the epileptogenic networks of patients with TLE-AE.
Kazutoshi Konomatsu, Makoto Ishida, Shiho Sato et al.· Clinical Neurophysiology· 0 citations
PURPOSE
Synthetic Q-space learning (synQSL), which uses only synthetic data in the training of regressors, has demonstrated promising results in parameter estimation for brain diffusion MRI (dMRI). This study aimed to evaluate the performance of synQSL in breast dMRI for intravoxel incoherent motion-diffusional kurtosis imaging (IVIM-DKI) parameter estimation, with comparing several types of regressors and conventional fitting by nonlinear least squares fitting (NL-LSF).
METHODS
In synthesizing data for synQSL, IVIM-DKI parameters were sampled from uniform distributions and substituted into the signal model along with b-values to generate diffusion-weighted imaging (DWI) signals. In addition, Rician noise was mixed to the signals. We prepared datasets of 105 and 106 samples, and trained multi-layer perceptron (MLP), Kolmogorov-Arnold networks (KAN), and random forest (RF) regressors for synQSL. The performance of the parameter estimation methods including NL-LSF was evaluated using a digital phantom including various value combinations of IVIM-DKI parameters and clinical data of 67 cases (13 benign and 54 malignant lesions) through quantitative analysis and visual assessment.
RESULTS
In the digital phantom, the regressors of synQSL achieved significantly lower root mean square error (RMSE) for f, D∗, and D than NL-LSF (P < 0.05) with less variation among the parameter sets. In clinical datasets, synQSL not only improved the visual quality of parameter maps but also showed significant differences between benign and malignant lesions in parameters while the NL-LSF failed. Furthermore, the estimation times for all synQSL regressors were substantially shorter than that of NL-LSF. Among the synQSL regressors, MLP showed superior properties including computational cost.
CONCLUSION
synQSL demonstrated superior parameter estimation performance compared to NL-LSF in breast IVIM-DKI analysis. In this study, MLP was considered the most suitable regressor for synQSL among those we examined, based on the balance between estimation accuracy and computational costs.
Kousei Konya, Yuki Ichinoseki, Erina Kato et al.· Magnetic Resonance in Medica...· 0 citations
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