Background: and Purpose Temporal dissociation between electrographic and clinical seizure onset in mesial temporal lobe epilepsy (MTLE) implies network heterogeneity. We hypothesized that MTLE consists of distinct electroclinical phenotypes defined by divergent seizure propagation dynamics. Methods: We retrospectively analyzed 167 seizures from 13 patients with drug-resistant MTLE who achieved Engel Class I surgical outcomes. Based on the predominant relationship between scalp electroencephalography (EEG) and clinical onset, patients were classified as EEG-first (n=7) or Clinical-first (n=6). Seizure onset patterns, propagation pathways, and temporal dynamics were assessed using scalp and stereoelectroencephalography (SEEG). Results: The cohort included 83 scalp and 84 SEEG seizures. By definition, onset order differed: in EEG-first seizures, scalp changes preceded clinical signs in 70.5%, whereas the reverse occurred in 65.8% of Clinical-first seizures (p<0.001). EEG-first seizures demonstrated longer SEEG-to-clinical latency (28.8±7.7 s vs. 13.0±7.7 s; p=0.004) and rhythmic theta onset on scalp EEG with focal SEEG low-voltage fast activity or preictal spikes. In contrast, Clinical-first seizures showed heterogeneous scalp patterns (theta, delta–theta, or delta slowing). Propagation tended to differ, with anterior pathways more common in EEG-first (69.6%) and posterior pathways in Clinical-first (26.3%), though with partial overlap (p=0.138). Conclusions MTLE comprises two pathophysiologically distinct phenotypes: EEG-first, reflecting a slowly propagating anterior mesial network, and Clinical-first, involving a rapidly spreading posterior network. This classification offers a mechanistic framework for understanding electroclinical dissociation and may refine presurgical evaluation.

Background: Currently, little is known about the relationship between the temporal radiographic latent trajectories, which are based on the extent of coronavirus disease 2019 (COVID-19) pneumonia and clinical outcomes. This study aimed to elucidate the differences in the temporal trends of critical laboratory biomarkers, utilization of critical care support, and clinical outcomes according to temporal radiographic latent trajectories. Methods: We enrolled 2,385 patients who were hospitalized with COVID-19 and underwent serial chest radiographs from December 2019 to March 2022. The extent of radiographic pneumonia was quantified as a percentage using a previously developed deep-learning algorithm. A latent class growth model was used to identify the trajectories of the longitudinal changes of COVID-19 pneumonia extents during hospitalization. We investigated the differences in the temporal trends of critical laboratory biomarkers among the temporal radiographic trajectory groups. Cox regression analyses were conducted to investigate differences in the utilization of critical care supports and clinical outcomes among the temporal radiographic trajectory groups. Results: The mean age of the enrolled patients was 58.0 ± 16.9 years old, with 1,149 (48.2%) being male. Radiographic pneumonia trajectories were classified into three groups: The steady group (n = 1,925, 80.7%) exhibited stable minimal pneumonia, the downhill group (n = 135, 5.7%) exhibited initial worsening followed by improving pneumonia, and the uphill group (n = 325, 13.6%) exhibited progressive deterioration of pneumonia. There were distinct differences in the patterns of temporal blood urea nitrogen (BUN) and C-reactive protein (CRP) levels between the uphill group and the other two groups. Cox regression analyses revealed that the hazard ratios (HRs) for the need for critical care support and the risk of intensive care unit admission were significantly higher in both the downhill and uphill groups compared to the steady group. However, regarding in-hospital mortality, only the uphill group demonstrated a significantly higher risk than the steady group (HR, 8.2; 95% confidence interval, 3.08–21.98). Conclusion Stratified pneumonia trajectories, identified through serial chest radiographs, are linked to different patterns of temporal changes in BUN and CRP levels. These changes can predict the need for critical care support and clinical outcomes in COVID-19 pneumonia.Appropriate therapeutic strategies should be tailored based on these disease trajectories.

Background and Objectives: This study aimed to analyze postoperative performance and mapping characteristics of cochlear implants (CIs) by comparing patients with autism spectrum disorder (ASD) to those without ASD, and to suggest CI mapping solutions in patients with ASD.Subjects and Method This retrospective study enrolled 10 children with ASD and hearing disabilities, who received simultaneous bilateral CI (ASD group), and 20 children with bilateral hearing disabilities, who received simultaneous bilateral CI at the same age (control group). CI performance was analyzed using speech perception tests (categorical auditory performance score and monosyllable, bisyllable, and Ling’s 6 tests) and a sound field test. The mapping characteristics focused on variables related to stimulus intensity and fine-tuning. Results: The performance of the ASD group was significantly poorer than that of the control group in all speech perception and sound field tests. At the comfortable (C) and threshold (T) levels, the ASD group scored significantly lower than the control group. The dynamic range of ASD group was significantly narrower than the control group. The ASD group had significantly lower pulse width, sensitivity, and volume than control group. Conclusion CI mapping in the ASD group showed practical limitations. To avoid overstimulation in patients with ASD, the dynamic range should be set narrow, or the C/T level should be set lower than normal. Key control factors, such as pulse width, sensitivity, and volume, should be set lower than the control group. Although lower performance from CI is generally expected in the ASD group, CI mapping in the ASD group requires a long-term approach with dedicated efforts and patience.

Background: Currently, little is known about the relationship between the temporal radiographic latent trajectories, which are based on the extent of coronavirus disease 2019 (COVID-19) pneumonia and clinical outcomes. This study aimed to elucidate the differences in the temporal trends of critical laboratory biomarkers, utilization of critical care support, and clinical outcomes according to temporal radiographic latent trajectories. Methods: We enrolled 2,385 patients who were hospitalized with COVID-19 and underwent serial chest radiographs from December 2019 to March 2022. The extent of radiographic pneumonia was quantified as a percentage using a previously developed deep-learning algorithm. A latent class growth model was used to identify the trajectories of the longitudinal changes of COVID-19 pneumonia extents during hospitalization. We investigated the differences in the temporal trends of critical laboratory biomarkers among the temporal radiographic trajectory groups. Cox regression analyses were conducted to investigate differences in the utilization of critical care supports and clinical outcomes among the temporal radiographic trajectory groups. Results: The mean age of the enrolled patients was 58.0 ± 16.9 years old, with 1,149 (48.2%) being male. Radiographic pneumonia trajectories were classified into three groups: The steady group (n = 1,925, 80.7%) exhibited stable minimal pneumonia, the downhill group (n = 135, 5.7%) exhibited initial worsening followed by improving pneumonia, and the uphill group (n = 325, 13.6%) exhibited progressive deterioration of pneumonia. There were distinct differences in the patterns of temporal blood urea nitrogen (BUN) and C-reactive protein (CRP) levels between the uphill group and the other two groups. Cox regression analyses revealed that the hazard ratios (HRs) for the need for critical care support and the risk of intensive care unit admission were significantly higher in both the downhill and uphill groups compared to the steady group. However, regarding in-hospital mortality, only the uphill group demonstrated a significantly higher risk than the steady group (HR, 8.2; 95% confidence interval, 3.08–21.98). Conclusion Stratified pneumonia trajectories, identified through serial chest radiographs, are linked to different patterns of temporal changes in BUN and CRP levels. These changes can predict the need for critical care support and clinical outcomes in COVID-19 pneumonia.Appropriate therapeutic strategies should be tailored based on these disease trajectories.

Background and Objectives: This study aimed to analyze postoperative performance and mapping characteristics of cochlear implants (CIs) by comparing patients with autism spectrum disorder (ASD) to those without ASD, and to suggest CI mapping solutions in patients with ASD.Subjects and Method This retrospective study enrolled 10 children with ASD and hearing disabilities, who received simultaneous bilateral CI (ASD group), and 20 children with bilateral hearing disabilities, who received simultaneous bilateral CI at the same age (control group). CI performance was analyzed using speech perception tests (categorical auditory performance score and monosyllable, bisyllable, and Ling’s 6 tests) and a sound field test. The mapping characteristics focused on variables related to stimulus intensity and fine-tuning. Results: The performance of the ASD group was significantly poorer than that of the control group in all speech perception and sound field tests. At the comfortable (C) and threshold (T) levels, the ASD group scored significantly lower than the control group. The dynamic range of ASD group was significantly narrower than the control group. The ASD group had significantly lower pulse width, sensitivity, and volume than control group. Conclusion CI mapping in the ASD group showed practical limitations. To avoid overstimulation in patients with ASD, the dynamic range should be set narrow, or the C/T level should be set lower than normal. Key control factors, such as pulse width, sensitivity, and volume, should be set lower than the control group. Although lower performance from CI is generally expected in the ASD group, CI mapping in the ASD group requires a long-term approach with dedicated efforts and patience.

Background and Objectives: This study aimed to analyze postoperative performance and mapping characteristics of cochlear implants (CIs) by comparing patients with autism spectrum disorder (ASD) to those without ASD, and to suggest CI mapping solutions in patients with ASD.Subjects and Method This retrospective study enrolled 10 children with ASD and hearing disabilities, who received simultaneous bilateral CI (ASD group), and 20 children with bilateral hearing disabilities, who received simultaneous bilateral CI at the same age (control group). CI performance was analyzed using speech perception tests (categorical auditory performance score and monosyllable, bisyllable, and Ling’s 6 tests) and a sound field test. The mapping characteristics focused on variables related to stimulus intensity and fine-tuning. Results: The performance of the ASD group was significantly poorer than that of the control group in all speech perception and sound field tests. At the comfortable (C) and threshold (T) levels, the ASD group scored significantly lower than the control group. The dynamic range of ASD group was significantly narrower than the control group. The ASD group had significantly lower pulse width, sensitivity, and volume than control group. Conclusion CI mapping in the ASD group showed practical limitations. To avoid overstimulation in patients with ASD, the dynamic range should be set narrow, or the C/T level should be set lower than normal. Key control factors, such as pulse width, sensitivity, and volume, should be set lower than the control group. Although lower performance from CI is generally expected in the ASD group, CI mapping in the ASD group requires a long-term approach with dedicated efforts and patience.

Background: Currently, little is known about the relationship between the temporal radiographic latent trajectories, which are based on the extent of coronavirus disease 2019 (COVID-19) pneumonia and clinical outcomes. This study aimed to elucidate the differences in the temporal trends of critical laboratory biomarkers, utilization of critical care support, and clinical outcomes according to temporal radiographic latent trajectories. Methods: We enrolled 2,385 patients who were hospitalized with COVID-19 and underwent serial chest radiographs from December 2019 to March 2022. The extent of radiographic pneumonia was quantified as a percentage using a previously developed deep-learning algorithm. A latent class growth model was used to identify the trajectories of the longitudinal changes of COVID-19 pneumonia extents during hospitalization. We investigated the differences in the temporal trends of critical laboratory biomarkers among the temporal radiographic trajectory groups. Cox regression analyses were conducted to investigate differences in the utilization of critical care supports and clinical outcomes among the temporal radiographic trajectory groups. Results: The mean age of the enrolled patients was 58.0 ± 16.9 years old, with 1,149 (48.2%) being male. Radiographic pneumonia trajectories were classified into three groups: The steady group (n = 1,925, 80.7%) exhibited stable minimal pneumonia, the downhill group (n = 135, 5.7%) exhibited initial worsening followed by improving pneumonia, and the uphill group (n = 325, 13.6%) exhibited progressive deterioration of pneumonia. There were distinct differences in the patterns of temporal blood urea nitrogen (BUN) and C-reactive protein (CRP) levels between the uphill group and the other two groups. Cox regression analyses revealed that the hazard ratios (HRs) for the need for critical care support and the risk of intensive care unit admission were significantly higher in both the downhill and uphill groups compared to the steady group. However, regarding in-hospital mortality, only the uphill group demonstrated a significantly higher risk than the steady group (HR, 8.2; 95% confidence interval, 3.08–21.98). Conclusion Stratified pneumonia trajectories, identified through serial chest radiographs, are linked to different patterns of temporal changes in BUN and CRP levels. These changes can predict the need for critical care support and clinical outcomes in COVID-19 pneumonia.Appropriate therapeutic strategies should be tailored based on these disease trajectories.