Stereoelectroencephalography-guided radiofrequency thermocoagulation of the epileptogenic zone: a potential treatment and prognostic indicator for subsequent excision surgery.

PURPOSE: To evaluate the safety and efficacy of stereoelectroencephalography (SEEG)-guided radiofrequency thermocoagulation (RFTC) for drug-resistant focal epilepsy and investigate the relationship between post-RFTC remission duration and delayed excision surgery effectiveness. METHODS: We conducted a retrospective analysis of 43 patients with drug-resistant focal epilepsy who underwent RFTC via SEEG electrodes. After excluding three, the remaining 40 were classified into subgroups based on procedures and outcomes. Twenty-four patients (60%) underwent a secondary excision surgery. We determined the predictive value of RFTC outcome upon subsequent surgical outcome by categorizing the delayed secondary surgery outcome as success (Engel I/II) versus failure (Engel III/IV). Demographic information, epilepsy characteristics, and the duration of seizure freedom after RFTC were assessed. RESULTS: Among 40 patients, 20% achieved Engel class I with RFTC alone, while 24 underwent delayed secondary excision surgery. Overall, 41.7% attained Engel class I, with a 66.7% success rate combining RFTC with delayed surgery. Seizure freedom duration was significantly longer in the success group (mean 4.9 months, SD = 2.7) versus the failure group (mean 1.9 months, SD = 1.1; P = 0.007). A higher proportion of RFTC-only and delayed surgical success group patients had preoperative lesional findings (p = 0.01), correlating with a longer time to seizure recurrence (p < 0.05). Transient postoperative complications occurred in 10%, resolving within a year. CONCLUSION: This study demonstrates that SEEG-guided RFTC is a safe and potential treatment option for patients with drug-resistant focal epilepsy. A prolonged duration of seizure freedom following RFTC may serve as a predictive marker for the success of subsequent excision surgery.

Analysis of Factors Related to the Efficacy of Consciousness-Regaining Therapy for Prolonged Disorder of Consciousness: A Retrospective Cohort Study.

OBJECTIVE: To analyze the factors related to the efficacy of consciousness-regaining therapy (CRT) for prolonged disorder of consciousness. METHODS: A retrospective analysis was conducted on the case data of 114 patients with prolonged disorder of consciousness (pDOC) admitted to the Department of Functional Neurosurgery of Tianjin Huanhu Hospital from January 2019 to January 2022 to explore the relevant factors that affect the efficacy of CRT for pDOC. Next, basic information on the cases, data on pDOC disease assessment, CRT methods, and efficacy evaluation were collected. RESULTS: These 114 patients were grouped, and a comparative analysis was done based on the efficacy at the end of treatment. Of these, 61 cases were allotted to the ineffective group and 53 cases to the effective group. There was a lack of statistical difference (P > 0.05) between the 2 groups based on gender, age, etiology, acute cerebral herniation, emergency craniotomy surgery, emergency decompressive craniectomy, time from onset to start of CRT, and CRT duration (P > 0.05). However, secondary hydrocephalus, CRT methods, JFK Coma Recovery Scale-Revised grading before treatment, and extended Glasgow Outcome Scale score at six months after treatment were found to be statistically different. The results of binary logistic regression analysis showed that the type of therapy (OR = 0.169, 95% CI: 0.057-0.508) affected the efficacy of CRT (P < 0.05). CONCLUSIONS: Personalized awakening therapy using various invasive CRT methods could improve the efficacy of therapy for pDOC compared with noninvasive therapy.

[Spinal cord electrical stimulation with neurophysiological monitoring for treatment of high-risk diabetic foot].

OBJECTIVE: To evaluate the clinical efficacy of a single-session implantation of spinal cord electrical stimulation with neurophysiological monitoring a spinal cord electrical stimulator under general anesthesia with neurophysiological monitoring for the treatment of high-risk diabetic foot. METHODS: The clinical data of seven patients with high-risk diabetic foot who underwent spinal cord electrical stimulation in neurosurgery ward nine of Tianjin Huanhu Hospital from May 2022 to May 2023 were collected. The operation was performed under general anesthesia with the "C" arm X ray machine guidance and neurophysiological monitoring. The arterial diameter and peak flow rate of lower extremity, lower extremity skin temperature (calf skin temperature, foot skin temperature), visual analog scale (VAS), continuous distance of movement, blood glucose level and toe wound were compared between patients before and after surgery. RESULTS: A total of seven patients with high-risk diabetic foot were included. The diameters and peak flow rates of femoral artery, popliteal artery, anterior tibial artery, posterior tibial artery and dorsal foot artery in both lower limbs were significantly improved after surgery. All patients had different degrees of lower limb pain before operation. After operation, VAS score decreased significantly (1.1±0.9 vs. 6.8±3.4), the pain was significantly relieved, and the calf skin temperature and foot skin temperature were significantly higher than those before surgery [calf skin temperature (centigrade): 33.3±0.9 vs. 30.9±0.7, foot skin temperature (centigrade): 31.4±0.8 vs. 29.1±0.6], fasting blood glucose and postprandial blood glucose were significantly lower than those before surgery [fasting blood glucose (mmol/L): 7.6±1.4 vs. 10.5±1.2, postprandial blood glucose (mmol/L): 9.3±2.3 vs. 13.5±1.1], the differences were statistically significant (all P < 0.01). The lower limb movement of all seven patients was significantly improved after surgery, including one patient who needed wheelchair travel before surgery, and one patient who had intermittent claudication before surgery. Among them, one patient needed wheelchair travel and one patient had intermittent claudication before surgery. All patients could walk normally at 2 weeks after operation. Among the seven patients, two patients had the diabetic foot wound ulceration before surgery, which could not heal for a long time. One month after surgery, blood flow around the foot wound recovered and the healing was accelerated. The wound was dry and crusted around the wound, and the wound healed well. CONCLUSIONS: For diabetic high-risk foot patients who are intolerant to diabetic peripheral neuralgia and local anesthesia spinal cord electrical stimulation test, one-time implantation of spinal cord electrical stimulator under general anesthesia under neurophysiological monitoring can effectively alleviate peripheral neuralgia and other diabetic foot related symptoms, improve lower limb blood supply, and reduce the risk of toe amputation. Clinical practice has proved the effectiveness of this technique, especially for the early treatment of diabetic high-risk foot patients.

Effects of subthalamic nucleus deep brain stimulation using different frequency programming paradigms on axial symptoms in advanced Parkinson's disease.

BACKGROUND: In advanced Parkinson's disease (PD), axial symptoms are common and can be debilitating. Although deep brain stimulation (DBS) significantly improves motor symptoms, conventional high-frequency stimulation (HFS) has limited effectiveness in improving axial symptoms. In this study, we investigated the effects on multiple axial symptoms after DBS surgery with three different frequency programming paradigms comprising HFS, low-frequency stimulation (LFS), and variable-frequency stimulation (VFS). METHODS: This study involved PD patients who had significant preoperative axial symptoms and underwent bilateral subthalamic nucleus (STN) DBS. Axial symptoms, motor symptoms, medications, and quality of life were evaluated preoperatively (baseline). One month after surgery, HFS was applied. At 6 months post-surgery, HFS assessments were performed, and HFS was switched to LFS. A further month later, we conducted LFS assessments and switched LFS to VFS. At 8 months after surgery, VFS assessments were performed. RESULTS: Of the 21 PD patients initially enrolled, 16 patients were ultimately included in this study. Regarding HFS, all axial symptoms except for the Berg Balance Scale (p < 0.0001) did not improve compared with the baseline (all p > 0.05). As for LFS and VFS, all axial symptoms improved significantly compared with both the baseline and HFS (all p < 0.05). Moreover, motor symptoms and medications were significantly better than the baseline (all p < 0.05) after using LFS and VFS. Additionally, the quality of life of the PD patients after receiving LFS and VFS was significantly better than at the baseline and with HFS (all p < 0.0001). CONCLUSION: Our findings indicate that HFS is ineffective at improving the majority of axial symptoms in advanced PD. However, both the LFS and VFS programming paradigms exhibit significant improvements in various axial symptoms.

The Association Between Trajectory-Skull Angle and Accuracy of Stereoelectroencephalography Electrode Implantation in Drug-Resistant Epilepsy.

OBJECTIVE: To analyze the relationship between trajectory-skull angle and stereoelectroencephalography electrode implantation accuracy in drug-resistant epilepsy patients, aiming to guide clinical electrode placement and enhance surgical precision and safety. METHODS: We conducted a retrospective analysis of medical records and surgical characteristics of 32 consecutive patients diagnosed with drug-resistant epilepsy, who underwent stereoelectroencephalography procedures at our center from June 2020 to June 2023. To evaluate the accuracy of electrode implantation, we utilized preoperative and postoperative computed tomography scans fused with SinoPlan software-planned trajectories. Entry radial error and target vector error were assessed as measurements of electrode implantation accuracy. RESULTS: After adjusting for confounders, we found a significant positive correlation between trajectory-skull angle and entry radial error (ß = 0.02, 95% CI: 0.01-0.03, P < 0.001). Likewise, a significant positive correlation existed between trajectory-skull angle and target vector error in all three models (ß = 0.03, 95% CI: 0.01-0.04, P < 0.001). Additionally, a U-shaped relationship between trajectory-skull angle and target vector error was identified using smooth curve fitting. This U-shaped pattern persisted in both frame-based and robot-guided stereotactic techniques. According to the two-piecewise linear regression model, the inflection points were 9° in the frame-based group and 16° in the robot-guided group. CONCLUSIONS: This study establishes a significant positive linear correlation between trajectory-skull angle and entry radial error, along with a distinctive U-shaped pattern in the relationship between trajectory-skull angle and target vector error. Our findings suggest that trajectory-skull angles of 9° (frame-based) and 16° (robot-guided) may optimize the accuracy of target vector error.

A study of brain networks associated with Freezing of gait in Parkinson's disease using transfer entropy analysis.

BACKGROUND: Parkinson's disease (PD) is a common neurodegenerative disease in the elderly. Freezing of Gait (FOG) is one of the common motor symptoms of PD, but the potential mechanism remains unclear. This study aimed to investigate the changes of brain functional network topology in PD patients with FOG. METHODS: The resting electroencephalogram (EEG) were acquired from15 PD patients with FOG (PD-FOG), 13 PD patients without FOG (PD-nFOG), and 16 healthy control (HC). Cognitive and motor functions were assessed using subjective scales. The whole-brain functional networks were constructed based on transfer entropy. Transfer entropy was used to analyse the information flow and causality in the network and the network connectivity was analyzed by graph theory. The characteristics of PD-FOG and PD-nFOG were compared by receiver operator characteristic (ROC) curve analysis. RESULTS: The θ bands brain network of PD-FOG, PD-nFOG and HC group was significantly different (P < 0.05). The average characteristic path length of the θ bands brain network was positively correlated with FOG Questionnaire (FOGQ). PD-FOG and PD-nFOG get high classification accuracy according to this feature. The information inflow in the frontal and occipital lobes and information outflow in the temporal lobe of PD-FOG patients in the θ bands increased significantly. CONCLUSIONS: The whole-brain functional network characteristics of PD-FOG in the θ bands can serve as potential biomarkers for early diagnosis of PD-FOG. Abnormal information flow of the frontal, occipital, and temporal lobes in the θ bands may be an important factor leading to FOG.

An Integrated Multi-Channel Deep Neural Network for Mesial Temporal Lobe Epilepsy Identification Using Multi-Modal Medical Data.

Epilepsy is a chronic brain disease with recurrent seizures. Mesial temporal lobe epilepsy (MTLE) is the most common pathological cause of epilepsy. With the development of computer-aided diagnosis technology, there are many auxiliary diagnostic approaches based on deep learning algorithms. However, the causes of epilepsy are complex, and distinguishing different types of epilepsy accurately is challenging with a single mode of examination. In this study, our aim is to assess the combination of multi-modal epilepsy medical information from structural MRI, PET image, typical clinical symptoms and personal demographic and cognitive data (PDC) by adopting a multi-channel 3D deep convolutional neural network and pre-training PET images. The results show better diagnosis accuracy than using one single type of medical data alone. These findings reveal the potential of a deep neural network in multi-modal medical data fusion.

Exploring the network effects of deep brain stimulation for rapid eye movement sleep behavior disorder in Parkinson's disease.

BACKGROUND: The research findings on the effects of subthalamic nucleus (STN) deep brain stimulation (DBS) in Parkinson's disease (PD) with Rapid Eye Movement Sleep Behavior Disorder (RBD) are inconsistent, and there is a lack of research on DBS electrode sites and their network effects for the explanation of the differences. Our objective is to explore the optimal stimulation sites (that is the sweet spot) and the brain network effects of STN-DBS for RBD in PD. METHODS: In this study, among the 50 PD patients who underwent STN-DBS treatment, 24 PD patients with RBD were screened. According to clinical scores and imaging data, the sweet spot of STN-DBS was analyzed in PD patients with RBD, and the optimal structure and functional network models of subthalamic stimulation were constructed. RESULTS: Bilateral STN-DBS can effectively improve the symptoms of RBD and other non-motor symptoms in 24 PD patients with RBD. RBD Questionnaire-Hong Kong (RBDQ-HK) score was 41.33 ± 17.45 at baseline and 30.83 ± 15.83 at 1-year follow-up, with statistical significance between them (P < 0.01). However, the MoCA score was an exception with a baseline of 22.04 ± 4.28 and a 1-year follow-up of 21.58 ± 4.33, showing no statistical significance (P = 0.12). The sweet spot and optimal network connectivity models for RBD improvement have been validated as effective. CONCLUSIONS: Bilateral STN-DBS can improve the symptoms of RBD in PD. There exist the sweet spot and brain network effects of bilateral STN-DBS in the treatment of PD with RBD. Our study also demonstrates that RBD is a brain network disease.

[A research on epilepsy source localization from scalp electroencephalograph based on patient-specific head model and multi-dipole model].

Accurate source localization of the epileptogenic zone (EZ) is the primary condition of surgical removal of EZ. The traditional localization results based on three-dimensional ball model or standard head model may cause errors. This study intended to localize the EZ by using the patient-specific head model and multi-dipole algorithms using spikes during sleep. Then the current density distribution on the cortex was computed and used to construct the phase transfer entropy functional connectivity network between different brain areas to obtain the localization of EZ. The experiment result showed that our improved methods could reach the accuracy of 89.27% and the number of implanted electrodes could be reduced by (19.34 ± 7.15)%. This work can not only improve the accuracy of EZ localization, but also reduce the additional injury and potential risk caused by preoperative examination and surgical operation, and provide a more intuitive and effective reference for neurosurgeons to make surgical plans.

Electroencephalogram-Based Brain Connectivity Analysis in Prolonged Disorders of Consciousness.

Background: Prolonged disorders of consciousness (pDOC) are common in neurology and place a heavy burden on families and society. This study is aimed at investigating the characteristics of brain connectivity in patients with pDOC based on quantitative EEG (qEEG) and extending a new direction for the evaluation of pDOC. Methods: Participants were divided into a control group (CG) and a DOC group by the presence or absence of pDOC. Participants underwent magnetic resonance imaging (MRI) T1 three-dimensional magnetization with a prepared rapid acquisition gradient echo (3D-T1-MPRAGE) sequence, and video EEG data were collected. After calculating the power spectrum by EEG data analysis tool, DTABR ((δ + θ)/(α + ß) ratio), Pearson's correlation coefficient (Pearson r), Granger's causality, and phase transfer entropy (PTE), we performed statistical analysis between two groups. Finally, receiver operating characteristic (ROC) curves of connectivity metrics were made. Results: The proportion of power in frontal, central, parietal, and temporal regions in the DOC group was lower than that in the CG. The percentage of delta power in the DOC group was significantly higher than that in the CG, the DTABR in the DOC group was higher than that in the CG, and the value was inverted. The Pearson r of the DOC group was higher than that of CG. The Pearson r of the delta band (Z = -6.71, P < 0.01), theta band (Z = -15.06, P < 0.01), and alpha band (Z = -28.45, P < 0.01) were statistically significant. Granger causality showed that the intensity of directed connections between the two hemispheres in the DOC group at the same threshold was significantly reduced (Z = -82.43, P < 0.01). The PTE of each frequency band in the DOC group was lower than that in the CG. The PTE of the delta band (Z = -42.68, P < 0.01), theta band (Z = -56.79, P < 0.01), the alpha band (Z = -35.11, P < 0.01), and beta band (Z = -63.74, P < 0.01) had statistical significance. Conclusion: Brain connectivity analysis based on EEG has the advantages of being noninvasive, convenient, and bedside. The Pearson r of DTABR, delta, theta, and alpha bands, Granger's causality, and PTE of the delta, theta, alpha, and beta bands can be used as biological markers to distinguish between pDOC and healthy people, especially when behavior evaluation is difficult or ambiguous; it can supplement clinical diagnosis.

Patient-independent seizure detection based on long-term iEEG and a novel lightweight CNN.

Objective.Patient-dependent seizure detection based on intracranial electroencephalography (iEEG) has made significant progress. However, due to the difference in the locations and number of iEEG electrodes used for each patient, patient-independent seizure detection based on iEEG has not been carried out. Additionally, current seizure detection algorithms based on deep learning have outperformed traditional machine learning algorithms in many performance metrics. However, they still have shortcomings of large memory footprints and slow inference speed.Approach.To solve the above problems of the current study, we propose a novel lightweight convolutional neural network model combining the Convolutional Block Attention Module (CBAM). Its performance for patient-independent seizure detection is evaluated on two long-term continuous iEEG datasets: SWEC-ETHZ and TJU-HH. Finally, we reproduce four other patient-independent methods to compare with our method and calculate the memory footprints and inference speed for all methods.Main results.Our method achieves 83.81% sensitivity (SEN) and 85.4% specificity (SPE) on the SWEC-ETHZ dataset and 86.63% SEN and 92.21% SPE on the TJU-HH dataset. In particular, it takes only 11 ms to infer 10 min iEEG (128 channels), and its memory footprint is only 22 kB. Compared to baseline methods, our method not only achieves better patient-independent seizure detection performance but also has a smaller memory footprint and faster inference speed.Significance.To our knowledge, this is the first iEEG-based patient-independent seizure detection study. This facilitates the application of seizure detection algorithms to the future clinic.

Clinical Study of Intraoperative Microelectrode Recordings during Awake and Asleep Subthalamic Nucleus Deep Brain Stimulation for Parkinson's Disease: A Retrospective Cohort Study.

Our objective is to analyze the difference of microelectrode recording (MER) during awake and asleep subthalamic nucleus deep brain stimulation (STN-DBS) for Parkinson's disease (PD) and the necessity of MER during "Asleep DBS" under general anesthesia (GA). The differences in MER, target accuracy, and prognosis under different anesthesia methods were analyzed. Additionally, the MER length was compared with the postoperative electrode length by electrode reconstruction and measurement. The MER length of two groups was 5.48 ± 1.39 mm in the local anesthesia (LA) group and 4.38 ± 1.43 mm in the GA group, with a statistical significance between the two groups (p < 0.01). The MER length of the LA group was longer than its postoperative electrode length (p < 0.01), however, there was no significant difference between the MER length and postoperative electrode length in the GA group (p = 0.61). There were also no significant differences in the postoperative electrode length, target accuracy, and postoperative primary and secondary outcome scores between the two groups (p > 0.05). These results demonstrate that "Asleep DBS" under GA is comparable to "Awake DBS" under LA. GA has influences on MER during surgery, but typical STN discharges can still be recorded. MER is not an unnecessary surgical procedure.

Continuous Seizure Detection Based on Transformer and Long-Term iEEG.

Automatic seizure detection algorithms are necessary for patients with refractory epilepsy. Many excellent algorithms have achieved good results in seizure detection. Still, most of them are based on discontinuous intracranial electroencephalogram (iEEG) and ignore the impact of different channels on detection. This study aimed to evaluate the proposed algorithm using continuous, long-term iEEG to show its applicability in clinical routine. In this study, we introduced the ability of the transformer network to calculate the attention between the channels of input signals into seizure detection. We proposed an end-to-end model that included convolution and transformer layers. The model did not need feature engineering or format transformation of the original multi-channel time series. Through evaluation on two datasets, we demonstrated experimentally that the transformer layer could improve the performance of the seizure detection algorithm. For the SWEC-ETHZ iEEG dataset, we achieved 97.5% event-based sensitivity, 0.06/h FDR, and 13.7 s latency. For the TJU-HH iEEG dataset, we achieved 98.1% event-based sensitivity, 0.22/h FDR, and 9.9 s latency. In addition, statistics showed that the model allocated more attention to the channels close to the seizure onset zone within 20 s after the seizure onset, which improved the explainability of the model. This paper provides a new method to improve the performance and explainability of automatic seizure detection.

Stereoelectroencephalography-guided radiofrequency thermocoagulation in drug-resistant focal epilepsy.

Background: Stereoelectroencephalography (SEEG) has become a common diagnostic method in epilepsy surgery and is found to be safe for a wide range of clinical applications. SEEG combined with radiofrequency thermocoagulation (RF-TC) not only reveals the seizure onset zone by hypothesis, but also acts as a treatment option without any additional cost to institutions and patients. Thus, we analyzed the treatment of the SEEG-guided RF-TC. Methods: This retrospective study analyzed seventeen epileptic patients treated with RF-TC between April 2019 and December 2020. All patients underwent a single round of SEEG-guided RF-TC treatment after more than three habitual seizures were recorded. The demographic characteristics of the patients were retrospectively reviewed. Outcomes were assessed using the Engel classification system. Results: All patients underwent SEEG-guided RF-TC without catastrophic functional damage. Follow-up data of all patients were complete. The number of contacts per patients where RF-TC was applied ranged from 9 to 43 (mean: 17.7±10.2). After RF-TC, the types of anti-epileptic drugs used reduced from 2.4±0.7 to 1.6±0.7. With RF-TC alone, four (23.5%) patients achieved Engel Ia, two (11.8%) patients achieved Engel Ib, one patient underwent resection without seizure at the 5-month follow-up, five patients had a relapse after 3-10 months of seizure freedom, and five patients had recurrence after 1 month. After RF-TC, six patients underwent secondary interventions followed by resection. Overall, 12 patients achieved Engel Ia or Ib, three patients achieved Engel IIa or IIb, and two patients achieved Engel IIIa. There were no Engel IV cases. Conclusions: SEEG-guided RF-TC performed in our institution was found to be a safe ablation procedure for the treatment of drug-resistant focal epilepsy. All patients experienced a reduction in the frequency of seizures after receiving RF-TC. RF-TC can be used as a palliative treatment option for patients with epilepsy who refuse surgery or cannot undergo resection surgery. Recurrence of focal epilepsy after RF-TC can be treated with resection surgery to achieve the seizure-free status.

A meningioma and breast carcinoma metastasis collision tumor.

Co-existence of meningioma and breast carcinoma metastasis in the type of collision tumor is uncommon in the brain. A 65-year-old female with a history of breast carcinoma suffered from a mass in posterior fossa of brain. After a craniotomy, the pathological specimen showed a collision tumor with breast cancer metastasis and meningioma. In the clinical setting of previously vague diagnosis in imaging, craniotomy for removal of the mass is more recommended if conditions permit. Only the rare tumor is identified in pathology for further follow-up and research.

White matter structural connectivity as a biomarker for detecting juvenile myoclonic epilepsy by transferred deep convolutional neural networks with varying transfer rates.

Objective. By detecting abnormal white matter changes, diffusion magnetic resonance imaging (MRI) contributes to the detection of juvenile myoclonic epilepsy (JME). In addition, deep learning has greatly improved the detection performance of various brain disorders. However, there is almost no previous study effectively detecting JME by a deep learning approach with diffusion MRI.Approach. In this study, the white matter structural connectivity was generated by tracking the white matter fibers in detail based on Q-ball imaging and neurite orientation dispersion and density imaging. Four advanced deep convolutional neural networks (CNNs) were deployed by using the transfer learning approach, in which the transfer rate searching strategy was proposed to achieve the best detection performance.Main results. Our results showed: (a) Compared to normal control, the white matter' neurite density of JME was significantly decreased. The most significantly abnormal fiber tracts between the two groups were found to be cortico-cortical connection tracts. (b) The proposed transfer rate searching approach contributed to find each CNN's best performance, in which the best JME detection accuracy of 92.2% was achieved by using the Inception_resnet_v2 network with a 16% transfer rate.Significance. The results revealed: (a) Through detection of the abnormal white matter changes, the white matter structural connectivity can be used as a useful biomarker for detecting JME, which helps to characterize the pathophysiology of epilepsy. (b) The proposed transfer rate, as a new hyperparameter, promotes the CNNs transfer learning performance in detecting JME.

Automated Detection of Juvenile Myoclonic Epilepsy using CNN based Transfer Learning in Diffusion MRI.

Epilepsy is one of the largest neurological diseases in the world, and juvenile myoclonic epilepsy (JME) usually occurs in adolescents, giving patients tremendous burdens during growth, which really needs the early diagnosis. Advanced diffusion magnetic resonance imaging (MRI) could detect the subtle changes of the white matter, which could be a non-invasive early diagnosis biomarker for JME. Transfer learning can solve the problem of insufficient clinical samples, which could avoid overfitting and achieve a better detection effect. However, there is almost no research to detect JME combined with diffusion MRI and transfer learning. In this study, two advanced diffusion MRI methods, high angle resolved diffusion imaging (HARDI) and neurite orientation dispersion and density imaging (NODDI), were used to generate the connectivity matrix which can describe tiny changes in white matter. And three advanced convolutional neural networks (CNN) based transfer learning were applied to detect JME. A total of 30 participants (15 JME patients and 15 normal controls) were analyzed. Among the three CNN models, Inception_resnet_v2 based transfer learning is better at detecting JME than Inception_v3 and Inception_v4, indicating that the "short cut" connection can improve the ability to detect JME. Inception_resnet_v2 achieved to detect JME with the accuracy of 75.2% and the AUC of 0.839. The results support that diffusion MRI and CNN based transfer learning have the potential to improve the automated detection of JME.

Quantitative volumetric analysis of primary glioblastoma multiforme on MRI and 11C-methionine PET: initial study on five patients.

To investigate the discrepancy between 11C-methionine (MET) positron emission tomography (PET) and MRI results in primary glioblastoma multiforme (GBM) through three-dimensional (3D) volumetric analysis, we retrospectively analysed patients with primary GBM who underwent preoperative 3D MRI and MET PET and were operated between June 2016 and January 2017. Tumour delineation and volumetric analysis were conducted using MRIcron software. Tumour volumes defined by MRI (VMRI) were manually drawn slice by slice in axial and sagittal or coronal images of enhanced T1 sequence, while metabolic tumour volumes were automatically segmented in MET PET (VMET) based on three (frontal, occipital and temporal) 3D reference volumes of interest (VOI). Discrepancies were evaluated in terms of both absolute volume and percentage on the combined images. MET PET contours contained and extended beyond MRI contours in all five patients; in a subset of cases, MET PET contours extended to the contralateral hemisphere. The discrepancy between MET uptake and MRI results was 27.67 cm3 (4.20-51.20 cm3), i.e. approximately 39.0% (17.4-64.3%) of the metabolic tumour volume was located outside the volumes of the Gd-enhanced area. Metabolic tumour volume is substantially underestimated by Gd-enhanced area in patients with primary GBM. Quantitative volumetric information derived from MET uptake is useful in defining tumour targets and designing individualised therapy strategies in primary GBM.

Complete prefrontal lobe isolation surgery for recurrent epilepsy: A case report.

Epileptogenic focus resection is less effective for the treatment of frontal lobe epilepsy compared with temporal lobe epilepsy. However, there is currently a lack of effective therapeutic options for patients with frontal lobe epilepsy who are unsuitable for epileptogenic focus resection (such patients with epileptogenic foci in one frontal lobe in which the precise epileptic foci cannot be determined), or who experience recurrent epilepsy following epileptogenic focus resection. The present study reports a patient with frontal lobe epilepsy who underwent successful frontal lobe isolation surgery following a previous unsuccessful epileptogenic focus resection surgery. To ensure complete isolation of the prefrontal lobe, the surgery included division of the anterior commissure and the anterior part of the corpus callosum. The patient was followed-up for 16 months. Although the follow-up electroencephalogram presented a number of sharp waves on the affected side, the patient did not experience any seizures. The results suggest that prefrontal lobe isolation is an effective method of treating frontal lobe epilepsy, as division of the anterior commissure and the anterior part of the corpus callosum ensures disconnection of the prefrontal lobe from other regions of the brain.