Altered dynamic functional connectivity of motor cerebellum with sensorimotor network and default mode network in juvenile myoclonic epilepsy.

Objective: To investigate whether changes occur in the dynamic functional connectivity (dFC) of motor cerebellum with cerebral cortex in juvenile myoclonic epilepsy (JME). Methods: We adopted resting-state electroencephalography-functional magnetic resonance imaging (EEG-fMRI) and a sliding-window approach to explore the dFC of motor cerebellum with cortex in 36 JME patients compared with 30 and age-matched health controls (HCs). The motor cerebellum was divided into five lobules (I-V, VI, VIIb, VIIIa, and VIIIb). Additionally, correlation analyses were conducted between the variability of dFC and clinical variables in the Juvenile Myoclonic Epilepsy (JME) group, such as disease duration, age at disease onset, and frequency score of myoclonic seizures. Results: Compared to HCs, the JME group presented increased dFC between the motor cerebellum with SMN and DMN. Specifically, connectivity between lobule VIIb and left precentral gyrus and right inferior parietal lobule (IPL); between lobule VIIIa and right inferior frontal gyrus (IFG) and left IPL; and between lobule VIIIb and left middle frontal gyrus (MFG), bilateral superior parietal gyrus (SPG), and left precuneus. In addition, within the JME group, the strength of dFC between lobule VIIIb and left precuneus was negatively (r = -0.424, p = 0.025, Bonferroni correction) related with the frequency score of myoclonic seizures. Conclusion: In patients with JME, there is a functional dysregulation between the motor cerebellum with DMN and SMN, and the variability of dynamic functional connectivity may be closely associated with the occurrence of motor symptoms in JME.

Genetic and phenotypic analyses of PRRT2 positive and negative paroxysmal kinesigenic dyskinesia.

Background: Paroxysmal kinesigenic dyskinesia (PKD) is a rare neurological disorder, characterized by attacks of involuntary movements triggered by sudden action. Variants in proline-rich transmembrane protein 2 (PRRT2) are the most common genetic cause of PKD. Objective: The objective was to investigate the clinical and genetic characteristics of PKD and to establish genotype-phenotype correlations. Methods: We enrolled 219 PKD patients, documented their clinical information and performed PRRT2 screening using Sanger sequencing. Whole exome sequencing was performed on 49 PKD probands without PRRT2 variants. Genotype-phenotype correlation analyses were conducted on the probands. Results: Among 219 PKD patients (99 cases from 39 families and 120 sporadic cases), 16 PRRT2 variants were identified. Nine variants (c.879+4A>G, c.879+5G>A, c.856G>A, c.955G>T, c.884G>C, c.649C>T, c.649dupC, c.649delC and c.696_697delCA) were previously known, while seven were novel (c.367_403del, c.347_348delAA, c.835C>T, c.116dupC, c.837_838insC, c.916_937del and c.902G>A). The mean interval from onset to diagnosis was 7.94 years. Compared to patients without PRRT2 variants, patients with the variants were more likely to have a positive family history, an earlier age of onset and a higher prevalence of falls during pre-treatment attacks (27.14% versus 8.99%, respectively). Patients with truncated PRRT2 variants tend to have bilateral attacks. We identified two transmembrane protein 151A (TMEM151A) variants including a novel variant (c.368G>C) and a reported variant (c.203C>T) in two PRRT2-negative probands with PKD. Conclusion: These findings provide insights on the clinical characteristics, diagnostic timeline and treatment response of PKD patients. PKD patients with truncated PRRT2 variants may tend to have more severe paroxysmal symptoms. This study expands the spectrum of PRRT2 and TMEM151A variants. Carbamazepine and oxcarbazepine are both used as a first-line treatment choice for PKD patients.

Abnormal white matter structural network topological property in patients with temporal lobe epilepsy.

BACKGROUND: Diffusion tensor imaging (DTI) studies have demonstrated white matter (WM) abnormalities in patients with temporal lobe epilepsy (TLE). However, alterations in the topological properties of the WM structural network in patients with TLE remain unclear. Graph theoretical analysis provides a new perspective for evaluating the connectivity of WM structural networks. METHODS: DTI was used to map the structural networks of 18 patients with TLE (10 males and 8 females) and 29 (17 males and 12 females) age- and gender-matched normal controls (NC). Graph theory was used to analyze the whole-brain networks and their topological properties between the two groups. Finally, partial correlation analyses were performed on the weighted network properties and clinical characteristics, namely, duration of epilepsy, verbal intelligence quotient (IQ), and performance IQ. RESULTS: Patients with TLE exhibited reduced global efficiency and increased characteristic path length. A total of 31 regions with nodal efficiency alterations were detected in the fractional anisotropy_ weighted network of the patients. Communication hubs, such as the middle temporal gyrus, right inferior temporal gyrus, left calcarine, and right superior parietal gyrus, were also differently distributed in the patients compared with the NC. Several node regions showed close relationships with duration of epilepsy, verbal IQ, and performance IQ. CONCLUSIONS: Our results demonstrate the disruption of the WM structural network in TLE patients. This study may contribute to the further understanding of the pathological mechanism of TLE.

Cerebral glucose hypometabolism and hypoperfusion of cingulate gyrus: an imaging biomarker of autoimmune encephalitis with psychiatric symptoms.

BACKGROUND: About 60% of autoimmune encephalitis (AE) patients present psychiatric symptoms, but the underlying mechanism remains unknown. This study examined the role of the cingulate cortex in such patients to identify predictive poor psychiatric factors. METHODS: In this study, 49 AE patients and 39 healthy controls were enrolled. AE patients were further divided into two groups based on the presence/absence of psychiatric symptoms. The ratio of the standardized uptake value (SUVR) and relative cerebral blood flow (rCBF) in different regions of the cingulate cortex were calculated through positron emission tomography-computed tomography (PET/CT) and arterial spin labeling (ASL) MRI, and the results were compared among the three groups. In addition, we followed-up on the psychiatric outcomes and identified the risk factors for poor psychiatric prognosis, focusing on the cingulate cortex. RESULTS: More than half of the AE patients (27/49) exhibited psychiatric symptoms. Agitation and thought blocking were typical psychiatric phenotypes, except for anti-glutamic acid decarboxylase 65 (GAD65) encephalitis, which mainly presented with catatonia and a depressed mood. AE patients with psychiatric symptoms experienced reduced metabolism and perfusion of the anterior cingulate cortex (ACC), midcingulate cortex (MCC), and posterior cingulate cortex (PCC). The SUVR of ACC can be used as an independent risk factor of poor psychiatric outcomes, which had an area under the ROC curve (AUC) of 0.865. CONCLUSION: Impaired cingulate cortex function in AE may be the potential mechanism of psychiatric symptoms. Hypometabolism of ACC is an independent prognostic factor predicting an unfavorable psychiatric prognosis in AE.

An Integrative Nomogram for Identifying Cognitive Impairment Using Seizure Type and Cerebral Small Vessel Disease Neuroimaging Markers in Patients with Late-Onset Epilepsy of Unknown Origin.

INTRODUCTION: Cognitive impairment (CI) is a common comorbidity in patients with late-onset epilepsy of unknown origin (LOEU). However, limited data are available on effective screening methods for CI at an early stage. We aimed to develop and internally validate a nomogram for identifying patients with LOEU at risk of CI and investigate the potential moderating effect of education on the relationship between periventricular white matter hyperintensities (PVHs) and cognitive function. METHODS: We retrospectively reviewed the clinical data of 61 patients aged ≥ 55 years diagnosed with LOEU. The main outcome was CI, reflected as an adjusted Montreal Cognition Assessment score of < 26 points. A nomogram based on a multivariable logistic regression model was constructed. Its discriminative ability, calibration, and clinical applicability were tested using calibration plots, the area under the curve (AUC), and decision curves. Internal model validation was conducted using the bootstrap method. The moderating effect of education on the relationship between PVH and cognitive function was examined using hierarchical linear regression. RESULTS: Forty-four of 61 (72.1%) patients had CI. A nomogram incorporating seizure type, total cerebral small vessel disease burden score, and PVH score was built to identify the risk factors for CI. The AUC of the model was 0.881 (95% confidence interval: 0.771-0.994) and 0.78 (95% confidence interval: 0.75-0.8) after internal validation. Higher educational levels blunted the negative impact of PVH on cognitive function. CONCLUSION: Our nomogram provides a convenient tool for identifying patients with LOEU who are at risk of CI. Moreover, our findings demonstrate the importance of education for these patients.

Dynamic alterations of striatal-related functional networks in juvenile absence epilepsy.

PURPOSE: To explore the features of dynamic functional connectivity (dFC) variability of striatal-cortical/subcortical networks in juvenile absence epilepsy (JAE). METHODS: We collected resting-state functional magnetic imaging data from 18 JAE patients and 28 healthy controls. The striatum was divided into six pairs of regions: the inferior-ventral striatum (VSi), superior-ventral striatum (VSs), dorsal-caudal putamen, dorsal-rostral putamen, dorsal-caudate (DC) and ventral-rostral putamen. We assessed the dFC variability of each subdivision in the whole brain using the sliding-window method, and correlated altered circuit with clinical variables in JAE patients. RESULTS: We found altered dFC variability of striatal-cortical/subcortical networks in patients with JAE. The VSs exhibited decreased dFC variability with subcortical regions, and dFC variability between VSs and thalamus was negatively correlated with epilepsy duration. For the striatal-cortical networks, the dFC variability was decreased in VSi-affective network but increased in DC-executive network. The altered dynamics of striatal-cortical networks involved crucial nodes of the default mode network (DMN). CONCLUSION: JAE patients exhibit excessive stability in the striatal-subcortical networks. For striatal-cortical networks in JAE, the striatal-affective circuit was more stable, while the striatal-executive circuit was more variable. Furthermore, crucial nodes of DMN were changed in striatal-cortical networks in JAE.

Age differentially modulates the cortical tracking of the lower and higher level linguistic structures during speech comprehension.

Speech comprehension requires listeners to rapidly parse continuous speech into hierarchically-organized linguistic structures (i.e. syllable, word, phrase, and sentence) and entrain the neural activities to the rhythm of different linguistic levels. Aging is accompanied by changes in speech processing, but it remains unclear how aging affects different levels of linguistic representation. Here, we recorded magnetoencephalography signals in older and younger groups when subjects actively and passively listened to the continuous speech in which hierarchical linguistic structures of word, phrase, and sentence were tagged at 4, 2, and 1 Hz, respectively. A newly-developed parameterization algorithm was applied to separate the periodically linguistic tracking from the aperiodic component. We found enhanced lower-level (word-level) tracking, reduced higher-level (phrasal- and sentential-level) tracking, and reduced aperiodic offset in older compared with younger adults. Furthermore, we observed the attentional modulation on the sentential-level tracking being larger for younger than for older ones. Notably, the neuro-behavior analyses showed that subjects' behavioral accuracy was positively correlated with the higher-level linguistic tracking, reversely correlated with the lower-level linguistic tracking. Overall, these results suggest that the enhanced lower-level linguistic tracking, reduced higher-level linguistic tracking and less flexibility of attentional modulation may underpin aging-related decline in speech comprehension.

Magnetoencephalography-based approaches to epilepsy classification.

Epilepsy is a chronic central nervous system disorder characterized by recurrent seizures. Not only does epilepsy severely affect the daily life of the patient, but the risk of premature death in patients with epilepsy is three times higher than that of the normal population. Magnetoencephalography (MEG) is a non-invasive, high temporal and spatial resolution electrophysiological data that provides a valid basis for epilepsy diagnosis, and used in clinical practice to locate epileptic foci in patients with epilepsy. It has been shown that MEG helps to identify MRI-negative epilepsy, contributes to clinical decision-making in recurrent seizures after previous epilepsy surgery, that interictal MEG can provide additional localization information than scalp EEG, and complete excision of the stimulation area defined by the MEG has prognostic significance for postoperative seizure control. However, due to the complexity of the MEG signal, it is often difficult to identify subtle but critical changes in MEG through visual inspection, opening up an important area of research for biomedical engineers to investigate and implement intelligent algorithms for epilepsy recognition. At the same time, the use of manual markers requires significant time and labor costs, necessitating the development and use of computer-aided diagnosis (CAD) systems that use classifiers to automatically identify abnormal activity. In this review, we discuss in detail the results of applying various different feature extraction methods on MEG signals with different classifiers for epilepsy detection, subtype determination, and laterality classification. Finally, we also briefly look at the prospects of using MEG for epilepsy-assisted localization (spike detection, high-frequency oscillation detection) due to the unique advantages of MEG for functional area localization in epilepsy, and discuss the limitation of current research status and suggestions for future research. Overall, it is hoped that our review will facilitate the reader to quickly gain a general understanding of the problem of MEG-based epilepsy classification and provide ideas and directions for subsequent research.

Distinguishing Patients with MRI-Negative Temporal Lobe Epilepsy from Normal Controls Based on Individual Morphological Brain Network.

Temporal Lobe Epilepsy (TLE) is the most common subtype of focal epilepsy and the most refractory to drug treatment. Roughly 30% of patients do not have easily identifiable structural abnormalities. In other words, MRI-negative TLE has normal MRI scans on visual inspection. Thus, MRI-negative TLE is a diagnostic and therapeutic challenge. In this study, we investigate the cortical morphological brain network to identify MRI-negative TLE. The 210 cortical ROIs based on the Brainnetome atlas were used to define the network nodes. The least absolute shrinkage and selection operator (LASSO) algorithm and Pearson correlation methods were used to calculate the inter-regional morphometric features vector correlation respectively. As a result, two types of networks were constructed. The topological characteristics of networks were calculated by graph theory. Then after, a two-stage feature selection strategy, including a two-sample t-test and support vector machine-based recursive feature elimination (SVM-RFE), was performed in feature selection. Finally, classification with support vector machine (SVM) and leave-one-out cross-validation (LOOCV) was employed for the training and evaluation of the classifiers. The performance of two constructed brain networks was compared in MRI-negative TLE classification. The results indicated that the LASSO algorithm achieved better performance than the Pearson pairwise correlation method. The LASSO algorithm provides a robust method of individual morphological network construction for distinguishing patients with MRI-negative TLE from normal controls.

Differences in the Default Mode Network of Temporal Lobe Epilepsy Patients Detected by Hilbert-Huang Transform Based Dynamic Functional Connectivity.

Resting-state functional connectivity, constructed via functional magnetic resonance imaging, has become an essential tool for exploring brain functions. Aside from the methods focusing on the static state, investigating dynamic functional connectivity can better uncover the fundamental properties of brain networks. Hilbert-Huang transform (HHT) is a novel time-frequency technique that can adapt to both non-linear and non-stationary signals, which may be an effective tool for investigating dynamic functional connectivity. To perform the present study, we investigated time-frequency dynamic functional connectivity among 11 brain regions of the default mode network by first projecting the coherence into the time and frequency domains, and subsequently by identifying clusters in the time-frequency domain using k-means clustering. Experiments on 14 temporal lobe epilepsy (TLE) patients and 21 age and sex-matched healthy controls were performed. The results show that functional connections in the brain regions of the hippocampal formation, parahippocampal gyrus, and retrosplenial cortex (Rsp) were reduced in the TLE group. However, the connections in the brain regions of the posterior inferior parietal lobule, ventral medial prefrontal cortex, and the core subsystem could hardly be detected in TLE patients. The findings not only demonstrate the feasibility of utilizing HHT in dynamic functional connectivity for epilepsy research, but also indicate that TLE may cause damage to memory functions, disorders of processing self-related tasks, and impairment of constructing a mental scene.

Causal effect of psychiatric disorders on epilepsy: A two-sample Mendelian randomization study.

BACKGROUND: This study aims to explore the relationship between psychiatric disorders and the risk of epilepsy using Mendelian randomization (MR) analysis. METHODS: We collected summary statistics of seven psychiatric traits from recent largest genome-wide association study (GWAS), including major depressive disorder (MDD), anxiety disorder, autism spectrum disorder (ASD), bipolar disorder (BIP), attention deficit hyperactivity disorder (ADHD), schizophrenia (SCZ), and insomnia. Then, MR analysis estimates were performed based on International League Against Epilepsy (ILAE) consortium data (ncase  = 15,212 and ncontrol  = 29,677), the results of which were subsequently validated in FinnGen consortium (ncase  = 6260 and ncontrol  = 176,107). Finally, a meta-analysis was conducted based on the ILAE and FinnGen data. RESULTS: We found significant causal effects of MDD and ADHD on epilepsy in the meta-analysis of the ILAE and FinnGen, with corresponding odds ratios (OR) of 1.20 (95% CI 1.08-1.34, p = .001) and 1.08 (95% CI 1.01-1.16, p = .020) by the inverse-variance weighted (IVW) method respectively. MDD increases the risk of focal epilepsy while ADHD has a risk effect on generalized epilepsy. No reliable evidence regarding causal effects of other psychiatric traits on epilepsy was identified. CONCLUSIONS: This study suggests that major depressive disorder and attention deficit hyperactivity disorder may causally increase the risk of epilepsy.

Positron emission tomography in autoimmune encephalitis: Clinical implications and future directions.

18 F-fluoro-deoxyglucose position emission tomography (18 F-FDG-PET) has been proven as a sensitive and reliable tool for diagnosis of autoimmune encephalitis (AE). More attention was paid to this kind of imaging because of the shortage of MRI, EEG, and CSF findings. FDG-PET has been assessed in a few small studies and case reports showing apparent abnormalities in cases where MRI does not. Here, we summarized the patterns (specific or not) in AE with different antibodies detected and the clinical outlook for the wide application of FDG-PET considering some limitations. Specific patterns based on antibody subtypes and clinical symptoms were critical for identifying suspicious AE, the most common of which was the anteroposterior gradient in anti- N -methyl- d -aspartate receptor (NMDAR) encephalitis and the medial temporal lobe hypermetabolism in limbic encephalitis. And the dynamic changes of metabolic presentations in different phases provided us the potential to inspect the evolution of AE and predict the functional outcomes. Except for the visual assessment, quantitative analysis was recently reported in some voxel-based studies of regions of interest, which suggested some clues of the future evaluation of metabolic abnormalities. Large prospective studies need to be conducted controlling the time from symptom onset to examination with the same standard of FDG-PET scanning.

Altered dynamic functional connectivity of striatal-cortical circuits in Juvenile Myoclonic Epilepsy.

OBJECTIVE: To investigate whether the dynamic functional connectivity (dFC) of striatal-cortical circuits changes in juvenile myoclonic epilepsy (JME). METHODS: The resting-state EEG-fMRI and the sliding-window approach were adopted to explore the dynamic striatal-cortical circuitry in thirty JME patients compared with 30 well-matched health controls (HCs). Six pairs of striatal seeds were selected as regions of interests. The correlation analysis was performed to reveal the relationship between the altered dFC variability and clinical variables in JME group. RESULTS: JME patients exhibited increased dFC variability mainly involved in fronto-striatal and striatal-thalamic networks; decreased dFC variability between striatum subdivisions and default mode network (DMN) regions compared with HCs (p<0.05, GRF corrected). In addition, the hypervariability between left ventral-rostral putamen and left medial superior frontal gyrus was positively (r= 0.493, p=0.008) correlated with the mean frequency score of myoclonic seizures in JME group. CONCLUSION: JME presented altered dFC variability in striatal-cortical circuits. The pattern of altered circuits showed increased variability in fronto-striatal and striatal-thalamic networks and decreased variability in striatal-DMN. These results provide novel information about the dynamic neural striatal-cortical circuitry of JME.

A resting-state fMRI study of temporal lobe epilepsy using multivariate pattern analysis and Granger causality analysis.

BACKGROUND AND PURPOSE: Understanding the pathogenesis of temporal lobe epilepsy (TLE) is essential for its diagnosis and treatment. The study aimed to explore regional homogeneity (ReHo) and changes in effective connectivity (EC) between brain regions in TLE patients, hoping to discover potential abnormalities in certain brain regions in TLE patients. METHODS: Resting-state functional magnetic resonance data were collected from 23 TLE patients and 32 normal controls (NC). ReHo was used as a feature of multivariate pattern analysis (MVPA) to explore the ability of its alterations in identifying TLE. Based on the results of the MVPA, certain brain regions were selected as seed points to further explore alterations in EC between brain regions using Granger causality analysis. RESULTS: MVPA results showed that the classification accuracy for the TLE and NC groups was 87.27%, and the right posterior cerebellum lobe, right lingual gyrus (LING_R), right cuneus (CUN_R), and left superior temporal gyrus (STG_L) provided significant contributions. Moreover, the EC from STG_L to right fusiform gyrus (FFG_R) and LING_R and the EC from CUN_R to the right occipital superior gyrus (SOG_R) and right occipital middle gyrus (MOG_R) were altered compared to the NC group. CONCLUSION: The MVPA results indicated that ReHo abnormalities in brain regions may be an important feature in the identification of TLE. The enhanced EC from STG_L to FFG_R and LING_R indicates a shift in language processing to the right hemisphere, and the weakened EC from SOG_R and MOG_R to CUN_R may reveal an underlying mechanism of TLE.

A review of MEG dynamic brain network research.

The dynamic description of neural networks has attracted the attention of researchers for dynamic networks may carry more information compared with resting-state networks. As a non-invasive electrophysiological data with high temporal and spatial resolution, magnetoencephalogram (MEG) can provide rich information for the analysis of dynamic functional brain networks. In this review, the development of MEG brain network was summarized. Several analysis methods such as sliding window, Hidden Markov model, and time-frequency based methods used in MEG dynamic brain network studies were discussed. Finally, the current research about multi-modal brain network analysis and their applications with MEG neurophysiology, which are prospected to be one of the research directions in the future, were concluded.

Predicting the functional outcomes of anti-LGI1 encephalitis using a random forest model.

OBJECTIVES: To establish a model in order to predict the functional outcomes of patients with anti-leucine-rich glioma-inactivated 1 (LGI1) encephalitis and identify significant predictive factors using a random forest algorithm. METHODS: Seventy-nine patients with confirmed LGI1 antibodies were retrospectively reviewed between January 2015 and July 2020. Clinical information was obtained from medical records and functional outcomes were followed up in interviews with patients or their relatives. Neurological functional outcome was assessed using a modified Rankin Scale (mRS), the cutoff of which was 2. The prognostic model was established using the random forest algorithm, which was subsequently compared with logistic regression analysis, Naive Bayes and Support vector machine (SVM) metrics based on the area under the curve (AUC) and the accuracy. RESULTS: A total of 79 patients were included in the final analysis. After a median follow-up of 24 months (range, 8-60 months), 20 patients (25%) experienced poor functional outcomes. A random forest model consisting of 16 variables used to predict the poor functional outcomes of anti-LGI1 encephalitis was successfully constructed with an accuracy of 83% and an F1 score of 60%. In addition, the random forest algorithm demonstrated a more precise predictive performance for poor functional outcomes in patients with anti-LGI1 encephalitis compared with three other models (AUC, 0.90 vs 0.80 vs 0.70 vs 0.64). CONCLUSIONS: The random forest model can predict poor functional outcomes of patients with anti-LGI1 encephalitis. This model was more accurate and reliable than the logistic regression, Naive Bayes, and SVM algorithm.

Pretreatment topological disruptions of whole-brain networks exist in childhood absence epilepsy: A resting-state EEG-fMRI study.

PURPOSE: Childhood absence epilepsy (CAE) is associated with functional changes in specific brain regions and connections. However, little is known about the topological properties of the functional brain connectome in drug naive CAE. METHODS: We adopted the resting-state EEG-fMRI and graph theoretic approach to investigate both local and global brain functional network properties of drug naive CAE during interictal resting state compared with healthy control. In addition, we computed the partial correlation coefficient to estimate the correlation between the functional network metrics and the measured disease duration or the age at seizure onset. RESULTS: The functional connectome in drug naive CAE showed decreased small-worldness and normalized clustering coefficient at the global level. At the nodal level, decreased nodal centralities were mainly in bilateral prefrontal-thalamocortical circuit and increased nodal centralities mainly in left hippocampus and right middle temporal gyrus (p < 0.05). In addition, the duration of the epilepsy was significantly correlated with the nodal efficiency in left middle frontal gyrus (r = -0.627, p = 0.012). CONCLUSION: The pretreatment topological disruptions of whole-brain networks exist in drug naive patients with CAE and the functional impairment mainly involve the prefrontal-thalamocortical circuit. These findings in the homogeneous group of CAE indicate that the aberrant topological organization of functional brain network is an intrinsic feature of CAE and provide topologic insights into understanding the pathophysiological mechanisms of CAE.

Distinguishing patients with temporal lobe epilepsy from normal controls with the directed graph measures of resting-state fMRI.

PURPOSE: Changes in the brain networks of patients with temporal lobe epilepsy (TLE) have been extensively explored, but the biological mechanisms underlying these alterations remain unclear. Here, we aim to identify changes in brain networks in patients with TLE and provide an accurate algorithm for distinguishing these patients from normal controls (NC) with graph-theoretical approach and advanced machine learning methods. METHODS: Directed network construction was applied to resting-state functional magnetic resonance imaging (rs-fMRI) data from 55 subjects (23 TLE patients and 32 NC), and 13 directed graph measures were calculated. Two-sample t-test selected features were used as inputs to a support vector machine (SVM). The leave-one-out cross-validation method was used in measuring classification performance. RESULTS: An accuracy of 94.55% (sensitivity = 91.30%, f1-score = 93.33%, Cohen's kappa = 0.9345) was achieved for the classification of TLE patients and NC with optimal features and SVM classifier. According to the results of the two-sample t-test results, TLE disease impacted several areas of the brain, including the temporal, parietal, occipital, posterior cingulate, angular gyrus, superior frontal gyrus, and cerebellum regions in degree centrality, flow coefficient and node efficiency. There was a significant correlation between performance IQ and the flow coefficient of the left posterior cerebellum lobe in TLE group. CONCLUSION: The study confirmed the validity of Granger causality analysis in constructing directed brain networks. The proposed machine learning approach based on directed graph measures may serve as a biomarker for the diagnosis of TLE to assist in the early diagnosis of TLE patients and intervention in treatment plans.

The prognosis of late-onset anti-N-methyl-D-aspartate receptor encephalitis in China.

OBJECTIVES: Early-onset anti-N-methyl-D-aspartate receptor encephalitis (anti-NMDARE) differs from late-onset anti-NMDARE regarding clinical characteristics. Until recently, research focusing on prognosis of elder adults has been scarce and showed inconsistent results. This study aims to evaluate the prognosis of late-onset anti-NMDARE in China. MATERIALS & METHODS: One hundred and twelve adults diagnosed as anti-NMDARE in four hospitals in China were reviewed retrospectively. Outcome data were assessed using modified Rankin Scale (mRS) score in short term (3 months after discharge) and long term (≥12 months after discharge). The relapse rate was also computed. Multivariable logistic regression was used to evaluate whether there are substantial differences in functional outcomes and recurrence rate across two groups. RESULTS: Of the 112 patients with anti-NMDARE, 81 (72.3%) were early-onset disease and 31 (27.7%) were late-onset disease. Of these, all had short-term follow-up and 70 completed long-term follow-up. Late-onset anti-NMDARE group showed better short-term (OR 2.70, 95% CI 1.09-6.71) and long-term prognoses (OR 10.25, 95% CI 1.90-55.15). Recurrence rates were statistically different between the groups (OR 4.25, 95% CI 1.22-14.75). CONCLUSION: The prognosis for anti-NMDARE in China was poorer for older adults relative to younger adults. The relapse rates were higher in late-onset group compared to early-onset group.

Identification of Epileptogenic and Non-epileptogenic High-Frequency Oscillations Using a Multi-Feature Convolutional Neural Network Model.

Accurately identifying epileptogenic zone (EZ) using high-frequency oscillations (HFOs) is a challenge that must be mastered to transfer HFOs into clinical use. We analyzed the ability of a convolutional neural network (CNN) model to distinguish EZ and non-EZ HFOs. Nineteen medically intractable epilepsy patients with good surgical outcomes 2 years after surgery were studied. Five-minute interictal intracranial electroencephalogram epochs of slow-wave sleep were selected randomly. Then 5 s segments of ripples (80-200 Hz) and fast ripples (FRs, 200-500 Hz) were detected automatically. The EZs and non-EZs were identified using the surgery resection range. We innovatively converted all epochs into four types of images using two scales: original waveforms, filtered waveforms, wavelet spectrum images, and smoothed pseudo Wigner-Ville distribution (SPWVD) spectrum images. Two scales were fixed and fitted scales. We then used a CNN model to classify the HFOs into EZ and non-EZ categories. As a result, 7,000 epochs of ripples and 2,000 epochs of FRs were randomly selected from the EZ and non-EZ data for analysis. Our CNN model can distinguish EZ and non-EZ HFOs successfully. Except for original ripple waveforms, the results from CNN models that are trained using fixed-scale images are significantly better than those from models trained using fitted-scale images (p < 0.05). Of the four fixed-scale transformations, the CNN based on the adjusted SPWVD (ASPWVD) produced the best accuracies (80.89 ± 1.43% and 77.85 ± 1.61% for ripples and FRs, respectively, p < 0.05). The CNN using ASPWVD transformation images is an effective deep learning method that can be used to classify EZ and non-EZ HFOs.