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.

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.

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.

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.

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.

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.

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.

Disruption of gray matter morphological networks in patients with paroxysmal kinesigenic dyskinesia.

This study explores the topological properties of brain gray matter (GM) networks in patients with paroxysmal kinesigenic dyskinesia (PKD) and asks whether GM network features have potential diagnostic value. We used 3D T1-weighted magnetic resonance imaging and graph theoretical approaches to investigate the topological organization of GM morphological networks in 87 PKD patients and 115 age- and sex-matched healthy controls. We applied a support vector machine to GM morphological network matrices to classify PKD patients versus healthy controls. Compared with the HC group, the GM morphological networks of PKD patients showed significant abnormalities at the global level, including an increase in characteristic path length (Lp) and decreases in local efficiency (Eloc ), clustering coefficient (Cp), normalized clustering coefficient (γ), and small-worldness (σ). The decrease in Cp was significantly correlated with disease duration and age of onset. The GM morphological networks of PKD patients also showed significant changes in nodal topological characteristics, mainly in the basal ganglia-thalamus circuitry, default-mode network and central executive network. Finally, we used the GM morphological network matrices to classify individuals as PKD patients versus healthy controls, achieving 87.8% accuracy. Overall, this study demonstrated disruption of GM morphological networks in PKD, which might extend our understanding of the pathophysiology of PKD; further, GM morphological network matrices might have the potential to serve as network neuroimaging biomarkers for the diagnosis of PKD.

Individual-level morphological hippocampal networks in patients with Alzheimer's disease.

In patients with Alzheimer's Disease (AD), the hippocampal network has been extensively investigated in previous studies; however, little is known about the morphological network associated with the hippocampus in the AD patients. A total of 68 patients with AD and another 68 gender and age matched healthy subjects were studied. Individual-level morphological hippocampal networks were constructed based on volume and texture features extracted from MRI to study the connections between bilateral hippocampus and 11 other subcortical gray matter structures. The relationship between morphological connections and Mini-Mental State Examination (MMSE) scores was also studied. Connections between bilateral hippocampus and bilateral thalamus, bilateral putamen were significant differences between the AD patients and controls (p < 0.05). There were significantly different in bilateral hippocampal connectivity, and for the left hippocampus, the connection to the right caudate were found to be statistically significant. The morphological connections between left hippocampus and bilateral thalamus (left: R = 0.371, p < 0.001; right: R = 0.411, p < 0.001), bilateral putamen (left: R = 0.383, p < 0.001; right: R = 0.348, p < 0.001), right hippocampus and bilateral thalamus (left: R = 0.370, p < 0.001; right: R = 0.387, p < 0.001), left putamen (R = 0.377, p < 0.001) were significantly positively correlated with the MMSE scores. Similar patterns were observed for left and right hippocampal connectivity and the connections highly associated with MMSE scores were also within the abnormal connections in AD patients.

Brain structural connectome in relation to PRRT2 mutations in paroxysmal kinesigenic dyskinesia.

This study explored the topological characteristics of brain white matter structural networks in patients with Paroxysmal Kinesigenic Dyskinesia (PKD), and the potential influence of the brain network stability gene PRRT2 on the structural connectome in PKD. Thirty-five PKD patients with PRRT2 mutations (PKD-M), 43 PKD patients without PRRT2 mutations (PKD-N), and 40 demographically-matched healthy control (HC) subjects underwent diffusion tensor imaging. Graph theory and network-based statistic (NBS) approaches were performed; the topological properties of the white matter structural connectome were compared across the groups, and their relationships with the clinical variables were assessed. Both disease groups PKD-M and PKD-N showed lower local efficiency (implying decreased segregation ability) compared to the HC group; PKD-M had longer characteristic path length and lower global efficiency (implying decreased integration ability) compared to PKD-N and HC, independently of the potential effects of medication. Both PKD-M and PKD-N had decreased nodal characteristics in the left thalamus and left inferior frontal gyrus, the alterations being more pronounced in PKD-M patients, who also showed abnormalities in the left fusiform and bilateral middle temporal gyrus. In the connectivity characteristics assessed by NBS, the alterations were more pronounced in the PKD-M group versus HC than in PKD-N versus HC. As well as the white matter alterations in the basal ganglia-thalamo-cortical circuit related to PKD with or without PRRT2 mutations, findings in the PKD-M group of weaker small-worldness and more pronounced regional disturbance show the adverse effects of PRRT2 gene mutations on brain structural connectome.

Real-time effects of interictal spikes on hippocampus and amygdala functional connectivity in unilateral temporal lobe epilepsy: An EEG-fMRI study.

OBJECTIVE: To explore the real-time effects of interictal epileptiform discharges (IEDs) on hippocampus and amygdala functional connectivity (FC) in unilateral temporal lobe epilepsy (TLE). METHODS: Patients with unilateral TLE were recruited and underwent simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) scanning. Simultaneous EEG was used to define the prespike (10 s before spike) and postspike (10 s after spike) periods. Dynamic FC analysis was applied using the left/right hippocampus and amygdala separately as a seed region to map the network alterations after IED occurrence. RESULTS: A total of 261 IED events (133 left, 128 right) from 21 patients with unilateral TLE (10 left, 11 right) were analyzed. Left IEDs had a greater influence on the hippocampus-seeded networks, whereas right IEDs affected the amygdala-seeded networks more. Left IEDs disconnected the ipsilateral hippocampus and the default mode network, which might be related to cognitive impairment in TLE. The reward-emotion network (more of the prefrontal-limbic system) and visual network also showed FC changes after left IEDs. The reward-emotion network (more of the reward system) was coactivated after right IEDs, indicating a possible mechanism of forced normalization. SIGNIFICANCE: This study directly linked the disrupted functional networks in TLE to epileptic activities and offered a unique tool for future research to investigate mechanisms of comorbidities in TLE.

Assessment of Utilization and Cost-Effectiveness of Telemedicine Program in Western Regions of China: A 12-Year Study of 249 Hospitals Across 112 Cities.

BACKGROUND: The imbalance in healthcare between urban and rural areas is still a problem in China. In recent decades, China has aimed to develop telemedicine. We assessed the implementation, utilization, and cost-effectiveness of a large telemedicine program across western China. MATERIALS AND METHODS: In 2002-2013, a government-sponsored major telemedicine program was established by West China Hospital of Sichuan University (hub), covering 249 spoke hospitals in 112 cities throughout western China and in 40 medical expertise areas. We analyzed the cross-sectional data from 11,987 consultations conducted at West China Hospital using the telemedicine network over a 12-year period. The types of diseases as well as the diagnosis and treatment changes were assessed. We also performed a cost-savings analysis and a one-way sensitivity analysis. RESULTS: Of the 11,987 teleconsultations, we noted that neoplasms (19.4%), injuries (13.9%), and circulatory diseases (10.3%) were the three most common diagnoses. Teleconsultations resulted in a change of diagnosis in 4,772 (39.8%) patients, and 3,707 (77.7%) of them underwent major diagnosis changes. Moreover, it led to a change of treatment in 6,591 (55.0%) patients, including 3,677 (55.8%) changes not linked to diagnosis changes. The telemedicine network resulted in an estimated net saving of $2,364,525 (if the patients traveled to the hub) or $3,759,014 (if the specialists traveled to the spoke hospitals). CONCLUSIONS: The introduction of telemedicine in China, linking highly specialized major hospitals (hub) with hundreds of small rural hospitals (spoke), can greatly improve the quality, efficiency, and cost-effectiveness of healthcare delivery and utilization. This new Internet-based healthcare model should be utilized more widely in developing countries.

Altered attention networks in benign childhood epilepsy with centrotemporal spikes (BECTS): A resting-state fMRI study.

It is noteworthy that some children with benign childhood epilepsy with centrotemporal spikes (BECTS) show attention problems despite their favorable seizure outcome. Resting-state functional magnetic resonance imaging (fMRI) is a method widely used to detect brain network alterations in neuropsychiatric diseases. We used resting-state functional magnetic resonance imaging (fMRI) to investigate specific brain networks related to attention deficit in children with BECTS. Resting-state fMRI was performed in patients with BECTS with ADHD (n=15) and those with BECTS without ADHD (n=15) and in healthy controls (n=15). Unbiased seed-based whole-brain functional connectivity analysis was used to study the connectivity pattern of three resting-state networks, including the ventral attention network (VAN) and the dorsal attention network (DAN) and the default mode network (DMN). Patients with BECTS with ADHD displayed decreased functional connectivity in the DAN compared with other two groups, while patients with BECTS without ADHD showed increased functional connectivity in the DAN. Moreover, we found increased functional connectivity in the VAN and in the DMN in patients with BECTS with or without ADHD when comparing with controls. These results showed that the newly-diagnosed children with BECTS displayed brain activity alterations in the ventral and dorsal attention networks. The difference in the extent of impairment in the dorsal attention network of patients with BECTS with ADHD and patients with BECTS without ADHD may lead to improved understanding of the underlying neuropathophysiology and treatment of BECTS with ADHD and BECTS without ADHD.

Driving among patients with epilepsy in West China.

PURPOSE: This study was conducted to survey the driving status of PWE in West China and to explore the sociodemographic and clinical factors associated with driving. METHODS: Between October 2012 and October 2013, all adult patients who came to our epilepsy clinic in the West China Hospital were invited to participate. Logistic regression was used to detect the patient factors associated with driving. RESULTS: A total of 657 patients completed this study. We found that 128 (19.5%) of these patients had driven recently (during the past year); among them, 80 (62.5%) experienced at least one seizure in the previous year. A logistic regression suggested that age, being male, being married, having a higher personal income, experiencing no seizure while awake, and taking fewer antiepileptic drugs were independently associated with recent driving. CONCLUSION: This study showed that a considerable proportion of patients continue driving despite uncontrolled seizures. More detailed and operational driving restrictions may be needed for patients in China in order to strike a better balance between patients' quality of life and public safety.

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.

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.

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.

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.