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An emerging literature suggests that the neuropsychological sequelae of pediatric temporal lobe epilepsy (TLE) are characterized by a continuum of cognitive phenotypes that range in type and severity. The goal of the present investigation was to better characterize the neuropsychological networks that underlie these phenotypes.The study included 59 patients with TLE who were empirically categorized into three cognitive phenotypes (normal, focal, and generalized impairment). Nine neuropsychological measures representing multiple cognitive domains (i.e., reasoning, language, visouperception, memory, and executive function) were examined by graph theory to characterize the global network properties of the cognitive phenotypes.Across the cognitive phenotype groups (i.e., normal, focal, generalized impaired) the following findings emerged: (1) the adjacency matrices demonstrated different patterns of association between cognitive measures within the neuropsychological network; (2) global measures including global efficiency (GE) and average clustering coefficient (aCC) showed a stepwise increase across the range of impaired pediatric TLE phenotypes; however, modularity (M) demonstrated the opposite pattern.Cognitive networks in pediatric TLE demonstrate stepwise perturbation in underlying neuropsychological networks. Graph theory offers a novel approach to examine cognitive abnormalities in pediatric TLE that may be applied to other pediatric epilepsies.

Identification of the seizure onset zone (SOZ) is a challenging task in epilepsy surgery. Patients with epilepsy have an altered brain network, allowing connectivity-based analyses to have a great potential in SOZ identification. We investigated a dynamical directed connectivity analysis utilizing ictal intracranial electroencephalographic (iEEG) recordings and proposed an algorithm for SOZ identification based on grouping iEEG contacts.Granger Causality was used for directed connectivity analysis in this study. The intracranial contacts were grouped into visually detected contacts (VDCs), which were identified as SOZ by epileptologists, and non-resected contacts (NRCs). The intragroup and intergroup directed connectivity for VDCs and NRCs were calculated around seizure onset. We then proposed an algorithm for SOZ identification based on the cross-correlation of intragroup outflow and inflow of SOZ candidate contacts.Our results revealed that the intragroup connectivity of VDCs (VDC→VDC) was significantly larger than the intragroup connectivity of NRCs (NRC→NRC) and the intergroup connectivity between NRCs and VDCs (NRC→VDC) around seizure onset. We found that the proposed algorithm had 90.1% accuracy for SOZ identification in the seizure-free patients. Comparison with Existing Methods The existing connectivity-based methods for SOZ identification often use either outflow or inflow. In this study, SOZ contacts were identified by integrating outflow and inflow based on the cross correlation between these two measures.The proposed group-based dynamical connectivity analysis in this study can aid our understanding of underlying seizure network and may be used to assist in identifying the SOZ contacts before epilepsy surgery.

Focal epilepsy originates within networks in one hemisphere. However, previous studies have investigated network topologies for the entire brain. In this study, magnetoencephalography (MEG) was used to investigate functional intra‐hemispheric networks of healthy controls (HCs) and patients with left‐ or right‐hemispheric temporal lobe or temporal plus extra‐temporal lobe epilepsy. 22 HCs, 25 left patients (LPs), and 16 right patients (RPs) were enrolled. The debiased weighted phase lag index was used to calculate functional connectivity between 246 brain regions in six frequency bands. Global efficiency, characteristic path length, and transitivity were computed for left and right intra‐hemispheric networks. The right global graph measures (GGMs) in the theta band were significantly different (p

Brain extraction is one of the most important preprocessing steps in cerebral magnetic resonance (MR) image analysis. Brain extraction from neonatal MR images is particularly challenging due to significant differences in head size and shape between neonates and rapid changes in neonatal brain structure in the weeks and months after birth. In this work, a multi-atlas-based neonatal brain extraction method using atlas library clustering and local label fusion (NOBELL) is presented. In NOBELL, an affinity propagation (AP) approach is first applied to cluster images of an atlas library into clusters represented by exemplars, which are used to select best matching clusters for target images. A local weighted voting strategy based on Jacobian determinant ranking is then employed to extract brain from target images using training images in best matching clusters. The performance of NOBELL was evaluated on T2- and T1-weighted scans of 40 neonates aged between 37 and 44 weeks. NOBELL outperformed two popular brain extraction tools, FSL’s Brain Extraction Tool (BET) and BrainSuite’s Brain Surface Extractor (BSE), and achieved higher accuracy with brain masks very close to manually extracted ones. NOBELL showed an average Jaccard coefficient of 0.974 (0.942) on T2 (T1)-weighted images in comparison with 0.908 (0.602) and 0.845 (0.762) achieved by BSE, and BET, respectively. NOBELL allows for accurate and efficient brain extraction, a crucial step in brain MRI applications such as accurate brain tissue segmentation and volume estimation as well as accurate cortical surface delineation in neonates.

A broad spectrum of emotional-behavioral problems have been reported in pediatric temporal lobe epilepsy (TLE), but with considerable variability in their presence and nature of expression, which hampers precise identification and treatment. The present study aimed to empirically identify latent patterns or behavioral phenotypes and their correlates.Data included parental ratings of emotional-behavioral status on the Behavior Assessment System for Children, 2nd Edition (BASC-2) of 81 children (mean age = 11.79, standard deviation [SD] = 3.93) with TLE. The nine clinical subscales were subjected to unsupervised machine learning to identify behavioral subgroups. To explore concurrent validity and the underlying composition of the identified clusters, we examined demographic factors, seizure characteristics, psychosocial factors, neuropsychological performance, psychiatric status, and health-related quality of life (HRQoL).Three behavioral phenotypes were identified, which included no behavioral concerns (Cluster 1, 43% of sample), externalizing problems (Cluster 2, 41% of sample), and internalizing problems (Cluster 3, 16% of sample). Behavioral phenotypes were characterized by important differences across clinical seizure variables, psychosocial/familial factors, everyday executive functioning, and HRQoL. Cluster 2 was associated with younger child age, lower maternal education, and higher rate of single-parent households. Cluster 3 was associated with older age at epilepsy onset and higher rates of hippocampal sclerosis and parental psychiatric history. Both Cluster 2 and 3 demonstrated elevated family stress. Concurrent validity was demonstrated through the association of psychiatric (i.e., rate of Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) disorders and psychotropic medication) and parent-rated HRQoL variables.Youth with TLE present with three distinct behavioral phenotypes that correspond with important clinical and sociodemographic markers. The current findings demonstrate the variability of behavioral presentations in youth with TLE and provide a preliminary framework for screening and targeting intervention to enhance support for youth with TLE and their families.

Background: In recent years, predicting and modeling the progression of Alzheimer’s disease (AD) based on neuropsychological tests has become increasingly appealing in AD research. Objective: In this study, we aimed to predict the neuropsychological scores and investigate the non-linear progression trend of the cognitive declines based on multimodal neuroimaging data. Methods: We utilized unimodal/bimodal neuroimaging measures and a non-linear regression method (based on artificial neural networks) to predict the neuropsychological scores in a large number of subjects (n = 1143), including healthy controls (HC) and patients with mild cognitive impairment non-converter (MCI-NC), mild cognitive impairment converter (MCI-C), and AD. We predicted two neuropsychological scores, i.e., the clinical dementia rating sum of boxes (CDRSB) and Alzheimer’s disease assessment scale cognitive 13 (ADAS13), based on structural magnetic resonance imaging (sMRI) and positron emission tomography (PET) biomarkers. Results: Our results revealed that volumes of the entorhinal cortex and hippocampus and the average fluorodeoxyglucose (FDG)-PET of the angular gyrus, temporal gyrus, and posterior cingulate outperform other neuroimaging features in predicting ADAS13 and CDRSB scores. Compared to a unimodal approach, our results showed that a bimodal approach of integrating the top two neuroimaging features (i.e., the entorhinal volume and the average FDG of the angular gyrus, temporal gyrus, and posterior cingulate) increased the prediction performance of ADAS13 and CDRSB scores in the converting and stable stages of MCI and AD. Finally, a non-linear AD progression trend was modeled to describe the cognitive decline based on neuroimaging biomarkers in different stages of AD. Conclusion: Findings in this study show an association between neuropsychological scores and sMRI and FDG-PET biomarkers from normal aging to severe AD.

Cortical stimulation mapping is the gold standard for presurgical language mapping; however, it cannot be reliably performed in very young patients. Language mapping using noninvasive modalities is also challenging in very young patients. Although utility of language mapping using power of high-gamma in electrocorticographic recordings was demonstrated in adults and older children, there is a gap of knowledge in the ability of this procedure for localizing language-specific cortex in very young patients. We describe a case of a 2-year-old patient who, to our knowledge, is the youngest person to undergo successful high-gamma electrocorticographic presurgical language mapping for localization of the expressive language cortex (Broca area). The surgical plan was to resect a cortical tuber within the left inferior frontal gyrus and there was a strong concern about postoperative language deficit after resection. Presurgical language mapping using noninvasive modalities were attempted without success. Cortical stimulation mapping was not feasible in this patient. Therefore, high-gamma electrocorticography was the only viable option for language mapping, and it successfully localized the expressive language cortex. The patient underwent surgery for resection of the IFG tuber based on results of high-gamma electrocorticography and had no postoperative language deficit. High-gamma electrocorticography can be used for localizing language-specific cortex, especially Broca’s area, in very young patients.

Statistical significance testing is a necessary step in connectivity analysis. Several statistical test methods have been employed to assess the significance of functional connectivity, but the performance of these methods has not been thoroughly evaluated. In addition, the effects of the intrinsic brain connectivity and background couplings on performance of statistical test methods in task-based studies have not been investigated yet. The background couplings may exist independent of cognitive state and can be observed on both pre- and post-stimulus time intervals. The background couplings may be falsely detected by a statistical test as task-related connections, which can mislead interpretations of the task-related functional networks. The aim of this study was to investigate the relative performance of four commonly used non-parametric statistical test methods-surrogate, demeaned surrogate, bootstrap resampling, and Monte Carlo permutation methods-in the presence of background couplings and noise, with different signal-to-noise ratios (SNRs). Using simulated electrocorticographic (ECoG) datasets and phase locking value (PLV) as a measure of functional connectivity, we evaluated the performances of the statistical test methods utilizing sensitivity, specificity, accuracy, and receiver operating curve (ROC) analysis. Furthermore, we calculated optimal p values for each statistical test method using the ROC analysis, and found that the optimal p values were increased by decreasing the SNR. We also found that the optimal p value of the bootstrap resampling was greater than that of other methods. Our results from the simulation datasets and a real ECoG dataset, as an illustrative case report, revealed that the bootstrap resampling is the most efficient non-parametric statistical test for identifying the significant PLV of ECoG data, especially in the presence of background couplings.

Purpose Resting‐state functional magnetic resonance imaging (Rs‐fMRI) can be used to investigate the alteration of resting‐state brain networks (RSNs) in patients with Parkinson's disease (PD) when compared with healthy controls (HCs). The aim of this study was to identify the differences between individual RSNs and reveal the most important discriminatory characteristic of RSNs between the HCs and PDs. Methods This study used Rs‐fMRI data of 23 patients with PD and 18 HCs. Group independent component analysis (ICA) was performed, and 23 components were extracted by spatially overlapping the components with a template RSN. The extracted components were used in the following three methods to compare RSNs of PD patients and HCs: (1) a subject‐specific score based on group RSNs and a dual‐regression approach (namely RSN scores); (2) voxel‐wise comparison of the RSNs in the PD patient and HC groups using a nonparametric permutation test; and (3) a hierarchical clustering analysis of RSNs in the PD patient and HC groups. Results The results of RSN scores showed a significant decrease in connectivity in seven ICs in patients with PD compared with HCs, and this decrease was particularly striking on the lateral and medial posterior occipital cortices. The results of hierarchical clustering of the RSNs revealed that the cluster of the default mode network breaks down into the three other clusters in PD patients. Conclusion We found various characteristics of the alteration of the RSNs in PD patients compared with HCs. Our results suggest that different characteristics of RSNs provide insights into the biological mechanism of PD., Deriving resting‐state networks using group independent component analysis. Applying an RSN score based on dual regression approach and statistical analysis. Identification of the significantly different regions of the cerebellum and occipital pole. The disintegration of the default mode network cluster into the other RSN clusters in PD.

Since its adoption in clinical practice, analysis and interpretation of magnetoencephalography (MEG) recordings in patients with epilepsy have evolved as a result of multidisciplinary input, with the aim of developing formalized criteria that can yield reliable results and that are complementary to other modalities used for diagnostic assessment in this cohort. The purpose of this chapter is to familiarize the interested practitioner with the process of analyzing interictal MEG recordings, using examples from clinical cases to illustrate the utility of this modality and acknowledging some of its limitations. To achieve this, the reader will first be presented with consideration of basic quality assessment measures when interpreting clinical MEG recordings. Subsequently, a description will be provided on a clinically valid approach that is standard of practice for performing source localization of epileptiform transients. Specifically, the latter is discussed in the broader context of identifying various interictal epileptiform transients in MEG recordings and determining their anatomical extent in order to better characterize the irritative zone. Finally, a review is provided for emerging methods in MEG connectivity analysis and their potential utility in clinical practice for elucidating epileptogenic networks.

Purpose To determine the preirradiation baseline association of white matter integrity with neurocognitive function and to assess posttreatment changes in pediatric patients with craniopharyngioma treated with proton therapy. Methods and Materials Ninety children and adolescents (2-20 years old) with craniopharyngioma were treated with proton therapy (54 Gy[RBE]) in a prospective therapeutic trial. Neurocognitive performance at the postoperative baseline before proton therapy and diffusion tensor imaging (DTI) data acquired at baseline and at annual follow-up were analyzed. Tract-based spatial statistics and structural connectomics were used to derive global and local white matter features from DTI. Baseline DTI features were compared for patients with average and below-average neurocognitive performance. Longitudinal DTI data were analyzed to determine the proton dose effect on white matter structures in relation to the irradiated brain volume and baseline age. Results Before proton therapy, patients with below-average working memory, processing speed, verbal fluency, verbal learning, or fine motor dexterity exhibited more globally degraded white matter structures compared with their counterparts with average performance, as indicated by lower mean fractional anisotropy, decreased global efficiency, or higher modularity. Surgery, obstructive hydrocephalus, and preoperative hypothalamic involvement appeared to be related to this degradation. In local analyses, tract-based spatial statistics revealed left-lateralized associations with verbal and motor functions, which supported surgical approaches to midline tumors via the right hemisphere. The mean fractional anisotropy of the brain and the global efficiency derived from DTI increased over the 5 years after proton therapy. The rate of increase was lower with larger irradiated brain volumes and in older children. Conclusions Below-average baseline neurocognitive performance in patients with craniopharyngioma before proton therapy appeared to be related to structural degradation of white matter tracts. Posttherapy longitudinal DTI showed improving trends in global integrity and efficiency measures, particularly in children in whom a smaller brain volume was irradiated.

Background The effects of statistical testing on the results of multivariate autoregressive (MVAR)-based effective connectivity analysis have not been adequately investigated, and it is still unclear which statistical test can provide the most accurate results. New methods Using simulated and real electrocorticographic (ECoG) data, we investigated the performance of three nonparametric statistical tests – Monte Carlo permutation, bootstrap resampling, and surrogate data method in MVAR-based effective connectivity analysis. Receiver operating characteristic (ROC) analysis and area under the ROC curve (AUC) were used to assess the performance of each statistical test method. In addition, we found optimal p-values for each method based on ROC analysis. Finally, we investigated the application of statistical tests on partial directed coherence analysis of ECoG data collected in a patient with epilepsy. Results The bootstrap statistical test performed more accurately than other methods. The surrogate method slightly outperformed the Monte Carlo permutation method. Optimal p-values of statistical tests depended on signal-to-noise ratio (SNR) of data, and its value increased by reducing SNR of data. By considering the typical SNR range of electrophysiological data, we recommended an optimal p-value range for the application of each statistical test method. Comparison with existing methods Limited studies have investigated the performance of statistical tests for MVAR-based effective connectivity analysis. For the first time, we have investigated the effects of baseline connections on the various performances of statistical tests. Conclusions We recommend utilizing the bootstrap statistical test with p-value between 0.05 and 0.1 for effective connectivity analysis of ECoG data.

Vagus nerve stimulation (VNS) is a low-risk surgical option for patients with drug resistant epilepsy, although it is impossible to predict which patients may respond to VNS treatment. Resting-state magnetoencephalography (rs-MEG) connectivity analysis has been increasingly utilized to investigate the impact of epilepsy on brain networks and identify alteration of these networks after different treatments; however, there is no study to date utilizing this modality to predict the efficacy of VNS treatment. We investigated whether the rs-MEG network topology before VNS implantation can be used to predict efficacy of VNS treatment. Twenty-three patients with epilepsy who had MEG before VNS implantation were included in this study. We also included 89 healthy control subjects from the Human Connectome Project. Using the phase-locking value in the theta, alpha, and beta frequency bands as a measure of rs-MEG functional connectivity, we calculated three global graph measures: modularity, transitivity, and characteristic path length (CPL). Our results revealed that the rs-MEG graph measures were significantly heritable and had an overall good test-retest reliability, and thus these measures may be used as potential biomarkers of the network topology. We found that the modularity and transitivity in VNS responders were significantly larger and smaller, respectively, than those observed in VNS non-responders. We also observed that the modularity and transitivity in three frequency bands and CPL in delta and beta bands were significantly different in controls than those found in responders or non-responders, although the values of the graph measures in controls were closer to those of responders than non-responders. We used the modularity and transitivity as input features of a naïve Bayes classifier, and achieved an accuracy of 87% in classification of non-responders, responders, and controls. The results of this study revealed that MEG-based graph measures are reliable biomarkers, and that these measures may be used to predict seizure outcome of VNS treatment, Highlights • We investigated whether rs-MEG network topology can predict efficacy of VNS treatment • We calculated graph measures based on PLV as a measure of rs-MEG functional connectivity • The graph measures were significantly heritable and had an overall good test-retest reliability • The modularity and transitivity were significantly different in responders versus non-responders • We achieved an accuracy of 87% in classification of non-responders, responders, and controls • Modularity and transitivity of rs-MEG may be used as clinical biomarkers for prediction of VNS outcome

As a noninvasive imaging modality able to show the dynamic changes in neurologic activity, functional magnetic resonance imaging has revolutionized the ability to both map and further understand the functional regions of the brain. Current applications range from neurosurgical planning to an enormous variety of investigational applications across many diverse specialties. The main purpose of this article is to provide a foundational understanding of how functional magnetic resonance imaging is being used in research by outlining the underlying basic science, specific methods, and direct investigational and clinical applications. In addition, the use of functional magnetic resonance imaging in current dermatological research, especially in relation to studies concerning the skin‒brain axis, is explicitly addressed. This article also touches on the advantages and limitations concerning functional magnetic resonance imaging in comparison with other similar techniques.

Purpose Using a novel technique based on phase locking value (PLV), we investigated the potential for features extracted from electrocorticographic (ECoG) recordings to serve as biomarkers to identify the seizure onset zone (SOZ). Methods We computed the PLV between the phase of the amplitude of high gamma activity (80–150Hz) and the phase of lower frequency rhythms (4–30Hz) from ECoG recordings obtained from 10 patients with epilepsy (21 seizures). We extracted five features from the PLV and used a machine learning approach based on logistic regression to build a model that classifies electrodes as SOZ or non-SOZ. Results More than 96% of electrodes identified as the SOZ by our algorithm were within the resected area in six seizure-free patients. In four non-seizure-free patients, more than 31% of the identified SOZ electrodes by our algorithm were outside the resected area. In addition, we observed that the seizure outcome in non-seizure-free patients correlated with the number of non-resected SOZ electrodes identified by our algorithm. Conclusion This machine learning approach, based on features extracted from the PLV, effectively identified electrodes within the SOZ. The approach has the potential to assist clinicians in surgical decision-making when pre-surgical intracranial recordings are utilized.

Brain network alterations in patients with Alzheimer's disease (AD) has been the subject of much investigation, but the biological mechanisms underlying these alterations remain poorly understood. Here, we aim to identify the changes in brain networks in patients with AD and mild cognitive impairment (MCI), and provide an accurate algorithm for classification of these patients from healthy control subjects (HC) by using a graph theoretical approach and advanced machine learning methods. Multivariate Granger causality analysis was performed on resting-state functional magnetic resonance imaging (rs-fMRI) data of 34 AD, 89 MCI, and 45 HC to calculate various directed graph measures. The graph measures were used as the original feature set for the machine learning algorithm. Filter and wrapper feature selection methods were applied to the original feature set to select an optimal subset of features. An accuracy of 93.3% was achieved for classification of AD, MCI, and HC using the optimal features and the naïve Bayes classifier. We also performed a hub node analysis and found that the number of hubs in HC, MCI, and AD were 12, 10, and 9, respectively, suggesting that patients with AD experience disturbance of critical communication areas in their brain network as AD progresses. The findings of this study provide insight into the neurophysiological mechanisms underlying MCI and AD. The proposed classification method highlights the potential of directed graph measures of rs-fMRI data for identification of the early stage of AD.

Comprehension of narratives constitutes a fundamental part of our everyday life experience. Although the neural mechanism of auditory narrative comprehension has been investigated in some studies, the neural correlates underlying this mechanism and its heritability remain poorly understood. We investigated comprehension of naturalistic speech in a large, healthy adult population (n = 429; 176/253 M/F; 22–36 years of age) consisting of 192 twin pairs (49 monozygotic and 47 dizygotic pairs) and 237 of their siblings. We used high quality functional MRI datasets from the Human Connectome Project (HCP) in which a story-based paradigm was utilized for the auditory narrative comprehension. Our results revealed that narrative comprehension was associated with activations of the classical language regions including superior temporal gyrus (STG), middle temporal gyrus (MTG), and inferior frontal gyrus (IFG) in both hemispheres, though STG and MTG were activated symmetrically and activation in IFG were left-lateralized. Our results further showed that the narrative comprehension was associated with activations in areas beyond the classical language regions, e.g. medial superior frontal gyrus (SFGmed), middle frontal gyrus (MFG), and supplementary motor area (SMA). Of subcortical structures, only the hippocampus was involved. The results of heritability analysis revealed that the oral reading recognition and picture vocabulary comprehension were significantly heritable (h 2 > 0.56, p 0.33, p

Objective We studied spatiotemporal dynamics of electrocorticographic (ECoG) high-gamma modulation (HGM) during visual naming. Methods In 8 patients, aged 4–19 years, with left hemisphere subdural electrodes, propagation of ECoG HGM during overt visual naming was mapped with trial-averaged time-frequency analysis. Group-level synthesis was performed by transforming all electrodes to a standard space and assigning cortical parcels based on a reference atlas. Results After image display following cortical parcels were activated: inferior occipital, caudal angular, fusiform, and middle temporal gyri, and superior temporal sulcus [0–400 ms]; rostral pars triangularis (A45r), inferior frontal sulcus, caudal dorsolateral premotor cortex (A6cdl) [300–600 ms]; caudal ventrolateral premotor cortex (A6cvl), caudal pars triangularis (A45c), pars opercularis (A44) [400–800 ms]; primary sensorimotor cortex [600–1400 ms], with most prominent HGM in glossolaryngeal region (A4tl). Lastly, auditory cortex (A41/A42) and superior temporal gyrus (A22) were activated [900 ms–1.4 s]. After 1.5 s, HGM decreased globally, except in ventrolateral premotor cortex. Conclusions During visual naming, ECoG HGM shows a sequential but overlapping spatiotemporal course through cortical regions. We provide neurophysiologic validation for a model of visual naming incorporating both modular and distributed cortical processing. This may explain cognitive deficits seen in some patients after surgery involving HGM naming sites outside perisylvian language cortex.

Accurate assessment of motor function is critical, particularly in the context of presurgical functional mapping. There are a few noninvasive tools available for preoperative motor mapping in older children and adults undergoing surgery for brain tumor or refractory epilepsy including fMRI, magnetoencephalography (MEG), and transcranial magnetic stimulation (TMS).1,2 However, assessing motor function is challenging in very young children. Direct cortical stimulation, a standard procedure in adults, is associated with a high incidence of intraoperative seizures, and is often unsuccessful in young children.3,4 Alternately, noninvasive motor mapping using MEG and fMRI attempted during sleep or under sedation primarily localize the somatosensory cortex from which the location of motor cortex is inferred.5,6 Furthermore, these methods have a low success rate (∼25%) and have risks associated with sedation.6 However, TMS has the advantage of mapping the motor cortex directly without requiring sedation. We have previously reported on successful mapping of the motor cortex in young children aged between 17 and 35 months.7 Here we describe a case of an 11-month-old infant who is, to our knowledge, the youngest person to undergo successful presurgical motor mapping with TMS.

Highlights • There is a need to preoperatively identify children who will respond to VNS. • There is similarity between the afferent vagus and median nerve projections to S1. • Median nerve somatosensory evoked fields identify those who will respond to VNS., There is an unmet need to develop robust predictive algorithms to preoperatively identify pediatric epilepsy patients who will respond to vagus nerve stimulation (VNS). Given the similarity in the neural circuitry between vagus and median nerve afferent projections to the primary somatosensory cortex, the current study hypothesized that median nerve somatosensory evoked field(s) (SEFs) could be used to predict seizure response to VNS. Retrospective data from forty-eight pediatric patients who underwent VNS at two different institutions were used in this study. Thirty-six patients (“Discovery Cohort”) underwent preoperative electrical median nerve stimulation during magnetoencephalography (MEG) recordings and 12 patients (“Validation Cohort”) underwent preoperative pneumatic stimulation during MEG. SEFs and their spatial deviation, waveform amplitude and latency, and event-related connectivity were calculated for all patients. A support vector machine (SVM) classifier was trained on the Discovery Cohort to differentiate responders from non-responders based on these input features and tested on the Validation Cohort by comparing the model-predicted response to VNS to the known response. We found that responders to VNS had significantly more widespread SEF localization and greater functional connectivity within limbic and sensorimotor networks in response to median nerve stimulation. No difference in SEF amplitude or latencies was observed between the two cohorts. The SVM classifier demonstrated 88.9% accuracy (0.93 area under the receiver operator characteristics curve) on cross-validation, which decreased to 67% in the Validation cohort. By leveraging overlapping neural circuitry, we found that median nerve SEF characteristics and functional connectivity could identify responders to VNS.

Structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) have provided promising results in the diagnosis of Alzheimer's disease (AD), though the utility of integrating sMRI with rs-fMRI has not been explored thoroughly. We investigated the performances of rs-fMRI and sMRI in single modality and multi-modality approaches for classifying patients with mild cognitive impairment (MCI) who progress to probable AD-MCI converter (MCI-C) from those with MCI who do not progress to probable AD-MCI non-converter (MCI-NC). The cortical and subcortical measurements, e.g. cortical thickness, extracted from sMRI and graph measures extracted from rs-fMRI functional connectivity were used as features in our algorithm. We trained and tested a support vector machine to classify MCI-C from MCI-NC using rs-fMRI and sMRI features. Our algorithm for classifying MCI-C and MCI-NC utilized a small number of optimal features and achieved accuracies of 89% for sMRI, 93% for rs-fMRI, and 97% for the combination of sMRI with rs-fMRI. To our knowledge, this is the first study that investigated integration of rs-fMRI and sMRI for identification of the early stage of AD. Our findings shed light on integration of sMRI with rs-fMRI for identification of the early stages of AD.

Mild traumatic brain injury (mTBI) may affect normal cognition and behavior by disrupting the functional connectivity networks that mediate efficient communication among brain regions. In this study, we analyzed brain connectivity profiles from resting state Magnetoencephalographic (MEG) recordings obtained from 31 mTBI patients and 55 normal controls. We used phase-locking value estimates to compute functional connectivity graphs to quantify frequency-specific couplings between sensors at various frequency bands. Overall, normal controls showed a dense network of strong local connections and a limited number of long-range connections that accounted for approximately 20% of all connections, whereas mTBI patients showed networks characterized by weak local connections and strong long-range connections that accounted for more than 60% of all connections. Comparison of the two distinct general patterns at different frequencies using a tensor representation for the connectivity graphs and tensor subspace analysis for optimal feature extraction showed that mTBI patients could be separated from normal controls with 100% classification accuracy in the alpha band. These encouraging findings support the hypothesis that MEG-based functional connectivity patterns may be used as biomarkers that can provide more accurate diagnoses, help guide treatment, and monitor effectiveness of intervention in mTBI., Highlights • We analyzed resting state connectivity profiles in 31 mTBI patients and 55 controls. • We quantified frequency-specific connectivity couplings using phase-locking values. • Normal control networks showed dense local and sparse long-range connections. • TBI patient networks showed sparse local and dense long-range connections. • Tensor subspace analysis could classify subjects with 100% accuracy in the α band

Objective To predict the postoperative language outcome using the support vector regression (SVR) and results of multimodal presurgical language mapping. Methods Eleven patients with epilepsy received presurgical language mapping using functional MRI (fMRI), magnetoencephalography (MEG), transcranial magnetic stimulation (TMS), and high-gamma electrocorticography (hgECoG), as well as pre- and postoperative neuropsychological evaluation of language. We constructed 15 (24–1) SVR models by considering the extent of resected language areas identified by all subsets of four modalities as input feature vector and the postoperative language outcome as output. We trained and cross-validated SVR models, and compared the cross-validation (CV) errors of all models for prediction of language outcome. Results Seven patients had some level of postoperative language decline and two of them had significant postoperative decline in naming. Some parts of language areas identified by four modalities were resected in these patients. We found that an SVR model consisting of fMRI, MEG, and hgECoG provided minimum CV error, although an SVR model consisting of fMRI and MEG was the optimal model that facilitated the best trade-off between model complexity and prediction accuracy. Conclusions A multimodal SVR can be used to predict the language outcome. Significance The developed multimodal SVR models in this study can be utilized to calculate the language outcomes of different resection plans prior to surgery and select the optimal surgical plan.

The results of this magnetoencephalography study challenge two long-standing assumptions regarding the brain mechanisms of language processing: First, that linguistic processing proper follows sensory feature processing effected by bilateral activation of the primary sensory cortices that lasts about 100 msec from stimulus onset. Second, that subsequent linguistic processing is effected by left hemisphere networks outside the primary sensory areas, including Broca's and Wernicke's association cortices. Here we present evidence that linguistic analysis begins almost synchronously with sensory, prelinguistic verbal input analysis and that the primary cortices are also engaged in these linguistic analyses and become, consequently, part of the left hemisphere language network during language tasks. These findings call for extensive revision of our conception of linguistic processing in the brain.

Cortical Stimulation Mapping (CSM) and the Wada procedure have long been considered the gold standard for localizing motor and language-related cortical areas and for determining the language and memory-dominant hemisphere, respectively. In recent years, however, non-invasive methods such as magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), and transcranial magnetic stimulation (TMS) have emerged as promising alternatives to the aforementioned procedures, particularly in cases where the invasive localization of eloquent cortex has proven to be challenging. To illustrate this point, we will first introduce the evidence of the compatibility of invasive and non-invasive methods and subsequently outline the rationale and the conditions where the latter methods are applicable.

Background We investigated identifying patients with mild cognitive impairment (MCI) who progress to Alzheimer’s disease (AD), MCI converter (MCI-C), from those with MCI who do not progress to AD, MCI non-converter (MCI-NC), based on resting-state fMRI (rs-fMRI). New method Graph theory and machine learning approach were utilized to predict progress of patients with MCI to AD using rs-fMRI. Eighteen MCI converts (average age 73.6 years; 11 male) and 62 age-matched MCI non-converters (average age 73.0 years, 28 male) were included in this study. We trained and tested a support vector machine (SVM) to classify MCI-C from MCI-NC using features constructed based on the local and global graph measures. A novel feature selection algorithm was developed and utilized to select an optimal subset of features. Results Using subset of optimal features in SVM, we classified MCI-C from MCI-NC with an accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve of 91.4%, 83.24%, 90.1%, and 0.95, respectively. Furthermore, results of our statistical analyses were used to identify the affected brain regions in AD. Comparison with existing method(s) To the best of our knowledge, this is the first study that combines the graph measures (constructed based on rs-fMRI) with machine learning approach and accurately classify MCI-C from MCI-NC. Conclusion Results of this study demonstrate potential of the proposed approach for early AD diagnosis and demonstrate capability of rs-fMRI to predict conversion from MCI to AD by identifying affected brain regions underlying this conversion.