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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

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.

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.

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.

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

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.

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.

The complexity of the widespread language network makes it challenging for accurate localization and lateralization. Using large-scale connectivity and graph-theoretical analyses of task-based magnetoencephalography (MEG), we aimed to provide robust representations of receptive and expressive language processes, comparable with spatial profiles of corresponding functional magnetic resonance imaging (fMRI). We examined MEG and fMRI data from 12 healthy young adults (age 20-37 years) completing covert auditory word-recognition task (WRT) and covert auditory verb-generation task (VGT). For MEG language mapping, broadband (3-30 Hz) beamformer sources were estimated, voxel-level connectivity was quantified using phase locking value, and highly connected hubs were characterized using eigenvector centrality graph measure. fMRI data were analyzed using a classic general linear model approach. A laterality index (LI) was computed for 20 language-specific frontotemporal regions for both MEG and fMRI. MEG network analysis showed bilateral and symmetrically distributed hubs within the left and right superior temporal gyrus (STG) during WRT and predominant hubs in left inferior prefrontal gyrus (IFG) during VGT. MEG and fMRI localization maps showed high correlation values within frontotemporal regions during WRT and VGT (r = 0.63, 0.74, q 0.05, respectively). Despite good concordance in localization, notable discordances were observed in lateralization between MEG and fMRI. During WRT, MEG favored a left-hemispheric dominance of left STG (LI = 0.25 ± 0.22) whereas fMRI supported a bilateral representation of STG (LI = 0.08 ± 0.2). Laterality of MEG and fMRI during VGT consistently showed a strong asymmetry in left IFG regions (MEG-LI = 0.45 ± 0.35 and fMRI-LI = 0.46 ± 0.13). Our results demonstrate the utility of a large-scale connectivity and graph theoretical analyses for robust identification of language-specific regions. MEG hubs are in great agreement with the literature in revealing with canonical and extra-canonical language sites, thus providing additional support for the underlying topological organization of receptive and expressive language cortices. Discordances in lateralization may emphasize the need for multimodal integration of MEG and fMRI to obtain an excellent predictive value in a heterogeneous healthy population and patients with neurosurgical conditions.

Ongoing fluctuations of neuronal activity have long been considered intrinsic noise that introduces unavoidable and unwanted variability into neuronal processing, which the brain eliminates by averaging across population activity (Georgopoulos et al., 1986; Lee et al., 1988; Shadlen and Newsome, 1994; Maynard et al., 1999). It is now understood, that the seemingly random fluctuations of cortical activity form highly structured patterns, including oscillations at various frequencies, that modulate evoked neuronal responses (Arieli et al., 1996; Poulet and Petersen, 2008; He, 2013) and affect sensory perception (Linkenkaer-Hansen et al., 2004; Boly et al., 2007; Sadaghiani et al., 2009; Vinnik et al., 2012; Palva et al., 2013). Ongoing cortical activity is driven by proprioceptive and interoceptive inputs. In addition, it is partially intrinsically generated in which case it may be related to mental processes (Fox and Raichle, 2007; Deco et al., 2011). Here we argue that respiration, via multiple sensory pathways, contributes a rhythmic component to the ongoing cortical activity. We suggest that this rhythmic activity modulates the temporal organization of cortical neurodynamics, thereby linking higher cortical functions to the process of breathing.

The aim of this study was to identify brain regions involved in motor imagery and differentiate two alternative strategies in its implementation: imagining a motor act using kinesthetic or visual imagery. Fourteen adults were precisely instructed and trained on how to imagine themselves or others perform a movement sequence, with the aim of promoting kinesthetic and visual imagery, respectively, in the context of an fMRI experiment using block design. We found that neither modality of motor imagery elicits activation of the primary motor cortex and that each of the two modalities involves activation of the premotor area which is also activated during action execution and action observation conditions, as well as of the supplementary motor area. Interestingly, the visual and the posterior cingulate cortices show reduced BOLD signal during both imagery conditions. Our results indicate that the networks of regions activated in kinesthetic and visual imagery of motor sequences show a substantial, while not complete overlap, and that the two forms of motor imagery lead to a differential suppression of visual areas.

SummaryThe brain generates oscillatory neuronal activity at a broad range of frequencies and the presence and amplitude of certain oscillations at specific times and in specific brain regions are highly correlated with states of arousal, sleep, and with a wide range of cognitive processes. The neuronal mechanisms underlying the generation of brain rhythms are poorly understood, particularly for low-frequency oscillations. We recently reported that respiration-locked olfactory bulb activity causes delta band (0.5-4 Hz) oscillatory neuronal activity in the whisker sensory (barrel) cortex in mice. Furthermore, gamma oscillations (30 – 100Hz), which are widely implicated in cognitive processing, were power-modulated in synchrony with the respiratory rhythm. These findings link afferent sensory activity caused by respiration directly to cortical rhythms associated with cognitive functions. Here we review the related literature and present new evidence to propose that respiration has a direct influence on oscillatory cortical activity, including gamma oscillations, and on transitions between synchronous and asynchronous cortical network states (marked by phase transitions). Oscillatory cortical activity, as well as phase transitions, has been implicated in cognitive functions, potentially linking respiratory phase to cognitive processing. We further argue that respiratory influence on cortical activity is present in most, and possibly in all areas of the neocortex in mice and humans. We furthermore suggest that respiration had a role in modulating cortical rhythms from early mammalian evolution. Early mammals relied strongly on their olfactory sense and had proportionately large olfactory bulbs. We propose that to this day the respiratory rhythm remains an integral element of dynamic cortical activity in mammals. We argue that breathing modulates all cortical functions, including cognitive and emotional processes, which could elucidate the well-documented but largely unexplained effects of respiratory exercises on mood and cognitive function.

Available online 22 February 2016 Cross-frequency coupling (CFC) is thought to represent a basic mechanism of functional integration of neural networks across distant brain regions. In this study, we analyzed CFC profiles from resting state Magnetoencephalographic (MEG) recordings obtained from 30 mild traumatic brain injury (mTBI) patients and 50 controls. We used mutual information (MI) to quantify the phase-to-amplitude coupling (PAC) of activity among the recording sensors in six nonoverlapping frequency bands. After forming the CFC-based functional connectivity graphs, we employed a tensor representation and tensor subspace analysis to identify the optimal set of features for subject classification as mTBI or control. Our results showed that controls formed a dense network of stronger local and global connections indicating higher functional integration compared to mTBI patients. Furthermore, mTBI patients could be separated from controls with more than 90% classification accuracy. These findings indicate that analysis of brain networks computed from resting-state MEG with PAC and tensorial representation of connectivity profiles may provide a valuable biomarker for the diagnosis of mTBI. The project reported here is part of a larger study, the Integrated Clinical Protocol, conducted by the investigators and staff of theMission ConnectMild Traumatic Brain Injury Translational Research Consortium and supported by the Department of Defense Congressionally Directed Medical Research Program. The graph representation and analysis is supported by a THALES project (CYBERSENSORS — High Frequency Monitoring Systemfor IntegratedWater ResourcesManagement of Rivers) funded by the NSRF2007-13 of the GreekMinistry of Development. The analysis methodology was partly supported by the “YPERTHEN” project under the Interreg framework of Greek-Cyprus Co-operation 2007–2013.