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The main objective of this paper is to present methods and results for the estimation of parameters of our proposed integrated magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) model. We use real auditory MEG and fMRI datasets from 7 normal subjects to estimate the parameters of the model. The MEG and fMRI data were acquired at different times, but the stimulus profile was the same for both techniques. We use independent component analysis (ICA) to extract activation-related signal from the MEG data. The stimulus-correlated ICA component is used to estimate MEG parameters of the model. The temporal and spatial information of the fMRI datasets are used to estimate fMRI parameters of the model. The estimated parameters have reasonable means and standard deviations for all subjects. Goodness of fit of the real data to our model shows the possibility of using the proposed model to simulate realistic datasets for evaluation of integrated MEG/fMRI analysis methods.

The study of brain networks by resting-state functional magnetic resonance imaging (rs-fMRI) is a promising method for identifying patients with dementia from healthy controls (HC). Using graph theory, different aspects of the brain network can be efficiently characterized by calculating measures of integration and segregation. In this study, we combined a graph theoretical approach with advanced machine learning methods to study the brain network in 89 patients with mild cognitive impairment (MCI), 34 patients with Alzheimer’s disease (AD), and 45 age-matched HC. The rs-fMRI connectivity matrix was constructed using a brain parcellation based on a 264 putative functional areas. Using the optimal features extracted from the graph measures, we were able to accurately classify three groups (i.e., HC, MCI, and AD) with accuracy of 88.4 %. We also investigated performance of our proposed method for a binary classification of a group (e.g., MCI) from two other groups (e.g., HC and AD). The classification accuracies for identifying HC from AD and MCI, AD from HC and MCI, and MCI from HC and AD, were 87.3, 97.5, and 72.0 %, respectively. In addition, results based on the parcellation of 264 regions were compared to that of the automated anatomical labeling atlas (AAL), consisted of 90 regions. The accuracy of classification of three groups using AAL was degraded to 83.2 %. Our results show that combining the graph measures with the machine learning approach, on the basis of the rs-fMRI connectivity analysis, may assist in diagnosis of AD and MCI.

Several neuroimaging studies have suggested that functional brain connectivity networks exhibit "small-world" characteristics, whereas recent studies based on structural data have proposed a "rich-club" organization of brain networks, whereby hubs of high connection density tend to connect among themselves compared to nodes of lower density. In this study, we adopted an "attack strategy" to compare the rich-club and small-world organizations and identify the model that describes best the topology of brain connectivity. We hypothesized that the highest reduction in global efficiency caused by a targeted attack on each model's hubs would reveal the organization that better describes the topology of the underlying brain networks. We applied this approach to magnetoencephalographic data obtained at rest from neurologically intact controls and mild traumatic brain injury patients. Functional connectivity networks were computed using phase-to-amplitude cross-frequency coupling between the δ and β frequency bands. Our results suggest that resting state MEG connectivity networks follow a rich-club organization.

Objective The aim of the present study was to compare localization of the language cortex using cortical stimulation mapping (CSM), high gamma electrocorticography (hgECoG), functional magnetic resonance imaging (fMRI), and transcranial magnetic stimulation (TMS). Methods Language mapping using CSM, hgECoG, fMRI, and TMS were compared in nine patients with epilepsy. Considering CSM as reference, we compared language mapping approaches based on hgECoG, fMRI, and TMS using their sensitivity, specificity, and the results of receiver operating characteristic (ROC) analyses. Results Our results show that areas involved in language processing can be identified by hgECoG, fMRI, and TMS. The average sensitivity/specificity of hgECoG, fMRI, and TMS across all patients was 100%/85%, 50%/80%, and 67%/66%, respectively. The average area under the ROC curve of hgECoG, fMRI, and TMS across CSM-positive patients was 0.98, 0.76, and 0.68, respectively. Conclusions There is considerable concordance between CSM, hgECoG, fMRI, and TMS language mapping. Our results reveal that hgECoG, fMRI, and TMS are valuable tools for presurgical language mapping. Significance Language mapping on the basis of hgECoG, fMRI, and TMS can provide important additional information, therefore, these methods can be used in conjunction with CSM or as an alternative, when the latter is deemed impractical.

The American Academy of Neurology (AAN) endorsement for the use of fMRI evaluation of hemispheric dominance for language in epilepsy surgery candidates1 appears as an odd and disconcerting sequel to the article by Eckland et al.,2 which reported that the clustering approach in functional MRI (fMRI) data processing may be responsible for an excessively high rate of false-positive findings. The latter report implied that up to 70% of the significant fMRI effects reported in approximately 40,000 peer-reviewed publications could represent no brain physiology facts, but type I statistical errors to the degree that they employed clustering of activated voxels.2 Consequently, it is fitting to suggest users of these popular fMRI software packages (SPM, FSL, AFNI) reconsider conclusions based on differences in activation levels of voxel clusters between task conditions. This raises the question of whether those who use the clustering procedure for the purpose of assessing hemisphere dominance for language should be alarmed for having made incorrect assessments in the past and refrain from using them for the same purpose in the future. Our answer is no, since language laterality judgments are typically made on the basis of laterality indices (i.e., the relative degree of activation defined as the ratio of left- and right-hemisphere activated voxels that are equally likely or unlikely to be activated). However, depending on how users of these automated software packages derive estimates of localization of the language network hubs (a practice not endorsed by the AAN),1 they may need to exercise more caution now that the shortcomings of these data reduction and analysis procedures were directly demonstrated and made widely known.

Supplemental Digital Content is available in the text., In this study, we have addressed the question of functional brain reorganization for language in the presence and absence of anatomical lesions in two patients with epilepsy using cortical stimulation mapping and high gamma (HG) activity in subdural grid recordings. In both, the expressive language cortex was defined as the cortical patch below the electrode(s) that when stimulated resulted in speech arrest, and during speech expression tasks generated HG activity. This patch fell within the borders of Broca’s area, as defined anatomically, in the case of the patient with a lesion, but outside that area in the other, lesion-free patient. Such results highlight the necessity for presurgical language mapping in all cases of surgery involving the language-dominant hemisphere and suggest that HG activity during expressive language tasks can be informative and helpful in conjunction with cortical stimulation mapping for expressive language mapping.

Study of brain network on the basis of resting-state functional magnetic resonance imaging (fMRI) has provided promising results to investigate changes in connectivity among different brain regions because of diseases. In this study, we combine graph theoretical approaches with advanced machine learning methods to study functional brain network alteration in patients with Alzheimer's disease (AD). Support vector machine (SVM) was used to explore the ability of graph measures in diagnosis of AD. We applied our method on the resting-state fMRI data of twenty patients with AD and twenty age and gender matched healthy subjects. After preprocessing of data, signals from 90 brain regions, segmented based on the automated anatomical labeling (AAL) atlas, were extracted and edges of the graph were calculated using the correlation between the signals of all pairs of the brain regions. Then a weighted undirected graph was constructed and graph measures were calculated. Fisher score feature selection algorithm were employed to choose most significant features. Finally, using the selected features, we were able to accurately classify patients with AD from healthy control ones with accuracy of 97.5%. Results of this study show that pattern recognition and graph of brain network, on the basis of the resting state fMRI data, can efficiently assist in the diagnosis of AD.

Objective Study of brain network on the basis of resting-state functional magnetic resonance imaging (fMRI) has provided promising results to investigate changes in connectivity among different brain regions because of diseases. Graph theory can efficiently characterize different aspects of the brain network by calculating measures of integration and segregation. Method In this study, we combine graph theoretical approaches with advanced machine learning methods to study functional brain network alteration in patients with Alzheimer’s disease (AD). Support vector machine (SVM) was used to explore the ability of graph measures in diagnosis of AD. We applied our method on the resting-state fMRI data of twenty patients with AD and twenty age and gender matched healthy subjects. The data were preprocessed and each subject’s graph was constructed by parcellation of the whole brain into 90 distinct regions using the automated anatomical labeling (AAL) atlas. The graph measures were then calculated and used as the discriminating features. Extracted network-based features were fed to different feature selection algorithms to choose most significant features. In addition to the machine learning approach, statistical analysis was performed on connectivity matrices to find altered connectivity patterns in patients with AD. Results Using the selected features, we were able to accurately classify patients with AD from healthy subjects with accuracy of 100%. Conclusion Results of this study show that pattern recognition and graph of brain network, on the basis of the resting state fMRI data, can efficiently assist in the diagnosis of AD. Significance Classification based on the resting-state fMRI can be used as a non-invasive and automatic tool to diagnosis of Alzheimer’s disease.

Objective Accurate noninvasive assessment of motor function using functional MRI (fMRI) and magnetoencephalography (MEG) is a challenge in patients who are very young or who are developmentally delayed. In such cases, passive mapping of the sensorimotor cortex is performed under sedation. We examined the feasibility of using transcranial magnetic stimulation (TMS) as a motor mapping tool in awake children younger than 3 years of age. Methods Six children underwent motor mapping with TMS while awake as well as passive sensorimotor mapping under conscious sedation with MEG during tactile stimulation (n = 5) and fMRI during passive hand movements (n = 4). Results Stimulation of the motor cortex via TMS successfully elicited evoked responses in contralateral hand muscles in 5 patients. The location of primary motor cortex in the precentral gyrus identified by TMS corresponded with the postcentral location of the primary sensory cortex identified by MEG in 2 patients and to the sensorimotor cortex identified by fMRI in 3 children. In this cohort, we demonstrate that TMS can illuminate abnormalities in motor physiology including motor reorganization. We also demonstrate the feasibility of using TMS-derived contralateral silent periods to approximate the location of motor cortex in the absence of an evoked response. When compared to chronological age, performance functioning level appears to be better in predicting successful mapping outcome with TMS. Conclusions Our findings indicate that awake TMS is a safe alternative to MEG and fMRI performed under sedation to localize the motor cortex and provides additional insight into the underlying pathophysiology and motor plasticity in toddlers.

Editors' Note: In WriteClick this week, Dr. Nirenberg responds to “Augmentation and impulsive behaviors in restless legs syndrome: Coexistence or association?” with the hypothesis that augmentation in restless legs syndrome may be a forme fruste of dopamine agonist withdrawal syndrome. Authors Djamshidian et al. report no association between augmentation and medication nonadherence, but agree with monitoring these patients closely for addictive behaviors. In reference to “Spatial-temporal functional mapping of language at the bedside with electrocorticography,” Drs. Babajani-Feremi et al. and authors Wang et al. discuss the use of high-gamma electrocorticography, electrocortical stimulation mapping, …

The Human Connectome Project (HCP) seeks to map the structural and functional connections between network elements in the human brain. Magnetoencephalography (MEG) provides a temporally rich source of information on brain network dynamics and represents one source of functional connectivity data to be provided by the HCP. High quality MEG data will be collected from 50 twin pairs both in the resting state and during performance of motor, working memory and language tasks. These data will be available to the general community. Additionally, using the cortical parcellation scheme common to all imaging modalities, the HCP will provide processing pipelines for calculating connection matrices as a function of time and frequency. Together with structural and functional data generated using magnetic resonance imaging methods, these data represent a unique opportunity to investigate brain network connectivity in a large cohort of normal adult human subjects. The analysis pipeline software and the dynamic connectivity matrices that it generates will all be made freely available to the research community.

Item does not contain fulltext The Human Connectome Project (HCP) seeks to map the structural and functional connections between network elements in the human brain. Magnetoencephalography (MEG) provides a temporally rich source of information on brain network dynamics and represents one source of functional connectivity data to be provided by the HCP. High quality MEG data will be collected from 50 twin pairs both in the resting state and during performance of motor, working memory and language tasks. These data will be available to the general community. Additionally, using the cortical parcellation scheme common to all imaging modalities, the HCP will provide processing pipelines for calculating connection matrices as a function of time and frequency. Together with structural and functional data generated using magnetic resonance imaging methods, these data represent a unique opportunity to investigate brain network connectivity in a large cohort of normal adult human subjects. The analysis pipeline software and the dynamic connectivity matrices that it generates will all be made freely available to the research community. 12 p.

A negative blood oxygen level - dependent (BOLD) has been associated with a high concentration of GABA using Magnetic Resonance Spectroscopy and fMRI. Subjects with long-allele carriers have seen with high concentration of serotonin in Rostral Subgenual portion of the anterior cingulate cortex (rACC). In this paper, we investigate the effect of serotonin concentration on hemodynamic responses. Our results show a negative BOLD signal in rACC in the subjects with long-allele carriers. In contrast, the subjects with short-allele carriers showed positive BOLD signals in rACC. These results suggest that the serotonin transporter gene impacts the neuronal activity and eventually the BOLD signal similar to GABA.

Emotional tasks may result in a strong blood oxygen level-dependent (BOLD) signal in the amygdala in 5- HTTLRP short-allele. Reduced anterior cingulate cortex (ACC)-amygdala connectivity in short-allele provides a potential mechanistic account for the observed increase in amygdala activity. In our study, fearful and threatening facial expressions were presented to two groups of 12 subjects with long- and short-allele carriers. The BOLD signals of the left amygdala of each group were averaged to increase the signal-to-noise ratio. A Bayesian approach was used to estimate the model parameters to elucidate the underlying hemodynamic mechanism. Our results showed a positive BOLD signal in the left amygdala for short-allele individuals, and a negative BOLD signal in the same region for long-allele individuals. This is due to the fact that short-allele is associated with lower availability of serotonin transporter (5-HTT) and this leads to an increase of serotonin (5-HT) concentration in the cACC-amygdala synapse.

Neurophysiological processes underlying auditory memory and attention are impaired in habitually short sleepers. The aim of this study was to use dynamic causal modeling (DCM) to study the mechanisms of these impairments in short sleepers. Eight normal sleepers (total sleep time (TST) = 7–8 h) and nine habitual short sleepers (TST ≤ 6 h) participated. The time in bed was increased from habitual (≤ 6 h) to extended (~ 8.5 h) for one week in the short sleep group. Event related potentials (ERPs) were collected using an auditory novelty task in “IGNORE” and “ATTEND” conditions. Fourteen DCM models were considered using different configurations of connections among the following six areas: left and right primary auditory cortices, superior temporal gyri (STG), and inferior temporal gyri (IFG). After fitting the ERPs to the 14 models (separately for the IGNORE and ATTEND conditions), the best model (across subjects) was chosen using the Bayesian model comparison. For both conditions, the connection from right-STG to right-IFG for normal sleepers was significantly greater than habitual short sleepers. This connection did not differ in habitual short sleepers before and after one week of extended sleep time. This connection for normal sleepers was not significantly greater than the habitual short sleepers after one week of extended sleep. These results show that the deficiency of novelty processing, seen in short sleepers, can be explained by the differences in connectivity of the pathway between frontal and temporal brain areas as compared to the normal sleepers. In addition, one week of extended time in bed was not enough to fully normalize this neuronal pathway between STG and IFG in short sleepers.

Differentiating treatment-induced necrosis (TIN) from recurrent/progressive tumor (RPT) in brain tumor patients using conventional morphologic imaging features is a very challenging task. Functional imaging techniques also offer moderate success due to the complexity of the tissue microenvironment and the inherent limitation of the various modalities and techniques. The purpose of this retrospective study was to assess the utility of nonmodel-based semiquantitative indices derived from dynamic contrast-enhanced T1-weighted MR perfusion (DCET1MRP) in differentiating TIN from RPT. Twenty-nine patients with previously treated brain tumors who showed recurrent or progressive enhancing lesion on follow-up MRI underwent DCET1MRP. Another 8 patients with treatment-naive high-grade gliomas who also underwent DCET1MRP were included as the control group. Semiquantitative indices derived from DCET1MRP included maximum slope of enhancement in initial vascular phase (MSIVP), normalized MSIVP (nMSIVP), normalized slope of delayed equilibrium phase (nSDEP), and initial area under the time-intensity curve (IAUC) at 60 and 120 s (IAUC(60) and IAUC(120)) obtained from the enhancement curve. There was a statistically significant difference between the 2 groups (P.01), with the RPT group showing higher MSIVP (15.78 vs 8.06), nMSIVP (0.046 vs 0.028), nIAUC(60) (33.07 vs 6.44), and nIAUC(120) (80.14 vs 65.55) compared with the TIN group. nSDEP was significantly lower in the RPT group (7.20 × 10(-5) vs 15.35 × 10(-5)) compared with the TIN group. Analysis of the receiver-operating-characteristic curve showed nMSIVP to be the best single predictor of RPT, with very high (95%) sensitivity and high (78%) specificity. Thus, nonmodel-based semiquantitative indices derived from DCET1MRP that are relatively easy to derive and do not require a complex model-based approach may aid in differentiating RPT from TIN and can be used as robust noninvasive imaging biomarkers.

We develop and evaluate a variational Bayesian expectation maximization (VBEM) method for model inversion of our multi-area extended neural mass model (MEN) using EEG/MEG data. Parameters of MEN have suitable prior distributions that enable us to use properties of a conjugate-exponential model in implementing VBEM. Consequently, VBEM leads to analytically tractable forms that starts with initialization and consists of repeated iterations of a variational Bayesian expectation step (VB E-step) and a variational Bayesian maximization step (VB M-step). Posterior distributions of model parameters are updated in the VB M-step. Distribution of the hidden state is updated in the VB E-step using variational extended Kalman smoother. We evaluate and validate performance of VBEM method for model inversion of MEN using simulation studies in various signal-to-noise ratios. The proposed approach provides a useful technique for analyzing effective connectivity using non-invasive EEG and MEG methods.

The gold standard for the localization of epileptic activities in the cerebral cortex is intracranial electrocorticography (ECoG) electrodes placed directly on the brain surface. However, it has limitations in being able to localize deep brain epileptic sources. As a means to improve the localization of epileptic activities from these subdural electrical recordings, we developed a simple source monitoring system to detect and localize these deep sources. The method is based on an independent component analysis (ICA) algorithm known as joint approximate diagonalization of eigen-matrices (JADE). This method was tested on patients with neocortical epileptic foci focusing on the interictal spikes from ECoG signals. The proposed method localizes the interictal epileptic discharges to both superficial as well as deep regions of the human neocortex and has the potential to improve the localization and surgical outcomes of patients with medically refractory epilepsy undergoing surgical resections.

Image thresholding is an important technique for image processing and pattern recognition. Several thresholding techniques have been proposed in the literature. In this paper for segmentation of magnetic resonance images, a novel method using a combination of the multilevel thresholding algorithm and the hierarchical evolutionary algorithm (HEA) is proposed. The HEA can be viewed as a variant of conventional genetic algorithms. The proposed technique is based on the participle swarm optimization (PSO) and, in fact, is an unsupervised clustering method based on an automatic multilevel thresholding approach. One advantage of the proposed method is that the number of clusters in the given image does not need to be known in advance. We evaluate and validate performance of the proposed method using simulation studies. The simulation results show that the accuracy of the proposed method is about 96%.

In Part I and Part II of these two companion papers (henceforth called Part I and Part II), we develop and evaluate a variational Bayesian expectation maximization (VBEM) method for model inversion of our multi-area extended neural mass model (MEN). In this paper, we develop the VBEM method to estimate posterior distributions of parameters of MEN. We choose suitable prior distributions for the model parameters in order to use properties of a conjugate-exponential model in implementing VBEM. Consequently, VBEM leads to analytically tractable forms. The proposed VBEM algorithm starts with initialization and consists of repeated iterations of a variational Bayesian expectation step (VB E-step) and a variational Bayesian maximization step (VB M-step). Posterior distributions of the model parameters are updated in the VB M-step. Distribution of the hidden state is updated in the VB E-step. We develop a variational extended Kalman smoother (VEKS) to infer the distribution of the hidden state in the VB E-step and derive the forward and backward passes of VEKS, analogous to the Kalman smoother. In Part I, we evaluate and validate the VBEM method using simulation studies.

We previously developed an integrated model of the brain within a single cortical area for functional Magnetic Resonance Imaging (fMRI), electroencephalography (EEG), and magnetoencephalography (MEG) using an extended neural mass model (ENMM). We then extended ENMM from a single-area to a multi-area model to develop a neural mass model of the entire brain. To this end, we derived a nonlinear state-space representation of the multi-area model. In Parts I and II of these two companion papers (henceforth called Part I and Part II), we develop and evaluate a variational Bayesian expectation maximization (VBEM) method to estimate parameters of multi-area ENMM (MEN) using E/MEG data. In Part I, we derive a state-space representation of MEN and use VBEM method for model inversion (parameter estimation). We evaluate and validate performance of VBEM method for model inversion of MEN using simulation studies in various signal-to-noise ratios. Details of VBEM method are presented in Part II. The proposed approach provides a useful technique for analyzing effective connectivity using non-invasive EEG and MEG methods.

Functional magnetic resonance imaging (fMRI) has complementary spatiotemporal resolution compared to Electroencephalography (EEG) as well as Magnetoencephalography (MEG). Thus, their integrated analysis should improve the overall resolution. To integrate analysis of E/MEG and fMRI, we extend our previously proposed integrated E/MEG and fMRI neural mass model to a multi-area model by defining two types of connections: the Short-Range Connections (SRCs) between minicolumns within the areas and Long-Range Connections (LRCs) between inter-areas minicolumns. The nonlinear input/output relationship in the proposed model is derived from the state space representation of the multi-area model. The E/MEG signals are originated from the overall synaptic activities of the pyramidal cells of all minicolumns and can be calculated using the lead field matrix (i.e., forward electromagnetic model). The fMRI signal is extracted from the proposed integrated model by calculating the overall neural activities in the areas and using it as the input of the extended balloon model (EBM). Using the simulation results, the capabilities of the proposed model to generate E/MEG and fMRI signals is shown. In addition, changes in the dynamics of the model to variations of its parameters were evaluated and lead us to the appropriate ranges for the parameters. In conclusion, this work proposes an effective method to integrate E/MEG and fMRI for the more effective use of these techniques in functional neuroimaging.

Magnetoencephalography (MEG) is a neuroimaging technique for brain activation detection. This technique does not provide a unique solution due to ill-posedness of its inverse solution. Several methods are proposed to improve the MEG inverse solution. Minimum Norm (MN) is a simple method whose solution is distributed and biased toward the superficial sources. In addition, its solution is sensitive to the noise. Several methods are proposed to improve performance of the MN method. In this paper, we propose a method whose solution is less sensitive to the noise and spatially unbiased toward the superficial sources. Control of focal solution properties is achieved by specifying a parameter in the proposed method. Performance of the proposed method is compared to others using simulation studies consisting of single and multiple dipole sources as well as an extended source model. Proposed method has superior performance compared to non-iterative methods. Its performance is similar to the iterative methods but its computational load is lower.

Functional magnetic resonance imaging (fMRI) has been widely used in cognitive neuroscience. Brain connectivity analysis such as dynamic causal modelling (DCM) using fMRI has drawn increasing attention of neuroimaging researchers. However, performance of DCM under low SNR has not been explored and validated thoroughly. A reasonable question is what levels of noise can be handled by the DCM. Therefore, simulation is an appropriate methodology to address this question. We propose the Euler method to simulate fMRI signal. The results were compared to those of the SPM-generate. Some parameters estimated by DCM were significantly different. Unlike SPM-generate, DCM always converged when using signals generated by Euler method. Although DCM performed similarly in linear scenario for both methods, DCM handled higher noise levels for Euler compared to SPM-generate. The higher performance of DCM when using Euler method might be due to direct discretisation of the dynamic system of the neuronal state variables.