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The epilepsies are commonly accompanied by widespread abnormalities in cerebral white matter. ENIGMA-Epilepsy is a large quantitative brain imaging consortium, aggregating data to investigate patterns of neuroimaging abnormalities in common epilepsy syndromes, including temporal lobe epilepsy, extratemporal epilepsy, and genetic generalized epilepsy. Our goal was to rank the most robust white matter microstructural differences across and within syndromes in a multicentre sample of adult epilepsy patients. Diffusion-weighted MRI data were analysed from 1069 healthy controls and 1249 patients: temporal lobe epilepsy with hippocampal sclerosis (n = 599), temporal lobe epilepsy with normal MRI (n = 275), genetic generalized epilepsy (n = 182) and non-lesional extratemporal epilepsy (n = 193). A harmonized protocol using tract-based spatial statistics was used to derive skeletonized maps of fractional anisotropy and mean diffusivity for each participant, and fibre tracts were segmented using a diffusion MRI atlas. Data were harmonized to correct for scanner-specific variations in diffusion measures using a batch-effect correction tool (ComBat). Analyses of covariance, adjusting for age and sex, examined differences between each epilepsy syndrome and controls for each white matter tract (Bonferroni corrected at P < 0.001). Across 'all epilepsies' lower fractional anisotropy was observed in most fibre tracts with small to medium effect sizes, especially in the corpus callosum, cingulum and external capsule. There were also less robust increases in mean diffusivity. Syndrome-specific fractional anisotropy and mean diffusivity differences were most pronounced in patients with hippocampal sclerosis in the ipsilateral parahippocampal cingulum and external capsule, with smaller effects across most other tracts. Individuals with temporal lobe epilepsy and normal MRI showed a similar pattern of greater ipsilateral than contralateral abnormalities, but less marked than those in patients with hippocampal sclerosis. Patients with generalized and extratemporal epilepsies had pronounced reductions in fractional anisotropy in the corpus callosum, corona radiata and external capsule, and increased mean diffusivity of the anterior corona radiata. Earlier age of seizure onset and longer disease duration were associated with a greater extent of diffusion abnormalities in patients with hippocampal sclerosis. We demonstrate microstructural abnormalities across major association, commissural, and projection fibres in a large multicentre study of epilepsy. Overall, patients with epilepsy showed white matter abnormalities in the corpus callosum, cingulum and external capsule, with differing severity across epilepsy syndromes. These data further define the spectrum of white matter abnormalities in common epilepsy syndromes, yielding more detailed insights into pathological substrates that may explain cognitive and psychiatric co-morbidities and be used to guide biomarker studies of treatment outcomes and/or genetic research., (© The Author(s) (2020). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Emotion regulation by neurofeedback involves interactions among multiple brain regions, including prefrontal cortex and subcortical regions. Previous studies focused on connections of specific brain regions such as amygdala with other brain regions. New method: Electroencephalography (EEG) neurofeedback is used to upregulate positive emotion by retrieving positive autobiographical memories and functional magnetic resonance imaging (fMRI) data acquired simultaneously. A global data-driven approach, group independent component analysis, is applied to the fMRI data and functional network connectivity (FNC) estimated. Results: The proposed approach identified all functional networks engaged in positive autobiographical memories and evaluated effects of neurofeedback. The results revealed two pairs of networks with significantly different functional connectivity among emotion regulation blocks (relative to other blocks of the experiment) and between experimental and control groups (false discovery rate corrected for multiple comparisons, q  = 0.05). FNC distribution showed significant connectivity differences between neurofeedback blocks and other blocks, revealing more synchronized brain networks during neurofeedback. Comparison with Existing Methods: Although the results are consistent with those of previous model-based studies, some of the connections found in this study were not found previously. These connections are between (a) occipital and other regions including limbic system/sublobar, prefrontal/frontal cortex, inferior parietal, and middle temporal gyrus and (b) posterior cingulate cortex and hippocampus. Conclusions: This study provided a global insight into brain connectivity for emotion regulation. The brain network interactions may be used to develop connectivity-based neurofeedback methods and alternative therapeutic approaches, which may be more effective than the traditional activity-based neurofeedback methods.

Background and Objective: Accurate seizure onset zone (SOZ) localization is an essential step in pre-surgical assessment of patients with refractory focal epilepsy. Complex pathophysiology of epileptic cerebral structures, seizure types and frequencies have not been considered as influential features for accurate identification of SOZ using EEG-fMRI. There is a crucial need to quantitatively measure concordance between presumed SOZ and IED-related BOLD response in different brain regions to improve SOZ delineation., Methods: A novel component-based EEG-fMRI approach is proposed to measure physical distance between BOLD clusters and selected component dipole location using patient-specific high resolution anatomical images. The method is applied on 18 patients with refractory focal epilepsy to localize epileptic focus and determine concordance quantitatively and compare between maximum BOLD cluster with identified component dipole. To measure concordance, distance from a voxel with maximal z-score of maximum BOLD to center of extracted component dipole is measured., Results: BOLD clusters to spikes distances for concordant (<25 mm), partially concordant (25-50 mm), and discordant (>50 mm) groups were significantly different (p < 0.0001). The results showed full concordance in 17 IED types (17.85 ± 4.69 mm), partial concordance in 4 (36.47 ± 8.84 mm), and nodiscordance, which is a significant rise compared to the existing literature. The proposed method is premised on the cross-correlation between the spike template outside the scanner and the highly-ranked extracted components. It successfully surpasses the limitations of conventional EEG-fMRI studies which are largely dependent on inside-scanner spikes. More significantly, the proposed method improves localization accuracy to 97% which marks a dramatic rise compared to conventional works., Conclusions: This study demonstrated that BOLD changes were related to epileptic spikes in different brain regions in patients with refractory focal epilepsy. In a systematic quantitative approach, concordance levels based on the distance between center of maximum BOLD cluster and dipole were determined by component-based EEG-fMRI method. Therefore, component-based EEG-fMRI can be considered as a reliable predictor of SOZ in patients with focal epilepsy and included as part of clinical evaluation for patients with medically resistant epilepsy., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Background: Simultaneous EEG-fMRI experiments record spatiotemporal dynamics of epileptic activity. A shortcoming of spike-based EEG-fMRI studies is their inability to provide information about behavior of epileptic generators when no spikes are visible., New Method: We extract time series of epileptic components identified on EEG and fit them with Generalized Linear Model (GLM) model. This allows a precise and reliable localization of epileptic foci in addition to predicting generator's behavior. The proposed method works in the source domain and delineates generators considering spatial correlation between spike template and candidate components in addition to patient's medical records., Results: The proposed method was applied on 20 patients with refractory epilepsy and 20 age- and gender-matched healthy controls. The identified components were examined statistically and threshold of localization accuracy was determined as 86% based on Receiver Operating Characteristic (ROC) curve analysis. Accuracy, sensitivity, and specificity were found to be 88%, 85%, and 95%, respectively. Contribution of EEG-fMRI and concordance between EEG and fMRI were also evaluated. Concordance was found in 19 patients and contribution in 17., Comparison With Existing Methods: We compared the proposed method with conventional methods. Our comparisons showed superiority of the proposed method. In particular, when epileptogenic zone was located deep in the brain, the method outperformed existing methods., Conclusions: This study contributes substantially to increasing the yield of EEG-fMRI and presents a realistic estimate of the neural behavior of epileptic generators, to the best of our knowledge, for the first time in the literature., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Background: Medial temporal lobe epilepsy (mTLE) has been associated with widespread white mater (WM) alternations in addition to mesial temporal sclerosis (MTS). Herein, we aimed to investigate the correlation between disease duration and WM structural abnormalities in mTLE using diffusion MRI (DMRI) connectometry approach., Method: DMRI connectometry was conducted on 24 patients with mTLE. A multiple regression model was used to investigate white matter tracts with microstructural correlates to disease duration, controlling for age and sex. DMRI data were processed in the MNI space using q-space diffeomorphic reconstruction to obtain the spin distribution function (SDF). The SDF values were converted to quantitative anisotropy (QA) and used in further analyses., Results: Connectometry analysis identified impaired white matter QA of the following fibers to be correlated with disease duration: bilateral retrosplenial cingulum, bilateral fornix, right inferior longitudinal fasciculus (ILF), and genu of corpus callosum (CC) (FDR = 0.009)., Conclusion: Our results were obtained from DMRI connectometry, which indicates the connectivity and the level of diffusion in nerve fibers rather just the direction of diffusion. Compared to previous studies investigating the correlation between duration of epilepsy and white matter integrity in mTLE patients, we detected broader and somewhat different associations in midline structures and component of limbic system. However, further studies with larger sample sizes are required to elucidate previous and current results.

Real-time detection of seizure activity in epilepsy patients is critical in averting seizure activity and improving patients' quality of life. Accurate evaluation, presurgical assessment, seizure prevention, and emergency alerts all depend on the rapid detection of seizure onset. A new method of feature selection and classification for rapid and precise seizure detection is discussed wherein informative components of electroencephalogram (EEG)-derived data are extracted and an automatic method is presented using infinite independent component analysis (I-ICA) to select independent features. The feature space is divided into subspaces via random selection and multichannel support vector machines (SVMs) are used to classify these subspaces. The result of each classifier is then combined by majority voting to establish the final output. In addition, a random subspace ensemble using a combination of SVM, multilayer perceptron (MLP) neural network and an extended k-nearest neighbors (k-NN), called extended nearest neighbor (ENN), is developed for the EEG and electrocorticography (ECoG) big data problem. To evaluate the solution, a benchmark ECoG of eight patients with temporal and extratemporal epilepsy was implemented in a distributed computing framework as a multitier cloud-computing architecture. Using leave-one-out cross-validation, the accuracy, sensitivity, specificity, and both false positive and false negative ratios of the proposed method were found to be 0.97, 0.98, 0.96, 0.04, and 0.02, respectively. Application of the solution to cases under investigation with ECoG has also been effected to demonstrate its utility., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Background and Objective: Registration is a critical step in group analysis of diffusion weighted images (DWI). Image registration is also necessary for construction of white matter atlases that can be used to identify white matter changes. A challenge in the registration of DWI is that the orientation of the fiber bundles should be considered in the process, making their registration more challenging than that of the scalar images. Most of the current registration methods use a model of diffusion profile, limiting the method to the used model., Methods: We propose a model-independent method for DWI registration. The proposed method uses a multi-level free-form deformation (FFD), a sparse similarity measure, and a dictionary. We also propose a synthesis K-SVD algorithm for sparse interpolation of images during the registration process. We utilize two dictionaries: analysis dictionary is learned based on diffusion signals while synthesis dictionary is generated based on image patches. The proposed multi-level approach registers anatomical structures at different scales. T-test is used to determine the significance of the differences between different methods., Results: We have shown the efficiency of the proposed approach using real data. The method results in smaller generalized fractional anisotropy (GFA) root mean square (RMS) error (0.05 improvements, p = 0.0237) and angular error (0.37 ° improvement, p = 0.0330) compared to the large deformation diffeomorphic metric mapping (LDDMM) method and advanced normalization tools (ANTs)., Conclusion: Sparse registration of diffusion signals enables registration of diffusion weighted images without using a diffusion model., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Multi-modal data fusion has recently emerged as a comprehensive neuroimaging analysis approach, which usually uses canonical correlation analysis (CCA). However, the current CCA-based fusion approaches face problems like high-dimensionality, multi-collinearity, unimodal feature selection, asymmetry, and loss of spatial information in reshaping the imaging data into vectors. This paper proposes a structured and sparse CCA (ssCCA) technique as a novel CCA method to overcome the above problems. To investigate the performance of the proposed algorithm, we have compared three data fusion techniques: standard CCA, regularized CCA, and ssCCA, and evaluated their ability to detect multi-modal data associations. We have used simulations to compare the performance of these approaches and probe the effects of non-negativity constraint, the dimensionality of features, sample size, and noise power. The results demonstrate that ssCCA outperforms the existing standard and regularized CCA-based fusion approaches. We have also applied the methods to real functional magnetic resonance imaging (fMRI) and structural MRI data of Alzheimer's disease (AD) patients (n = 34) and healthy control (HC) subjects (n = 42) from the ADNI database. The results illustrate that the proposed unsupervised technique differentiates the transition pattern between the subject-course of AD patients and HC subjects with a p-value of less than 1×10 -6 . Furthermore, we have depicted the brain mapping of functional areas that are most correlated with the anatomical changes in AD patients relative to HC subjects.

Integration of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is an open problem, which has motivated many researches. The most important challenge in EEG-fMRI integration is the unknown relationship between these two modalities. In this paper, we extract the same features (spatial map of neural activity) from both modality. Therefore, the proposed integration method does not need any assumption about the relationship of EEG and fMRI. We present a source localization method from scalp EEG signal using jointly fMRI analysis results as prior spatial information and source separation for providing temporal courses of sources of interest. The performance of the proposed method is evaluated quantitatively along with multiple sparse priors method and sparse Bayesian learning with the fMRI results as prior information. Localization bias and source distribution index are used to measure the performance of different localization approaches with or without a variety of fMRI-EEG mismatches on simulated realistic data. The method is also applied to experimental data of face perception of 16 subjects. Simulation results show that the proposed method is significantly stable against the noise with low localization bias. Although the existence of an extra region in the fMRI data enlarges localization bias, the proposed method outperforms the other methods. Conversely, a missed region in the fMRI data does not affect the localization bias of the common sources in the EEG-fMRI data. Results on experimental data are congruent with previous studies and produce clusters in the fusiform and occipital face areas (FFA and OFA, respectively). Moreover, it shows high stability in source localization against variations in different subjects.

A prerequisite of temporal lobe epilepsy (TLE) surgery is to lateralize the disease. Recent studies have shown the capability of diffusion weighted MRI (DWMRI) in lateralizing TLE patients. This has been achieved by analyzing diffusion parameters of specific white matter tracts or regions known to be involved in the disease; however, other brain regions and connections have not been investigated for TLE lateralization. Whole brain structural connectivity using DWMRI provides a wealth of information regarding the structural connections in the brain. This information can be explored to find the most effective connections for TLE lateralization. In this work, we investigate the connectivity matrices calculated from DWMRI of 10 left and 10 right TLE patients to find the most effective connections for lateralizing the disease. Linear support vector machine (LSVM) classifier and leave-one-out cross validation scheme are used to estimate classification performance of the connectivity feature subsets. A subset of three connections with 100% classification accuracy is found. The corresponding LSVM classifier may be used to lateralize prospective TLE patients.

Neural degeneration in Alzheimer's disease (AD) leads to structural topology deformation that in turn changes brain functionality. The main aim of the present study is to find the brain's functional connectivity network (FCN) correlates of Alzheimer's psychological test scores. To this end, the brain's FCN is extracted from the resting state functional magnetic resonance images (rs-fMRI) of healthy controls and patients with AD and represented as a graph. Then, network measures are calculated from the graphs. The correlations between the brain network measures and five AD psychological assessment test scores are evaluated. The results show positive correlation between the Mini-Mental State Examination (MMSE) and nodal strength in the left insula and negative correlation between the Clinical Dementia Rating (CDR) score and local efficiency of left olfactory cortex and also eigenvector centrality of left supramarginal cortex. The Neuropsychiatric Inventory Questionnaire (NPI-Q) also seems to be correlated with network measures of the left superior parietal gyrus. Moreover, notable decreased continuity are spotted in the limbic system. These measures can be used to provide an objective tool for AD diagnosis.

Background: Diffusion weighted imaging (DWI) is a non-invasive method for investigating the brain white matter structure and can be used to evaluate fiber bundles. However, due to practical constraints, DWI data acquired in clinics are low resolution., New Method: This paper proposes a method for interpolation of orientation distribution functions (ODFs). To this end, fuzzy clustering is applied to segment ODFs based on the principal diffusion directions (PDDs). Next, a cluster is modeled by a tensor so that an ODF is represented by a mixture of tensors. For interpolation, each tensor is rotated separately., Results: The method is applied on the synthetic and real DWI data of control and epileptic subjects. Both experiments illustrate capability of the method in increasing spatial resolution of the data in the ODF field properly. The real dataset show that the method is capable of reliable identification of differences between temporal lobe epilepsy (TLE) patients and normal subjects., Comparison With Existing Methods: The method is compared to existing methods. Comparison studies show that the proposed method generates smaller angular errors relative to the existing methods. Another advantage of the method is that it does not require an iterative algorithm to find the tensors., Conclusions: The proposed method is appropriate for increasing resolution in the ODF field and can be applied to clinical data to improve evaluation of white matter fibers in the brain., (Copyright © 2015 Elsevier B.V. All rights reserved.)

To spatially cluster resting state-functional magnetic resonance imaging (rs-fMRI) data into potential networks, there are only a few general approaches that determine the number of networks/clusters, despite a wide variety of techniques proposed for clustering. For individual subjects, extraction of a large number of spatially disjoint clusters results in multiple small networks that are spatio-temporally homogeneous but irreproducible across subjects. Alternatively, extraction of a small number of clusters creates spatially large networks that are temporally heterogeneous but spatially reproducible across subjects. We propose a fully automatic, iterative reclustering framework in which a small number of spatially large, heterogeneous networks are initially extracted to maximize spatial reproducibility. Subsequently, the large networks are iteratively subdivided to create spatially reproducible subnetworks until the overall within-network homogeneity does not increase substantially. The proposed approach discovers a rich network hierarchy in the brain while simultaneously optimizing spatial reproducibility of networks across subjects and individual network homogeneity. We also propose a novel metric to measure the connectivity of brain regions, and in a simulation study show that our connectivity metric and framework perform well in the face of low signal to noise and initial segmentation errors. Experimental results generated using real fMRI data show that the proposed metric improves stability of network clusters across subjects, and generates a meaningful pattern for spatially hierarchical structure of the brain., (© 2015 Wiley Periodicals, Inc.)

Mild traumatic brain injury (mTBI) accounts for more than 1 million emergency visits each year. Most of the injured stay in the emergency department for a few hours and are discharged home without a specific follow-up plan because of their negative clinical structural imaging. Advanced magnetic resonance imaging (MRI), particularly functional MRI (fMRI), has been reported as being sensitive to functional disturbances after brain injury. In this study, a cohort of 12 patients with mTBI were prospectively recruited from the emergency department of our local Level-1 trauma center for an advanced MRI scan at the acute stage. Sixteen age- and sex-matched controls were also recruited for comparison. Both group-based and individual-based independent component analysis of resting-state fMRI (rsfMRI) demonstrated reduced functional connectivity in both posterior cingulate cortex (PCC) and precuneus regions in comparison with controls, which is part of the default mode network (DMN). Further seed-based analysis confirmed reduced functional connectivity in these two regions and also demonstrated increased connectivity between these regions and other regions of the brain in mTBI. Seed-based analysis using the thalamus, hippocampus, and amygdala regions further demonstrated increased functional connectivity between these regions and other regions of the brain, particularly in the frontal lobe, in mTBI. Our data demonstrate alterations of multiple brain networks at the resting state, particularly increased functional connectivity in the frontal lobe, in response to brain concussion at the acute stage. Resting-state functional connectivity of the DMN could serve as a potential biomarker for improved detection of mTBI in the acute setting.

In this paper, we present a fast method to extract the sources related to interictal epileptiform state. The method is based on general eigenvalue decomposition using two correlation matrices during: 1) periods including interictal epileptiform discharges (IED) as a reference activation model and 2) periods excluding IEDs or abnormal physiological signals as background activity. After extracting the most similar sources to the reference or IED state, IED regions are estimated by using multiobjective optimization. The method is evaluated using both realistic simulated data and actual intracerebral electroencephalography recordings of patients suffering from focal epilepsy. These patients are seizure-free after the resective surgery. Quantitative comparisons of the proposed IED regions with the visually inspected ictal onset zones by the epileptologist and another method of identification of IED regions reveal good performance.

White matter (WM) microstructure can be evaluated by diffusion tensor imaging (DTI). Tract-based spatial statistical (TBSS) analysis provides a means of assessing alterations in WM tracts. In this paper, both voxel-based morphometry (VBM) and TBSS are examined using DTI data of temporal lobe epilepsy (TLE) patients and nonepileptic subjects. In addition to fractional anisotropy (FA), ellipsoidal area ratio (EAR) is used in this study. Significant reductions of FA and EAR are identified by TBSS in the parahippocampal white matter. Because of methodological differences, TBSS detects more localized abnormalities than VBM, while the EAR is more sensitive to WM alteration than FA., (2011 Elsevier Ltd. All rights reserved.)

Diffusion tensor magnetic resonance imaging (DTMRI) is a non-invasive tool for the investigation of white matter structure within the brain. However, the traditional tensor model is unable to characterize anisotropies of orders higher than two in heterogeneous areas containing more than one fiber population. To resolve this issue, high angular resolution diffusion imaging (HARDI) with a large number of diffusion encoding gradients is used along with reconstruction methods such as Q-ball. Using HARDI data, the fiber orientation distribution function (ODF) on the unit sphere is calculated and used to extract the principal diffusion directions (PDDs). Fast and accurate estimation of PDDs is a prerequisite for tracking algorithms that deal with fiber crossings. In this paper, the PDDs are defined as the directions around which the ODF data is concentrated. Estimates of the PDDs based on this definition are less sensitive to noise in comparison with the previous approaches. A clustering approach to estimate the PDDs is proposed which is an extension of fuzzy c-means clustering developed for orientation of points on a sphere. MDL (Minimum description length) principle is proposed to estimate the number of PDDs. Using both simulated and real diffusion data, the proposed method has been evaluated and compared with some previous protocols. Experimental results show that the proposed clustering algorithm is more accurate, more resistant to noise, and faster than some of techniques currently being utilized., (Copyright © 2011 Elsevier Inc. All rights reserved.)

In this paper, we study temporal couplings between interictal events of spatially remote regions in order to localize the leading epileptic regions from intracerebral EEG (iEEG). We aim to assess whether quantitative epileptic graph analysis during interictal period may be helpful to predict the seizure onset zone of ictal iEEG. Using wavelet transform, cross-correlation coefficient, and multiple hypothesis test, we propose a differential connectivity graph (DCG) to represent the connections that change significantly between epileptic and nonepileptic states as defined by the interictal events. Postprocessings based on mutual information and multiobjective optimization are proposed to localize the leading epileptic regions through DCG. The suggested approach is applied on iEEG recordings of five patients suffering from focal epilepsy. Quantitative comparisons of the proposed epileptic regions within ictal onset zones detected by visual inspection and using electrically stimulated seizures, reveal good performance of the present method.

This paper presents a visualization and analysis framework for evaluating changes in structural organization of fiber bundles in human brain white matter. Statistical analysis of fiber bundle organization is conducted using an anisotropy measure, volume ratio (VR), which is ratio of anisotropic and isotropic components. Initially fiber bundles are tracked using a probabilistic algorithm starting from seed voxels. To ensure accurate selection of seed voxels and to prevent operator bias, a reference brain (MNI&#95;152) is used when marking ROIs. Individual structural MRI brain scans are mapped to the reference using volumetric conformal parameterization. This mapping preserves topology and aligns features perfectly making it a robust and accurate registration technique. One-to-one mapping to the template allows ROI selection and subsequent transfer of ROI to structural MRI of subject. Affine registration coregisters structural MRI and DTI. Seed voxels are mapped to DTI using the resulting transformation parameters. To evaluate the proposed approach, MRI and DTI of 12 normal volunteers and 15 medial temporal lobe epilepsy patients are used. First, a statistical hypothesis testing is conducted to test for anisotropy changes in cingulum and fornix fiber bundles of epileptic patients. Experimental results reveal a 40% decrease in anisotropy levels of cingulum in patients compared to volunteers. They also show a 25% overall decrease in anisotropy of fornix. Secondly, shapes of the bundles are visualized in 3D illustrating that the bundles of epileptic patients are bumpy while those of normal volunteers are smooth., (Copyright © 2010 Elsevier Inc. All rights reserved.)

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., (© 2010 Elsevier Inc. All rights reserved.)

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., (© 2010 Elsevier Inc. All rights reserved.)

Purpose: To develop an automatic atlas-based method for segmentation of fiber bundles using High Angular Resolution Diffusion Imaging (HARDI) data., Hypothesis: Quantitative evaluation of diffusion characteristics inside specific fiber bundles provides new insights into disease developments, evolutions, therapy effects, and surgical interventions., Background: Most of previous segmentation methods use similarity measures and strategies that do not lead to accurate segmentation results. They also suffer from subjectivity of initial seeds and regions of interest (ROI) defined by operator., Materials and Methods: We propose a novel method that uses Spherical Harmonic Coefficients (SHC) of HARDI diffusion signals to compute Orientation Distribution Function (ODF) and to extract Principal Diffusion Directions (PDDs). The proposed method selects most collinear PDD of neighbors of each voxel. Then, based on SHC and selected PDD, a similarity measure is proposed and used as a speed function in the level set framework that segments fiber bundles. To automate the process, an atlas-based method is used to select initial seeds for fiber bundles. To generate data for evaluation of the proposed method, an artificial pattern consisting of three crossing bundles intersected by a circular bundle is created. Also, two normal controls are imaged by two different HARDI protocols., Results: Segmentation results for different fiber bundles in simulated data and normal control data are presented. Influence of threshold selection on the proposed segmentation method is evaluated using Dice coefficient. Also, effect of increasing the number of gradient directions on accuracy of extracted PDDs is evaluated., Conclusion: The proposed segmentation method has advantages over previous methods especially those that use similarity measures based on diffusion tensor imaging (DTI) data. These advantages are achieved by proper propagation of a hyper-surface in fiber crossing areas without making assumptions about diffusivity profile and selection of initial seeds or ROI., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Background: Pre-operative imaging devices generate high-resolution images but intra-operative imaging devices generate low-resolution images. To use high-resolution pre-operative images during surgery, they must be deformed to reflect intra-operative geometry of brain., Methods: We employ biomechanical models, guided by low resolution intra-operative images, to determine location of normal and abnormal regions of brain after craniotomy. We also employ finite element methods to discretize and solve the related differential equations. In the process, pre- and intra-operative images are utilized and corresponding points are determined and used to optimize parameters of the models. This paper develops a nonlinear model and compares it with linear models while our previous work developed and compared linear models (mechanical and elastic)., Results: Nonlinear model is evaluated and compared with linear models using simulated and real data. Partial validation using intra-operative images indicates that the proposed models reduce the localization error caused by brain deformation after craniotomy., Conclusions: The proposed nonlinear model generates more accurate results than the linear models. When guided by limited intra-operative surface data, it predicts deformation of entire brain. Its execution time is however considerably more than those of linear models.

We previously proposed an integrated electroencephalography (EEG), magnetoencephalography (MEG), and functional Magnetic Resonance Imaging (fMRI) model based on an extended neural mass model (ENMM) within a single cortical area. In the ENMM, a cortical area contains several minicolumns where strengths of their connections diminish exponentially with their distances. The ENMM was derived based on the physiological principles of the cortical minicolumns and their connections within a single cortical area to generate EEG, MEG, and fMRI signals. The purpose of this paper is to further extend the ENMM model from a single-area to a multi-area model to develop a neural mass model of the entire brain that generates EEG and MEG signals. For multi-area modeling, two connection types are considered: short-range connections (SRCs) and long-range connections (LRCs). The intra-area SRCs among the minicolumns within the areas were previously modeled in the ENMM. To define inter-area LRCs among the cortical areas, we consider that the cell populations of all minicolumns in the destination area are affected by the excitatory afferent of the pyramidal cells of all minicolumns in the source area. The state-space representation of the multi-area model is derived considering the intra-minicolumn, SRCs', and LRCs' parameters. Using simulations, we evaluate effects of parameters of the model on its dynamics and, based on stability analysis, find valid ranges for parameters of the model. In addition, we evaluate reducing redundancy of the model parameters using simulation results and conclude that the parameters of the model can be limited to the LRCs and SRCs while the intra-minicolumn parameters stay at their physiological mean values. The proposed multi-area integrated E/MEG model provides an efficient neuroimaging technique for effective connectivity analysis in healthy subjects as well as neurological and psychiatric patients., (Copyright (c) 2010 Elsevier Inc. All rights reserved.)

Purpose: The authors propose a fast, robust, nonparametric, entropy-based, coupled, multishape approach to segment subcortical brain structures from magnetic resonance images (MRIs)., Methods: The proposed method uses three types of information: Image intensity, tissue types, and locations of structures. The image intensity information is captured by estimating the probability density function (pdf) of the image intensities in each structure. The tissue type information is captured by applying an unsupervised tissue segmentation method to the image and estimating a probability mass function (pmf) for the tissue type of each structure. The location information is captured by estimating pdf of the location of each structure from the training datasets. The resulting pmf's and pdf's are used to define an entropy function whose minimum corresponds to a desirable segmentation of the structures. The authors propose a three-step optimization strategy for the segmentation method. In the first step, a powerful automatic initialization method is developed based on tissue type and location information of the structures. In the second step, a quasi-Newton method is used to optimize the parameters of the energy function. To speed up the iterations, derivatives of the energy function with respect to its parameters are analytically derived and used in the optimization process. In the last step, the limitations related to the prior shape model are removed and a level-set method is applied for the fine tuning of the segmentation results., Results: The proposed method is applied to two different datasets and the results are compared to those of previous methods in literature. Experimental results are presented for lateral ventricles, caudate, thalamus, putamen, pallidum, hippocampus, and amygdala., Conclusions: The results illustrate superior performance of the proposed segmentation method compared to other methods in literature. The execution time of the algorithm is a few minutes, suitable for a variety of applications.

This paper presents an automatic method for segmentation of brain structures using their symmetry and tissue type information. The proposed method generates segmented structures that have homogenous tissues. It benefits from general symmetry of the brain structures in the two hemispheres. It also benefits from the tissue regions generated by fuzzy c-means clustering. All in all, the proposed method can be described as a dynamic knowledge-based method that eliminates the need for statistical shape models of the structures while generating accurate segmentation results. The proposed approach is implemented in MATLAB and tested on the Internet Brain Segmentation Repository (IBSR) datasets. To this end, it is applied to the segmentation of caudate and ventricles three-dimensionally in magnetic resonance images (MRI) of the brain. Impacts of each of the steps of the proposed approach are demonstrated through experiments. It is shown that the proposed method generates accurate segmentation results that are insensitive to initialization and parameter selection. The proposed method is compared to four previous methods illustrating advantages and limitations of each method., (Copyright (c) 2009 Elsevier Ltd. All rights reserved.)

Classifying brain white matter fibers into bundles is of growing interest in neuroscience. Quantification of diffusion characteristics inside a fiber bundle provides new insights for disease evolutions, therapy effects, and surgical interventions. In this paper, we present a novel method for segmenting fiber bundles using spherical harmonic coefficients (SHC) that describe diffusion signal obtained from High Angular Resolution Diffusion Imaging (HARDI) protocols. Based on SHC, we define a similarity measure and use it as a speed function term in level set framework. We show advantages of the proposed measure over similarity measures based on Diffusion Tensor Imaging (DTI) indices. Without any assumptions about diffusion model, we deal with diffusion signal instead of orientation distribution function (ODF) calculated using complicated mathematics, inaccurate simplifications, and time-consuming implementation. By applying the proposed algorithm on synthetic data, its superior accuracy and robustness in low SNR conditions are shown. Application of the proposed method on real HARDI MRI data also illustrates its superior performance, especially in heterogeneous diffusion areas with low traditional diffusion anisotropies., (Copyright (c) 2009 Elsevier Ltd. All rights reserved.)

We propose a new method to estimate the random effect variance in group analysis of fMRI data. In the first level of analysis, general linear model (GLM) is used to estimate a parameter map ("effect") for each subject. After applying discrete wavelet transform to the "effect" maps, noise is reduced through a vertical energy thresholding (VET). The fixed effect component in each coefficient is derived by averaging the wavelet coefficients along all subjects. Then, the wavelet coefficients containing significant random effect are identified by their higher sample variance along the subjects. Wavelet coefficients containing random effect component in each subject are used to reconstruct the random effect maps through an inverse wavelet transform. Random effect variance is obtained from random effect maps for use in random effect analysis. The proposed method and other methods like GLM group analysis and variance ratio smoothing are applied to both experimental and artificial fMRI data. ROC curves, obtained from the simulated data, show improved group activation detection compared to existing random effect analysis methods. For the experimental data, the proposed method shows its high sensitivity by detecting multiple activation regions, namely visual cortex, cuneus, precuneus, thalamus, and cerebellum. From these regions, precuneus and cerebellum are not detected by majority of the previously published methods.

We propose an automatic method for the segmentation of the brain structures in three dimensional (3D) Magnetic Resonance Images (MRI). The proposed method consists of two stages. In the first stage, we represent the shape of the structure using Geometric Moment Invariants (GMIs) in 8 scales. For each scale, an Artificial Neural Network (ANN) is designed to approximate the signed distance function of a desired structure. The GMIs along with the voxel intensities and coordinates are used as the input features of the ANN and the signed distance function as its output. In the second stage, we combine the outputs of the ANNs of the first stage and design another ANN to classify the image voxels into two classes, inside or outside of the structure. We introduce a fast method for moment calculations. The proposed method is applied to the segmentation of caudate, putamen, and thalamus in MRI where it has outperformed other methods in the literature.

Unknown low frequency fluctuations called "trend" are observed in noisy time-series measured for different applications. In some disciplines, they carry primary information while in other fields such as functional magnetic resonance imaging (fMRI) they carry nuisance effects. In all cases, however, it is necessary to estimate them accurately. In this paper, a method for estimating trend in the presence of fractal noise is proposed and applied to fMRI time-series. To this end, a partly linear model (PLM) is fitted to each time-series. The parametric and nonparametric parts of PLM are considered as contributions of hemodynamic response and trend, respectively. Using the whitening property of wavelet transform, the unknown components of the model are estimated in the wavelet domain. The results of the proposed method are compared to those of other parametric trend-removal approaches such as spline and polynomial models. It is shown that the proposed method improves activation detection and decreases variance of the estimated parameters relative to the other methods.

We present a novel and efficient method for localization of human brain structures such as hippocampus. Landmark localization is important for segmentation and registration. This method follows a statistical roadmap, consisting of anatomical landmarks, to reach the desired structures. Using a set of desired and undesired landmarks, identified on a training set, we estimate Gaussian models and determine optimal search areas for desired landmarks. The statistical models form a set of rules to evaluate the extracted landmarks during the search procedure. When applied on 900 MR images of 10 epileptic patients, this method demonstrated an overall success rate of 83%.

This article presents a new method for estimating the leakage of a contrast agent out of a vessel. The proposed method is developed based on tissue homogeneity (TH) model, modified Patlak model, and Monte Carlo simulation. The analytical methods published in the literature estimate the contrast agent leakage by solving the coupled differential equations associated with the TH model under adiabatic conditions. These methods employ unrealistic simplifying assumptions and become intractable in their applications to the vessels that have a non-uniform permeability. Without making any unrealistic assumptions, our approach simply tracks the passage of the contrast agent through the capillary and its crossing of the vessel walls based on the blood flow in the vessel, the vessel's permeability, and the condition of the blood-brain barrier (BBB). These are treated as statistical processes that can be modeled reasonably well using the Monte Carlo method. In the proposed approach, the intra- and extra-vascular spaces are divided into multiple compartments, similar to the Patlak model. A real, measured arterial input function (AIF) is used as the capillary input and the concentration of the contrast agent is found as a function of time and distance, inside and outside of the capillary. This is done for normal and abnormal capillaries with uniform and non-uniform permeability. The proposed method generates concentration curves similar to those of the analytical method for simple AIF models. It also generates reasonable concentration curves for a real AIF. The proposed method does not fit a mathematical function to the measured AIF and does not make unrealistic simplifying assumptions. It is not therefore prone to the fitting errors and generates more realistic and more accurate results than the analytical methods.

We introduce a bottom-up model for integrating electroencephalography (EEG) or magnetoencephalography (MEG) with functional magnetic resonance imaging (fMRI). An extended neural mass model is proposed based on the physiological principles of cortical minicolumns and their connections. The fMRI signal is extracted from the proposed neural mass model by introducing a relationship between the stimulus and the neural activity and using the resultant neural activity as input of the extended Balloon model. The proposed model, validated using simulations, is instrumental in evaluating the upcoming combined methods for simultaneous analysis of MEG/EEG and fMRI.

The authors assessed the effect of IV abciximab on early neurologic improvement and ischemic lesion growth in 29 patients with supratentorial stroke and NIH stroke scale score (NIHSSS) > or = 4 (11.1 +/- 5.9), treated within 3 to 24 (13.6 +/- 5.5) hours of onset. The 48 to 72-hour NIHSSS improvement was 4.4 +/- 3.2 and the 24-hour lesion growth on DWI was +23% (-50%, +103%); 7/26 (27%) patients experienced lesion size decrease. Treatment of sub-24-hour stroke with abciximab improves early post-treatment neurologic status and often attenuates ischemic lesion growth.

Despite its potential advantages for fMRI analysis, fuzzy C-means (FCM) clustering suffers from limitations such as the need for a priori knowledge of the number of clusters, and unknown statistical significance and instability of the results. We propose a randomization-based method to control the false-positive rate and estimate statistical significance of the FCM results. Using this novel approach, we develop an fMRI activation detection method. The ability of the method in controlling the false-positive rate is shown by analysis of false positives in activation maps of resting-state fMRI data. Controlling the false-positive rate in FCM allows comparison of different fuzzy clustering methods, using different feature spaces, to other fMRI detection methods. In this article, using simulation and real fMRI data, we compare a novel feature space that takes the variability of the hemodynamic response function into account (HRF-based feature space) to the conventional cross-correlation analysis and FCM using the cross-correlation feature space. In both cases, the HRF-based feature space provides a greater sensitivity compared to the cross-correlation feature space and conventional cross-correlation analysis. Application of the proposed method to finger-tapping fMRI data, using HRF-based feature space, detected activation in sub-cortical regions, whereas both of the FCM with cross-correlation feature space and the conventional cross-correlation method failed to detect them.

This paper presents a fast method for delineation of activated areas of the brain from functional magnetic resonance imaging (fMRI) time series data. The steps of the work accomplished are as follows. 1) It is shown that the detection performance evaluated by the area under the receiver operating characteristic curve is directly related to the signal-to-noise ratio (SNR) of the composite image generated in the detection process. 2) Detection and segmentation of activated areas are formulated in a vector space framework. In this formulation, a linear transformation (image combination method) is shown to be desirable to maximize the SNR of the activated areas subject to the constraint of removing inactive areas. 3) An analytical solution for the problem is found. 4) Image pixel vectors and expected time series pattern (signature) for inactive pixels are used to calculate weighting vector and identify activated regions. 5) Signatures of the activated regions are used to segment different activities. 6) Segmented images by the proposed method are compared with those generated by the conventional methods (correlation, t-statistic, and z statistic). Detection performance and SNRs of the images are compared. The proposed approach outperforms the conventional methods of fMRI analysis. In addition, it is model-independent and does not require a priori knowledge of the fMRI response to the paradigm. Since the method is linear and most of the work is done analytically, numerical implementation and execution of the method are much faster than the conventional methods.

An adaptive spatial filtering method is proposed that takes into account contextual information in fMRI activation detection. This filter replaces the time series of each voxel with a weighted average of time series of a small neighborhood around it. The filter coefficients at each voxel are derived so as to maximize a test statistic designed to indicate the presence of activation. This statistic is the ratio of the energy of the filtered time series in a signal subspace to the energy of the residuals. It is shown that the filter coefficients and the maximum energy ratio can be found through a generalized eigenproblem. This approach equates the filter coefficients to the elements of an eigenvector corresponding to the largest eigenvalue of a specific matrix, while the largest eigenvalue itself becomes the maximum energy ratio that can be used as a statistic for detecting activation. The distribution of this statistic under the null hypothesis is derived by a nonparametric permutation technique in the wavelet domain. Also, in this paper we introduce a new set of basis vectors that define the signal subspace. The space spanned by these basis vectors covers a wide range of possible hemodynamic response functions (HRF) and is applicable to both event related and block design fMRI signal analysis. This approach circumvents the need for a priori assumptions about the exact shape of the HRF. Resting-state experimental fMRI data were used to assess the specificity of the method, showing that the actual false-alarm rate of the proposed method is equal or less than its expected value. Analysis of simulated data and motor task fMRI datasets from six volunteers using the method proposed here showed an improved sensitivity as compared to a conventional test with a similar statistic applied to spatially smoothed data.

A new method is proposed for activation detection in event-related functional magnetic resonance imaging (fMRI). The method is based on the analysis of selected resolution levels (a subspace) in translation invariant wavelet transform (TIWT) domain. Using a priori knowledge about the activation signal and trends, we analyze their power in different resolution levels in TIWT domain and select an optimal set of resolution levels. A randomization-based statistical test is then applied in the wavelet domain for activation detection. This approach suppresses the effects of trends and enhances the detection sensitivity. In addition, since TIWT is insensitive to signal translations, the power analysis is robust with respect to signal shifts. The randomization test alleviates the need for assumptions about fMRI noise. The method has been applied to simulated and experimental fMRI datasets. Comparisons have been made between the results of the proposed method, a similar method in the time domain and the cross-correlation method. The proposed method has shown superior sensitivity compared to the other methods.

The goal of this work was to develop a warping technique for mapping a brain image to another image or atlas data, with minimum user interaction and independent of gray level information. We have developed and tested three different methods for warping magnetic resonance (MR) brain images. We utilize a deformable contour to extract and warp the boundaries of the two images. A mesh-grid coordinate system is constructed for each brain, by applying a distance transformation to the resulting contours, and scaling. In the first method (MGC), the first image is mapped to the second image based on a one-to-one mapping between different layers defined by the mesh-grid. In the second method (IDW), the corresponding pixels in the two images are found using the above mesh-grid system and a local inverse-distance weights interpolation. In the third proposed method (TSB), a subset of grid points is used for finding the parameters of a spline transformation, which defines the global warping. The warping methods were applied to clinical MR consisting of diffusion-weighted and T2-weighted images of the human brain. The IDW and TSB methods were superior in ranking of diagnostic quality of the warped MR images to the MGC (P < 0.01) as defined by a neuroradiologist. The deformable contour warping produced excellent diagnostic quality for the diffusion-weighted images coregistered and warped to T2 weighted images. J. Magn. Reson. Imaging 2000;12:417-429., (Copyright 2000 Wiley-Liss, Inc.)

This study presents histological validation of an objective (unsupervised) computer segmentation algorithm, the iterative self-organizing data analysis technique (ISODATA), for analysis of multiparameter magnetic resonance imaging (MRI) data in experimental focal cerebral ischemia. T2-, T1-, and diffusion (DWI) weighted coronal images were acquired from 4 to 168 hours after stroke on separate groups of animals. Animals were killed immediately after MRI for histological analysis. MR images were coregistered/warped to histology. MRI lesion areas were defined using DWI, apparent diffusion coefficient (ADC) maps, T2-weighted images, and ISODATA. The last techniques clearly discriminated between ischemia-altered and morphologically intact tissue. ISODATA areas were congruent and significantly correlated (r = 0.99, P < 0.05) with histologically defined lesions. In contrast, DWI, ADC, and T2 lesion areas showed no significant correlation with histologically evaluated lesions until subacute time points. These data indicate that multiparameter ISODATA methodology can accurately detect and identify ischemic cell damage early and late after ischemia, with ISODATA outperforming ADC, DWI, and T2-weighted images in identification of ischemic lesions from 4 to 168 hours after stroke., (Copyright 2000 Wiley-Liss, Inc.)

An accurate noninvasive time-independent identification of an ischemic cerebral lesion is an important objective of magnetic resonance imaging (MRI). This study describes a novel application of a multiparameter MRI analysis algorithm, the Eigenimage (EI) filter, to experimental stroke. The EI is a linear filter that maximizes the projection of a desired tissue (ischemic tissue) while it minimizes the projection of undesired tissues (nonischemic tissue) onto a composite image called an eigenimage. Rats (n=26) were subjected to permanent middle cerebral artery occlusion. T2- and T1-weighted coronal MRI were acquired on separate groups of animals. The animals were immediately sacrificed after each imaging session for histopathological analysis of tissue at 4-8 h, 16-24 h, and 48-168 h after stroke onset. Lesion areas from MRI were defined using EI. The EI defined lesion areas were coregistered and warped to the corresponding histopathological sections. The ischemic lesion as defined by EI exhibited ischemic cell damage ranging from scattered acute cell damage to pan necrosis. Ischemic cellular damage was not detected in homologous contralateral hemisphere regions. EI lesion areas overlaid on histopathological sections were significantly correlated (r=0.92, p<0.05) acutely, (r=0.98, p<0.05) subacutely, and (r=0.99, p<0.05) chronically. These data indicate that EI methodology can accurately segment ischemic damage after MCA occlusion from 4-168 h after stroke., (Copyright 1999 Elsevier Science B.V.)

The application of a discrete dynamic contour model for segmentation of the hippocampus from brain MRI has been investigated. Solutions to several common problems of dynamic contours in this case and similar cases have been developed. A new method for extracting the discontinuous boundary of a structure with multiple edges near the structure has been developed. The method is based on detecting and following edges by external forces. The reliability of the final contour and the model stability have been improved by using a continuous mapping of the external energy and limiting movements of the contour. The problem of optimizing the internal force weight has been overcome by making it dependent on the amount of the external force. Finally, the results of applying the proposed algorithm, which implements the above modifications, to multiple applications have been evaluated.

Many image registration methods use head surface, brain surface, or inner/outer surface of the skull to estimate rotation and translation parameters. The inner surface of the skull is also used for intracranial volume segmentation which is considered the first step in segmentation and analysis of brain images. The surface is usually characterized by a set of edge or contour points extracted from cross-sectional images. Automatic extraction of contour points is complicated by discontinuity of edges in the back of the eyes and ears and sometimes by a previous surgery or an inadequate field of view. We have developed an automated method for contour extraction that connects discontinuities using a multiresolution pyramid. Steps of the method are: (1) Contour points are found by an edge-tracking algorithm; (2) A multiresolution pyramid of contour points is constructed; (3) Contour points of reduced images are found; (4) From the continuous contour found at the lowest resolution, contour points at a higher resolution are found; (5) Step 4 is repeated until contour points at the highest resolution (original image) are found. The method runs fast and has been successful in extracting contours from MRI and CT images. We illustrate the method and its performance using MRI and CT images of the human brain.

Highlights • New approaches for detecting the overlapping communities of the brain network are introduced using rs-fMRI data. • Non-negative Tensor Factorization techniques are proposed to decompose the association matrices of the individuals. • It has been shown that the resultant community structures through the proposed methods are accurate and stable. Abstract It has been found that specific regions in the brain are dedicated to specific functions. Detection and analysis of the constituent functional networks of the brain is of great importance for understanding the brain functionality and diagnosing some neuropsychiatric illnesses. In this paper, we introduce Non-negative Tensor Factorization (NTF) methods to identify the overlapping communities in brain networks using resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. Instead of taking average over a group of subjects, we use individual subject connectivity matrices to build the tensor data. Decomposed factors indicate the community membership probabilities and inter-subject variability indices modeling the community strengths over subjects. In contrast to the methods based on Non-negative Matrix Factorization (NMF) which are generally applied to the average connectivity matrices, using tensor factorization modeling preserves the information conveyed by the individual subjects. The experiments are carried out on simulated data as well as real Human Connectome Project (HCP) rs-fMRI datasets. To evaluate the effectiveness of the proposed framework, we have computed reproducibility over time and groups of subjects. Test-retest reliability is also examined through computing the intra-class correlation coefficient (ICC) index. The results show that the proposed NTF-based frameworks lead to stable and accurate results. [ABSTRACT FROM AUTHOR]