Identify Brain Networks Research Articles

Identification of epileptogenic networks from dense EEG: A model-based study.

Epilepsy is a network disease. Identifying the epileptogenic networks from noninvasive recordings is a challenging issue. In this context, M/EEG source connectivity is a promising tool to identify brain networks with high temporal and spatial resolution. In this paper, we analyze the impact of the two main factors that intervene in EEG source connectivity processing: i) the algorithm used to solve the EEG inverse problem and ii) the method used to measure the functional connectivity. We evaluate three inverse solutions algorithms (dSPM, wMNE and cMEM) and three connectivity measures (r(2), h(2) and MI) on data simulated from a combined biophysical/physiological model able to generate realistic interictal epileptic spikes reflected in scalp EEG. The performance criterion used here is the similarity between the network identified by each of the inverse/connectivity combination and the original network used in the source model. Results show that the choice of the combination has a high impact on the identified network. Results suggest also that nonlinear methods (nonlinear correlation coefficient and mutual information) for measuring the connectivity are more efficient than the linear one (the cross correlation coefficient). The dSPM as inverse solution shows the lowest performance compared to cMEM and wMNE.

Brain Network Disturbance Related to Posttraumatic Stress and Traumatic Brain Injury in Veterans

BackgroundUnderstanding the neural causes and consequences of posttraumatic stress disorder (PTSD) and mild traumatic brain injury (mTBI) is a high research priority, given the high rates of associated disability and suicide. Despite remarkable progress in elucidating the brain mechanisms of PTSD and mTBI, a comprehensive understanding of these conditions at the level of brain networks has yet to be achieved. The present study sought to identify functional brain networks and topological properties (measures of network organization and function) related to current PTSD severity and mTBI. MethodsGraph theoretic tools were used to analyze resting-state functional magnetic resonance imaging data from 208 veterans of Operation Enduring Freedom, Operation Iraqi Freedom, and Operation New Dawn, all of whom had experienced a traumatic event qualifying for PTSD criterion A. Analyses identified brain networks and topological network properties linked to current PTSD symptom severity, mTBI, and the interaction between PTSD and mTBI. ResultsTwo brain networks were identified in which weaker connectivity was linked to higher PTSD re-experiencing symptoms, one of which was present only in veterans with comorbid mTBI. Re-experiencing was also linked to worse functional segregation (necessary for specialized processing) and diminished influence of key regions on the network, including the hippocampus. ConclusionsFindings of this study demonstrate that PTSD re-experiencing symptoms are linked to weakened connectivity in a network involved in providing contextual information. A similar relationship was found in a separate network typically engaged in the gating of working memory, but only in veterans with mTBI.

Redistribution of neural phase coherence reflects establishment of feedforward map in speech motor adaptation.

Despite recent progress in our understanding of sensorimotor integration in speech learning, a comprehensive framework to investigate its neural basis is lacking at behaviorally relevant timescales. Structural and functional imaging studies in humans have helped us identify brain networks that support speech but fail to capture the precise spatiotemporal coordination within the networks that takes place during speech learning. Here we use neuronal oscillations to investigate interactions within speech motor networks in a paradigm of speech motor adaptation under altered feedback with continuous recording of EEG in which subjects adapted to the real-time auditory perturbation of a target vowel sound. As subjects adapted to the task, concurrent changes were observed in the theta-gamma phase coherence during speech planning at several distinct scalp regions that is consistent with the establishment of a feedforward map. In particular, there was an increase in coherence over the central region and a decrease over the fronto-temporal regions, revealing a redistribution of coherence over an interacting network of brain regions that could be a general feature of error-based motor learning in general. Our findings have implications for understanding the neural basis of speech motor learning and could elucidate how transient breakdown of neuronal communication within speech networks relates to speech disorders.

EEG source connectivity analysis: from dense array recordings to brain networks.

The recent past years have seen a noticeable increase of interest for electroencephalography (EEG) to analyze functional connectivity through brain sources reconstructed from scalp signals. Although considerable advances have been done both on the recording and analysis of EEG signals, a number of methodological questions are still open regarding the optimal way to process the data in order to identify brain networks. In this paper, we analyze the impact of three factors that intervene in this processing: i) the number of scalp electrodes, ii) the combination between the algorithm used to solve the EEG inverse problem and the algorithm used to measure the functional connectivity and iii) the frequency bands retained to estimate the functional connectivity among neocortical sources. Using High-Resolution (hr) EEG recordings in healthy volunteers, we evaluated these factors on evoked responses during picture recognition and naming task. The main reason for selection this task is that a solid literature background is available about involved brain networks (ground truth). From this a priori information, we propose a performance criterion based on the number of connections identified in the regions of interest (ROI) that belong to potentially activated networks. Our results show that the three studied factors have a dramatic impact on the final result (the identified network in the source space) as strong discrepancies were evidenced depending on the methods used. They also suggest that the combination of weighted Minimum Norm Estimator (wMNE) and the Phase Synchronization (PS) methods applied on High-Resolution EEG in beta/gamma bands provides the best performance in term of topological distance between the identified network and the expected network in the above-mentioned cognitive task.

Merging clinical neuropsychology and functional neuroimaging to evaluate the construct validity and neural network engagement of the n-back task.

The n-back task is a widely used neuroimaging paradigm for studying the neural basis of working memory (WM); however, its neuropsychometric properties have received little empirical investigation. The present study merged clinical neuropsychology and functional magnetic resonance imaging (fMRI) to explore the construct validity of the letter variant of the n-back task (LNB) and to further identify the task-evoked networks involved in WM. Construct validity of the LNB task was investigated using a bootstrapping approach to correlate LNB task performance across clinically validated neuropsychological measures of WM to establish convergent validity, as well as measures of related but distinct cognitive constructs (i.e., attention and short-term memory) to establish discriminant validity. Independent component analysis (ICA) identified brain networks active during the LNB task in 34 healthy control participants, and general linear modeling determined task-relatedness of these networks. Bootstrap correlation analyses revealed moderate to high correlations among measures expected to converge with LNB (|ρ|≥ 0.37) and weak correlations among measures expected to discriminate (|ρ|≤ 0.29), controlling for age and education. ICA identified 35 independent networks, 17 of which demonstrated engagement significantly related to task condition, controlling for reaction time variability. Of these, the bilateral frontoparietal networks, bilateral dorsolateral prefrontal cortices, bilateral superior parietal lobules including precuneus, and frontoinsular network were preferentially recruited by the 2-back condition compared to 0-back control condition, indicating WM involvement. These results support the use of the LNB as a measure of WM and confirm its use in probing the network-level neural correlates of WM processing.

Prevalence of smokeless tobacco use among individuals with comorbid other substance dependence

Aims: Functional magnetic resonance imaging (fMRI) research has indicated the involvement of frontal network in cocaine addiction. Resting-state f MRI approaches have revealed reduced (Kelly, 2011;Munier, 2012) aswell as enhanced (Camchong, 2011) resting state functional connectivity (RSFC) in frontal network in cocaine users relative to control participants. Also, task fMRI research has identified the insula as an important region activated during cocaine craving (Bonson, 2002). We examined the relationship between resting state networks and measures of cocaine use [frequency, money spent per week; duration] in nontreatmentseeking, chronic cocaine smokers. Methods: Ten participants (39–53yrs; 6M;4F) abstinent from cocaine for 72h completed a resting state scan and an anatomical MPRAGE scan. Whole-brain Independent component analysis was performed on resting state fMRI data using FSL’sMELODIC program that identified brain networks representing RSFC of frontalparietal, dorsal-attention, and insula networks. FSL’s regression model then determined any correlations between the activity of these brain networks and cocaine use measures. Results: Activity in frontal-parietal and insula networks was positively correlated with all three outcomes: frequency of use, money spent/week and cocaine use duration. Medial frontal gyrus and inferior parietal lobule [a part of dorsal-attention network (DAN)] positively correlated with each measure. Thus, the increased activity in these brain regions was associated with increased cocaine use. Conclusions: To the best of our knowledge, this is the first study to demonstrate a relationship between RSFC in frontal and insula networks and measures of cocaine use in chronic cocaine smokers. Relationship between the DAN and cocaine use may suggest increased attentional bias to cocaine cues in the environment. As potential network level biomarkers of chronic cocaine use, these identified resting state networks may be usefully applied in treatment development. Financial support: Research supported byNIDAK01DA029047.

Poster #M35 DISRUPTIONS IN SMALL-WORLD CORTICAL FUNCTIONAL CONNECTIVITY NETWORK DURING AN AUDITORY ODDBALL PARADIGM TASK IN PATIENTS WITH SCHIZOPHRENIA

A primary goal of the field of behavioral neuroendocrinology is to understand how the brain modulates complex behavior. Over the last 20 years we have proposed various brain networks to explain behavioral regulation, however, the parameters by which these networks are identified are often ill-defined and reflect our personal scientific biases based on our area of expertise. In this perspective article, I question our characterization of brain networks underlying behavior and their utility. Using the Social Behavior Network as a primary example, I outline issues with brain networks commonly discussed in the field of behavioral neuroendocrinology, argue that we reconsider how we identify brain networks underlying behavior, and urge the future use of analytical tools developed by the field of Network Neuroscience. With modern statistical/mathematical tools and state of the art technology for brain imaging, we can strive to minimize our bias and generate brain networks that may more accurately reflect how the brain produces behavior.

Time-course of cortical networks involved in working memory

Working memory (WM) is one of the most studied cognitive constructs. Although many neuroimaging studies have identified brain networks involved in WM, the time course of these networks remains unclear. In this paper we use dense-array electroencephalography (dEEG) to capture neural signals during performance of a standard WM task, the n-back task, and a blend of principal components analysis and independent components analysis (PCA/ICA) to statistically identify networks of WM and their time courses. Results reveal a visual cortex centric network, that also includes the posterior cingulate cortex, that is active prior to stimulus onset and that appears to reflect anticipatory, attention-related processes. After stimulus onset, the ventromedial prefrontal cortex, lateral prefrontal prefrontal cortex, and temporal poles become associated with the prestimulus network. This second network appears to reflect executive control processes. Following activation of the second network, the cortices of the temporo-parietal junction with the temporal lobe structures seen in the first and second networks re-engage. This third network appears to reflect activity of the ventral attention network involved in control of attentional reorientation. The results point to important temporal features of network dynamics that integrate multiple subsystems of the ventral attention network with the default mode network in the performance of working memory tasks.

An evaluation of independent component analyses with an application to resting‐state fMRI

We examine differences between independent component analyses (ICAs) arising from different assumptions, measures of dependence, and starting points of the algorithms. ICA is a popular method with diverse applications including artifact removal in electrophysiology data, feature extraction in microarray data, and identifying brain networks in functional magnetic resonance imaging (fMRI). ICA can be viewed as a generalization of principal component analysis (PCA) that takes into account higher-order cross-correlations. Whereas the PCA solution is unique, there are many ICA methods-whose solutions may differ. Infomax, FastICA, and JADE are commonly applied to fMRI studies, with FastICA being arguably the most popular. Hastie and Tibshirani (2003) demonstrated that ProDenICA outperformed FastICA in simulations with two components. We introduce the application of ProDenICA to simulations with more components and to fMRI data. ProDenICA was more accurate in simulations, and we identified differences between biologically meaningful ICs from ProDenICA versus other methods in the fMRI analysis. ICA methods require nonconvex optimization, yet current practices do not recognize the importance of, nor adequately address sensitivity to, initial values. We found that local optima led to dramatically different estimates in both simulations and group ICA of fMRI, and we provide evidence that the global optimum from ProDenICA is the best estimate. We applied a modification of the Hungarian (Kuhn-Munkres) algorithm to match ICs from multiple estimates, thereby gaining novel insights into how brain networks vary in their sensitivity to initial values and ICA method.

Effect of scanner acoustic background noise on strict resting-state fMRI

Functional MRI (fMRI) resting-state experiments are aimed at identifying brain networks that support basal brain function. Although most investigators consider a ‘resting-state’ fMRI experiment with no specific external stimulation, subjects are unavoidably under heavy acoustic noise produced by the equipment. In the present study, we evaluated the influence of auditory input on the resting-state networks (RSNs). Twenty-two healthy subjects were scanned using two similar echo-planar imaging sequences in the same 3T MRI scanner: a default pulse sequence and a reduced “silent” pulse sequence. Experimental sessions consisted of two consecutive 7-min runs with noise conditions (default or silent) counterbalanced across subjects. A self-organizing group independent component analysis was applied to fMRI data in order to recognize the RSNs. The insula, left middle frontal gyrus and right precentral and left inferior parietal lobules showed significant differences in the voxel-wise comparison between RSNs depending on noise condition. In the presence of low-level noise, these areas Granger-cause oscillations in RSNs with cognitive implications (dorsal attention and entorhinal), while during high noise acquisition, these connectivities are reduced or inverted. Applying low noise MR acquisitions in research may allow the detection of subtle differences of the RSNs, with implications in experimental planning for resting-state studies, data analysis, and ergonomic factors.

Dissociable effects of methylphenidate, atomoxetine and placebo on regional cerebral blood flow in healthy volunteers at rest: A multi-class pattern recognition approach

The stimulant drug methylphenidate (MPH) and the non-stimulant drug atomoxetine (ATX) are both widely used for the treatment of attention deficit/hyperactivity disorder (ADHD), but their differential effects on human brain function are poorly understood. PET and blood oxygen level dependent (BOLD) fMRI have been used to study the effects of MPH and BOLD fMRI is beginning to be used to delineate the effects of MPH and ATX in the context of cognitive tasks. The BOLD signal is a proxy for neuronal activity and is dependent on three physiological parameters: regional cerebral blood flow (rCBF), cerebral metabolic rate of oxygen and cerebral blood volume. To identify areas sensitive to MPH and ATX and assist interpretation of BOLD studies in healthy volunteers and ADHD patients, it is therefore of interest to characterize the effects of these drugs on rCBF. In this study, we used arterial spin labeling (ASL) MRI to measure rCBF non-invasively in healthy volunteers after administration of MPH, ATX or placebo. We employed multi-class pattern recognition (PR) to discriminate the neuronal effects of the drugs, which accurately discriminated all drug conditions from one another and provided activity patterns that precisely localized discriminating brain regions. We showed common and differential effects in cortical and subcortical brain regions. The clearest differential effects were observed in four regions: (i) in the caudate body where MPH but not ATX increased rCBF, (ii) in the midbrain/substantia nigra and (iii) thalamus where MPH increased and ATX decreased rCBF plus (iv) a large region of cerebellar cortex where ATX increased rCBF relative to MPH. Our results demonstrate that combining ASL and PR yields a sensitive method for detecting the effects of these drugs and provides insights into the regional distribution of brain networks potentially modulated by these compounds.

Impaired consciousness in epilepsy investigated by a prospective responsiveness in epilepsy scale (RES)

Impaired consciousness in epileptic seizures has a major negative impact on patient quality of life. Prior work on epileptic unconsciousness has mainly used retrospective and nonstandardized methods. Our goal was to validate and to obtain initial data using a standardized prospective testing battery. The responsiveness in epilepsy scale (RES) was used on 52 patients during continuous video-electroencephalography (EEG) monitoring. RES begins with higher-level questions and commands, and switches adaptively to more basic sensorimotor responses depending on patient performance. RES continues after seizures and includes postictal memory testing. Scoring was conducted based on video review. Testing on standardized seizure simulations yielded good intrarater and interrater reliability. We captured 59 seizures from 18 patients (35% of participants) during 1,420 h of RES monitoring. RES impairment was greatest during and after tonic-clonic seizures, less in partial seizures, and minimal in auras and subclinical seizures. In partial seizures, ictal RES impairment was significantly greater if EEG changes were present. Maximum RES impairment (lowest ictal score) was also significantly correlated with long postictal recovery time, and poor postictal memory. We found that prospective testing of responsiveness during seizures is feasible and reliable. RES impairment was related to EEG changes during seizures, as well as to postictal memory deficits and recovery time. With a larger patient sample it is hoped that this approach can identify brain networks underlying specific components of impaired consciousness in seizures. This may allow the development of improved treatments targeted at preventing dysfunction in these networks.

Optimizing Experimental Design for Comparing Models of Brain Function

This article presents the first attempt to formalize the optimization of experimental design with the aim of comparing models of brain function based on neuroimaging data. We demonstrate our approach in the context of Dynamic Causal Modelling (DCM), which relates experimental manipulations to observed network dynamics (via hidden neuronal states) and provides an inference framework for selecting among candidate models. Here, we show how to optimize the sensitivity of model selection by choosing among experimental designs according to their respective model selection accuracy. Using Bayesian decision theory, we (i) derive the Laplace-Chernoff risk for model selection, (ii) disclose its relationship with classical design optimality criteria and (iii) assess its sensitivity to basic modelling assumptions. We then evaluate the approach when identifying brain networks using DCM. Monte-Carlo simulations and empirical analyses of fMRI data from a simple bimanual motor task in humans serve to demonstrate the relationship between network identification and the optimal experimental design. For example, we show that deciding whether there is a feedback connection requires shorter epoch durations, relative to asking whether there is experimentally induced change in a connection that is known to be present. Finally, we discuss limitations and potential extensions of this work.

The Impact of Continuity Editing in Narrative Film on Event Segmentation

Filmmakers use continuity editing to engender a sense of situational continuity or discontinuity at editing boundaries. The goal of this study was to assess the impact of continuity editing on how people perceive the structure of events in a narrative film and to identify brain networks that are associated with the processing of different types of continuity editing boundaries. Participants viewed a commercially produced film and segmented it into meaningful events, while brain activity was recorded with functional magnetic resonance imaging (MRI). We identified three degrees of continuity that can occur at editing locations: edits that are continuous in space, time, and action; edits that are discontinuous in space or time but continuous in action; and edits that are discontinuous in action as well as space or time. Discontinuities in action had the biggest impact on behavioral event segmentation, and discontinuities in space and time had minor effects. Edits were associated with large transient increases in early visual areas. Spatial-temporal changes and action changes produced strikingly different patterns of transient change, and they provided evidence that specialized mechanisms in higher order perceptual processing regions are engaged to maintain continuity of action in the face of spatiotemporal discontinuities. These results suggest that commercial film editing is shaped to support the comprehension of meaningful events that bridge breaks in low-level visual continuity, and even breaks in continuity of spatial and temporal location.

Distinct functional networks associated with improvement of affective symptoms and cognitive function during citalopram treatment in geriatric depression

Variability in the affective and cognitive symptom response to antidepressant treatment has been observed in geriatric depression. The underlying neural circuitry is poorly understood. This study evaluated the cerebral glucose metabolic effects of citalopram treatment and applied multivariate, functional connectivity analyses to identify brain networks associated with improvements in affective symptoms and cognitive function. Sixteen geriatric depressed patients underwent resting positron emission tomography (PET) studies of cerebral glucose metabolism and assessment of affective symptoms and cognitive function before and after 8 weeks of selective serotonin reuptake inhibitor treatment (citalopram). Voxel-wise analyses of the normalized glucose metabolic data showed decreased cerebral metabolism during citalopram treatment in the anterior cingulate gyrus, middle temporal gyrus, precuneus, amygdala, and parahippocampal gyrus. Increased metabolism was observed in the putamen, occipital cortex, and cerebellum. Functional connectivity analyses revealed two networks which were uniquely associated with improvement of affective symptoms and cognitive function during treatment. A subcortical-limbic-frontal network was associated with improvement in affect (depression and anxiety), while a medial temporal-parietal-frontal network was associated with improvement in cognition (immediate verbal learning/memory and verbal fluency). The regions that comprise the cognitive network overlap with the regions that are affected in Alzheimer's dementia. Thus, alterations in specific brain networks associated with improvement of affective symptoms and cognitive function are observed during citalopram treatment in geriatric depression.

Gray matter correlates of cognitive ability tests used for vocational guidance

BackgroundIndividual differences in cognitive abilities provide information that is valuable for vocational guidance, but there is an ongoing debate about the role of ability factors, including general intelligence (g), compared to individual tests. Neuroimaging can help identify brain parameters that may account for individual differences in both factors and tests. Here we investigate how eight tests used in vocational guidance correlate to regional gray matter. We compare brain networks identified by using scores for ability factors (general and specific) to those identified by using individual tests to determine whether these relatively broad and narrow approaches yield similar results.FindingsUsing MRI and voxel-based morphometry (VBM), we correlated gray matter with independent ability factors (general intelligence, speed of reasoning, numerical, spatial, memory) and individual test scores from a battery of cognitive tests completed by 40 individuals seeking vocational guidance. Patterns of gray matter correlations differed between group ability factors and individual tests. Moreover, tests within the same factor showed qualitatively different brain correlates to some degree.ConclusionsThe psychometric factor structure of cognitive tests can help identify brain networks related to cognitive abilities beyond a general intelligence factor (g). Correlates of individual ability tests with gray matter, however, appear to have some differences from the correlates for group factors.

Multi-level bootstrap analysis of stable clusters in resting-state fMRI

A variety of methods have been developed to identify brain networks with spontaneous, coherent activity in resting-state functional magnetic resonance imaging (fMRI). We propose here a generic statistical framework to quantify the stability of such resting-state networks (RSNs), which was implemented with k-means clustering. The core of the method consists in bootstrapping the available datasets to replicate the clustering process a large number of times and quantify the stable features across all replications. This bootstrap analysis of stable clusters (BASC) has several benefits: (1) it can be implemented in a multi-level fashion to investigate stable RSNs at the level of individual subjects and at the level of a group; (2) it provides a principled measure of RSN stability; and (3) the maximization of the stability measure can be used as a natural criterion to select the number of RSNs. A simulation study validated the good performance of the multi-level BASC on purely synthetic data. Stable networks were also derived from a real resting-state study for 43 subjects. At the group level, seven RSNs were identified which exhibited a good agreement with the previous findings from the literature. The comparison between the individual and group-level stability maps demonstrated the capacity of BASC to establish successful correspondences between these two levels of analysis and at the same time retain some interesting subject-specific characteristics, e.g. the specific involvement of subcortical regions in the visual and fronto-parietal networks for some subjects.

Connectivity of the Primate Superior Colliculus Mapped by Concurrent Microstimulation and Event-Related fMRI

BackgroundNeuroanatomical studies investigating the connectivity of brain areas have heretofore employed procedures in which chemical or viral tracers are injected into an area of interest, and connected areas are subsequently identified using histological techniques. Such experiments require the sacrifice of the animals and do not allow for subsequent electrophysiological studies in the same subjects, rendering a direct investigation of the functional properties of anatomically identified areas impossible.Methodology/Principal FindingsHere, we used a combination of microstimulation and fMRI in an anesthetized monkey preparation to study the connectivity of the superior colliculus (SC). Microstimulation of the SC resulted in changes in the blood oxygenation level-dependent (BOLD) signals in the SC and in several cortical and subcortical areas consistent with the known connectivity of the SC in primates.Conclusions/SignificanceThese findings demonstrates that the concurrent use of microstimulation and fMRI can be used to identify brain networks for further electrophysiological or fMRI investigation.

Sex differences in brain activity during aversive visceral stimulation and its expectation in patients with chronic abdominal pain: A network analysis

Differences in brain responses to aversive visceral stimuli may underlie previously reported sex differences in symptoms as well as perceptual and emotional responses to such stimuli in patients with irritable bowel syndrome (IBS). The goal of the current study was to identify brain networks activated by expected and delivered aversive visceral stimuli in male and female patients with chronic abdominal pain, and to test for sex differences in the effective connectivity of the circuitry comprising these networks. Network analysis was applied to assess the brain response of 46 IBS patients (22 men and 24 women) recorded using [ 15O] water positron emission tomography during rest/baseline and expected and delivered aversive rectal distension. Functional connectivity results from partial least squares analyses provided support for the hypothesized involvement of 3 networks corresponding to: 1) visceral afferent information processing (thalamus, insula and dorsal anterior cingulate cortex, orbital frontal cortex), 2) emotional-arousal (amygdala, rostral and subgenual cingulate regions, and locus coeruleus complex) and 3) cortical modulation (frontal and parietal cortices). Effective connectivity results obtained via structural equation modeling indicated that sex-related differences in brain response are largely due to alterations in the effective connectivity of emotional-arousal circuitry rather than visceral afferent processing circuits. Sex differences in the cortico-limbic circuitry involved in emotional-arousal, pain facilitation and autonomic responses may underlie the observed differences in symptoms, and in perceptual and emotional responses to aversive visceral stimuli.