Identify Brain Networks Research Articles

Neural Basis of Sensorimotor Plasticity in Speech Motor Adaptation.

When we speak, we get correlated sensory feedback from speech sounds and from the muscles and soft tissues of the vocal tract. Here we dissociate the contributions of auditory and somatosensory feedback to identify brain networks that underlie the somatic contribution to speech motor learning. The technique uses a robotic device that selectively alters somatosensory inputs in combination with resting-state fMRI scans that reveal learning-related changes in functional connectivity. A partial correlation analysis is used to identify connectivity changes that are not explained by the time course of activity in any other learning-related areas. This analysis revealed changes related to behavioral improvements in movement and separately, to changes in auditory perception: Speech motor adaptation itself was associated with connectivity changes that were primarily in non-motor areas of brain, specifically, to a strengthening of connectivity between auditory and somatosensory cortex and between presupplementary motor area and the inferior parietal lobule. In contrast, connectively changes associated with alterations to auditory perception were restricted to speech motor areas, specifically, primary motor cortex and inferior frontal gyrus. Overall, our findings show that during adaptation, somatosensory inputs result in a broad range of changes in connectivity in areas associated with speech motor control and learning.

Beyond the limbic system: disruption and functional compensation of large-scale brain networks in patients with anti-LGI1 encephalitis

ObjectiveHippocampal inflammation in anti-LGI1 encephalitis causes memory deficits, seizures and behavioural abnormalities. Recent findings suggest that extralimbic brain areas are additionally affected and that patients also suffer from non-limbic cognitive...

77. Intrinsic Brain Architecture Predicts Future Attentional and Mood Problems in a Normative Pediatric Sample

Previously we and others have discovered ways in which neuroimaging could be used to identify brain network pathologies in children who are at familial risk for psychiatric disorders but who are not currently diagnosed with any disorder. Here, we expand on the previous findings by investigating longitudinally a normative sample of children.

‘Identifying Brain Networks Controlling Micturition and Continence in Mouse’

ObjectiveLower urinary tract symptoms (LUTS) are extremely common and enormously debilitating. A significant component of LUTS is due to failure of nervous control of bladder function, or otherwise failure of neural pathways to compensate for bladder dysfunction. It remains however unclear how the brain controls bladder filling and voiding and how the micturition reflex is inhibited at times when voiding is undesirable. Prior studies have shown that the pontine micturition center (PMC, aka Barrington's nucleus) directly controls voiding. PMC neurons provide direct innervation of sacral spinal cord nuclei that control bladder contraction and sphincter relaxation. Here we identify the neurochemical identity of PMC neurons that contribute to micturition control as well as identify a network of afferent neurons, spanning several forebrain and brainstem regions, which directly modulates these PMC neurons to influence voiding behavior.MethodsMicro‐injections of adeno‐associated viruses (AAVs), modified Rabies virus, Cholera Toxin b (CTb) or Diphtheria Toxin a (dtA), were placed into anatomically defined regions of the mouse brain, enabling highly selective expression of proteins in target neuron populations. A novel non‐invasive assay; micturition video thermography (MVT), was used to track voiding behavior in awake‐behaving mice. MVT was combined with telemetric measurement of bladder pressure, urethral sphincter EMG and optogenetic stimulation or inhibition. Following MVT these manipulations were repeated whilst recording cystometry, in either the awake or anesthetized state.ResultsStimulating PMCCRH neurons using Gq DREADDs produced urinary frequency in awake‐behaving mice and on the anesthetized cystometrogram (CMG). Conversely, selective ablation of neurons in the PMC using dtA resulted in urinary retention, and delayed or eliminated the CMG voiding reflex. To identify the input connections controlling PMC neurons, we used CTb and modified Rabies virus. Afferents to PMCCRH neurons were found in the vlPAG, the preoptic area, the lateral hypothalamic area, and other sites. Monosynaptic connectivity was confirmed for select sites using electrophysiological recordings. To test the functional consequence of afferent neuron activity, we light‐stimulated their axon terminals within the PMC and observed micturition events within seconds (glutamatergic/excitatory afferents) or increased inter‐void intervals (GABAergic/inhibitory afferents). Finally, we recorded Ca2+‐dependent fluorescence changes in distinct neuron populations to study the timing of neuronal activity with respect to detrusor contraction and voiding.ConclusionsOur results taken together, identify a network of neurons that can control urinary voiding and continence by acting on PMC neurons. We have demonstrated necessity of select afferent sites for voiding, and the effects of removing their modulatory input for continence. This information helps us with a detailed understanding of how forebrain, brainstem and spinal inputs converge to control bladder filling and voiding, and hence the neurologic mechanisms of LUTS in mice and humans. As we define the brain pathways controlling normal micturition, we will evaluate how they are altered in settings of abnormal micturition, such as brain degeneration and prostatic enlargement.Support or Funding InformationNational Institutes of Health (NIH); R01 DK113030‐01This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Sensorimotor network alterations in children and youth with prenatal alcohol exposure.

Children with prenatal alcohol exposure (PAE) often have impaired sensorimotor function. While altered brain structure has been noted in sensorimotor areas, the functional brain alterations remain unclear. This study aims to investigate sensorimotor brain networks in children and youth with PAE using resting-state functional magnetic resonance imaging (rs-fMRI). A parcellation-based network analysis was performed to identify brain networks related to hand/lower limb and face/upper limb function in 59 children and youth with PAE and 50 typically developing controls. Participants with PAE and controls had similar organization of the hand and face areas within the primary sensorimotor cortex, but participants with PAE had altered functional connectivity (FC) between the sensorimotor regions and the rest of the brain. The sensorimotor regions in the PAE group showed less connectivity to certain hubs of the default mode network and more connectivity to areas of the salience network. Overall, our results show that despite similar patterns of organization in the sensorimotor network, subjects with PAE have increased FC between this network and other brain areas, perhaps suggesting overcompensation. These alterations in the sensorimotor network lay the foundation for future studies to evaluate interventions and treatments to improve motor function in children with PAE.

Cortical parcellation based on structural connectivity: A case for generative models

One of the major challenges in systems neuroscience is to identify brain networks and unravel their significance for brain function –this has led to the concept of the ‘connectome’. Connectomes are currently extensively studied in large-scale international efforts at multiple scales, and follow different definitions with respect to their connections as well as their elements.Perhaps the most promising avenue for defining the elements of connectomes originates from the notion that individual brain areas maintain distinct (long-range) connection profiles. These connectivity patterns determine the areas’ functional properties and also allow for their anatomical delineation and mapping. This rationale has motivated the concept of connectivity-based cortex parcellation.In the past ten years, non-invasive mapping of human brain connectivity has led to immense advances in the development of parcellation techniques and their applications. Unfortunately, many of these approaches primarily aim for confirmation of well-known, existing architectonic maps and, to that end, unsuitably incorporate prior knowledge and frequently build on circular argumentation. Often, current approaches also tend to disregard the specific apertures of connectivity measurements, as well as the anatomical specificities of cortical areas, such as spatial compactness, regional heterogeneity, inter-subject variability, the multi-scaling nature of connectivity information, and potential hierarchical organisation. From a methodological perspective, however, a useful framework that regards all of these aspects in an unbiased way is technically demanding.In this commentary, we first outline the concept of connectivity-based cortex parcellation and discuss its prospects and limitations in particular with respect to structural connectivity. To improve reliability and efficiency, we then strongly advocate for connectivity-based cortex parcellation as a modelling approach; that is, an approximation of the data based on (model) parameter inference. As such, a parcellation algorithm can be formally tested for robustness –the precision of its predictions can be quantified and statistics about potential generalization of the results can be derived. Such a framework also allows the question of model constraints to be reformulated in terms of hypothesis testing through model selection and offers a formative way to integrate anatomical knowledge in terms of prior distributions.

Resting-State fMRI Functional Connectivity: Big Data Preprocessing Pipelines and Topological Data Analysis

Resting state functional magnetic resonance imaging (rfMRI) can be used to measure functional connectivity and then identify brain networks and related brain disorders and diseases. To explore these complex networks, however, huge amounts of data are necessary. Recent advances in neuroimaging technologies, and the unique methodological approach of rfMRI, have enabled us to an era of Biomedical Big Data. The recent progress of big data sharing projects with their challenges are discussed. This increasing amount of neuroimaging data has greatly increased the importance of developing preprocessing pipelines and advanced analytic techniques, which are better at handling large-scale datasets. Before applying any analysis method on rfMRI data, several preprocessing steps need to be applied to reduce all unwanted effects. Three alternative ways to get access to big preprocessed rfMRI data are presented involving the minimal preprocessing pipelines. There are several commonly used methods to examine functional connectivity. However, they become limited in the analysis of big data, and a new tool to explore such data is necessary. We propose a number of novel methods rooted in algebraic topology and collectively referred to as Topological Data Analysis to rfMRI functional connectivity. Their properties for big data analysis are also discussed.

Functional Connectivity of Multiple Brain Regions Required for the Consolidation of Social Recognition Memory.

Social recognition memory is an essential and basic component of social behavior that is used to discriminate familiar and novel animals/humans. Previous studies have shown the importance of several brain regions for social recognition memories; however, the mechanisms underlying the consolidation of social recognition memory at the molecular and anatomic levels remain unknown. Here, we show a brain network necessary for the generation of social recognition memory in mice. A mouse genetic study showed that cAMP-responsive element-binding protein (CREB)-mediated transcription is required for the formation of social recognition memory. Importantly, significant inductions of the CREB target immediate-early genes c-fos and Arc were observed in the hippocampus (CA1 and CA3 regions), medial prefrontal cortex (mPFC), anterior cingulate cortex (ACC), and amygdala (basolateral region) when social recognition memory was generated. Pharmacological experiments using a microinfusion of the protein synthesis inhibitor anisomycin showed that protein synthesis in these brain regions is required for the consolidation of social recognition memory. These findings suggested that social recognition memory is consolidated through the activation of CREB-mediated gene expression in the hippocampus/mPFC/ACC/amygdala. Network analyses suggested that these four brain regions show functional connectivity with other brain regions and, more importantly, that the hippocampus functions as a hub to integrate brain networks and generate social recognition memory, whereas the ACC and amygdala are important for coordinating brain activity when social interaction is initiated by connecting with other brain regions. We have found that a brain network composed of the hippocampus/mPFC/ACC/amygdala is required for the consolidation of social recognition memory.SIGNIFICANCE STATEMENT Here, we identify brain networks composed of multiple brain regions for the consolidation of social recognition memory. We found that social recognition memory is consolidated through CREB-meditated gene expression in the hippocampus, medial prefrontal cortex, anterior cingulate cortex (ACC), and amygdala. Importantly, network analyses based on c-fos expression suggest that functional connectivity of these four brain regions with other brain regions is increased with time spent in social investigation toward the generation of brain networks to consolidate social recognition memory. Furthermore, our findings suggest that hippocampus functions as a hub to integrate brain networks and generate social recognition memory, whereas ACC and amygdala are important for coordinating brain activity when social interaction is initiated by connecting with other brain regions.

Audio-visual speech perception in adult readers with dyslexia: an fMRI study.

Developmental dyslexia is a specific deficit in reading and spelling that often persists into adulthood. In the present study, we used slow event-related fMRI and independent component analysis to identify brain networks involved in perception of audio-visual speech in a group of adult readers with dyslexia (RD) and a group of fluent readers (FR). Participants saw a video of a female speaker saying a disyllabic word. In the congruent condition, audio and video input were identical whereas in the incongruent condition, the two inputs differed. Participants had to respond to occasionally occurring animal names. The independent components analysis (ICA) identified several components that were differently modulated in FR and RD. Two of these components including fusiform gyrus and occipital gyrus showed less activation in RD compared to FR possibly indicating a deficit to extract face information that is needed to integrate auditory and visual information in natural speech perception. A further component centered on the superior temporal sulcus (STS) also exhibited less activation in RD compared to FR. This finding is corroborated in the univariate analysis that shows less activation in STS for RD compared to FR. These findings suggest a general impairment in recruitment of audiovisual processing areas in dyslexia during the perception of natural speech.

Dysfunction of Large-Scale Brain Networks in Schizophrenia: A Meta-analysis of Resting-State Functional Connectivity.

Schizophrenia is a complex mental disorder with disorganized communication among large-scale brain networks, as demonstrated by impaired resting-state functional connectivity (rsFC). Individual rsFC studies, however, vary greatly in their methods and findings. We searched for consistent patterns of network dysfunction in schizophrenia by using a coordinate-based meta-analysis. Fifty-six seed-based voxel-wise rsFC datasets from 52 publications (2115 patients and 2297 healthy controls) were included in this meta-analysis. Then, coordinates of seed regions of interest (ROI) and between-group effects were extracted and coded. Seed ROIs were categorized into seed networks by their location within an a priori template. Multilevel kernel density analysis was used to identify brain networks in which schizophrenia was linked to hyper-connectivity or hypo-connectivity with each a priori network. Our results showed that schizophrenia was characterized by hypo-connectivity within the default network (DN, self-related thought), affective network (AN, emotion processing), ventral attention network (VAN, processing of salience), thalamus network (TN, gating information) and somatosensory network (SS, involved in sensory and auditory perception). Additionally, hypo-connectivity between the VAN and TN, VAN and DN, VAN and frontoparietal network (FN, external goal-directed regulation), FN and TN, and FN and DN were found in schizophrenia. Finally, the only instance of hyper-connectivity in schizophrenia was observed between the AN and VAN. Our meta-analysis motivates an empirical foundation for a disconnected large-scale brain networks model of schizophrenia in which the salience processing network (VAN) plays the core role, and its imbalanced communication with other functional networks may underlie the core difficulty of patients to differentiate self-representation (inner world) and environmental salience processing (outside world).

SU86. Multimodal Social Brain Network and Facial Affect Recognition in Schizophrenia

Abstract Background: Facial Affect Recognition (FAR) is the capacity to understand and interpret facial expressions. It is previously shown that schizophrenia patients and siblings (albeit to a lesser extent) perform worse on FAR (Meijer et al, Psychol Med, 2012). The social brain network (SBN) is associated with emotion processing, and the interaction between grey and white matter within the network might explain poor FAR performance. Here, we investigate whether disturbed FAR can be explained by a differential association between the following modalities: cortical volume [CV] and white matter integrity (fractional anisotropy [FA], radial [RD], and axial diffusivity [AD]) within the SBN between patients, siblings, and controls. Methods: We acquired brain scans at 1.5 T of 103 patients, 125 nonpsychotic siblings, and 78 controls. Using FreeSurfer 5.1, volumes of the medial orbitofrontal, fusiform gyrus, insula, caudal/rostral cingulated, and superior/middle temporal cortex were extracted, standardized, and summed to represent cortical volume of the SBN. With TRACULA 5.3, an anatomy restricted, automated, probabilistic tractography package, FA, RD, and AD were extracted of the arcuate, superior longitudinal, and uncinate fasciculus, and the cingulum-cingulate and cingulum-angular pathway then standardized and summed. SBN areas and tracts were registered to the same space. SBN Modality-by-Modality interactions and Task-by-Modality interactions were compared between groups using linear mixed-effects modeling. FDR correction was applied. Partial-omegas-squared (ωp2) were calculated. Interpretation of this effect size is as follows: small: .01 .35. Results: Within the SBN, patients had significantly higher RD (P = .018), lower AD (P = .001), and lower CV (P = .007) than controls. Siblings had smaller CV than controls (P = .030). No group effect was found in the interaction between modalities (CV*AD, CV*FA, or CV*RD). The difference in FAR between groups was significantly explained by 2 interactions, ie, FAR*AD and FAR*(CV*AD). This implicates that in patients smaller AD as well as the association between lower CV and lower AD were related to poor FAR, which was not present in controls (P = .041, ωp2 = .29 and P = .038, ωp2 = .47, respectively). Conclusion: We showed that in patients, FAR is related to an interaction of reduced fiber-tract integrity connected to smaller cortical volume within the SBN. Poorer performance in siblings did not show interacting brain structure differences related to FAR. Possibly, the extent of the abnormality in FAR performance and brain structure were too subtle. Our findings suggest that with a multimodal network approach we can identify brain networks that are related to emotion processing deficits in schizophrenia patients. Assessing structural covariance within this network is the next step.

Brain networks associated with cognitive and hedonic responses to a meal.

We recently reported interrelated digestive, cognitive, and hedonic responses to a meal. The aim of this study was to identify brain networks related to the hedonic response to eating. Thirty-eight healthy subjects (20-38 age range) were evaluated after a 5-hour fast and after ingestion of a test meal (juice and warm ham and cheese sandwich, 300mL, 425 kcal). Perceptual and affective responses (satiety, abdominal fullness, digestive well-being, and positive mood), and resting scans of the brain using functional MRI (3T Trio, Siemens, Germany) were evaluated immediately before and after the test meal. A high-order group independent component analysis was performed to investigate ingestion-related changes in the intrinsic connectivity of brain networks, with a focus on thalamic and insular networks. Ingestion induced satiation (3.3±0.4 score increase; P<.001) and abdominal fullness (2.4±0.3 score increase; P<.001). These sensations included an affective dimension involving digestive well-being (2.8±0.3 score increase; P<.001) and positive mood (1.8±0.2 score increase; P<.001). In general, thalamo-cortical connectivity increased with meal ingestion while insular-cortical connectivity mainly decreased. Furthermore, larger meal-induced changes (increase/decrease) in specific thalamic connections were associated with smaller changes in satiety/fullness. In contrast, a larger meal-induced decrease in insular-anterior cingulate cortex connectivity was associated with increased satiety, fullness, and digestive well-being. Perceptual and emotional responses to food intake are related to brain connectivity in defined functional networks. Brain imaging may provide objective biomarkers of subjective effects of meal ingestion.

Dual Temporal and Spatial Sparse Representation for Inferring Group-Wise Brain Networks From Resting-State fMRI Dataset.

Recently, sparse representation has been successfully used to identify brain networks from task-based fMRI dataset. However, when using the strategy to analyze resting-state fMRI dataset, it is still a challenge to automatically infer the group-wise brain networks under consideration of group commonalities and subject-specific characteristics. In the paper, a novel method based on dual temporal and spatial sparse representation (DTSSR) is proposed to meet this challenge. First, the brain functional networks with subject-specific characteristics are obtained via sparse representation with online dictionary learning for the fMRI time series (temporal domain) of each subject. Next, based on the current brain science knowledge, a simple mathematical model is proposed to describe the complex nonlinear dynamic coupling mechanism of the brain networks, with which the group-wise intrinsic connectivity networks (ICNs) can be inferred by sparse representation for these brain functional networks (spatial domain) of all subjects. Experiments on Leiden_2180 dataset show that most group-wise ICNs obtained by the proposed DTSSR are interpretable by current brain science knowledge and are consistent with previous literature reports. The robustness of DTSSR and the reproducibility of the results are demonstrated by experiments on three different datasets (Leiden_2180, Leiden_2200, and our own dataset). The present work also shed new light on exploring the coupling mechanism of brain networks from perspective of information science.

Functional connectivity decreases in autism in emotion, self, and face circuits identified by Knowledge-based Enrichment Analysis

A powerful new method is described called Knowledge based functional connectivity Enrichment Analysis (KEA) for interpreting resting state functional connectivity, using circuits that are functionally identified using search terms with the Neurosynth database. The method derives its power by focusing on neural circuits, sets of brain regions that share a common biological function, instead of trying to interpret single functional connectivity links. This provides a novel way of investigating how task- or function-related networks have resting state functional connectivity differences in different psychiatric states, provides a new way to bridge the gap between task and resting-state functional networks, and potentially helps to identify brain networks that might be treated. The method was applied to interpreting functional connectivity differences in autism. Functional connectivity decreases at the network circuit level in 394 patients with autism compared with 473 controls were found in networks involving the orbitofrontal cortex, anterior cingulate cortex, middle temporal gyrus cortex, and the precuneus, in networks that are implicated in the sense of self, face processing, and theory of mind. The decreases were correlated with symptom severity.

Intrinsic functional connectivity alterations in cognitively intact elderly APOE ε4 carriers measured by eigenvector centrality mapping are related to cognition and CSF biomarkers: a preliminary study.

Apolipoprotein E (APOE) ε4 allele is the best established genetic risk factor for sporadic Alzheimer's disease (AD). However, there is a need to understand the effects of this genotype on the brain by simultaneously assessing intrinsic brain network and cerebral spinal fluid (CSF) biomarkers changes in healthy older ε4 carriers. Thirteen cognitively intact, elderly APOE ε4 carriers and 22 ε3 homozygotes were included in the present study. Eigenvector centrality mapping (ECM) was used to identify brain network hub organization based on resting-state functional MRI (rsfMRI). We evaluated comprehensive cognitive ability and tested levels of Aβ1-42, total-tau (t-tau) and phosphorylated-tau (p-tau181) in CSF. Comparisons of ECM between two groups were conducted, followed by correlations analyses between EC values with significant group differences and cognitive ability/CSF biomarkers. APOE ε4 carriers showed significantly decreased EC values in left medial temporal lobe (MTL), left lingual gyrus (LG) and increased EC values in left middle frontal gyrus (MFG) as compared to non-carriers. Correlation analysis demonstrated that left LG EC value correlated with Rey Auditory Verbal Learning Test total learning (RAVLT, r=0.57, p<0.05) and t-tau level (r=-0.57, p<0.05), while left MFG EC values correlated with log-transformed Trail-Making Test B (TMT-B, r=-0.67, p<0.05) in APOE ε4 carriers. This study suggests the APOE ε4 allele contributes to disruption of brain connectedness in certain functional nodes, which may result from neuronal death caused by toxicity of neurofibrillary tangles.

Fooling the brain by mirroring the hand: Brain correlates of the perceptual capture of limb ownership.

Mirror therapy (MT) is an increasingly employed method aimed at reducing phantom pain and other negative sensations following loss of a limb or damage to sensorimotor systems. However, the brain processes associated with the perception of limb ownership, a key correlate of MT, are unknown. To examine whether transient perceptions of limb ownership together with associated neural activity can be elucidated using a purpose-developed mirror reflection task combined with electrophysiological (EEG) measures and cutting-edge analyses. Brain activity was measured online using EEG in 20 healthy controls while they produced opening-closing movements of one hand in control conditions or while viewing the mirror reflection of the movements. The key experimental condition required participants to make a foot pedal response whenever a change in perception of ownership (of a mirror-reflected limb) occurred (Mirror condition). Control conditions and a strict epoching regime were employed using standard subtractive logic to isolate the perception of limb ownership (which was further verified by self-reports). Data from 15 participants were suitable for complete analysis; the remaining reported no experience of ownership. Significant spectral power increases were found in central-parietal regions in association with perceptions of ownership, with the most prominent effect specific to the alpha frequency band (8-13 Hz) measured at the right parietal area. Source localization analyses further identified brain networks associated with the mirror reflection condition in the alpha frequency (parietal lobe) and the beta frequency (middle temporal areas). These were distinct from localized networks associated with the foot pedal response. Transient perceptions of ownership can be captured experimentally, and are associated with localized sites of neural activation. This is an initial step toward eventual development of therapeutic targets for interventions including brain computer interfaces (BCIs) aimed at ameliorating the negative effects associated with impaired or missing limbs.

Getting the Cocktail Party Started: Masking Effects in Speech Perception.

Spoken conversations typically take place in noisy environments, and different kinds of masking sounds place differing demands on cognitive resources. Previous studies, examining the modulation of neural activity associated with the properties of competing sounds, have shown that additional speech streams engage the superior temporal gyrus. However, the absence of a condition in which target speech was heard without additional masking made it difficult to identify brain networks specific to masking and to ascertain the extent to which competing speech was processed equivalently to target speech. In this study, we scanned young healthy adults with continuous fMRI, while they listened to stories masked by sounds that differed in their similarity to speech. We show that auditory attention and control networks are activated during attentive listening to masked speech in the absence of an overt behavioral task. We demonstrate that competing speech is processed predominantly in the left hemisphere within the same pathway as target speech but is not treated equivalently within that stream and that individuals who perform better in speech in noise tasks activate the left mid-posterior superior temporal gyrus more. Finally, we identify neural responses associated with the onset of sounds in the auditory environment; activity was found within right lateralized frontal regions consistent with a phasic alerting response. Taken together, these results provide a comprehensive account of the neural processes involved in listening in noise.

Post-concussive complaints after mild traumatic brain injury associated with altered brain networks during working memory performance.

The aim was to investigate brain network function during working memory (WM) task performance in patients with uncomplicated mild traumatic brain injury (mTBI) in the sub-acute phase post-injury. We were particularly interested in differences between patients with (PCC-present) and without post-concussive complaints (PCC-absent). Fifty-two patients and twenty healthy controls (HCs) (matched for age, sex, education and handedness) were included. Two patient groups were created based on reported post-concussive complaints at two weeks post-injury: PCC-present (n = 32) and PCC-absent (n = 20). Functional MRI scans were made at approximately four weeks post-injury. Participants performed an n-back task consisting of three conditions (0-, 1- and 2-back) with increasing difficulty. General linear model analysis was performed to investigate activation patterns. Independent component analysis was used to identify brain networks. The frontal executive network (FEN), frontoparietal network (FPN) and default mode network (DMN) were selected for further analyses based on their highest task-relatedness. Task accuracy and reaction times were similar for patients with mTBI and HCs. During high WM load (2-vs.0-back contrast), mTBI patients exhibited lower activation within the medial prefrontal cortex compared to HCs. No differences were found between PCC-present and PCC-absent patients. Regarding network function, PCC-absent patients showed stronger deactivation of the DMN compared to PCC-present patients and HCs, especially during difficult task conditions. Furthermore, functional connectivity between the DMN and FEN was lower in PCC-absent patients compared to PCC-present patients. Interestingly, network function did not differ between PCC-present patients and HCs, suggesting that non-injury related factors may underlie post-concussive complaints after mTBI.Electronic supplementary materialThe online version of this article (doi:10.1007/s11682-015-9489-y) contains supplementary material, which is available to authorized users.

Tracking dynamics of functional brain networks using dense EEG

Cognition is formed from networks between functionally specific but distributed brain regions. A very challenging issue in cognition is how to precisely track brain networks at very short temporal scales (often very short <1 s). So far, very few studies have addressed this problem as it requires high temporal and spatial resolution simultaneously. Due to its excellent temporal resolution, electroencephalography (EEG) is a key neuroimaging technique to access real-time information flow among large scale neuronal networks.Here, we propose a new method based on EEG source connectivity to map large-scale networks at high temporal (in the order of ms) and spatial (∼1000 regions of interest) resolution. We show clear evidence of the ability of EEG source connectivity to track brain networks with high time/space resolutions during picture naming task. Our results reveal that the cognitive process can be decomposed into a sequence of transiently-stable and partially-overlapping networks. Our qualitative and quantitative observations show that the identified brain networks are in accordance with results reported in the literature regarding involved brain areas during the analyzed task.

EEGNET: An Open Source Tool for Analyzing and Visualizing M/EEG Connectome.

The brain is a large-scale complex network often referred to as the “connectome”. Exploring the dynamic behavior of the connectome is a challenging issue as both excellent time and space resolution is required. In this context Magneto/Electroencephalography (M/EEG) are effective neuroimaging techniques allowing for analysis of the dynamics of functional brain networks at scalp level and/or at reconstructed sources. However, a tool that can cover all the processing steps of identifying brain networks from M/EEG data is still missing. In this paper, we report a novel software package, called EEGNET, running under MATLAB (Math works, inc), and allowing for analysis and visualization of functional brain networks from M/EEG recordings. EEGNET is developed to analyze networks either at the level of scalp electrodes or at the level of reconstructed cortical sources. It includes i) Basic steps in preprocessing M/EEG signals, ii) the solution of the inverse problem to localize / reconstruct the cortical sources, iii) the computation of functional connectivity among signals collected at surface electrodes or/and time courses of reconstructed sources and iv) the computation of the network measures based on graph theory analysis. EEGNET is the unique tool that combines the M/EEG functional connectivity analysis and the computation of network measures derived from the graph theory. The first version of EEGNET is easy to use, flexible and user friendly. EEGNET is an open source tool and can be freely downloaded from this webpage: https://sites.google.com/site/eegnetworks/.