A MATLAB toolbox for Granger causal connectivity analysis

Reduced Information Transmission of Medial Prefrontal Cortex to Basolateral Amygdala Inhibits Exploratory Behavior in Depressed Rats

Depression is a mental and neurological disease that reduces the desire for exploration. Dysregulation of the information transmission between medial prefrontal cortex (mPFC) and basolateral amygdala (BLA) is associated with depression. However, which direction of information transmission (mPFC-BLA or BLA-mPFC) related to the decline of exploratory interests in depression is unclear. Therefore, it is important to determine what specific changes occur in mPFC and BLA information transmission in depressed rats during exploratory behavior. In the present study, local field potentials (LFPs) were recorded via multi-electrodes implanted in the mPFC and BLA for the control and depression groups of rats when they were exploring in an open field. The theta band was determined to be the characteristic band of exploratory behavior. The direct transfer function (DTF) was used to calculate the mPFC and BLA bidirectional information flow (IF) to measure information transmission. Compared with the control group, the theta IF of mPFC-BLA in the depression group was significantly reduced, and there was no significant difference in theta IF of BLA-mPFC between the two groups. Our results indicated that the reduction of mPFC-BLA information transmission can inhibit the exploratory behavior of depressed rats.

Habenula and left angular gyrus circuit contributes to response of electroconvulsive therapy in major depressive disorder

The habenula (Hb), one of the hottest structures in depression, has been widely demonstrated to be involved in the neurobiology of depression. Although the structural and functional abnormalities of Hb have been reported in major depressive disorders (MDD) patients, the role of Hb in treatment response in MDD remains unclear. In this study, resting-state functional connectivity (RSFC) and Granger causality analysis (GCA) were performed to investigate the intrinsic and causal changes of Hb in MDD after ECT. Moreover, support vector classification was applied to find out whether the changed functional and causal connections of Hb can effectively distinguish the MDD patients from healthy controls. The RSFC and GCA identified increased RSFC strength between bilateral Hb and left angular gyrus (AG), decreased causal connectivity strength from left AG to left Hb, from right Hb to left AG, and bidirectional interactions between left and right Hb in MDD patients after ECT. The changed causal connectivities from left AG to left Hb, and from right Hb to left AG were correlated with the changed depression symptoms and impaired delay memory recall performances. Furthermore, the functional and causal connectivities between left AG and bilateral Hb could serve as a biomarker to differentiate MDD from HCs. These results provided new evidence for the importance of Hb in depression and revealed that the interactions between Hb and left AG contribute to ECT response in MDD. Our findings will facilitate the future treatment of depression with the target of Hb in MDD and other brain disorders.

Optical health analysis of visual comfort for bright screen display based on back propagation neural network

BACKGROUND

The visual comfort of liquid crystal display (LCD) is the subjective evaluation of the user. It is a multi-dimensional and multi-factor problem, which is affected by the luminous characteristics of the LCD screen, the physiological factors of the user, and some other environmental factors.

METHODS

Based on the theory of visual comfort under the guidance of ergonomics, this paper adopts a combination of objective measurement and subjective evaluation to obtain objective data such as blink frequency and pupil size changes, and subjective evaluation data on screen parameters. Correlation analysis was used to screen subjective and objective data, and an LCD visual comfort evaluation using the back propagation (BP) neural network was constructed with the aim of a concise evaluation of the LCD's own light-emitting characteristics, user's physiological factors, and environmental factors.

RESULTS

After testing, the model can successfully predict the optimal visual level of the screen. After training, the relative error between the predicted value of visual comfort and the actual evaluation value is mostly within 10%. Based on this model, the display brightness and color temperature control system combined with the ambient light sensor can automatically adjust the brightness of the screen and the temperature of color parameters in correlation to user's gender, age, and ambient light changes to achieve the effect of improving visual comfort. Setting and user parameter adjustment provide a new method. The maximum adjustment error of the system after testing is 5.378%.

CONCLUSION

Our proposed technique can serve as a useful analysis platform for understanding and evaluating the visual comfort of the bright LCD screen at home or in the workplace, and enhancing optical health of humans.

Whole-brain estimates of directed connectivity for human connectomics

Connectomics is essential for understanding large-scale brain networks but requires that individual connection estimates are neurobiologically interpretable. In particular, a principle of brain organization is that reciprocal connections between cortical areas are functionally asymmetric. This is a challenge for fMRI-based connectomics in humans where only undirected functional connectivity estimates are routinely available. By contrast, whole-brain estimates of effective (directed) connectivity are computationally challenging, and emerging methods require empirical validation. Here, using a motor task at 7T, we demonstrate that a novel generative model can infer known connectivity features in a whole-brain network (>200 regions, >40,000 connections) highly efficiently. Furthermore, graph-theoretical analyses of directed connectivity estimates identify functional roles of motor areas more accurately than undirected functional connectivity estimates. These results, which can be achieved in an entirely unsupervised manner, demonstrate the feasibility of inferring directed connections in whole-brain networks and open new avenues for human connectomics.

Differential Covariance: A New Method to Estimate Functional Connectivity in fMRI

Measuring functional connectivity from fMRI recordings is important in understanding processing in cortical networks. However, because the brain's connection pattern is complex, currently used methods are prone to producing false functional connections. We introduce differential covariance analysis, a new method that uses derivatives of the signal for estimating functional connectivity. We generated neural activities from dynamical causal modeling and a neural network of Hodgkin-Huxley neurons and then converted them to hemodynamic signals using the forward balloon model. The simulated fMRI signals, together with the ground-truth connectivity pattern, were used to benchmark our method with other commonly used methods. Differential covariance achieved better results in complex network simulations. This new method opens an alternative way to estimate functional connectivity.

Task-independent Electrophysiological Correlates of Motor Imagery Ability from Kinaesthetic and Visual Perspectives

Motor imagery (MI) ability is highly subjective, as indicated by the individual scores of the MIQ-3 questionnaire, and poor imagers compensate for the difficulty in performing MI with larger cerebral activations, as demonstrated by MI studies involving hands/limbs. In order to identify general, task-independent MI ability correlates, 16 volunteers were stratified with MIQ-3. The scores in the kinaesthetic (K) and 1st-person visual (V) perspectives were associated with EEG patterns obtained during K-MI and V-MI of the same complex MIQ-3 movements during these MI tasks (Spearman's correlation, significance at <0.05, SnPM corrected). EEG measures were relative to rest (relaxation, closed eyes), and based on six electrode clusters both for band spectral content and connectivity (Granger causality). Lower K-MI ability was associated with greater theta decreases during tasks in fronto-central clusters and greater inward information flow to prefrontal clusters for theta, high alpha and beta bands. On the other hand, power band relative decreases were associated with V-MI ability in fronto-central clusters for low alpha and left fronto-central and both centro-parietal clusters for beta bands. The results thus suggest different computational mechanisms for MI-V and MI-K. The association between low alpha/beta desynchronization and V-MIQ scores and between theta changes and K-MIQ scores suggest a cognitive effort with greater cerebral activation in participants with lower V-MI ability. The association between information flow to prefrontal hub and K-MI ability suggest the need for a continuous update of information to support MI-related executive functions in subjects with poor K-MI ability.

Modeling time-varying brain networks with a self-tuning optimized Kalman filter

Brain networks are complex dynamical systems in which directed interactions between different areas evolve at the sub-second scale of sensory, cognitive and motor processes. Due to the highly non-stationary nature of neural signals and their unknown noise components, however, modeling dynamic brain networks has remained one of the major challenges in contemporary neuroscience. Here, we present a new algorithm based on an innovative formulation of the Kalman filter that is optimized for tracking rapidly evolving patterns of directed functional connectivity under unknown noise conditions. The Self-Tuning Optimized Kalman filter (STOK) is a novel adaptive filter that embeds a self-tuning memory decay and a recursive regularization to guarantee high network tracking accuracy, temporal precision and robustness to noise. To validate the proposed algorithm, we performed an extensive comparison against the classical Kalman filter, in both realistic surrogate networks and real electroencephalography (EEG) data. In both simulations and real data, we show that the STOK filter estimates time-frequency patterns of directed connectivity with significantly superior performance. The advantages of the STOK filter were even clearer in real EEG data, where the algorithm recovered latent structures of dynamic connectivity from epicranial EEG recordings in rats and human visual evoked potentials, in excellent agreement with known physiology. These results establish the STOK filter as a powerful tool for modeling dynamic network structures in biological systems, with the potential to yield new insights into the rapid evolution of network states from which brain functions emerge.

During normal behavior, brains transition between functional network states several times per second. This allows humans to quickly read a sentence, and a frog to catch a fly. Understanding these fast network dynamics is fundamental to understanding how brains work, but up to now it has proven very difficult to model fast brain dynamics for various methodological reasons. To overcome these difficulties, we designed a new Kalman filter (STOK) by innovating on previous solutions from control theory and state-space modelling. We show that STOK accurately models fast network changes in simulations and real neural data, making it an essential new tool for modelling fast brain networks in the time and frequency domain.

Changes of Effective Connectivity in the Alpha Band Characterize Differential Processing of Audiovisual Information in Cross-Modal Selective Attention

Cross-modal selective attention enhances the processing of sensory inputs that are most relevant to the task at hand. Such differential processing could be mediated by a swift network reconfiguration on the macroscopic level, but this remains a poorly understood process. To tackle this issue, we used a behavioral paradigm to introduce a shift of selective attention between the visual and auditory domains, and recorded scalp electroencephalographic signals from eight healthy participants. The changes in effective connectivity caused by the cross-modal attentional shift were delineated by analyzing spectral Granger Causality (GC), a metric of frequency-specific effective connectivity. Using data-driven methods of pattern-classification and feature-analysis, we found that a change in the α band (12 Hz-15 Hz) of GC is a stable feature across different individuals that can be used to decode the attentional shift. Specifically, auditory attention induces more pronounced information flow in the α band, especially from the parietal-occipital areas to the temporal-parietal areas, compared to the case of visual attention, reflecting a reconfiguration of interaction in the macroscopic brain network accompanying different processing. Our results support the role of α oscillation in organizing the information flow across spatially-separated brain areas and, thereby, mediating cross-modal selective attention.

The boundaries of state-space Granger causality analysis applied to BOLD simulated data: A comparative modelling and simulation approach

BACKGROUND

The analysis of connectivity has become a fundamental tool in human neuroscience. Granger Causality Mapping is a data-driven method that uses Granger Causality (GC) to assess the existence and direction of influence between signals, based on temporal precedence of information. More recently, a theory of Granger causality has been developed for state-space (SS-GC) processes, but little is known about its statistical validation and application on functional magnetic resonance imaging (fMRI) data.

NEW METHOD

We explored different multivariate computational frameworks to define the optimal combination for GC estimation. We hypothesized a new heuristic, combining SS-GC with a distinct statistical validation technique, Time Reversed Testing, validating it on synthetic data. We test its performance with a number of experimental parameters, including block structure, sampling frequency, noise and system mean pairwise correlation, using a statistical framework of binary classification.

RESULTS

We found that SS-GC with time reversed testing outperforms other frameworks. The results validate the application of SS-GC to generative models. When estimating reliable causal relations, SS-GC returns promising results, especially when considering synthetic data with a high impact of noise and sampling rate.

CONCLUSIONS

In this study, we empirically explored the boundaries of SS-GC with time reversed testing, a data-driven causality analysis framework with potential applicability to fMRI data.

Construction of functional brain connectivity networks from fMRI data with driving and modulatory inputs: an extended conditional Granger causality approach

We propose a numerical-based approach extending the conditional MVAR Granger causality (MVGC) analysis for the construction of directed connectivity networks in the presence of both exogenous/stimuli and modulatory inputs. The performance of the proposed scheme is validated using both synthetic stochastic data considering also the influence of haemodynamics latencies and a benchmark fMRI dataset related to the role of attention in the perception of visual motion. The particular fMRI dataset has been used in many studies to evaluate alternative model hypotheses using the Dynamic Causal Modelling (DCM) approach. Based on the use of the Bayes factor, we show that the obtained GC connectivity network compares well to a reference model that has been selected through DCM analysis among other candidate models. Thus, our findings suggest that the proposed scheme can be successfully used as a stand-alone or complementary to DCM approach to find directed causal connectivity patterns in task-related fMRI studies.

Time-variant Epileptic Brain Functional Connectivity of Focal and Generalized Seizure in Chronic Temporal Lobe Epilepsy Rat. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020;2020:2833-2836. [PMID: 33018596 DOI: 10.1109/embc44109.2020.9175924]

Seizure types and characteristics may vary with time in a patient with distinct mechanisms underlying the propagation of ictal activity. Similarly, we found that both focal and generalized seizures coexist in some pilocarpine-induced chronic temporal lobe epilepsy (TLE) rats. In different seizure patterns, mapping complex networks and analyzing epileptic characteristics involved in seizure propagation are likely to reflect seizure propagation mechanisms, and indicate the establishment of stimulation strategy for epilepsy treatment, especially on the selection of stimulation targets. In our study, we used Granger causality method to track the time-variant epileptic brain functional connectivity in focal and generalized seizures from multi-site local field potentials (LFPs). Results showed that these two major types of seizures had different propagation patterns during ictal period. When comparing them, generalized seizures involved in a network with more complex relationships and spread to more extensive brain regions than in local seizures at mid-ictal stage. Moreover, we observed that focal seizures had a focused causal hub with strong interactions, while generalized seizures had relative distributed causal hubs to drive the development of seizure during seizure-onset stage. These findings suggest that stimulation strategy might need to be adapted to different seizure types thus allowing for retuning abnormal epileptic brain network and obtaining better treatment effect on seizure suppression.

Imaging the effects of age on proactive control in healthy adults

Previous research has reported reduced efficiency in reactive inhibition, along with reduced brain activations, in older adults. The current study investigated age-related behavioral and neural changes in proactive inhibition, and whether age may influence the relationship between proactive and reactive inhibition. One-hundred-and-forty-nine adults (18 to 72 years) underwent fMRI while performing a stop signal task (SST). Proactive inhibition was defined by the sequential effect, the correlation between the estimated probability of stop signal - p(Stop) - and go trial reaction time (goRT). P(Stop) was estimated trial by trial with a Bayesian belief model; reactive inhibition was defined by the stop signal reaction time (SSRT). Behaviorally the magnitude of sequential effect was not correlated with age, replicating earlier reports of spared proactive control in older adults. Age was associated with greater activations to p(Stop) in the lateral prefrontal cortex (PFC), paracentral lobule, superior parietal lobule, and cerebellum, and activations to goRT in the inferior occipital gyrus (IOG). Granger Causality analysis demonstrated that the PFC Granger caused IOG, with the PFC-IOG connectivity significantly correlated with p(Stop) in older but not younger adults. These findings suggest that the PFC and IOG activations and PFC-IOG connectivity may compensate for proactive control during aging. In contrast, while the activations of the ventromedial prefrontal cortex and caudate head to p(Stop) were negatively correlated with SSRT, relating proactive to reactive control, these activities did not vary with age. These findings highlighted distinct neural processes underlying proactive inhibition and limited neural plasticity to support cognitive control in the aging brain.

The Influence of Listening to Music on Adults with Left-behind Experience Revealed by EEG-based Connectivity

The human brain has a close relationship with music. Music-induced structural and functional brain changes have been demonstrated in the healthy adult. In the present study, adults with left-behind experience (ALB) were divided into two groups. The experimental group (ALB-E) took part in the music therapy experiment with three stages, including before listening to music (pre-stage), initially listening to music (mid-stage) and after listening to music (post-stage). The control group (ALB-C) did not participate in music therapy. Scalp resting-state EEGs of ALB were recorded during the three stages. We found no significant frequency change in the ALB-C group. In the ALB-E group, only the theta power spectrum was significantly different at all stages. The topographical distributions of the theta power spectrum represented change in trends from the frontal regions to the occipital regions. The result of Granger causal analysis (GCA), based on theta frequency, showed a stronger information flow from the middle frontal gyrus to the middle temporal gyrus (MFG → MTG) in the left hemisphere at the pre-stage compared to the post-stage. Additionally, the experimental group showed a weaker information flow from inferior gyrus to superior temporal gyrus (IFG → STG) in the right hemisphere at post-test stage compared to the ALB-C group. Our results demonstrate that listening to music can play a positive role on improving negative feelings for individuals with left behind experience.

Sleep apnea: Tracking effects of a first session of CPAP therapy by means of Granger causality

Connectivity between physiological networks is an issue of particular importance for understanding the complex interaction brain-heart. In the present study, this interaction was analyzed in polysomnography recordings of 28 patients diagnosed with obstructive sleep apnea (OSA) and compared with a group of 10 control subjects. Electroencephalography and electrocardiography signals from these polysomnography time series were characterized employing Granger causality computation to measure the directed connectivity among five brain waves and three spectral subbands of heart rate variability. Polysomnography data from OSA patients were recorded before and during a first session of continuous positive air pressure (CPAP) therapy in a split-night study. Results showed that CPAP therapy allowed the recovery of inner brain connectivities, mainly in subsystems involving the theta wave. In addition, differences between control and OSA patients were established in connections that involve lower frequency ranges of heart rate variability. This information can be potentially useful in the initial diagnosis of OSA, and determine the role of cardiac activity in sleep dynamics based on the use of three subbands of heart rate variability.

Signal Information Prediction of Mortality Identifies Unique Patient Subsets after Severe Traumatic Brain Injury: A Decision-Tree Analysis Approach

Nonlinear physiological signal features that reveal information content and causal flow have recently been shown to be predictors of mortality after severe traumatic brain injury (TBI). The extent to which these features interact together, and with traditional measures to describe patients in a clinically meaningful way remains unclear. In this study, we incorporated basic demographics (age and initial Glasgow Coma Scale [GCS]) with linear and non-linear signal information based features (approximate entropy [ApEn], and multivariate conditional Granger causality [GC]) to evaluate their relative contributions to mortality using cardio-cerebral monitoring data from 171 severe TBI patients admitted to a single neurocritical care center over a 10 year period. Beyond linear modelling, we employed a decision tree analysis approach to define a predictive hierarchy of features. We found ApEn (p = 0.009) and GC (p = 0.004) based features to be independent predictors of mortality at a time when mean intracranial pressure (ICP) was not. Our combined model with both signal information-based features performed the strongest (area under curve = 0.86 vs. 0.77 for linear features only). Although low "intracranial" complexity (ApEn-ICP) outranked both age and GCS as crucial drivers of mortality (fivefold increase in mortality where ApEn-ICP <1.56, 36.2% vs. 7.8%), decision tree analysis revealed clear subsets of patient populations using all three predictors. Patients with lower ApEn-ICP who were >60 years of age died, whereas those with higher ApEn-ICP and GCS ≥5 all survived. Yet, even with low initial intracranial complexity, as long as patients maintained robust GC and "extracranial" complexity (ApEn of mean arterial pressure), they all survived. Incorporating traditional linear and novel, non-linear signal information features, particularly in a framework such as decision trees, may provide better insight into "health" status. However, caution is required when interpreting these results in a clinical setting prior to external validation.

Human Anterior Insula Encodes Performance Feedback and Relays Prediction Error to the Medial Prefrontal Cortex

Adaptive behavior requires the comparison of outcome predictions with actual outcomes (e.g., performance feedback). This process of performance monitoring is computed by a distributed brain network comprising the medial prefrontal cortex (mPFC) and the anterior insular cortex (AIC). Despite being consistently co-activated during different tasks, the precise neuronal computations of each region and their interactions remain elusive. In order to assess the neural mechanism by which the AIC processes performance feedback, we recorded AIC electrophysiological activity in humans. We found that the AIC beta oscillations amplitude is modulated by the probability of performance feedback valence (positive or negative) given the context (task and condition difficulty). Furthermore, the valence of feedback was encoded by delta waves phase-modulating the power of beta oscillations. Finally, connectivity and causal analysis showed that beta oscillations relay feedback information signals to the mPFC. These results reveal that structured oscillatory activity in the anterior insula encodes performance feedback information, thus coordinating brain circuits related to reward-based learning.

Granger Causality Network Methods for Analyzing Cross-Border Electricity Trading between Greece, Italy, and Bulgaria

Italy, Greece, and, to a lesser degree, Bulgaria have experienced fast growth in their renewable generation capacity (RESc) over the last several years. The consequences of this fact include a decrease in spot wholesale prices in electricity markets and a significant effect on cross border trading (CBT) among neighboring interconnected countries. In this work, we empirically analyzed historical data on fundamental market variables (i.e., spot prices, load, RES generation) as well as CBT data (imports, exports, commercial schedules, net transfer capacities, etc.) on the Greek, Italian, and Bulgarian electricity markets by applying the Granger causality connectivity analysis (GCCA) approach. The aim of this analysis was to detect all possible interactions among the abovementioned variables, focusing in particular on the effects of growing shares of RES generation on the commercial electricity trading among the abovementioned countries for the period 2015–2018. The key findings of this paper are summarized as the following: The RES generation in Italy, for the period examined, drives the spot prices in Greece via commercial schedules. In addition, on average, spot price fluctuations do not affect the commercial schedules of energy trading between Greece and Bulgaria.

Revealing Relationships Among Cognitive Functions Using Functional Connectivity and a Large-Scale Meta-Analysis Database

To characterize each cognitive function per se and to understand the brain as an aggregate of those functions, it is vital to relate dozens of these functions to each other. Knowledge about the relationships among cognitive functions is informative not only for basic neuroscientific research but also for clinical applications and developments of brain-inspired artificial intelligence. In the present study, we propose an exhaustive data mining approach to reveal relationships among cognitive functions based on functional brain mapping and network analysis. We began our analysis with 109 pseudo-activation maps (cognitive function maps; CFM) that were reconstructed from a functional magnetic resonance imaging meta-analysis database, each of which corresponds to one of 109 cognitive functions such as ‘emotion,’ ‘attention,’ ‘episodic memory,’ etc. Based on the resting-state functional connectivity between the CFMs, we mapped the cognitive functions onto a two-dimensional space where the relevant functions were located close to each other, which provided a rough picture of the brain as an aggregate of cognitive functions. Then, we conducted so-called conceptual analysis of cognitive functions using clustering of voxels in each CFM connected to the other 108 CFMs with various strengths. As a result, a CFM for each cognitive function was subdivided into several parts, each of which is strongly associated with some CFMs for a subset of the other cognitive functions, which brought in sub-concepts (i.e., sub-functions) of the cognitive function. Moreover, we conducted network analysis for the network whose nodes were parcels derived from whole-brain parcellation based on the whole-brain voxel-to-CFM resting-state functional connectivities. Since each parcel is characterized by associations with the 109 cognitive functions, network analyses using them are expected to inform about relationships between cognitive and network characteristics. Indeed, we found that informational diversities of interaction between parcels and densities of local connectivity were dependent on the kinds of associated functions. In addition, we identified the homogeneous and inhomogeneous network communities about the associated functions. Altogether, we suggested the effectiveness of our approach in which we fused the large-scale meta-analysis of functional brain mapping with the methods of network neuroscience to investigate the relationships among cognitive functions.

Directed EEG neural network analysis by LAPPS (p≤1) Penalized sparse Granger approach

The conventional multivariate Granger Analysis (GA) of directed interactions has been widely applied in brain network construction based on EEG recordings as well as fMRI. Nevertheless, EEG is usually inevitably contaminated by strong noise, which may cause network distortion due to the L2-norm used in GAs for directed network recovery. The Lp (p ≤1) norm has been shown to be more robust to outliers as compared to LASSO and L2-GAs. Motivated to construct the sparse brain networks under strong noise condition, we hereby introduce a new approach for GA analysis, termed LAPPS (Least Absolute LP (0<p<1) Penalized Solution). LAPPS utilizes the L1-loss function for the residual error to alleviate the effect of outliers, and another Lp-penalty term (p=0.5) to obtain the sparse connections while suppressing the spurious linkages in the networks. The simulation results reveal that LAPPS obtained the best performance under various noise conditions. In a real EEG data test when subjects performed the left and right hand Motor Imagery (MI) for brain network estimation, LAPPS also obtained a sparse network pattern with the hub at the contralateral brain primary motor areas consistent with the physiological basis of MI.

Hyperammonemia alters the mismatch negativity in the auditory evoked potential by altering functional connectivity and neurotransmission

Minimal hepatic encephalopathy (MHE) is a neuropsychiatric syndrome produced by central nervous system dysfunction subsequent to liver disease. Hyperammonemia and inflammation act synergistically to alter neurotransmission, leading to the cognitive and motor alterations in MHE, which are reproduced in rat models of chronic hyperammonemia. Patients with MHE show altered functional connectivity in different neural networks and a reduced response in the cognitive potential mismatch negativity (MMN), which correlates with attention deficits. The mechanisms by which MMN is altered in MHE remain unknown. The objectives of this work are as follows: To assess if rats with chronic hyperammonemia reproduce the reduced response in the MMN found in patients with MHE. Analyze the functional connectivity between the areas (CA1 area of the dorsal hippocampus, prelimbic cortex, primary auditory cortex, and central inferior colliculus) involved in the generation of the MMN and its possible alterations in hyperammonemia. Granger causality analysis has been applied to detect the net flow of information between the population neuronal activities recorded from a local field potential approach. Analyze if altered MMN response in hyperammonemia is associated with alterations in glutamatergic and GABAergic neurotransmission. Extracellular levels of the neurotransmitters and/or membrane expression of their receptors have been analyzed after the tissue isolation of the four target sites. The results show that rats with chronic hyperammonemia show reduced MMN response in hippocampus, mimicking the reduced MMN response of patients with MHE. This is associated with altered functional connectivity between the areas involved in the generation of the MMN. Hyperammonemia also alters membrane expression of glutamate and GABA receptors in hippocampus and reduces the changes in extracellular GABA and glutamate induced by the MMN paradigm of auditory stimulus in hippocampus of control rats. The changes in glutamatergic and GABAergic neurotransmission and in functional connectivity between the brain areas analyzed would contribute to the impairment of the MMN response in rats with hyperammonemia and, likely, also in patients with MHE.

ACOEC-FD: Ant Colony Optimization for Learning Brain Effective Connectivity Networks From Functional MRI and Diffusion Tensor Imaging

Identifying brain effective connectivity (EC) networks from neuroimaging data has become an effective tool that can evaluate normal brain functions and the injuries associated with neurodegenerative diseases. So far, there are many methods used to identify EC networks. However, most of the research currently focus on learning EC networks from single modal imaging data such as functional magnetic resonance imaging (fMRI) data. This paper proposes a new method, called ACOEC-FD, to learn EC networks from fMRI and diffusion tensor imaging (DTI) using ant colony optimization (ACO). First, ACOEC-FD uses DTI data to acquire some positively correlated relations among regions of interest (ROI), and takes them as anatomical constraint information to effectively restrict the search space of candidate arcs in an EC network. ACOEC-FD then achieves multi-modal imaging data integration by incorporating anatomical constraint information into the heuristic function of probabilistic transition rules to effectively encourage ants more likely to search for connections between structurally connected regions. Through simulation studies on generated datasets and real fMRI-DTI datasets, we demonstrate that the proposed approach results in improved inference results on EC compared to some methods that only used fMRI data.

Emergence of the Affect from the Variation in the Whole-Brain Flow of Information

Over the past few decades, the quest for discovering the brain substrates of the affect to understand the underlying neural basis of the human's emotions has resulted in substantial and yet contrasting results. Whereas some point at distinct and independent brain systems for the Positive and Negative affects, others propose the presence of flexible brain regions. In this respect, there are two factors that are common among these previous studies. First, they all focused on the change in brain activation, thereby neglecting the findings that indicate that the stimuli with equivalent sensory and behavioral processing demands may not necessarily result in differential brain activation. Second, they did not take into consideration the brain regional interactivity and the findings that identify that the signals from individual cortical neurons are shared across multiple areas and thus concurrently contribute to multiple functional pathways. To address these limitations, we performed Granger causal analysis on the electroencephalography (EEG) recordings of the human subjects who watched movie clips that elicited Negative, Neutral, and Positive affects. This allowed us to look beyond the brain regional activation in isolation to investigate whether the brain regional interactivity can provide further insights for understanding the neural substrates of the affect. Our results indicated that the differential affect states emerged from subtle variation in information flow of the brain cortical regions that were in both hemispheres. They also showed that these regions that were rather common between affect states than distinct to a specific affect were characterized with both short- as well as long-range information flow. This provided evidence for the presence of simultaneous integration and differentiation in the brain functioning that leads to the emergence of different affects. These results are in line with the findings on the presence of intrinsic large-scale interacting brain networks that underlie the production of psychological events. These findings can help advance our understanding of the neural basis of the human's emotions by identifying the signatures of differential affect in subtle variation that occurs in the whole-brain cortical flow of information.

Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models

It is well recognised that animal and plant pathogens form complex ecological communities of interacting organisms within their hosts, and there is growing interest in the health implications of such pathogen interactions. Although community ecology approaches have been used to identify pathogen interactions at the within-host scale, methodologies enabling robust identification of interactions from population-scale data such as that available from health authorities are lacking. To address this gap, we developed a statistical framework that jointly identifies interactions between multiple viruses from contemporaneous non-stationary infection time series. Our conceptual approach is derived from a Bayesian multivariate disease mapping framework. Importantly, our approach captures within- and between-year dependencies in infection risk while controlling for confounding factors such as seasonality, demographics and infection frequencies, allowing genuine pathogen interactions to be distinguished from simple correlations. We validated our framework using a broad range of synthetic data. We then applied it to diagnostic data available for five respiratory viruses co-circulating in a major urban population between 2005 and 2013: adenovirus, human coronavirus, human metapneumovirus, influenza B virus and respiratory syncytial virus. We found positive and negative covariances indicative of epidemiological interactions among specific virus pairs. This statistical framework enables a community ecology perspective to be applied to infectious disease epidemiology with important utility for public health planning and preparedness.

Disease-causing microorganisms, including viruses, bacteria, protozoa and fungi, form complex communities within animals and plants. These microorganisms can coexist harmoniously or even beneficially, or they may competitively interact for host resources. Well-studied examples include interactions between viruses and bacteria in the respiratory tract. Whilst ecological studies have revealed that some pathogens do interact within their hosts, identifying interactions from available population scale data from health authorities is challenging. This is exacerbated by a lack of large-scale data describing the infection patterns of multiple pathogens within single populations over long time frames. Furthermore, methods for evaluating whether infection frequencies of different pathogens fluctuate together or not over time cannot readily account for alternative explanations. For example, human pathogens may have related seasonal patterns depending on the age groups they infect and the weather conditions they survive in, and not because they are interacting. We developed a robust statistical framework to identify pathogen-pathogen interactions from population scale diagnostic data. This framework serves as a crucial step in identifying such important interactions and will guide new studies to elucidate their underpinning mechanisms. This will have important consequences for public health preparedness and the design of effective disease control interventions.

A Data-Driven Measure of Effective Connectivity Based on Renyi's α-Entropy

Transfer entropy (TE) is a model-free effective connectivity measure based on information theory. It has been increasingly used in neuroscience because of its ability to detect unknown non-linear interactions, which makes it well suited for exploratory brain effective connectivity analyses. Like all information theoretic quantities, TE is defined regarding the probability distributions of the system under study, which in practice are unknown and must be estimated from data. Commonly used methods for TE estimation rely on a local approximation of the probability distributions from nearest neighbor distances, or on symbolization schemes that then allow the probabilities to be estimated from the symbols' relative frequencies. However, probability estimation is a challenging problem, and avoiding this intermediate step in TE computation is desirable. In this work, we propose a novel TE estimator using functionals defined on positive definite and infinitely divisible kernels matrices that approximate Renyi's entropy measures of order α. Our data-driven approach estimates TE directly from data, sidestepping the need for probability distribution estimation. Also, the proposed estimator encompasses the well-known definition of TE as a sum of Shannon entropies in the limiting case when α → 1. We tested our proposal on a simulation framework consisting of two linear models, based on autoregressive approaches and a linear coupling function, respectively, and on the public electroencephalogram (EEG) database BCI Competition IV, obtained under a motor imagery paradigm. For the synthetic data, the proposed kernel-based TE estimation method satisfactorily identifies the causal interactions present in the data. Also, it displays robustness to varying noise levels and data sizes, and to the presence of multiple interaction delays in the same connected network. Obtained results for the motor imagery task show that our approach codes discriminant spatiotemporal patterns for the left and right-hand motor imagination tasks, with classification performances that compare favorably to the state-of-the-art.

Aging Changes Effective Connectivity of Motor Networks During Motor Execution and Motor Imagery

Age-related neurodegenerative and neurochemical changes are considered to be the basis for the decline of motor function; however, the change of effective connections in cortical motor networks that come with aging remains unclear. Here, we investigated the age-related changes of the dynamic interaction between cortical motor regions. Twenty young subjects and 20 older subjects underwent both right hand motor execution (ME) and right hand motor imagery (MI) tasks by using functional magnetic resonance imaging. Conditional Granger causality analysis (CGCA) was used to compare young and older adults’ effective connectivity among regions of the motor network during the tasks. The more effective connections among motor regions in older adults were found during ME; however, effective within-domain hemisphere connections were reduced, and the blood oxygenation level dependent (BOLD) signal was significantly delayed in older adults during MI. Supplementary motor area (SMA) had a significantly higher In+Out degree within the network during ME and MI in older adults. Our results revealed a dynamic interaction within the motor network altered with aging during ME and MI, which suggested that the interaction with cortical motor neurons caused by the mental task was more difficult with aging. The age-related effects on the motor cortical network provide a new insight into our understanding of neurodegeneration in older individuals.

Inferring correlations associated to causal interactions in brain signals using autoregressive models

The specific connectivity of a neuronal network is reflected in the dynamics of the signals recorded on its nodes. The analysis of how the activity in one node predicts the behaviour of another gives the directionality in their relationship. However, each node is composed of many different elements which define the properties of the links. For instance, excitatory and inhibitory neuronal subtypes determine the functionality of the connection. Classic indexes such as the Granger causality (GC) quantifies these interactions, but they do not infer into the mechanism behind them. Here, we introduce an extension of the well-known GC that analyses the correlation associated to the specific influence that a transmitter node has over the receiver. This way, the G-causal link has a positive or negative effect if the predicted activity follows directly or inversely, respectively, the dynamics of the sender. The method is validated in a neuronal population model, testing the paradigm that excitatory and inhibitory neurons have a differential effect in the connectivity. Our approach correctly infers the positive or negative coupling produced by different types of neurons. Our results suggest that the proposed approach provides additional information on the characterization of G-causal connections, which is potentially relevant when it comes to understanding interactions in the brain circuits.

The Effects of Audiovisual Inputs on Solving the Cocktail Party Problem in the Human Brain: An fMRI Study

At cocktail parties, our brains often simultaneously receive visual and auditory information. Although the cocktail party problem has been widely investigated under auditory-only settings, the effects of audiovisual inputs have not. This study explored the effects of audiovisual inputs in a simulated cocktail party. In our fMRI experiment, each congruent audiovisual stimulus was a synthesis of 2 facial movie clips, each of which could be classified into 1 of 2 emotion categories (crying and laughing). Visual-only (faces) and auditory-only stimuli (voices) were created by extracting the visual and auditory contents from the synthesized audiovisual stimuli. Subjects were instructed to selectively attend to 1 of the 2 objects contained in each stimulus and to judge its emotion category in the visual-only, auditory-only, and audiovisual conditions. The neural representations of the emotion features were assessed by calculating decoding accuracy and brain pattern-related reproducibility index based on the fMRI data. We compared the audiovisual condition with the visual-only and auditory-only conditions and found that audiovisual inputs enhanced the neural representations of emotion features of the attended objects instead of the unattended objects. This enhancement might partially explain the benefits of audiovisual inputs for the brain to solve the cocktail party problem.

Evaluation of Granger Causality Measures for Constructing Networks from Multivariate Time Series

Granger causality and variants of this concept allow the study of complex dynamical systems as networks constructed from multivariate time series. In this work, a large number of Granger causality measures used to form causality networks from multivariate time series are assessed. These measures are in the time domain, such as model-based and information measures, the frequency domain, and the phase domain. The study aims also to compare bivariate and multivariate measures, linear and nonlinear measures, as well as the use of dimension reduction in linear model-based measures and information measures. The latter is particular relevant in the study of high-dimensional time series. For the performance of the multivariate causality measures, low and high dimensional coupled dynamical systems are considered in discrete and continuous time, as well as deterministic and stochastic. The measures are evaluated and ranked according to their ability to provide causality networks that match the original coupling structure. The simulation study concludes that the Granger causality measures using dimension reduction are superior and should be preferred particularly in studies involving many observed variables, such as multi-channel electroencephalograms and financial markets.

Alcohol exposure in utero disrupts cortico-striatal coordination required for behavioral flexibility

Deficits in behavioral flexibility are a hallmark of multiple psychiatric, neurological, and substance use disorders. These deficits are often marked by decreased function of the prefrontal cortex (PFC); however, the genesis of such executive deficits remains understudied. Here we report how the most preventable cause of developmental disability, in utero exposure to alcohol, alters cortico-striatal circuit activity leading to impairments in behavioral flexibility in adulthood. We utilized a translational touch-screen task coupled with in vivo electrophysiology in adult mice to examine single unit and coordinated activity of the lateral orbital frontal cortex (OFC) and dorsolateral striatum (DS) during flexible behavior. Prenatal alcohol exposure (PAE) decreased OFC, and increased DS, single unit activity during reversal learning and altered the number of choice responsive neurons in both regions. PAE also decreased coordinated activity within the OFC and DS as measured by oscillatory field activity and altered spike-field coupling. Furthermore, PAE led to sustained connectivity between regions past what was seen in control animals. These findings suggest that PAE causes altered coordination within and between the OFC and DS, promoting maladaptive perseveration. Our model suggests that in optimally functioning mice OFC disengages the DS and updates the newly changed reward contingency, whereas in PAE animals, aberrant and persistent OFC to DS signaling drives behavioral inflexibility during early reversal sessions. Together, these findings demonstrate how developmental exposure alters circuit-level activity leading to behavioral deficits and suggest a critical role for coordination of neural timing during behaviors requiring executive function.

Learning Brain Effective Connectivity Network Structure Using Ant Colony Optimization Combining With Voxel Activation Information

Learning brain effective connectivity (EC) networks from functional magnetic resonance imaging (fMRI) data has become a new hot topic in the neuroinformatics field. However, how to accurately and efficiently learn brain EC networks is still a challenging problem. In this paper, we propose a new algorithm to learn the brain EC network structure using ant colony optimization (ACO) algorithm combining with voxel activation information, named as VACOEC. First, VACOEC uses the voxel activation information to measure the independence between each pair of brain regions and effectively restricts the space of candidate solutions, which makes many unnecessary searches of ants be avoided. Then, by combining the global score increase of a solution with the voxel activation information, a new heuristic function is designed to guide the process of ACO to search for the optimal solution. The experimental results on simulated datasets show that the proposed method can accurately and efficiently identify the directions of the brain EC networks. Moreover, the experimental results on real-world data show that patients with Alzheimers disease (AD) exhibit decreased effective connectivity not only in the intra-network within the default mode network (DMN) and salience network (SN), but also in the inter-network between DMN and SN, compared with normal control (NC) subjects. The experimental results demonstrate that VACOEC is promising for practical applications in the neuroimaging studies of geriatric subjects and neurological patients.

G-Causality Brain Connectivity Differences of Finger Movements between Motor Execution and Motor Imagery

Motor imagery is one of the classical paradigms which have been used in brain-computer interface and motor function recovery. Finger movement-based motor execution is a complex biomechanical architecture and a crucial task for establishing most complicated and natural activities in daily life. Some patients may suffer from alternating hemiplegia after brain stroke and lose their ability of motor execution. Fortunately, the ability of motor imagery might be preserved independently and worked as a backdoor for motor function recovery. The efficacy of motor imagery for achieving significant recovery for the motor cortex after brain stroke is still an open question. In this study, we designed a new paradigm to investigate the neural mechanism of thirty finger movements in two scenarios: motor execution and motor imagery. Eleven healthy participants performed or imagined thirty hand gestures twice based on left and right finger movements. The electroencephalogram (EEG) signal for each subject during sixty trials left and right finger motor execution and imagery were recorded during our proposed experimental paradigm. The Granger causality (G-causality) analysis method was employed to analyze the brain connectivity and its strength between contralateral premotor, motor, and sensorimotor areas. Highest numbers for G-causality trials of 37 ± 7.3, 35.5 ± 8.8, 36.3 ± 10.3, and 39.2 ± 9.0 and lowest Granger causality coefficients of 9.1 ± 3.2, 10.9 ± 3.7, 13.2 ± 0.6, and 13.4 ± 0.6 were achieved from the premotor to motor area during execution/imagination tasks of right and left finger movements, respectively. These results provided a new insight into motor execution and motor imagery based on hand gestures, which might be useful to build a new biomarker of finger motor recovery for partially or even completely plegic patients. Furthermore, a significant difference of the G-causality trial number was observed during left finger execution/imagery and right finger imagery, but it was not observed during the right finger execution phase. Significant difference of the G-causality coefficient was observed during left finger execution and imagery, but it was not observed during right finger execution and imagery phases. These results suggested that different MI-based brain motor function recovery strategies should be taken for right-hand and left-hand patients after brain stroke.

Using Partial Directed Coherence to Study Alpha-Band Effective Brain Networks during a Visuospatial Attention Task

Previous studies have shown that the neural mechanisms underlying visual spatial attention rely on top-down control information from the frontal and parietal cortexes, which ultimately amplifies sensory processing of stimulus occurred at the attended location relative to those at unattended location. However, the modulations of effective brain networks in response to stimulus at attended and unattended location are not yet clear. In present study, we collected event-related potentials (ERPs) from 15 subjects during a visual spatial attention task, and a partial directed coherence (PDC) method was used to construct alpha-band effective brain networks of two conditions (targets at attended and nontargets at unattended location). Flow gain mapping, effective connectivity pattern, and graph measures including clustering coefficient (C), characteristic path length (L), global efficiency (E_global), and local efficiency (E_local) were compared between two conditions. Flow gain mapping showed that the frontal region seemed to serve as the main source of information transmission in response to targets at attended location while the parietal region served as the main source in nontarget condition. Effective connectivity pattern indicated that in response to targets, there existed obvious top-down connections from the frontal, temporal, and parietal cortexes to the visual cortex compared with in response to nontargets. Graph theory analysis was used to quantify the topographical properties of the brain networks, and results revealed that in response to targets, the brain networks were characterized by significantly smaller characteristic path length and larger global efficiency than in response to nontargets. Our findings suggested that smaller characteristic path length and larger global efficiency could facilitate global integration of information and provide a substrate for more efficient perceptual processing of targets at attended location compared with processing of nontargets at ignored location, which revealed the neural mechanisms underlying visual spatial attention from the perspective of effective brain networks and graph theory for the first time and opened new vistas to interpret a cognitive process.

Classification of propofol-induced sedation states using brain connectivity analysis

connectivity measurements can provide key information about ongoing brain processes. In this paper, we propose to investigate the performance of the binary classification of Propofol-induced sedation states using partial granger causality analysis. Based on the brain connectivity measurements obtained from EEG signals in a database that contains four sedation states: baseline, mild, moderate, and recovery, we consider eight sensors and evaluate the area under the ROC curve with five classifiers: the k-nearest neighbor (density method), support vector machine, linear discriminant analysis, Bayesian discriminant analysis, and a model based on extreme learning machine. The results support the conclusion that the different Propofol-induced sedation states can be identified with an AUC of around 0.75, by considering signal segments of only 4 second. These results highlight the discriminant power that can be obtained from scalp level connectivity measures for online brain monitoring.

Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review

Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of human cognition and neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective connectivity) among various neural units. Computational methods, especially graph theory-based methods, have recently played a significant role in understanding brain connectivity architecture. Objectives: Thanks to the emergence of graph theoretical analysis, the main purpose of the current paper is to systematically review how brain properties can emerge through the interactions of distinct neuronal units in various cognitive and neurological applications using fMRI. Moreover, this article provides an overview of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls. Methods: In this systematic review, the databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, and SpringerLink are employed for exploring the evolution of computational methods in human brain connectivity from 1990 to the present, focusing on graph theory. The Cochrane Collaboration's tool was used to assess the risk of bias in individual studies. Results: Our results show that graph theory and its implications in cognitive neuroscience have attracted the attention of researchers since 2009 (as the Human Connectome Project launched), because of their prominent capability in characterizing the behavior of complex brain systems. Although graph theoretical approach can be generally applied to either functional or effective connectivity patterns during rest or task performance, to date, most articles have focused on the resting-state functional connectivity. Conclusions: This review provides an insight into how to utilize graph theoretical measures to make neurobiological inferences regarding the mechanisms underlying human cognition and behavior as well as different brain disorders.

Evaluation of connectivity estimates using spiking neuronal network models

The flow of information between different regions of the cortex is fundamental for brain function. Researchers use causality detection techniques, such as Granger causality, to infer connectivity among brain areas from time series. Generalized partial directed coherence (GPDC) is a frequency domain linear method based on vector autoregressive model, which has been applied in electroencephalography, local field potential, and blood oxygenation level-dependent signals. Despite its widespread usage, previous attempts to validate GPDC use oversimplified simulated data, which do not reflect the nonlinearities and network couplings present in biological signals. In this work, we evaluated the GPDC performance when applied to simulated LFP signals, i.e., generated from networks of spiking neuronal models. We created three models, each containing five interacting networks, and evaluated whether the GPDC method could accurately detect network couplings. When using a stronger coupling, we showed that GPDC correctly detects all existing connections from simulated LFP signals in the three models, without false positives. Varying the coupling strength between networks, by changing the number of connections or synaptic strengths, and adding noise in the times series, altered the receiver operating characteristic (ROC) curve, ranging from perfect to chance level retrieval. We also showed that GPDC values correlated with coupling strength, indicating that GPDC values can provide useful information regarding coupling strength. These results reinforce that GPDC can be used to detect causality relationships over neural signals.

Causal Cortical Network for Arithmetic Problem-Solving Represents Brain's Planning Rather than Reasoning

Arithmetic problem-solving whose components mainly involve the calculation, planning and reasoning, is an important mathematical skill. To date, the neural mechanism underlying arithmetic problem-solving remains unclear. In this study, a scheme that combined a novel 24 points game paradigm, conditional Granger causality analysis, and near-infrared spectroscopy (fNIRS) neuroimaging technique was developed to examine the differences in brain activation and effective connectivity between the calculation, planning, and reasoning. We discovered that the performance of planning was correlated with the activation in frontal cortex, whereas the performance of reasoning showed the relationship with the activation in parietal cortex. In addition, we also discovered that the directional effective connectivity between the anterior frontal and posterior parietal cortex was more closely related to planning rather than reasoning. It is expected that this work will pave a new avenue for an improved understanding of the neural underpinnings underlying arithmetic problem-solving, which also provides a novel indicator to evaluate the efficacy of mathematical education.

Investigation of the emotional network in depression after stroke: A study of multivariate Granger causality analysis of fMRI data

OBJECTIVE

Depression after stroke (DAS) is a serious complication of stroke that significantly restricts rehabilitation. Brain imaging technology is an important method for studying the emotional network of DAS. However, few studies have focused on dynamic interactions within the network. The aim of this study was to investigate the emotional network of frontal lobe DAS using the multivariate Granger causality analysis (GCA) method, a technique that can estimate the association among the brain areas to analyze functional magnetic resonance imaging (fMRI) data collected from DAS and no depression after stroke (NDAS).

METHOD

Thirty-six first-time ischemic right frontal lobe stroke patients underwent resting-state fMRI (rs-fMRI) scans. The clinical assessment scale used for screening subjects was as follows: the 24-item Hamilton Rating Scale for Depression (HAMD-24), the National Institutes of Health Stroke Scale (NIHSS), the Mini-Mental State Examination (MMSE), and the Barthel Index (BI). The multivariate GCA method was used to analyze fMRI data collected from DAS and NDAS.

RESULTS

The results showed positive regulations in the order from the ventromedial prefrontal cortex (VMPFC), the anterior cingulate cortex (ACC), and the amygdala (AMYG) to the thalamus, and when the interaction order is opposite, the moderating effect is negative. The thalamus could predict the negative activity of the insular (IC) via the ACC. The dorsolateral prefrontal cortex (DLPFC) could predict the activity of the ACC via the temporal pole (TP).

CONCLUSION

This study found a VMPFC-ACC-AMYG-thalamus emotional circuit to explain the network between different brain regions associated with DAS. The DLPFC and TP play an important role in the emotional regulation of DAS, and the function of the IC is regulated negatively by the thalamus. These findings advance the neural theory of DAS, which is based on the functional relationship between different brain areas.

Impaired cognitive flexibility following NMDAR-GluN2B deletion is associated with altered orbitofrontal-striatal function

A common feature across neuropsychiatric disorders is inability to discontinue an action or thought once it has become detrimental. Reversal learning, a hallmark of executive control, requires plasticity within cortical, striatal and limbic circuits and is highly sensitive to disruption of N-methyl-D-aspartate receptor (NMDAR) function. In particular, selective deletion or antagonism of GluN2B containing NMDARs in cortical regions including the orbitofrontal cortex (OFC), promotes maladaptive perseveration. It remains unknown whether GluN2B functions to maintain local cortical activity necessary for reversal learning, or if it exerts a broader influence on the integration of neural activity across cortical and subcortical systems. To address this question, we utilized in vivo electrophysiology to record neuronal activity and local field potentials (LFP) in the orbitofrontal cortex and dorsal striatum (dS) of mice with deletion of GluN2B in neocortical and hippocampal principal cells while they performed touchscreen reversal learning. Reversal impairment produced by corticohippocampal GluN2B deletion was paralleled by an aberrant increase in functional connectivity between the OFC and dS. These alterations in coordination were associated with alterations in local OFC and dS firing activity. These data demonstrate highly dynamic patterns of cortical and striatal activity concomitant with reversal learning, and reveal GluN2B as a molecular mechanism underpinning the timing of these processes.

Multi-layer adaptation of group coordination in musical ensembles

Group coordination passes through an efficient integration of multimodal sources of information. This study examines complex non-verbal communication by recording movement kinematics from conductors and two sections of violinists of an orchestra adapting to a perturbation affecting their normal pattern of sensorimotor communication (rotation of half a turn of the first violinists’ section). We show that different coordination signals are channeled through ancillary (head kinematics) and instrumental movements (bow kinematics). Each one of them affect coordination either at the inter-group or intra-group levels, therefore tapping into different modes of cooperation: complementary versus imitative coordination. Our study suggests that the co-regulation of group behavior is based on the exchange of information across several layers, each one of them tuned to carry specific coordinative signals. Multi-layer sensorimotor communication may be the key musicians and, more generally humans, use to flexibly communicate between each other in interactive sensorimotor tasks.

Cause or effect: Altered brain and network activity in cervical dystonia is partially normalized by botulinum toxin treatment

Background

Idiopathic cervical dystonia (CD) is a chronic movement disorder characterized by impressive clinical symptoms and the lack of clear pathological findings in clinical diagnostics and imaging. At present, the injection of botulinum toxin (BNT) in dystonic muscles is an effective therapy to control motor symptoms and pain in CD.

Objectives

We hypothesized that, although it is locally injected to dystonic muscles, BNT application leads to changes in brain and network activity towards normal brain function.

Methods

Using 3 T functional MR imaging along with advanced analysis techniques (functional connectivity, Granger causality, and regional homogeneity), we aimed to characterize brain activity in CD (17 CD patients vs. 17 controls) and to uncover the effects of BNT treatment (at 6 months).

Results

In CD, we observed an increased information flow within the basal ganglia, the thalamus, and the sensorimotor cortex. In parallel, some of these structures became less responsive to regulating inputs. Furthermore, our results suggested an altered somatosensory integration. Following BNT administration, we noted a shift towards normal brain function in the CD patients, especially within the motor cortex, the somatosensory cortex, and the basal ganglia.

Conclusion

The changes in brain function and network activity in CD can be interpreted as related to the underlying cause, the effort to compensate or a mixture of both. Although BNT is applied in the last stage of the cortico-neuromuscular pathway, brain patterns are shifted towards those of healthy controls.

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we characterized brain activity in CD and the effects of BNT using 3T fMR imaging and network analysis techniques

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following treatment with botulinum toxin (BNT), abnormal brain activity patterns in primary dystonia are attenuated

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critical key regions for both the pathophysiology and BNT-induced improvement in cervical dystonia are the BG

A closed-loop, music-based brain-computer interface for emotion mediation

Emotions play a critical role in rational and intelligent behavior; a better fundamental knowledge of them is indispensable for understanding higher order brain function. We propose a non-invasive brain-computer interface (BCI) system to feedback a person’s affective state such that a closed-loop interaction between the participant’s brain responses and the musical stimuli is established. We realized this concept technically in a functional prototype of an algorithm that generates continuous and controllable patterns of synthesized affective music in real-time, which is embedded within a BCI architecture. We evaluated our concept in two separate studies. In the first study, we tested the efficacy of our music algorithm by measuring subjective affective responses from 11 participants. In a second pilot study, the algorithm was embedded in a real-time BCI architecture to investigate affective closed-loop interactions in 5 participants. Preliminary results suggested that participants were able to intentionally modulate the musical feedback by self-inducing emotions (e.g., by recalling memories), suggesting that the system was able not only to capture the listener’s current affective state in real-time, but also potentially provide a tool for listeners to mediate their own emotions by interacting with music. The proposed concept offers a tool to study emotions in the loop, promising to cast a complementary light on emotion-related brain research, particularly in terms of clarifying the interactive, spatio-temporal dynamics underlying affective processing in the brain.