A MATLAB toolbox for Granger causal connectivity analysis

Estimating coupling strength between multivariate neural series with multivariate permutation conditional mutual information

Recently, coupling between groups of neurons or different brain regions has been widely studied to provide insights into underlying mechanisms of brain functions. To comprehensively understand the effect of such coupling, it is necessary to accurately extract the coupling strength information among multivariate neural signals from the whole brain. This study proposed a new method named multivariate permutation conditional mutual information (MPCMI) to quantitatively estimate the coupling strength of multivariate neural signals (MNS). The performance of the MPCMI method was validated on the simulated MNS generated by multi-channel neural mass model (MNMM). The coupling strength feature of simulated MNS extracted by MPCMI showed better performance compared with standard methods, such as permutation conditional mutual information (PCMI), multivariate Granger causality (MVGC), and Granger causality analysis (GCA). Furthermore, the MPCMI was applied to estimate the coupling strengths of two-channel resting-state electroencephalographic (rsEEG) signals from different brain regions of 19 patients with amnestic mild cognitive impairment (aMCI) with type 2 diabetes mellitus (T2DM) and 20 normal control (NC) with T2DM in Alpha1 and Alpha2 frequency bands. Empirical results showed that the MPCMI could effectively extract the coupling strength features that were significantly different between the aMCI and the NC. Hence, the proposed MPCMI method could be an effective estimate of coupling strengths of MNS, and might be a viable biomarker for clinical applications.

A Comparison of Evoked and Non-evoked Functional Networks

The growing interest in brain networks to study the brain's function in cognition and diseases has produced an increase in methods to extract these networks. Typically, each method yields a different network. Therefore, one may ask what the resulting networks represent. To address this issue we consider electrocorticography (ECoG) data where we compare three methods. We derive networks from on-going ECoG data using two traditional methods: cross-correlation (CC) and Granger causality (GC). Next, connectivity is probed actively using single pulse electrical stimulation (SPES). We compare the overlap in connectivity between these three methods as well as their ability to reveal well-known anatomical connections in the language circuit. We find that strong connections in the CC network form more or less a subset of the SPES network. GC and SPES are related more weakly, although GC connections coincide more frequently with SPES connections compared to non-existing SPES connections. Connectivity between the two major hubs in the language circuit, Broca's and Wernicke's area, is only found in SPES networks. Our results are of interest for the use of patient-specific networks obtained from ECoG. In epilepsy research, such networks form the basis for methods that predict the effect of epilepsy surgery. For this application SPES networks are interesting as they disclose more physiological connections compared to CC and GC networks.

Current source density estimates improve the discriminability of scalp-level brain connectivity features related to motor-imagery tasks

Recent progress in the number of studies involving brain connectivity analysis of motor imagery (MI) tasks for brain-computer interface (BCI) systems has warranted the need for pre-processing methods. The objective of this study is to evaluate the impact of current source density (CSD) estimation from raw electroencephalogram (EEG) signals on the classification performance of scalp level brain connectivity feature based MI-BCI. In particular, time-domain partial Granger causality (PGC) method was implemented on the raw EEG signals and CSD signals of a publicly available dataset for the estimation of brain connectivity features. Moreover, pairwise binary classifications of four different MI tasks were performed in inter-session and intra-session conditions using a support vector machine classifier. The results showed that CSD provided a statistically significant increase of the AUC: 20.28% in the inter-session condition; 12.54% and 13.92% with session 01 and session 02, respectively, in the intra-session condition. These results show that pre-processing of EEG signals is crucial for single-trial connectivity features based MI-BCI systems and CSD can enhance their overall performance.

Interhemispheric connectivity during lateralized lexical decision

The well-established right visual field (RVF-lh) advantage in word recognition is commonly attributed to the typical left hemisphere dominance in language; words presented to the LVF-rh are processed less efficiently due to the need for transcallosal transfer from the right to left hemisphere. The exact stage for this hemispheric transfer is currently unsettled. Some studies suggest that transfer occurs at very early stages between primary visual regions, whereas other studies suggest that transfer occurs between the left visual word form area and its right hemisphere homolog. This study explores these conflicting accounts and finds evidence for both. Participants conducted a lateralized lexical decision task with both unilateral and bilateral display conditions. Connectivity analyses were conducted from magnetoencephalography signals that were localized to the left middle occipital gyrus (LMOG), right middle occipital gyrus (RMOG), left visual word form area (LVWFA), and right visual word form area (RVWA). Results from unilateral trials showed asymmetrical interhemispheric connectivity from the RMOG to LMOG and symmetrical interhemispheric connectivity between the LVWFA and RVWFA. Furthermore, bilateral presentations led to reduced interhemispheric connectivity between both homologous region of interest pairs. Together, these results suggest that lateralized word recognition involves multiple stages of interhemispheric interactions and that these interactions are reduced with bilateral displays.

A procedure to increase the power of Granger-causal analysis through temporal smoothing

BACKGROUND

How the human brain coordinates network activity to support cognition and behavior remains poorly understood. New high-resolution recording modalities facilitate a more detailed understanding of the human brain network. Several approaches have been proposed to infer functional networks, indicating the transient coordination of activity between brain regions, from neural time series. One category of approach is based on statistical modeling of time series recorded from multiple sensors (e.g., multivariate Granger causality). However, fitting such models remains computationally challenging as the history structure may be long in neural activity, requiring many model parameters to fully capture the dynamics.

NEW METHOD

We develop a method based on Granger causality that makes the assumption that the history dependence varies smoothly. We fit multivariate autoregressive models such that the coefficients of the lagged history terms are smooth functions. We do so by modelling the history terms with a lower dimensional spline basis, which requires many fewer parameters than the standard approach and increases the statistical power of the model.

RESULTS

We show that this procedure allows accurate estimation of brain dynamics and functional networks in simulations and examples of brain voltage activity recorded from a patient with pharmacoresistant epilepsy.

COMPARISON WITH EXISTING METHOD

The proposed method has more statistical power than the Granger method for networks of signals that exhibit extended and smooth history dependencies.

CONCLUSIONS

The proposed tool permits conditional inference of functional networks from many brain regions with extended history dependence, furthering the applicability of Granger causality to brain network science.

Hippocampal CA1 gamma power predicts the precision of spatial memory judgments

The hippocampus plays a critical role in spatial memory. However, the exact neural mechanisms underlying high-fidelity spatial memory representations are unknown. We report findings from presurgical epilepsy patients with bilateral hippocampal depth electrodes performing an object-location memory task that provided a broad range of spatial memory precision. During encoding, patients were shown a series of objects along the circumference of an invisible circle. At test, the same objects were shown at the top of the circle (0°), and patients used a dial to move the object to its location shown during encoding. Angular error between the correct location and the indicated location was recorded as a continuous measure of performance. By registering pre- and postimplantation MRI scans, we were able to localize the electrodes to specific hippocampal subfields. We found a correlation between increased gamma power, thought to reflect local excitatory activity, and the precision of spatial memory retrieval in hippocampal CA1 electrodes. Additionally, we found a similar relationship between gamma power and memory precision in the dorsolateral prefrontal cortex and a directional relationship between activity in this region and in the CA1, suggesting that the dorsolateral prefrontal cortex is involved in postretrieval processing. These results indicate that local processing in hippocampal CA1 and dorsolateral prefrontal cortex supports high-fidelity spatial memory representations.

The blessing of Dimensionality: Feature Selection outperforms functional connectivity-based feature transformation to classify ADHD subjects from EEG patterns of phase synchronisation

Functional connectivity (FC) characterizes brain activity from a multivariate set of N brain signals by means of an NxN matrix A, whose elements estimate the dependence within each possible pair of signals. Such matrix can be used as a feature vector for (un)supervised subject classification. Yet if N is large, A is highly dimensional. Little is known on the effect that different strategies to reduce its dimensionality may have on its classification ability. Here, we apply different machine learning algorithms to classify 33 children (age [6-14 years]) into two groups (healthy controls and Attention Deficit Hyperactivity Disorder patients) using EEG FC patterns obtained from two phase synchronisation indices. We found that the classification is highly successful (around 95%) if the whole matrix A is taken into account, and the relevant features are selected using machine learning methods. However, if FC algorithms are applied instead to transform A into a lower dimensionality matrix, the classification rate drops to less than 80%. We conclude that, for the purpose of pattern classification, the relevant features should be selected among the elements of A by using appropriate machine learning algorithms.

The Study of Visual-Auditory Interactions on Lower Limb Motor Imagery

In order to improve the activation of the mirror neuron system and the ability of the visual-cued motor imagery further, the multi-stimuli-cued unilateral lower limb motor imagery is studied in this paper. The visual-auditory evoked pathway is proposed and the sensory process is studied. To analyze the visual-auditory interactions, the kinesthetic motor imagery with the visual-auditory stimulus, visual stimulus and no stimulus are involved. The motor-related rhythm suppression is applied on quantitative evaluation. To explore the statistical sensory process, the causal relationships among the functional areas and the event-related potentials are investigated. The results have demonstrated the outstanding performances of the visual-auditory evoked motor imagery on the improvement of the mirror neuron system activation and the motor imagery ability. Besides, the abundant information interactions among functional areas and the positive impacts of the auditory stimulus in the motor and the visual areas have been revealed. The possibility that the sensory processes evoked by the visual-auditory interactions differ from the one elicited by kinesthetic motor imagery, has also been indicated. This study will promisingly offer an efficient way to motor rehabilitation, thus favorable for hemiparesis and partial paralysis patients.

Response to Ruby et al: On a 'failed' attempt to manipulate conscious perception with transcranial magnetic stimulation to prefrontal cortex

Does disruption of prefrontal cortical activity using transcranial magnetic stimulation (TMS) impair visual metacognition? An initial study supporting this idea (Rounis, Maniscalco, Rothwell, Passingham, & Lau, 2010) motivated an attempted replication and extension (Bor, Schwartzman, Barrett, & Seth, 2017). Bor et al. failed to replicate the initial study, concluding that there was not good evidence that TMS to dorsolateral prefrontal cortex impairs visual metacognition. This failed replication has recently been critiqued by some of the authors of the initial study (Ruby, Maniscalco, & Peters, 2018). Here we argue that these criticisms are misplaced. In our response, we encounter some more general issues concerning good practice in replication of cognitive neuroscience studies, and in setting criteria for excluding data when employing statistical analyses like signal detection theory. We look forward to further studies investigating the role of prefrontal cortex in metacognition, with increasingly refined methodologies, motivated by the discussions in this series of papers.

Hierarchical Algorithms for Causality Retrieval in Atrial Fibrillation Intracavitary Electrograms

Multichannel intracavitary electrograms (EGMs) are acquired at the electrophysiology laboratory to guide radio frequency catheter ablation of patients suffering from atrial fibrillation. These EGMs are used by cardiologists to determine candidate areas for ablation (e.g., areas corresponding to high dominant frequencies or complex fractionated electrograms). In this paper, we introduce two hierarchical algorithms to retrieve the causal interactions among these multiple EGMs. Both algorithms are based on Granger causality, but other causality measures can be easily incorporated. In both cases, they start by selecting a root node, but they differ on the way in which they explore the set of signals to determine their cause-effect relationships: either testing the full set of unexplored signals (GS-CaRe) or performing a local search only among the set of neighbor EGMs (LS-CaRe). The ensuing causal model provides important information about the propagation of the electrical signals inside the atria, uncovering wavefronts and activation patterns that can guide cardiologists towards candidate areas for catheter ablation. Numerical experiments, on both synthetic signals and annotated real-world signals, show the good performance of the two proposed approaches.

Electroconvulsive therapy selectively enhanced feedforward connectivity from fusiform face area to amygdala in major depressive disorder

Electroconvulsive therapy (ECT) has been widely used to treat the major depressive disorder (MDD), especially for treatment-resistant depression. However, the neuroanatomical basis of ECT remains an open problem. In our study, we combined the voxel-based morphology (VBM), resting-state functional connectivity (RSFC) and granger causality analysis (GCA) to identify the longitudinal changes of structure and function in 23 MDD patients before and after ECT. In addition, multivariate pattern analysis using linear support vector machine (SVM) was applied to classify 23 depressed patients from 25 gender, age and education matched healthy controls. VBM analysis revealed the increased gray matter volume of left superficial amygdala after ECT. The following RSFC and GCA analyses further identified the enhanced functional connectivity between left amygdala and left fusiform face area (FFA) and effective connectivity from FFA to amygdala after ECT, respectively. Moreover, SVM-based classification achieved an accuracy of 83.33%, a sensitivity of 82.61% and a specificity of 84% by leave-one-out cross-validation. Our findings indicated that ECT may facilitate the neurogenesis of amygdala and selectively enhance the feedforward cortical-subcortical connectivity from FFA to amygdala. This study may shed new light on the pathological mechanism of MDD and may provide the neuroanatomical basis for ECT.

Granger Causality Analysis of Interictal iEEG Predicts Seizure Focus and Ultimate Resection

BACKGROUND

A critical conceptual step in epilepsy surgery is to locate the causal region of seizures. In practice, the causal region may be inferred from the set of electrodes showing early ictal activity. There would be advantages in deriving information about causal regions from interictal data as well. We applied Granger's statistical approach to baseline interictal data to calculate causal interactions. We hypothesized that maps of the Granger causality network (or GC maps) from interictal data might inform about the seizure network, and set out to see if “causality” in the Granger sense correlated with surgical targets.

OBJECTIVE

To determine whether interictal baseline data could produce GC maps, and whether the regions of high GC would statistically resemble the topography of the ictally active electrode (IAE) set and resection.

METHODS

Twenty-minute interictal baselines obtained from 25 consecutive patients were analyzed. The “GC maps” were quantitatively compared to conventionally constructed surgical plans, by using rank order and Cartesian distance statistics.

RESULTS

In 16 of 25 cases, the interictal GC rankings of the electrodes in the IAE set were lower than predicted by chance (P < .05). The aggregate probability of such a match by chance alone is very small (P < 10⁻²⁰) suggesting that interictal GC maps correlated with ictal networks. The distance of the highest GC electrode to the IAE set and to the resection averaged 4 and 6 mm (Wilcoxon P < .001).

CONCLUSION

GC analysis has the potential to help localize ictal networks from interictal data.

Time-varying MVAR algorithms for directed connectivity analysis: Critical comparison in simulations and benchmark EEG data

Human brain function depends on directed interactions between multiple areas that evolve in the subsecond range. Time-varying multivariate autoregressive (tvMVAR) modeling has been proposed as a way to help quantify directed functional connectivity strengths with high temporal resolution. While several tvMVAR approaches are currently available, there is a lack of unbiased systematic comparative analyses of their performance and of their sensitivity to parameter choices. Here, we critically compare four recursive tvMVAR algorithms and assess their performance while systematically varying adaptation coefficients, model order, and signal sampling rate. We also compared two ways of exploiting repeated observations: single-trial modeling followed by averaging, and multi-trial modeling where one tvMVAR model is fitted across all trials. Results from numerical simulations and from benchmark EEG recordings showed that: i) across a broad range of model orders all algorithms correctly reproduced patterns of interactions; ii) signal downsampling degraded connectivity estimation accuracy for most algorithms, although in some cases downsampling was shown to reduce variability in the estimates by lowering the number of parameters in the model; iii) single-trial modeling followed by averaging showed optimal performance with larger adaptation coefficients than previously suggested, and showed slower adaptation speeds than multi-trial modeling. Overall, our findings identify strengths and weaknesses of existing tvMVAR approaches and provide practical recommendations for their application to modeling dynamic directed interactions from electrophysiological signals.

Dynamics of corticospinal motor control during overground and treadmill walking in humans

Increasing evidence suggests cortical involvement in the control of human gait. However, the nature of corticospinal interactions remains poorly understood. We performed time-frequency analysis of electrophysiological activity acquired during treadmill and overground walking in 22 healthy, young adults. Participants walked at their preferred speed (4.2, SD 0.4 km/h), which was matched across both gait conditions. Event-related power, corticomuscular coherence (CMC), and intertrial coherence (ITC) were assessed for EEG from bilateral sensorimotor cortices and EMG from the bilateral tibialis anterior (TA) muscles. Cortical power, CMC, and ITC at theta, alpha, beta, and gamma frequencies (4-45 Hz) increased during the double support phase of the gait cycle for both overground and treadmill walking. High beta (21-30 Hz) CMC and ITC of EMG was significantly increased during overground compared with treadmill walking, as well as EEG power in theta band (4-7 Hz). The phase spectra revealed positive time lags at alpha, beta, and gamma frequencies, indicating that the EEG response preceded the EMG response. The parallel increases in power, CMC, and ITC during double support suggest evoked responses at spinal and cortical populations rather than a modulation of ongoing corticospinal oscillatory interactions. The evoked responses are not consistent with the idea of synchronization of ongoing corticospinal oscillations but instead suggest coordinated cortical and spinal inputs during the double support phase. Frequency-band dependent differences in power, CMC, and ITC between overground and treadmill walking suggest differing neural control for the two gait modalities, emphasizing the task-dependent nature of neural processes during human walking. NEW & NOTEWORTHY We investigated cortical and spinal activity during overground and treadmill walking in healthy adults. Parallel increases in power, corticomuscular coherence, and intertrial coherence during double support suggest evoked responses at spinal and cortical populations rather than a modulation of ongoing corticospinal oscillatory interactions. These findings identify neurophysiological mechanisms that are important for understanding cortical control of human gait in health and disease.

Motor Preparation Disrupts Proactive Control in the Stop Signal Task

In a study of the stop signal task (SST) we employed Bayesian modeling to compute the estimated likelihood of stop signal or P(Stop) trial by trial and identified regional processes of conflict anticipation and response slowing. A higher P(Stop) is associated with prolonged go trial reaction time (goRT)-a form of sequential effect-and reflects proactive control of motor response. However, some individuals do not demonstrate a sequential effect despite similar go and stop success (SS) rates. We posited that motor preparation may disrupt proactive control more in certain individuals than others. Specifically, the time interval between trial and go signal onset-the fore-period (FP)-varies across trials and a longer FP is associated with a higher level of motor preparation and shorter goRT. Greater motor preparatory activities may disrupt proactive control. To test this hypothesis, we compared brain activations and Granger causal connectivities of 81 adults who demonstrated a sequential effect (SEQ) and 35 who did not (nSEQ). SEQ and nSEQ did not differ in regional activations to conflict anticipation, motor preparation, goRT slowing or goRT speeding. In contrast, SEQ and nSEQ demonstrated different patterns of Granger causal connectivities. P(Stop) and FP activations shared reciprocal influence in SEQ but FP activities Granger caused P(Stop) activities unidirectionally in nSEQ, and FP activities Granger caused goRT speeding activities in nSEQ but not SEQ. These findings support the hypothesis that motor preparation disrupts proactive control in nSEQ and provide direct neural evidence for interactive go and stop processes.

Midfrontal Theta and Posterior Parietal Alpha Band Oscillations Support Conflict Resolution in a Masked Affective Priming Task

Past attempts to characterize the neural mechanisms of affective priming have conceptualized it in terms of classic cognitive conflict, but have not examined the neural oscillatory mechanisms of subliminal affective priming. Using behavioral and electroencephalogram (EEG) time frequency (TF) analysis, the current study examines the oscillatory dynamics of unconsciously triggered conflict in an emotional facial expressions version of the masked affective priming task. The results demonstrate that the power dynamics of conflict are characterized by increased midfrontal theta activity and suppressed parieto-occipital alpha activity. Across-subject and within-trial correlation analyses further confirmed this pattern. Phase synchrony and Granger causality analyses (GCAs) revealed that the fronto-parietal network was involved in unconscious conflict detection and resolution. Our findings support a response conflict account of affective priming, and reveal the role of the fronto-parietal network in unconscious conflict control.

Disruption of frontal-parietal connectivity during conscious sedation by propofol administration

The sedative state is a transitional state from wakefulness to general anesthesia. However, little is understood regarding the mechanism of conscious sedation, different from general anesthesia while maintaining wakefulness. In this study, we aimed to investigate changes in functional connectivity of the parietal-frontal network, implicated in wakefulness during conscious sedation induced by propofol infusion. The electroencephalography was obtained at the frontal and parietal areas of adult volunteers who maintain wakefulness during low-dose propofol infusion (1.5 mg/kg/h) over 1 h. Spectral Granger causality (GC) (δ, θ, α, β, and γ frequency bands) and time-domain GC were calculated during each stage of awake (before propofol administration), sedation, and recovery (after discontinuation of propofol). We also calculated the phase-locking index and compared it with GC during each stage. A decrease in GC from the frontal to parietal areas was observed particularly in the low-frequency bands during propofol administration. Contrary to the GC changes in the frontoparietal direction, GC from the parietal to frontal areas was increased in the high-frequency bands during propofol administration and significantly decreased after discontinuation of propofol. In summary, we showed that frontal-parietal neural networks were significantly changed differently by the frequency of the brain rhythm and the directions of connections during sedation by propofol administration. Our result suggests that the alteration of brain interaction may induce sedative state lying between awake and general anesthesia.

Ultra-slow mechanical stimulation of olfactory epithelium modulates consciousness by slowing cerebral rhythms in humans

The coupling between respiration and neural activity within olfactory areas and hippocampus has recently been unambiguously demonstrated, its neurophysiological basis sustained by the well-assessed mechanical sensitivity of the olfactory epithelium. We herein hypothesize that this coupling reverberates to the whole brain, possibly modulating the subject's behavior and state of consciousness. The olfactory epithelium of 12 healthy subjects was stimulated with periodical odorless air-delivery (frequency 0.05 Hz, 8 s on, 12 off). Cortical electrical activity (High Density-EEG) and perceived state of consciousness have been studied. The stimulation induced i) an enhancement of delta-theta EEG activity over the whole cortex mainly involving the Limbic System and Default Mode Network structures, ii) a reversal of the overall information flow directionality from wake-like postero-anterior to NREM sleep-like antero-posterior, iii) the perception of having experienced an Altered State of Consciousness. These findings could shed further light via a neurophenomenological approach on the links between respiration, cerebral activity and subjective experience, suggesting a plausible neurophysiological basis for interpreting altered states of consciousness induced by respiration-based meditative practices.

Strengthened functional connectivity among LFPs in rat medial prefrontal cortex during anxiety

Theta oscillations in medial prefrontal cortex (mPFC) have been consistently implicated in the regulation of anxiety-related behaviors. However, the theta-band functional connectivity in mPFC is less well characterized. Therefore, we simultaneously recorded local filed potentials (LFPs) from mPFC in freely behaving rats in the elevated plus maze (EPM). Functional connectivity among LFPs was measured by directed transfer function (DTF) via spectral Granger causal connectivity analysis. Causal network was then identified based on DTF. Global efficiency (E_glob) was selected to quantitatively describe the characteristic of the network. Our results showed that a significant difference in theta-band functional connectivity between safe and aversive location in the maze anxiety test. Strikingly, DTF and E_glob were higher specifically in the closed arms and decreased sharply prior to entrying into the open arms. These results indicate strengthened theta-band functional connectivity may be related to anxiety.

Multivariate Granger causality unveils directed parietal to prefrontal cortex connectivity during task-free MRI

While a large body of research has focused on the study of functional brain "connectivity", few investigators have focused on directionality of brain-brain interactions which, in spite of the mostly bidirectional anatomical substrates, cannot be assumed to be symmetrical. We employ a multivariate Granger Causality-based approach to estimating directed in-network interactions and quantify its advantages using extensive realistic synthetic BOLD data simulations to match Human Connectome Project (HCP) data specification. We then apply our framework to resting state functional MRI (rs-fMRI) data provided by the HCP to estimate the directed connectome of the human brain. We show that the functional interactions between parietal and prefrontal cortices commonly observed in rs-fMRI studies are not symmetrical, but consists of directional connectivity from parietal areas to prefrontal cortices rather than vice versa. These effects are localized within the same hemisphere and do not generalize to cross-hemispheric functional interactions. Our data are consistent with neurophysiological evidence that posterior parietal cortices involved in processing and integration of multi-sensory information modulate the function of more anterior prefrontal regions implicated in action control and goal-directed behaviour. The directionality of functional connectivity can provide an additional layer of information in interpreting rs-fMRI studies both in health and disease.

Bidirectional Cardio-Respiratory Interactions in Heart Failure

We investigated cardio-respiratory coupling in patients with heart failure by quantification of bidirectional interactions between cardiac (RR intervals) and respiratory signals with complementary measures of time series analysis. Heart failure patients were divided into three groups of twenty, age and gender matched, subjects: with sinus rhythm (HF-Sin), with sinus rhythm and ventricular extrasystoles (HF-VES), and with permanent atrial fibrillation (HF-AF). We included patients with indication for implantation of implantable cardioverter defibrillator or cardiac resynchronization therapy device. ECG and respiratory signals were simultaneously acquired during 20 min in supine position at spontaneous breathing frequency in 20 healthy control subjects and in patients before device implantation. We used coherence, Granger causality and cross-sample entropy analysis as complementary measures of bidirectional interactions between RR intervals and respiratory rhythm. In heart failure patients with arrhythmias (HF-VES and HF-AF) there is no coherence between signals (p < 0.01), while in HF-Sin it is reduced (p < 0.05), compared with control subjects. In all heart failure groups causality between signals is diminished, but with significantly stronger causality of RR signal in respiratory signal in HF-VES. Cross-sample entropy analysis revealed the strongest synchrony between respiratory and RR signal in HF-VES group. Beside respiratory sinus arrhythmia there is another type of cardio-respiratory interaction based on the synchrony between cardiac and respiratory rhythm. Both of them are altered in heart failure patients. Respiratory sinus arrhythmia is reduced in HF-Sin patients and vanished in heart failure patients with arrhythmias. Contrary, in HF-Sin and HF-VES groups, synchrony increased, probably as consequence of some dominant neural compensatory mechanisms. The coupling of cardiac and respiratory rhythm in heart failure patients varies depending on the presence of atrial/ventricular arrhythmias and it could be revealed by complementary methods of time series analysis.

Time, frequency, and time-varying Granger-causality measures in neuroscience

This article proposes a systematic methodological review and an objective criticism of existing methods enabling the derivation of time, frequency, and time-varying Granger-causality statistics in neuroscience. The capacity to describe the causal links between signals recorded at different brain locations during a neuroscience experiment is indeed of primary interest for neuroscientists, who often have very precise prior hypotheses about the relationships between recorded brain signals. The increasing interest and the huge number of publications related to this topic calls for this systematic review, which describes the very complex methodological aspects underlying the derivation of these statistics. In this article, we first present a general framework that allows us to review and compare Granger-causality statistics in the time domain, and the link with transfer entropy. Then, the spectral and the time-varying extensions are exposed and discussed together with their estimation and distributional properties. Although not the focus of this article, partial and conditional Granger causality, dynamical causal modelling, directed transfer function, directed coherence, partial directed coherence, and their variant are also mentioned.

Isoflurane Impairs Low-Frequency Feedback but Leaves High-Frequency Feedforward Connectivity Intact in the Fly Brain

Hierarchically organized brains communicate through feedforward (FF) and feedback (FB) pathways. In mammals, FF and FB are mediated by higher and lower frequencies during wakefulness. FB is preferentially impaired by general anesthetics in multiple mammalian species. This suggests FB serves critical functions in waking brains. The brain of Drosophila melanogaster (fruit fly) is also hierarchically organized, but the presence of FB in these brains is not established. Here, we studied FB in the fly brain, by simultaneously recording local field potentials (LFPs) from low-order peripheral structures and higher-order central structures. We analyzed the data using Granger causality (GC), the first application of this analysis technique to recordings from the insect brain. Our analysis revealed that low frequencies (0.1–5 Hz) mediated FB from the center to the periphery, while higher frequencies (10–45 Hz) mediated FF in the opposite direction. Further, isoflurane anesthesia preferentially reduced FB. Our results imply that the spectral characteristics of FF and FB may be a signature of hierarchically organized brains that is conserved from insects to mammals. We speculate that general anesthetics may induce unresponsiveness across species by targeting the mechanisms that support FB.

Auditory neural networks involved in attention modulation prefer biologically significant sounds and exhibit sexual dimorphism in anurans

Allocating attention to biologically relevant stimuli in a complex environment is critically important for survival and reproductive success. In humans, attention modulation is regulated by the frontal cortex, and is often reflected by changes in specific components of the event-related potential (ERP). Although brain networks for attention modulation have been widely studied in primates and avian species, little is known about attention modulation in amphibians. The present study aimed to investigate the attention modulation networks in an anuran species, the Emei music frog (Babina daunchina). Male music frogs produce advertisement calls from within underground nest burrows that modify the acoustic features of the calls, and both males and females prefer calls produced from inside burrows. We broadcast call stimuli to male and female music frogs while simultaneously recording electroencephalographic (EEG) signals from the telencephalon and mesencephalon. Granger causal connectivity analysis was used to elucidate functional brain networks within the time window of ERP components. The results show that calls produced from inside nests which are highly sexually attractive result in the strongest brain connections; both ascending and descending connections involving the left telencephalon were stronger in males while those in females were stronger with the right telencephalon. Our findings indicate that the frog brain allocates neural attention resources to highly attractive sounds within the window of early components of ERP, and that such processing is sexually dimorphic, presumably reflecting the different reproductive strategies of males and females.

Direct Recordings from Human Anterior Insula Reveal its Leading Role within the Error-Monitoring Network

The ability to monitor our own errors is mediated by a network that includes dorsomedial prefrontal cortex (dmPFC) and anterior insula (AI). However, the dynamics of the underlying neurophysiological processes remain unclear. In particular, whether AI is on the receiving or driving end of the error-monitoring network is unresolved. Here, we recorded intracerebral electroencephalography signals simultaneously from AI and dmPFC in epileptic patients while they performed a stop-signal task. We found that errors selectively modulated broadband neural activity in human AI. Granger causality estimates revealed that errors were immediately followed by a feedforward influence from AI onto anterior cingulate cortex and, subsequently, onto presupplementary motor area. The reverse pattern of information flow was observed on correct responses. Our findings provide the first direct electrophysiological evidence indicating that the anterior insula rapidly detects and conveys error signals to dmPFC, while the latter might use this input to adapt behavior following inappropriate actions.

Study on the mechanisms of seizure-like events suppression effect by electrical stimulation using a microelectrode array

In this paper, we studied the mechanisms underlying the suppression of seizure-like events (SLEs) by electrical stimulation. We conducted an in-vitro experiment using entorhinal cortex combined hippocampal slices and two convulsant drugs, bicuculline and 4-aminopyridine, to induce spontaneous SLEs. We used a microelectrode array to observe network dynamics over the entire hippocampal area simultaneously, including regions far from the stimulation site. We stimulated the entorhinal cortex region, which has been determined to be a focus of SLEs by Granger causality analysis of multichannel time series data, by an external electrode. In bicuculline application, electrical stimulation showed a marked suppression effect, even though the sizes of the effective region differed. In 4-aminopyridine application, however, stimulation under the same conditions did not suppress the activities in ∼80% of SLEs. The suppression effect was more remarkable in the areas surrounding the stimulation site in both cases. Our experimental results could support the neuronal depolarization blockade mechanism by accumulation of extracellular potassium ions, which is one of the most convincing mechanisms to understand seizure suppression phenomena because of electrical stimulation. Computer simulation using a neuronal network model also confirmed the mechanism.

Gender differences in brain networks during verbal Sternberg tasks: A simultaneous near-infrared spectroscopy and electro-encephalography study

Gender differences in psychological processes have been of great interest in a variety of fields including verbal fluency, emotion processing and working memory. Previous studies suggested that women outperform men in verbal working memory (VWM). However, the inherent mechanisms are still unclear. To obtain a deeper insight into the gender differences in brain networks in VWM, this study used near-infrared spectroscopy (NIRS) and electro-encephalography (EEG) simultaneously to investigate gender-related brain networks during verbal Sternberg tasks. NIRS results confirmed that women surpass men in VWM from the perspective of both brain activation and connectivity. Results of EEG (effective connectivity and event-related spectral power) showed that men tend to use a more visuospatial strategy to encode memory. In addition, novel analysis methods of brain networks can provide useful information about the gender specifics of brain functions. Gender-related pseudo-color maps constructed from all channels of average HbO₂ activity during low- and high-load tasks (from 0 to 6 seconds after beginning).

Large-Scale Brain Networks Supporting Divided Attention across Spatial Locations and Sensory Modalities

Higher-order cognitive processes were shown to rely on the interplay between large-scale neural networks. However, brain networks involved with the capability to split attentional resource over multiple spatial locations and multiple stimuli or sensory modalities have been largely unexplored to date. Here I re-analyzed data from Santangelo et al. (2010) to explore the causal interactions between large-scale brain networks during divided attention. During fMRI scanning, participants monitored streams of visual and/or auditory stimuli in one or two spatial locations for detection of occasional targets. This design allowed comparing a condition in which participants monitored one stimulus/modality (either visual or auditory) in two spatial locations vs. a condition in which participants monitored two stimuli/modalities (both visual and auditory) in one spatial location. The analysis of the independent components (ICs) revealed that dividing attentional resources across two spatial locations necessitated a brain network involving the left ventro- and dorso-lateral prefrontal cortex plus the posterior parietal cortex, including the intraparietal sulcus (IPS) and the angular gyrus, bilaterally. The analysis of Granger causality highlighted that the activity of lateral prefrontal regions were predictive of the activity of all of the posteriors parietal nodes. By contrast, dividing attention across two sensory modalities necessitated a brain network including nodes belonging to the dorsal frontoparietal network, i.e., the bilateral frontal eye-fields (FEF) and IPS, plus nodes belonging to the salience network, i.e., the anterior cingulated cortex and the left and right anterior insular cortex (aIC). The analysis of Granger causality highlights a tight interdependence between the dorsal frontoparietal and salience nodes in trials requiring divided attention between different sensory modalities. The current findings therefore highlighted a dissociation among brain networks implicated during divided attention across spatial locations and sensory modalities, pointing out the importance of investigating effective connectivity of large-scale brain networks supporting complex behavior.

dynGENIE3: dynamical GENIE3 for the inference of gene networks from time series expression data

The elucidation of gene regulatory networks is one of the major challenges of systems biology. Measurements about genes that are exploited by network inference methods are typically available either in the form of steady-state expression vectors or time series expression data. In our previous work, we proposed the GENIE3 method that exploits variable importance scores derived from Random forests to identify the regulators of each target gene. This method provided state-of-the-art performance on several benchmark datasets, but it could however not specifically be applied to time series expression data. We propose here an adaptation of the GENIE3 method, called dynamical GENIE3 (dynGENIE3), for handling both time series and steady-state expression data. The proposed method is evaluated extensively on the artificial DREAM4 benchmarks and on three real time series expression datasets. Although dynGENIE3 does not systematically yield the best performance on each and every network, it is competitive with diverse methods from the literature, while preserving the main advantages of GENIE3 in terms of scalability.

Evidence for parallel activation of the pre-supplementary motor area and inferior frontal cortex during response inhibition: a combined MEG and TMS study

This pre-registered experiment sought to uncover the temporal relationship between the inferior frontal cortex (IFC) and the pre-supplementary motor area (pre-SMA) during stopping of an ongoing action. Both regions have previously been highlighted as being central to cognitive control of actions, particularly response inhibition. Here we tested which area is activated first during the stopping process using magnetoencephalography, before assessing the relative chronometry of each region using functionally localized transcranial magnetic stimulation. Both lines of evidence pointed towards simultaneous activity across both regions, suggesting that parallel, mutually interdependent processing may form the cortical basis of stopping. Additional exploratory analysis, however, provided weak evidence in support of previous suggestions that the pre-SMA may provide an ongoing drive of activity to the IFC.

Causality analysis of leading singular value decomposition modes identifies rotor as the dominant driving normal mode in fibrillation

Cardiac fibrillation is a major clinical and societal burden. Rotors may drive fibrillation in many cases, but their role and patterns are often masked by complex propagation. We used Singular Value Decomposition (SVD), which ranks patterns of activation hierarchically, together with Wiener-Granger causality analysis (WGCA), which analyses direction of information among observations, to investigate the role of rotors in cardiac fibrillation. We hypothesized that combining SVD analysis with WGCA should reveal whether rotor activity is the dominant driving force of fibrillation even in cases of high complexity. Optical mapping experiments were conducted in neonatal rat cardiomyocyte monolayers (diameter, 35 mm), which were genetically modified to overexpress the delayed rectifier K⁺ channel I_Kr only in one half of the monolayer. Such monolayers have been shown previously to sustain fast rotors confined to the I_Kr overexpressing half and driving fibrillatory-like activity in the other half. SVD analysis of the optical mapping movies revealed a hierarchical pattern in which the primary modes corresponded to rotor activity in the I_Kr overexpressing region and the secondary modes corresponded to fibrillatory activity elsewhere. We then applied WGCA to evaluate the directionality of influence between modes in the entire monolayer using clear and noisy movies of activity. We demonstrated that the rotor modes influence the secondary fibrillatory modes, but influence was detected also in the opposite direction. To more specifically delineate the role of the rotor in fibrillation, we decomposed separately the respective SVD modes of the rotor and fibrillatory domains. In this case, WGCA yielded more information from the rotor to the fibrillatory domains than in the opposite direction. In conclusion, SVD analysis reveals that rotors can be the dominant modes of an experimental model of fibrillation. Wiener-Granger causality on modes of the rotor domains confirms their preferential driving influence on fibrillatory modes.

Decoding the different states of visual attention using functional and effective connectivity features in fMRI data

The present paper concentrates on the impact of visual attention task on structure of the brain functional and effective connectivity networks using coherence and Granger causality methods. Since most studies used correlation method and resting-state functional connectivity, the task-based approach was selected for this experiment to boost our knowledge of spatial and feature-based attention. In the present study, the whole brain was divided into 82 sub-regions based on Brodmann areas. The coherence and Granger causality were applied to construct functional and effective connectivity matrices. These matrices were converted into graphs using a threshold, and the graph theory measures were calculated from it including degree and characteristic path length. Visual attention was found to reveal more information during the spatial-based task. The degree was higher while performing a spatial-based task, whereas characteristic path length was lower in the spatial-based task in both functional and effective connectivity. Primary and secondary visual cortex (17 and 18 Brodmann areas) were highly connected to parietal and prefrontal cortex while doing visual attention task. Whole brain connectivity was also calculated in both functional and effective connectivity. Our results reveal that Brodmann areas of 17, 18, 19, 46, 3 and 4 had a significant role proving that somatosensory, parietal and prefrontal regions along with visual cortex were highly connected to other parts of the cortex during the visual attention task. Characteristic path length results indicated an increase in functional connectivity and more functional integration in spatial-based attention compared with feature-based attention. The results of this work can provide useful information about the mechanism of visual attention at the network level.

Oscillatory Dynamics in the Frontoparietal Attention Network during Sustained Attention in the Ferret

Sustained attention requires the coordination of neural activity across multiple cortical areas in the frontoparietal network, in particular the prefrontal cortex (PFC) and posterior parietal cortex (PPC). Previous work has demonstrated that activity in these brain regions is coordinated by neuronal oscillations of the local field potential (LFP). However, the underlying coordination of activity in terms of organization of single unit (SU) spiking activity has remained poorly understood, particularly in the freely moving animal. We found that long-range functional connectivity between anatomically connected PFC and PPC was mediated by oscillations in the theta frequency band. SU activity in PFC was phase locked to theta oscillations in PPC, and spiking activity in PFC and PPC was locked to local high-gamma activity. Together, our results support a model in which frequency-specific synchronization mediates functional connectivity between and within PFC and PPC of the frontoparietal attention network in the freely moving animal.

Dynamics of the human brain network revealed by time-frequency effective connectivity in fNIRS

Functional near infrared spectroscopy (fNIRS) is a promising neuroimaging method for investigating networks of cortical regions over time. We propose a directed effective connectivity method (TPDC) allowing the capture of both time and frequency evolution of the brain's networks using fNIRS data acquired from healthy subjects performing a continuous finger-tapping task. Using this method we show the directed connectivity patterns among cortical motor regions involved in the task and their significant variations in the strength of information flow exchanges. Intra and inter-hemispheric connections during the motor task with their temporal evolution are also provided. Characterisation of the fluctuations in brain connectivity opens up a new way to assess the organisation of the brain to adapt to changing task constraints, or under pathological conditions.

Effective Connectivity Analysis of the Brain Network in Drivers during Actual Driving Using Near-Infrared Spectroscopy

Driving a vehicle is a complex activity that requires high-level brain functions. This study aimed to assess the change in effective connectivity (EC) between the prefrontal cortex (PFC), motor-related areas (MA) and vision-related areas (VA) in the brain network among the resting, simple-driving and car-following states. Twelve young male right-handed adults were recruited to participate in an actual driving experiment. The brain delta [HbO₂] signals were continuously recorded using functional near infrared spectroscopy (fNIRS) instruments. The conditional Granger causality (GC) analysis, which is a data-driven method that can explore the causal interactions among different brain areas, was performed to evaluate the EC. The results demonstrated that the hemodynamic activity level of the brain increased with an increase in the cognitive workload. The connection strength among PFC, MA and VA increased from the resting state to the simple-driving state, whereas the connection strength relatively decreased during the car-following task. The PFC in EC appeared as the causal target, while the MA and VA appeared as the causal sources. However, l-MA turned into causal targets with the subtask of car-following. These findings indicate that the hemodynamic activity level of the cerebral cortex increases linearly with increasing cognitive workload. The EC of the brain network can be strengthened by a cognitive workload, but also can be weakened by a superfluous cognitive workload such as driving with subtasks.

Cooperative cortical network for categorical processing of Chinese lexical tone

The variation of pitch in speech not only creates the intonation for affective communication but also signals different meaning of a word in tonal languages, like Chinese. Due to its subtle and brisk pitch contour distinction between tone categories, the underlying neural processing mechanism is largely unknown. Using direct recordings of the human brain, we found categorical neural responses to lexical tones over a distributed cooperative network that included not only the auditory areas in the temporal cortex but also motor areas in the frontal cortex. Strong causal links from the temporal cortex to the motor cortex were discovered, which provides new evidence of top-down influence and sensory–motor interaction during speech perception.

In tonal languages such as Chinese, lexical tone with varying pitch contours serves as a key feature to provide contrast in word meaning. Similar to phoneme processing, behavioral studies have suggested that Chinese tone is categorically perceived. However, its underlying neural mechanism remains poorly understood. By conducting cortical surface recordings in surgical patients, we revealed a cooperative cortical network along with its dynamics responsible for this categorical perception. Based on an oddball paradigm, we found amplified neural dissimilarity between cross-category tone pairs, rather than between within-category tone pairs, over cortical sites covering both the ventral and dorsal streams of speech processing. The bilateral superior temporal gyrus (STG) and the middle temporal gyrus (MTG) exhibited increased response latencies and enlarged neural dissimilarity, suggesting a ventral hierarchy that gradually differentiates the acoustic features of lexical tones. In addition, the bilateral motor cortices were also found to be involved in categorical processing, interacting with both the STG and the MTG and exhibiting a response latency in between. Moreover, the motor cortex received enhanced Granger causal influence from the semantic hub, the anterior temporal lobe, in the right hemisphere. These unique data suggest that there exists a distributed cooperative cortical network supporting the categorical processing of lexical tone in tonal language speakers, not only encompassing a bilateral temporal hierarchy that is shared by categorical processing of phonemes but also involving intensive speech–motor interactions over the right hemisphere, which might be the unique machinery responsible for the reliable discrimination of tone identities.

MULAN: Evaluation and ensemble statistical inference for functional connectivity

Many analysis methods exist to extract graphs of functional connectivity from neuronal networks. Confidence in the results is limited because, (i) different methods give different results, (ii) parameter setting directly influences the final result, and (iii) systematic evaluation of the results is not always performed. Here, we introduce MULAN (MULtiple method ANalysis), which assumes an ensemble based approach combining multiple analysis methods and fuzzy logic to extract graphs with the most probable structure. In order to reduce the dependency on parameter settings, we determine the best set of parameters using a genetic algorithm on simulated datasets, whose temporal structure is similar to the experimental one. After a validation step, the selected set of parameters is used to analyze experimental data. The final step cross-validates experimental subsets of data and provides a direct estimate of the most likely graph and our confidence in the proposed connectivity. A systematic evaluation validates our strategy against empirical stereotactic electroencephalography (SEEG) and functional magnetic resonance imaging (fMRI) data.

Direct Medial Entorhinal Cortex Input to Hippocampal CA1 Is Crucial for Extended Quiet Awake Replay

Hippocampal replays have been demonstrated to play a crucial role in memory. Chains of ripples (ripple bursts) in CA1 have been reported to co-occur with long-range place cell sequence replays during the quiet awake state, but roles of neural inputs to CA1 in ripple bursts and replays are unknown. Here we show that ripple bursts in CA1 and medial entorhinal cortex (MEC) are temporally associated. An inhibition of MECIII input to CA1 during quiet awake reduced ripple bursts in CA1 and restricted the spatial coverage of replays to a shorter distance corresponding to single ripple events. The reduction did not occur with MECIII input inhibition during slow-wave sleep. Inhibition of CA3 activity suppressed ripples and replays in CA1 regardless of behavioral state. Thus, MECIII input to CA1 is crucial for ripple bursts and long-range replays specifically in quiet awake, whereas CA3 input is essential for both, regardless of behavioral state.

Dynamic Correlations between Intrinsic Connectivity and Extrinsic Connectivity of the Auditory Cortex in Humans

The arrival of sound signals in the auditory cortex (AC) triggers both local and inter-regional signal propagations over time up to hundreds of milliseconds and builds up both intrinsic functional connectivity (iFC) and extrinsic functional connectivity (eFC) of the AC. However, interactions between iFC and eFC are largely unknown. Using intracranial stereo-electroencephalographic recordings in people with drug-refractory epilepsy, this study mainly investigated the temporal dynamic of the relationships between iFC and eFC of the AC. The results showed that a Gaussian wideband-noise burst markedly elicited potentials in both the AC and numerous higher-order cortical regions outside the AC (non-auditory cortices). Granger causality analyses revealed that in the earlier time window, iFC of the AC was positively correlated with both eFC from the AC to the inferior temporal gyrus and that to the inferior parietal lobule. While in later periods, the iFC of the AC was positively correlated with eFC from the precentral gyrus to the AC and that from the insula to the AC. In conclusion, dual-directional interactions occur between iFC and eFC of the AC at different time windows following the sound stimulation and may form the foundation underlying various central auditory processes, including auditory sensory memory, object formation, integrations between sensory, perceptional, attentional, motor, emotional, and executive processes.

A study of problems encountered in Granger causality analysis from a neuroscience perspective

Granger causality methods were developed to analyze the flow of information between time series. These methods have become more widely applied in neuroscience. Frequency-domain causality measures, such as those of Geweke, as well as multivariate methods, have particular appeal in neuroscience due to the prevalence of oscillatory phenomena and highly multivariate experimental recordings. Despite its widespread application in many fields, there are ongoing concerns regarding the applicability of Granger causality methods in neuroscience. When are these methods appropriate? How reliably do they recover the system structure underlying the observed data? What do frequency-domain causality measures tell us about the functional properties of oscillatory neural systems? In this paper, we analyze fundamental properties of Granger-Geweke (GG) causality, both computational and conceptual. Specifically, we show that (i) GG causality estimates can be either severely biased or of high variance, both leading to spurious results; (ii) even if estimated correctly, GG causality estimates alone are not interpretable without examining the component behaviors of the system model; and (iii) GG causality ignores critical components of a system's dynamics. Based on this analysis, we find that the notion of causality quantified is incompatible with the objectives of many neuroscience investigations, leading to highly counterintuitive and potentially misleading results. Through the analysis of these problems, we provide important conceptual clarification of GG causality, with implications for other related causality approaches and for the role of causality analyses in neuroscience as a whole.

Temporal Dynamics of Sensorimotor Networks in Effort-Based Cost-Benefit Valuation: Early Emergence and Late Net Value Integration

Although physical effort can impose significant costs on decision-making, when and how effort cost information is incorporated into choice remains contested, reflecting a larger debate over the role of sensorimotor networks in specifying behavior. Serial information processing models, in which motor circuits simply implement the output of cognitive systems, hypothesize that effort cost factors into decisions relatively late, via integration with stimulus values into net (combined) value signals in dorsomedial frontal cortex (dmFC). In contrast, ethology-inspired approaches suggest a more active role for the dorsal sensorimotor stream, with effort cost signals emerging rapidly after stimulus onset. Here we investigated the time course of effort cost integration using event-related potentials in hungry human subjects while they made decisions about expending physical effort for appetitive foods. Consistent with the ethological perspective, we found that effort cost was represented from as early as 100-250 ms after stimulus onset, localized to dorsal sensorimotor regions including middle cingulate, somatosensory, and motor/premotor cortices. However, examining the same data time-locked to motor output revealed net value signals combining stimulus value and effort cost approximately -400 ms before response, originating from sensorimotor areas including dmFC, precuneus, and posterior parietal cortex. Granger causal connectivity analysis of the motor effector signal in the time leading to response showed interactions between these sensorimotor regions and ventrolateral prefrontal cortex, a structure associated with adjusting behavior-response mappings. These results suggest that rapid activation of sensorimotor regions interacts with cognitive valuation systems, producing a net value signal reflecting both physical effort and reward contingencies.

SIGNIFICANCE STATEMENT

Although physical effort imposes a cost on choice, when and how effort cost influences neural correlates of decision-making remains contested. This dispute reflects a larger disagreement between cognitive neuroscience and ethology over the role of sensorimotor systems in behavior: are sensorimotor circuits merely implementing the late-stage output of cognitive systems, or engaged rapidly and interactively from early in decision-making? We find that, although early representation of effort cost is associated with sensorimotor regions, these signals are also integrated with cognitive stimulus value representations in the time leading up to motor response. These data suggest that sensorimotor networks interact dynamically with cognitive systems to guide decision-making, providing a first step toward reconciling differing perspectives on sensorimotor roles in valuation and choice.

Combining Multiple Resting-State fMRI Features during Classification: Optimized Frameworks and Their Application to Nicotine Addiction

Machine learning techniques have been applied to resting-state fMRI data to predict neurological or neuropsychiatric disease states. Existing studies have used either a single type of resting-state feature or a few feature types (<4) in the prediction model. However, resting-state data can be processed in many different ways, yielding different feature types containing complementary and/or novel information, leaving uncertain the most informative features to provide to the classifier. In this study, multiple resting-state features were calculated from two main analytical categories: local measures and network measures. Feature selection was adopted using an optimized grid-search approach selecting top ranked features from statistical tests. We then tested three optimized frameworks: feature combination, kernel combination, and classifier combination, all using the support vector machine as an elementary classifier, to combine these resting-state feature types. When applied to nicotine addiction, with a cohort size of 100 smokers and 100 non-smokers, via a 10-fold cross-validation procedure, the feature combination and the classifier combination achieved an accuracy of 75.5%, while the kernel combination achieved a 73.0% accuracy; all three combination frameworks improved classification performance compared to the single feature type based results (best accuracy 70.5%). This study not only reveals the discriminative power of resting-state data, but also demonstrates the efficiency of combining multiple features from one data phenotype to improve classification performance.

Exploring connectivity with large-scale Granger causality on resting-state functional MRI

BACKGROUND

Large-scale Granger causality (lsGC) is a recently developed, resting-state functional MRI (fMRI) connectivity analysis approach that estimates multivariate voxel-resolution connectivity. Unlike most commonly used multivariate approaches, which establish coarse-resolution connectivity by aggregating voxel time-series avoiding an underdetermined problem, lsGC estimates voxel-resolution, fine-grained connectivity by incorporating an embedded dimension reduction.

NEW METHOD

We investigate application of lsGC on realistic fMRI simulations, modeling smoothing of neuronal activity by the hemodynamic response function and repetition time (TR), and empirical resting-state fMRI data. Subsequently, functional subnetworks are extracted from lsGC connectivity measures for both datasets and validated quantitatively. We also provide guidelines to select lsGC free parameters.

RESULTS

Results indicate that lsGC reliably recovers underlying network structure with area under receiver operator characteristic curve (AUC) of 0.93 at TR=1.5s for a 10-min session of fMRI simulations. Furthermore, subnetworks of closely interacting modules are recovered from the aforementioned lsGC networks. Results on empirical resting-state fMRI data demonstrate recovery of visual and motor cortex in close agreement with spatial maps obtained from (i) visuo-motor fMRI stimulation task-sequence (Accuracy=0.76) and (ii) independent component analysis (ICA) of resting-state fMRI (Accuracy=0.86).

COMPARISON WITH EXISTING METHOD(S)

Compared with conventional Granger causality approach (AUC=0.75), lsGC produces better network recovery on fMRI simulations. Furthermore, it cannot recover functional subnetworks from empirical fMRI data, since quantifying voxel-resolution connectivity is not possible as consequence of encountering an underdetermined problem.

CONCLUSIONS

Functional network recovery from fMRI data suggests that lsGC gives useful insight into connectivity patterns from resting-state fMRI at a multivariate voxel-resolution.

Distinct neural processes support post-success and post-error slowing in the stop signal task

Executive control requires behavioral adaptation to environmental contingencies. In the stop signal task (SST), participants exhibit slower go trial reaction time (RT) following a stop trial, whether or not they successfully interrupt the motor response. In previous fMRI studies, we demonstrated activation of the right-hemispheric ventrolateral prefrontal cortex, in the area of inferior frontal gyrus, pars opercularis (IFGpo) and anterior insula (AI), during post-error slowing (PES). However, in similar analyses we were not able to identify regional activities during post-success slowing (PSS). Here, we revisited this issue in a larger sample of participants (n=100) each performing the SST for 40 min during fMRI. We replicated IFGpo/AI activation to PES (p≤0.05, FWE corrected). Further, PSS engages decreased activation in a number of cortical regions including the left inferior frontal cortex (IFC; p≤0.05, FWE corrected). We employed Granger causality mapping to identify areas that provide inputs each to the right IFGpo/AI and left IFC, and computed single-trial amplitude (STA) of stop trials of these input regions as well as the STA of post-stop trials of the right IFGpo/AI and left IFC. The STAs of the right inferior precentral sulcus and supplementary motor area (SMA) and right IFGpo/AI were positively correlated and the STAs of the left SMA and left IFC were positively correlated (slope>0, p's≤0.01, one-sample t test), linking regional responses during stop success and error trials to those during PSS and PES. These findings suggest distinct neural mechanisms to support PSS and PES.

Single-trial effective brain connectivity patterns enhance discriminability of mental imagery tasks

OBJECTIVE

The majority of the current approaches of connectivity based brain-computer interface (BCI) systems focus on distinguishing between different motor imagery (MI) tasks. Brain regions associated with MI are anatomically close to each other, hence these BCI systems suffer from low performances. Our objective is to introduce single-trial connectivity feature based BCI system for cognition imagery (CI) based tasks wherein the associated brain regions are located relatively far away as compared to those for MI.

APPROACH

We implemented time-domain partial Granger causality (PGC) for the estimation of the connectivity features in a BCI setting. The proposed hypothesis has been verified with two publically available datasets involving MI and CI tasks.

MAIN RESULTS

The results support the conclusion that connectivity based features can provide a better performance than a classical signal processing framework based on bandpass features coupled with spatial filtering for CI tasks, including word generation, subtraction, and spatial navigation. These results show for the first time that connectivity features can provide a reliable performance for imagery-based BCI system.

SIGNIFICANCE

We show that single-trial connectivity features for mixed imagery tasks (i.e. combination of CI and MI) can outperform the features obtained by current state-of-the-art method and hence can be successfully applied for BCI applications.