BSMART: a Matlab/C toolbox for analysis of multichannel neural time series

White matter microstructural integrity as a key to effective propagation of gamma entrainment in humans

Gamma entrainment through sensory stimulation has the potential to reduce the pathology of Alzheimer's disease in mouse models. However, clinical trials in Alzheimer's disease (AD) patients have yielded inconsistent results, necessitating further investigation. This single-center pre-post intervention study aims to explore the influence of white matter microstructural integrity on gamma rhythm propagation from the visual cortex to AD-affected regions in 31 cognitively normal volunteers aged ≥ 65. Gamma rhythm propagation induced by optimal FLS was measured. Diffusion tensor imaging was employed to assess the integrity of white matter tracts of interest. After excluding 5 participants with a deficit in steady-state visually evoked potentials, 26 participants were included in the final analysis. In the linear regression analyses, gamma entrainment was identified as a significant predictor of gamma propagation (p < 0.001). Furthermore, the study identified white matter microstructural integrity as a significant predictor of gamma propagation by flickering light stimulation (p < 0.05), which was specific to tracts that connect occipital and temporal or frontal regions. These findings indicate that, despite robust entrainment of gamma rhythms in the visual cortex, their propagation to other regions may be impaired if the microstructural integrity of the white matter tracts connecting the visual cortex to other areas is compromised. Consequently, our findings have expanded our understanding of the prerequisites for effective gamma entrainment and suggest that future clinical trials utilizing visual stimulation for gamma entrainment should consider white matter tract microstructural integrity for candidate selection and outcome analysis.

Theta Oscillations Support Prefrontal-hippocampal Interactions in Sequential Working Memory

The prefrontal cortex and hippocampus may support sequential working memory beyond episodic memory and spatial navigation. This stereoelectroencephalography (SEEG) study investigated how the dorsolateral prefrontal cortex (DLPFC) interacts with the hippocampus in the online processing of sequential information. Twenty patients with epilepsy (eight women, age 27.6 ± 8.2 years) completed a line ordering task with SEEG recordings over the DLPFC and the hippocampus. Participants showed longer thinking times and more recall errors when asked to arrange random lines clockwise (random trials) than to maintain ordered lines (ordered trials) before recalling the orientation of a particular line. First, the ordering-related increase in thinking time and recall error was associated with a transient theta power increase in the hippocampus and a sustained theta power increase in the DLPFC (3-10 Hz). In particular, the hippocampal theta power increase correlated with the memory precision of line orientation. Second, theta phase coherences between the DLPFC and hippocampus were enhanced for ordering, especially for more precisely memorized lines. Third, the theta band DLPFC → hippocampus influence was selectively enhanced for ordering, especially for more precisely memorized lines. This study suggests that theta oscillations may support DLPFC-hippocampal interactions in the online processing of sequential information.

Dynamic Changes in Local Activity and Network Interactions among the Anterior Cingulate, Amygdala, and Cerebellum during Associative Learning

Communication between the cerebellum and forebrain structures is necessary for motor learning and has been implicated in a variety of cognitive functions. The exact nature of cerebellar-forebrain interactions supporting behavior and cognition is not known. We examined how local and network activity support learning by simultaneously recording neural activity in the cerebellum, amygdala, and anterior cingulate cortex while male and female rats were trained in trace eyeblink conditioning. Initially, the cerebellum and forebrain signal the contingency between external stimuli through increases in theta power and synchrony. Neuronal activity driving expression of the learned response was observed in the cerebellum and became evident in the anterior cingulate and amygdala as learning progressed. Aligning neural activity to the training stimuli or learned response provided a way to differentiate between learning-related activity driven by different mechanisms. Stimulus and response-related increases in theta power and coherence were observed across all three areas throughout learning. However, increases in slow gamma power and coherence were only observed when oscillations were aligned to the cerebellum-driven learned response. Percentage of learned responses, learning-related local activity, and slow gamma communication from cerebellum to forebrain all progressively increased during training. The relatively fast frequency of slow gamma provides an ideal mechanism for the cerebellum to communicate learned temporal information to the forebrain. This cerebellar response-aligned slow gamma then provides enrichment of behavior-specific temporal information to local neuronal activity in the forebrain. These dynamic network interactions likely support a wide range of behaviors and cognitive tasks that require coordination between the forebrain and cerebellum.SIGNIFICANCE STATEMENT This study presents new evidence for how dynamic learning-related changes in single neurons and neural oscillations in a cerebellar-forebrain network support associative motor learning. The current results provide an integrated mechanism for how bidirectional communication between the cerebellum and forebrain represents important external events and internal neural drive. This bidirectional communication between the cerebellum and forebrain likely supports a wide range of behaviors and cognitive tasks that require temporal precision.

Granger Geweke Causality reveals information exchange during physical interaction is modulated by task difficulty

The haptic sense is an important mode of communication during physical interactions, and it is known to enable humans to estimate key features of their partner's behavior. It is proposed that such estimations are based upon the exchange of information mediated by the interaction forces, resulting in role distribution and coordination between partners. In the present study, we examined whether the information exchange is functionally modified to adapt to the task, or whether it is a fixed process, leaving the adaptation to individual's behaviors. We analyzed the forces during an empirical dyadic interaction task using Granger-Geweke causality analysis, which allowed us to quantify the causal influence of each individual's forces on their partner's. The dynamics of relative phase were also examined. We observed an increase of inter-partner influence with an increase in the spatial accuracy required by the task, demonstrating an adaptation of information flow to the task. This increase of exchange with the spatial accuracy constraint was accompanied by an increase of errors and of the variability of the relative phase between forces. The influence was dominated by participants in a specific role, showing a clear role division as well as task division between the dyad partners. Moreover, the influence occurred in the [2.15-7] Hz frequency band, demonstrating its importance as a frequency band of interest during cooperation involving haptic interaction. Several interpretations are introduced, ranging from sub-division of motion control to phase-amplitude coupling.

5-HT2AR and NMDAR psychedelics induce similar hyper-synchronous states in the rat cognitive-limbic cortex-basal ganglia system

The profound changes in perception and cognition induced by psychedelic drugs are thought to act on several levels, including increased glutamatergic activity, altered functional connectivity and an aberrant increase in high-frequency oscillations. To bridge these different levels of observation, we have here performed large-scale multi-structure recordings in freely behaving rats treated with 5-HT2AR psychedelics (LSD, DOI) and NMDAR psychedelics (ketamine, PCP). While interneurons and principal cells showed disparate firing rate modulations for the two classes of psychedelics, the local field potentials revealed a shared pattern of synchronized high-frequency oscillations in the ventral striatum and several cortical areas. Remarkably, the phase differences between structures were close to zero, corresponding to <1 ms delays. Likely, this hypersynchrony has major effects on the integration of information across neuronal systems and we propose that it is a key contributor to changes in perception and cognition during psychedelic drug use. Potentially, similar mechanisms could induce hallucinations and delusions in psychotic disorders and would constitute promising targets for new antipsychotic treatments.

Aversive memory formation in humans involves an amygdala-hippocampus phase code

Memory for aversive events is central to survival but can become maladaptive in psychiatric disorders. Memory enhancement for emotional events is thought to depend on amygdala modulation of hippocampal activity. However, the neural dynamics of amygdala-hippocampal communication during emotional memory encoding remain unknown. Using simultaneous intracranial recordings from both structures in human patients, here we show that successful emotional memory encoding depends on the amygdala theta phase to which hippocampal gamma activity and neuronal firing couple. The phase difference between subsequently remembered vs. not-remembered emotional stimuli translates to a time period that enables lagged coherence between amygdala and downstream hippocampal gamma. These results reveal a mechanism whereby amygdala theta phase coordinates transient amygdala -hippocampal gamma coherence to facilitate aversive memory encoding. Pacing of lagged gamma coherence via amygdala theta phase may represent a general mechanism through which the amygdala relays emotional content to distant brain regions to modulate other aspects of cognition, such as attention and decision-making.

Interacting humans use forces in specific frequencies to exchange information by touch

Object-mediated joint action is believed to be enabled by implicit information exchange between interacting individuals using subtle haptic signals within their interaction forces. The characteristics of these haptic signals have, however, remained unclear. Here we analyzed the interaction forces during an empirical dyadic interaction task using Granger–Geweke causality analysis, which allowed us to quantify the causal influence of each individual’s forces on their partner’s. We observed that the inter-partner influence was not the same at every frequency. Specifically, in the frequency band of [2.15–7] Hz, we observed inter-partner differences of causal influence that were invariant of the movement frequencies in the task and present only when information exchange was indispensable for task performance. Moreover, the inter-partner difference in this frequency band was observed to be correlated with the task performance by the dyad. Our results suggest that forces in the [2.15–7] Hz band constitute task related information exchange between individuals during physical interactions.

Optimal flickering light stimulation for entraining gamma rhythms in older adults

With aging, optimal parameters of flickering light stimulation (FLS) for gamma entrainment may change in the eyes and brain. We investigated the optimal FLS parameters for gamma entrainment in 35 cognitively normal old adults by comparing event-related synchronization (ERS) and spectral Granger causality (sGC) of entrained gamma rhythms between different luminance intensities, colors, and flickering frequencies of FLSs. ERS entrained by 700 cd/m² FLS and 32 Hz or 34 Hz FLSs was stronger than that entrained by 400 cd/m² at Pz (p < 0.01) and 38 Hz or 40 Hz FLSs, respectively, at both Pz (p < 0.05) and Fz (p < 0.01). Parieto-occipital-to-frontotemporal connectivities of gamma rhythm entrained by 700 cd/m² FLS and 32 Hz or 34 Hz FLSs were also stronger than those entrained by 400 cd/m² at Pz (p < 0.01) and 38 Hz or 40 Hz FLSs, respectively (p < 0.001). ERS and parieto-occipital-to-frontotemporal connectivities of entrained gamma rhythms did not show significant difference between white and red lights. Adverse effects were comparable between different parameters. In older adults, 700 cd/m² FLS at 32 Hz or 34 Hz can entrain a strong gamma rhythm in the whole brain with tolerable adverse effects.

MEA-ToolBox: an Open Source Toolbox for Standardized Analysis of Multi-Electrode Array Data

Functional assessment of in vitro neuronal networks-of relevance for disease modelling and drug testing-can be performed using multi-electrode array (MEA) technology. However, the handling and processing of the large amount of data typically generated in MEA experiments remains a huge hurdle for researchers. Various software packages have been developed to tackle this issue, but to date, most are either not accessible through the links provided by the authors or only tackle parts of the analysis. Here, we present ''MEA-ToolBox'', a free open-source general MEA analytical toolbox that uses a variety of literature-based algorithms to process the data, detect spikes from raw recordings, and extract information at both the single-channel and array-wide network level. MEA-ToolBox extracts information about spike trains, burst-related analysis and connectivity metrics without the need of manual intervention. MEA-ToolBox is tailored for comparing different sets of measurements and will analyze data from multiple recorded files placed in the same folder sequentially, thus considerably streamlining the analysis pipeline. MEA-ToolBox is available with a graphic user interface (GUI) thus eliminating the need for any coding expertise while offering functionality to inspect, explore and post-process the data. As proof-of-concept, MEA-ToolBox was tested on earlier-published MEA recordings from neuronal networks derived from human induced pluripotent stem cells (hiPSCs) obtained from healthy subjects and patients with neurodevelopmental disorders. Neuronal networks derived from patient's hiPSCs showed a clear phenotype compared to those from healthy subjects, demonstrating that the toolbox could extract useful parameters and assess differences between normal and diseased profiles.

Dorsal visual stream is preferentially engaged during externally guided action selection in Parkinson Disease

OBJECTIVE

In patients with Parkinson Disease (PD), self-imitated or internally cued (IC) actions are thought to be compromised by the disease process, as exemplified by impairments in action initiation. In contrast, externally-cued (EC) actions which are made in response to sensory prompts can restore a remarkable degree of movement capability in PD, particularly alleviating freezing-of-gait. This study investigates the electrophysiological underpinnings of movement facilitation in PD through visuospatial cuing, with particular attention to the dynamics within the posterior parietal cortex (PPC) and lateral premotor cortex (LPMC) axis of the dorsal visual stream.

METHODS

Invasive cortical recordings over the PPC and LPMC were obtained during deep brain stimulation lead implantation surgery. Thirteen PD subjects performed an action selection task, which was constituted by left or right joystick movement with directional visual cuing in the EC condition and internally generated direction selection in the IC condition. Time-resolved neural activities within and between the PPC and LPMC were compared between EC and IC conditions.

RESULTS

Reaction times (RT) were significantly faster in the EC condition relative to the IC condition (paired t-test, p = 0.0015). PPC-LPMC inter-site phase synchrony within the β-band (13-35 Hz) was significantly greater in the EC relative to the IC condition. Greater PPC-LPMC β debiased phase lag index (dwPLI) prior to movement onset was correlated with faster reaction times only in the EC condition. Multivariate granger causality (GC) was greater in the EC condition relative to the IC condition, prior to and during movement.

CONCLUSION

Relative to IC actions, we report relative increase in inter-site phase synchrony and directional PPC to LPMC connectivity in the β-band during preparation and execution of EC actions. Furthermore, increased strength of connectivity is predictive of faster RT, which are pathologically slow in PD patients. Stronger engagement of the PPC-LPMC cortical network by an EC specifically through the channel of β-modulation is implicated in correcting the pathological slowing of action initiation seen in Parkinson's patients.

SIGNIFICANCE

These findings shed light on the electrophysiological mechanisms that underlie motor facilitation in PD patients through visuospatial cuing.

Brain network effects related to physical and virtual surgical training revealed by Granger causality

this study investigates the difference in effective connectivity among novice medical students trained on physical and virtual simulators to perform the Fundamental laparoscopic surgery (FLS) pattern cutting task (PC). We propose using dynamic spectral Granger causality (GC) in the frequency band of [0.01-0.07]Hz to measure the effect of surgical training on effective brain connectivity. To obtain the dynamics relationship between the cortical regions, we propose to use the short-time Fourier transform (STFT) method. FLS pattern cutting is a complex bimanual task requiring fine motor skills and increased brain activity. With this in mind, we have used high resolution functional near-infrared spectroscopy to leverage its high temporal resolution for capturing the change in hemodynamics (HbO2) in 14 healthy subjects. Analysis of variance (ANOVA) found a statistically significant difference in "LPMC granger causes RPMC" (LPMC→ RPMC) in the subject trained on these two simulator in the first 40 sec of the task. We showed that the directed brain connectivity was affected by the type of surgical simulator used for training the medical students.

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.

Optimal flickering light stimulation for entraining gamma waves in the human brain

Although light flickering at 40 Hz reduced Alzheimer’s disease (AD) pathologies in mice by entraining gamma waves, it failed to reduce cerebral amyloid burden in a study on six patients with AD or mild cognitive impairment. We investigated the optimal color, intensity, and frequency of the flickering light stimulus for entraining gamma waves in young adults. We compared the event-related synchronization (ERS) values of entrained gamma waves between four different light colors (white, red, green, and blue) in the first experiment and four different luminance intensities in the second experiment. In both experiments, we compared the ERS values of entrained gamma waves between 10 different flickering frequencies from 32 to 50 Hz. We also examined the severity of six adverse effects in both experiments. We compared the propagation of gamma waves in the visual cortex to other brain regions between different luminance intensities and flickering frequencies. We found that red light entrained gamma waves most effectively, followed by white light. Lights of higher luminance intensities (700 and 400 cd/m²) entrained stronger gamma waves than those of lower luminance intensities (100 and 10 cd/m²). Lights flickering at 34–38 Hz entrained stronger and more widely spread beyond the visual cortex than those flickering at 40–50 Hz. Light of 700 cd/m² resulted in more moderate-to-severe adverse effects than those of other luminance intensities. In humans, 400 cd/m² white light flickering at 34–38 Hz was most optimal for gamma entrainment.

SANTIA: a Matlab-based open-source toolbox for artifact detection and removal from extracellular neuronal signals

Neuronal signals generally represent activation of the neuronal networks and give insights into brain functionalities. They are considered as fingerprints of actions and their processing across different structures of the brain. These recordings generate a large volume of data that are susceptible to noise and artifacts. Therefore, the review of these data to ensure high quality by automatically detecting and removing the artifacts is imperative. Toward this aim, this work proposes a custom-developed automatic artifact removal toolbox named, SANTIA (SigMate Advanced: a Novel Tool for Identification of Artifacts in Neuronal Signals). Developed in Matlab, SANTIA is an open-source toolbox that applies neural network-based machine learning techniques to label and train models to detect artifacts from the invasive neuronal signals known as local field potentials.

No Evidence That Cognitive and Physical Activities Are Related to Changes in EEG Markers of Cognition in Older Adults at Risk of Dementia

An active lifestyle as well as cognitive and physical training (PT) may benefit cognition by increasing cognitive reserve, but the underlying neurobiological mechanisms of this reserve capacity are not well understood. To investigate these mechanisms of cognitive reserve, we focused on electrophysiological correlates of cognitive performance, namely on an event-related measure of auditory memory and on a measure of global coherence. Both measures have shown to be sensitive markers for cognition and might therefore be suitable to investigate potential training- and lifestyle-related changes. Here, we report on the results of an electrophysiological sub-study that correspond to previously published behavioral findings. Altogether, 65 older adults with subjective or objective cognitive impairment and aged 60-88 years were assigned to a 10-week cognitive (n = 19) or a 10-week PT (n = 21) or to a passive control group (n = 25). In addition, self-reported lifestyle was assessed at baseline. We did not find an effect of both training groups on electroencephalography (EEG) measures of auditory memory decay or global coherence (ps ≥ 0.29) and a more active lifestyle was not associated with improved global coherence (p = 0.38). Results suggest that a 10-week unimodal cognitive or PT and an active lifestyle in older adults at risk for dementia are not strongly related to improvements in electrophysiological correlates of cognition.

Conflict processing networks: A directional analysis of stimulus-response compatibilities using MEG

The suppression of distracting information in order to focus on an actual cognitive goal is a key feature of executive functions. The use of brain imaging methods to investigate the underlying neurobiological brain activations that occur during conflict processing have demonstrated a strong involvement of the fronto-parietal attention network (FPAN). Surprisingly, the directional interconnections, their time courses and activations at different frequency bands remain to be elucidated, and thus, this constitutes the focus of this study. The shared information flow between brain areas of the FPAN is provided for frequency bands ranging from the theta to the lower gamma band (4–40 Hz). We employed an adaptation of the Simon task utilizing Magnetoencephalography (MEG). Granger causality was applied to investigate interconnections between the active brain regions, as well as their directionality. Following stimulus onset, the middle frontal precentral cortex and superior parietal cortex were significantly activated during conflict processing in a time window of between 300 to 600ms. Important differences in causality were found across frequency bands between processing of conflicting stimuli in the left as compared to the right visual hemifield. The exchange of information from and to the FPAN was most prominent in the beta band. Moreover, the anterior cingulate cortex and the anterior insula represented key areas for conflict monitoring, either by receiving input from other areas of the FPAN or by generating output themselves. This indicates that the salience network is at least partly involved in processing conflict information. The present study provides detailed insights into the underlying neural mechanisms of the FPAN, especially regarding its temporal characteristics and directional interconnections.

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.

EEG Decoding of Dynamic Facial Expressions of Emotion: Evidence from SSVEP and Causal Cortical Network Dynamics

The neural cognitive mechanism in processing static facial expressions (FEs) has been well documented, whereas the one underlying perceiving dynamic faces remains unclear. In this study, Fourier transformation and time-frequency analysis of Electroencephalography (EEG) data were carried out to detect the brain activation underlying dynamic or static FEs while twenty-one participants were viewing dynamic or static faces flicking at 10 Hz. In particular, steady-state visual evoked potentials (SSVEPs) were quantified through spectral power analysis of EEG recordings. Besides, Granger causality (GC) analysis (GCA) was also performed to capture the causal cortical network dynamics during dynamic or static FEs of emotion. It was discovered that the dynamic (from neural to happy (N2H) or vice versa (H2N)) FEs elicited larger SSVEPs than the static ones. Additionally, GCA demonstrated that the H2N case, in which happy FEs were being gradually changed into neutral ones, exhibited larger GC measure during the late processing stage than that from the early stage. Consequently, enhanced SSVEPs and effective brain connectivity for dynamic FEs illustrated that participants might need consume more attentional resources to process the dynamic faces, particularly for the change from happy to neutral faces. The new neural index might facilitate us to better understand the cognitive processing of dynamic and static FEs.

Characterization of the Stages of Creative Writing With Mobile EEG Using Generalized Partial Directed Coherence

Two stages of the creative writing process were characterized through mobile scalp electroencephalography (EEG) in a 16-week creative writing workshop. Portable dry EEG systems (four channels: TP09, AF07, AF08, TP10) with synchronized head acceleration, video recordings, and journal entries, recorded mobile brain-body activity of Spanish heritage students. Each student's brain-body activity was recorded as they experienced spaces in Houston, Texas (“Preparation” stage), and while they worked on their creative texts (“Generation” stage). We used Generalized Partial Directed Coherence (gPDC) to compare the functional connectivity among both stages. There was a trend of higher gPDC in the Preparation stage from right temporo-parietal (TP10) to left anterior-frontal (AF07) brain scalp areas within 1–50 Hz, not reaching statistical significance. The opposite directionality was found for the Generation stage, with statistical significant differences (p < 0.05) restricted to the delta band (1–4 Hz). There was statistically higher gPDC observed for the inter-hemispheric connections AF07–AF08 in the delta and theta bands (1–8 Hz), and AF08 to TP09 in the alpha and beta (8–30 Hz) bands. The left anterior-frontal (AF07) recordings showed higher power localized to the gamma band (32–50 Hz) for the Generation stage. An ancillary analysis of Sample Entropy did not show significant difference. The information transfer from anterior-frontal to temporal-parietal areas of the scalp may reflect multisensory interpretation during the Preparation stage, while brain signals originating at temporal-parietal toward frontal locations during the Generation stage may reflect the final decision making process to translate the multisensory experience into a creative text.

From shape to meaning: Evidence for multiple fast feedforward hierarchies of concept processing in the human brain

A number of fMRI studies have provided support for the existence of multiple concept representations in areas of the brain such as the anterior temporal lobe (ATL) and inferior parietal lobule (IPL). However, the interaction among different conceptual representations remains unclear. To better understand the dynamics of how the brain extracts meaning from sensory stimuli, we conducted a human high-density electroencephalography (EEG) study in which we first trained participants to associate pseudowords with various animal and tool concepts. After training, multivariate pattern classification of EEG signals in sensor and source space revealed the representation of both animal and tool concepts in the left ATL and tool concepts within the left IPL within 250 ​ms. Finally, we used Granger Causality analyses to show that orthography-selective sensors directly modulated activity in the parietal-tool selective cluster. Together, our results provide evidence for distinct but parallel "perceptual-to-conceptual" feedforward hierarchies in the brain.

Measuring directed functional connectivity using non-parametric directionality analysis: Validation and comparison with non-parametric Granger Causality

Background

‘Non-parametric directionality’ (NPD) is a novel method for estimation of directed functional connectivity (dFC) in neural data. The method has previously been verified in its ability to recover causal interactions in simulated spiking networks in Halliday et al. (2015).

Methods

This work presents a validation of NPD in continuous neural recordings (e.g. local field potentials). Specifically, we use autoregressive models to simulate time delayed correlations between neural signals. We then test for the accurate recovery of networks in the face of several confounds typically encountered in empirical data. We examine the effects of NPD under varying: a) signal-to-noise ratios, b) asymmetries in signal strength, c) instantaneous mixing, d) common drive, e) data length, and f) parallel/convergent signal routing. We also apply NPD to data from a patient who underwent simultaneous magnetoencephalography and deep brain recording.

Results

We demonstrate that NPD can accurately recover directed functional connectivity from simulations with known patterns of connectivity. The performance of the NPD measure is compared with non-parametric estimators of Granger causality (NPG), a well-established methodology for model-free estimation of dFC. A series of simulations investigating synthetically imposed confounds demonstrate that NPD provides estimates of connectivity that are equivalent to NPG, albeit with an increased sensitivity to data length. However, we provide evidence that: i) NPD is less sensitive than NPG to degradation by noise; ii) NPD is more robust to the generation of false positive identification of connectivity resulting from SNR asymmetries; iii) NPD is more robust to corruption via moderate amounts of instantaneous signal mixing.

Conclusions

The results in this paper highlight that to be practically applied to neural data, connectivity metrics should not only be accurate in their recovery of causal networks but also resistant to the confounding effects often encountered in experimental recordings of multimodal data. Taken together, these findings position NPD at the state-of-the-art with respect to the estimation of directed functional connectivity in neuroimaging.

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Non-parametric directionality (NPD) is a novel directed connectivity measure.

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NPD estimates are equivalent to estimates of Granger causality but are more robust to signal confounds.

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Multivariate extensions of NPD can correctly identify signal routing.

Global EEG coherence as a marker for cognition in older adults at risk for dementia

Quantitative electroencephalography (EEG) provides useful information about neurophysiological health of the aging brain. Current studies investigating EEG coherence and power for specific brain areas and frequency bands have yielded inconsistent results. This study assessed EEG coherence and power indices at rest measured over the whole skull and for a wide frequency range as global EEG markers for cognition in a sample at risk for dementia. Since global markers are more reliable and less error-prone than region- and frequency-specific indices they might help to overcome previous inconsistencies. Global EEG coherence (1-30 Hz) and an EEG slowing score were assessed. The EEG slowing score was calculated by low-frequency power (1-8 Hz) divided by high-frequency power (9-30 Hz). In addition, the prognostic value of the two EEG indices for cognition and cognitive decline was assessed in a 5-year follow-up pilot study. Baseline global coherence correlated positively with cognition at baseline, but not with cognitive decline or with cognition at the 5-year follow-up. The EEG slowing ratio showed no significant association, neither with cognition at baseline or follow-up, nor with cognitive decline over a period of 5 years. The results indicate that the resting state global EEG coherence might be a useful and easy to assess electrophysiological correlate for neurocognitive health in older adults at risk for dementia. Because of the small statistical power for the follow-up analyses, the prognostic value of global coherence could not be determined in the present study. Future studies should assess its prognostic value with larger sample sizes.

Brain Connectivity Analysis Under Semantic Vigilance and Enhanced Mental States

In this paper, we present a method to quantify the coupling between brain regions under vigilance and enhanced mental states by utilizing partial directed coherence (PDC) and graph theory analysis (GTA). The vigilance state is induced using a modified version of stroop color-word task (SCWT) while the enhancement state is based on audio stimulation with a pure tone of 250 Hz. The audio stimulation was presented to the right and left ears simultaneously for one-hour while participants perform the SCWT. The quantification of mental states was performed by means of statistical analysis of indexes based on GTA, behavioral responses of time-on-task (TOT), and Brunel Mood Scale (BRMUS). The results show that PDC is very sensitive to vigilance decrement and shows that the brain connectivity network is significantly reduced with increasing TOT, p < 0.05. Meanwhile, during the enhanced state, the connectivity network maintains high connectivity as time passes and shows significant improvements compared to vigilance state. The audio stimulation enhances the connectivity network over the frontal and parietal regions and the right hemisphere. The increase in the connectivity network correlates with individual differences in the magnitude of the vigilance enhancement assessed by response time to stimuli. Our results provide evidence for enhancement of cognitive processing efficiency with audio stimulation. The BRMUS was used to evaluate the emotional states of vigilance task before and after using the audio stimulation. BRMUS factors, such as fatigue, depression, and anger, significantly decrease in the enhancement group compared to vigilance group. On the other hand, happy and calmness factors increased with audio stimulation, p < 0.05.

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.

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.

A Dynamic Interplay within the Frontoparietal Network Underlies Rhythmic Spatial Attention

Classic studies of spatial attention assumed that its neural and behavioral effects were continuous over time. Recent behavioral studies have instead revealed that spatial attention leads to alternating periods of heightened or diminished perceptual sensitivity. Yet, the neural basis of these rhythmic fluctuations has remained largely unknown. We show that a dynamic interplay within the macaque frontoparietal network accounts for the rhythmic properties of spatial attention. Neural oscillations characterize functional interactions between the frontal eye fields (FEF) and the lateral intraparietal area (LIP), with theta phase (3-8 Hz) coordinating two rhythmically alternating states. The first is defined by FEF-dominated beta-band activity, associated with suppressed attentional shifts, and LIP-dominated gamma-band activity, associated with enhanced visual processing and better behavioral performance. The second is defined by LIP-specific alpha-band activity, associated with attenuated visual processing and worse behavioral performance. Our findings reveal how network-level interactions organize environmental sampling into rhythmic cycles.

Comparing Open-Source Toolboxes for Processing and Analysis of Spike and Local Field Potentials Data

Analysis of spike and local field potential (LFP) data is an essential part of neuroscientific research. Today there exist many open-source toolboxes for spike and LFP data analysis implementing various functionality. Here we aim to provide a practical guidance for neuroscientists in the choice of an open-source toolbox best satisfying their needs. We overview major open-source toolboxes for spike and LFP data analysis as well as toolboxes with tools for connectivity analysis, dimensionality reduction and generalized linear modeling. We focus on comparing toolboxes functionality, statistical and visualization tools, documentation and support quality. To give a better insight, we compare and illustrate functionality of the toolboxes on open-access dataset or simulated data and make corresponding MATLAB scripts publicly available.

Highly Flexible Precisely Braided Multielectrode Probes and Combinatorics for Future Neuroprostheses

The braided multielectrode probe (BMEP) is an ultrafine microwire bundle interwoven into a precise tubular braided structure, which is designed to be used as an invasive neural probe consisting of multiple microelectrodes for electrophysiological neural recording and stimulation. Significant advantages of BMEPs include highly flexible mechanical properties leading to decreased immune responses after chronic implantation in neural tissue and dense recording/stimulation sites (24 channels) within the 100-200 μm diameter. In addition, because BMEPs can be manufactured using various materials in any size and shape without length limitations, they could be expanded to applications in deep central nervous system (CNS) regions as well as peripheral nervous system (PNS) in larger animals and humans. Finally, the 3D topology of wires supports combinatoric rearrangements of wires within braids, and potential neural yield increases. With the newly developed next generation micro braiding machine, we can manufacture more precise and complex microbraid structures. In this article, we describe the new machine and methods, and tests of simulated combinatoric separation methods. We propose various promising BMEP designs and the potential modifications to these designs to create probes suitable for various applications for future neuroprostheses.

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.

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.

Preliminary Neurophysiological Evidence of Altered Cortical Activity and Connectivity With Neurologic Music Therapy in Parkinson's Disease

Neurologic Music Therapy (NMT) is a novel impairment-focused behavioral intervention system whose techniques are based on the clinical neuroscience of music perception, cognition, and production. Auditory Stimulation (RAS) is one of the NMT techniques, which aims to develop and maintain a physiological rhythmic motor activity through rhythmic auditory cues. In a series of breakthrough studies beginning in the mid-nineties, we discovered that RAS durably improves gait velocity, stride length, and cadence in Parkinson's disease (PD). No study to date reports the neurophysiological evidence of auditory-motor frequency entrainment after a NMT intervention in the Parkinson's community. We hypothesized that NMT-related motor improvements in PD are due to entrainment-related coupling between auditory and motor activity resulting from an increased functional communication between the auditory and the motor cortices. Spectral analysis in the primary motor and auditory cortices during a cued finger tapping task showed a simultaneous increase in evoked power in the beta-range along with an increased functional connectivity after a course of NMT in a small sample of three older adults with PD. This case study provides preliminary evidence that NMT-based motor rehabilitation may enhance cortical activation in the auditory and motor areas in a synergic manner. With a lack of both control subjects and control conditions, this neuroimaging case-proof of concept series of visible changes suggests potential mechanisms and offers further education on the clinical applications of musical interventions for motor impairments.

The mediodorsal pulvinar coordinates the macaque fronto-parietal network during rhythmic spatial attention

Spatial attention is discontinuous, sampling behaviorally relevant locations in theta-rhythmic cycles (3-6 Hz). Underlying this rhythmic sampling are intrinsic theta oscillations in frontal and parietal cortices that provide a clocking mechanism for two alternating attentional states that are associated with either engagement at the presently attended location (and enhanced perceptual sensitivity) or disengagement (and diminished perceptual sensitivity). It has remained unclear, however, how these theta-dependent states are coordinated across the large-scale network that directs spatial attention. The pulvinar is a candidate for such coordination, having been previously shown to regulate cortical activity. Here, we examined pulvino-cortical interactions during theta-rhythmic sampling by simultaneously recording from macaque frontal eye fields (FEF), lateral intraparietal area (LIP), and pulvinar. Neural activity propagated from pulvinar to cortex during periods of engagement, and from cortex to pulvinar during periods of disengagement. A rhythmic reweighting of pulvino-cortical interactions thus defines functional dissociations in the attention network.

Cortical Network Topology in Prodromal and Mild Dementia Due to Alzheimer's Disease: Graph Theory Applied to Resting State EEG

Graph theory analysis on resting state electroencephalographic rhythms disclosed topological properties of cerebral network. In Alzheimer's disease (AD) patients, this approach showed mixed results. Granger causality matrices were used as input to the graph theory allowing to estimate the strength and the direction of information transfer between electrode pairs. The number of edges (degree), the number of inward edges (in-degree), of outgoing edges (out-degree) were statistically compared among healthy controls, patients with mild cognitive impairment due to AD (AD-MCI) and AD patients with mild dementia (ADD) to evaluate if degree abnormality could involve low and/or high degree vertices, the so called hubs, in both prodromal and over dementia stage. Clustering coefficient and local efficiency were evaluated as measures of network segregation, path length and global efficiency as measures of integration, the assortativity coefficient as a measure of resilience. Degree, in-degree and out-degree values were lower in AD-MCI and ADD than the control group for non-hubs and hubs vertices. The number of edges was preserved for frontal electrodes, where patients' groups showed an additional hub in F3. Clustering coefficient was lower in ADD compared with AD-MCI in the right occipital electrode, and it was positively correlated with mini mental state examination. Local and global efficiency values were lower in patients' than control groups. Our results show that the topology of the network is altered in AD patients also in its prodromal stage, begins with the reduction of the number of edges and the loss of the local and global efficiency.

Open-Source Tools for Processing and Analysis of In Vitro Extracellular Neuronal Signals

The recent years have seen unprecedented growth in the manufacturing of neurotechnological tools. The latest technological advancements presented the neuroscientific community with neuronal probes containing thousands of recording sites. These next-generation probes are capable of simultaneously recording neuronal signals from a large number of channels. Numerically, a simple 128-channel neuronal data acquisition system equipped with a 16 bits A/D converter digitizing the acquired analog waveforms at a sampling frequency of 20 kHz will generate approximately 17 GB uncompressed data per hour. Today's biggest challenge is to mine this staggering amount of data and find useful information which can later be used in decoding brain functions, diagnosing diseases, and devising treatments. To this goal, many automated processing and analysis tools have been developed and reported in the literature. A good amount of them are also available as open source for others to adapt them to individual needs. Focusing on extracellularly recorded neuronal signals in vitro, this chapter provides an overview of the popular open-source tools applicable on these signals for spike trains and local field potentials analysis, and spike sorting. Towards the end, several future research directions have also been outlined.

GraphVar 2.0: A user-friendly toolbox for machine learning on functional connectivity measures

BACKGROUND

We previously presented GraphVar as a user-friendly MATLAB toolbox for comprehensive graph analyses of functional brain connectivity. Here we introduce a comprehensive extension of the toolbox allowing users to seamlessly explore easily customizable decoding models across functional connectivity measures as well as additional features.

NEW METHOD

GraphVar 2.0 provides machine learning (ML) model construction, validation and exploration. Machine learning can be performed across any combination of graph measures and additional variables, allowing for a flexibility in neuroimaging applications.

RESULTS

In addition to previously integrated functionalities, such as network construction and graph-theoretical analyses of brain connectivity with a high-speed general linear model (GLM), users can now perform customizable ML across connectivity matrices, graph measures and additionally imported variables. The new extension also provides parametric and nonparametric testing of classifier and regressor performance, data export, figure generation and high quality export.

COMPARISON WITH EXISTING METHODS

Compared to other existing toolboxes, GraphVar 2.0 offers (1) comprehensive customization, (2) an all-in-one user friendly interface, (3) customizable model design and manual hyperparameter entry, (4) interactive results exploration and data export, (5) automated queue system for modelling multiple outcome variables within the same session, (6) an easy to follow introductory review.

CONCLUSIONS

GraphVar 2.0 allows comprehensive, user-friendly exploration of encoding (GLM) and decoding (ML) modelling approaches on functional connectivity measures making big data neuroscience readily accessible to a broader audience of neuroimaging investigators.

Alpha-band functional connectivity during cued versus implicit modality-specific anticipatory attention: EEG-source coherence analysis

The anticipation of future events based on a background experience is one of the main components of any goal-directed behavior. Anticipatory attention can be either voluntary (explicit) or involuntary (implicit). We presumed that these two types of anticipatory attention differed in terms of cortical functional organization. We examined this assumption with an experimental model consisting of three experimental sessions (cued attention, implicit learning, and baseline) that were equal in terms of stimuli, motor responses, and cognitive task. Participants were asked to discriminate the temporal order of stimuli within a pair presented in either the visual or auditory sensory modality. Prestimulus functional connectivity was assessed via alpha-band coherence computed in the source space for preselected regions of interests. Functional links between the cortices of the frontoparietal control system increased during the cued attention condition and did not increase during the implicit anticipation condition. The buildup of implicit anticipation was accompanied by the strengthening of functional links between the intraparietal, ventral premotor, and presupplementary motor areas. It was discovered that both cued and implicit types of anticipation were underlain by functional modality-specific cortical links.

Effective brain network analysis with resting-state EEG data: a comparison between heroin abstinent and non-addicted subjects

OBJECTIVE

Neuro-electrophysiological tools have been widely used in heroin addiction studies. Previous studies indicated that chronic heroin abuse would result in abnormal functional organization of the brain, while few heroin addiction studies have applied the effective connectivity tool to analyze the brain functional system (BFS) alterations induced by heroin abuse. The present study aims to identify the abnormality of resting-state heroin abstinent BFS using source decomposition and effective connectivity tools.

APPROACH

The resting-state electroencephalograph (EEG) signals were acquired from 15 male heroin abstinent (HA) subjects and 14 male non-addicted (NA) controls. Multivariate autoregressive models combined independent component analysis (MVARICA) was applied for blind source decomposition. Generalized partial directed coherence (GPDC) was applied for effective brain connectivity analysis. Effective brain networks of both HA and NA groups were constructed. The two groups of effective cortical networks were compared by the bootstrap method. Abnormal causal interactions between decomposed source regions were estimated in the 1-45 Hz frequency domain.

MAIN RESULTS

This work suggested: (a) there were clear effective network alterations in heroin abstinent subject groups; (b) the parietal region was a dominant hub of the abnormally weaker causal pathways, and the left occipital region was a dominant hub of the abnormally stronger causal pathways.

SIGNIFICANCE

These findings provide direct evidence that chronic heroin abuse induces brain functional abnormalities. The potential value of combining effective connectivity analysis and brain source decomposition methods in exploring brain alterations of heroin addicts is also implied.

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.

Oscillations Synchronize Amygdala-to-Prefrontal Primate Circuits during Aversive Learning

The contribution of oscillatory synchrony in the primate amygdala-prefrontal pathway to aversive learning remains largely unknown. We found increased power and phase synchrony in the theta range during aversive conditioning. The synchrony was linked to single-unit spiking and exhibited specific directionality between input and output measures in each region. Although it was correlated with the magnitude of conditioned responses, it declined once the association stabilized. The results suggest that amygdala spikes help to synchronize ACC activity and transfer error signal information to support memory formation.

A Comparative Study of the Robustness of Frequency-Domain Connectivity Measures to Finite Data Length

In this work we use numerical simulation to investigate how the temporal length of the data affects the reliability of the estimates of brain connectivity from EEG time-series. We assume that the neural sources follow a stable MultiVariate AutoRegressive model, and consider three connectivity metrics: imaginary part of coherency (IC), generalized partial directed coherence (gPDC) and frequency-domain granger causality (fGC). In order to assess the statistical significance of the estimated values, we use the surrogate data test by generating phase-randomized and autoregressive surrogate data. We first consider the ideal case where we know the source time courses exactly. Here we show how, expectedly, even exact knowledge of the source time courses is not sufficient to provide reliable estimates of the connectivity when the number of samples gets small; however, while gPDC and fGC tend to provide a larger number of false positives, the IC becomes less sensitive to the presence of connectivity. Then we proceed with more realistic simulations, where the source time courses are estimated using eLORETA, and the EEG signal is affected by biological noise of increasing intensity. Using the ideal case as a reference, we show that the impact of biological noise on IC estimates is qualitatively different from the impact on gPDC and fGC.

A Critical Assessment of Directed Connectivity Estimates with Artificially Imposed Causality in the Supramammillary-Septo-Hippocampal Circuit

Algorithms for estimating directed connectivity have become indispensable to further understand the neurodynamics between functionally coupled brain areas. The evaluation of directed connectivity on the propagation of brain activity has largely been based on simulated data or toy models, where various hidden properties of neurophysiological data may not be fully recapitulated. In this study, directionality was unequivocally manipulated in the freely moving rat in a unique dataset, where normal oscillatory interactions between the supramammillary nucleus (SuM) and hippocampus (HPC) were attenuated by temporary medial septal (MS) inactivation, and replaced by electrical stimulation of the fornix to evaluate the performance of several directed connectivity assessment methods. The directed transfer function, partial directed coherence, directed coherence, pair-wise Geweke-Granger causality, phase slope index, and phase transfer entropy, all found SuM to HPC theta propagation when the MS is inactivated, and HPC activity was driven by peaks of simultaneously recorded SuM theta. As expected from theoretical expectations and simulated data, signal features including coupling strength, signal-to-noise ratio, and stationarity all weakly affected directed connectivity measures. We conclude that all the examined directed connectivity estimates correctly identify artificially imposed uni-directionality of brain oscillations in freely moving animals. Non-auto-regressive modeling based methods appear to be the most robust, and are least affected by inherent features in data such as signal-to-noise ratio and stationarity.

BRAPH: A graph theory software for the analysis of brain connectivity

The brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory, where the brain is represented as a set of nodes connected by edges. This representation of the brain as a connectome can be used to assess important measures that reflect its topological architecture. We have developed a freeware MatLab-based software (BRAPH-BRain Analysis using graPH theory) for connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized. To demonstrate the abilities of BRAPH, we performed structural and functional graph theory analyses in two separate studies. In the first study, using MRI data, we assessed the differences in global and nodal network topology in healthy controls, patients with amnestic mild cognitive impairment, and patients with Alzheimer's disease. In the second study, using resting-state fMRI data, we compared healthy controls and Parkinson's patients with mild cognitive impairment.

Theta Oscillations during Active Sleep Synchronize the Developing Rubro-Hippocampal Sensorimotor Network

Neuronal oscillations comprise a fundamental mechanism by which distant neural structures establish and express functional connectivity. Long-range functional connectivity between the hippocampus and other forebrain structures is enabled by theta oscillations. Here, we show for the first time that the infant rat red nucleus (RN)-a brainstem sensorimotor structure-exhibits theta (4-7 Hz) oscillations restricted primarily to periods of active (REM) sleep. At postnatal day 8 (P8), theta is expressed as brief bursts immediately following myoclonic twitches; by P12, theta oscillations are expressed continuously across bouts of active sleep. Simultaneous recordings from the hippocampus and RN at P12 show that theta oscillations in both structures are coherent, co-modulated, and mutually interactive during active sleep. Critically, at P12, inactivation of the medial septum eliminates theta in both structures. The developmental emergence of theta-dependent functional coupling between the hippocampus and RN parallels that between the hippocampus and prefrontal cortex. Accordingly, disruptions in the early expression of theta could underlie the cognitive and sensorimotor deficits associated with neurodevelopmental disorders such as autism and schizophrenia.