Complexity synchronization in emergent intelligence.

In this work, we use a simple multi-agent-based-model (MABM) of a social network, implementing selfish algorithm (SA) agents, to create an adaptive environment and show, using a modified diffusion entropy analysis (DEA), that the mutual-adaptive interaction between the parts of such a network manifests complexity synchronization (CS). CS has been shown to exist by processing simultaneously measured time series from among organ-networks (ONs) of the brain (neurophysiology), lungs (respiration), and heart (cardiovascular reactivity) and to be explained theoretically as a synchronization of the multifractal dimension (MFD) scaling parameters characterizing each time series. Herein, we find the same kind of CS in the emergent intelligence of groups formed in a self-organized social interaction without macroscopic control but with biased self-interest between two groups of agents playing an anti-coordination game.

A topological deep learning framework for neural spike decoding.

The brain's spatial orientation system uses different neuron ensembles to aid in environment-based navigation. Two of the ways brains encode spatial information are through head direction cells and grid cells. Brains use head direction cells to determine orientation, whereas grid cells consist of layers of decked neurons that overlay to provide environment-based navigation.

Complexity Synchronization of Organ Networks.

The transdisciplinary nature of science as a whole became evident as the necessity for the complex nature of phenomena to explain social and life science, along with the physical sciences, blossomed into complexity theory and most recently into complexitysynchronization. This science motif is based on the scaling arising from the 1/f-variability in complex dynamic networks and the need for a network of networks to exchange information internally during intra-network dynamics and externally during inter-network dynamics. The measure of complexity adopted herein is the multifractal dimension of the crucial event time series generated by an organ network, and the difference in the multifractal dimensions of two organ networks quantifies the relative complexity between interacting complex networks.

Complexity synchronization: a measure of interaction between the brain, heart and lungs.

Herein we address the measurable consequences of the network effect (NE) on time series generated by different parts of the brain, heart, and lung organ-networks (ONs), which are directly related to their inter-network and intra-network interactions. Moreover, these same physiologic ONs have been shown to generate crucial event (CE) time series, and herein are shown, using modified diffusion entropy analysis (MDEA) to have scaling indices with quasiperiodic changes in complexity, as measured by scaling indices, over time. Such time series are generated by different parts of the brain, heart, and lung ONs, and the results do not depend on the underlying coherence properties of the associated time series but demonstrate a generalized synchronization of complexity.

Temporal complexity measure of reaction time series: Operational versus event time.

Introduction: Detrended fluctuation analysis (DFA) is a well-established method to evaluate scaling indices of time series, which categorize the dynamics of complex systems. In the literature, DFA has been used to study the fluctuations of reaction time Y(n) time series, where n is the trial number.

Methods: Herein we propose treating each reaction time as a duration time that changes the representation from operational (trial number) time n to event (temporal) time t, or X(t).

Significance of event related causality (ERC) in eloquent neural networks.

Neural activity emerges and propagates swiftly between brain areas. Investigation of these transient large-scale flows requires sophisticated statistical models. We present a method for assessing the statistical confidence of event-related neural propagation.

Assessing the Impact of I Conductance on Cross-Frequency Coupling in Model Pyramidal Neurons.

Large cortical and hippocampal pyramidal neurons are elements of neuronal circuitry that have been implicated in cross-frequency coupling (CFC) during cognitive tasks. We investigate potential mechanisms for CFC within these neurons by examining the role that the hyperpolarization-activated mixed cation current (I) plays in modulating CFC characteristics in multicompartment neuronal models. We quantify CFC along the soma-apical dendrite axis and tuft of three models configured to have different spatial distributions of I conductance density: (1) exponential gradient along the soma-apical dendrite axis, (2) uniform distribution, and (3) no I conductance.

Enabling Large-Scale Simulations With the GENESIS Neuronal Simulator.

In this paper, we evaluate the computational performance of the GEneral NEural SImulation System (GENESIS) for large scale simulations of neural networks. While many benchmark studies have been performed for large scale simulations with leaky integrate-and-fire neurons or neuronal models with only a few compartments, this work focuses on higher fidelity neuronal models represented by 50-74 compartments per neuron. After making some modifications to the source code for GENESIS and its parallel implementation, PGENESIS, particularly to improve memory usage, we find that PGENESIS is able to efficiently scale on supercomputing resources to network sizes as large as 9 × 10 neurons with 18 × 10 synapses and 2.

Complex Modulation Method for Measuring Cross-Frequency Coupling of Neural Oscillations.

There is growing evidence from human intracranial electrocorticography (ECoG) studies that interactions between cortical frequencies are important for sensory perception, cognition and inter-regional neuronal communication. Recent studies have focused mainly on the strength of phase-amplitude coupling in cross-frequency interactions. Here, we introduce a complex modulation method based on measures of coherence to investigate cross-frequency coupling in the neural time series.

Resonance Analysis as a Tool for Characterizing Functional Division of Layer 5 Pyramidal Neurons.

Evidence suggests that layer 5 pyramidal neurons can be divided into functional zones with unique afferent connectivity and membrane characteristics that allow for post-synaptic integration of feedforward and feedback inputs. To assess the existence of these zones and their interaction, we characterized the resonance properties of a biophysically-realistic compartmental model of a neocortical layer 5 pyramidal neuron. Consistent with recently published theoretical and empirical findings, our model was configured to have a "hot zone" in distal apical dendrite and apical tuft where both high- and low-threshold Ca ionic conductances had densities 1-2 orders of magnitude higher than anywhere else in the apical dendrite.

Overlapping brain network and alpha power changes suggest visuospatial attention effects on driving performance.

When humans perform prolonged, continuous tasks, their performance fluctuates. The etiology of these fluctuations is multifactorial, but they are influenced by changes in attention reflected in underlying neural dynamics. Previous work with electroencephalography has suggested that prestimulus alpha power is a neural signature of attention allocation with higher power portending relatively poorer performance.

Impact of Neuronal Membrane Damage on the Local Field Potential in a Large-Scale Simulation of Cerebral Cortex.

Within multiscale brain dynamics, the structure-function relationship between cellular changes at a lower scale and coordinated oscillations at a higher scale is not well understood. This relationship may be particularly relevant for understanding functional impairments after a mild traumatic brain injury (mTBI) when current neuroimaging methods do not reveal morphological changes to the brain common in moderate to severe TBI such as diffuse axonal injury or gray matter lesions. Here, we created a physiology-based model of cerebral cortex using a publicly released modeling framework (GEneral NEural SImulation System) to explore the possibility that performance deficits characteristic of blast-induced mTBI may reflect dysfunctional, local network activity influenced by microscale neuronal damage at the cellular level.

A novel method linking neural connectivity to behavioral fluctuations: Behavior-regressed connectivity.

Background: During an experimental session, behavioral performance fluctuates, yet most neuroimaging analyses of functional connectivity derive a single connectivity pattern. These conventional connectivity approaches assume that since the underlying behavior of the task remains constant, the connectivity pattern is also constant.

New Method: We introduce a novel method, behavior-regressed connectivity (BRC), to directly examine behavioral fluctuations within an experimental session and capture their relationship to changes in functional connectivity.

BLASST: Band Limited Atomic Sampling With Spectral Tuning With Applications to Utility Line Noise Filtering.

Objective: In this paper, we present and test a new method for the identification and removal of nonstationary utility line noise from biomedical signals.

Methods: The method, band limited atomic sampling with spectral tuning (BLASST), is an iterative approach that is designed to 1) fit nonstationarities in line noise by searching for best-fit Gabor atoms at predetermined time points, 2) self-modulate its fit by leveraging information from frequencies surrounding the target frequency, and 3) terminate based on a convergence criterion obtained from the same surrounding frequencies. To evaluate the performance of the proposed algorithm, we generate several simulated and real instances of nonstationary line noise and test BLASST along with alternative filtering approaches.

A study of the dynamics of seizure propagation across micro domains in the vicinity of the seizure onset zone.

Objective: The use of micro-electrode arrays to measure electrical activity from the surface of the brain is increasingly being investigated as a means to improve seizure onset zone (SOZ) localization. In this work, we used a multivariate autoregressive model to determine the evolution of seizure dynamics in the [Formula: see text] Hz high frequency band across micro-domains sampled by such micro-electrode arrays. We showed that a directed transfer function (DTF) can be used to estimate the flow of seizure activity in a set of simulated micro-electrode data with known propagation pattern.

Redefining the role of Broca's area in speech.

For over a century neuroscientists have debated the dynamics by which human cortical language networks allow words to be spoken. Although it is widely accepted that Broca's area in the left inferior frontal gyrus plays an important role in this process, it was not possible, until recently, to detail the timing of its recruitment relative to other language areas, nor how it interacts with these areas during word production. Using direct cortical surface recordings in neurosurgical patients, we studied the evolution of activity in cortical neuronal populations, as well as the Granger causal interactions between them.

Afferent inputs to cortical fast-spiking interneurons organize pyramidal cell network oscillations at high-gamma frequencies (60-200 Hz).

High-gamma activity, ranging in frequency between ∼60 Hz and 200 Hz, has been observed in local field potential, electrocorticography, EEG and magnetoencephalography signals during cortical activation, in a variety of functional brain systems. The origin of these signals is yet unknown. Using computational modeling, we show that a cortical network model receiving thalamic input generates high-gamma responses comparable to those observed in local field potential recorded in monkey somatosensory cortex during vibrotactile stimulation.

Electrocorticographic functional mapping identifies human cortex critical for auditory and visual naming.

More comprehensive, and efficient, mapping strategies are needed to avoid post-operative language impairments in patients undergoing epilepsy surgery. Conservative resection of dominant anterior frontal or temporal cortex frequently results in post-operative naming deficits despite standard pre-operative electrocortical stimulation mapping of visual object (picture) naming. Naming to auditory description may better simulate word retrieval in human conversation but is not typically tested, in part due to the time demands of electrocortical stimulation mapping.

Reliability of early cortical auditory gamma-band responses.

Objective: To evaluate the test-retest reliability of event-related power changes in the 30-150 Hz gamma frequency range occurring in the first 150 ms after presentation of an auditory stimulus.

Methods: Repeat intracranial electrocorticographic (ECoG) recordings were performed with 12 epilepsy patients, at ≥1-day intervals, using a passive odd-ball paradigm with steady-state tones. Time-frequency matching pursuit analysis was used to quantify changes in gamma-band power relative to pre-stimulus baseline.

Epileptic seizures from abnormal networks: why some seizures defy predictability.

Seizure prediction has proven to be difficult in clinically realistic environments. Is it possible that fluctuations in cortical firing could influence the onset of seizures in an ictal zone? To test this, we have now used neural network simulations in a computational model of cortex having a total of 65,536 neurons with intercellular wiring patterned after histological data. A spatially distributed Poisson driven background input representing the activity of neighboring cortex affected 1% of the neurons.

Improving early seizure detection.

Over the last decade, the search for a method able to reliably predict seizures hours in advance has been largely replaced by the more realistic goal of very early detection of seizure onset, which would allow therapeutic or warning devices to be triggered prior to the onset of disabling clinical symptoms. We explore in this article the steps along the pathway from data acquisition to closed-loop applications that can and should be considered to design the most efficient early seizure detection. Microelectrodes, high-frequency oscillations, high sampling rate, high-density arrays, and modern analysis techniques are all elements of the recording and detection process that in combination with modeling studies can provide new insights into the dynamics of seizure onsets.

Attention to painful cutaneous laser stimuli evokes directed functional interactions between human sensory and modulatory pain-related cortical areas.

The human 'pain network' includes cortical areas that are activated during the response to painful stimuli (termed category 1) or during psychological processes that modulate pain, for example, distraction (termed category 2). These categories include parts of the parasylvian (PS), medial frontal (MF), and paracentral cortex (S1&M1). Here we test the hypothesis that causal interactions both within and between category 1 and category 2 modules occur during attention to a painful stimulus.

Connectivity between perisylvian and bilateral basal temporal cortices.

Language processing requires the orchestrated action of different neuronal populations, and some studies suggest that the role of the basal temporal (BT) cortex in language processing is bilaterally distributed. Our aim was to demonstrate connectivity between perisylvian cortex and both BT areas. We recorded corticocortical evoked potentials (CCEPs) in 8 patients with subdural electrodes implanted for surgical evaluation of intractable epilepsy.

Dynamics of large-scale cortical interactions at high gamma frequencies during word production: event related causality (ERC) analysis of human electrocorticography (ECoG).

Intracranial EEG studies in humans have shown that functional brain activation in a variety of functional-anatomic domains of human cortex is associated with an increase in power at a broad range of high gamma (>60Hz) frequencies. Although these electrophysiological responses are highly specific for the location and timing of cortical processing and in animal recordings are highly correlated with increased population firing rates, there has been little direct empirical evidence for causal interactions between different recording sites at high gamma frequencies. Such causal interactions are hypothesized to occur during cognitive tasks that activate multiple brain regions.

Language mapping in multilingual patients: electrocorticography and cortical stimulation during naming.

Multilingual patients pose a unique challenge when planning epilepsy surgery near language cortex because the cortical representations of each language may be distinct. These distinctions may not be evident with routine electrocortical stimulation mapping (ESM). Electrocorticography (ECoG) has recently been used to detect task-related spectral perturbations associated with functional brain activation.