Cinematic Operation of the Cerebral Cortex Interpreted via Critical Transitions in Self-Organized Dynamic Systems.

Measurements of local field potentials over the cortical surface and the scalp of animals and human subjects reveal intermittent bursts of beta and gamma oscillations. During the bursts, narrow-band metastable amplitude modulation (AM) patters emerge for a fraction of a second and ultimately dissolve to the broad-band random background activity. The burst process depends on previously learnt conditioned stimuli (CS), thus different AM patterns may emerge in response to different CS. This observation leads to our cinematic theory of cognition when perception happens in discrete steps manifested in the sequence of AM patterns. Our article summarizes findings in the past decades on experimental evidence of cinematic theory of cognition and relevant mathematical models. We treat cortices as dissipative systems that self-organize themselves near a critical level of activity that is a non-equilibrium metastable state. Criticality is arguably a key aspect of brains in their rapid adaptation, reconfiguration, high storage capacity, and sensitive response to external stimuli. Self-organized criticality (SOC) became an important concept to describe neural systems. We argue that transitions from one AM pattern to the other require the concept of phase transitions, extending beyond the dynamics described by SOC. We employ random graph theory (RGT) and percolation dynamics as fundamental mathematical approaches to model fluctuations in the cortical tissue. Our results indicate that perceptions are formed through a phase transition from a disorganized (high entropy) to a well-organized (low entropy) state, which explains the swiftness of the emergence of the perceptual experience in response to learned stimuli.

Breathing as a Fundamental Rhythm of Brain Function.

Ongoing fluctuations of neuronal activity have long been considered intrinsic noise that introduces unavoidable and unwanted variability into neuronal processing, which the brain eliminates by averaging across population activity (Georgopoulos et al., 1986; Lee et al., 1988; Shadlen and Newsome, 1994; Maynard et al., 1999). It is now understood, that the seemingly random fluctuations of cortical activity form highly structured patterns, including oscillations at various frequencies, that modulate evoked neuronal responses (Arieli et al., 1996; Poulet and Petersen, 2008; He, 2013) and affect sensory perception (Linkenkaer-Hansen et al., 2004; Boly et al., 2007; Sadaghiani et al., 2009; Vinnik et al., 2012; Palva et al., 2013). Ongoing cortical activity is driven by proprioceptive and interoceptive inputs. In addition, it is partially intrinsically generated in which case it may be related to mental processes (Fox and Raichle, 2007; Deco et al., 2011). Here we argue that respiration, via multiple sensory pathways, contributes a rhythmic component to the ongoing cortical activity. We suggest that this rhythmic activity modulates the temporal organization of cortical neurodynamics, thereby linking higher cortical functions to the process of breathing.

Mechanism and significance of global coherence in scalp EEG.

What distinguishes animals from robots is the neurodynamics of intention. The mechanism is the action-perception cycle that creates and applies knowledge. Knowledge is the condensed, categorized information brains accumulate over lifetimes of experience. Vertebrate intention emerged in the Ordovician period as a tool to prowl first olfactory environments, then environments of other modalities. Action necessitates remembering space-time trajectories. Hence the sensory, motor, and hippocampal cortices interact intimately. Brains create the contextual richness of relevant knowledge almost instantly by exploiting the capacity of cortical neuropil to transit between a gas-like phase with sparse, random firing and a liquid-liked phase of high-energy, narrow band oscillation synchronized widely. They express remembrances in spatial patterns of amplitude modulation (AM) of beta and gamma waves.

Open loop optogenetic control of simulated cortical epileptiform activity.

We present a model for the use of open loop optogenetic control to inhibit epileptiform activity in a meso scale model of the human cortex. The meso scale cortical model first developed by Liley et al. (2001) is extended to two dimensions and the nature of the seizure waves is studied. We adapt to the meso scale a 4 state functional model of Channelrhodopsin-2 (ChR2) ion channels. The effects of pulsed and constant illumination on the conductance of these ion channels is presented. The inhibitory cell population is targeted for the application of open loop control. Seizure waves are successfully suppressed and the inherent properties of the optogenetic channels ensures charge balance in the cortex, protecting it from damage.

Dissipation of 'dark energy' by cortex in knowledge retrieval.

We have devised a thermodynamic model of cortical neurodynamics expressed at the classical level by neural networks and at the quantum level by dissipative quantum field theory. Our model is based on features in the spatial images of cortical activity newly revealed by high-density electrode arrays. We have incorporated the mechanism and necessity for so-called dark energy in knowledge retrieval. We have extended the model first using the Carnot cycle to define our measures for energy, entropy and temperature, and then using the Rankine cycle to incorporate criticality and phase transitions. We describe the dynamics of two interactive fields of neural activity that express knowledge, one at high and the other at low energy density, and the two operators that create and annihilate the fields. We postulate that the extremely high density of energy sequestered briefly in cortical activity patterns can account for the vividness, richness of associations, and emotional intensity of memories recalled by stimuli.

Spatial patterning of the neonatal EEG suggests a need for a high number of electrodes.

There is an increasing demand for source analysis of neonatal EEG, but currently there is inadequate knowledge about i) the spatial patterning of neonatal scalp EEG and hence ii) the number of electrodes needed to capture neonatal EEG in full spatial detail. This study addresses these issues by using a very high density (2.5mm interelectrode spacing) linear electrode array to assess the spatial power spectrum, by using a high density (64 electrodes) EEG cap to assess the spatial extent of the common oscillatory bouts in the neonatal EEG and by using a neonatal size spherical head model to assess the effects of source depth and skull conductivities on the spatial frequency spectrum. The linear array recordings show that the spatial power spectrum decays rapidly until about 0.5-0.8 cycles per centimeter. The dense array EEG recordings show that the amplitude of oscillatory events decays within 4-6 cm to the level of global background activity, and that the higher frequencies (12-20 Hz) show the most rapid spatial decline in amplitude. Simulation with spherical head model showed that realistic variation in skull conductivity and source depths can both introduce orders of magnitude difference in the spatial frequency of the scalp EEG. Calculation of spatial Nyquist frequencies from the spatial power spectra suggests that an interelectrode distance of about 6-10mm would suffice to capture the full spatial texture of the raw EEG signal at the neonatal scalp without spatial aliasing or under-sampling. The spatial decay of oscillatory events suggests that a full representation of their spatial characteristics requires an interelectrode distance of 10-20mm. The findings show that the conventional way of recording neonatal EEG with about 10 electrodes ignores most spatial EEG content, that increasing the electrode density is necessary to improve neonatal EEG source localization and information extraction, and that prospective source models will need to carefully consider the neonatally relevant ranges of tissue conductivities and source depths when source localizing cortical activity in neonates.

Locating spatial patterns of waveforms during sensory perception in scalp EEG.

The spatio-temporal oscillations in EEG waves are indicative of sensory and cognitive processing. We propose a method to find the spatial amplitude patterns of a time-limited waveform across multiple EEG channels. It consists of a single iteration of multichannel matching pursuit where the base waveform is obtained via the Hilbert transform of a time-limited tone. The vector of extracted amplitudes across channels is used for classification, and we analyze the effect of deviation in temporal alignment of the waveform on classification performance. Results for a previously published dataset of 6 subjects show comparable results versus a more complicated criteria-based method.

Behavioral states may be associated with distinct spatial patterns in electrocorticogram.

To determine if behavioral states are associated with unique spatial electrocorticographic (ECoG) patterns, we obtained recordings with a microgrid electrode array applied to the cortical surface of a human subject. The array was constructed with the intent of extracting maximal spatial information by optimizing interelectrode distances. A 34-year-old patient with intractable epilepsy underwent intracranial ECoG monitoring after standard methods failed to reveal localization of seizures. During the 8-day period of invasive recording, in addition to standard clinical electrodes a square 1 × 1 cm microgrid array with 64 electrodes (1.25 mm separation) was placed on the right inferior temporal gyrus. Careful review of video recordings identified four extended naturalistic behaviors: reading, conversing on the telephone, looking at photographs, and face-to-face interactions. ECoG activity recorded with the microgrid that corresponded to these behaviors was collected and ECoG spatial patterns were analyzed. During periods of ECoG selected for analysis, no electrographic seizures or epileptiform patterns were present. Moments of maximal spatial variance are shown to cluster by behavior. Comparisons between conditions using a permutation test reveal significantly different spatial patterns for each behavior. We conclude that ECoG recordings obtained on the cortical surface with optimal high spatial frequency resolution reveal distinct local spatial patterns that reflect different behavioral states, and we predict that similar patterns will be found in many if not most cortical areas on which a microgrid is placed.

Understanding perception through neural "codes".

A major challenge for cognitive scientists is to deduce and explain the neural mechanisms of the rapid transposition between stimulus energy and recalled memory-between the specific (sensation) and the generic (perception)-in both material and mental aspects. Researchers are attempting three explanations in terms of neural codes. The microscopic code: cellular neurobiologists correlate stimulus properties with the rates and frequencies of trains of action potentials induced by stimuli and carried by topologically organized axons. The mesoscopic code: cognitive scientists formulate symbolic codes in trains of action potentials from feature-detector neurons of phonemes, lines, odorants, vibrations, faces, etc., that object-detector neurons bind into representations of stimuli. The macroscopic code: neurodynamicists extract neural correlates of stimuli and associated behaviors in spatial patterns of oscillatory fields of dendritic activity, which self-organize and evolve on trajectories through high-dimensional brain state space. This multivariate code is expressed in landscapes of chaotic attractors. Unlike other scientific codes, such as DNA and the periodic table, these neural codes have no alphabet or syntax. They are epistemological metaphors that experimentalists need to measure neural activity and engineers need to model brain functions. My aim is to describe the main properties of the macroscopic code and the grand challenge it poses: how do very large patterns of textured synchronized oscillations form in cortex so quickly?

The emergence of mind and emotion in the evolution of neocortex.

The most deeply transformative concept for the growth of 21st Century psychiatry is the constellation of the chaotic dynamics of the brain. Brains are no longer seen as rational systems that are plagued with emotional disorders reflecting primitives inherited from our animal ancestors. Brains are dynamical systems that continually create patterns by acting intentionally into the environment and shaping themselves in accord with the sensory consequences of their intended actions. Emotions are now seen not as reversions to animal behaviors but as the sources of force and energy that brains require for the actions they take to understand the world and themselves. Humans are unique in experiencing consciousness of their own actions, which they experience as conscience: guilt, shame, pride and joy. Chaotic brain dynamics strives always for unity and harmony, but as a necessary condition for adaptation to a changing world, it repeatedly lapses into disorder. The successes are seen in the normal unity of consciousness; the failures are seen in the disorders that we rightly label the schizophrenias and the less severe character disorders. The foundation for healthy unity is revealed by studies in the evolution of brains, in particular the way in which neocortex of mammals emerged from the primitive allocortex of reptiles. The amazing facts of brain dynamics are now falling into several places. The power-law connectivity of cortex supports the scale-free dynamics of the global workspace in brains ranging from mouse to whale. That dynamics in humans holds the secrets of speech and symbol utilization. By recursive interactions in vast areas of human neocortex the scale-free connectivity supports our unified consciousness. Here in this dynamics are to be sought the keys to understanding and treating the disorders that uniquely plague the human mind.

Controversies in epilepsy: debates held during the Fourth International Workshop on Seizure Prediction.

Debates on six controversial topics were held during the Fourth International Workshop on Seizure Prediction (IWSP4) convened in Kansas City, KS, USA, July 4-7, 2009. The topics were (1) Ictogenesis: Focus versus Network? (2) Spikes and Seizures: Step-relatives or Siblings? (3) Ictogenesis: A Result of Hyposynchrony? (4) Can Focal Seizures Be Caused by Excessive Inhibition? (5) Do High-Frequency Oscillations Provide Relevant Independent Information? (6) Phase Synchronization: Is It Worthwhile as Measured? This article, written by the IWSP4 organizing committee and the debaters, summarizes the arguments presented during the debates.

A method to study global spatial patterns related to sensory perception in scalp EEG.

Prior studies of multichannel ECoG from animals showed that beta and gamma oscillations carried perceptual information in both local and global spatial patterns of amplitude modulation, when the subjects were trained to discriminate conditioned stimuli (CS). Here the hypothesis was tested that similar patterns could be found in the scalp EEG human subjects trained to discriminate simultaneous visual-auditory CS. Signals were continuously recorded from 64 equispaced scalp electrodes and band-pass filtered. The Hilbert transform gave the analytic phase, which segmented the EEG into temporal frames, and the analytic amplitude, which expressed the pattern in each frame as a feature vector. Methods applied to the ECoG were adapted to the EEG for systematic search of the beta-gamma spectrum, the time period after CS onset, and the scalp surface to locate patterns that could be classified with respect to type of CS. Spatial patterns of EEG amplitude modulation were found from all subjects that could be classified with respect to stimulus combination type significantly above chance levels. The patterns were found in the beta range (15-22 Hz) but not in the gamma range. They occurred in three short bursts following CS onset. They were non-local, occupying the entire array. Our results suggest that the scalp EEG can yield information about the timing of episodically synchronized brain activity in higher cognitive function, so that future studies in brain-computer interfacing can be better focused. Our methods may be most valuable for analyzing data from dense arrays with very high spatial and temporal sampling rates.

Vortices in brain activity: their mechanism and significance for perception.

Brains interface with the world through perception. The process extracts information from microscopic sensory inputs and incorporates it into the mesoscopic memory store for retrieval in recognition. The process requires creation of spatiotemporal patterns of neural activity. The construction is done through phase transitions in cortical populations that condense the background activity through spontaneous symmetry breaking. Large-scale interactions create fields of synaptically driven activity that is observed by measuring brain waves (electrocorticogram, ECoG) and evaluated by constructing a mesoscopic vectorial order parameter as follows. The negative feedback among excitatory and inhibitory neurons creates spatially and spectrally distributed gamma oscillations (20-80 Hz) in the background activity. Band pass filtering reveals beats in ECoG log analytic power. In some beats that recur at theta rates (3-7 Hz), the order parameter transiently approaches zero, giving a null spike in which the microscopic activity is uniformly disordered (symmetric). A phase transition that is manifested in an analytic phase discontinuity breaks the symmetry. As the null spike terminates, the resurgent order parameter imposes mesoscopic order seen in spatial patterns of ECoG amplitude modulation (AM) that actualize and update the memory of a stimulus. Read-out is through a divergent/convergent projection that performs on cortical output an irreversible spatiotemporal integral transformation. The ECoG reveals a conic phase gradient that accompanies an AM pattern. The phase cone manifests a vortex, which is initiated by the null spike, and which is inferred to help stabilize and prolong its accompanying AM pattern that might otherwise be rapidly degraded by the turbulent neural noise from which it emerges.

Similarities between simulated spatial spectra of scalp EEG, MEG and structural MRI.

Electrical dipoles oriented perpendicular to the cortical surface are the primary source of the scalp EEGs and MEGs. Thus, in particular, gyri and sulci structures on the cortical surface have a definite possibility to influence the EEGs and MEGs. This was examined by comparing the spatial power spectral density (PSD) of the upper portion of the human cortex in MRI slices to that of simulated scalp EEGs and MEGs. The electrical activity was modeled with 2,650 dipolar sources oriented normal to the local cortical surface. The resulting scalp potentials were calculated with a finite element model of the head constructed from 51 segmented sagittal MR images. The PSD was computed after taking the fast Fourier transform of scalp potentials. The PSD of the cortical contour in each slice was also computed. The PSD was then averaged over all the slices. This was done for sagittal and coronal view both. The PSD of EEG and MEG showed two broad peaks, one from 0.05 to 0.22 cycles/cm (wavelength 20-4.545 cm) and the other from 0.22 to 1.2 cycles/cm (wavelength 4.545-0.834 cm). The PSD of the cortex showed a broad peak from 0.08 to 0.32 cycles/cm (wavelength 12.5-3.125 cm) and other two peaks within the range of 0.32 to 0.9 cycles/cm (wavelength 3.125-1.11 cm). These peaks are definitely due to the gyri structures and associated larger patterns on the cortical surface. Smaller peaks in the range of 1-3 cycles/cm were also observed which are possibly due to sulci structures. These results suggest that the spatial information was present in the EEG and MEG at the spatial frequencies of gyri. This also implies that the practical Nyquist frequency for sampling scalp EEGs should be 3.0 cycles/cm and an optimal interelectrode spacing of about 3 mm is needed for extraction of cortical patterns from scalp EEGs in humans.

The KIV model of intentional dynamics and decision making.

Human cognition performs granulation of the seemingly homogeneous temporal sequences of perceptual experiences into meaningful and comprehensible chunks of fuzzy concepts and behaviors. These knowledge granules are stored and consequently accessed during action selection and decisions. A dynamical approach is presented here to interpret experimental findings using K (Katchalsky) models. In the K model, meaningful knowledge is repetitiously created and processed in the form of sequences of oscillatory patterns of neural activity distributed across space and time. These patterns are not rigid but flexible and intermittent; soon after they arise through phase transitions, they dissipate. Computational implementations demonstrate the operation of the model based on the principles of intentional brain dynamics.

EEG synchrony during a perceptual-cognitive task: widespread phase synchrony at all frequencies.

OBJECTIVES: (1) To examine the validity of comparing the phase of broad-band signals. (2) To measure phase synchrony over the whole head, at a variety of frequencies. METHODS: The concept of broad band phase is investigated (a) by visual comparison of the time series of two channels of filtered data with the time series of the spatial analytic phase difference (SAPD) between the two channels and (b) using artificial sinusoids. Phase synchrony is then measured in 64-channel EEG recorded while human subjects performed a perceptual-cognitive task, by calculation of analytic phase differences between each channel and a frontal synchrony reference channel. The number of channels in synchrony with the reference channel at a series of frequency passbands is compared for data acquired using a common recording reference, the same data re-referenced to an average reference and artificial noise. RESULTS: Analytic phase is shown to represent the resultant of the phasor angles of all the narrow band signals incorporated in a composite waveform. Episodic global phase synchrony is identified in background EEG, in all passbands from theta to epsilon. Many of the episodes of widespread synchrony occur in both common-referenced and average-referenced data, but some common-reference episodes are not seen in average-referenced data. In both forms of data, synchrony is about equally widespread in all subjects at lower passbands, but more widespread in some subjects than others at higher passbands. CONCLUSIONS: (1) It is valid to measure the analytic phase of broad band EEG signals. (2) Non-local phase synchrony is intermittently present in all frequency bands from theta to epsilon, not only during and after external stimuli, but also in background EEG. (3) In some subjects synchrony is more widespread in gamma and epsilon bands than in beta, alpha or delta bands, but in others the reverse is true. (4) Some of the episodes of synchrony seen in common referenced data may be artifacts of a sudden decrease in power at the recording electrodes in comparison with the common reference electrode. However, most of the episodes of synchrony in common-referenced data cannot be explained in this fashion. (5) Episodes of widespread synchrony are not established instantaneously. During the establishment of most episodes of '40 Hz' synchrony, the number of channels in synchrony peaks after about 100 ms. SIGNIFICANCE: If long-range phase synchrony really is a hallmark of consciousness, it should be present most of the time the subject is conscious. Our results confirm this prediction, and suggest that consciousness may involve not only gamma frequencies, but the whole range from theta to epsilon. The mechanism of synchrony establishment at the scalp as shown by the present method is relatively slow and thus more likely to involve chemical synapses than gap junctions, electric fields or quantum non-locality.

Deep analysis of perception through dynamic structures that emerge in cortical activity from self-regulated noise.

The statistical properties of the spontaneous background electrocorticogram (ECoG) were modeled, starting with random numbers, constraining the distributions, and identifying characteristic deviations from randomness in ECoG from subjects at rest and during intentional behaviors. The ECoG had been recorded through 8 x 8 arrays of 64 electrodes, from the surfaces of auditory, visual, or somatic cortices of 9 rabbits, and from the inferotemporal cortex of a human subject. Power spectral densities (PSD) in coordinates of log(10) power versus log(10) frequency of ECoG from subjects at rest usually conformed to noise in power-law distributions in a continuum. PSD of ECoG from active subjects usually deviated from noise in having peaks in log(10) power above the power-law line in various frequency bands. The analytic signals from the Hilbert transform after band pass filtering in the beta and gamma ranges revealed beats from interference among distributed frequencies in band pass filtered noise called Rayleigh noise. The beats were displayed as repetitive down spikes in log(10) analytic power. Repetition rates were proportional to filter bandwidths for all center frequencies. Resting ECoG often gave histograms of the magnitudes and intervals of down spikes that conformed to noise. Histograms from active ECoG often deviated from noise in Rayleigh distributions of down spike intervals by giving what are called Rice (Mathematical analysis of random noise-and appendixes-technical publications monograph B-1589. Bell Telephone Labs Inc., New York, 1950) distributions. Adding power to noise as signals at single frequencies simulated those deviations. The beats in dynamic theory are deemed essential for perception, by gating beta and gamma bursts at theta rates through enhancement of the cortical signal-to-noise ratio in exceptionally deep down spikes called null spikes.

Combining fMRI with EEG and MEG in order to relate patterns of brain activity to cognition.

The common factor that underlies several types of functional brain imaging is the electric current of masses of dendrites. The prodigious demands for the energy that is required to drive the dendritic currents are met by hemodynamic and metabolic responses that are visualized with fMRI and PET techniques. The high current densities in parallel dendritic shafts and the broad distributions of the loop currents outside the dendrites generate both the scalp EEG and the magnetic fields seen in the MEG. The measurements of image intensities and potential fields provide state variables for modeling. The relationships between the intensities of current density and the electric, magnetic, and hemodynamic state variables are complex and far from proportionate. The state variables are complementary, because the information they convey comes from differing albeit overlapping neural populations, so that efforts to cross-validate localization of neural activity relating to specified cognitive behaviors have not always been successful. We propose an alternative way to use the three methods in combination through studies of hemisphere-wide, high-resolution spatiotemporal patterns of neural activity recorded non-invasively and analyzed with multivariate statistics. Success in this proposed endeavor requires specification of what patterns to look for. At the present level of understanding, an appropriate pattern is any significant departure from random noise in the spectral, temporal and spatial domains that can be scaled into the coarse-graining of time by fMRI/BOLD and the coarse-graining of space by EEG and MEG. Here the requisite patterns are predicted to be large-scale spatial amplitude modulation (AM) of synchronized neuronal signals in the beta and gamma ranges that are coordinated but not correlated with fMRI intensities.

Power spectral density changes and language lateralization during covert object naming tasks measured with high-density EEG recordings.

Our objective was to study changes in EEG time-domain power spectral density (PSDt) and localization of language areas during covert object naming tasks in human subjects with epilepsy. EEG data for subjects with epilepsy were acquired during the covert object naming tasks using a net of 256 electrodes. The trials required each subject to provide the names of common objects presented every 4 seconds on slides. Each trial comprised the 1.0 second before and 3.0 seconds after initial object presentation. PSDt values at baseline and during tasks were calculated in the theta, alpha, beta, low gamma, and high gamma bands. The spatial contour plots reveal that PSDt values during object naming were 10-20% higher than the baseline values for different bands. Language was lateralized to left frontal or temporal areas. In all cases, the Wada test disclosed language lateralization to the left hemisphere as well.