Spatiotemporal dynamics of human high gamma discriminate naturalistic behavioral states

In analyzing the neural correlates of naturalistic and unstructured behaviors, features of neural activity that are ignored in a trial-based experimental paradigm can be more fully studied and investigated. Here, we analyze neural activity from two patients using electrocorticography (ECoG) and stereo-electroencephalography (sEEG) recordings, and reveal that multiple neural signal characteristics exist that discriminate between unstructured and naturalistic behavioral states such as “engaging in dialogue” and “using electronics”. Using the high gamma amplitude as an estimate of neuronal firing rate, we demonstrate that behavioral states in a naturalistic setting are discriminable based on long-term mean shifts, variance shifts, and differences in the specific neural activity’s covariance structure. Both the rapid and slow changes in high gamma band activity separate unstructured behavioral states. We also use Gaussian process factor analysis (GPFA) to show the existence of salient spatiotemporal features with variable smoothness in time. Further, we demonstrate that both temporally smooth and stochastic spatiotemporal activity can be used to differentiate unstructured behavioral states. This is the first attempt to elucidate how different neural signal features contain information about behavioral states collected outside the conventional experimental paradigm.     

The Cerebral Cost of Breathing: An fMRI Case-Study in Congenital Central Hypoventilation Syndrome

Certain motor activities - like walking or breathing - present the interesting property of proceeding either automatically or under voluntary control. In the case of breathing, brainstem structures located in the medulla are in charge of the automatic mode, whereas cortico-subcortical brain networks - including various frontal lobe areas - subtend the voluntary mode. We speculated that the involvement of cortical activity during voluntary breathing could impact both on the “resting state” pattern of cortical-subcortical connectivity, and on the recruitment of executive functions mediated by the frontal lobe. In order to test this prediction we explored a patient suffering from central congenital hypoventilation syndrome (CCHS), a very rare developmental condition secondary to brainstem dysfunction. Typically, CCHS patients demonstrate efficient cortically-controlled breathing while awake, but require mechanically-assisted ventilation during sleep to overcome the inability of brainstem structures to mediate automatic breathing. We used simultaneous EEG-fMRI recordings to compare patterns of brain activity between these two types of ventilation during wakefulness. As compared with spontaneous breathing (SB), mechanical ventilation (MV) restored the default mode network (DMN) associated with self-consciousness, mind-wandering, creativity and introspection in healthy subjects. SB on the other hand resulted in a specific increase of functional connectivity between brainstem and frontal lobe. Behaviorally, the patient was more efficient in cognitive tasks requiring executive control during MV than during SB, in agreement with her subjective reports in everyday life. Taken together our results provide insight into the cognitive and neural costs of spontaneous breathing in one CCHS patient, and suggest that MV during waking periods may free up frontal lobe resources, and make them available for cognitive recruitment. More generally, this study reveals how the active maintenance of cortical control over a continuous motor activity impacts on brain functioning and cognition.     

Characterization and Generation of Male Courtship Song in Cotesia congregata (Hymenoptera: Braconidae)

Background

Male parasitic wasps attract females with a courtship song produced by rapid wing fanning. Songs have been described for several parasitic wasp species; however, beyond association with wing fanning, the mechanism of sound generation has not been examined. We characterized the male courtship song of Cotesia congregata (Hymenoptera: Braconidae) and investigated the biomechanics of sound production.

Methods and Principal Findings

Courtship songs were recorded using high-speed videography (2,000 fps) and audio recordings. The song consists of a long duration amplitude-modulated “buzz” followed by a series of pulsatile higher amplitude “boings,” each decaying into a terminal buzz followed by a short inter-boing pause while wings are stationary. Boings have higher amplitude and lower frequency than buzz components. The lower frequency of the boing sound is due to greater wing displacement. The power spectrum is a harmonic series dominated by wing repetition rate ∼220 Hz, but the sound waveform indicates a higher frequency resonance ∼5 kHz. Sound is not generated by the wings contacting each other, the substrate, or the abdomen. The abdomen is elevated during the first several wing cycles of the boing, but its position is unrelated to sound amplitude. Unlike most sounds generated by volume velocity, the boing is generated at the termination of the wing down stroke when displacement is maximal and wing velocity is zero. Calculation indicates a low Reynolds number of ∼1000.

Conclusions and Significance

Acoustic pressure is proportional to velocity for typical sound sources. Our finding that the boing sound was generated at maximal wing displacement coincident with cessation of wing motion indicates that it is caused by acceleration of the wing tips, consistent with a dipole source. The low Reynolds number requires a high wing flap rate for flight and predisposes wings of small insects for sound production.     

Bumblebee Homing: The Fine Structure of Head Turning Movements

Changes in flight direction in flying insects are largely due to roll, yaw and pitch rotations of their body. Head orientation is stabilized for most of the time by counter rotation. Here, we use high-speed video to analyse head- and body-movements of the bumblebee Bombus terrestris while approaching and departing from a food source located between three landmarks in an indoor flight-arena. The flight paths consist of almost straight flight segments that are interspersed with rapid turns. These short and fast yaw turns (“saccades”) are usually accompanied by even faster head yaw turns that change gaze direction. Since a large part of image rotation is thereby reduced to brief instants of time, this behavioural pattern facilitates depth perception from visual motion parallax during the intersaccadic intervals. The detailed analysis of the fine structure of the bees’ head turning movements shows that the time course of single head saccades is very stereotypical. We find a consistent relationship between the duration, peak velocity and amplitude of saccadic head movements, which in its main characteristics resembles the so-called "saccadic main sequence" in humans. The fact that bumblebee head saccades are highly stereotyped as in humans, may hint at a common principle, where fast and precise motor control is used to reliably reduce the time during which the retinal images moves.     

Re-Examination of Globally Flat Space-Time

In the following, we offer a novel approach to modeling the observed effects currently attributed to the theoretical concepts of “dark energy,” “dark matter,” and “dark flow.” Instead of assuming the existence of these theoretical concepts, we take an alternative route and choose to redefine what we consider to be inertial motion as well as what constitutes an inertial frame of reference in flat space-time. We adopt none of the features of our current cosmological models except for the requirement that special and general relativity be local approximations within our revised definition of inertial systems. Implicit in our ideas is the assumption that at “large enough” scales one can treat objects within these inertial systems as point-particles having an insignificant effect on the curvature of space-time. We then proceed under the assumption that time and space are fundamentally intertwined such that time- and spatial-translational invariance are not inherent symmetries of flat space-time (i.e., observable clock rates depend upon both relative velocity and spatial position within these inertial systems) and take the geodesics of this theory in the radial Rindler chart as the proper characterization of inertial motion. With this commitment, we are able to model solely with inertial motion the observed effects expected to be the result of “dark energy,” “dark matter,” and “dark flow.” In addition, we examine the potential observable implications of our theory in a gravitational system located within a confined region of an inertial reference frame, subsequently interpreting the Pioneer anomaly as support for our redefinition of inertial motion. As well, we extend our analysis into quantum mechanics by quantizing for a real scalar field and find a possible explanation for the asymmetry between matter and antimatter within the framework of these redefined inertial systems.     

Long Distance Transport in Macrocystis integrifolia: III. MOVEMENT OF THO

Movement of THO and tritium-labeled photoassimilate was studied in intact fronds and frond cuttings of Macrocystis integrifolia following labeling of a mature blade by tritiated water. Both THO and tritium-labeled assimilate moved from the source blade to sink areas at velocities comparable to those recorded earlier for ¹⁴C- and ³²P-labeled compounds. In intact fronds and frond cuttings, THO and tritium-labeled assimilate showed a declining gradient with increasing distance from the source. In the exudate collected from the basal cut end of the frond, there was a marked increase in radioactivity with time in the photoassimilate, but no such gradient was evident for THO. These results support the idea that, although both tritium-labeled assimilate and THO move in the sieve elements, THO is rapidly exchanged with water in the tissues surrounding the sieve elements. Finally, it is shown that THO is transported to the sink and there “unloaded”; indeed, it can move out of the plant itself. The data on velocity and directionality of transport as well as unloading of THO at the sink are discussed, along with computations on specific mass transfer, and favor the idea that Münch's pressure-flow hypothesis is applicable in Macrocystis for long distance translocation of photoassimilates.     

Treacherous Pavements: Paving Slab Patterns Modify Intended Walking Directions

Current understanding in locomotion research is that, for humans, navigating natural environments relies heavily on visual input; in contrast, walking on even ground in man-made obstacle and hazard-free environments is so highly automated that visual information derived from floor patterns should not affect locomotion and in particular have no impact on the direction of travel. The vision literature on motion perception would suggest otherwise; specifically that oblique floor patterns may induce substantial veering away from the intended direction of travel due to the so-called aperture problem. Here, we tested these contrasting predictions by letting participants walk over commonly encountered floor patterns (paving slabs) and investigating participants’ ability to walk “straight ahead” for different pattern orientations. We show that, depending on pattern orientation, participants veered considerably over the measured travel distance (up to 8% across trials), in line with predictions derived from the literature on motion perception. We argue that these findings are important to the study of locomotion, and, if also observed in real world environments, might have implications for architectural design.     

Inferring Models of Bacterial Dynamics toward Point Sources

Experiments have shown that bacteria can be sensitive to small variations in chemoattractant (CA) concentrations. Motivated by these findings, our focus here is on a regime rarely studied in experiments: bacteria tracking point CA sources (such as food patches or even prey). In tracking point sources, the CA detected by bacteria may show very large spatiotemporal fluctuations which vary with distance from the source. We present a general statistical model to describe how bacteria locate point sources of food on the basis of stochastic event detection, rather than CA gradient information. We show how all model parameters can be directly inferred from single cell tracking data even in the limit of high detection noise. Once parameterized, our model recapitulates bacterial behavior around point sources such as the “volcano effect”. In addition, while the search by bacteria for point sources such as prey may appear random, our model identifies key statistical signatures of a targeted search for a point source given any arbitrary source configuration.     

What Is Stochastic Resonance? Definitions, Misconceptions, Debates, and Its Relevance to Biology

Stochastic resonance is said to be observed when increases in levels of unpredictable fluctuations—e.g., random noise—cause an increase in a metric of the quality of signal transmission or detection performance, rather than a decrease. This counterintuitive effect relies on system nonlinearities and on some parameter ranges being “suboptimal”. Stochastic resonance has been observed, quantified, and described in a plethora of physical and biological systems, including neurons. Being a topic of widespread multidisciplinary interest, the definition of stochastic resonance has evolved significantly over the last decade or so, leading to a number of debates, misunderstandings, and controversies. Perhaps the most important debate is whether the brain has evolved to utilize random noise in vivo, as part of the “neural code”. Surprisingly, this debate has been for the most part ignored by neuroscientists, despite much indirect evidence of a positive role for noise in the brain. We explore some of the reasons for this and argue why it would be more surprising if the brain did not exploit randomness provided by noise—via stochastic resonance or otherwise—than if it did. We also challenge neuroscientists and biologists, both computational and experimental, to embrace a very broad definition of stochastic resonance in terms of signal-processing “noise benefits”, and to devise experiments aimed at verifying that random variability can play a functional role in the brain, nervous system, or other areas of biology.     

Emerging Object Representations in the Visual System Predict Reaction Times for Categorization

Recognizing an object takes just a fraction of a second, less than the blink of an eye. Applying multivariate pattern analysis, or “brain decoding”, methods to magnetoencephalography (MEG) data has allowed researchers to characterize, in high temporal resolution, the emerging representation of object categories that underlie our capacity for rapid recognition. Shortly after stimulus onset, object exemplars cluster by category in a high-dimensional activation space in the brain. In this emerging activation space, the decodability of exemplar category varies over time, reflecting the brain’s transformation of visual inputs into coherent category representations. How do these emerging representations relate to categorization behavior? Recently it has been proposed that the distance of an exemplar representation from a categorical boundary in an activation space is critical for perceptual decision-making, and that reaction times should therefore correlate with distance from the boundary. The predictions of this distance hypothesis have been born out in human inferior temporal cortex (IT), an area of the brain crucial for the representation of object categories. When viewed in the context of a time varying neural signal, the optimal time to “read out” category information is when category representations in the brain are most decodable. Here, we show that the distance from a decision boundary through activation space, as measured using MEG decoding methods, correlates with reaction times for visual categorization during the period of peak decodability. Our results suggest that the brain begins to read out information about exemplar category at the optimal time for use in choice behaviour, and support the hypothesis that the structure of the representation for objects in the visual system is partially constitutive of the decision process in recognition.     

Seeds of Locally Aligned Motion and Stress Coordinate a Collective Cell Migration

We find how collective migration emerges from mechanical information transfer between cells. Local alignment of cell velocity and mechanical stress orientation—a phenomenon dubbed “plithotaxis”—plays a crucial role in inducing coordinated migration. Leader cells at the monolayer edge better align velocity and stress to migrate faster toward the open space. Local seeds of enhanced motion then generate stress on neighboring cells to guide their migration. Stress-induced motion propagates into the monolayer as well as along the monolayer boundary to generate increasingly larger clusters of coordinately migrating cells that move faster with enhanced alignment of velocity and stress. Together, our analysis provides a model of long-range mechanical communication between cells, in which plithotaxis translates local mechanical fluctuations into globally collective migration of entire tissues.     

A Model of Functional Brain Connectivity and Background Noise as a Biomarker for Cognitive Phenotypes: Application to Autism

We present an efficient approach to discriminate between typical and atypical brains from macroscopic neural dynamics recorded as magnetoencephalograms (MEG). Our approach is based on the fact that spontaneous brain activity can be accurately described with stochastic dynamics, as a multivariate Ornstein-Uhlenbeck process (mOUP). By fitting the data to a mOUP we obtain: 1) the functional connectivity matrix, corresponding to the drift operator, and 2) the traces of background stochastic activity (noise) driving the brain. We applied this method to investigate functional connectivity and background noise in juvenile patients (n = 9) with Asperger’s syndrome, a form of autism spectrum disorder (ASD), and compared them to age-matched juvenile control subjects (n = 10). Our analysis reveals significant alterations in both functional brain connectivity and background noise in ASD patients. The dominant connectivity change in ASD relative to control shows enhanced functional excitation from occipital to frontal areas along a parasagittal axis. Background noise in ASD patients is spatially correlated over wide areas, as opposed to control, where areas driven by correlated noise form smaller patches. An analysis of the spatial complexity reveals that it is significantly lower in ASD subjects. Although the detailed physiological mechanisms underlying these alterations cannot be determined from macroscopic brain recordings, we speculate that enhanced occipital-frontal excitation may result from changes in white matter density in ASD, as suggested in previous studies. We also venture that long-range spatial correlations in the background noise may result from less specificity (or more promiscuity) of thalamo-cortical projections. All the calculations involved in our analysis are highly efficient and outperform other algorithms to discriminate typical and atypical brains with a comparable level of accuracy. Altogether our results demonstrate a promising potential of our approach as an efficient biomarker for altered brain dynamics associated with a cognitive phenotype.     

Understanding auditory distance estimation by humpback whales: a computational approach

Ranging, the ability to judge the distance to a sound source, depends on the presence of predictable patterns of attenuation. We measured long-range sound propagation in coastal waters to assess whether humpback whales might use frequency degradation cues to range singing whales. Two types of neural networks, a multi-layer and a single-layer perceptron, were trained to classify recorded sounds by distance traveled based on their frequency content. The multi-layer network successfully classified received sounds, demonstrating that the distorting effects of underwater propagation on frequency content provide sufficient cues to estimate source distance. Normalizing received sounds with respect to ambient noise levels increased the accuracy of distance estimates by single-layer perceptrons, indicating that familiarity with background noise can potentially improve a listening whale's ability to range. To assess whether frequency patterns predictive of source distance were likely to be perceived by whales, recordings were pre-processed using a computational model of the humpback whale's peripheral auditory system. Although signals processed with this model contained less information than the original recordings, neural networks trained with these physiologically based representations estimated source distance more accurately, suggesting that listening whales should be able to range singers using distance-dependent changes in frequency content.     

Establishing a Statistical Link between Network Oscillations and Neural Synchrony

Pairs of active neurons frequently fire action potentials or “spikes” nearly synchronously (i.e., within 5 ms of each other). This spike synchrony may occur by chance, based solely on the neurons’ fluctuating firing patterns, or it may occur too frequently to be explicable by chance alone. When spike synchrony above chances levels is present, it may subserve computation for a specific cognitive process, or it could be an irrelevant byproduct of such computation. Either way, spike synchrony is a feature of neural data that should be explained. A point process regression framework has been developed previously for this purpose, using generalized linear models (GLMs). In this framework, the observed number of synchronous spikes is compared to the number predicted by chance under varying assumptions about the factors that affect each of the individual neuron’s firing-rate functions. An important possible source of spike synchrony is network-wide oscillations, which may provide an essential mechanism of network information flow. To establish the statistical link between spike synchrony and network-wide oscillations, we have integrated oscillatory field potentials into our point process regression framework. We first extended a previously-published model of spike-field association and showed that we could recover phase relationships between oscillatory field potentials and firing rates. We then used this new framework to demonstrate the statistical relationship between oscillatory field potentials and spike synchrony in: 1) simulated neurons, 2) in vitro recordings of hippocampal CA1 pyramidal cells, and 3) in vivo recordings of neocortical V4 neurons. Our results provide a rigorous method for establishing a statistical link between network oscillations and neural synchrony.     

Balancing with Vibration: A Prelude for “Drift and Act” Balance Control

Stick balancing at the fingertip is a powerful paradigm for the study of the control of human balance. Here we show that the mean stick balancing time is increased by about two-fold when a subject stands on a vibrating platform that produces vertical vibrations at the fingertip (0.001 m, 15–50 Hz). High speed motion capture measurements in three dimensions demonstrate that vibration does not shorten the neural latency for stick balancing or change the distribution of the changes in speed made by the fingertip during stick balancing, but does decrease the amplitude of the fluctuations in the relative positions of the fingertip and the tip of the stick in the horizontal plane, A(x,y). The findings are interpreted in terms of a time-delayed “drift and act” control mechanism in which controlling movements are made only when controlled variables exceed a threshold, i.e. the stick survival time measures the time to cross a threshold. The amplitude of the oscillations produced by this mechanism can be decreased by parametric excitation. It is shown that a plot of the logarithm of the vibration-induced increase in stick balancing skill, a measure of the mean first passage time, versus the standard deviation of the A(x,y) fluctuations, a measure of the distance to the threshold, is linear as expected for the times to cross a threshold in a stochastic dynamical system. These observations suggest that the balanced state represents a complex time–dependent state which is situated in a basin of attraction that is of the same order of size. The fact that vibration amplitude can benefit balance control raises the possibility of minimizing risk of falling through appropriate changes in the design of footwear and roughness of the walking surfaces.     

Effects of Virtual Speaker Density and Room Reverberation on Spatiotemporal Thresholds of Audio-Visual Motion Coherence

The present study examined the effects of spatial sound-source density and reverberation on the spatiotemporal window for audio-visual motion coherence. Three different acoustic stimuli were generated in Virtual Auditory Space: two acoustically “dry” stimuli via the measurement of anechoic head-related impulse responses recorded at either 1° or 5° spatial intervals (Experiment 1), and a reverberant stimulus rendered from binaural room impulse responses recorded at 5° intervals in situ in order to capture reverberant acoustics in addition to head-related cues (Experiment 2). A moving visual stimulus with invariant localization cues was generated by sequentially activating LED''s along the same radial path as the virtual auditory motion. Stimuli were presented at 25°/s, 50°/s and 100°/s with a random spatial offset between audition and vision. In a 2AFC task, subjects made a judgment of the leading modality (auditory or visual). No significant differences were observed in the spatial threshold based on the point of subjective equivalence (PSE) or the slope of psychometric functions (β) across all three acoustic conditions. Additionally, both the PSE and β did not significantly differ across velocity, suggesting a fixed spatial window of audio-visual separation. Findings suggest that there was no loss in spatial information accompanying the reduction in spatial cues and reverberation levels tested, and establish a perceptual measure for assessing the veracity of motion generated from discrete locations and in echoic environments.     

The resistance of the septum of the medium giant axon of the earthworm

It is generally thought that nexuses constitute low-resistance pathways between cell interiors in epithelial, neural, muscular, and even connective tissues. However, there are no reliable estimates of the specific resistance of a nexus. The reason for this is that in most cases the surfaces of nexuses between cells are geometrically complex and therefore it has been very hard to accurately estimate nexal areas. However, the septa of the median giant axon have a relatively simple shape. Moreover, in this preparation, it is possible to make a measuring current flow parallel to the axon axis so that from the voltage difference appearing between intracellular electrodes during current flow, the specific septal membrane resistance could be calculated. The average specific nexal resistance obtained was 5.9 ω cm(2) if one assumes that 100 percent of the septum is nexus. The steady state I-V curve for the septum is linear (+/- 10 mV). Placement of electrodes was validated by septa even though the septa were found to be permeable to fluorescein and TEA. Exposure of the axon to hypertonic saline impedes the movement of fluorescein across the septa. By analogy with other tissues it is concluded that hypertonic solutions disrupt nexuses.

A mathematical model was derived which predicts the steady- state transmembrane potential vs. distance from a point source of intracellular current. When the specific nexal membrane resistance is 5.9 ω cm(2), the prediction closely approximates the fall of transmembrane potential vs. distance in an ordinary infinite cable. This is commensurate with the electrophysiological behavior of this multicellular “axon.”

     

Cochlear Responses to Condensation and Rarefaction Clicks

Auditory nerve responses to condensation and rarefaction clicks (CC and RC) have been recorded over a wide intensity range with gross electrodes. At low intensities the RC responses are nearly identical to CC responses. At high intensities RC and CC response waveforms are similar, but the latency of the N₁ peak in the RC response is 0.2 msec. shorter than that for the corresponding CC response. At intermediate intensities the RC and CC response waveforms are quite different. These results can be interpreted in terms of a model in which there are two excitatory mechanisms for the neural response, which are operative in different intensity ranges. The cochlear microphonic potential and a “slow” potential are suggested as possible excitatory mechanisms.     

Motion-based prediction explains the role of tracking in motion extrapolation

During normal viewing, the continuous stream of visual input is regularly interrupted, for instance by blinks of the eye. Despite these frequents blanks (that is the transient absence of a raw sensory source), the visual system is most often able to maintain a continuous representation of motion. For instance, it maintains the movement of the eye such as to stabilize the image of an object. This ability suggests the existence of a generic neural mechanism of motion extrapolation to deal with fragmented inputs. In this paper, we have modeled how the visual system may extrapolate the trajectory of an object during a blank using motion-based prediction. This implies that using a prior on the coherency of motion, the system may integrate previous motion information even in the absence of a stimulus. In order to compare with experimental results, we simulated tracking velocity responses. We found that the response of the motion integration process to a blanked trajectory pauses at the onset of the blank, but that it quickly recovers the information on the trajectory after reappearance. This is compatible with behavioral and neural observations on motion extrapolation. To understand these mechanisms, we have recorded the response of the model to a noisy stimulus. Crucially, we found that motion-based prediction acted at the global level as a gain control mechanism and that we could switch from a smooth regime to a binary tracking behavior where the dot is tracked or lost. Our results imply that a local prior implementing motion-based prediction is sufficient to explain a large range of neural and behavioral results at a more global level. We show that the tracking behavior deteriorates for sensory noise levels higher than a certain value, where motion coherency and predictability fail to hold longer. In particular, we found that motion-based prediction leads to the emergence of a tracking behavior only when enough information from the trajectory has been accumulated. Then, during tracking, trajectory estimation is robust to blanks even in the presence of relatively high levels of noise. Moreover, we found that tracking is necessary for motion extrapolation, this calls for further experimental work exploring the role of noise in motion extrapolation.