Temporal Patterns Of Neural Activity Research Articles

Entropy, complexity, and maturity in children's neural responses to naturalistic video lessons

Temporal characteristics of neural signals are often overlooked in traditional fMRI developmental studies but are critical to studying brain functions in ecologically valid settings. In the present study, we explore the temporal properties of children's neural responses during naturalistic mathematics and grammar tasks. To do so, we introduce a novel measure in developmental fMRI: neural entropy, which indicates temporal complexity of BOLD signals. We show that temporal patterns of neural activity have lower complexity and greater variability in children than in adults in the association cortex but not in the sensory-motor cortex. We also show that neural entropy is associated with both child-adult similarity in functional connectivity and neural synchrony, and that neural entropy increases with the size of functionally connected networks in the association cortex. In addition, neural entropy increases with functional maturity (i.e., child-adult neural synchrony) in content-specific regions. These exploratory findings suggest the hypothesis that neural entropy indexes the increasing breadth and diversity of neural processes available to children for analyzing mathematical information over development.

Recording from Fly Olfactory Sensilla.

Olfactory systems detect and discriminate an enormous diversity of volatile environmental stimuli and provide important paradigms to investigate how sensory cues are represented in the brain. Key stimulus-coding events occur in peripheral olfactory sensory neurons, which typically express a single olfactory receptor-from a large repertoire encoded in the genome-with a defined ligand-response profile. These receptors convert odor ligand recognition into spatial and temporal patterns of neural activity that are transmitted to, and interpreted in, central brain regions. Drosophila provides an attractive model to study olfactory coding because it possesses a relatively simple peripheral olfactory system that displays many organizational parallels to those of vertebrates. Moreover, nearly all olfactory sensory neurons have been molecularly characterized and are accessible for physiological analysis, as they are exposed on the surface of sensory organs (antennae and maxillary palps) housed in specialized hairs called sensilla. This protocol describes how to perform recordings of odor-evoked activity from Drosophila olfactory sensilla, covering the basics of sample preparation, setting up the electrophysiology rig, assembling an odor stimulus-delivery device, and data analysis. The methodology can be used to characterize the ligand-recognition properties of most olfactory sensory neurons and the role of olfactory receptors (and other molecular components) in signal transduction.

Chemosensory Coding in Drosophila Single Sensilla.

The chemical senses-smell and taste-detect and discriminate an enormous diversity of environmental stimuli and provide fascinating but challenging models to investigate how sensory cues are represented in the brain. Important stimulus-coding events occur in peripheral sensory neurons, which express specific combinations of chemosensory receptors with defined ligand-response profiles. These receptors convert ligand recognition into spatial and temporal patterns of neural activity that are transmitted to, and interpreted in, central brain regions. Drosophila melanogaster provides an attractive model to study chemosensory coding because it possesses relatively simple peripheral olfactory and gustatory systems that display many organizational parallels to those of vertebrates. Moreover, nearly all peripheral chemosensory neurons have been molecularly characterized and are accessible for physiological analysis, as they are exposed on the surface of sensory organs housed in specialized hairs called sensilla. Here, we briefly review anatomical, molecular, and physiological properties of adult Drosophila olfactory and gustatory systems and provide background to methods for electrophysiological recordings of ligand-evoked activity from different types of chemosensory sensilla.

Optogenetic and chemogenetic control of an enzymatic reporter of neural activity

The brain's control of our thoughts, feelings, and behaviors stems from neural circuits, which perform logical operations based on the temporal patterns of neural activity and the connectivity of the neurons as the circuit traverses the brain. Recent studies have produced strategies for visualizing and controlling a circuit's neural activity. In some cases, specialized microscopy systems have enabled imaging of entire brain volumes on the timescale of neural activity and during behavior, enabling the reconstruction of neural circuitry at cellular resolution. However, recording neural activity from whole brain volumes to reconstruct neural circuitry is a significant challenge when working with larger brains, less specialized microscopes, or stimuli and behaviors that are not conducive to concurrent imaging. In these situations, strategies that enable permanent recording of neural activity during defined time windows for later readout would be highly beneficial. Here, we describe an approach for permanent recording of neural activity using calcium-dependent enzymatic activation of genetically encoded substrates. Our efforts focus on an engineered Tobacco Etch Virus (TEV) protease, which does not have endogenous substrates in vertebrate systems. We describe our strategy for engineering a calcium-dependent TEV protease that involves a split version of the enzyme with each half attached to the calcium-binding partners calmodulin and M13. These TEV fragments are designed so that, in the absence of neural activity, they will remain separate and inactive, but in the presence of neural activity and the resulting high calcium concentrations, the association of calmodulin and M13 will permit TEV reconstitution and enzyme activity. Once active, the split TEV protease can cleave a variety of genetically encoded substrates that link TEV protease activity with transcription of a desired gene or direct creation of a fluorophore. Finally, we will discuss controlling the split TEV protease and substrate association using opto- and chemogenetics approaches to provide precise spatial and temporal control over the time period of neural recording. This strategy will enable visualization and control of neurons activated by a sensory stimulus or behavior, permitting detailed analysis of neural circuitry over distant brain regions.

Topographic gradients of intrinsic dynamics across neocortex.

The intrinsic dynamics of neuronal populations are shaped by both microscale attributes and macroscale connectome architecture. Here we comprehensively characterize the rich temporal patterns of neural activity throughout the human brain. Applying massive temporal feature extraction to regional haemodynamic activity, we systematically estimate over 6000 statistical properties of individual brain regions' time-series across the neocortex. We identify two robust spatial gradients of intrinsic dynamics, one spanning a ventromedial-dorsolateral axis and dominated by measures of signal autocorrelation, and the other spanning a unimodal-transmodal axis and dominated by measures of dynamic range. These gradients reflect spatial patterns of gene expression, intracortical myelin and cortical thickness, as well as structural and functional network embedding. Importantly, these gradients are correlated with patterns of meta-analytic functional activation, differentiating cognitive versus affective processing and sensory versus higher-order cognitive processing. Altogether, these findings demonstrate a link between microscale and macroscale architecture, intrinsic dynamics, and cognition.

Taking the sublexical route: brain dynamics of reading in the semantic variant of primary progressive aphasia.

Reading aloud requires mapping an orthographic form to a phonological one. The mapping process relies on sublexical statistical regularities (e.g. 'oo' to |uː|) or on learned lexical associations between a specific visual form and a series of sounds (e.g. yacht to/jɑt/). Computational, neuroimaging, and neuropsychological evidence suggest that sublexical, phonological and lexico-semantic processes rely on partially distinct neural substrates: a dorsal (occipito-parietal) and a ventral (occipito-temporal) route, respectively. Here, we investigated the spatiotemporal features of orthography-to-phonology mapping, capitalizing on the time resolution of magnetoencephalography and the unique clinical model offered by patients with semantic variant of primary progressive aphasia (svPPA). Behaviourally, patients with svPPA manifest marked lexico-semantic impairments including difficulties in reading words with exceptional orthographic to phonological correspondence (irregular words). Moreover, they present with focal neurodegeneration in the anterior temporal lobe, affecting primarily the ventral, occipito-temporal, lexical route. Therefore, this clinical population allows for testing of specific hypotheses on the neural implementation of the dual-route model for reading, such as whether damage to one route can be compensated by over-reliance on the other. To this end, we reconstructed and analysed time-resolved whole-brain activity in 12 svPPA patients and 12 healthy age-matched control subjects while reading irregular words (e.g. yacht) and pseudowords (e.g. pook). Consistent with previous findings that the dorsal route is involved in sublexical, phonological processes, in control participants we observed enhanced neural activity over dorsal occipito-parietal cortices for pseudowords, when compared to irregular words. This activation was manifested in the beta-band (12-30 Hz), ramping up slowly over 500 ms after stimulus onset and peaking at ∼800 ms, around response selection and production. Consistent with our prediction, svPPA patients did not exhibit this temporal pattern of neural activity observed in controls this contrast. Furthermore, a direct comparison of neural activity between patients and controls revealed a dorsal spatiotemporal cluster during irregular word reading. These findings suggest that the sublexical/phonological route is involved in processing both irregular and pseudowords in svPPA. Together these results provide further evidence supporting a dual-route model for reading aloud mediated by the interplay between lexico-semantic and sublexical/phonological neurocognitive systems. When the ventral route is damaged, as in the case of neurodegeneration affecting the anterior temporal lobe, partial compensation appears to be possible by over-recruitment of the slower, serial attention-dependent, dorsal one.

Dysregulation of temporal dynamics of synchronous neural activity in adolescents on autism spectrum.

Autism spectrum disorder is increasingly understood to be based on atypical signal transfer among multiple interconnected networks in the brain. Relative temporal patterns of neural activity have been shown to underlie both the altered neurophysiology and the altered behaviors in a variety of neurogenic disorders. We assessed brain network dynamics variability in autism spectrum disorders (ASD) using measures of synchronization (phase-locking) strength, and timing of synchronization and desynchronization of neural activity (desynchronization ratio) across frequency bands of resting-state electroencephalography (EEG). Our analysis indicated that frontoparietal synchronization is higher in ASD but with more short periods of desynchronization. It also indicates that the relationship between the properties of neural synchronization and behavior is different in ASD and typically developing populations. Recent theoretical studies suggest that neural networks with a high desynchronization ratio have increased sensitivity to inputs. Our results point to the potential significance of this phenomenon to the autistic brain. This sensitivity may disrupt the production of an appropriate neural and behavioral responses to external stimuli. Cognitive processes dependent on the integration of activity from multiple networks maybe, as a result, particularly vulnerable to disruption. Autism Res 2020, 13: 24-31. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Parts of the brain can work together by synchronizing the activity of the neurons. We recorded the electrical activity of the brain in adolescents with autism spectrum disorder and then compared the recording to that of their peers without the diagnosis. We found that in participants with autism, there were a lot of very short time periods of non-synchronized activity between frontal and parietal parts of the brain. Mathematical models show that the brain system with this kind of activity is very sensitive to external events.

Average firing rate rather than temporal pattern determines metabolic cost of activity in thalamocortical relay neurons

Thalamocortical (TC) relay cells exhibit different temporal patterns of activity, including tonic mode and burst mode, to transmit sensory information to the cortex. Our aim was to quantify the metabolic cost of different temporal patterns of neural activity across a range of average firing rates. We used a biophysically-realistic model of a TC relay neuron to simulate tonic and burst patterns of firing. We calculated the metabolic cost by converting the calculated ion fluxes into the demand for ATP to maintain homeostasis of intracellular ion concentrations. Most energy was expended on reversing Na+ entry during action potentials and pumping Ca2+ out of the cell. Average firing rate determined the ATP cost across firing patterns by controlling the overall number of spikes. Varying intraburst frequency or spike number in each burst influenced the metabolic cost by altering the interactions of inward and outward currents on multiple timescales, but temporal pattern contributed substantially less to the metabolic demand of neural activity as compared to average firing rate. These predictions should be considered when interpreting findings of functional imaging studies that rely of estimates of neuronal metabolic demand, e.g., functional magnetic resonance imaging.

Coriolis and centrifugal forces drive haltere deformations and influence spike timing.

The halteres of flies are mechanosensory organs that serve a crucial role in the control of agile flight, providing sensory input for rapid course corrections to perturbations. Derived from hind wings, halteres are actively flapped and are thus subject to a variety of inertial forces as the fly undergoes complex flight trajectories. Previous analyses of halteres modelled them as a point mass, showing that Coriolis forces lead to subtle deflections orthogonal to the plane of flapping. By design, these models could not consider the effects of force gradients associated with a mass distribution, nor could they reveal three-dimensional spatio-temporal patterns of strain that result from those forces. In addition, diversity in the geometry of halteres, such as shape and asymmetries, could not be simply modelled with a point mass on a massless rod. To study the effects of mass distributions and asymmetries, we examine the haltere subject to both flapping and body rotations using three-dimensional finite-element simulations. We focus on a set of simplified geometries, in which we vary the stalk and bulb shape. We find that haltere mass distribution gives rise to two unreported deformation modes: (i) halteres twist with a magnitude that strongly depends on stalk and bulb geometry and (ii) halteres with an asymmetric mass distribution experience out-of-plane bending due to centrifugal forces, independent of body rotation. Since local strains at the base of the haltere drive deformations of mechanosensory neurons, we combined measured neural encoding mechanisms with our structural analyses to predict the spatial and temporal patterns of neural activity. This activity depends on both the flapping and rotation dynamics, and we show how the timing of neural activity is a viable mechanism for rotation-rate encoding. Our results provide new insights in haltere dynamics and show the viability for timing-based encoding of fly body rotations by halteres.

Specific lexico-semantic predictions are associated with unique spatial and temporal patterns of neural activity.

We used Magnetoencephalography (MEG) in combination with Representational Similarity Analysis to probe neural activity associated with distinct, item-specific lexico-semantic predictions during language comprehension. MEG activity was measured as participants read highly constraining sentences in which the final words could be predicted. Before the onset of the predicted words, both the spatial and temporal patterns of brain activity were more similar when the same words were predicted than when different words were predicted. The temporal patterns localized to the left inferior and medial temporal lobe. These findings provide evidence that unique spatial and temporal patterns of neural activity are associated with item-specific lexico-semantic predictions. We suggest that the unique spatial patterns reflected the prediction of spatially distributed semantic features associated with the predicted word, and that the left inferior/medial temporal lobe played a role in temporally 'binding' these features, giving rise to unique lexico-semantic predictions.

High-throughput brain activity mapping and machine learning as a foundation for systems neuropharmacology

Technologies for mapping the spatial and temporal patterns of neural activity have advanced our understanding of brain function in both health and disease. An important application of these technologies is the discovery of next-generation neurotherapeutics for neurological and psychiatric disorders. Here, we describe an in vivo drug screening strategy that combines high-throughput technology to generate large-scale brain activity maps (BAMs) with machine learning for predictive analysis. This platform enables evaluation of compounds’ mechanisms of action and potential therapeutic uses based on information-rich BAMs derived from drug-treated zebrafish larvae. From a screen of clinically used drugs, we found intrinsically coherent drug clusters that are associated with known therapeutic categories. Using BAM-based clusters as a functional classifier, we identify anti-seizure-like drug leads from non-clinical compounds and validate their therapeutic effects in the pentylenetetrazole zebrafish seizure model. Collectively, this study provides a framework to advance the field of systems neuropharmacology.

Spectral mapping of brain functional connectivity from diffusion imaging

Understanding the relationship between the dynamics of neural processes and the anatomical substrate of the brain is a central question in neuroscience. On the one hand, modern neuroimaging technologies, such as diffusion tensor imaging, can be used to construct structural graphs representing the architecture of white matter streamlines linking cortical and subcortical structures. On the other hand, temporal patterns of neural activity can be used to construct functional graphs representing temporal correlations between brain regions. Although some studies provide evidence that whole-brain functional connectivity is shaped by the underlying anatomy, the observed relationship between function and structure is weak, and the rules by which anatomy constrains brain dynamics remain elusive. In this article, we introduce a methodology to map the functional connectivity of a subject at rest from his or her structural graph. Using our methodology, we are able to systematically account for the role of structural walks in the formation of functional correlations. Furthermore, in our empirical evaluations, we observe that the eigenmodes of the mapped functional connectivity are associated with activity patterns associated with different cognitive systems.

Reconstruction of Spatial Thermal Gradient Encoded in Thermosensory Neuron AFD in Caenorhabditis elegans.

Deciphering how information is encoded in the nervous system is an important challenge for understanding the principles of information processing in neural circuits. During navigation behavior, animals transform spatial information to temporal patterns of neural activity. To elucidate how a sensory system achieves this transformation, we focused on a thermosensory neuron in Caenorhabditis elegans called AFD, which plays a major role in a sensory behavior. Using tracking and calcium imaging system for freely moving animals, we identified the response property of the AFD. The identified response property enabled us to reconstruct both neural activity from a temperature stimulus and a spatial thermal environment from neural activity. These results shed light on how a sensory system encodes the environment.

Decoding visual object categories from temporal correlations of ECoG signals

How visual object categories are represented in the brain is one of the key questions in neuroscience. Studies on low-level visual features have shown that relative timings or phases of neural activity between multiple brain locations encode information. However, whether such temporal patterns of neural activity are used in the representation of visual objects is unknown. Here, we examined whether and how visual object categories could be predicted (or decoded) from temporal patterns of electrocorticographic (ECoG) signals from the temporal cortex in five patients with epilepsy. We used temporal correlations between electrodes as input features, and compared the decoding performance with features defined by spectral power and phase from individual electrodes. While using power or phase alone, the decoding accuracy was significantly better than chance, correlations alone or those combined with power outperformed other features. Decoding performance with correlations was degraded by shuffling the order of trials of the same category in each electrode, indicating that the relative time series between electrodes in each trial is critical. Analysis using a sliding time window revealed that decoding performance with correlations began to rise earlier than that with power. This earlier increase in performance was replicated by a model using phase differences to encode categories. These results suggest that activity patterns arising from interactions between multiple neuronal units carry additional information on visual object categories.

Day–night differences in neural activation in histaminergic and serotonergic areas with putative projections to the cerebrospinal fluid in a diurnal brain

In nocturnal rodents, brain areas that promote wakefulness have a circadian pattern of neural activation that mirrors the sleep/wake cycle, with more neural activation during the active phase than during the rest phase. To investigate whether differences in temporal patterns of neural activity in wake-promoting regions contribute to differences in daily patterns of wakefulness between nocturnal and diurnal species, we assessed Fos expression patterns in the tuberomammillary (TMM), supramammillary (SUM), and raphe nuclei of male grass rats maintained in a 12:12 h light–dark cycle. Day–night profiles of Fos expression were observed in the ventral and dorsal TMM, in the SUM, and in specific subpopulations of the raphe, including serotonergic cells, with higher Fos expression during the day than during the night. Next, to explore whether the cerebrospinal fluid is an avenue used by the TMM and raphe in the regulation of target areas, we injected the retrograde tracer cholera toxin subunit beta (CTB) into the ventricular system of male grass rats. While CTB labeling was scarce in the TMM and other hypothalamic areas including the suprachiasmatic nucleus, which contains the main circadian pacemaker, a dense cluster of CTB-positive neurons was evident in the caudal dorsal raphe, and the majority of these neurons appeared to be serotonergic. Since these findings are in agreement with reports for nocturnal rodents, our results suggest that the evolution of diurnality did not involve a change in the overall distribution of neuronal connections between systems that support wakefulness and their target areas, but produced a complete temporal reversal in the functioning of those systems.

Contribution of within- and cross-channel information to vibrotactile frequency discrimination

Vibrotactile stimuli normally activate multiple information-processing channels starting from different types of mechanoreceptors, and the most sensitive channel alternates depending on the range of vibration frequency. How the tactile system encodes vibration frequency using within-channel information (e.g., the temporal pattern of neural activity of each channel) and/or cross-channel information (the relationship between activities of channels) has been examined, and the usefulness of the former has been evidently shown, while that of the latter remains controversial. To see the contribution of within- and cross-channel information to vibrotactile frequency encoding, we investigated frequency discrimination for near-threshold vibration, which can activate the channels more selectively than conventional supra-threshold vibration. At near-threshold intensity, the contribution of cross-channel information, if it exists, would be observed only when two vibrations activate different channels. In the first experiment, we examined the frequency discrimination thresholds for a wide range of frequencies. The results did not show a clear contribution of the cross-channel information, though that of the within-channel information was evident. In the second experiment, we compared the signal detection threshold and the frequency identification threshold between frequency pairs. We found that for certain pairs of stimuli whose frequencies were far enough apart from each other to activate different channels, each stimulus was identified as soon as it was detected. This suggests that each channel is a labeled line as expected from cross-channel encoding of vibration frequency, but not all data were consistent with this idea. We conclude that though a labeled-line structure might exist as a basis of cross-channel encoding, the cross-channel information does not play a dominant role in frequency discrimination.

The mind-brain relationship as a mathematical problem.

This paper aims to frame certain fundamental aspects of the human mind (content and meaning of mental states) and foundational elements of brain computation (spatial and temporal patterns of neural activity) so as to enable at least in principle their integration within one and the same quantitative representation. Through the history of science, similar approaches have been instrumental to bridge other seemingly mysterious scientific phenomena, such as thermodynamics and statistical mechanics, optics and electromagnetism, or chemistry and quantum physics, among several other examples. Identifying the relevant levels of analysis is important to define proper mathematical formalisms for describing the brain and the mind, such that they could be mapped onto each other in order to explain their equivalence. Based on these premises, we overview the potential of neural connectivity to provide highly informative constraints on brain computational process. Moreover, we outline approaches for representing cognitive and emotional states geometrically with semantic maps. Next, we summarize leading theoretical framework that might serve as an explanatory bridge between neural connectivity and mental space. Furthermore, we discuss the implications of this framework for human communication and our view of reality. We conclude by analyzing the practical requirements to manage the necessary data for solving the mind-brain problem from this perspective.

M-channels modulate the intrinsic excitability and synaptic responses of layer 2/3 pyramidal neurons in auditory cortex

Neurons in the auditory cortex are believed to utilize temporal patterns of neural activity to accurately process auditory information but the intrinsic neuronal mechanism underlying the control of auditory neural activity is not known. The slowly activating, persistent K+ channel, also called M-channel that belongs to the Kv7 family, is already known to be important in regulating subthreshold neural excitability and synaptic summation in neocortical and hippocampal pyramidal neurons. However, its functional role in the primary auditory cortex (A1) has never been characterized. In this study, we investigated the roles of M-channels on neuronal excitability, short-term plasticity, and synaptic summation of A1 layer 2/3 regular spiking pyramidal neurons with whole-cell current-clamp recordings in vitro. We found that blocking M-channels with a selective M-channel blocker, XE991, significantly increased neural excitability of A1 layer 2/3 pyramidal neurons. Furthermore, M-channels controled synaptic responses of intralaminar-evoked excitatory postsynaptic potentials (EPSPs); XE991 significantly increased EPSP amplitude, decreased the rate of short-term depression, and increased the synaptic summation. These results suggest that M-channels are involved in controlling spike output patterns and synaptic responses of A1 layer 2/3 pyramidal neurons, which would have important implications in auditory information processing.

Loss of reliable temporal structure in event-related averaging of naturalistic stimuli

To separate neural signals from noise, brain responses measured in neuroimaging are routinely averaged across space and time. However, such procedures may obscure some properties of neural activity. Recently, multi-voxel pattern analysis methods have demonstrated that patterns of activity across voxels contain valuable information that is concealed by spatial averaging. Here we show that temporal patterns of neural activity contain information that can discriminate different stimuli, even within brain regions that show no net activation to that stimulus class. Furthermore, we find that in many brain regions, responses to natural stimuli are highly context dependent. In such cases, prototypical event-related responses do not even exist for individual stimuli, so that averaging responses to the same stimulus within different contexts may worsen the effective signal-to-noise. As a result, analysis of the temporal structures of single events can reveal aspects of neural dynamics which cannot be detected using standard event-related averaging methods.

Stimulus and Network Dynamics Collide in a Ratiometric Model of the Antennal Lobe Macroglomerular Complex

Time is considered to be an important encoding dimension in olfaction, as neural populations generate odour-specific spatiotemporal responses to constant stimuli. However, during pheromone mediated anemotactic search insects must discriminate specific ratios of blend components from rapidly time varying input. The dynamics intrinsic to olfactory processing and those of naturalistic stimuli can therefore potentially collide, thereby confounding ratiometric information. In this paper we use a computational model of the macroglomerular complex of the insect antennal lobe to study the impact on ratiometric information of this potential collision between network and stimulus dynamics. We show that the model exhibits two different dynamical regimes depending upon the connectivity pattern between inhibitory interneurons (that we refer to as fixed point attractor and limit cycle attractor), which both generate ratio-specific trajectories in the projection neuron output population that are reminiscent of temporal patterning and periodic hyperpolarisation observed in olfactory antennal lobe neurons. We compare the performance of the two corresponding population codes for reporting ratiometric blend information to higher centres of the insect brain. Our key finding is that whilst the dynamically rich limit cycle attractor spatiotemporal code is faster and more efficient in transmitting blend information under certain conditions it is also more prone to interference between network and stimulus dynamics, thus degrading ratiometric information under naturalistic input conditions. Our results suggest that rich intrinsically generated network dynamics can provide a powerful means of encoding multidimensional stimuli with high accuracy and efficiency, but only when isolated from stimulus dynamics. This interference between temporal dynamics of the stimulus and temporal patterns of neural activity constitutes a real challenge that must be successfully solved by the nervous system when faced with naturalistic input.