Sequential Activity Patterns Research Articles

Neural Sequences Underlying Directed Turning in C. elegans.

Complex behaviors like navigation rely on sequenced motor outputs that combine to generate effective movement. The brain-wide organization of the circuits that integrate sensory signals to select and execute appropriate motor sequences is not well understood. Here, we characterize the architecture of neural circuits that control C. elegans olfactory navigation. We identify error-correcting turns during navigation and use whole-brain calcium imaging and cell-specific perturbations to determine their neural underpinnings. These turns occur as motor sequences accompanied by neural sequences, in which defined neurons activate in a stereotyped order during each turn. Distinct neurons in this sequence respond to sensory cues, anticipate upcoming turn directions, and drive movement, linking key features of this sensorimotor behavior across time. The neuromodulator tyramine coordinates these sequential brain dynamics. Our results illustrate how neuromodulation can act on a defined neural architecture to generate sequential patterns of activity that link sensory cues to motor actions.

The Type I Interferon Signature Reflects Multiple Phenotypic and Activity Measures in Dermatomyositis.

The type 1 interferon (IFN) pathway is up-regulated in dermatomyositis (DM). We sought to define how organ-specific disease activity as well as autoantibodies and other clinical factors are independently associated with systemic type I IFN activity in adult patients with DM. RNA sequencing was performed on 355 whole blood samples collected from 202 well-phenotyped DM patients followed up during the course of their clinical care. A previously defined 13-gene type I IFN score was modeled as a function of demographic, serologic, and clinical variables using both cross-sectional and longitudinal data. The pattern of type I IFN-driven transcriptional response was stereotyped across samples with a sequential modular activation pattern strikingly similar to systemic lupus erythematosus. The median type I IFN score was higher or lower in patients with anti-melanoma differentiation-associated protein 5 (anti-MDA-5) or anti-Mi-2 antibodies, respectively, compared to patients without these antibodies. Absolute type I IFN score was independently associated with muscle and skin disease activity, interstitial lung disease, and anti-MDA-5 antibodies. Changes in the type I IFN score over time were significantly associated with changes in skin or muscle disease activity. Stratified analysis accounting for heterogeneity in organ involvement and antibody class revealed high correlation between changes in the type I IFN score and skin disease activity (Spearman's ρ=0.84-0.95). The type I IFN score is independently associated with skin and muscle disease activity as well as certain clinical and serologic features in DM. Accounting for the effect of muscle disease and anti-MDA-5 status revealed that the type I IFN score is strongly correlated with skin disease activity, providing support for type I IFN blockade as a therapeutic strategy for DM.

Anatomical organization of temporally correlated neural calcium activity in the hippocampal CA1 region

Hippocampal CA1 neuronal ensembles generate sequential patterns of firing activity that contribute to episodic memory formation and spatial cognition. Here we used invivo calcium imaging to record neural ensemble activities in mouse hippocampal CA1 and identified CA1 excitatory neuron sub-populations whose members are active across the same second-long period of time. We identified groups of hippocampal neurons sharing temporally correlated neural calcium activity during behavioral exploration and found that they also organized as clusters in anatomical space. Such clusters vary in membership and activity dynamics with respect to movement in different environments, but also appear during immobility in the dark suggesting an internal dynamic. The strong covariance between dynamics and anatomical location within the CA1 sub-region reveals a previously unrecognized form of topographic representation in hippocampus that may guide generation of hippocampal sequences across time and therefore organize the content of episodic memory.

The secret life of baby turtles: A novel system to predict hatchling emergence, detect infertile nests, and remotely monitor sea turtle nest events.

Current understanding of sea turtle nesting, hatching, and emergence events has been largely limited to observable events on the surface of the sand, though recent approaches using audio or visual equipment have allowed scientists to better understand some underground nest phenomena. We used a technology-based approach to define motion-related Caretta caretta hatching and emergence nest events. We describe a novel low-cost, accelerometer-based system called TurtleSense that can detect movement and temperature within sea turtle nests remotely. TurtleSense is successfully able to specifically detect motion within sea turtle nests over the entire course of incubation. This system allows for the identification of infertile nests and the detection of four predictable sequential developmental activity patterns in viable nests, including a hatch and posthatch period, the timing of which can be used to tightly predict hatchling emergence events almost to the day. TurtleSense provides a much better understanding about what is happening in the nest before emergence and allows for the generation of a theory of the mechanism that triggers mass emergence. Our results suggest that motion plays a large role in hatchling communication and that the timing of emergence events may be related to the cessation of movement within the nest. Current management of sea turtle nesting events is primarily driven by counting the number of days since the nest was laid, with further safeguards placed at the nest upon subsequent visual observation of depression or emergence events. Use of TurtleSense technology can impact nest management and conservation efforts, allowing organizations to use this motion data to more tightly predict emergence dates for sea turtle hatchlings and to use viability data to inform nest management decisions.

Multiscale representations of community structures in attractor neural networks.

Our cognition relies on the ability of the brain to segment hierarchically structured events on multiple scales. Recent evidence suggests that the brain performs this event segmentation based on the structure of state-transition graphs behind sequential experiences. However, the underlying circuit mechanisms are poorly understood. In this paper we propose an extended attractor network model for graph-based hierarchical computation which we call the Laplacian associative memory. This model generates multiscale representations for communities (clusters) of associative links between memory items, and the scale is regulated by the heterogenous modulation of inhibitory circuits. We analytically and numerically show that these representations correspond to graph Laplacian eigenvectors, a popular method for graph segmentation and dimensionality reduction. Finally, we demonstrate that our model exhibits chunked sequential activity patterns resembling hippocampal theta sequences. Our model connects graph theory and attractor dynamics to provide a biologically plausible mechanism for abstraction in the brain.

Robust Trajectory Generation for Robotic Control on the Neuromorphic Research Chip Loihi.

Neuromorphic hardware has several promising advantages compared to von Neumann architectures and is highly interesting for robot control. However, despite the high speed and energy efficiency of neuromorphic computing, algorithms utilizing this hardware in control scenarios are still rare. One problem is the transition from fast spiking activity on the hardware, which acts on a timescale of a few milliseconds, to a control-relevant timescale on the order of hundreds of milliseconds. Another problem is the execution of complex trajectories, which requires spiking activity to contain sufficient variability, while at the same time, for reliable performance, network dynamics must be adequately robust against noise. In this study we exploit a recently developed biologically-inspired spiking neural network model, the so-called anisotropic network. We identified and transferred the core principles of the anisotropic network to neuromorphic hardware using Intel's neuromorphic research chip Loihi and validated the system on trajectories from a motor-control task performed by a robot arm. We developed a network architecture including the anisotropic network and a pooling layer which allows fast spike read-out from the chip and performs an inherent regularization. With this, we show that the anisotropic network on Loihi reliably encodes sequential patterns of neural activity, each representing a robotic action, and that the patterns allow the generation of multidimensional trajectories on control-relevant timescales. Taken together, our study presents a new algorithm that allows the generation of complex robotic movements as a building block for robotic control using state of the art neuromorphic hardware.

Spatial Information Encoding across Multiple Neocortical Regions Depends on an Intact Hippocampus.

There has been considerable research showing populations of neurons encoding for different aspects of space in the brain. Recently, several studies using two-photon calcium imaging and virtual navigation have identified "spatially" modulated neurons in the posterior cortex. We enquire here whether the presence of such spatial representations may be a cortex-wide phenomenon and, if so, whether these representations can be organized in the absence of the hippocampus. To this end, we imaged the dorsal cortex of mice running on a treadmill populated with tactile cues. A high percentage (40-80%) of the detected neurons exhibited sparse, spatially localized activity, with activity fields uniformly localized over the track. The development of this location specificity was impaired by hippocampal damage. Thus, there is a substantial population of neurons distributed widely over the cortex that collectively form a continuous representation of the explored environment, and hippocampal outflow is necessary to organize this phenomenon.SIGNIFICANCE STATEMENT Increasing evidence points to the role of the neocortex in encoding spatial information. Whether this feature is linked to hippocampal functions is largely unknown. Here, we systematically surveyed multiple regions in the dorsal cortex of the same animal for the presence of signals encoding for spatial position. We described populations of cortical neurons expressing sequential patterns of activity localized in space in primary, secondary, and associational areas. Furthermore, we showed that the formation of these spatial representations was impacted by hippocampal lesion. Our results indicate that hippocampal inputs are necessary to maintain a precise cortical representation of space.

Dynamics of language reorganization after left temporo-parietal and frontal stroke.

The loss and recovery of language functions are still incompletely understood. This longitudinal functional MRI study investigated the neural mechanisms underlying language recovery in patients with post-stroke aphasia putting particular emphasis on the impact of lesion site. To identify patterns of language-related activation, an auditory functional MRI sentence comprehension paradigm was administered to patients with circumscribed lesions of either left frontal (n = 17) or temporo-parietal (n = 17) cortex. Patients were examined repeatedly during the acute (≤1 week, t1), subacute (1-2 weeks, t2) and chronic phase (>6 months, t3) post-stroke; healthy age-matched control subjects (n = 17) were tested once. The separation into two patient groups with circumscribed lesions allowed for a direct comparison of the contributions of distinct lesion-dependent network components to language reorganization between both groups. We hypothesized that activation of left hemisphere spared and perilesional cortex as well as lesion-homologue cortex in the right hemisphere varies between patient groups and across time. In addition, we expected that domain-general networks serving cognitive control independently contribute to language recovery. First, we found a global network disturbance in the acute phase that is characterized by reduced functional MRI language activation including areas distant to the lesion (i.e. diaschisis) and subsequent subacute network reactivation (i.e. resolution of diaschisis). These phenomena were driven by temporo-parietal lesions. Second, we identified a lesion-independent sequential activation pattern with increased activity of perilesional cortex and bilateral domain-general networks in the subacute phase followed by reorganization of left temporal language areas in the chronic phase. Third, we observed involvement of lesion-homologue cortex only in patients with frontal but not temporo-parietal lesions. Fourth, irrespective of lesion location, language reorganization predominantly occurred in pre-existing networks showing comparable activation in healthy controls. Finally, we detected different relationships of performance and activation in language and domain-general networks demonstrating the functional relevance for language recovery. Our findings highlight that the dynamics of language reorganization clearly depend on lesion location and hence open new perspectives for neurobiologically motivated strategies of language rehabilitation, such as individually-tailored targeted application of neuro-stimulation.

Navigating Through Time: A Spatial Navigation Perspective on How the Brain May Encode Time.

Interval timing, which operates on timescales of seconds to minutes, is distributed across multiple brain regions and may use distinct circuit mechanisms as compared to millisecond timing and circadian rhythms. However, its study has proven difficult, as timing on this scale is deeply entangled with other behaviors. Several circuit and cellular mechanisms could generate sequential or ramping activity patterns that carry timing information. Here we propose that a productive approach is to draw parallels between interval timing and spatial navigation, where direct analogies can be made between the variables of interest and the mathematical operations necessitated. Along with designing experiments that isolate or disambiguate timing behavior from other variables, new techniques will facilitate studies that directly address the neural mechanisms that are responsible for interval timing.

Sequential replay of nonspatial task states in the human hippocampus.

Sequential neural activity patterns related to spatial experiences are "replayed" in the hippocampus of rodents during rest. We investigated whether replay of nonspatial sequences can be detected noninvasively in the human hippocampus. Participants underwent functional magnetic resonance imaging (fMRI) while resting after performing a decision-making task with sequential structure. Hippocampal fMRI patterns recorded at rest reflected sequentiality of previously experienced task states, with consecutive patterns corresponding to nearby states. Hippocampal sequentiality correlated with the fidelity of task representations recorded in the orbitofrontal cortex during decision-making, which were themselves related to better task performance. Our findings suggest that hippocampal replay may be important for building representations of complex, abstract tasks elsewhere in the brain and establish feasibility of investigating fast replay signals with fMRI.

A diverse range of factors affect the nature of neural representations underlying short-term memory.

Sequential and persistent activity models are two prominent models of short-term memory in neural circuits. In persistent activity models, memories are represented in persistent or nearly persistent activity patterns across a population of neurons, whereas in sequential models, memories are represented dynamically by a sequential activity pattern across the population. Experimental evidence for both models has been reported previously. However, it has been unclear under what conditions these two qualitatively different types of solutions emerge in neural circuits. Here, we address this question by training recurrent neural networks on several short-term memory tasks under a wide range of circuit and task manipulations. We show that both sequential and nearly persistent solutions are part of a spectrum that emerges naturally in trained networks under different conditions. Our results help to clarify some seemingly contradictory experimental results on the existence of sequential versus persistent activity-based short-term memory mechanisms in the brain.

Abnormal phasic activity in saliency network, motor areas, and basal ganglia in Parkinson's disease during rhythm perception.

Behavioral studies indicate that persons with Parkinson's disease have complexity dependent problems with the discrimination of auditory rhythms. Furthermore, neuroimaging studies show that rhythm processing activates many brain areas that overlap with areas affected by Parkinson's disease (PD). This study sought to investigate the neural correlates of rhythm processing in PD and healthy controls, with a particular focus on rhythmic complexity. We further aimed to investigate differences in brain activation during initial phases of rhythm processing. Functional magnetic resonance imaging was used to scan 15 persons with Parkinson's disease and 15 healthy controls while they listened to musical rhythms with two different levels of complexity. Rhythmic complexity had no significant effect on brain activations, but patients and controls showed differences in areas related to temporal auditory processing, notably bilateral planum temporale and inferior parietal lobule. We found indications of a particular sequential or phasic activation pattern of brain activity, where activity in caudate nucleus in the basal ganglia was time‐displaced by activation in the saliency network—comprised of anterior cingulate cortex and bilateral anterior insula—and cortical and subcortical motor areas, during the initial phases of listening to rhythms. We relate our findings to core PD pathology, and discuss the overall, rhythm processing related hyperactivity in PD as a possible dysfunction in specific basal ganglia mechanisms, and the phasic activation pattern in PD as a reflection of a lack of preparatory activation of task‐relevant brain networks for rhythm processing in PD.

Similarity analysis of frequent sequential activity pattern mining

Activity pattern classification is extensively studied using multi-person single-day mobile traces. However, human mobility exhibits intra-personal variability and thus single-day activity may not fully capture one’s activity patterns. This paper creates a methodological framework to analyze similarity of activity patterns using frequent sequential pattern mining when multi-person multi-day data is available. Frequent sequential pattern mining discovers the frequently occurring ordered subsequences and is a natural approach of analyzing multi-day travel patterns. Prefix-Span algorithm is implemented to extract frequent patterns for each individual. Then similarity measures are defined to describe the extent to which two travelers’ activity patterns are alike and the regularity of how one repeats her activity patterns from day to day. Based upon the pairwise similarity values between two individuals, hierarchical clustering is conducted to divide travelers into communities. To illustrate these methodologies, 349 travelers’ 19,130 travel activity sequences are extracted from the world’s first connected vehicle testbed in Ann Arbor, Michigan. Three major clusters are identified. Coupled with demographics, these clusters are characterized as “seniors”, “working class”, and “parents”, respectively. Multinomial logistic regression is employed to model to what extent the similarity of socio-demographics can explain that of travel patterns. This work can be extended to either infer an unknown user’s demographics (or customer profiling) based on her activity patterns, or to reconstruct an unknown user’s frequent activity patterns based on her demographics and other similar travelers’ patterns.

The Hippocampal Ensemble Code for Spatial Navigation and Episodic Memory.

In our minds we can vividly re-experience a series of past events or episodes that occurred along space and time. In 1957, a highly influential clinical investigation published by Scoville and Milner suggested that episodic memory retrieval is severely impaired after physical damage occurs to the hippocampus. In fact, loss of the hippocampus can lead to a profound disturbance to spatial navigation. Within the hippocampus reside place cells: excitatory pyramidal cells that maximally fire at a particular location. The presence of hippocampal place cells across several mammalian species, including mice, rats, bats, and primates, has led to a theory that the hippocampus is a locus of the 'where' element of episodic memory. However, growing evidence suggests that place cell's firings do not merely encode spatial information, but also represent a series of experienced events that occur along the spatiotemporal continuum. This suggestion inspires the following questions: is the ensemble activity of place cells a neuronal substrate for forming or recalling episodic memory? And, why does the hippocampus enable flexible spatial navigation?To address these questions, in this chapter I review several compelling investigations on the firing pattern of place cells, and the temporal flow and reactivation of their ensemble activity. I argue that the sequential activity pattern of hippocampal place cells is capable of encoding not only future trajectory but also future events that occur along space and time. Furthermore, their activity can be reactivated in a temporally compressed fashion while the animal briefly pauses. These pivotal reports strongly support a view that the hippocampal neuronal ensemble enables flexible spatial navigation and contributes to the capacities of episodic memory: mental time travel and foreseeing future situations.

Physical activity phenotyping with activity bigrams, and their association with BMI.

BackgroundAnalysis of physical activity usually focuses on a small number of summary statistics derived from accelerometer recordings: average counts per minute and the proportion of time spent in moderate-vigorous physical activity or in sedentary behaviour. We show how bigrams, a concept from the field of text mining, can be used to describe how a person’s activity levels change across (brief) time points. These variables can, for instance, differentiate between two people spending the same time in moderate activity, where one person often stays in moderate activity from one moment to the next and the other does not.MethodsWe use data on 4810 participants of the Avon Longitudinal Study of Parents and Children (ALSPAC). We generate a profile of bigram frequencies for each participant and test the association of each frequency with body mass index (BMI), as an exemplar.ResultsWe found several associations between changes in bigram frequencies and BMI. For instance, a one standard deviation decrease in the number of adjacent minutes in sedentary then moderate activity (or vice versa), with a corresponding increase in the number of adjacent minutes in moderate then vigorous activity (or vice versa), was associated with a 2.36 kg/m2 lower BMI [95% confidence interval (CI): −3.47, −1.26], after accounting for the time spent in sedentary, low, moderate and vigorous activity.ConclusionsActivity bigrams are novel variables that capture how a person’s activity changes from one moment to the next. These variables can be used to investigate how sequential activity patterns associate with other traits.

Learning multiple variable-speed sequences in striatum via cortical tutoring.

Sparse, sequential patterns of neural activity have been observed in numerous brain areas during timekeeping and motor sequence tasks. Inspired by such observations, we construct a model of the striatum, an all-inhibitory circuit where sequential activity patterns are prominent, addressing the following key challenges: (i) obtaining control over temporal rescaling of the sequence speed, with the ability to generalize to new speeds; (ii) facilitating flexible expression of distinct sequences via selective activation, concatenation, and recycling of specific subsequences; and (iii) enabling the biologically plausible learning of sequences, consistent with the decoupling of learning and execution suggested by lesion studies showing that cortical circuits are necessary for learning, but that subcortical circuits are sufficient to drive learned behaviors. The same mechanisms that we describe can also be applied to circuits with both excitatory and inhibitory populations, and hence may underlie general features of sequential neural activity pattern generation in the brain.

The complement and contact activation systems: partnership in pathogenesis beyond angioedema.

The blood plasma contains four biologically important proteolytic cascades, which probably evolved from the same ancestral gene. This in part may explain why each cascade has very similar "initiating trigger" followed by sequential and cascade-like downstream enzymatic activation pattern. The four cascades are: the complement system, the blood clotting cascade, the fibrinolytic system, and the kallikrein-kinin system. Although much has been written about the interplay between all these enzymatic cascades, the cross-talk between the complement and the kinin generating systems has become particularly relevant as this interaction results in the generation of nascent molecules that have significant impact in various inflammatory diseases including angioedema and cancer. In this review, we will focus on the consequences of the interplay between the two systems by highlighting the role of a novel molecular link called gC1qR. Although this protein was first identified as a receptor for C1q, it is now recognized as a multiligand binding cellular protein, which serves not only as C1q receptor, but also as high affinity (KD ≤0.8nM) binding site for both high molecular weight kininogen (HK) and factor XII (FXII). At inflammatory sites, where atherogenic factors such as immune complexes and/or pathogens can activate the endothelial cell into a procoagulant and proinflammatory surface, the two pathways are activated to generate vasoactive peptides that contribute in various ways to the inflammatory processes associated with numerous diseases. More importantly, since recent observations strongly suggest an important role for both pathways in cancer, we will focus on how a growing tumor cluster can employ the byproducts derived from the two activation systems to ensure not only its survival and growth, but also its escape into distal sites of colonization.

Correlations between prefrontal neurons form a small-world network that optimizes the generation of multineuron sequences of activity

Sequential patterns of prefrontal activity are believed to mediate important behaviors, e.g., working memory, but it remains unclear exactly how they are generated. In accordance with previous studies of cortical circuits, we found that prefrontal microcircuits in young adult mice spontaneously generate many more stereotyped sequences of activity than expected by chance. However, the key question of whether these sequences depend on a specific functional organization within the cortical microcircuit, or emerge simply as a by-product of random interactions between neurons, remains unanswered. We observed that correlations between prefrontal neurons do follow a specific functional organization-they have a small-world topology. However, until now it has not been possible to directly link small-world topologies to specific circuit functions, e.g., sequence generation. Therefore, we developed a novel analysis to address this issue. Specifically, we constructed surrogate data sets that have identical levels of network activity at every point in time but nevertheless represent various network topologies. We call this method shuffling activity to rearrange correlations (SHARC). We found that only surrogate data sets based on the actual small-world functional organization of prefrontal microcircuits were able to reproduce the levels of sequences observed in actual data. As expected, small-world data sets contained many more sequences than surrogate data sets with randomly arranged correlations. Surprisingly, small-world data sets also outperformed data sets in which correlations were maximally clustered. Thus the small-world functional organization of cortical microcircuits, which effectively balances the random and maximally clustered regimes, is optimal for producing stereotyped sequential patterns of activity.

Cell Assembly Signatures Defined by Short-Term Synaptic Plasticity in Cortical Networks.

The cell assembly (CA) hypothesis has been used as a conceptual framework to explain how groups of neurons form memories. CAs are defined as neuronal pools with synchronous, recurrent and sequential activity patterns. However, neuronal interactions and synaptic properties that define CAs signatures have been difficult to examine because identities and locations of assembly members are usually unknown. In order to study synaptic properties that define CAs, we used optical and electrophysiological approaches to record activity of identified neurons in mouse cortical cultures. Population analysis and graph theory techniques allowed us to find sequential patterns that represent repetitive transitions between network states. Whole cell pair recordings of neurons participating in repeated sequences demonstrated that synchrony is exhibited by groups of neurons with strong synaptic connectivity (concomitant firing) showing short-term synaptic depression (STD), whereas alternation (sequential firing) is seen in groups of neurons with weaker synaptic connections showing short-term synaptic facilitation (STF). Decreasing synaptic weights of a network promoted the generation of sequential activity patterns, whereas increasing synaptic weights restricted state transitions. Thus in simple cortical networks of real neurons, basic signatures of CAs, the properties that underlie perception and memory in Hebb's original description, are already present.

Different propagation speeds of recalled sequences in plastic spiking neural networks

Neural networks can generate spatiotemporal patterns of spike activity. Sequential activity learning and retrieval have been observed in many brain areas, and e.g. is crucial for coding of episodic memory in the hippocampus or generating temporal patterns during song production in birds. In a recent study, a sequential activity pattern was directly entrained onto the neural activity of the primary visual cortex (V1) of rats and subsequently successfully recalled by a local and transient trigger. It was observed that the speed of activity propagation in coordinates of the retinotopically organized neural tissue was constant during retrieval regardless how the speed of light stimulation sweeping across the visual field during training was varied. It is well known that spike-timing dependent plasticity (STDP) is a potential mechanism for embedding temporal sequences into neural network activity. How training and retrieval speeds relate to each other and how network and learning parameters influence retrieval speeds, however, is not well described. We here theoretically analyze sequential activity learning and retrieval in a recurrent neural network with realistic synaptic short-term dynamics and STDP. Testing multiple STDP rules, we confirm that sequence learning can be achieved by STDP. However, we found that a multiplicative nearest-neighbor (NN) weight update rule generated weight distributions and recall activities that best matched the experiments in V1. Using network simulations and mean-field analysis, we further investigated the learning mechanisms and the influence of network parameters on recall speeds. Our analysis suggests that a multiplicative STDP rule with dominant NN spike interaction might be implemented in V1 since recall speed was almost constant in an NMDA-dominant regime. Interestingly, in an AMPA-dominant regime, neural circuits might exhibit recall speeds that instead follow the change in stimulus speeds. This prediction could be tested in experiments.