Spontaneous and evoked activity patterns diverge over development

The immature brain is highly spontaneously active. Over development this activity must be integrated with emerging patterns of stimulus-evoked activity, but little is known about how this occurs. Here we investigated this question by recording spontaneous and evoked neural activity in the larval zebrafish tectum from 4 to 15 days post fertilisation. Correlations within spontaneous and evoked activity epochs were comparable over development, and their neural assemblies properties refined in similar ways. However both the similarity between evoked and spontaneous assemblies, and also the geometric distance between spontaneous and evoked patterns, decreased over development. At all stages of development evoked activity was of higher dimension than spontaneous activity. Thus spontaneous and evoked activity do not converge over development in this system, and these results do not support the hypothesis that spontaneous activity evolves to form a Bayesian prior for evoked activity.

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Model-based decoupling of evoked and spontaneous neural activity in calcium imaging data

PLoS Computational Biology ◽

10.1371/journal.pcbi.1008330 ◽

2020 ◽

Vol 16 (11) ◽

pp. e1008330

Author(s):

Marcus A. Triplett ◽

Zac Pujic ◽

Biao Sun ◽

Lilach Avitan ◽

Geoffrey J. Goodhill

Keyword(s):

Spontaneous Activity ◽

Neural Activity ◽

Calcium Imaging ◽

Temporal Dynamics ◽

Population Level ◽

Imaging Data ◽

Evoked Activity ◽

Zebrafish Optic Tectum ◽

Low Dimensional ◽

Mouse Visual Cortex

The pattern of neural activity evoked by a stimulus can be substantially affected by ongoing spontaneous activity. Separating these two types of activity is particularly important for calcium imaging data given the slow temporal dynamics of calcium indicators. Here we present a statistical model that decouples stimulus-driven activity from low dimensional spontaneous activity in this case. The model identifies hidden factors giving rise to spontaneous activity while jointly estimating stimulus tuning properties that account for the confounding effects that these factors introduce. By applying our model to data from zebrafish optic tectum and mouse visual cortex, we obtain quantitative measurements of the extent that neurons in each case are driven by evoked activity, spontaneous activity, and their interaction. By not averaging away potentially important information encoded in spontaneous activity, this broadly applicable model brings new insight into population-level neural activity within single trials.

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Structured spike series specify gene expression patterns for olfactory circuit formation

Science ◽

10.1126/science.aaw5030 ◽

2019 ◽

Vol 365 (6448) ◽

pp. eaaw5030 ◽

Cited By ~ 12

Author(s):

Ai Nakashima ◽

Naoki Ihara ◽

Mayo Shigeta ◽

Hiroshi Kiyonari ◽

Yuji Ikegaya ◽

...

Keyword(s):

Spontaneous Activity ◽

Neural Activity ◽

Activity Patterns ◽

Expression Patterns ◽

Temporal Patterns ◽

Dependent Manner ◽

Specific Expression ◽

Circuit Formation ◽

Combinatorial Code ◽

Olfactory Map

Neural circuits emerge through the interplay of genetic programming and activity-dependent processes. During the development of the mouse olfactory map, axons segregate into distinct glomeruli in an olfactory receptor (OR)–dependent manner. ORs generate a combinatorial code of axon-sorting molecules whose expression is regulated by neural activity. However, it remains unclear how neural activity induces OR-specific expression patterns of axon-sorting molecules. We found that the temporal patterns of spontaneous neuronal spikes were not spatially organized but were correlated with the OR types. Receptor substitution experiments demonstrated that ORs determine spontaneous activity patterns. Moreover, optogenetically differentiated patterns of neuronal activity induced specific expression of the corresponding axon-sorting molecules and regulated axonal segregation. Thus, OR-dependent temporal patterns of spontaneous activity play instructive roles in generating the combinatorial code of axon-sorting molecules during olfactory map formation.

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Spatiotemporal structure of sensory-evoked and spontaneous activity revealed by mesoscale imaging in anesthetized and awake mice

10.1101/2020.05.22.111021 ◽

2020 ◽

Cited By ~ 1

Author(s):

Navvab Afrashteh ◽

Samsoon Inayat ◽

Edgar Bermudez Contreras ◽

Artur Luczak ◽

Bruce L. McNaughton ◽

...

Keyword(s):

Spontaneous Activity ◽

Brain Activity ◽

Activity Patterns ◽

Sensory Stimulation ◽

Wide Field ◽

Evoked Responses ◽

Evoked Activity ◽

Cortical Regions ◽

The One ◽

Sensory Evoked

AbstractBrain activity propagates across the cortex in diverse spatiotemporal patterns, both as a response to sensory stimulation and during spontaneous activity. Despite been extensively studied, the relationship between the characteristics of such patterns during spontaneous and evoked activity is not completely understood. To investigate this relationship, we compared visual, auditory, and tactile evoked activity patterns elicited with different stimulus strengths and spontaneous activity motifs in lightly anesthetized and awake mice using mesoscale wide-field voltage-sensitive dye and glutamate imaging respectively. The characteristics of cortical activity that we compared include amplitude, speed, direction, and complexity of propagation trajectories in spontaneous and evoked activity patterns. We found that the complexity of the propagation trajectories of spontaneous activity, quantified as their fractal dimension, is higher than the one from sensory evoked responses. Moreover, the speed and direction of propagation, are modulated by the amplitude during both, spontaneous and evoked activity. Finally, we found that spontaneous activity had similar amplitude and speed when compared to evoked activity elicited with low stimulus strengths. However, this similarity gradually decreased when the strength of stimuli eliciting evoked responses increased. Altogether, these findings are consistent with the fact that even primary sensory areas receive widespread inputs from other cortical regions, and that, during rest, the cortex tends to reactivate traces of complex, multi-sensory experiences that may have occurred in a range of different behavioural contexts.

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Model-based decoupling of evoked and spontaneous neural activity in calcium imaging data

10.1101/691261 ◽

2019 ◽

Cited By ~ 1

Author(s):

Marcus A. Triplett ◽

Zac Pujic ◽

Biao Sun ◽

Lilach Avitan ◽

Geoffrey J. Goodhill

Keyword(s):

Spontaneous Activity ◽

Neural Activity ◽

Calcium Imaging ◽

Temporal Dynamics ◽

Population Level ◽

Imaging Data ◽

Evoked Activity ◽

Zebrafish Optic Tectum ◽

Low Dimensional ◽

Mouse Visual Cortex

AbstractThe pattern of neural activity evoked by a stimulus can be substantially affected by ongoing spontaneous activity. Separating these two types of activity is particularly important for calcium imaging data given the slow temporal dynamics of calcium indicators. Here we present a statistical model that decouples stimulus-driven activity from low dimensional spontaneous activity in this case. The model identifies hidden factors giving rise to spontaneous activity while jointly estimating stimulus tuning properties that account for the confounding effects that these factors introduce. By applying our model to data from zebrafish optic tectum and mouse visual cortex, we obtain quantitative measurements of the extent that neurons in each case are driven by evoked activity, spontaneous activity, and their interaction. This broadly applicable model brings new insight into population-level neural activity in single trials without averaging away potentially important information encoded in spontaneous activity.

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Spontaneous activity and functional connectivity in the developing cerebellorubral system

Journal of Neurophysiology ◽

10.1152/jn.00461.2016 ◽

2016 ◽

Vol 116 (3) ◽

pp. 1316-1327 ◽

Cited By ~ 12

Author(s):

Carlos Del Rio-Bermudez ◽

Alan M. Plumeau ◽

Nicholas J. Sattler ◽

Greta Sokoloff ◽

Mark S. Blumberg

Keyword(s):

Functional Connectivity ◽

Spontaneous Activity ◽

Neural Activity ◽

Activity Patterns ◽

Red Nucleus ◽

Downstream Target ◽

State Dependent ◽

Age Related ◽

Extracellular Activity

The development of the cerebellar system depends in part on the emergence of functional connectivity in its input and output pathways. Characterization of spontaneous activity within these pathways provides insight into their functional status in early development. In the present study we recorded extracellular activity from the interpositus nucleus (IP) and its primary downstream target, the red nucleus (RN), in unanesthetized rats at postnatal days 8 (P8) and P12, a period of dramatic change in cerebellar circuitry. The two structures exhibited state-dependent activity patterns and age-related changes in rhythmicity and overall firing rate. Importantly, sensory feedback (i.e., reafference) from myoclonic twitches (spontaneous, self-generated movements that are produced exclusively during active sleep) drove neural activity in the IP and RN at both ages. Additionally, anatomic tracing confirmed the presence of cerebellorubral connections as early as P8. Finally, inactivation of the IP and adjacent nuclei using the GABAA receptor agonist muscimol caused a substantial decrease in neural activity in the contralateral RN at both ages, as well as the disappearance of rhythmicity; twitch-related activity in the RN, however, was preserved after IP inactivation, indicating that twitch-related reafference activates the two structures in parallel. Overall, the present findings point to the contributions of sleep-related spontaneous activity to the development of cerebellar networks.

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Context-Dependent Relationships between Locus Coeruleus Activation and Coordinated Neural Activity Patterns in the Anterior Cingulate Cortex

SSRN Electronic Journal ◽

10.2139/ssrn.3413098 ◽

2019 ◽

Author(s):

Siddhartha Joshi ◽

Joshua I. Gold

Keyword(s):

Locus Coeruleus ◽

Anterior Cingulate Cortex ◽

Neural Activity ◽

Activity Patterns ◽

Cingulate Cortex ◽

Anterior Cingulate ◽

Context Dependent

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Dynamic relationships between spontaneous and evoked electrophysiological activity

Communications Biology ◽

10.1038/s42003-021-02240-9 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Soren Wainio-Theberge ◽

Annemarie Wolff ◽

Georg Northoff

Keyword(s):

Neural Activity ◽

Large Scale ◽

Behavioral Outcomes ◽

Low Frequencies ◽

Scale Free ◽

Non Invasive ◽

Spontaneous Neural Activity ◽

Electrophysiological Signals ◽

Evoked Activity ◽

Dynamic Relationships

AbstractSpontaneous neural activity fluctuations have been shown to influence trial-by-trial variation in perceptual, cognitive, and behavioral outcomes. However, the complex electrophysiological mechanisms by which these fluctuations shape stimulus-evoked neural activity remain largely to be explored. Employing a large-scale magnetoencephalographic dataset and an electroencephalographic replication dataset, we investigate the relationship between spontaneous and evoked neural activity across a range of electrophysiological variables. We observe that for high-frequency activity, high pre-stimulus amplitudes lead to greater evoked desynchronization, while for low frequencies, high pre-stimulus amplitudes induce larger degrees of event-related synchronization. We further decompose electrophysiological power into oscillatory and scale-free components, demonstrating different patterns of spontaneous-evoked correlation for each component. Finally, we find correlations between spontaneous and evoked time-domain electrophysiological signals. Overall, we demonstrate that the dynamics of multiple electrophysiological variables exhibit distinct relationships between their spontaneous and evoked activity, a result which carries implications for experimental design and analysis in non-invasive electrophysiology.

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Visual learning and classification of human epithelial type 2 cell images through spontaneous activity patterns

Pattern Recognition ◽

10.1016/j.patcog.2013.10.013 ◽

2014 ◽

Vol 47 (7) ◽

pp. 2325-2337 ◽

Cited By ~ 13

Author(s):

Yan Yang ◽

Arnold Wiliem ◽

Azadeh Alavi ◽

Brian C. Lovell ◽

Peter Hobson

Keyword(s):

Spontaneous Activity ◽

Activity Patterns ◽

Visual Learning

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Harmonic Brain Modes: A Unifying Framework for Linking Space and Time in Brain Dynamics

The Neuroscientist ◽

10.1177/1073858417728032 ◽

2017 ◽

Vol 24 (3) ◽

pp. 277-293 ◽

Cited By ~ 29

Author(s):

Selen Atasoy ◽

Gustavo Deco ◽

Morten L. Kringelbach ◽

Joel Pearson

Keyword(s):

Neural Activity ◽

Brain Activity ◽

Activity Patterns ◽

Structural Connectivity ◽

Building Blocks ◽

Mental States ◽

Brain Dynamics ◽

Space And Time ◽

Wide Range ◽

The Brain

A fundamental characteristic of spontaneous brain activity is coherent oscillations covering a wide range of frequencies. Interestingly, these temporal oscillations are highly correlated among spatially distributed cortical areas forming structured correlation patterns known as the resting state networks, although the brain is never truly at “rest.” Here, we introduce the concept of harmonic brain modes—fundamental building blocks of complex spatiotemporal patterns of neural activity. We define these elementary harmonic brain modes as harmonic modes of structural connectivity; that is, connectome harmonics, yielding fully synchronous neural activity patterns with different frequency oscillations emerging on and constrained by the particular structure of the brain. Hence, this particular definition implicitly links the hitherto poorly understood dimensions of space and time in brain dynamics and its underlying anatomy. Further we show how harmonic brain modes can explain the relationship between neurophysiological, temporal, and network-level changes in the brain across different mental states ( wakefulness, sleep, anesthesia, psychedelic). Notably, when decoded as activation of connectome harmonics, spatial and temporal characteristics of neural activity naturally emerge from the interplay between excitation and inhibition and this critical relation fits the spatial, temporal, and neurophysiological changes associated with different mental states. Thus, the introduced framework of harmonic brain modes not only establishes a relation between the spatial structure of correlation patterns and temporal oscillations (linking space and time in brain dynamics), but also enables a new dimension of tools for understanding fundamental principles underlying brain dynamics in different states of consciousness.

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