Transient cortical circuits match spontaneous and sensory-driven activity during development

At the earliest developmental stages, spontaneous activity synchronizes local and large-scale cortical networks. These networks form the functional template for the establishment of global thalamocortical networks and cortical architecture. The earliest connections are established autonomously. However, activity from the sensory periphery reshapes these circuits as soon as afferents reach the cortex. The early-generated, largely transient neurons of the subplate play a key role in integrating spontaneous and sensory-driven activity. Early pathological conditions—such as hypoxia, inflammation, or exposure to pharmacological compounds—alter spontaneous activity patterns, which subsequently induce disturbances in cortical network activity. This cortical dysfunction may lead to local and global miswiring and, at later stages, can be associated with neurological and psychiatric conditions.

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Oxytocin shapes spontaneous activity patterns in the developing visual cortex by activating somatostatin interneurons

10.1101/866251 ◽

2019 ◽

Cited By ~ 1

Author(s):

Paloma P Maldonado ◽

Alvaro Nuno-Perez ◽

Jan Kirchner ◽

Elizabeth Hammock ◽

Julijana Gjorgjieva ◽

...

Keyword(s):

Visual Cortex ◽

Spontaneous Activity ◽

Sensory Processing ◽

Activity Patterns ◽

Network Activity ◽

Synaptic Connections ◽

Pairwise Correlations ◽

Early Brain Development

SummarySpontaneous network activity shapes emerging neuronal circuits during early brain development, however how neuromodulation influences this activity is not fully understood. Here, we report that the neuromodulator oxytocin powerfully shapes spontaneous activity patterns. In vivo, oxytocin strongly decreased the frequency and pairwise correlations of spontaneous activity events in visual cortex (V1), but not in somatosensory cortex (S1). This differential effect was a consequence of oxytocin only increasing inhibition in V1 and increasing both inhibition and excitation in S1. The increase in inhibition was mediated by the depolarization and increase in excitability of somatostatin+ (SST) interneurons specifically. Accordingly, silencing SST+ neurons pharmacogenetically fully blocked oxytocin’s effect on inhibition in vitro as well its effect on spontaneous activity patterns in vivo. Thus, oxytocin decreases the excitatory/inhibitory ratio and modulates specific features of V1 spontaneous activity patterns that are crucial for refining developing synaptic connections and sensory processing later in life.

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Selective entrainment of gamma subbands by different slow network oscillations

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1617249114 ◽

2017 ◽

Vol 114 (17) ◽

pp. 4519-4524 ◽

Cited By ~ 54

Author(s):

Weiwei Zhong ◽

Mareva Ciatipis ◽

Thérèse Wolfenstetter ◽

Jakob Jessberger ◽

Carola Müller ◽

...

Keyword(s):

Large Scale ◽

Dorsal Hippocampus ◽

Activity Patterns ◽

Specific Interaction ◽

Brain Regions ◽

Network Activity ◽

Gamma Frequency ◽

Cornu Ammonis ◽

Oscillatory Coupling ◽

Fast Oscillations

Theta oscillations (4–12 Hz) are thought to provide a common temporal reference for the exchange of information among distant brain networks. On the other hand, faster gamma-frequency oscillations (30–160 Hz) nested within theta cycles are believed to underlie local information processing. Whether oscillatory coupling between global and local oscillations, as showcased by theta-gamma coupling, is a general coding mechanism remains unknown. Here, we investigated two different patterns of oscillatory network activity, theta and respiration-induced network rhythms, in four brain regions of freely moving mice: olfactory bulb (OB), prelimbic cortex (PLC), parietal cortex (PAC), and dorsal hippocampus [cornu ammonis 1 (CA1)]. We report differential state- and region-specific coupling between the slow large-scale rhythms and superimposed fast oscillations. During awake immobility, all four regions displayed a respiration-entrained rhythm (RR) with decreasing power from OB to CA1, which coupled exclusively to the 80- to 120-Hz gamma subband (γ2). During exploration, when theta activity was prevailing, OB and PLC still showed exclusive coupling of RR with γ2 and no theta-gamma coupling, whereas PAC and CA1 switched to selective coupling of theta with 40- to 80-Hz (γ1) and 120- to 160-Hz (γ3) gamma subbands. Our data illustrate a strong, specific interaction between neuronal activity patterns and respiration. Moreover, our results suggest that the coupling between slow and fast oscillations is a general brain mechanism not limited to the theta rhythm.

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Revealing neural correlates of behavior without behavioral measurements

Nature Communications ◽

10.1038/s41467-019-12724-2 ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 20

Author(s):

Alon Rubin ◽

Liron Sheintuch ◽

Noa Brande-Eilat ◽

Or Pinchasof ◽

Yoav Rechavi ◽

...

Keyword(s):

Internal Structure ◽

Large Scale ◽

Tuning Curve ◽

Activity Patterns ◽

A Priori ◽

Network Activity ◽

Head Direction ◽

External Variable ◽

Unknown Variable ◽

Tuning Curves

Abstract Measuring neuronal tuning curves has been instrumental for many discoveries in neuroscience but requires a priori assumptions regarding the identity of the encoded variables. We applied unsupervised learning to large-scale neuronal recordings in behaving mice from circuits involved in spatial cognition and uncovered a highly-organized internal structure of ensemble activity patterns. This emergent structure allowed defining for each neuron an ‘internal tuning-curve’ that characterizes its activity relative to the network activity, rather than relative to any predefined external variable, revealing place-tuning and head-direction tuning without relying on measurements of place or head-direction. Similar investigation in prefrontal cortex revealed schematic representations of distances and actions, and exposed a previously unknown variable, the ‘trajectory-phase’. The internal structure was conserved across mice, allowing using one animal’s data to decode another animal’s behavior. Thus, the internal structure of neuronal activity itself enables reconstructing internal representations and discovering new behavioral variables hidden within a neural code.

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Memory Replay in Balanced Recurrent Networks

10.1101/069641 ◽

2016 ◽

Author(s):

Nikolay Chenkov ◽

Henning Sprekeler ◽

Richard Kempter

Keyword(s):

Large Scale ◽

Neuronal Excitability ◽

Activity Patterns ◽

Recurrent Network ◽

Network Activity ◽

Sharp Waves ◽

Sequence Of Events ◽

Up States ◽

Plausible Model ◽

Network States

AbstractComplex patterns of neural activity appear during up-states in the neocortex and sharp waves in the hippocampus, including sequences that resemble those during prior behavioral experience. The mechanisms underlying this replay are not well understood. How can small synaptic footprints engraved by experience control large-scale network activity during memory retrieval and consolidation? We hypothesize that sparse and weak synaptic connectivity between Hebbian assemblies are boosted by pre-existing recurrent connectivity within them. To investigate this idea, we connect sequences of assemblies in randomly connected spiking neuronal networks with a balance of excitation and inhibition. Simulations and analytical calculations show that recurrent connections within assemblies allow for a fast amplification of signals that indeed reduces the required number of inter-assembly connections. Replay can be evoked by small sensory-like cues or emerge spontaneously by activity fluctuations. Global—potentially neuromodulatory—alterations of neuronal excitability can switch between network states that favor retrieval and consolidation.Author SummarySynaptic plasticity is the basis for learning and memory, and many experiments indicate that memories are imprinted in synaptic connections. However, basic mechanisms of how such memories are retrieved and consolidated remain unclear. In particular, how can one-shot learning of a sequence of events achieve a sufficiently strong synaptic footprint to retrieve or replay this sequence? Using both numerical simulations of spiking neural networks and an analytic approach, we provide a biologically plausible model for understanding how minute synaptic changes in a recurrent network can nevertheless be retrieved by small cues or even manifest themselves as activity patterns that emerge spontaneously. We show how the retrieval of exceedingly small changes in the connections across assemblies is robustly facilitated by recurrent connectivity within assemblies. This interaction between recurrent amplification within an assembly and the feed-forward propagation of activity across the network establishes a basis for the retrieval of memories.

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Refinement of corticospinal neuron activity during skilled motor learning

10.1101/2021.03.22.436415 ◽

2021 ◽

Author(s):

Najet Serradj ◽

Francesca Marino ◽

Yunuen Moreno-López ◽

Sydney Agger ◽

Andrew Sloan ◽

...

Keyword(s):

Large Scale ◽

Primary Motor Cortex ◽

Corticospinal Tract ◽

Activity Patterns ◽

Critical Role ◽

Network Activity ◽

Neuron Activity ◽

Cortical Motor ◽

Dependent Behavior

AbstractThe learning of motor skills relies on plasticity of the primary motor cortex as task acquisition drives the remodeling of cortical motor networks1,2. Large scale cortical remodeling of evoked motor outputs occurs in response to the learning of skilled, corticospinal-dependent behavior, but not simple, unskilled tasks1. Here we determine the response of corticospinal neurons to both skilled and unskilled motor training and assess the role of corticospinal neuron activity in the execution of the trained behaviors. Using in vivo calcium imaging, we found that refinement of corticospinal activity correlated with the development of skilled, but not unskilled, motor expertise. Animals that failed to learn our skilled task exhibited a limited repertoire of dynamic movements and a corresponding absence of network modulation. Transection of the corticospinal tract and aberrant activation of corticospinal neurons show the necessity for corticospinal network activity patterns in the execution of skilled, but not unskilled, movement. We reveal a critical role for corticospinal network modulation in the learning and execution of skilled motor movements. The integrity of the corticospinal tract is essential to the recovery of voluntary movement after central nervous system injuries and these findings should help to shape translational approaches to motor recovery.

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Synchronizing Brain Rhythms to Improve Cognition

Annual Review of Medicine ◽

10.1146/annurev-med-060619-022857 ◽

2020 ◽

Vol 72 (1) ◽

Author(s):

Shrey Grover ◽

John A. Nguyen ◽

Robert M.G. Reinhart

Keyword(s):

Large Scale ◽

Activity Patterns ◽

Neuropsychiatric Disorders ◽

Network Activity ◽

Human Cognition ◽

Annual Review ◽

Publication Date ◽

Early Results ◽

Transcranial Alternating Current Stimulation ◽

Communication Dynamics

Impaired cognition is common in many neuropsychiatric disorders and severely compromises quality of life. Synchronous electrophysiological rhythms represent a core mechanism for sculpting communication dynamics among large-scale brain networks that underpin cognition and its breakdown in neuropsychiatric disorders. Here, we review an emerging neuromodulation technology called transcranial alternating current stimulation that has shown remarkable early results in rapidly improving various domains of human cognition by modulating properties of rhythmic network synchronization. Future noninvasive neuromodulation research holds promise for potentially rescuing network activity patterns and improving cognition, setting groundwork for the development of drug-free, circuit-based therapeutics for people with cognitive brain disorders. Expected final online publication date for the Annual Review of Medicine, Volume 72 is January 27, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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The Functional Role of Large-scale Brain Network Coordination in Placebo-induced Anxiolysis

Cerebral Cortex ◽

10.1093/cercor/bhy188 ◽

2018 ◽

Vol 29 (8) ◽

pp. 3201-3210 ◽

Cited By ~ 4

Author(s):

Benjamin Meyer ◽

Kenneth S L Yuen ◽

Victor Saase ◽

Raffael Kalisch

Keyword(s):

Functional Role ◽

Large Scale ◽

Activity Patterns ◽

Clinical Importance ◽

Mental States ◽

Brain Network ◽

Anxiety Reduction ◽

Anterior Cingulate ◽

Network Activity

Abstract Anxiety reduction through mere expectation of anxiolytic treatment effects (placebo anxiolysis) has enormous clinical importance. Recent behavioral and electrophysiological data suggest that placebo anxiolysis involves reduced vigilance and enhanced internalization of attention; however, the underlying neurobiological mechanisms are not yet clear. Given the fundamental function of intrinsic connectivity networks (ICNs) in basic cognitive processes, we investigated ICN activity patterns associated with externally and internally directed mental states under the influence of an anxiolytic placebo medication. Based on recent findings, we specifically analyzed the functional role of the rostral anterior cingulate cortex (rACC) in coordinating placebo-dependent cue-related (phasic) and cue-unrelated (sustained) network activity. Under placebo, we observed a down-regulation of the entire salience network (SN), particularly in response to threatening cues. The rACC exhibited enhanced cue-unrelated functional connectivity (FC) with the SN, which correlated with reductions in tonic arousal and anxiety. Hence, apart from the frequently reported modulation of aversive cue responses, the rACC appears to be crucially involved in exerting a tonically dampening control over salience-responsive structures. In line with a more internally directed mental state, we also found enhanced FC within the default mode network (DMN), again predicting reductions in anxiety under placebo.

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Revealing neural correlates of behavior without behavioral measurements

10.1101/540195 ◽

2019 ◽

Cited By ~ 1

Author(s):

Alon Rubin ◽

Liron Sheintuch ◽

Noa Brande-Eilat ◽

Or Pinchasof ◽

Yoav Rechavi ◽

...

Keyword(s):

Internal Structure ◽

Large Scale ◽

Tuning Curve ◽

Activity Patterns ◽

A Priori ◽

Network Activity ◽

Head Direction ◽

External Variable ◽

Unknown Variable ◽

Ensemble Activity

Measuring neuronal tuning curves has been instrumental for many discoveries in neuroscience but requires a-priori assumptions regarding the identity of the encoded variables. We applied unsupervised learning to large-scale neuronal recordings in behaving mice from circuits involved in spatial cognition, and uncovered a highly-organized internal structure of ensemble activity patterns. This emergent structure allowed defining for each neuron an ‘internal tuning-curve’ that characterizes its activity relative to the network activity, rather than relative to any pre-defined external variable – revealing place-tuning in the hippocampus and head-direction tuning in the thalamus and postsubiculum, without relying on measurements of place or head-direction. Similar investigation in prefrontal cortex revealed schematic representations of distances and actions, and exposed a previously unknown variable, the ‘trajectory-phase’. The structure of ensemble activity patterns was conserved across mice, allowing using one animal’s data to decode another animal’s behavior. Thus, the internal structure of neuronal activity itself enables reconstructing internal representations and discovering new behavioral variables hidden within a neural code.

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A supervised machine learning approach to characterize spinal network function

Journal of Neurophysiology ◽

10.1152/jn.00763.2018 ◽

2019 ◽

Vol 121 (6) ◽

pp. 2001-2012 ◽

Cited By ~ 4

Author(s):

A. N. Dalrymple ◽

S. A. Sharples ◽

N. Osachoff ◽

A. P. Lognon ◽

P. J. Whelan

Keyword(s):

Machine Learning ◽

Spinal Cord ◽

Nervous System ◽

Spontaneous Activity ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Network Activity ◽

Multilayer Perceptrons ◽

Network Development ◽

Newborn Mice

Spontaneous activity is a common feature of immature neuronal networks throughout the central nervous system and plays an important role in network development and consolidation. In postnatal rodents, spontaneous activity in the spinal cord exhibits complex, stochastic patterns that have historically proven challenging to characterize. We developed a software tool for quickly and automatically characterizing and classifying episodes of spontaneous activity generated from developing spinal networks. We recorded spontaneous activity from in vitro lumbar ventral roots of 16 neonatal [postnatal day (P)0–P3] mice. Recordings were DC coupled and detrended, and episodes were separated for analysis. Amplitude-, duration-, and frequency-related features were extracted from each episode and organized into five classes. Paired classes and features were used to train and test supervised machine learning algorithms. Multilayer perceptrons were used to classify episodes as rhythmic or multiburst. We increased network excitability with potassium chloride and tested the utility of the tool to detect changes in features and episode class. We also demonstrate usability by having a novel experimenter use the program to classify episodes collected at a later time point (P5). Supervised machine learning-based classification of episodes accounted for changes that traditional approaches cannot detect. Our tool, named SpontaneousClassification, advances the detail in which we can study not only developing spinal networks, but also spontaneous networks in other areas of the nervous system.NEW & NOTEWORTHY Spontaneous activity is important for nervous system network development and consolidation. Our software uses machine learning to automatically and quickly characterize and classify episodes of spontaneous activity in the spinal cord of newborn mice. It detected changes in network activity following KCl-enhanced excitation. Using our software to classify spontaneous activity throughout development, in pathological models, or with neuromodulation, may offer insight into the development and organization of spinal circuits.

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