scholarly journals Breathing modulates cortico-hippocampal dynamics during offline states

2021 ◽  
Author(s):  
Nikolaos Karalis ◽  
Anton Sirota
Keyword(s):  
Information Flow ◽  
Neural Activity ◽  
Information Transfer ◽  
Large Scale ◽  
Brain Regions ◽  
State Transitions ◽  
Population Activity ◽  
Brain Rhythm ◽  
Regional Population ◽  
Brain Circuit

Abstract Network dynamics have been proposed as a mechanistic substrate for the information transfer across cortical and hippocampal circuits. During sleep and offline states, synchronous reactivation across these regions underlies the consolidation of memories. However, little is known about the mechanisms that synchronize and coordinate these processes across widespread brain regions. Here we address the hypothesis that breathing acts as an oscillatory pacemaker, persistently coupling distributed brain circuit dynamics. Using large-scale recordings from seven cortical and subcortical brain regions in quiescent and sleeping mice, we identified a novel global mechanism, termed respiratory corollary discharge, that co-modulates neural activity across these circuits. Analysis of inter-regional population activity and optogenetic perturbations revealed that breathing rhythm couples hippocampal sharp-wave ripples and cortical DOWN/UP state transitions by jointly modulating excitability in these circuits. These results highlight breathing, a perennial brain rhythm, as an oscillatory scaffold for the functional coordination of the limbic circuit, supporting the segregation and integration of information flow across neuronal networks during offline states.

scholarly journals Altered topology of large-scale structural brain networks in chronic stroke

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Bastian Cheng ◽  
Eckhard Schlemm ◽  
Robert Schulz ◽  
Marlene Boenstrup ◽  
Arnaud Messé ◽  
...  
Keyword(s):  
Information Transfer ◽  
Large Scale ◽  
Neuronal Degeneration ◽  
Brain Networks ◽  
Brain Regions ◽  
Local Network ◽  
Structural Connectome ◽  
The Impact ◽  
Structural Brain ◽  
Topological Changes

Abstract Beyond disruption of neuronal pathways, focal stroke lesions induce structural disintegration of distant, yet connected brain regions via retrograde neuronal degeneration. Stroke lesions alter functional brain connectivity and topology in large-scale brain networks. These changes are associated with the degree of clinical impairment and recovery. In contrast, changes of large scale, structural brain networks after stroke are less well reported. We therefore aimed to analyse the impact of focal lesions on the structural connectome after stroke based on data from diffusion-weighted imaging and probabilistic fibre tracking. In total, 17 patients (mean age 64.5 ± 8.4 years) with upper limb motor deficits in the chronic stage after stroke and 21 healthy participants (mean age 64.9 ± 10.3 years) were included. Clinical deficits were evaluated by grip strength and the upper extremity Fugl-Meyer assessment. We calculated global and local graph theoretical measures to characterize topological changes in the structural connectome. Results from our analysis demonstrated significant alterations of network topology in both ipsi- and contralesional, primarily unaffected, hemispheres after stroke. Global efficiency was significantly lower in stroke connectomes as an indicator of overall reduced capacity for information transfer between distant brain areas. Furthermore, topology of structural connectomes was shifted toward a higher degree of segregation as indicated by significantly higher values of global clustering and modularity. On a level of local network parameters, these effects were most pronounced in a subnetwork of cortico-subcortical brain regions involved in motor control. Structural changes were not significantly associated with clinical measures. We propose that the observed network changes in our patients are best explained by the disruption of inter- and intrahemispheric, long white matter fibre tracts connecting distant brain regions. Our results add novel insights on topological changes of structural large-scale brain networks in the ipsi- and contralesional hemisphere after stroke.

scholarly journals Induced cortical oscillations in turtle cortex are coherent at the mesoscale of population activity, but not at the microscale of the membrane potential of neurons

2017 ◽  
Vol 118 (5) ◽  
pp. 2579-2591 ◽  
Author(s):  
Mahmood S. Hoseini ◽  
Jeff Pobst ◽  
Nathaniel Wright ◽  
Wesley Clawson ◽  
Woodrow Shew ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Membrane Potential ◽  
Neural Activity ◽  
Visual Stimulation ◽  
Field Potential ◽  
Brain Regions ◽  
Large Trial ◽  
Population Activity ◽  
Potential Oscillations ◽  
Time Frequency

Bursts of oscillatory neural activity have been hypothesized to be a core mechanism by which remote brain regions can communicate. Such a hypothesis raises the question to what extent oscillations are coherent across spatially distant neural populations. To address this question, we obtained local field potential (LFP) and membrane potential recordings from the visual cortex of turtle in response to visual stimulation of the retina. The time-frequency analysis of these recordings revealed pronounced bursts of oscillatory neural activity and a large trial-to-trial variability in the spectral and temporal properties of the observed oscillations. First, local bursts of oscillations varied from trial to trial in both burst duration and peak frequency. Second, oscillations of a given recording site were not autocoherent; i.e., the phase did not progress linearly in time. Third, LFP oscillations at spatially separate locations within the visual cortex were more phase coherent in the presence of visual stimulation than during ongoing activity. In contrast, the membrane potential oscillations from pairs of simultaneously recorded pyramidal neurons showed smaller phase coherence, which did not change when switching from black screen to visual stimulation. In conclusion, neuronal oscillations at distant locations in visual cortex are coherent at the mesoscale of population activity, but coherence is largely absent at the microscale of the membrane potential of neurons. NEW & NOTEWORTHY Coherent oscillatory neural activity has long been hypothesized as a potential mechanism for communication across locations in the brain. In this study we confirm the existence of coherent oscillations at the mesoscale of integrated cortical population activity. However, at the microscopic level of neurons, we find no evidence for coherence among oscillatory membrane potential fluctuations. These results raise questions about the applicability of the communication through coherence hypothesis to the level of the membrane potential.

scholarly journals Perception and propagation of activity through the cortical hierarchy is determined by neural variability

2021 ◽  
Author(s):  
James M Rowland ◽  
Thijs L van der Plas ◽  
Matthias Loidolt ◽  
Robert Michael Lees ◽  
Joshua Keeling ◽  
...  
Keyword(s):  
Neural Activity ◽  
Signal To Noise Ratio ◽  
Brain Regions ◽  
Cortical Function ◽  
Population Activity ◽  
Two Photon ◽  
Secondary Somatosensory Cortex ◽  
Cortical Hierarchy ◽  
Robust Transmission ◽  
Neural Variability

The brains of higher organisms are composed of anatomically and functionally distinct regions performing specialised tasks; but regions do not operate in isolation. Orchestration of complex behaviours requires communication between brain regions, but how neural activity dynamics are organised to facilitate reliable transmission is not well understood. We studied this process directly by generating neural activity that propagates between brain regions and drives behaviour, allowing us to assess how populations of neurons in sensory cortex cooperate to transmit information. We achieved this by imaging two hierarchically organised and densely interconnected regions, the primary and secondary somatosensory cortex (S1 and S2) in mice while performing two-photon photostimulation of S1 neurons and assigning behavioural salience to the photostimulation. We found that the probability of perception is determined not only by the strength of the photostimulation signal, but also by the variability of S1 neural activity. Therefore, maximising the signal-to-noise ratio of the stimulus representation in cortex is critical to its continued propagation downstream. Further, we show that propagated, behaviourally salient activity elicits balanced, persistent, and generalised activation of the downstream region. Hence, our work adds to existing understanding of cortical function by identifying how population activity is formatted to ensure robust transmission of information, allowing specialised brain regions to communicate and coordinate behaviour.

scholarly journals Large- and multi-scale networks in the rodent brain during novelty exploration

2020 ◽  
Author(s):  
Michael X Cohen ◽  
Bernhard Englitz ◽  
Arthur S C França
Keyword(s):  
Prefrontal Cortex ◽  
Parietal Cortex ◽  
Local Field ◽  
Neural Activity ◽  
Large Scale ◽  
Field Potential ◽  
Brain Regions ◽  
Data Availability ◽  
Data Matrix ◽  
Temporal Scales

AbstractNeural activity is coordinated across multiple spatial and temporal scales, and these patterns of coordination are implicated in both healthy and impaired cognitive operations. However, empirical cross-scale investigations are relatively infrequent, due to limited data availability and to the difficulty of analyzing rich multivariate datasets. Here we applied frequency-resolved multivariate source-separation analyses to characterize a large-scale dataset comprising spiking and local field potential activity recorded simultaneously in three brain regions (prefrontal cortex, parietal cortex, hippocampus) in freely-moving mice. We identified a constellation of multidimensional, inter-regional networks across a range of frequencies (2-200 Hz). These networks were reproducible within animals across different recording sessions, but varied across different animals, suggesting individual variability in network architecture. The theta band (~4-10 Hz) networks had several prominent features, including roughly equal contribution from all regions and strong inter-network synchronization. Overall, these findings demonstrate a multidimensional landscape of large-scale functional activations of cortical networks operating across multiple spatial, spectral, and temporal scales during open-field exploration.Significance statementNeural activity is synchronized over space, time, and frequency. To characterize the dynamics of large-scale networks spanning multiple brain regions, we recorded data from the prefrontal cortex, parietal cortex, and hippocampus in awake behaving mice, and pooled data from spiking activity and local field potentials into one data matrix. Frequency-specific multivariate decomposition methods revealed a cornucopia of neural networks defined by coherent spatiotemporal patterns over time. These findings reveal a rich, dynamic, and multivariate landscape of large-scale neural activity patterns during foraging behavior.

Scalable Semisupervised Functional Neurocartography Reveals Canonical Neurons in Behavioral Networks

2016 ◽  
Vol 28 (8) ◽  
pp. 1453-1497 ◽  
Author(s):  
E. Paxon Frady ◽  
Ashish Kapoor ◽  
Eric Horvitz ◽  
William B. Kristan Jr.
Keyword(s):  
Neural Activity ◽  
Large Scale ◽  
Feature Space ◽  
Brain Regions ◽  
Machine Learning Techniques ◽  
Intersubject Variability ◽  
High Dimensions ◽  
Spatial And Temporal Scales ◽  
Canonical Neurons ◽  
The Rich

Large-scale data collection efforts to map the brain are underway at multiple spatial and temporal scales, but all face fundamental problems posed by high-dimensional data and intersubject variability. Even seemingly simple problems, such as identifying a neuron/brain region across animals/subjects, become exponentially more difficult in high dimensions, such as recognizing dozens of neurons/brain regions simultaneously. We present a framework and tools for functional neurocartography—the large-scale mapping of neural activity during behavioral states. Using a voltage-sensitive dye (VSD), we imaged the multifunctional responses of hundreds of leech neurons during several behaviors to identify and functionally map homologous neurons. We extracted simple features from each of these behaviors and combined them with anatomical features to create a rich medium-dimensional feature space. This enabled us to use machine learning techniques and visualizations to characterize and account for intersubject variability, piece together a canonical atlas of neural activity, and identify two behavioral networks. We identified 39 neurons (18 pairs, 3 unpaired) as part of a canonical swim network and 17 neurons (8 pairs, 1 unpaired) involved in a partially overlapping preparatory network. All neurons in the preparatory network rapidly depolarized at the onsets of each behavior, suggesting that it is part of a dedicated rapid-response network. This network is likely mediated by the S cell, and we referenced VSD recordings to an activity atlas to identify multiple cells of interest simultaneously in real time for further experiments. We targeted and electrophysiologically verified several neurons in the swim network and further showed that the S cell is presynaptic to multiple neurons in the preparatory network. This study illustrates the basic framework to map neural activity in high dimensions with large-scale recordings and how to extract the rich information necessary to perform analyses in light of intersubject variability.

scholarly journals Recording extracellular neural activity in the behaving monkey using a semichronic and high-density electrode system

2016 ◽  
Vol 116 (2) ◽  
pp. 563-574 ◽  
Author(s):  
Germán Mendoza ◽  
Adrien Peyrache ◽  
Jorge Gámez ◽  
Luis Prado ◽  
György Buzsáki ◽  
...  
Keyword(s):  
Neural Activity ◽  
Large Scale ◽  
Low Cost ◽  
Pyramidal Cells ◽  
Macaque Monkey ◽  
Electrode System ◽  
High Density ◽  
Large Species ◽  
Population Activity ◽  
Different Depths

We describe a technique to semichronically record the cortical extracellular neural activity in the behaving monkey employing commercial high-density electrodes. After the design and construction of low cost microdrives that allow varying the depth of the recording locations after the implantation surgery, we recorded the extracellular unit activity from pools of neurons at different depths in the presupplementary motor cortex (pre-SMA) of a rhesus monkey trained in a tapping task. The collected data were processed to classify cells as putative pyramidal cells or interneurons on the basis of their waveform features. We also demonstrate that short time cross-correlogram occasionally yields unit pairs with high short latency (<5 ms), narrow bin (<3 ms) peaks, indicative of monosynaptic spike transmission from pre- to postsynaptic neurons. These methods have been verified extensively in rodents. Finally, we observed that the pattern of population activity was repetitive over distinct trials of the tapping task. These results show that the semichronic technique is a viable option for the large-scale parallel recording of local circuit activity at different depths in the cortex of the macaque monkey and other large species.

scholarly journals Large-scale cellular-resolution imaging of neural activity in freely behaving mice

2021 ◽  
Author(s):  
D.P. Leman ◽  
I.A. Chen ◽  
K.A. Bolding ◽  
J. Tai ◽  
L.K. Wilmerding ◽  
...  
Keyword(s):  
Neural Activity ◽  
Large Scale ◽  
First Generation ◽  
Brain Regions ◽  
Field Of View ◽  
Cellular Resolution ◽  
Neural Populations ◽  
Resolution Imaging ◽  
Freely Behaving ◽  
Freely Moving

AbstractMiniaturized microscopes for head-mounted fluorescence imaging are powerful tools for visualizing neural activity during naturalistic behaviors, but the restricted field of view of first-generation ‘miniscopes’ limits the size of neural populations accessible for imaging. Here we describe a novel miniaturized mesoscope offering cellular-resolution imaging over areas spanning several millimeters in freely moving mice. This system enables comprehensive visualization of activity across entire brain regions or interactions across areas.

scholarly journals A spatiotemporal complexity architecture of human brain activity

2021 ◽  
Author(s):  
Stephan Krohn ◽  
Nina von Schwanenflug ◽  
Leonhard Waschke ◽  
Amy Romanello ◽  
Martin Gell ◽  
...  
Keyword(s):  
Human Brain ◽  
Neural Activity ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain Organization ◽  
Functional Connections ◽  
Signal Complexity ◽  
The Brain

The human brain operates in large-scale functional networks, collectively subsumed as the functional connectome1-13. Recent work has begun to unravel the organization of the connectome, including the temporal dynamics of brain states14-20, the trade-off between segregation and integration9,15,21-23, and a functional hierarchy from lower-order unimodal to higher-order transmodal processing systems24-27. However, it remains unknown how these network properties are embedded in the brain and if they emerge from a common neural foundation. Here we apply time-resolved estimation of brain signal complexity to uncover a unifying principle of brain organization, linking the connectome to neural variability6,28-31. Using functional magnetic resonance imaging (fMRI), we show that neural activity is marked by spontaneous "complexity drops" that reflect episodes of increased pattern regularity in the brain, and that functional connections among brain regions are an expression of their simultaneous engagement in such episodes. Moreover, these complexity drops ubiquitously propagate along cortical hierarchies, suggesting that the brain intrinsically reiterates its own functional architecture. Globally, neural activity clusters into temporal complexity states that dynamically shape the coupling strength and configuration of the connectome, implementing a continuous re-negotiation between cost-efficient segregation and communication-enhancing integration9,15,21,23. Furthermore, complexity states resolve the recently discovered association between anatomical and functional network hierarchies comprehensively25-27,32. Finally, brain signal complexity is highly sensitive to age and reflects inter-individual differences in cognition and motor function. In sum, we identify a spatiotemporal complexity architecture of neural activity — a functional "complexome" that gives rise to the network organization of the human brain.

scholarly journals Towards an information theoretical description of communication in brain networks

2021 ◽  
pp. 1-23
Author(s):  
Enrico Amico ◽  
Kausar Abbas ◽  
Duy Anh Duong-Tran ◽  
Uttara Tipnis ◽  
Meenusree Rajapandian ◽  
...  
Keyword(s):  
Information Transfer ◽  
Large Scale ◽  
Brain Networks ◽  
Brain Regions ◽  
Brain Network ◽  
Theoretical Description ◽  
Structural Topology ◽  
Communication Dynamics ◽  
The Brain ◽  
The Way

Modeling communication dynamics in the brain is a key challenge in network neuroscience. We present here a framework that combines two measurements for any system where different communication processes are taking place on top of a fixed structural topology: Path Processing Score (PPS) estimates how much the brain signal has changed or has been transformed between any two brain regions (source and target); Path Broadcasting Strength (PBS) estimates the propagation of the signal through edges adjacent to the path being assessed. We use PPS and PBS to explore communication dynamics in large-scale brain networks. We show that brain communication dynamics can be divided into three main “communication regimes” of information transfer: absent communication (no communication happening); relay communication (information is being transferred almost intact); transducted communication (the information is being transformed). We use PBS to categorize brain regions based on the way they broadcast information. Subcortical regions are mainly direct broadcasters to multiple receivers; Temporal and frontal nodes mainly operate as broadcast relay brain stations; Visual and somato-motor cortices act as multi-channel transducted broadcasters. This work paves the way towards the field of brain network information theory by providing a principled methodology to explore communication dynamics in large-scale brain networks.

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