The asynchronous state's relation to large-scale potentials in cortex

Understanding the relation between large-scale potentials (M/EEG) and their underlying neural activity can improve the precision of research and clinical diagnosis. Recent insights into cortical dynamics highlighted a state of strongly reduced spike count correlations, termed the asynchronous state (AS). The AS has received considerable attention from experimenters and theorists alike, regarding its implications for cortical dynamics and coding of information. However, how reconcilable are these vanishing correlations in the AS with large-scale potentials such as M/EEG observed in most experiments? Typically the latter are assumed to be based on underlying correlations in activity, in particular between subthreshold potentials. We survey the occurrence of the AS across brain states, regions, and layers and argue for a reconciliation of this seeming disparity: large-scale potentials are either observed, first, at transitions between cortical activity states, which entail transient changes in population firing rate, as well as during the AS, and, second, on the basis of sufficiently large, asynchronous populations that only need to exhibit weak correlations in activity. Cells with no or little spiking activity can contribute to large-scale potentials via their subthreshold currents, while they do not contribute to the estimation of spiking correlations, defining the AS. Furthermore, third, the AS occurs only within particular cortical regions and layers associated with the currently selected modality, allowing for correlations at other times and between other areas and layers.

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Striatal activity reflects cortical activity patterns

10.1101/703710 ◽

2019 ◽

Cited By ~ 3

Author(s):

Andrew J Peters ◽

Nicholas A Steinmetz ◽

Kenneth D Harris ◽

Matteo Carandini

Keyword(s):

Large Scale ◽

Activity Patterns ◽

Dorsal Striatum ◽

Cortical Activity ◽

Task Engagement ◽

Visually Guided ◽

Cortical Inputs ◽

Cortical Regions ◽

The Relationship ◽

Different Parts

The dorsal striatum is organized into domains that drive characteristic behaviors1–7, and receive inputs from different parts of the cortex8,9 which modulate similar behaviors10–12. Striatal responses to cortical inputs, however, can be affected by changes in connection strength13–15, local striatal circuitry16,17, and thalamic inputs18,19. Therefore, it is unclear whether the pattern of activity across striatal domains mirrors that across the cortex20–23 or differs from it24–28. Here we use simultaneous large-scale recordings in the cortex and the striatum to show that striatal activity can be accurately predicted by spatiotemporal activity patterns in the cortex. The relationship between activity in the cortex and the striatum was spatially consistent with corticostriatal anatomy, and temporally consistent with a feedforward drive. Each striatal domain exhibited specific sensorimotor responses that predictably followed activity in the associated cortical regions, and the corticostriatal relationship remained unvaried during passive states or performance of a task probing visually guided behavior. However, the task’s visual stimuli and corresponding behavioral responses evoked relatively more activity in the striatum than in associated cortical regions. This increased striatal activity involved an additive offset in firing rate, which was independent of task engagement but only present in animals that had learned the task. Thus, striatal activity largely reflects patterns of cortical activity, deviating from them in a simple additive fashion for learned stimuli or actions.

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Large-scale imaging of cortical dynamics during sensory perception and behavior

Journal of Neurophysiology ◽

10.1152/jn.01056.2015 ◽

2016 ◽

Vol 115 (6) ◽

pp. 2852-2866 ◽

Cited By ~ 107

Author(s):

Joseph B. Wekselblatt ◽

Erik D. Flister ◽

Denise M. Piscopo ◽

Cristopher M. Niell

Keyword(s):

Large Scale ◽

Temporal Dynamics ◽

Dorsal Surface ◽

Global Scale ◽

Orientation Discrimination ◽

Cortical Dynamics ◽

Two Photon Microscopy ◽

Two Photon ◽

Cortical Regions ◽

And Behavior

Sensory-driven behaviors engage a cascade of cortical regions to process sensory input and generate motor output. To investigate the temporal dynamics of neural activity at this global scale, we have improved and integrated tools to perform functional imaging across large areas of cortex using a transgenic mouse expressing the genetically encoded calcium sensor GCaMP6s, together with a head-fixed visual discrimination behavior. This technique allows imaging of activity across the dorsal surface of cortex, with spatial resolution adequate to detect differential activity in local regions at least as small as 100 μm. Imaging during an orientation discrimination task reveals a progression of activity in different cortical regions associated with different phases of the task. After cortex-wide patterns of activity are determined, we demonstrate the ability to select a region that displayed conspicuous responses for two-photon microscopy and find that activity in populations of individual neurons in that region correlates with locomotion in trained mice. We expect that this paradigm will be a useful probe of information flow and network processing in brain-wide circuits involved in many sensory and cognitive processes.

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Decoding of cortex-wide brain activity from local recordings of neural potentials

10.1101/2021.10.14.464468 ◽

2021 ◽

Author(s):

Xin Liu ◽

Chi Ren ◽

Zhisheng Huang ◽

Madison Wilson ◽

Jeong-Hoon Kim ◽

...

Keyword(s):

Neural Activity ◽

Large Scale ◽

Neural Circuit ◽

Brain Activity ◽

Wide Field ◽

Dorsal Cortex ◽

Surface Potentials ◽

Brain Surface ◽

Neural Activities ◽

Cortical Regions

Objective. Electrical recordings of neural activity from brain surface have been widely employed in basic neuroscience research and clinical practice for investigations of neural circuit functions, brain-computer interfaces, and treatments for neurological disorders. Traditionally, these surface potentials have been believed to mainly reflect local neural activity. It is not known how informative the locally recorded surface potentials are for the neural activities across multiple cortical regions. Approach. To investigate that, we perform simultaneous local electrical recording and wide-field calcium imaging in awake head-fixed mice. Using a recurrent neural network model, we try to decode the calcium fluorescence activity of multiple cortical regions from local electrical recordings. Main results. The mean activity of different cortical regions could be decoded from locally recorded surface potentials. Also, each frequency band of surface potentials differentially encodes activities from multiple cortical regions so that including all the frequency bands in the decoding model gives the highest decoding performance. Despite the close spacing between recording channels, surface potentials from different channels provide complementary information about the large-scale cortical activity and the decoding performance continues to improve as more channels are included. Finally, we demonstrate the successful decoding of whole dorsal cortex activity at pixel-level using locally recorded surface potentials. Significance. These results show that the locally recorded surface potentials indeed contain rich information of the large-scale neural activities, which could be further demixed to recover the neural activity across individual cortical regions. In the future, our cross-modality inference approach could be adapted to virtually reconstruct cortex-wide brain activity, greatly expanding the spatial reach of surface electrical recordings without increasing invasiveness. Furthermore, it could be used to facilitate imaging neural activity across the whole cortex in freely moving animals, without requirement of head-fixed microscopy configurations.

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Task-Modulated Corticocortical Synchrony in the Cognitive-Motor Network Supporting Handwriting

Cerebral Cortex ◽

10.1093/cercor/bhz210 ◽

2019 ◽

Vol 30 (3) ◽

pp. 1871-1886

Author(s):

Timo Saarinen ◽

Jan Kujala ◽

Hannu Laaksonen ◽

Antti Jalava ◽

Riitta Salmelin

Keyword(s):

Large Scale ◽

Phase Synchronization ◽

Task Demands ◽

Low Frequencies ◽

Simple Loop ◽

Cortical Dynamics ◽

Motor Network ◽

Neural Processes ◽

Hand Coordination ◽

Cortical Regions

Abstract Both motor and cognitive aspects of behavior depend on dynamic, accurately timed neural processes in large-scale brain networks. Here, we studied synchronous interplay between cortical regions during production of cognitive-motor sequences in humans. Specifically, variants of handwriting that differed in motor variability, linguistic content, and memorization of movement cues were contrasted to unveil functional sensitivity of corticocortical connections. Data-driven magnetoencephalography mapping (n = 10) uncovered modulation of mostly left-hemispheric corticocortical interactions, as quantified by relative changes in phase synchronization. At low frequencies (~2–13 Hz), enhanced frontoparietal synchrony was related to regular handwriting, whereas premotor cortical regions synchronized for simple loop production and temporo-occipital areas for a writing task substituting normal script with loop patterns. At the beta-to-gamma band (~13–45 Hz), enhanced synchrony was observed for regular handwriting in the central and frontoparietal regions, including connections between the sensorimotor and supplementary motor cortices and between the parietal and dorsal premotor/precentral cortices. Interpreted within a modular framework, these modulations of synchrony mainly highlighted interactions of the putative pericentral subsystem of hand coordination and the frontoparietal subsystem mediating working memory operations. As part of cortical dynamics, interregional phase synchrony varies depending on task demands in production of cognitive-motor sequences.

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Brain Mechanisms of Conscious Awareness: Detect, Pulse, Switch, and Wave

The Neuroscientist ◽

10.1177/10738584211049378 ◽

2021 ◽

pp. 107385842110493

Author(s):

Hal Blumenfeld

Keyword(s):

Neural Activity ◽

Large Scale ◽

Memory Systems ◽

Attention And Memory ◽

Conscious Awareness ◽

Conscious Perception ◽

Discrete Events ◽

Cortical Regions ◽

Large Scale Networks ◽

Experimental Findings

Consciousness is a fascinating field of neuroscience research where questions often outnumber the answers. We advocate an open and optimistic approach where converging mechanisms in neuroscience may eventually provide a satisfactory understanding of consciousness. We first review several characteristics of conscious neural activity, including the involvement of dedicated systems for content and levels of consciousness, the distinction and overlap of mechanisms contributing to conscious states and conscious awareness of transient events, nonlinear transitions and involvement of large-scale networks, and finally the temporal nexus where conscious awareness of discrete events occurs when mechanisms of attention and memory meet. These considerations and recent new experimental findings lead us to propose an inclusive hypothesis involving four phases initiated shortly after an external sensory stimulus: (1) Detect—primary and higher cortical and subcortical circuits detect the stimulus and select it for conscious perception. (2) Pulse—a transient and massive neuromodulatory surge in subcortical-cortical arousal and salience networks amplifies signals enabling conscious perception to proceed. (3) Switch—networks that may interfere with conscious processing are switched off. (4) Wave—sequential processing through hierarchical lower to higher cortical regions produces a fully formed percept, encoded in frontoparietal working memory and medial temporal episodic memory systems for subsequent report of experience. The framework hypothesized here is intended to be nonexclusive and encourages the addition of other mechanisms with further progress. Ultimately, just as many mechanisms in biology together distinguish living from nonliving things, many mechanisms in neuroscience synergistically may separate conscious from nonconscious neural activity.

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Delta-band Cortical Tracking of Acoustic and Linguistic Features in Natural Spoken Narratives

10.1101/2020.07.31.231431 ◽

2020 ◽

Author(s):

Cheng Luo ◽

Nai Ding

Keyword(s):

Large Scale ◽

Cortical Activity ◽

Natural Speech ◽

Speech Comprehension ◽

Neural Encoding ◽

Linguistic Features ◽

Linguistic Information ◽

Cortical Dynamics ◽

Delta Band ◽

Linguistic Units

AbstractSpeech contains rich acoustic and linguistic information. During speech comprehension, cortical activity tracks the acoustic envelope of speech. Recent studies also observe cortical tracking of higher-level linguistic units, such as words and phrases, using synthesized speech deprived of delta-band acoustic envelope. It remains unclear, however, how cortical activity jointly encodes the acoustic and linguistic information in natural speech. Here, we investigate the neural encoding of words and demonstrate that delta-band cortical activity tracks the rhythm of multi-syllabic words when naturally listening to narratives. Furthermore, by dissociating the word rhythm from acoustic envelope, we find cortical activity primarily tracks the word rhythm during speech comprehension. When listeners’ attention is diverted, however, neural tracking of words diminishes, and delta-band activity becomes phase locked to the acoustic envelope. These results suggest that large-scale cortical dynamics in the delta band are primarily coupled to the rhythm of linguistic units during natural speech comprehension.

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Faculty Opinions recommendation of Resting-state neural activity across face-selective cortical regions is behaviorally relevant.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.13135958.14465056 ◽

2011 ◽

Author(s):

Cheryl Grady

Keyword(s):

Neural Activity ◽

Resting State ◽

Cortical Regions

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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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Fundamental Dynamical Modes Underlying Human Brain Synchronization

Computational and Mathematical Methods in Medicine ◽

10.1155/2012/912729 ◽

2012 ◽

Vol 2012 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Catalina Alvarado-Rojas ◽

Michel Le Van Quyen

Keyword(s):

Large Scale ◽

Brain Regions ◽

Electrode Placement ◽

High Dimensional ◽

Sleep Cycle ◽

Epileptic Patients ◽

Brain States ◽

Intracranial Recordings ◽

Characteristic Dynamic

Little is known about the long-term dynamics of widely interacting cortical and subcortical networks during the wake-sleep cycle. Using large-scale intracranial recordings of epileptic patients during seizure-free periods, we investigated local- and long-range synchronization between multiple brain regions over several days. For such high-dimensional data, summary information is required for understanding and modelling the underlying dynamics. Here, we suggest that a compact yet useful representation is given by a state space based on the first principal components. Using this representation, we report, with a remarkable similarity across the patients with different locations of electrode placement, that the seemingly complex patterns of brain synchrony during the wake-sleep cycle can be represented by a small number of characteristic dynamic modes. In this space, transitions between behavioral states occur through specific trajectories from one mode to another. These findings suggest that, at a coarse level of temporal resolution, the different brain states are correlated with several dominant synchrony patterns which are successively activated across wake-sleep states.

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