scholarly journals A future for neuronal oscillation research

2018 ◽  
Vol 2 ◽  
pp. 239821281879482
Author(s):  
Miles A. Whittington ◽  
Roger D. Traub ◽  
Natalie E. Adams
Keyword(s):  
Cognitive Processing ◽  
Brain Function ◽  
Large Scale ◽  
Activity Patterns ◽  
Neuronal Population ◽  
Local Network ◽  
Brain State ◽  
Whole Brain ◽  
Temporal Properties ◽  
Global Brain

Neuronal oscillations represent the most obvious feature of electrical activity in the brain. They are linked in general with global brain state (awake, asleep, etc.) and specifically with organisation of neuronal outputs during sensory perception and cognitive processing. Oscillations can be generated by individual neurons on the basis of interaction between inputs and intrinsic conductances but are far more commonly seen at the local network level in populations of interconnected neurons with diverse arrays of functional properties. It is at this level that the brain’s rich and diverse library of oscillatory time constants serve to temporally organise large-scale neural activity patterns. The discipline is relatively mature at the microscopic (cell, local network) level – although novel discoveries are still commonplace – but requires a far greater understanding of mesoscopic and macroscopic brain dynamics than we currently hold. Without this, extrapolation from the temporal properties of neurons and their communication strategies up to whole brain function will remain largely theoretical. However, recent advances in large-scale neuronal population recordings and more direct, higher fidelity, non-invasive measurement of whole brain function suggest much progress is just around the corner.

scholarly journals δ-Oscillation Correlates of Anesthesia-induced Unconsciousness in Large-scale Brain Networks of Human Infants

2019 ◽  
Vol 131 (6) ◽  
pp. 1239-1253 ◽  
Author(s):  
Ioannis Pappas ◽  
Laura Cornelissen ◽  
David K. Menon ◽  
Charles B. Berde ◽  
Emmanuel A. Stamatakis
Keyword(s):  
Functional Connectivity ◽  
General Anesthesia ◽  
Information Transfer ◽  
Large Scale ◽  
Aminobutyric Acid ◽  
Brain State ◽  
Whole Brain ◽  
Efficient System ◽  
Median Difference ◽  
Infant Brain

Abstract Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New Background Functional brain connectivity studies can provide important information about changes in brain-state dynamics during general anesthesia. In adults, γ-aminobutyric acid–mediated agents disrupt integration of information from local to the whole-brain scale. Beginning around 3 to 4 months postnatal age, γ-aminobutyric acid–mediated anesthetics such as sevoflurane generate α-electroencephalography oscillations. In previous studies of sevoflurane-anesthetized infants 0 to 3.9 months of age, α-oscillations were absent, and power spectra did not distinguish between anesthetized and emergence from anesthesia conditions. Few studies detailing functional connectivity during general anesthesia in infants exist. This study’s aim was to identify changes in functional connectivity of the infant brain during anesthesia. Methods A retrospective cohort study was performed using multichannel electroencephalograph recordings of 20 infants aged 0 to 3.9 months old who underwent sevoflurane anesthesia for elective surgery. Whole-brain functional connectivity was evaluated during maintenance of a surgical state of anesthesia and during emergence from anesthesia. Functional connectivity was represented as networks, and network efficiency indices (including complexity and modularity) were computed at the sensor and source levels. Results Sevoflurane decreased functional connectivity at the δ-frequency (1 to 4 Hz) in infants 0 to 3.9 months old when comparing anesthesia with emergence. At the sensor level, complexity decreased during anesthesia, showing less whole-brain integration with prominent alterations in the connectivity of frontal and parietal sensors (median difference, 0.0293; 95% CI, −0.0016 to 0.0397). At the source level, similar results were observed (median difference, 0.0201; 95% CI, −0.0025 to 0.0482) with prominent alterations in the connectivity between default-mode and frontoparietal regions. Anesthesia resulted in fragmented modules as modularity increased at the sensor (median difference, 0.0562; 95% CI, 0.0048 to 0.1298) and source (median difference, 0.0548; 95% CI, −0.0040 to 0.1074) levels. Conclusions Sevoflurane is associated with decreased capacity for efficient information transfer in the infant brain. Such findings strengthen the hypothesis that conscious processing relies on an efficient system of integrated information transfer across the whole brain.

scholarly journals Locomotion-dependent remapping of distributed cortical networks

2018 ◽  
Author(s):  
Kelly B. Clancy ◽  
Ivana Orsolic ◽  
Thomas D. Mrsic-Flogel
Keyword(s):  
Activity Patterns ◽  
Local Area ◽  
Local Network ◽  
Brain State ◽  
Dorsal Cortex ◽  
Cortical Networks ◽  
V1 Neurons ◽  
State Dependent ◽  
Visual Areas ◽  
Sensory Cortices

AbstractThe interactions between areas of the neocortex are fluid and state-dependent, but how individual neurons couple to cortex-wide network dynamics remains poorly understood. We correlated the spiking of individual neurons in primary visual (V1) and retrosplenial (RSP) cortex to activity across dorsal cortex, recorded simultaneously by calcium imaging. Individual neurons were correlated with distinct and reproducible patterns of activity across the cortical surface; while some fired predominantly with their local area, others coupled to activity in subsets of distal areas. The extent of distal coupling was predicted by how strongly neurons correlated with the local network. Changes in brain state triggered by locomotion re-structured how neurons couple to cortical activity patterns: running strengthened affiliations of V1 neurons with visual areas, while strengthening distal affiliations of RSP neurons with sensory cortices. Thus, individual neurons within a cortical area can independently engage in different cortical networks depending on the animal's behavioral state.

scholarly journals Turbulent-like dynamics in the human brain

2019 ◽  
Author(s):  
Gustavo Deco ◽  
Morten L. Kringelbach
Keyword(s):  
Human Brain ◽  
Brain Function ◽  
Information Transfer ◽  
Large Scale ◽  
Brain Dynamics ◽  
Whole Brain ◽  
Large Scale Network ◽  
Maximal Sensitivity ◽  
Scale Network ◽  
Best Fit

SummaryTurbulence facilitates fast energy/information transfer across scales in physical systems. These qualities are important for brain function, but it is currently unknown if the dynamic intrinsic backbone of brain also exhibits turbulence. Using large-scale neuroimaging empirical data from 1003 healthy participants, we demonstrate Kuramoto’s amplitude turbulence in human brain dynamics. Furthermore, we build a whole-brain model with coupled oscillators to demonstrate that the best fit to the data corresponds to a region of maximally developed amplitude turbulence, which also corresponds to maximal sensitivity to the processing of external stimulations (information capability). The model shows the economy of anatomy by following the Exponential Distance Rule of anatomical connections as a cost-of-wiring principle. This establishes a firm link between turbulence and optimal brain function. Overall, our results reveal a way of analysing and modelling whole-brain dynamics that suggests turbulence as the dynamic intrinsic backbone facilitating large scale network communication.

scholarly journals Resting Brain Fluctuations Are Intrinsically Coupled to Visual Response Dynamics

2020 ◽  
Author(s):  
Michaël E Belloy ◽  
Jacob Billings ◽  
Anzar Abbas ◽  
Amrit Kashyap ◽  
Wen-Ju Pan ◽  
...  
Keyword(s):  
Reticular Formation ◽  
Visual Stimulation ◽  
Activity Patterns ◽  
Brain Dynamics ◽  
Brain State ◽  
Visual Response ◽  
Response Dynamics ◽  
Sensory Stimuli ◽  
Prominent Component ◽  
Global Brain

Abstract How do intrinsic brain dynamics interact with processing of external sensory stimuli? We sought new insights using functional magnetic resonance imaging to track spatiotemporal activity patterns at the whole brain level in lightly anesthetized mice, during both resting conditions and visual stimulation trials. Our results provide evidence that quasiperiodic patterns (QPPs) are the most prominent component of mouse resting brain dynamics. These QPPs captured the temporal alignment of anticorrelation between the default mode (DMN)- and task-positive (TPN)-like networks, with global brain fluctuations, and activity in neuromodulatory nuclei of the reticular formation. Specifically, the phase of QPPs prior to stimulation could significantly stratify subsequent visual response magnitude, suggesting QPPs relate to brain state fluctuations. This is the first observation in mice that dynamics of the DMN- and TPN-like networks, and particularly their anticorrelation, capture a brain state dynamic that affects sensory processing. Interestingly, QPPs also displayed transient onset response properties during visual stimulation, which covaried with deactivations in the reticular formation. We conclude that QPPs appear to capture a brain state fluctuation that may be orchestrated through neuromodulation. Our findings provide new frontiers to understand the neural processes that shape functional brain states and modulate sensory input processing.

scholarly journals The Locus Coeruleus Noradrenaline System in Delirium

2021 ◽  
Vol 13 ◽  
Author(s):  
Niels Hansen ◽  
Alina Isabel Rediske
Keyword(s):  
Prefrontal Cortex ◽  
Locus Coeruleus ◽  
Cognitive Processing ◽  
Large Scale ◽  
Brain Regions ◽  
Brain Dysfunction ◽  
Brain State ◽  
Release Of Noradrenaline ◽  
Noradrenergic Transmission ◽  
The Relationship

Delirium is a brain state involving severe brain dysfunction affecting cognitive and attentional capacities. Our opinion statement review aims to elucidate the relationship between abnormal arousal and locus coeruleus (LC) activity in cognitive dysfunction and inattention in delirium states. We propose (1) that enhanced noradrenaline release caused by altered arousal in hyperactive delirium states leads to increased noradrenergic transmission within the LC and subcortical and cortical brain regions including the prefrontal cortex and hippocampus, thus affecting how attention and cognition function. In hypoactive delirium states, however, we are presuming (2) that less arousal will cause the release of noradrenaline to diminish in the LC, followed by reduced noradrenergic transmission in cortical and subcortical brain areas concentrated within the prefrontal cortex and hippocampus, leading to deficient attention and cognitive processing. Studies addressing the measurement of noradrenaline and its derivatives in biomaterial probes regarding delirium are also covered in this article. In conclusion, the LC-NA system plays a crucial role in generating delirium. Yet there have been no large-scale studies investigating biomarkers of noradrenaline to help us draw conclusions for improving delirium’s diagnosis, treatment, and prognosis, and to better understand its pathogenesis.

scholarly journals Indexing brain state-dependent pupil dynamics with simultaneous fMRI and optical fiber calcium recording

2020 ◽  
Vol 117 (12) ◽  
pp. 6875-6882 ◽  
Author(s):  
Patricia Pais-Roldán ◽  
Kengo Takahashi ◽  
Filip Sobczak ◽  
Yi Chen ◽  
Xiaoning Zhao ◽  
...  
Keyword(s):  
Cognitive Processing ◽  
Resting State Fmri ◽  
Brain Network ◽  
Cell Group ◽  
Brain State ◽  
Correlation Pattern ◽  
Activation Patterns ◽  
State Dependent ◽  
Group 5 ◽  
Global Brain

Pupillometry, a noninvasive measure of arousal, complements human functional MRI (fMRI) to detect periods of variable cognitive processing and identify networks that relate to particular attentional states. Even under anesthesia, pupil dynamics correlate with brain-state fluctuations, and extended dilations mark the transition to more arousable states. However, cross-scale neuronal activation patterns are seldom linked to brain state-dependent pupil dynamics. Here, we complemented resting-state fMRI in rats with cortical calcium recording (GCaMP-mediated) and pupillometry to tackle the linkage between brain-state changes and neural dynamics across different scales. This multimodal platform allowed us to identify a global brain network that covaried with pupil size, which served to generate an index indicative of the brain-state fluctuation during anesthesia. Besides, a specific correlation pattern was detected in the brainstem, at a location consistent with noradrenergic cell group 5 (A5), which appeared to be dependent on the coupling between different frequencies of cortical activity, possibly further indicating particular brain-state dynamics. The multimodal fMRI combining concurrent calcium recordings and pupillometry enables tracking brain state-dependent pupil dynamics and identifying unique cross-scale neuronal dynamic patterns under anesthesia.

scholarly journals Multiscale brain modelling

2005 ◽  
Vol 360 (1457) ◽  
pp. 1043-1050 ◽  
Author(s):  
P. A Robinson ◽  
C. J Rennie ◽  
D. L Rowe ◽  
S. C O'Connor ◽  
E Gordon
Keyword(s):  
Electrical Activity ◽  
Brain Function ◽  
Large Scale ◽  
Multiple Scales ◽  
Brain Anatomy ◽  
Brain Electrical Activity ◽  
Whole Brain ◽  
Non Invasive ◽  
Spatio Temporal ◽  
Evoked Response Potentials

A central difficulty of brain modelling is to span the range of spatio-temporal scales from synapses to the whole brain. This paper overviews results from a recent model of the generation of brain electrical activity that incorporates both basic microscopic neurophysiology and large-scale brain anatomy to predict brain electrical activity at scales from a few tenths of a millimetre to the whole brain. This model incorporates synaptic and dendritic dynamics, nonlinearity of the firing response, axonal propagation and corticocortical and corticothalamic pathways. Its relatively few parameters measure quantities such as synaptic strengths, corticothalamic delays, synaptic and dendritic time constants, and axonal ranges, and are all constrained by independent physiological measurements. It reproduces quantitative forms of electroencephalograms seen in various states of arousal, evoked response potentials, coherence functions, seizure dynamics and other phenomena. Fitting model predictions to experimental data enables underlying physiological parameters to be inferred, giving a new non-invasive window into brain function that complements slower, but finer-resolution, techniques such as fMRI. Because the parameters measure physiological quantities relating to multiple scales, and probe deep structures such as the thalamus, this will permit the testing of a range of hypotheses about vigilance, cognition, drug action and brain function. In addition, referencing to a standardized database of subjects adds strength and specificity to characterizations obtained.

scholarly journals Comparing Task-Relevant Information Across Different Methods of Extracting Functional Connectivity

2018 ◽  
Author(s):  
Sophie Benitez Stulz ◽  
Andrea Insabato ◽  
Gustavo Deco ◽  
Matthieu Gilson ◽  
Mario Senden
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Cross Validation ◽  
Activity Patterns ◽  
Relevant Information ◽  
Brain Regions ◽  
Classification Performance ◽  
Phase Coherence ◽  
Brain State ◽  
Brain States

AbstractThe concept of brain states, functionally relevant large-scale activity patterns, has become popular in neuroimaging. Not all components of such patterns are equally characteristic for each brain state, but machine learning provides a possibility for extracting and comparing the structure of brain states from functional data. However, their characterization in terms of functional connectivity measures varies widely, from cross-correlation to phase coherence, and the idea that different measures provide similar or coherent information is a common assumption made in neuroimaging. Here, we compare the brain state signatures extracted from of phase coherence, pairwise covariance, correlation, regularized covariance and regularized precision for a dataset of subjects performing five different cognitive tasks. In addition, we compare the classification performance in identifying the tasks for each connectivity measure. The measures are evaluated in their ability to discriminate the five tasks with two types of cross-validation: within-subject cross-validation, which reflects the stability of the signature over time; and between-subject cross-validation, which aims at extracting signatures that generalize across subjects. Secondly, we compare the informative features (connections or links between brain regions/areas) across measures to test the assumption that similar information is obtained about brain state signatures from different connectivity measures. In our results, the different types of cross-validation give different classification performance and emphasize that functional connectivity measures on fMRI require observation windows of sufficient duration. Furthermore, we find that informative links for the classification, meaning changes between tasks that are consistent across subjects, are entirely uncorrelated between BOLD correlations and covariances. These results indicate that the corresponding FC signature can strongly differ across FC methods used and that interpretation is subject to caution in terms of subnetworks related to a task.

scholarly journals Feedforward and feedback interactions between visual cortical areas use different population activity patterns

2021 ◽  
Author(s):  
João D. Semedo ◽  
Anna I. Jasper ◽  
Amin Zandvakili ◽  
Amir Aschner ◽  
Christian K. Machens ◽  
...  
Keyword(s):  
Brain Function ◽  
Activity Patterns ◽  
Sensory Information ◽  
Neuronal Population ◽  
Stimulus Onset ◽  
Population Activity ◽  
Cortical Areas ◽  
Visual Areas ◽  
Population Interactions ◽  
Visual Cortical

AbstractBrain function relies on the coordination of activity across multiple, recurrently connected, brain areas. For instance, sensory information encoded in early sensory areas is relayed to, and further processed by, higher cortical areas and then fed back. However, the way in which feedforward and feedback signaling interact with one another is incompletely understood. Here we investigate this question by leveraging simultaneous neuronal population recordings in early and midlevel visual areas (V1-V2 and V1-V4). Using a dimensionality reduction approach, we find that population interactions are feedforward-dominated shortly after stimulus onset and feedback-dominated during spontaneous activity. The population activity patterns most correlated across areas were distinct during feedforward- and feedback-dominated periods. These results suggest that feedforward and feedback signaling rely on separate “channels”, such that feedback signaling does not directly affect activity that is fed forward.

Implantable neural interfaces

2018 ◽  
Author(s):  
Stefano Vassanelli
Keyword(s):  
Brain Function ◽  
Large Scale ◽  
Ionic Channels ◽  
Patch Clamp Technique ◽  
Advantages And Disadvantages ◽  
Optogenetic Stimulation ◽  
Physical Interfaces ◽  
The Brain

Establishing direct communication with the brain through physical interfaces is a fundamental strategy to investigate brain function. Starting with the patch-clamp technique in the seventies, neuroscience has moved from detailed characterization of ionic channels to the analysis of single neurons and, more recently, microcircuits in brain neuronal networks. Development of new biohybrid probes with electrodes for recording and stimulating neurons in the living animal is a natural consequence of this trend. The recent introduction of optogenetic stimulation and advanced high-resolution large-scale electrical recording approaches demonstrates this need. Brain implants for real-time neurophysiology are also opening new avenues for neuroprosthetics to restore brain function after injury or in neurological disorders. This chapter provides an overview on existing and emergent neurophysiology technologies with particular focus on those intended to interface neuronal microcircuits in vivo. Chemical, electrical, and optogenetic-based interfaces are presented, with an analysis of advantages and disadvantages of the different technical approaches.

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