scholarly journals Catecholamines Alter the Intrinsic Variability of Cortical Population Activity and Perception

2017 ◽  
Author(s):  
Thomas Pfeffer ◽  
Arthur-Ervin Avramiea ◽  
Guido Nolte ◽  
Andreas K. Engel ◽  
Klaus Linkenkaer-Hansen ◽  
...  
Keyword(s):  
Temporal Structure ◽  
Neuropsychiatric Disorders ◽  
Pharmacological Intervention ◽  
Brain State ◽  
Intrinsic Variability ◽  
Perceptual Inference ◽  
Population Activity ◽  
Scale Free ◽  
Neural Computations ◽  
Cortical Computation

ABSTRACTThe ascending modulatory systems of the brainstem are powerful regulators of global brain state. Disturbances of these systems are implicated in several major neuropsychiatric disorders. Yet, how these systems interact with specific neural computations in the cerebral cortex to shape perception, cognition, and behavior remains poorly understood. Here, we probed into the effect of two such systems, the catecholaminergic (dopaminergic and noradrenergic) and cholinergic systems, on an important aspect of cortical computation: its intrinsic variability. To this end, we combined placebo-controlled pharmacological intervention in humans, magnetoencephalographic (MEG) recordings of cortical population activity, and psychophysical measurements of the perception of ambiguous visual input. A low-dose catecholaminergic, but not cholinergic, manipulation altered the rate of spontaneous perceptual fluctuations as well as the temporal structure of “scale-free” population activity of large swaths of visual and parietal cortex. Computational analyses indicate that both effects were consistent with an increase in excitatory relative to inhibitory activity in the cortical areas underlying visual perceptual inference. We propose that catecholamines regulate the variability of perception and cognition through dynamically changing the cortical excitation-inhibition ratio. The combined read-out of fluctuations in perception and cortical activity we established here may prove useful as an efficient, and easily accessible marker of altered cortical computation in neuropsychiatric disorders.

scholarly journals Mutual Interactions between Brain States and Alzheimer’s Disease Pathology: A Focus on Gamma and Slow Oscillations

Biology ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 707
Author(s):  
Nicole Byron ◽  
Anna Semenova ◽  
Shuzo Sakata
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Mouse Models ◽  
Therapeutic Potential ◽  
Brain Diseases ◽  
Pharmacological Intervention ◽  
Brain State ◽  
Population Activity ◽  
Slow Oscillations ◽  
Brain States

Brain state varies from moment to moment. While brain state can be defined by ongoing neuronal population activity, such as neuronal oscillations, this is tightly coupled with certain behavioural or vigilant states. In recent decades, abnormalities in brain state have been recognised as biomarkers of various brain diseases and disorders. Intriguingly, accumulating evidence also demonstrates mutual interactions between brain states and disease pathologies: while abnormalities in brain state arise during disease progression, manipulations of brain state can modify disease pathology, suggesting a therapeutic potential. In this review, by focusing on Alzheimer’s disease (AD), the most common form of dementia, we provide an overview of how brain states change in AD patients and mouse models, and how controlling brain states can modify AD pathology. Specifically, we summarise the relationship between AD and changes in gamma and slow oscillations. As pathological changes in these oscillations correlate with AD pathology, manipulations of either gamma or slow oscillations can modify AD pathology in mouse models. We argue that neuromodulation approaches to target brain states are a promising non-pharmacological intervention for neurodegenerative diseases.

scholarly journals Scale-free behaviour and metastable brain-state switching driven by human cognition, an empirical approach

2019 ◽  
Vol 13 (5) ◽  
pp. 437-452 ◽  
Author(s):  
Aldo Mora-Sánchez ◽  
Gérard Dreyfus ◽  
François-Benoît Vialatte
Keyword(s):  
Empirical Approach ◽  
Human Cognition ◽  
Brain State ◽  
Scale Free ◽  
State Switching

Spontaneous Brain Activity Predicts Task-Evoked Activity During Animate Versus Inanimate Touch

2019 ◽  
Vol 29 (11) ◽  
pp. 4628-4645 ◽  
Author(s):  
Andrea Scalabrini ◽  
Sjoerd J H Ebisch ◽  
Zirui Huang ◽  
Simone Di Plinio ◽  
Mauro Gianni Perrucci ◽  
...  
Keyword(s):  
Anterior Cingulate Cortex ◽  
Spontaneous Activity ◽  
Brain Activity ◽  
Temporal Structure ◽  
Cingulate Cortex ◽  
Anterior Cingulate ◽  
Scale Free ◽  
Spontaneous Brain Activity ◽  
Posterior Insula ◽  
Evoked Activity

Abstract The spontaneous activity of the brain is characterized by an elaborate temporal structure with scale-free properties as indexed by the power law exponent (PLE). We test the hypothesis that spontaneous brain activity modulates task-evoked activity during interactions with animate versus inanimate stimuli. For this purpose, we developed a paradigm requiring participants to actively touch either animate (real hand) or inanimate (mannequin hand) stimuli. Behaviorally, participants perceived the animate target as closer in space, temporally more synchronous with their own self, and more personally relevant, compared with the inanimate. Neuronally, we observed a modulation of task-evoked activity by animate versus inanimate interactions in posterior insula, in medial prefrontal cortex, comprising anterior cingulate cortex, and in medial superior frontal gyrus. Among these regions, an increased functional connectivity was shown between posterior insula and perigenual anterior cingulate cortex (PACC) during animate compared with inanimate interactions and during resting state. Importantly, PLE during spontaneous brain activity in PACC correlated positively with PACC task-evoked activity during animate versus inanimate stimuli. In conclusion, we demonstrate that brain spontaneous activity in PACC can be related to the distinction between animate and inanimate stimuli and thus might be specifically tuned to align our brain with its animate environment.

scholarly journals Predictive Neural Computations Support Spoken Word Recognition: Evidence from MEG and Competitor Priming

2020 ◽  
Author(s):  
Yingcan Carol Wang ◽  
Ediz Sohoglu ◽  
Rebecca A. Gilbert ◽  
Richard N. Henson ◽  
Matthew H. Davis
Keyword(s):  
Word Recognition ◽  
Prediction Error ◽  
Spoken Word Recognition ◽  
Predictive Coding ◽  
Spoken Word ◽  
Speech Comprehension ◽  
Speech Sounds ◽  
Neural Responses ◽  
Perceptual Inference ◽  
Neural Computations

AbstractHuman listeners achieve quick and effortless speech comprehension through computations of conditional probability using Bayes rule. However, the neural implementation of Bayesian perceptual inference remains unclear. Competitive-selection accounts (e.g. TRACE) propose that word recognition is achieved through direct inhibitory connections between units representing candidate words that share segments (e.g. hygiene and hijack share /haid3/). Manipulations that increase lexical uncertainty should increase neural responses associated with word recognition when words cannot be uniquely identified (during the first syllable). In contrast, predictive-selection accounts (e.g. Predictive-Coding) proposes that spoken word recognition involves comparing heard and predicted speech sounds and using prediction error to update lexical representations. Increased lexical uncertainty in words like hygiene and hijack will increase prediction error and hence neural activity only at later time points when different segments are predicted (during the second syllable). We collected MEG data to distinguish these two mechanisms and used a competitor priming manipulation to change the prior probability of specific words. Lexical decision responses showed delayed recognition of target words (hygiene) following presentation of a neighbouring prime word (hijack) several minutes earlier. However, this effect was not observed with pseudoword primes (higent) or targets (hijure). Crucially, MEG responses in the STG showed greater neural responses for word-primed words after the point at which they were uniquely identified (after /haid3/ in hygiene) but not before while similar changes were again absent for pseudowords. These findings are consistent with accounts of spoken word recognition in which neural computations of prediction error play a central role.Significance StatementEffective speech perception is critical to daily life and involves computations that combine speech signals with prior knowledge of spoken words; that is, Bayesian perceptual inference. This study specifies the neural mechanisms that support spoken word recognition by testing two distinct implementations of Bayes perceptual inference. Most established theories propose direct competition between lexical units such that inhibition of irrelevant candidates leads to selection of critical words. Our results instead support predictive-selection theories (e.g. Predictive-Coding): by comparing heard and predicted speech sounds, neural computations of prediction error can help listeners continuously update lexical probabilities, allowing for more rapid word identification.

Configuration and dynamics of dominant inspiratory multineuronal activity patterns during eupnea and gasping generation in vitro

2021 ◽  
Author(s):  
Josué de Jesús Juárez-Vidales ◽  
Jesús Esteban Pérez-Ortega ◽  
Jonathan Julio Ismael Lorea-Hernández ◽  
Felipe A. Méndez-Salcido ◽  
Fernando Pena-Ortega
Keyword(s):  
Activity Patterns ◽  
Respiratory Neurons ◽  
Core Network ◽  
Population Activity ◽  
Scale Free ◽  
Rich Club ◽  
Wide Range ◽  
Multineuronal Activity ◽  
The Rich

The preBötzinger complex (preBötC), located within the ventral respiratory column, produces inspiratory bursts in varying degrees of synchronization/amplitude. This wide range of population burst patterns reflects the flexibility of the preBötC neurons, which is expressed in variations in the onset/offset times of their activations and their activity during the population bursts, with respiratory neurons exhibiting a large cycle-to-cycle timing jitter both at the population activity onset and at the population activity peak; suggesting that respiratory neurons are stochastically activated before and during the inspiratory bursts. However, it is still unknown whether this stochasticity is maintained while evaluating the coactivity of respiratory neuronal ensembles. Moreover, the preBötC topology also remains unknown. Here, by simultaneously recording tens of preBötC neurons and using coactivation analysis during the inspiratory periods, we found that the preBötC has a scale-free configuration (mixture of not many highly connected nodes -hubs- with abundant poorly connected elements) exhibiting the rich-club phenomenon (hubs more likely interconnected with each other). PreBötC neurons also produce multineuronal activity patterns (MAPs) that are highly stable and change during the hypoxia-induced reconfiguration. Moreover, preBötC contains a coactivating core network shared by all its MAPs. Finally, we found a distinctive pattern of sequential coactivation of core network neurons at the beginning of the inspiratory periods, indicating that, when evaluated at the multicellular level, the coactivation of respiratory neurons seems not to be stochastic.

scholarly journals Variability of perceptual multistability: from brain state to individual trait

2012 ◽  
Vol 367 (1591) ◽  
pp. 988-1000 ◽  
Author(s):  
Andreas Kleinschmidt ◽  
Philipp Sterzer ◽  
Geraint Rees
Keyword(s):  
Sensory Input ◽  
Parietal Lobe ◽  
Individual Variability ◽  
Brain State ◽  
Perceptual Inference ◽  
External Stimulation ◽  
Switching Behaviour ◽  
Individual Trait ◽  
The Individual ◽  
The Brain

Few phenomena are as suitable as perceptual multistability to demonstrate that the brain constructively interprets sensory input. Several studies have outlined the neural circuitry involved in generating perceptual inference but only more recently has the individual variability of this inferential process been appreciated. Studies of the interaction of evoked and ongoing neural activity show that inference itself is not merely a stimulus-triggered process but is related to the context of the current brain state into which the processing of external stimulation is embedded. As brain states fluctuate, so does perception of a given sensory input. In multistability, perceptual fluctuation rates are consistent for a given individual but vary considerably between individuals. There has been some evidence for a genetic basis for these individual differences and recent morphometric studies of parietal lobe regions have identified neuroanatomical substrates for individual variability in spontaneous switching behaviour. Moreover, disrupting the function of these latter regions by transcranial magnetic stimulation yields systematic interference effects on switching behaviour, further arguing for a causal role of these regions in perceptual inference. Together, these studies have advanced our understanding of the biological mechanisms by which the brain constructs the contents of consciousness from sensory input.

scholarly journals Association between Scale-Free Brain Dynamics and Behavioral Performance: Functional MRI Study in Resting State and Face Processing Task

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Masato Kasagi ◽  
Zirui Huang ◽  
Kosuke Narita ◽  
Hitoshi Shitara ◽  
Tomokazu Motegi ◽  
...  
Keyword(s):  
Face Processing ◽  
Resting State ◽  
Cognitive Task ◽  
Temporal Structure ◽  
Power Laws ◽  
Posterior Cingulate Cortex ◽  
Scale Free ◽  
State Activity ◽  
Evoked Activity ◽  
And Task

The scale-free dynamics of human brain activity, characterized by an elaborate temporal structure with scale-free properties, can be quantified using the power-law exponent (PLE) as an index. Power laws are well documented in nature in general, particularly in the brain. Some previous fMRI studies have demonstrated a lower PLE during cognitive-task-evoked activity than during resting state activity. However, PLE modulation during cognitive-task-evoked activity and its relationship with an associated behavior remain unclear. In this functional fMRI study in the resting state and face processing + control task, we investigated PLE during both the resting state and task-evoked activities, as well as its relationship with behavior measured using mean reaction time (mRT) during the task. We found that (1) face discrimination-induced BOLD signal changes in the medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), amygdala, and fusiform face area; (2) PLE significantly decreased during task-evoked activity specifically in mPFC compared with resting state activity; (3) most importantly, in mPFC, mRT significantly negatively correlated with both resting state PLE and the resting-task PLE difference. These results may lead to a better understanding of the associations between task performance parameters (e.g., mRT) and the scale-free dynamics of spontaneous and task-evoked brain activities.

scholarly journals Fauna, ecology, and protection of birds in the vicinity of Meryasovo village, Baymaksky district, republic of Bashkortostan of the Russian Federation

2021 ◽  
Vol 296 ◽  
pp. 01001
Author(s):  
G.R. Bairamgulova ◽  
A.B. Zulkarnaev ◽  
S.M. Muzafarov ◽  
G.A. Yagafarova ◽  
F.G. Aminev
Keyword(s):  
Temporal Structure ◽  
Bird Species ◽  
Population Activity ◽  
Breeding Sites ◽  
Specific Distribution ◽  
Republic Of Bashkortostan ◽  
Bird Fauna ◽  
The Russian Federation ◽  
The Republic ◽  
The Village

Avifauna is of great importance in anthropogenic, natural biocenoses. In order to regulate and protect them, it is necessary to investigate firstly the composition of the ornithofauna, secondly the specific distribution, and thirdly the population activity of the species composition. We studied the spatial and temporal structure of the avifauna in the vicinity of the village of Meryasovo in the Baymaksky district. We have identified 34 bird species belonging to 10 orders and 22 families in the vicinity of Meryasovo, Baimaksky district of the Republic of Bashkortostan. The conducted measures to preserve the bird habitats and territories with the main conditions for birds as breeding sites, stopping-off points during migration, wintering, moulting have contributed to the conservation of the bird fauna in the vicinity of Meryasovo village, Baymaksky district of the Republic of Bashkortostan.

scholarly journals Lempel-Ziv complexity of cortical activity during sleep and waking in rats

2015 ◽  
Vol 113 (7) ◽  
pp. 2742-2752 ◽  
Author(s):  
Daniel Abásolo ◽  
Samantha Simons ◽  
Rita Morgado da Silva ◽  
Giulio Tononi ◽  
Vladyslav V. Vyazovskiy
Keyword(s):  
Sleep Deprivation ◽  
Eye Movement ◽  
Brain Activity ◽  
Temporal Structure ◽  
Nrem Sleep ◽  
Brain State ◽  
Rapid Eye Movement ◽  
Sleep Regulation ◽  
Spatio Temporal ◽  
Underlying Mechanisms

Understanding the dynamics of brain activity manifested in the EEG, local field potentials (LFP), and neuronal spiking is essential for explaining their underlying mechanisms and physiological significance. Much has been learned about sleep regulation using conventional EEG power spectrum, coherence, and period-amplitude analyses, which focus primarily on frequency and amplitude characteristics of the signals and on their spatio-temporal synchronicity. However, little is known about the effects of ongoing brain state or preceding sleep-wake history on the nonlinear dynamics of brain activity. Recent advances in developing novel mathematical approaches for investigating temporal structure of brain activity based on such measures, as Lempel-Ziv complexity (LZC) can provide insights that go beyond those obtained with conventional techniques of signal analysis. Here, we used extensive data sets obtained in spontaneously awake and sleeping adult male laboratory rats, as well as during and after sleep deprivation, to perform a detailed analysis of cortical LFP and neuronal activity with LZC approach. We found that activated brain states—waking and rapid eye movement (REM) sleep are characterized by higher LZC compared with non-rapid eye movement (NREM) sleep. Notably, LZC values derived from the LFP were especially low during early NREM sleep after sleep deprivation and toward the middle of individual NREM sleep episodes. We conclude that LZC is an important and yet largely unexplored measure with a high potential for investigating neurophysiological mechanisms of brain activity in health and disease.

scholarly journals A speech envelope landmark for syllable encoding in human superior temporal gyrus

2019 ◽  
Vol 5 (11) ◽  
pp. eaay6279 ◽  
Author(s):  
Yulia Oganian ◽  
Edward F. Chang
Keyword(s):  
Temporal Structure ◽  
Superior Temporal Gyrus ◽  
Rate Of Change ◽  
Speech Comprehension ◽  
Acoustic Features ◽  
Local Maxima ◽  
Neural Computations ◽  
Absolute Amplitude ◽  
Speech Envelope ◽  
Intracranial Recordings

The most salient acoustic features in speech are the modulations in its intensity, captured by the amplitude envelope. Perceptually, the envelope is necessary for speech comprehension. Yet, the neural computations that represent the envelope and their linguistic implications are heavily debated. We used high-density intracranial recordings, while participants listened to speech, to determine how the envelope is represented in human speech cortical areas on the superior temporal gyrus (STG). We found that a well-defined zone in middle STG detects acoustic onset edges (local maxima in the envelope rate of change). Acoustic analyses demonstrated that timing of acoustic onset edges cues syllabic nucleus onsets, while their slope cues syllabic stress. Synthesized amplitude-modulated tone stimuli showed that steeper slopes elicited greater responses, confirming cortical encoding of amplitude change, not absolute amplitude. Overall, STG encoding of the timing and magnitude of acoustic onset edges underlies the perception of speech temporal structure.

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