Errant ensembles: dysfunctional neuronal network dynamics in schizophrenia

2010 ◽  
Vol 38 (2) ◽  
pp. 516-521 ◽  
Author(s):  
Matt W. Jones
Keyword(s):  
Calcium Binding ◽  
Field Potential ◽  
Brain Regions ◽  
Aminobutyric Acid ◽  
Neural Population ◽  
Population Activity ◽  
Potential Oscillations ◽  
Disconnection Syndrome ◽  
Hyperactivity Disorder ◽  
Neuronal Network Dynamics

Most complex psychiatric disorders cannot be explained by pathology of a single brain region, but arise as a consequence of dysfunctional interactions between brain regions. Schizophrenia, in particular, has been described as a ‘disconnection syndrome’, but similar principles are likely to apply to depression and ADHD (attention deficit hyperactivity disorder). All these diseases are associated with impaired co-ordination of neural population activity, which manifests as abnormal EEG (electroencephalogram) and LFP (local field potential) oscillations both within and across subcortical and cortical brain regions. Importantly, it is increasingly possible to link oscillations and interactions at distinct frequencies to the physiology and/or pathology of distinct classes of neurons and interneurons. Such analyses increasingly implicate abnormal levels, timing or modulation of GABA (γ-aminobutyric acid)-ergic inhibition in brain disease. The present review discusses the evidence suggesting that dysfunction of a particular class of interneurons, marked by their expression of the calcium-binding protein parvalbumin, could contribute to the broad range of neurophysiological and behavioural symptoms characteristic of schizophrenia.

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 Transformation of Independent Oscillatory Inputs into Temporally Precise Rate Codes

2016 ◽  
Author(s):  
David Tingley ◽  
Andrew A. Alexander ◽  
Laleh K. Quinn ◽  
Andrea A. Chiba ◽  
Douglas Nitz
Keyword(s):  
Field Potential ◽  
Spatial Location ◽  
Brain Regions ◽  
Synaptic Activity ◽  
Specific Rate ◽  
Attention Task ◽  
Potential Oscillations ◽  
Phase Precession ◽  
Theta Phase ◽  
Theta Phase Precession

AbstractComplex behaviors demand temporal coordination among functionally distinct brain regions. The basal forebrain’s afferent and efferent structure suggests a capacity for mediating such coordination. During performance of a selective attention task, synaptic activity in this region was dominated by four amplitude-independent oscillations temporally organized by the phase of the slowest, a theta rhythm. Further, oscillatory amplitudes were precisely organized by task epoch and a robust input/output transform, from synchronous synaptic activity to spiking rates of basal forebrain neurons, was identified. For many neurons, spiking was temporally organized as phase precessing sequences against theta band field potential oscillations. Remarkably, theta phase precession advanced in parallel to task progression, rather than absolute spatial location or time. Together, the findings reveal a process by which associative brain regions can integrate independent oscillatory inputs and transform them into sequence-specific, rate-coded outputs that are adaptive to the pace with which organisms interact with their environment.

scholarly journals Coexistence of fast and slow gamma oscillations in one population of inhibitory spiking neurons

2019 ◽  
Author(s):  
Hongjie Bi ◽  
Marco Segneri ◽  
Matteo di Volo ◽  
Alessandro Torcini
Keyword(s):  
Gamma Oscillations ◽  
Brain Regions ◽  
Neural Population ◽  
Neural Firing ◽  
Phase Coupling ◽  
Population Activity ◽  
Brain Functions ◽  
Wide Range ◽  
Gamma Rhythms ◽  
Phase Phase

Oscillations are a hallmark of neural population activity in various brain regions with a spectrum covering a wide range of frequencies. Within this spectrum gamma oscillations have received particular attention due to their ubiquitous nature and to their correlation with higher brain functions. Recently, it has been reported that gamma oscillations in the hippocampus of behaving rodents are segregated in two distinct frequency bands: slow and fast. These two gamma rhythms correspond to different states of the network, but their origin has been not yet clarified. Here, we show theoretically and numerically that a single inhibitory population can give rise to coexisting slow and fast gamma rhythms corresponding to collective oscillations of a balanced spiking network. The slow and fast gamma rhythms are generated via two different mechanisms: the fast one being driven by the coordinated tonic neural firing and the slow one by endogenous fluctuations due to irregular neural activity. We show that almost instantaneous stimulations can switch the collective gamma oscillations from slow to fast and vice versa. Furthermore, to make a closer contact with the experimental observations, we consider the modulation of the gamma rhythms induced by a slower (theta) rhythm driving the network dynamics. In this context, depending on the strength of the forcing and the noise amplitude, we observe phase-amplitude and phase-phase coupling between the fast and slow gamma oscillations and the theta forcing. Phase-phase coupling reveals on average different theta-phases preferences for the two coexisting gamma rhythms joined to a wide cycle-to-cycle variability.

scholarly journals Learning improves decoding of odor identity with phase-referenced oscillations in the olfactory bulb

2019 ◽  
Author(s):  
Justin Losacco ◽  
Daniel Ramirez-Gordillo ◽  
Jesse Gilmer ◽  
Diego Restrepo
Keyword(s):  
Olfactory Bulb ◽  
Field Potential ◽  
Gamma Oscillations ◽  
Brain Regions ◽  
Spike Timing ◽  
Potential Oscillations ◽  
Theta Oscillation ◽  
Beta Power ◽  
Early Processing ◽  
Theta Phase

AbstractLocal field potential oscillations reflect temporally coordinated neuronal ensembles— coupling distant brain regions, gating processing windows, and providing a reference for spike timing-based codes. In phase amplitude coupling (PAC), the amplitude of the envelope of a faster oscillation is larger within a phase window of a slower carrier wave. Here, we characterized PAC, and the related theta phase-referenced high gamma and beta power (PRP), in the olfactory bulb of mice learning to discriminate odorants. PAC changes throughout learning, and odorant-elicited changes in PRP increase for rewarded and decrease for unrewarded odorants. Contextual odorant identity (is the odorant rewarded?) can be decoded from peak PRP in animals proficient in odorant discrimination, but not in naïve mice. As the animal learns to discriminate the odorants the dimensionality of PRP decreases. Therefore, modulation of phase-referenced chunking of information in the course of learning plays a role in early sensory processing in olfaction.SignificanceEarly processing of olfactory information takes place in circuits undergoing slow frequency theta oscillations generated by the interplay of olfactory input modulated by sniffing and centrifugal feedback from downstream brain areas. Studies in the hippocampus and cortex suggest that different information “chunks” are conveyed at different phases of the theta oscillation. Here we show that in the olfactory bulb, the first processing station in the olfactory system, the amplitude of high frequency gamma oscillations encodes for information on whether an odorant is rewarded when it is observed at the peak phase of the theta oscillation. Furthermore, encoding of information by the theta phase-referenced gamma oscillations becomes more accurate as the animal learns to differentiate two odorants.

scholarly journals Learning improves decoding of odor identity with phase-referenced oscillations in the olfactory bulb

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Justin Losacco ◽  
Daniel Ramirez-Gordillo ◽  
Jesse Gilmer ◽  
Diego Restrepo
Keyword(s):  
Olfactory Bulb ◽  
Field Potential ◽  
Brain Regions ◽  
Spike Timing ◽  
Potential Oscillations ◽  
Neuronal Ensembles ◽  
Beta Power ◽  
High Gamma ◽  
Theta Phase ◽  
Phase Amplitude

Local field potential oscillations reflect temporally coordinated neuronal ensembles—coupling distant brain regions, gating processing windows, and providing a reference for spike timing-based codes. In phase amplitude coupling (PAC), the amplitude of the envelope of a faster oscillation is larger within a phase window of a slower carrier wave. Here, we characterized PAC, and the related theta phase-referenced high gamma and beta power (PRP), in the olfactory bulb of mice learning to discriminate odorants. PAC changes throughout learning, and odorant-elicited changes in PRP increase for rewarded and decrease for unrewarded odorants. Contextual odorant identity (is the odorant rewarded?) can be decoded from peak PRP in animals proficient in odorant discrimination, but not in naïve mice. As the animal learns to discriminate the odorants the dimensionality of PRP decreases. Therefore, modulation of phase-referenced chunking of information in the course of learning plays a role in early sensory processing in olfaction.

Local Field Potential Oscillations in Primate Cerebellar Cortex: Synchronization With Cerebral Cortex During Active and Passive Expectancy

2005 ◽  
Vol 93 (4) ◽  
pp. 2039-2052 ◽  
Author(s):  
Richard Courtemanche ◽  
Yves Lamarre
Keyword(s):  
Local Field ◽  
Primary Motor Cortex ◽  
Field Potential ◽  
Brain Regions ◽  
Stimulus Onset ◽  
Somatosensory Processing ◽  
Potential Oscillations ◽  
Passive Condition ◽  
Right Hand ◽  
Active Condition

Many brain regions, such as the cerebellum, primary somatosensory cortex (SI), and primary motor cortex (MI), interact to produce coordinated actions. Synchronization of local field potentials (LFPs) in sensorimotor cerebral areas has been related to motor performance, often through 10- to 25-Hz oscillatory LFPs. The macaque cerebellar paramedian lobule (PM) also shows 10- to 25-Hz LFP oscillations, which are modulated in a stimulus–response lever press task to get reward (active condition), but also, albeit differently, in a similarly timed stimulus–reward relation (passive condition). This study focuses on simultaneous LFP activity in primate SI or MI and the PM cerebellum during the active (left- or right-hand lever presses) and passive conditions. Results show a similar modulation pattern of 10- to 25-Hz oscillations in the cerebellum, MI, and SI during the active condition (left or right hand), decreasing after stimulus onset, returning, and again decreasing after movement onset. In the passive condition, when the monkey did not move but got reward, all 3 areas show an oscillatory profile where oscillations increase after stimulus onset and last until reward, denoting a role for these oscillations in passive expectancy. However, synchronization between cerebellar LFPs and SI LFPs is higher during the active condition than during the passive condition, and highest for the interested hand. This greater PM–SI synchronization, when the monkey had to press the lever, could represent a form of cerebro-cerebellar communication, perhaps to serve somatosensory processing to accomplish the task; PM–MI synchronization was less selective for the hand used and might carry a more general type of information.

scholarly journals Coupling of pupil- and neuronal population dynamics reveals diverse influences of arousal on cortical processing

2021 ◽  
Author(s):  
Thomas Pfeffer ◽  
Christian Keitel ◽  
Daniel S. Kluger ◽  
Anne Keitel ◽  
Alena Russmann ◽  
...  
Keyword(s):  
Low Frequency ◽  
Intermediate Frequency ◽  
Brain Regions ◽  
Neuronal Population ◽  
Neural Population ◽  
Cortical Circuits ◽  
Population Activity ◽  
Linear Relationships ◽  
Cortical Regions ◽  
Aperiodic Component

Fluctuations in arousal, controlled by subcortical neuromodulatory systems, continuously shape cortical state, with profound consequences for information processing. Yet, how arousal signals influence cortical population activity in detail has only been characterized for a few selected brain regions so far. Traditional accounts conceptualize arousal as a homogeneous modulator of neural population activity across the cerebral cortex. Recent insights, however, point to a higher specificity of arousal effects on different components of neural activity and across cortical regions. Here, we provide a comprehensive account of the relationships between fluctuations in arousal and neuronal population activity across the human brain. Exploiting the established link between pupil size and central arousal systems, we performed concurrent magnetoencephalographic (MEG) and pupillographic recordings in a large number of participants, pooled across three laboratories. We found a cascade of effects relative to the peak timing of spontaneous pupil dilations: Decreases in low-frequency (2-8 Hz) activity in temporal and lateral frontal cortex, followed by increased high-frequency (>64 Hz) activity in mid-frontal regions, followed by linear and non-linear relationships with intermediate frequency-range activity (8-32 Hz) in occipito-parietal regions. The non-linearity resembled an inverted U-shape whereby intermediate pupil sizes coincided with maximum 8-32 Hz activity. Pupil-linked arousal also coincided with widespread changes in the structure of the aperiodic component of cortical population activity, indicative of changes in the excitation-inhibition balance in underlying microcircuits. Our results provide a novel basis for studying the arousal modulation of cognitive computations in cortical circuits.

scholarly journals Abstract representations emerge naturally in neural networks trained to perform multiple tasks

2021 ◽  
Author(s):  
W. Jeffrey Johnston ◽  
Stefano Fusi
Keyword(s):  
Neural Networks ◽  
Natural World ◽  
Brain Regions ◽  
Neural Population ◽  
Task Structure ◽  
Cognitive Variables ◽  
Population Activity ◽  
Data Set ◽  
Abstract Representations ◽  
Neurophysiological Studies

Humans and other animals demonstrate a remarkable ability to generalize knowledge across distinct contexts and objects during natural behavior. We posit that this ability depends on the geometry of the neural population representations of these objects and contexts. Specifically, abstract, or disentangled, neural representations -- in which neural population activity is a linear function of the variables important for making a decision -- are known to allow for this kind of generalization. Further, recent neurophysiological studies have shown that the brain has sufficiently abstract representations of some sensory and cognitive variables to enable generalization across distinct contexts. However, it is unknown how these abstract representations emerge. Here, using feedforward neural networks, we demonstrate a simple mechanism by which these abstract representations can be produced: The learning of multiple distinct classification tasks. We demonstrate that, despite heterogeneity in the task structure, abstract representations that enable reliable generalization can be produced from a variety of different inputs -- including standard nonlinearly mixed inputs, inputs that mimic putative representations from early sensory areas, and even simple image inputs from a standard machine learning data set. Thus, we conclude that abstract representations of sensory and cognitive variables emerge from the multiple behaviors that animals exhibit in the natural world, and may be pervasive in high-level brain regions. We make several specific predictions about which variables will be represented abstractly as well as show how these representations can be detected.

Midfrontal conflict-related theta-band power reflects neural oscillations that predict behavior

2013 ◽  
Vol 110 (12) ◽  
pp. 2752-2763 ◽  
Author(s):  
Michael X Cohen ◽  
Tobias H. Donner
Keyword(s):  
Specific Activity ◽  
Physiological Mechanism ◽  
Event Related Potentials ◽  
Brain Regions ◽  
Neural Population ◽  
Neural Oscillations ◽  
Theta Band ◽  
Population Activity ◽  
Theta Power ◽  
Related Potentials

Action monitoring and conflict resolution require the rapid and flexible coordination of activity in multiple brain regions. Oscillatory neural population activity may be a key physiological mechanism underlying such rapid and flexible network coordination. EEG power modulations of theta-band (4–8 Hz) activity over the human midfrontal cortex during response conflict have been proposed to reflect neural oscillations that support conflict detection and resolution processes. However, it has remained unclear whether this frequency-band-specific activity reflects neural oscillations or nonoscillatory responses (i.e., event-related potentials). Here, we show that removing the phase-locked component of the EEG did not reduce the strength of the conflict-related modulation of the residual (i.e., non-phase-locked) theta power over midfrontal cortex. Furthermore, within-subject regression analyses revealed that the non-phase-locked theta power was a significantly better predictor of the conflict condition than was the time-domain phase-locked EEG component. Finally, non-phase-locked theta power showed robust and condition-specific (high- vs. low-conflict) cross-trial correlations with reaction time, whereas the phase-locked component did not. Taken together, our results indicate that most of the conflict-related and behaviorally relevant midfrontal EEG signal reflects a modulation of ongoing theta-band oscillations that occurs during the decision process but is not phase-locked to the stimulus or to the response.

scholarly journals Movement Decomposition in the Primary Motor Cortex

2018 ◽  
Vol 29 (4) ◽  
pp. 1619-1633 ◽  
Author(s):  
Naama Kadmon Harpaz ◽  
David Ungarish ◽  
Nicholas G Hatsopoulos ◽  
Tamar Flash
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Temporal Dynamics ◽  
Single Cells ◽  
Brain Regions ◽  
Specific Pattern ◽  
Neural Representation ◽  
Neural Population ◽  
Population Activity ◽  
Acceleration And Deceleration

Abstract A complex action can be described as the composition of a set of elementary movements. While both kinematic and dynamic elements have been proposed to compose complex actions, the structure of movement decomposition and its neural representation remain unknown. Here, we examined movement decomposition by modeling the temporal dynamics of neural populations in the primary motor cortex of macaque monkeys performing forelimb reaching movements. Using a hidden Markov model, we found that global transitions in the neural population activity are associated with a consistent segmentation of the behavioral output into acceleration and deceleration epochs with directional selectivity. Single cells exhibited modulation of firing rates between the kinematic epochs, with abrupt changes in spiking activity timed with the identified transitions. These results reveal distinct encoding of acceleration and deceleration phases at the level of M1, and point to a specific pattern of movement decomposition that arises from the underlying neural activity. A similar approach can be used to probe the structure of movement decomposition in different brain regions, possibly controlling different temporal scales, to reveal the hierarchical structure of movement composition.

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