scholarly journals Evidence for two distinct thalamocortical circuits in retrosplenial cortex

2021 ◽  
Author(s):  
Eleonora Lomi ◽  
Mathias L Mathiasen ◽  
Han Yin Cheng ◽  
Ningyu Zhang ◽  
John P Aggleton ◽  
...  
Keyword(s):  
Neural Coding ◽  
Field Potential ◽  
Retrosplenial Cortex ◽  
Theta Oscillations ◽  
Single Unit Activity ◽  
Theta Band ◽  
Potential Oscillations ◽  
Anterior Thalamus ◽  
Differential Weighting ◽  
The Brain

Retrosplenial cortex (RSC) lies at the interface between perceptual and memory networks in the brain and mediates between these, although it is not yet known how. It has two distinct subregions, granular (gRSC) and dysgranular (dRSC). The present study investigated how these subregions differ with respect to their electrophysiology and connections, as a step towards understanding their functions. gRSC is more closely connected to the hippocampal system, in which theta-band local field potential oscillations are prominent. We therefore compared theta-rhythmic single-unit activity between the two RSC subregions and found, mostly in gRSC, a subpopulation of non-directional cells with spiking activity strongly entrained by theta oscillations, suggesting a stronger coupling of gRSC to the hippocampal system. We then used retrograde tracers to examine whether differences in neural coding between RSC subregions might reflect differential inputs from the anterior thalamus, which is a prominent source of RSC afferents. We found that gRSC and dRSC differ in their afferents from two AV subfields: dorsomedial (AVDM) and ventrolateral (AVVL). AVVL targets both gRSC and dRSC, while AVDM provides a selective projection to gRSC. These combined results suggest the existence of two distinct but interacting RSC subcircuits: one connecting AVDM to gRSC that may comprise part of the cognitive hippocampal system, and the other connecting AVVL to both RSC regions that may link hippocampal and perceptual regions. We suggest that these subcircuits are distinct to allow for differential weighting during integration of converging sensory and cognitive computations: an integration that may take place in thalamus, RSC or both.

scholarly journals Respiration competes with theta for modulating parietal cortex neurons

2019 ◽  
Author(s):  
Felix Jung ◽  
Yevgenij Yanovsky ◽  
Jurij Brankačk ◽  
Adriano BL Tort ◽  
Andreas Draguhn
Keyword(s):  
Neuronal Activity ◽  
Parietal Cortex ◽  
Field Potential ◽  
Anterior Cingulate ◽  
Theta Oscillations ◽  
Network Activity ◽  
Coupled Oscillations ◽  
Neuronal Network Activity ◽  
Posterior Parietal ◽  
The Brain

AbstractRecent work has shown that nasal respiration entrains local field potential (LFP) and neuronal activity in widespread regions of the brain. This includes non-olfactory regions where respiration-coupled oscillations have been described in different mammals, such as rodents, cats and humans. They may, thus, constitute a global signal aiding interregional communication. Nevertheless, the brain produces other widespread slow rhythms, such as theta oscillations, which also mediate long-range synchronization of neuronal activity. It is completely unknown how these different signals interact to control neuronal network activity. In this work, we characterized respiration- and theta-coupled activity in the posterior parietal cortex of mice. Our results show that respiration-coupled and theta oscillations have different laminar profiles, in which respiration preferentially entrains LFPs and units in more superficial layers, whereas theta modulation does not differ across the parietal cortex. Interestingly, we find that the percentage of theta-modulated units increases in the absence of respiration-coupled oscillations, suggesting that both rhythms compete for modulating parietal cortex neurons. We further show through intracellular recordings that synaptic inhibition is likely to play a role in generating respiration-coupled oscillations at the membrane potential level. Finally, we provide anatomical and electrophysiological evidence of reciprocal monosynaptic connections between the anterior cingulate and posterior parietal cortices, suggesting a possible source of respiration-coupled activity in the parietal cortex.

scholarly journals Multiplexed code of navigation variables in anterior limbic areas

2019 ◽  
Author(s):  
Jean Laurens ◽  
Amada Abrego ◽  
Henry Cham ◽  
Briana Popeney ◽  
Yan Yu ◽  
...  
Keyword(s):  
Navigation System ◽  
Neural Coding ◽  
Hippocampal Neurons ◽  
Large Fraction ◽  
Retrosplenial Cortex ◽  
Field Analysis ◽  
Head Direction ◽  
Thalamic Nuclei ◽  
Anterior Thalamus ◽  
Anterior Thalamic Nuclei

AbstractThe brain’s navigation system integrates multimodal cues to create a sense of position and orientation. Here we used a multimodal model to systematically assess how neurons in the anterior thalamic nuclei, retrosplenial cortex and anterior hippocampus of mice, as well as in the cingulum fiber bundle and the white matter regions surrounding the hippocampus, encode an array of navigational variables when animals forage in a circular arena. In addition to coding head direction, we found that some thalamic cells encode the animal’s allocentric position, similar to place cells. We also found that a large fraction of retrosplenial neurons, as well as some hippocampal neurons, encode the egocentric position of the arena’s boundary. We compared the multimodal model to traditional methods of head direction tuning and place field analysis, and found that the latter were inapplicable to multimodal regions such as the anterior thalamus and retrosplenial cortex. Our results draw a new picture of the signals carried and outputted by the anterior thalamus and retrosplenial cortex, offer new insights on navigational variables represented in the hippocampus and its vicinity, and emphasize the importance of using multimodal models to investigate neural coding throughout the navigation system.

scholarly journals Low activity microstates during sleep

2016 ◽  
Author(s):  
Hiroyuki Miyawaki ◽  
Yazan N. Billeh ◽  
Kamran Diba
Keyword(s):  
Rem Sleep ◽  
Activity Patterns ◽  
Field Potential ◽  
Ca1 Neurons ◽  
Anterior Thalamus ◽  
Large Populations ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Local Field Potential Lfp ◽  
Low Activity

AbstractA better understanding of sleep requires evaluating the distinct activity patterns of the brain during sleep. We performed extracellular recordings of large populations of hippocampal region CA1 neurons in freely moving rats across sleep and waking states. Throughout non-REM (non-rapid eye movement) sleep, we observed periods of diminished oscillatory and population spiking activity lasting on the order of seconds, which we refer to as “LOW” activity sleep states. LOW states featured enhanced firing in a subset of “LOW-active” cells, and greater firing in putative interneurons compared to DOWN/OFF states. LOW activity sleep was preceded and followed by increased sharp-wave ripple (SWR) activity. We also observed decreased slow-wave activity (SWA) and sleep spindles in the hippocampus local-field potential (LFP) and neocortical electroencephalogram (EEG) upon LOW onset, but only a partial rebound immediately after LOW. LOW states demonstrated LFP, EEG, and EMG patterns consistent with sleep, but frequently transitioned into microarousals (MAs) and showed EMG and LFP spectral differences from previously described small-amplitude irregular activity (SIA) during quiet waking. Their likelihood increased over the course of sleep, particularly following REM sleep. To confirm that LOW is a brain-wide phenomenon, we analyzed data from the entorhinal cortex of rats, medial prefrontal cortex, and anterior thalamus of mice, obtained from crcns.org and found that LOW states corresponded to markedly diminished activity simultaneously in all of these regions. We propose that LOW states are an important microstate within non-REM sleep that provide respite from high-activity sleep, and may serve a restorative function.

Adaptation of Rehabilitation System Based on User’s Mental Engagement

2015 ◽  
Author(s):  
Ehsan T. Esfahani ◽  
Shrey Pareek ◽  
Pramod Chembrammel ◽  
Mostafa Ghobadi ◽  
Thenkurussi Kesavadas
Keyword(s):  
Brain Activity ◽  
Difficulty Level ◽  
The Novel ◽  
Theta Band ◽  
Robotic Rehabilitation ◽  
Designed Experiments ◽  
Power Spectral ◽  
Rehabilitation System ◽  
Alpha Beta ◽  
The Brain

Recognition of user’s mental engagement is imperative to the success of robotic rehabilitation. The paper explores the novel paradigm in robotic rehabilitation of using Passive BCI as opposed to the conventional Active ones. We have designed experiments to determine a user’s level of mental engagement. In our experimental study, we record the brain activity of 3 healthy subjects during multiple sessions where subjects need to navigate through a maze using a haptic system with variable resistance/assistance. Using the data obtained through the experiments we highlight the drawbacks of using conventional workload metrics as indicators of human engagement, thus asserting that Motor and Cognitive Workloads be differentiated. Additionally we propose a new set of features: differential PSD of Cz-Poz at alpha, Beta and Sigma band, (Mental engagement) and relative C3-C4 at beta (Motor Workload) to distinguish Normal Cases from those instances when haptic where applied with an accuracy of 92.93%. Mental engagement is calculated using the power spectral density of the Theta band (4–7 Hz) in the parietal-midline (Pz) with respect to the central midline (Cz). The above information can be used to adjust robotic rehabilitation parameters I accordance with the user’s needs. The adjustment may be in the force levels, difficulty level of the task or increasing the speed of the task.

scholarly journals The Geometry of Information Coding in Correlated Neural Populations

2021 ◽  
Vol 44 (1) ◽  
Author(s):  
Rava Azeredo da Silveira ◽  
Fred Rieke
Keyword(s):  
Neural Coding ◽  
Neural Population ◽  
Annual Review ◽  
Publication Date ◽  
Information Coding ◽  
Population Code ◽  
New Approach ◽  
Neural Populations ◽  
The Brain ◽  
Theoretical Developments

Neurons in the brain represent information in their collective activity. The fidelity of this neural population code depends on whether and how variability in the response of one neuron is shared with other neurons. Two decades of studies have investigated the influence of these noise correlations on the properties of neural coding. We provide an overview of the theoretical developments on the topic. Using simple, qualitative, and general arguments, we discuss, categorize, and relate the various published results. We emphasize the relevance of the fine structure of noise correlation, and we present a new approach to the issue. Throughout this review, we emphasize a geometrical picture of how noise correlations impact the neural code. Expected final online publication date for the Annual Review of Neuroscience, Volume 44 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Optogenetic activation of SST-positive interneurons restores hippocampal theta oscillation impairment induced by soluble amyloid beta oligomersin vivo

2018 ◽  
Author(s):  
Hyowon Chung ◽  
Kyerl Park ◽  
Hyun Jae Jang ◽  
Michael M Kohl ◽  
Jeehyun Kwag
Keyword(s):  
Learning And Memory ◽  
Peak Power ◽  
Pyramidal Neurons ◽  
Hippocampal Slices ◽  
Field Potential ◽  
Theta Oscillations ◽  
Ca1 Pyramidal Neurons ◽  
Theta Frequency ◽  
Hippocampal Theta

AbstractAbnormal accumulation of amyloid β oligomers (AβO) is a hallmark of Alzheimer’s disease (AD), which leads to learning and memory deficits. Hippocampal theta oscillations that are critical in spatial navigation, learning and memory are impaired in AD. Since GABAergic interneurons, such as somatostatin-positive (SST+) and parvalbumin-positive (PV+) interneurons, are believed to play key roles in the hippocampal oscillogenesis, we asked whether AβO selectively impairs these SST+ and PV+ interneurons. To selectively manipulate SST+ or PV+ interneuron activity in mice with AβO pathologyin vivo, we co-injected AβO and adeno-associated virus (AAV) for expressing floxed channelrhodopsin-2 (ChR2) into the hippocampus of SST-Cre or PV-Cre mice. Local field potential (LFP) recordingsin vivoin these AβO–injected mice showed a reduction in the peak power of theta oscillations and desynchronization of spikes from CA1 pyramidal neurons relative to theta oscillations compared to those in control mice. Optogenetic-activation of SST+ but not PV+ interneurons in AβO–injected mice fully restored the peak power of theta oscillations and resynchronized the theta spike phases to a level observed in control mice.In vitrowhole-cell voltage-clamp recordings in CA1 pyramidal neurons in hippocampal slices treated with AβO revealed that short-term plasticity of SST+ interneuron inhibitory inputs to CA1 pyramidal neurons at theta frequency were selectively disrupted while that of PV+ interneuron inputs were unaffected. Together, our results suggest that dysfunction in inputs from SST+ interneurons to CA1 pyramidal neurons may underlie the impairment of theta oscillations observed in AβO-injected micein vivo.Our findings identify SST+ interneurons as a target for restoring theta-frequency oscillations in early AD.

scholarly journals Learning to synchronize: Midfrontal theta dynamics during rule switching

2020 ◽  
Author(s):  
Pieter Verbeke ◽  
Kate Ergo ◽  
Esther De Loof ◽  
Tom Verguts
Keyword(s):  
Negative Feedback ◽  
Human Subjects ◽  
Learning Task ◽  
Theta Oscillations ◽  
Prediction Errors ◽  
Hierarchical Learning ◽  
Theta Power ◽  
Negative Prediction ◽  
Theta Phase ◽  
The Brain

AbstractIn recent years, several hierarchical extensions of well-known learning algorithms have been proposed. For example, when stimulus-action mappings vary across time or context, the brain may learn two or more stimulus-action mappings in separate modules, and additionally (at a hierarchically higher level) learn to appropriately switch between those modules. However, how the brain mechanistically coordinates neural communication to implement such hierarchical learning, remains unknown. Therefore, the current study tests a recent computational model that proposed how midfrontal theta oscillations implement such hierarchical learning via the principle of binding by synchrony (Sync model). More specifically, the Sync model employs bursts at theta frequency to flexibly bind appropriate task modules by synchrony. 64-channel EEG signal was recorded while 27 human subjects (Female: 21, Male: 6) performed a probabilistic reversal learning task. In line with the Sync model, post-feedback theta power showed a linear relationship with negative prediction errors, but not with positive prediction errors. This relationship was especially pronounced for subjects with better behavioral fit (measured via AIC) of the Sync model. Also consistent with Sync model simulations, theta phase-coupling between midfrontal electrodes and temporo-parietal electrodes was stronger after negative feedback. Our data suggest that the brain uses theta power and synchronization for flexibly switching between task rule modules, as is useful for example when multiple stimulus-action mappings must be retained and used.Significance StatementEveryday life requires flexibility in switching between several rules. A key question in understanding this ability is how the brain mechanistically coordinates such switches. The current study tests a recent computational framework (Sync model) that proposed how midfrontal theta oscillations coordinate activity in hierarchically lower task-related areas. In line with predictions of this Sync model, midfrontal theta power was stronger when rule switches were most likely (strong negative prediction error), especially in subjects who obtained a better model fit. Additionally, also theta phase connectivity between midfrontal and task-related areas was increased after negative feedback. Thus, the data provided support for the hypothesis that the brain uses theta power and synchronization for flexibly switching between rules.

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