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Tort, A. B.

Publications and source records attributed to Tort, A. B..

17 recordsLinked to original sources

Nasal inhalations and exhalations evoke distinct prefrontal-cortex responses with constant latency

Neuronal activity synchronous to breathing is salient across the mammalian brain. These modulations are typically characterized as oscillating signals phase-locked to the ongoing breathing rhythm and treated as evidence for respiratory entrainment of cortical and subcortical oscillations. Since activation of the olfactory circuitry has been identified as their preponderant source, we wondered whether respiratory brain signals could be better understood as sequences of evoked sensory potentials. To help distinguish between these scenarios, we set out to quantify the detailed alignment of prefrontal cortex local field potentials (LFPs) to the nasal airflow cycle across the natural repertoire of rat breathing. We found that, while LFP and breathing show salient synchrony for all breathing modes, the phase of their locking is a strict linear function of respiratory rate. We explain this by showing that LFPs align to nasal airflow with a constant time lag of 100 ms, irrespective of breathing rate. Segmenting by breathing cycles revealed that LFP peaks and fast-gamma bursts are time-locked to inhalations, while LFP troughs and slow-gamma activity time-lock to exhalations. Our results support the view that respiratory signals across the frontal brain of rodents are sensory potentials evoked by cyclic ortho- and retronasal airflow.

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HIPPOCAMPAL THETA OSCILLATIONS DURING CONTROLLED SPEED RUNS ON A TREADMILL

Hippocampal theta (6-12 Hz) oscillations coordinate neural activity during spatial navigation and are strongly related to locomotion speed. However, recent research has yielded conflicting evidence on whether theta rhythms are primarily modulated by acceleration or instantaneous speed. Moreover, the role of movement transitions--at locomotion onset and offset--has often been overlooked, despite potentially involving distinct dynamics not explained by speed or acceleration alone. Previous studies have rarely controlled for locomotion timing and speed, limiting our ability to dissociate the contributions of speed, acceleration, and movement transitions. To address this, we used a computer-controlled treadmill to induce rat locomotion under three distinct conditions: (a) movement transitions, (b) steady running at constant speed, and (c) locomotion with continuous acceleration. This setup allowed precise spectral analysis of hippocampal theta oscillations across conditions. We found that treadmill-triggered movement transitions produced sustained increases in theta power and transient increases in theta frequency. Upon treadmill stop, theta power decreased slowly, whereas theta frequency dropped rapidly. Steady running elevated both theta power and frequency relative to rest. During constant-speed trials, both metrics increased with speed and remained stable over time. Notably, the acceleration rate itself had no effect on theta power or frequency. Instead, during accelerating trials, theta frequency increased progressively with instantaneous speed, underscoring speed as the primary modulator. In summary, our results show that movement transitions induce distinct, sustained changes in theta power and transient changes in theta frequency, while instantaneous speed--not acceleration--governs hippocampal theta frequency. Significance statementThe precise contributions of movement transitions, speed, and acceleration to hippocampal theta oscillations remain unclear due to confounding factors in freely moving paradigms. To resolve this, we employed a computer-controlled treadmill to systematically isolate each locomotor variable under tightly controlled conditions. Our results demonstrate that movement transitions induce distinct changes in theta power and frequency, and that instantaneous speed--not acceleration--robustly modulates theta frequency across hippocampal subregions. These findings clarify an ongoing debate and refine our understanding of how specific locomotor dynamics shape hippocampal activity during navigation.

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On CA1 ripple oscillations: reevaluating asynchronicity evidence

Sharp-wave ripples (SWRs) are hippocampal network oscillations associated with memory consolidation. They are characterized by the co-occurrence of fast and slow field potentials across CA1 layers: the fast-frequency oscillations, known as ripples, are prominent in the pyramidal cell layer, where they coincide with increased neuronal spiking, while slower negative transients, referred to as sharp waves, occur simultaneously in the stratum radiatum. SWRs have traditionally been considered globally synchronous across the hippocampus; however, recent evidence suggests that ripples may be less synchronous than previously thought, particularly between the two hemispheres (Villalobos et al., 2017). In this study, we revisited this question using a unique dataset from probes spanning the septotemporal axis of CA1. Our results demonstrate that ripples are phase-locked within but not between hemispheres, although their occurrence remains time-locked across both the septo-temporal axis and hemispheres. We also observed a similar synchronicity pattern for spiking activity: neurons are locally phase-coupled and globally time-coupled to ripple events. Interneurons exhibit a much stronger phase coupling to both ipsilateral and contralateral ripples than pyramidal neurons. These findings suggest that ripples are locally phase-coupled through pyramidal-interneuron interactions, with global time-locking likely driven by a common bilateral CA3 input and potentially modulated by interneuron circuits.

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Breathing modulates network activity in frontal brain regions during anxiety

Anxiety elicits various physiological responses, including changes in respiratory rate and neuronal activity within specific brain regions such as the medial prefrontal cortex (mPFC). Previous research suggests that the olfactory bulb (OB) modulates the mPFC through respiration-coupled neuronal oscillations (RCOs), which have been linked to fear-related freezing behavior. Nevertheless, the impact of breathing on frontal brain networks during other negative emotional responses, such as anxiety-related states characterized by higher breathing rates, remains unclear. To address this, we subjected rats to the elevated plus maze (EPM) paradigm while simultaneously recording respiration and local field potentials in the OB and mPFC. Our findings demonstrate distinct respiratory patterns during EPM exploration: slower breathing frequencies prevailed in the closed arms, whereas faster frequencies were observed in the open arms, independent of locomotor activity, indicating that anxiety-like states are associated with increased respiratory rates. Additionally, we identified RCOs at different frequencies, mirroring the bimodal distribution of respiratory frequencies. RCOs exhibited higher power during open arm exploration, when they showed greater coherence with breathing at faster frequencies. Furthermore, we confirmed that nasal respiration drives RCOs in frontal brain regions, and found a stronger effect during faster breathing. Interestingly, we observed that the frequency of prefrontal gamma oscillations modulated by respiration increased with heightened anxiety levels and breathing frequency. Overall, our study provides evidence for a significant influence of breathing on prefrontal cortex networks during anxious states, shedding light on the complex interplay between respiratory physiology and emotional processing. Significance StatementUnderstanding how breathing influences brain activity during anxious states could pave the way for novel therapeutic interventions targeting respiratory control to alleviate anxiety symptoms. Our study uncovers a crucial link between respiratory patterns and anxiety-related neural activity in the brain. By investigating the interplay between breathing, neuronal oscillations, and emotional states, we reveal that anxiety induces distinct respiratory patterns, with higher breathing rates correlating with anxious behavior. Importantly, we demonstrate that respiration drives oscillatory activity in the prefrontal cortex, and this effect is potentiated during the fast breathing associated with anxiety. Furthermore, the breathing cycle modulates the emergence of prefrontal gamma oscillations differentially across anxiety levels. This discovery sheds new light on the intricate relationship between respiratory physiology and emotional processing.

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Communication subspace dynamics of the canonical olfactory pathway

Understanding how different brain areas communicate is crucial for elucidating the mechanisms underlying cognition. A possible way for neural populations to interact is through a communication subspace, a specific region in the state-space enabling the transmission of behaviorally-relevant spiking patterns. In the olfactory system, it remains unclear if different populations employ such a mechanism. Our study reveals that neuronal ensembles in the main olfactory pathway (olfactory bulb to olfactory cortex) interact through a communication subspace, which is driven by nasal respiration and allows feedforward and feedback transmission to occur segregated along the sniffing cycle. Moreover, our results demonstrate that subspace communication depends causally on the activity of both areas, is hindered during anesthesia, and transmits a low-dimensional representation of odor.

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Waveform-based classification of dentate spikes

Synchronous excitatory discharges from the entorhinal cortex (EC) to the dentate gyrus (DG) generate fast and prominent patterns in the hilar local field potential (LFP), called dentate spikes (DSs). As sharp-wave ripples in CA1, DSs are more likely to occur in quiet behavioral states, when memory consolidation is thought to take place. However, their functions in mnemonic processes are yet to be elucidated. The classification of DSs into types 1 or 2 is determined by their origin in the lateral or medial EC, as revealed by current source density (CSD) analysis, which requires recordings from linear probes with multiple electrodes spanning the DG layers. To allow the investigation of the functional role of each DS type in recordings obtained from single electrodes and tetrodes, which are abundant in the field, we developed an unsupervised method using Gaussian mixture models to classify such events based on their waveforms. Our classification approach achieved high accuracies (> 80%) when validated in 8 mice with DG laminar profiles. The average CSDs, waveforms, rates, and widths of the DS types obtained through our method closely resembled those derived from the CSD-based classification. As an example of application, we used the technique to analyze single-electrode LFPs from apolipoprotein (apo) E3 and apoE4 knock-in mice. We observed that the latter group, which is a model for Alzheimers disease, exhibited wider DSs of both types from a young age, with a larger effect size for DS type 2, likely reflecting early pathophysiological alterations in the EC-DG network, such as hyperactivity. In addition to the applicability of the method in expanding the study of DS types, our results show that their waveforms carry information about their origins, suggesting different underlying network dynamics and roles in memory processing. Author summaryThe entorhinal cortex and the dentate gyrus are regions of the brains hippocampal formation that play a crucial role in learning and memory. Their synchronous activation generates fast and high-amplitude electrophysiological patterns in the dentate gyrus, called dentate spikes (DSs). However, the functional role of DSs is still poorly understood. A technical limitation for the study of DSs is that their classification into types 1 and 2 is only possible through laminar profiles obtained by multicontact linear probes. In the present work, we propose a method to classify the different types of DSs through their waveforms, thus expanding the investigation of their significance in mnemonic processes by making possible their study in single-site recordings.

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5-MeO-DMT induces sleep-like LFP spectral signatures in the hippocampus and prefrontal cortex of awake rats

5-methoxy-N,N-dimethyltryptamine (5-MeO-DMT) is a potent classical psychedelic known to induce changes in locomotion, behaviour, and sleep in rodents. However, there is limited knowledge regarding its acute neurophysiological effects. Local field potentials (LFPs) are commonly used as a proxy for neural activity, but previous studies investigating psychedelics have been hindered by confounding effects of behavioural changes and anaesthesia, which alter these signals. To address this gap, we investigated acute LFP changes in the hippocampus (HP) and medial prefrontal cortex (mPFC) of freely behaving rats, following 5-MeO-DMT administration. 5-MeO-DMT led to an increase of delta power and a decrease of theta power in the HP LFPs, which could not be accounted for by changes in locomotion. Furthermore, we observed a dose-dependent reduction in slow (20-50 Hz) and mid (50-100Hz) gamma power, as well as in theta phase modulation, even after controlling for the effects of speed and theta power. State map analysis of the spectral profile of awake behaviour induced by 5-MeO-DMT revealed similarities to electrophysiological states observed during slow-wave sleep (SWS) and rapid-eye-movement (REM) sleep. Our findings suggest that the psychoactive effects of classical psychedelics are associated with the integration of waking behaviours with sleep-like spectral patterns in LFPs.

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Cortical high-frequency oscillations ({approx} 110 Hz) in cats are state-dependent and enhanced by a subanesthetic dose of ketamine

Ketamine is an NMDA receptor antagonist that has both antidepressant and anesthetic properties. At subanesthetic doses, ketamine can cause transient psychosis in humans, and is used to model psychosis in experimental animals. In rodents, subanesthetic doses of ketamine increase the power of high-frequency oscillations (HFO, 100-180 Hz) in the electroencephalogram and field potentials, a frequency band linked to cognitive functions. However, the effects of ketamine in higher mammals, with more translatable relevance, are poorly investigated. Here, we have examined cortical HFO during wakefulness, sleep, and after administering a sub-anesthetic dose of ketamine (15 mg/kg), utilizing the cat as an animal model. Four cats were implanted with cortical electrodes for chronic polysomnographic recordings. HFO power, connectivity, information flow directionality, and their relationships with respiratory activity were analyzed. During wakefulness, but not during sleep, we found that HFO were coupled with the inspiratory phase of the respiration. After ketamine administration, HFO were enhanced significantly and remained associated with the inspiratory phase. The analysis of the information flow after ketamine suggest that HFO originate from the olfactory bulb and stream towards the prefrontal cortex. Accordingly, occluding the nostrils significantly reduced HFO power in both the olfactory bulb and prefrontal cortex. In contrast, auditory stimulation did not affect HFO. In conclusion, spontaneous cortical HFO show certain state-dependent features in cats, and enhancement of this rhythm by ketamine may disrupt cortical information processing, which could contribute to some of the neuropsychiatric manifestations associated with ketamine. HighlightsO_LIKetamine is used to model psychosis in humans and experimental animals C_LIO_LISubanesthetic doses of ketamine increase the power of high-frequency oscillations C_LIO_LIHigh-frequency oscillations are coupled with the inspiratory phase of respiration C_LIO_LIThese oscillations originate in the olfactory bulb and stream to the neocortex C_LIO_LINostril occlusion lowers high-frequency activity in the olfactory bulb and neocortex C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=69 SRC="FIGDIR/small/543142v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@e386feorg.highwire.dtl.DTLVardef@1b3c06aorg.highwire.dtl.DTLVardef@1d93ecorg.highwire.dtl.DTLVardef@1daa161_HPS_FORMAT_FIGEXP M_FIG C_FIG

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Mechanisms and functions of respiration-driven gamma oscillations in the piriform cortex

Gamma oscillations are believed to underlie cognitive processes by shaping the formation of transient neuronal partnerships on a millisecond scale. These oscillations are coupled to the phase of breathing cycles in several brain areas, possibly reflecting local computations driven by sensory inputs sampled at each breath. Here, we investigated the mechanisms and functions of gamma oscillations in the piriform (olfactory) cortex of awake mice to understand their dependence on breathing and how they relate to local spiking activity. Mechanistically, we find that respiration drives gamma oscillations in the piriform cortex, which correlate with local feedback inhibition and result from recurrent connections between local excitatory and inhibitory neuronal populations. Moreover, respiration-driven gamma oscillations are triggered by the activation of mitral/tufted cells in the olfactory bulb and are abolished during ketamine/xylazine anesthesia. Functionally, we demonstrate that they locally segregate neuronal assemblies through a winner-take-all computation leading to sparse odor coding during each breathing cycle. Our results shed new light on the mechanisms of gamma oscillations, bridging computation, cognition and physiology.

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OFF-periods reduce the complexity of neocortical activity during sleep

Complexity of electroencephalographic signals decreases during slow-wave sleep (SWS), however, the neural mechanism for this decrease remains elusive. Here, we show that this complexity reduction is caused by synchronous neuronal OFF-periods. We analyse in-vivo recordings from neocortical neuronal populations, finding that OFF-periods in SWS trap cortical dynamics, making the population activity more recurrent, deterministic, and less chaotic than during REM sleep or Wakefulness. Importantly, when we exclude OFF-periods, SWS becomes indistinguishable from Wakefulness or REM sleep. In fact, we show that spiking activity for all states has a universal scaling compatible with critical phenomena. We complement these results by a critical branching model that replicates our experimental findings, where we show that forcing OFF-periods to a percentage of neurons suffices to generate a decrease in complexity that replicates SWS.

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Causal relations between cortical network oscillations and breathing frequency

Nasal breathing generates a rhythmic signal which entrains cortical network oscillations in widespread brain regions on a cycle-to-cycle time scale. It is unknown, however, how respiration and neuronal network activity interact on a larger time scale: are breathing frequency and typical neuronal oscillation patterns correlated? Is there any directionality or causal relationship? To address these questions, we recorded field potentials from the posterior parietal cortex of mice together with respiration during REM sleep. In this state, the parietal cortex exhibits prominent theta and gamma oscillations while behavioral activity is minimal, reducing confounding signals. We found that the instantaneous breathing rate strongly correlates with the instantaneous frequency and amplitude of both theta and gamma oscillations. Granger causality analysis revealed specific directionalities for different rhythms: changes in theta activity precede and cause changes in breathing rate, suggesting control of breathing frequency by the functional state of the brain. On the other hand, the instantaneous breathing rate Granger-causes changes in gamma oscillations, suggesting that gamma is influenced by a peripheral reafference signal. These findings show that breathing causally relates to different patterns of rhythmic brain activity, revealing new and complex interactions between elementary physiological functions and neuronal information processing. Significance StatementThe study of the interactions between respiration and brain activity has been focused on phase-entrainment relations, in which cortical networks oscillate phase-locked to breathing cycles. Here we discovered new and much broader interactions which link respiration rate (frequency) to different patterns of oscillatory brain activity. Specifically, we show that the instantaneous breathing rate strongly correlates with the instantaneous frequency and amplitude of theta and gamma oscillations, two major network patterns associated with cognitive functions. Interestingly, causality analyses reveal that changes in breathing rate follow theta, suggesting a central drive, while in contrast, gamma activity follows changes in breathing rate, suggesting the role of a reafferent signal. Our results reveal new mechanisms by which nasal breathing patterns may influence brain functions.

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Theta-gamma coupling depends on breathing rate

Temporal coupling between theta and gamma oscillations is a hallmark activity pattern of several cortical networks and becomes especially prominent during REM sleep. In a parallel approach, nasal breathing has been recently shown to generate phase-entrained network oscillations which also modulate gamma. Both slow rhythms (theta and respiration-entrained oscillations) have been suggested to aid large-scale integration but they differ in frequency, display low coherence, and modulate different gamma sub-bands. Respiration and theta are therefore believed to be largely independent. In the present work, however, we report an unexpected but robust relation between theta-gamma coupling and respiration in mice. Interestingly, this relation takes place not through the phase of individual respiration cycles, but through respiration rate: the strength of theta-gamma coupling exhibits an inverted V-shaped dependence on breathing rate, leading to maximal coupling at breathing frequencies of 4-6 Hz. Noteworthy, when subdividing sleep epochs into phasic and tonic REM patterns, we find that breathing differentially relates to theta-gamma coupling in each state, providing new evidence for their physiological distinctiveness. Altogether, our results reveal that breathing correlates with brain activity not only through phase-entrainment but also through rate-dependent relations with theta-gamma coupling. Thus, the link between respiration and other patterns of cortical network activity is more complex than previously assumed.

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Gamma band alterations and REM-like traits underpin the acute effect of the atypical psychedelic ibogaine

Ibogaine is a psychedelic alkaloid that has attracted scientific interest because of its important antiaddictive properties evidenced in observational studies in humans, and in models for substance-use-disorders in rodents. Its subjective effect has been described as intense vivid dream-like experiences occurring while awake; hence, ibogaine is often referred to as an oneirogenic psychedelic. While this unique dream-like profile has been hypothesized to aid the antiaddictive effects in the past, the electrophysiological signatures of the ibogaine psychedelic state remain unknown. In our previous work, we showed in rats that ibogaine administration promotes a waking state with abnormal motor behavior, accompanied by a decrease in NREM and REM sleep. Here, we performed an in-depth analysis of the intracranial electroencephalogram during "ibogaine wakefulness". Ibogaine induced gamma oscillations with larger power than control levels but less coherent and less complex; i.e., this state shows clear REM sleep traits within the gamma frequency band. Thus, our results provide novel biological evidence for the association between the psychedelic state and REM sleep, and an empirical basis for the oneirogenic conjecture of ibogaine.

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Hippocampal-Prefrontal Interactions during Spatial Decision-Making

The hippocampus has been linked to memory encoding and spatial navigation, while the prefrontal cortex is associated with cognitive functions such as decision-making. These regions are hypothesized to communicate in tasks that demand both spatial navigation and decision-making processes. However, the electrophysiological signatures underlying this communication remain to be better elucidated. To investigate the dynamics of the hippocampal-prefrontal interactions, we have analyzed their local field potentials and spiking activity recorded from rats performing an odor-cued spatial alternation task in an 8-shaped maze. We found that the phase coherence of theta peaked around the choice point area of the maze. Moreover, Granger causality revealed a hippocampus->prefrontal cortex directionality of information flow at theta frequency, peaking at starting areas of the maze, and on the reverse direction at delta frequency, peaking near the turn onset. Additionally, the patterns of phase-amplitude cross-frequency coupling within and between the regions also showed spatial selectivity, and a new method revealed that hippocampal theta and prefrontal delta modulated not only gamma amplitude but also inter-regional gamma synchrony. Lastly, we found that the theta rhythm dynamically modulated neurons in both regions, with the highest modulation at the choice area; interestingly, prefrontal cortex neurons were more strongly modulated by the hippocampal theta rhythm than by their local field rhythm. In all, our results reveal maximum electrophysiological interactions between the hippocampus and the prefrontal cortex near the decision-making period of the spatial alternation task. These results corroborate the hypothesis that a dynamic interplay between these regions takes place during spatial decisions.

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Communication through coherence by means of cross-frequency coupling

The theory of communication through coherence (CTC) posits the synchronization of brain oscillations as a key mechanism for information sharing and perceptual binding. In a parallel literature, hippocampal theta activity (4 - 10 Hz) has been shown to modulate the appearance of neocortical fast gamma oscillations (100 - 150 Hz), a phenomenon known as cross-frequency coupling (CFC). Even though CFC has also been previously associated with information routing, it remains to be determined whether it directly relates to CTC. In particular, for the theta-fast gamma example at hand, a critical question is to know if the phase of the theta cycle influences gamma synchronization across the neocortex. To answer this question, we designed a new screening method for detecting the modulation of the cross-regional high-frequency synchronization by the phase of slower oscillations. Upon applying the method, we found that the long-distance synchronization of neocortical fast gamma during REM sleep depends on the instantaneous phase of the theta rhythm. These results show that CFC is likely to aid long-range information transfer by facilitating the cross-regional synchronization of faster rhythms, thus consistent with classical CTC views.

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On the Boundary Conditions of Avoidance Memory Reconsolidation: An Attractor Network Perspective

The reconsolidation and extinction of aversive memories and their boundary conditions have been extensively studied. Knowing their network mechanisms may lead to the development of better strategies for the treatment of fear and anxiety-related disorders. In 2011, Osan et al. developed a computational model for exploring such phenomena based on attractor dynamics, Hebbian plasticity and synaptic degradation induced by prediction error. This model was able to explain, in a single formalism, experimental findings regarding the freezing behavior of rodents submitted to contextual fear conditioning. In 2017, through the study of inhibitory avoidance in rats, Radiske et al. showed that the previous knowledge of a context as non-aversive is a boundary condition for the reconsolidation of the shock memory subsequently experienced in that context. In the present work, by adapting the model of Osan et al. (2011) to simulate the experimental protocols of Radiske et al. (2017), we show that such boundary condition is compatible with the dynamics of an attractor network that supports synaptic labilization common to reconsolidation and extinction. Additionally, by varying parameters such as the levels of protein synthesis and degradation, we predict behavioral outcomes, and thus boundary conditions that can be tested experimentally.

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Spike sorting with Gaussian mixture models

The shape of extracellularly recorded action potentials is a product of several variables, such as the biophysical and anatomical properties of the neuron and the relative position of the electrode. This allows for isolating spikes of different neurons recorded in the same channel into clusters based on waveform features. However, correctly classifying spike waveforms into their underlying neuronal sources remains a main challenge. This process, called spike sorting, typically consists of two steps: (1) extracting relevant waveform features (e.g., height, width), and (2) clustering them into non-overlapping groups believed to correspond to different neurons. In this study, we explored the performance of Gaussian mixture models (GMMs) in these two steps. We extracted relevant waveform features using a combination of common techniques (e.g., principal components and wavelets) and GMM fitting parameters (e.g., standard deviations and peak distances). Then, we developed an approach to perform unsupervised clustering using GMMs, which estimates cluster properties in a data-driven way. Our results show that the proposed GMM-based framework outperforms previously established methods when using realistic simulations of extracellular spikes and actual extracellular recordings to evaluate sorting performance. We also discuss potentially better techniques for feature extraction than the widely used principal components. Finally, we provide a friendly graphical user interface in MATLAB to run our algorithm, which allows for manual adjustment of the automatic results.

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