Local Field Potential Activity Research Articles

Neurofeedback training can modulate task-relevant memory replay rate in rats.

Hippocampal replay - the time-compressed, sequential reactivation of ensembles of neurons related to past experience - is a key neural mechanism of memory consolidation. Replay typically coincides with a characteristic pattern of local field potential activity, the sharp-wave ripple (SWR). Reduced SWR rates are associated with cognitive impairment in multiple models of neurodegenerative disease, suggesting that a clinically viable intervention to promote SWRs and replay would prove beneficial. We therefore developed a neurofeedback paradigm for rat subjects in which SWR detection triggered rapid positive feedback in the context of a memory-dependent task. This training protocol increased the prevalence of task-relevant replay during the targeted neurofeedback period by changing the temporal dynamics of SWR occurrence. This increase was also associated with neural and behavioral forms of compensation after the targeted period. These findings reveal short-timescale regulation of SWR generation and demonstrate that neurofeedback is an effective strategy for modulating hippocampal replay.

The primate cortical LFP exhibits multiple spectral and temporal gradients and widespread task dependence during visual short-term memory.

Although cognitive functions are hypothesized to be mediated by synchronous neuronal interactions in multiple frequency bands among widely distributed cortical areas, we still lack a basic understanding of the distribution and task dependence of oscillatory activity across the cortical map. Here, we ask how the spectral and temporal properties of the local field potential (LFP) vary across the primate cerebral cortex, and how they are modulated during visual short-term memory. We measured the LFP from 55 cortical areas in two macaque monkeys while they performed a visual delayed match to sample task. Analysis of peak frequencies in the LFP power spectra reveals multiple discrete frequency bands between 3 and 80 Hz that differ between the two monkeys. The LFP power in each band, as well as the sample entropy, a measure of signal complexity, display distinct spatial gradients across the cortex, some of which correlate with reported spine counts in cortical pyramidal neurons. Cortical areas can be robustly decoded using a small number of spectral and temporal parameters, and significant task-dependent increases and decreases in spectral power occur in all cortical areas. These findings reveal pronounced, widespread, and spatially organized gradients in the spectral and temporal activity of cortical areas. Task-dependent changes in cortical activity are globally distributed, even for a simple cognitive task.NEW & NOTEWORTHY We recorded extracellular electrophysiological signals from roughly the breadth and depth of a cortical hemisphere in nonhuman primates (NHPs) performing a visual memory task. Analyses of the band-limited local field potential (LFP) power displayed widespread, frequency-dependent cortical gradients in spectral power. Using a machine learning classifier, these features allowed robust cortical area decoding. Further task dependence in LFP power were found to be widespread, indicating large-scale gradients of LFP activity, and task-related activity.

Congenital deafness reduces alpha-gamma cross-frequency coupling in the auditory cortex

Neurons within a neuronal network can be grouped by bottom-up and top-down influences using synchrony in neuronal oscillations. This creates the representation of perceptual objects from sensory features. Oscillatory activity can be differentiated into stimulus-phase-locked (evoked) and non-phase-locked (induced). The former is mainly determined by sensory input, the latter by higher-level (cortical) processing. Effects of auditory deprivation on cortical oscillations have been studied in congenitally deaf cats (CDCs) using cochlear implant (CI) stimulation. CI-induced alpha, beta, and gamma activity were compromised in the auditory cortex of CDCs. Furthermore, top-down information flow between secondary and primary auditory areas in hearing cats, conveyed by induced alpha oscillations, was lost in CDCs. Here we used the matching pursuit algorithm to assess components of such oscillatory activity in local field potentials recorded in primary field A1. Additionally to the loss of induced alpha oscillations, we also found a loss of evoked theta activity in CDCs. The loss of theta and alpha activity in CDCs can be directly related to reduced high-frequency (gamma-band) activity due to cross-frequency coupling. Here we quantified such cross-frequency coupling in adult 1) hearing-experienced, acoustically stimulated cats (aHCs), 2) hearing-experienced cats following acute pharmacological deafening and subsequent CIs, thus in electrically stimulated cats (eHCs), and 3) electrically stimulated CDCs. We found significant cross-frequency coupling in all animal groups in > 70% of auditory-responsive sites. The predominant coupling in aHCs and eHCs was between theta/alpha phase and gamma power. In CDCs such coupling was lost and replaced by alpha oscillations coupling to delta/theta phase. Thus, alpha/theta oscillations synchronize high-frequency gamma activity only in hearing-experienced cats. The absence of induced alpha and theta oscillations contributes to the loss of induced gamma power in CDCs, thereby signifying impaired local network activity.

Subthalamic nucleus synchronization between beta band local field potential and single-unit activity in Parkinson's disease.

Local field potential (LFP) oscillations in the beta band (13-30 Hz) in the subthalamic nucleus (STN) of Parkinson's disease patients have been implicated in disease severity and treatment response. The relationship between single-neuron activity in the STN and regional beta power changes remains unclear. We used spike-triggered average (STA) to assess beta synchronization in STN. Beta power and STA magnitude at the beta frequency range were compared in three conditions: STN versus other subcortical structures, dorsal versus ventral STN, and high versus low beta power STN recordings. Magnitude of STA-LFP was greater within the STN compared to extra-STN structures along the trajectory path, despite no difference in percentage of the total power. Within the STN, there was a higher percent beta power in dorsal compared to ventral STN but no difference in STA-LFP magnitude. Further refining the comparison to high versus low beta peak power recordings inside the STN to evaluate if single-unit activity synchronized more strongly with beta band activity in areas of high beta power resulted in a significantly higher STA magnitude for areas of high beta power. Overall, these results suggest that STN single units strongly synchronize to beta activity, particularly units in areas ofhigh beta power.

Differential Modulation of Local Field Potentials in the Primary and Premotor Cortices during Ipsilateral and Contralateral Reach to Grasp in Macaque Monkeys.

Hand movements are associated with modulations of neuronal activity across several interconnected cortical areas, including the primary motor cortex (M1) and the dorsal and ventral premotor cortices (PMd and PMv). Local field potentials (LFPs) provide a link between neuronal discharges and synaptic inputs. Our current understanding of how LFPs vary in M1, PMd, and PMv during contralateral and ipsilateral movements is incomplete. To help reveal unique features in the pattern of modulations, we simultaneously recorded LFPs in these areas in two macaque monkeys performing reach and grasp movements with either the right or left hand. The greatest effector-dependent differences were seen in M1, at low (≤13 Hz) and γ frequencies. In premotor areas, differences related to hand use were only present in low frequencies. PMv exhibited the greatest increase in low frequencies during instruction cues and the smallest effector-dependent modulation during movement execution. In PMd, δ oscillations were greater during contralateral reach and grasp, and β activity increased during contralateral grasp. In contrast, β oscillations decreased in M1 and PMv. These results suggest that while M1 primarily exhibits effector-specific LFP activity, premotor areas compute more effector-independent aspects of the task requirements, particularly during movement preparation for PMv and production for PMd. The generation of precise hand movements likely relies on the combination of complementary information contained in the unique pattern of neural modulations contained in each cortical area. Accordingly, integrating LFPs from premotor areas and M1 could enhance the performance and robustness of brain-machine interfaces.

480 Effect of Chronic DBS on Ventral Capsule Ventral Striatum-Specific Circadian Rhythm in Refractory Obsessive-Compulsive Disorder

INTRODUCTION: Deep brain stimulation (DBS) of the ventral capsule/ventral striatum (VC/VS) improves symptoms in ∼66% of patients with treatment-resistant obsessive-compulsive disorder (OCD). However, a common adverse effect of VC/VS DBS is hypomania, marked by decreased need for sleep and increased risk-taking behaviors. Modern DBS devices have enabled 24-hour collection of neural data, allowing for tracking of circadian rhythms reflected by VC/VS local field potentials (LFPs). METHODS: We implanted six patients with treatment-resistant OCD with a DBS device configured for constant VC/VS stimulation and recording. Spectral power in the 9 ± 2.5 Hz band was estimated onboard the device every 10 minutes for a median of 249 days (range 77-608). We assessed the rhythmicity of power in this spectral band by fitting 7-day retrospective rolling windows of per-day normalized LFP power to cosine functions using least-squared regression. Output metrics included acrophase (time of peak activity), amplitude, and fit significance. We quantified trends in acrophase over time using Pearson correlation. RESULTS: All six patients demonstrated consistent 24-hour cyclic LFP activity bilaterally. Two of the six had diurnal peaks which shifted significantly earlier across the study period. The remaining four patients had nocturnal peaks, all shifting significantly later over time. Notably, a patient in the latter group experienced three days of hypomania after DBS initiation associated with circadian disruptions even after clinical symptom resolution. Only after clinical DBS adjustment 2 weeks later did circadian rhythm rapidly return to pre-hypomanic levels. CONCLUSIONS: Study patients demonstrated evolving circadian VC/VS LFP activity across the course of DBS therapy, usually independent of cyclic whole-brain activity (i.e. sleep). Conversely, disruptions to these circadian patterns, as observed here during hypomania, may serve as biomarkers of region-specific stimulation side effects.

Neural timescales reflect behavioral demands in freely moving rhesus macaques

Previous work demonstrated a highly reproducible cortical hierarchy of neural timescales at rest, with sensory areas displaying fast, and higher-order association areas displaying slower timescales. The question arises how such stable hierarchies give rise to adaptive behavior that requires flexible adjustment of temporal coding and integration demands. Potentially, this lack of variability in the hierarchical organization of neural timescales could reflect the structure of the laboratory contexts. We posit that unconstrained paradigms are ideal to test whether the dynamics of neural timescales reflect behavioral demands. Here we measured timescales of local field potential activity while male rhesus macaques foraged in an open space. We found a hierarchy of neural timescales that differs from previous work. Importantly, although the magnitude of neural timescales expanded with task engagement, the brain areas’ relative position in the hierarchy was stable. Next, we demonstrated that the change in neural timescales is dynamic and contains functionally-relevant information, differentiating between similar events in terms of motor demands and associated reward. Finally, we demonstrated that brain areas are differentially affected by these behavioral demands. These results demonstrate that while the space of neural timescales is anatomically constrained, the observed hierarchical organization and magnitude is dependent on behavioral demands.

Multimodal subspace identification for modeling discrete-continuous spiking and field potential population activity

Objective. Learning dynamical latent state models for multimodal spiking and field potential activity can reveal their collective low-dimensional dynamics and enable better decoding of behavior through multimodal fusion. Toward this goal, developing unsupervised learning methods that are computationally efficient is important, especially for real-time learning applications such as brain–machine interfaces (BMIs). However, efficient learning remains elusive for multimodal spike-field data due to their heterogeneous discrete-continuous distributions and different timescales. Approach. Here, we develop a multiscale subspace identification (multiscale SID) algorithm that enables computationally efficient learning for modeling and dimensionality reduction for multimodal discrete-continuous spike-field data. We describe the spike-field activity as combined Poisson and Gaussian observations, for which we derive a new analytical SID method. Importantly, we also introduce a novel constrained optimization approach to learn valid noise statistics, which is critical for multimodal statistical inference of the latent state, neural activity, and behavior. We validate the method using numerical simulations and with spiking and local field potential population activity recorded during a naturalistic reach and grasp behavior. Main results. We find that multiscale SID accurately learned dynamical models of spike-field signals and extracted low-dimensional dynamics from these multimodal signals. Further, it fused multimodal information, thus better identifying the dynamical modes and predicting behavior compared to using a single modality. Finally, compared to existing multiscale expectation-maximization learning for Poisson–Gaussian observations, multiscale SID had a much lower training time while being better in identifying the dynamical modes and having a better or similar accuracy in predicting neural activity and behavior. Significance. Overall, multiscale SID is an accurate learning method that is particularly beneficial when efficient learning is of interest, such as for online adaptive BMIs to track non-stationary dynamics or for reducing offline training time in neuroscience investigations.

Brain-wide human oscillatory local field potential activity during visual working memory

Oscillatory activity in the local field potential (LFP) is thought to be a marker of cognitive processes. To understand how it differentiates tasks and brain areas in humans, we recorded LFPs in 15 adults with intracranial depth electrodes, as they performed visual-spatial and shape working memory tasks. Stimulus appearance produced widespread, broad-band activation, including in occipital, parietal, temporal, insular, and prefrontal cortex, and the amygdala and hippocampus. Occipital cortex was characterized by most elevated power in the high-gamma (100-150Hz) range during the visual stimulus presentation. The most consistent feature of the delay period was a systematic pattern of modulation in the beta frequency (16-40Hz), which included a decrease in power of variable timing across areas, and rebound during the delay period. These results reveal the widespread nature of oscillatory activity across a broad brain network and region-specific signatures of oscillatory processes associated with visual working memory.

Aberrant Cortical Activity in 5xFAD Mice in Response to Social and Non-Social Olfactory Stimuli.

Neuroimaging studies investigating the behavioral and psychological symptoms of dementia (BPSD)- such as apathy, anxiety, and depression- have linked some of these symptoms with altered neural activity. However, inconsistencies in operational definitions and rating scales, limited scope of assessments, and poor temporal resolution of imaging techniques have hampered human studies. Many transgenic (Tg) mouse models of Alzheimer's disease (AD) exhibit BPSD-like behaviors concomitant with AD-related neuropathology, allowing examination of how neural activity may relate to BPSD-like behaviors with high temporal and spatial resolution. To examine task-dependent neural activity in the medial prefrontal cortex (mPFC) of AD-model mice in response to social and non-social olfactory stimuli. We previously demonstrated age-related decreases in social investigation in Tg 5xFAD females, and this reduced social investigation is evident in Tg 5xFAD females and males by 6 months of age. In the present study, we examine local field potential (LFP) in the mPFC of awake, behaving 5xFAD females and males at 6 months of age during exposure to social and non-social odor stimuli in a novel olfactometer. Our results indicate that Tg 5xFAD mice exhibit aberrant baseline and task-dependent LFP activity in the mPFC- including higher relative delta (1-4 Hz) band power and lower relative power in higher bands, and overall stronger phase-amplitude coupling- compared to wild-type controls. These results are consistent with previous human and animal studies examining emotional processing, anxiety, fear behaviors, and stress responses, and suggest that Tg 5xFAD mice may exhibit altered arousal or anxiety.

Sensing with deep brain stimulation device in epilepsy: Aperiodic changes in thalamic local field potential during seizures.

Thalamic deep brain stimulation (DBS) is an effective therapeutic option in patients with drug-resistant epilepsy. Recent DBS devices with sensing capabilities enable chronic, outpatient local field potential (LFP) recordings. Whereas beta oscillations have been demonstrated to be a useful biomarker in movement disorders, the clinical utility of DBS sensing in epilepsy remains unclear. Our aim was to determine LFP features that distinguish ictal from inter-ictal states, which may aid in tracking seizure outcomes with DBS. Electrophysiology data were obtained from DBS devices implanted in the anterior nucleus (N = 12) or centromedian nucleus (N = 2) of the thalamus. Power spectra recorded during patient/caregiver-marked seizure events were analyzed with a method that quantitatively separates the oscillatory and non-oscillatory/aperiodic components of the LFP using non-parametric statistics, without the need for pre-specification of the frequency bands of interest. Features of the LFP parameterized using this algorithm were compared with those from inter-ictal power spectra recorded in clinic. Oscillatory activity in multiple canonical frequency bands was identified from the power spectra in 86.48% of patient-marked seizure events. Delta oscillations were present in all patients, followed by theta (N = 10) and beta (N = 9). Although there were no differences in oscillatory LFP features between the ictal and inter-ictal states, there was a steeper decline in the 1/f slope of the aperiodic component of the LFP during seizures. Our work highlights the potential and shortcomings of chronic LFP recordings in thalamic DBS for epilepsy. Findings suggest that no single frequency band in isolation clearly differentiates seizures, and that features of aperiodic LFP activity may be clinically-relevant biomarkers of seizures.

Machine learning identifies experimental brain metastasis subtypes based on their influence on neural circuits

A high percentage of patients with brain metastases frequently develop neurocognitive symptoms; however, understanding how brain metastasis co-opts the function of neuronal circuits beyond a tumor mass effect remains unknown. We report a comprehensive multidimensional modeling of brain functional analyses in the context of brain metastasis. By testing different preclinical models of brain metastasis from various primary sources and oncogenic profiles, we dissociated the heterogeneous impact on local field potential oscillatory activity from cortical and hippocampal areas that we detected from the homogeneous inter-model tumor size or glial response. In contrast, we report a potential underlying molecular program responsible for impairing neuronal crosstalk by scoring the transcriptomic and mutational profiles in a model-specific manner. Additionally, measurement of various brain activity readouts matched with machine learning strategies confirmed model-specific alterations that could help predict the presence and subtype of metastasis.

Unraveling Sex Differences in Affect Processing: Unique Oscillatory Signaling Dynamics in the Infralimbic Cortex and Nucleus Accumbens Shell

BackgroundNegative affect is prevalent in psychiatric diseases such as depression and addiction. Projections from the infralimbic cortex (IL) to the nucleus accumbens shell (NAcSh) are causally linked to learned negative affect as 20 Hz optogenetic stimulation of this circuit reduces conditioned taste aversion (CTA) in male but not female rats. However, the prior study did not provide insight into how innate versus learned negative affect are processed in these areas across sex. MethodsTo address this issue, local field potential activity was simultaneously recorded in the IL and NAcSh in response to intraoral infusion of rewarding (saccharin) and aversive (quinine) tastants and following induction of a CTA in male and female Sprague Dawley rats. ResultsLocal field potential oscillatory activity within each brain region to saccharin varied across sex. In males, CTA increased IL resting-state power, which was correlated with the strength of the learned aversion, and reduced beta power and IL-NAcSh coherence. In females, CTA increased gamma power in the NAcSh. Similar effects were observed in males and females after CTA in theta-low gamma phase-amplitude coupling. Finally, while quinine produced similar effects in oscillatory power across sex, females showed differences in phase-amplitude coupling within the NAcSh that may be linked to aversion resistance. ConclusionsWe revealed sex-specific hedonic processing in the IL and NAcSh and how oscillatory signaling is disrupted in learned negative affect, revealing translationally relevant insight into potential treatment strategies that can help to reduce the deleterious effects of learned negative affect in psychiatric illnesses.

A Simple, Lightweight, and Low-Cost Customizable Multielectrode Array for Local Field Potential Recordings.

Local field potential (LFP) recording is a valuable method for assessing brain systems communication. Multiple methods have been developed to collect LFP data to study the rhythmic activity of the brain. These methods range from the use of single or bundled metal electrodes to electrode arrays that can target multiple brain regions. Although these electrodes are efficient in collecting LFP activity, they can be expensive, difficult to build, and less adaptable to different applications, which may include targeting multiple brain regions simultaneously. Here, the building process for a 16-channel customizable multielectrode array (CMEA) that can be used to collect LFP data from different brain regions simultaneously in rats is described. These CMEA electrode arrays are lightweight (<1 g), take little time to build (<1 h), and are affordable ($15 Canadian). The CMEA can also be modified to record single-unit and multiunit activity in addition to LFP activity using both wired and wireless neural data acquisition systems. Moreover, these CMEAs can be used to explore neural activity (LFP and single-unit/multiunit activity) in preliminary studies, before purchasing more expensive electrodes for targeted studies. Together, these characteristics make the described CMEA a competitive alternative to the commercially available multielectrode arrays for its simplicity, low cost, and efficiency in collecting LFP data in freely behaving animals.

Laminar specificity of the auditory perceptual awareness negativity: A biophysical modeling study.

How perception of sensory stimuli emerges from brain activity is a fundamental question of neuroscience. To date, two disparate lines of research have examined this question. On one hand, human neuroimaging studies have helped us understand the large-scale brain dynamics of perception. On the other hand, work in animal models (mice, typically) has led to fundamental insight into the micro-scale neural circuits underlying perception. However, translating such fundamental insight from animal models to humans has been challenging. Here, using biophysical modeling, we show that the auditory awareness negativity (AAN), an evoked response associated with perception of target sounds in noise, can be accounted for by synaptic input to the supragranular layers of auditory cortex (AC) that is present when target sounds are heard but absent when they are missed. This additional input likely arises from cortico-cortical feedback and/or non-lemniscal thalamic projections and targets the apical dendrites of layer-5 (L5) pyramidal neurons. In turn, this leads to increased local field potential activity, increased spiking activity in L5 pyramidal neurons, and the AAN. The results are consistent with current cellular models of conscious processing and help bridge the gap between the macro and micro levels of perception-related brain activity.

Wake-like skin patterning and neural activity during octopus sleep

While sleeping, many vertebrate groups alternate between at least two sleep stages: rapid eye movement and slow wave sleep1–4, in part characterized by wake-like and synchronous brain activity, respectively. Here we delineate neural and behavioural correlates of two stages of sleep in octopuses, marine invertebrates that evolutionarily diverged from vertebrates roughly 550 million years ago (ref. 5) and have independently evolved large brains and behavioural sophistication. ‘Quiet’ sleep in octopuses is rhythmically interrupted by approximately 60-s bouts of pronounced body movements and rapid changes in skin patterning and texture6. We show that these bouts are homeostatically regulated, rapidly reversible and come with increased arousal threshold, representing a distinct ‘active’ sleep stage. Computational analysis of active sleep skin patterning reveals diverse dynamics through a set of patterns conserved across octopuses and strongly resembling those seen while awake. High-density electrophysiological recordings from the central brain reveal that the local field potential (LFP) activity during active sleep resembles that of waking. LFP activity differs across brain regions, with the strongest activity during active sleep seen in the superior frontal and vertical lobes, anatomically connected regions associated with learning and memory function7–10. During quiet sleep, these regions are relatively silent but generate LFP oscillations resembling mammalian sleep spindles11,12 in frequency and duration. The range of similarities with vertebrates indicates that aspects of two-stage sleep in octopuses may represent convergent features of complex cognition.

Rhythmic oscillations in the midbrain dopaminergic nuclei in mice.

Dopamine release in the forebrain by midbrain ventral tegmental nucleus (VTA) and substantia nigra pars compacta (SNc) neurons is implicated in reward processing, goal-directed learning, and decision-making. Rhythmic oscillations of neural excitability underlie coordination of network processing, and have been reported in these dopaminergic nuclei at several frequency bands. This paper provides a comparative characterization of several frequencies of oscillations of local field potential and single unit activity, highlighting some behavioral correlates. We recorded from optogenetically identified dopaminergic sites in four mice training in operant olfactory and visual discrimination tasks. Rayleigh and Pairwise Phase Consistency (PPC) analyses revealed some VTA/SNc neurons phase-locked to each frequency range, with fast spiking interneurons (FSIs) prevalent at 1-2.5 Hz (slow) and 4 Hz bands, and dopaminergic neurons predominant in the theta band. More FSIs than dopaminergic neurons were phase-locked in the slow and 4 Hz bands during many task events. The highest incidence of phase-locking in neurons was in the slow and 4 Hz bands, and occurred during the delay between the operant choice and trial outcome (reward or punishment) signals. These data provide a basis for further examination of rhythmic coordination of activity of dopaminergic nuclei with other brain structures, and its impact for adaptive behavior.

Behavioral State-Dependent Modulation of Prefrontal Cortex Activity by Respiration.

Respiration-rhythmic oscillations in the local field potential emerge in the mPFC, a cortical region with a key role in the regulation of cognitive and emotional behavior. Respiration-driven rhythms coordinate local activity by entraining fast γ oscillations as well as single-unit discharges. To what extent respiration entrainment differently engages the mPFC network in a behavioral state-dependent manner, however, is not known. Here, we compared the respiration entrainment of mouse PFC local field potential and spiking activity (23 male and 2 female mice) across distinct behavioral states: during awake immobility in the home cage (HC), during passive coping in response to inescapable stress under tail suspension (TS), and during reward consumption (Rew). Respiration-driven rhythms emerged during all three states. However, prefrontal γ oscillations were more strongly entrained by respiration during HC than TS or Rew. Moreover, neuronal spikes of putative pyramidal cells and putative interneurons showed significant respiration phase-coupling throughout behaviors with characteristic phase preferences depending on the behavioral state. Finally, while phase-coupling dominated in deep layers in HC and Rew conditions, TS resulted in the recruitment of superficial layer neurons to respiration. These results jointly suggest that respiration dynamically entrains prefrontal neuronal activity depending on the behavioral state.SIGNIFICANCE STATEMENT The mPFC, through its extensive connections (e.g., to the amygdala, the striatum, serotoninergic and dopaminergic nuclei), flexibly regulates cognitive behaviors. Impairment of prefrontal functions can lead to disease states, such as depression, addiction, or anxiety disorders. Deciphering the complex regulation of PFC activity during defined behavioral states is thus an essential challenge. Here, we investigated the role of a prefrontal slow oscillation that has recently attracted rising interest, the respiration rhythm, in modulating prefrontal neurons during distinct behavioral states. We show that prefrontal neuronal activity is differently entrained by the respiration rhythm in a cell type- and behavior-dependent manner. These results provide first insight into the complex modulation of prefrontal activity patterns by rhythmic breathing.

Somatosensory-Evoked Early Sharp Waves in the Neonatal Rat Hippocampus.

The developing entorhinal-hippocampal system is embedded within a large-scale bottom-up network, where spontaneous myoclonic movements, presumably via somatosensory feedback, trigger hippocampal early sharp waves (eSPWs). The hypothesis, that somatosensory feedback links myoclonic movements with eSPWs, implies that direct somatosensory stimulation should also be capable of evoking eSPWs. In this study, we examined hippocampal responses to electrical stimulation of the somatosensory periphery in urethane-anesthetized, immobilized neonatal rat pups using silicone probe recordings. We found that somatosensory stimulation in ~33% of the trials evoked local field potential (LFP) and multiple unit activity (MUA) responses identical to spontaneous eSPWs. The somatosensory-evoked eSPWs were delayed from the stimulus, on average, by 188 ms. Both spontaneous and somatosensory-evoked eSPWs (i) had similar amplitude of ~0.5 mV and half-duration of ~40 ms, (ii) had similar current-source density (CSD) profiles, with current sinks in CA1 strata radiatum, lacunosum-moleculare and DG molecular layer and (iii) were associated with MUA increase in CA1 and DG. Our results indicate that eSPWs can be triggered by direct somatosensory stimulations and support the hypothesis that sensory feedback from movements is involved in the association of eSPWs with myoclonic movements in neonatal rats.

Deviance Distraction and Stimulus-Specific Adaptation in the Somatosensory Cortex Reduce with Experience.

Automatic detection of a surprising change in the sensory input is a central element of exogenous attentional control. Stimulus-specific adaptation (SSA) is a potential neuronal mechanism detecting such changes and has been robustly described across sensory modalities and different instances of the ascending sensory pathways. However, little is known about the relationship of SSA to perception. To assess how deviating stimuli influence target signal detection, we used a behavioral cross-modal paradigm in mice and combined it with extracellular recordings from the primary somatosensory whisker cortex. In this paradigm, male mice performed a visual detection task while task-irrelevant whisker stimuli were either presented as repetitive "standard" or as rare deviant stimuli. We found a deviance distraction effect on the animals' performance: Faster reaction times but worsened target detection was observed in the presence of a deviant stimulus. Multiunit activity and local field potentials exhibited enhanced neuronal responses to deviant compared with standard whisker stimuli across all cortical layers, as a result of SSA. The deviant-triggered behavioral distraction correlated with these enhanced neuronal deviant responses only in the deeper cortical layers. However, the layer-specific effect of SSA on perception reduced with increasing task experience as a result of statistical distractor learning. These results demonstrate a layer-specific involvement of SSA on perception that is susceptible to modulation over time.SIGNIFICANCE STATEMENT Detecting sudden changes in our immediate environment is behaviorally relevant and important for efficient perceptual processing. However, the connection between the underpinnings of cortical deviance detection and perception remains unknown. Here, we investigate how the cortical representation of deviant whisker stimuli impacts visual target detection by recording local field potential and multiunit activity in the primary somatosensory cortex of mice engaged in a cross-modal visual detection task. We find that deviant whisker stimuli distract animals in their task performance, which correlates with enhanced neuronal responses for deviants in a layer-specific manner. Interestingly, this effect reduces with the increased experience of the animal as a result of distractor learning on statistical regularities.