Fear and safety engage competing patterns of theta-gamma coupling in the basolateral amygdala.

paper Cited Public

Authors: Stujenske, Joseph M; Likhtik, Ekaterina; Topiwala, Mihir A; Gordon, Joshua A
Year: 2014
Journal: Neuron
PMID: 25144877
DOI: 10.1016/j.neuron.2014.07.026
PMCID: PMC4141236

Figure 1

Two types of BLA gamma frequency oscillations are coupled to theta during fear recall(A) Experimental protocol. See text for detailed description.(B) Wavelet transform (color plot) of BLA LFP (gray) recorded during recall session (Day 4). Lower traces, slow (40-70 Hz, green) and fast (70-120 Hz, blue) gamma events, occurring at distinct phases of the theta oscillation (black). Boxes indicate representative high-amplitude gamma events in each frequency band.(C) Phase-Amplitude comodugram of a representative BLA LFP recording demonstrating modulation of high frequency power (y-axis) by low frequency oscillation phase (x-axis). Warm colors indicate stronger modulation; note the prominent modulation of separate slow (40-70 Hz) and fast (70-120 Hz) gamma peaks.(D) Histograms for the occurrence of slow gamma troughs, fast gamma troughs and multi-unit spikes (top three panels) and the preferred phase of significantly phase-locked (p < .05, Rayleigh test) multi-units (n=48) and single units (n=38; bottom two panels) relative to phases of the theta (4-12 Hz) oscillation. Error bars, here and throughout, are +/− SEM, except as otherwise noted.

LLM interpretation

This figure illustrates the coupling of BLA gamma oscillations to theta during fear recall. Panel A shows the experimental timeline across training, recall, and extinction phases, while Panel B displays a wavelet transform and filtered LFP traces showing slow (40-70 Hz) and fast (70-120 Hz) gamma events occurring at different theta phases. Panel C is a phase-amplitude comodugram showing strong modulation of both gamma bands by low-frequency phases, and Panel D uses histograms to show the distribution of gamma troughs and unit spikes relative to the theta phase, with significance indicated for phase-locked units (p < .05).

Figure 2

BLA theta-gamma coupling increases during conditioned fear(A) Example power spectrum of BLA LFP during pretone (black), an aversive CS+ (red), and a neutral CS− (blue). Presentation of an aversive CS+ elicits higher BLA theta power (peak at 6Hz).(B) Example comodugrams of theta–gamma coupling during pretone (left; 30s before tone), CS+ (right; during tone), and shift predictor of CS+ data (middle).(C) Mean theta-fast gamma coupling strength for CS+ (red) and shift predictor (gray) normalized to pre-tone (black line at 0, n=15). Significance lines (top): CS+/pretone (black) and CS+/shift predictor (gray) differences (p < 0.05/21, Bonferroni-corrected, sign-rank).(D) Mean theta-fast gamma coupling strength as a function of instantaneous theta frequency (n=15). Significance lines (top): Gray (uncorrected, p < 0.05) and black (Bonferroni-corrected sign rank, p < 0.05/15).(E) Change in theta-gamma coupling strength from pretone to CS+ for fast gamma (blue) and slow gamma (green) as a function of instantaneous theta frequency (left) and averaged across the theta range (right). Significance lines (top): Differences from pre-tone for each gamma frequency band. Light blue (uncorrected) and dark blue (Bonferroni-corrected, sign-rank, p<0.5/15).(F) Theta phase-fast gamma amplitude coupling strength as a function of theta power, binned in multiples of SD from the mean of the pretone theta power. * p < .05, ** p < .01, *** p <.001, sign-rank.

LLM interpretation

This figure analyzes BLA theta-gamma coupling during conditioned fear across six panels. It includes power spectra (A), comodugrams showing theta-phase and gamma-frequency coupling (B), and line graphs comparing coupling strength across conditions (CS+, CS-, Pretone, and Shift Predictor) and frequencies (C, D, E). Panel F is a bar chart showing that theta phase-fast gamma amplitude coupling increases as theta power increases, with statistical significance indicated by asterisks (*p < .05 to ***p < .001).

Figure 3

BLA fast gamma power decreases during conditioned fear(A) Example multi-taper spectrograms of BLA LFP during a CS+ (top) and CS− (bottom) presentation. Power was normalized by z-scoring in each frequency range. Black lines, stimulus onset and offset.(B) Fast gamma power during CS+ (red) and CS− (blue) presentations for Discriminators (D) and Generalizers (G). ** p <.01, sign-rank.(C) The difference between CS− and CS+ fast gamma power plotted by animal, as a function of discrimination score (CS+ - CS− percent freezing), with Pearson's r and p-value indicated. Grey box spanning panels (B) and (C) indicates data from the discriminator group.(D) Fast gamma power (top) and theta-fast gamma coupling strength (bottom) as a function of freezing on a trial-by-trial basis for an example animal. Each symbol represents data from a single trial. Data are normalized to pretone values.(E) Population data showing fast gamma power (black) and theta-fast gamma coupling (purple) as a function of freezing level (p<.001 and p<.05, respectively, MLR). All data is mean-normalized.

LLM interpretation

This figure analyzes BLA fast gamma power during conditioned fear across five panels. (A) shows multi-taper spectrograms for CS+ and CS- stimuli, while (B) uses a bar chart to show significantly lower fast gamma power for CS+ compared to CS- in Discriminators (** p < .01), but not in Generalizers. (C) displays a positive correlation (r=.48, p=0.02) between the CS- minus CS+ power difference and discrimination scores. (D) and (E) use scatter plots and line graphs to show that fast gamma power decreases while theta-fast gamma coupling increases as freezing levels rise (p < .001 and p < .05, respectively).

Figure 4

Increased fast gamma power during the CS− reflects synchronous neural firing(A) Left, histogram of the preferred fast gamma phases for significantly (dark blue) and non-significantly (light blue) phase-locked multi-unit recordings (p < .05, Rayleigh test). Black line is a cartoon depiction of fast gamma oscillation phases.(B) Percentage of multi-units significantly phase-locked to the fast gamma oscillation, compared to shift predictor. *** p < .001, McNemar's test.(C) Left, pie charts illustrating the percentage of recordings demonstrating significant phase-locking to fast gamma during the CS+ only (red), CS− only (blue), or both (magenta) in Discriminators (top) and Generalizers (bottom). Right, percentage of significantly phase-locked units to CS+ and CS−, including overlap. * p < .05, McNemar's test.(D) Change in multi-unit phase-locking strength to fast gamma from CS+ to CS− for discriminators (black, D) and for generalizers (grey, G). Mean change in discriminators is significantly different from 0 (** p < .01, sign-rank) and greater than in generalizers (** p<.01, rank-sum).

LLM interpretation

This figure consists of four panels analyzing fast gamma phase-locking in BLA recordings. (A) A histogram shows the distribution of preferred fast gamma phases, with 33% of recordings significantly phase-locked (dark blue). (B) A bar chart compares the percentage of significantly phase-locked units in the original data (33%) versus a shift predictor (6%), with $p < .001$. (C) Pie charts and bar graphs compare phase-locking to CS+ and CS− between Discriminators and Generalizers, showing a significant difference in phase-locking percentages for Discriminators ($p < .05$). (D) A scatter plot shows the change in phase-locking strength from CS+ to CS−, indicating a significant difference from zero for Discriminators ($p < .01$) and a significant difference between Discriminators and Generalizers ($p < .01$).

Figure 5

A subset of BLA single units synchronize with BLA fast gamma(A) Left, fast gamma trough-triggered firing rate of an example single unit. Blue line, trough-triggered LFP. Right, distribution of spikes by gamma phase for this unit. Blue arrow indicates preferred phase.(B) Histogram of the preferred fast gamma phases for significantly (blue) and non-significantly (gray) phase-locked single units (p < .05, Rayleigh test for both distributions). Oscillatory cycle is repeated for clarity.(C) Spike distribution as a function of fast gamma power for significantly phase-locked cells (blue) and all other cells (gray). Fast gamma power was positively correlated with spike rate of both phase-locked (r=0.5510, p=4.5 × 10−6, MLR) and other cells (r=0.2048, p=0.0011, MLR), but this relationship was significantly stronger for phase-locked units (inset: phase-locked, r=0.35+/−.09; others, r=0.18+/−.04; p=.0232, rank-sum).(D) Trial-by-trial firing rate as a function of freezing rate for an example fast gamma phase-locked unit (r=-.7729, ** p < .01). Gray arrow indicates mean pretone firing rate.(E) Pretone-normalized firing rate as a function of mean-normalized freezing level averaged across all phase-locked (blue) and other (gray) single units. A significant effect of freezing was seen only on phase-locked cells (p < .001, MLR).(F) Change in firing rate from low- to high-freezing trials for phase-locked (blue) and other (gray) single units. Decrease in rate for phase-locked units was significantly different from both 0 (p < .05, one-sample t-test) and from that in other units (p < .05, unpaired t-test).

LLM interpretation

This figure consists of six panels (A-F) analyzing the relationship between BLA single-unit firing and fast gamma oscillations. Panel A shows a trough-triggered firing rate histogram and a polar plot of spike distribution, while Panel B displays a histogram of preferred phases for phase-locked (blue) and non-phase-locked (gray) units. Panel C is a line graph showing a stronger positive correlation between fast gamma power and spike percentage for phase-locked units compared to others. Panels D and E use scatter and line plots to show a negative correlation between firing rate and freezing level specifically for phase-locked units, and Panel F uses a bar chart to show a significant decrease in firing rate from low- to high-freezing trials for phase-locked units (p < .05).

Figure 6

Synchrony and directionality of fast gamma in mPFC-BLA-vHPC circuit(A) Top, BLA fast gamma trough-triggered LFPs from mPFC (black), BLA (green) and vHPC (purple). Bottom, fast-gamma trough-triggered spectral coherence for specific region pairs.(B) Left, fast gamma power in the mPFC (top) and vHPC (bottom) during the CS+ (red) and CS− (blue) in discriminators (D) and generalizers (G). ** p < .01, sign-rank. Right, difference in fast gamma power between CS− and CS+ as a function of discrimination score for mPFC (top) and vHPC (bottom).(C) Left, probability (over time) of observing near zero phase synchrony in the fast gamma range by CS type, for discriminators and generalizers. Right, ratio of probability by CS type, as a function of discrimination score. Each symbol represents data from an individual animal.(D) Mean fast gamma Granger Causality Index for the mPFC-BLA (top), BLA-vHPC (middle), and vHPC-mPFC (bottom). Green, BLA lead; gray, mPFC lead; cyan, vHPC lead. *p<0.05, sign-rank.(E) Difference in PFC to BLA Granger lead strength (see text) between CS− and CS+, as a function of discrimination score.(F) Schematic of predominant directionality of fast gamma between mPFC, BLA, and vHPC, inferred from the data presented in D. A safety signal from the mPFC is propagated to the BLA, synchronizing fast gamma activity within the mPFC-BLA circuit.

LLM interpretation

This figure analyzes fast gamma synchrony and directionality in the mPFC-BLA-vHPC circuit. It includes trough-triggered LFPs and spectral coherence plots (A), bar charts and scatter plots showing fast gamma power and phase synchrony relative to CS type and discrimination scores (B, C), and Granger Causality Index bar charts and scatter plots indicating directional lead strength (D, E). A schematic (F) summarizes the inferred directionality, showing a safety signal propagating from the mPFC to the BLA and then to the vHPC.

Figure 7

Increased BLA fast gamma is associated with mPFC-to-BLA theta directionality(A) Mean CS+-evoked theta phase modulation of gamma frequency activity in the BLA (left, n=23), mPFC (middle, n=27), vHPC (right, n=17).(B) CS+evoked phase-locking (MRL) of BLA fast gamma with its local theta oscillation (green) compared to BLA fast gamma phase locking with mPFC theta (grey, top panel), vHPC theta (cyan, middle panel), and dHPC theta (bottom panel). **p<.01, signrank.(C) The number of BLA multi-unit recordings significantly phase-locked (p<.05/4, bonferroni corrected) to fast gamma oscillations in the mPFC (gray), BLA (green), vHPC (blue), mPFC and BLA (gray/green), BLA and vHPC (green/blue), and all structures (black). All recordings that phase-locked to the dHPC gamma oscillation (2%) also phase-locked to the vHPC gamma oscillation and were thus included with the vHPC in this depiction.(D) Fast gamma power in the BLA, as a function of the percentage of time windows in which the BLA theta leads mPFC theta (top), or mPFC theta leads BLA theta (bottom). Data are from a representative animal; each symbol represents data from single trial.(E) Population averages quantifying BLA fast gamma power for periods when instantaneous theta directionality corresponds to a BLA lead (green), no lead (black), or mPFC lead (gray).(F) Gamma power as a function of the relative strength of coupling of BLA gamma to mPFC vs BLA theta (z-scored relative to BLA theta values).

LLM interpretation

This figure consists of multiple panels analyzing the relationship between BLA fast gamma oscillations and theta phase/directionality. Panel A shows heatmaps of normalized power across frequency and phase for BLA, mPFC, and vHPC theta; Panel B uses bar charts to show significantly higher phase-locking (MRL) of BLA fast gamma to local BLA theta compared to mPFC, vHPC, and dHPC theta (**p < .01). Panel C is a pie chart showing the distribution of units phase-locked to different structures, with the majority (59%) locked to the PFC. Panels D, E, and F use scatter plots and a bar chart to demonstrate that BLA fast gamma power increases specifically when mPFC theta leads BLA theta (r = .87, p = .0010) and when coupling to mPFC theta is stronger than to BLA theta (r = .7664, p = .026).

Figure 8

mPFC lead and BLA fast gamma power in extinction and the open field(A) Freezing values for mice undergoing extinction during extinction training (CS+ data only).(B) Top, power spectrogram of BLA LFP from a representative animal, showing trial to trial changes in fast gamma power through extinction. Bottom, population mean +/− SEM fast gamma amplitude through extinction for BLA (green) and mPFC (gray).(C) Mean +/− Granger causality index, normalized by pretone value, for mPFC→BLA (gray) and BLA→mPFC (green) directions as a function of trial number. GCImPFC→BLA significantly increased throughout extinction (p=4.7 × 10−5, MLR), without a corresponding change in the GCIBLA →mPFC (p=.97). Inset, relative mPFC granger lead strength (see text) from R1 to E10.(D) Representative paths (yellow) of an anxious (left) and a non-anxious (right) mouse during exploration of a novel open field. Data from center (red), periphery (blue), and transition (gray) epochs was analyzed separately.(E) Fast gamma power by open field zone for anxious (n=9, left) and non-anxious (n=6, right) mice.(F) mPFC Granger lead strength by open field zone for anxious (left) and non-anxious (right) mice.

LLM interpretation

This figure consists of multiple panels analyzing neural activity during extinction learning and open field exploration. Panels A-C show a decrease in freezing behavior (A), an increase in fast gamma power for both BLA and mPFC (B), and a significant increase in mPFC$\rightarrow$BLA Granger causality over trials (C, $p=4.7 \times 10^{-5}$). Panels D-F compare anxious and non-anxious mice in an open field, showing that anxious mice exhibit significantly higher fast gamma power and mPFC Granger lead strength in the periphery compared to the center (*).

#	Section	Preview
20	Results — Fast gamma oscillations reflect BLA neuronal activity	Given that discriminators demonstrated enhanced fast gamma power during the CS− compared to the…
21	Results — Fast gamma oscillations reflect BLA neuronal activity	We next confirmed these findings in recordings of 83 well-isolated single units from the BLA. 18…
22	Results — Fast gamma oscillations reflect BLA neuronal activity	If phase-locked cells are involved in generating the fast gamma oscillations associated with safety,…
23	Results — Gamma synchrony across the cortico-limbic system	Encoding of fear and safety is also believed to engage circuits in the mPFC and HPC, which are…
24	Results — Gamma synchrony across the cortico-limbic system	Indeed, we observed highly synchronous slow and fast gamma oscillations in all three brain regions…
25	Results — Gamma synchrony across the cortico-limbic system	Given the strong, zero-phase lag synchrony of mPFC-BLA-vHPC fast gamma oscillations, we tested the…
26	Results — Gamma synchrony across the cortico-limbic system	We reasoned that these corresponding changes in mPFC and BLA gamma power might reflect periods of…
27	Results — Gamma synchrony across the cortico-limbic system	To further explore the dynamics of the mPFC-BLA-vHPC circuit, we used the Granger causality index…
28	Results — Gamma synchrony across the cortico-limbic system	we defined for each animal as GCImPFC→BLA / (GCImPFC→BLA + GCIBLA→mPFC), was correlated with…
29	Results — Dynamic switches in theta-gamma coupling	Given that gamma couples strongly to theta oscillations, we reasoned that the observed mPFC-to-BLA…
30	Results — Dynamic switches in theta-gamma coupling	data not shown). We were concerned that strong mPFC theta-BLA fast gamma coupling could arise if the…
31	Results — Dynamic switches in theta-gamma coupling	In Figure 3 we showed that local BLA theta-BLA gamma coupling increases with CS+ presentation,…
32	Results — Dynamic switches in theta-gamma coupling	windows; the lag at the peak of this cross-correlation indicates predominant theta directionality…
33	Results — Dynamic switches in theta-gamma coupling	Along with shifts in theta frequency lead, theta-gamma coupling changed dynamically as well, such…
34	Results — Theta-gamma dynamics and safety	Our data from fear conditioning were highly suggestive of an mPFC-to-BLA safety signal in the gamma…
35	Results — Theta-gamma dynamics and safety	recall. At the same time, we saw an enhancement of the GCImPFC→BLA (Figure 8D; p=4.7 × 10−5,…
36	Results — Theta-gamma dynamics and safety	Both fear discrimination and extinction probe fear and safety using learned stimuli, but a true…
37	Results — Theta-gamma dynamics and safety	enhanced mPFC Granger lead strength towards the periphery for anxious mice (Figure 8F; p=.026, MLR)…
38	Discussion	Fear states involve amygdala interactions with an extended network and in particular, its dense…
39	Discussion	Based on these findings, along with results from a number of studies demonstrating enhanced…

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Breathing Modulates Network Activity in Frontal Brain Regions during Anxiety.	Dias ALA et al.	—	2025	→
Early Life Stress Impairs VTA Coordination of BLA Network and Behavioral States.	Stone BT et al.	—	2025	→
Fatty acid binding proteins and their involvement in anxiety and mood disorders.	Jones MJ et al.	—	2025	→
Gamma synchronization between the medial temporal lobe and medial frontal cortex for goal-directed visual attention in humans.	Zhang J et al.	—	2025	→
Impacts of linseed oil diet on anxiety and memory extinction after early life stress: A sex-specific analysis of mitochondrial dysfunction, astrocytic markers, and inflammation in the amygdala.	Andressa Caetano R et al.	—	2025	→
Loss of PV Interneurons in the BLA May Contribute to Altered Network and Behavioral States in Chronically Epileptic Mice.	Colmers PLW et al.	—	2025	→
Major individual and regional variations in unit entrainment by oscillations of different frequencies.	Badawy M et al.	—	2025	→
Modelling fragile X-associated neuropsychiatric disorders in young inducible 90CGG premutation mice.	Çalışkan G et al.	—	2025	→
Neuromodulation as a therapeutic approach for post-traumatic stress disorder: the evidence to date.	Manocchio F et al.	—	2025	→
Serotonergic modulation of the BNST-CeA pathway reveals sex differences in fear learning.	Ravenelle R et al.	—	2025	→
Theta oscillations between the ventromedial prefrontal cortex and amygdala support dynamic representations of threat and safety.	Lu P et al.	—	2025	→
α-linolenic acid-induced facilitation of GABAergic synaptic transmission is mediated via acid-sensing ion channel (ASIC1a) activity in the basolateral amygdala.	Pidoplichko VI et al.	—	2025	→
A brainstem circuit amplifies aversion.	Liang J et al.	—	2024	→
Alcohol Modulates Frontal Cortex and BLA Network States Which Correlate with Future Voluntary Alcohol Consumption.	DiLeo A et al.	—	2024	→
Alterations in GABA<sub>A</sub> receptor-mediated inhibition triggered by status epilepticus and their role in epileptogenesis and increased anxiety.	Aroniadou-Anderjaska V et al.	—	2024	→
Arousal effects on oscillatory dynamics in the non-human primate brain.	Anand SA et al.	—	2024	→
Basolateral amygdala oscillations enable fear learning in a biophysical model.	Cattani A et al.	—	2024	→
Basolateral amygdala parvalbumin interneurons coordinate oscillations to drive reward behaviors.	Amaya KA et al.	—	2024	→
Circadian Rhythms in Conditioned Threat Extinction Reflect Time-of-Day Differences in Ventromedial Prefrontal Cortex Neural Processing.	Hartsock MJ et al.	—	2024	→
Elevated fear states facilitate ventral hippocampal engagement of basolateral amygdala neuronal activity.	Ritger AC et al.	—	2024	→
Large-scale coupling of prefrontal activity patterns as a mechanism for cognitive control in health and disease: evidence from rodent models.	Negrón-Oyarzo I et al.	—	2024	→
Mouse Escape Behaviors and mPFC-BLA Activity Dataset: Understanding Flexible Defensive Strategies Under Threat.	Cho S et al.	—	2024	→
Multi-regional control of amygdalar dynamics reliably reflects fear memory age.	Makino Y et al.	—	2024	→
Neural circuits for the adaptive regulation of fear and extinction memory.	Plas SL et al.	—	2024	→
Neural decoding and feature selection methods for closed-loop control of avoidance behavior.	Liu J et al.	—	2024	→
Neuromodulation of inhibitory synaptic transmission in the basolateral amygdala during fear and anxiety.	Fu X et al.	—	2024	→
Olfactory bulb-medial prefrontal cortex theta synchronization is associated with anxiety.	Mooziri M et al.	—	2024	→
Prefrontal Regulation of Safety Learning during Ethologically Relevant Thermal Threat.	Felix-Ortiz AC et al.	—	2024	→
The gamma-band activity model of the near-death experience: a critique and a reinterpretation.	A Shaw N	—	2024	→
The ventromedial prefrontal cortex in response to threat omission is associated with subsequent explicit safety memory.	Wiemer J et al.	—	2024	→
Unraveling Sex Differences in Affect Processing: Unique Oscillatory Signaling Dynamics in the Infralimbic Cortex and Nucleus Accumbens Shell.	Douton JE et al.	—	2024	→
Allergen Induces Depression-like Behavior in Association with Altered Prefrontal-hippocampal Circuit in Male Rats.	Dehdar K et al.	—	2023	→
Altered neurotransmission in stress-induced depressive disorders: The underlying role of the amygdala in depression.	Asim M et al.	—	2023	→
Anxiety and depression: A top-down, bottom-up model of circuit function.	LeDuke DO et al.	—	2023	→
Anxious individuals shift emotion control from lateral frontal pole to dorsolateral prefrontal cortex.	Bramson B et al.	—	2023	→
A pilot study of closed-loop neuromodulation for treatment-resistant post-traumatic stress disorder.	Gill JL et al.	—	2023	→
Aversive memory reactivation: A possible role for delta oscillations in the hippocampus-amygdala circuit.	Couto Pereira NS et al.	—	2023	→
Brain response in asthma: the role of "lung-brain" axis mediated by neuroimmune crosstalk.	Wang Y et al.	—	2023	→
Brain structural and functional alterations related to anxiety in allergic asthma.	Dehdar K et al.	—	2023	→
Changes in brain rhythms and connectivity tracking fear acquisition and reversal.	Pirazzini G et al.	—	2023	→
Comparison of three common inbred mouse strains reveals substantial differences in hippocampal GABAergic interneuron populations and in vitro network oscillations.	Çalışkan G et al.	—	2023	→
Corticosteroid treatment attenuates anxiety and mPFC-amygdala circuit dysfunction in allergic asthma.	Dehdar K et al.	—	2023	→
Differential Regulation of Prelimbic and Thalamic Transmission to the Basolateral Amygdala by Acetylcholine Receptors.	Tryon SC et al.	—	2023	→
Distinct ACC Neural Mechanisms Underlie Authentic and Transmitted Anxiety Induced by Maternal Separation in Mice.	Jiang J et al.	—	2023	→
Dopamine D1-like receptors modulate synchronized oscillations in the hippocampal-prefrontal-amygdala circuit in contextual fear.	Stubbendorff C et al.	—	2023	→
Dynamic Changes in Local Activity and Network Interactions among the Anterior Cingulate, Amygdala, and Cerebellum during Associative Learning.	Halverson HE et al.	—	2023	→
Dynamic switching of neural oscillations in the prefrontal-amygdala circuit for naturalistic freeze-or-flight.	Han HB et al.	—	2023	→
Enriched environment attenuates hippocampal theta and gamma rhythms dysfunction in chronic cerebral hypoperfusion via improving imbalanced neural afferent levels.	Zheng J et al.	—	2023	→
Expanding the canon: An inclusive neurobiology of thalamic and subthalamic fear circuits.	Venkataraman A et al.	—	2023	→
High gamma coherence between task-responsive sensory-motor cortical regions in a motor reaction-time task.	Anand S et al.	—	2023	→
Identification of a novel fatty acid binding protein-5-CB2 receptor-dependent mechanism regulating anxiety behaviors in the prefrontal cortex.	Uzuneser TC et al.	—	2023	→
Influence of chronic stress on network states governing valence processing: Potential relevance to the risk for psychiatric illnesses.	Antonoudiou P et al.	—	2023	→
Neuronal Circuits Associated with Fear Memory: Potential Therapeutic Targets for Posttraumatic Stress Disorder.	Yan Y et al.	—	2023	→
Non-angry aggressive arousal and angriffsberietschaft: A narrative review of the phenomenology and physiology of proactive/offensive aggression motivation and escalation in people and other animals.	Potegal M et al.	—	2023	→
Npas4-mediated dopaminergic regulation of safety memory consolidation.	Ko B et al.	—	2023	→
Oscillatory and non-oscillatory brain activity reflects fear expression in an immediate and delayed fear extinction task.	Bierwirth P et al.	—	2023	→
Sex differences in amygdalohippocampal oscillations and neuronal activation in a rodent anxiety model and in response to infralimbic deep brain stimulation.	Vila-Merkle H et al.	—	2023	→
Threat gates visual aversion via theta activity in Tachykinergic neurons.	Tsuji M et al.	—	2023	→
ACC-BLA functional connectivity disruption in allergic inflammation is associated with anxiety.	Gholami-Mahtaj L et al.	—	2022	→
Allergen disrupts amygdala-respiration coupling.	Dehdar K et al.	—	2022	→
Allopregnanolone Mediates Affective Switching Through Modulation of Oscillatory States in the Basolateral Amygdala.	Antonoudiou P et al.	—	2022	→
Antiseizure and Neuroprotective Efficacy of Midazolam in Comparison with Tezampanel (LY293558) against Soman-Induced Status Epilepticus.	Figueiredo TH et al.	—	2022	→
Breaking Down a Rhythm: Dissecting the Mechanisms Underlying Task-Related Neural Oscillations.	Ibarra-Lecue I et al.	—	2022	→
Breathing coordinates cortico-hippocampal dynamics in mice during offline states.	Karalis N et al.	—	2022	→
Chronic corticosterone exposure impairs emotional regulation and cognitive function through disturbing neural oscillations in mice.	Zhao S et al.	—	2022	→
Control of Theta Oscillatory Activity Underlying Fear Expression by mGlu<sub>5</sub> Receptors.	Matulewicz P et al.	—	2022	→
Cross-species anxiety tests in psychiatry: pitfalls and promises.	Bach DR	—	2022	→
Extreme conditions affect neuronal oscillations of cerebral cortices in humans in the China Space Station and on Earth.	Zhang P et al.	—	2022	→
Gq neuromodulation of BLA parvalbumin interneurons induces burst firing and mediates fear-associated network and behavioral state transition in mice.	Fu X et al.	—	2022	→
Hyperactivity of basolateral amygdala mediates behavioral deficits in mice following exposure to bisphenol A and its analogue alternative.	Hu F et al.	—	2022	→
Neural Oscillations in Aversively Motivated Behavior.	Totty MS et al.	—	2022	→
Prefrontal-amygdalar oscillations related to social behavior in mice.	Kuga N et al.	—	2022	→
Prelimbic cortex drives discrimination of non-aversion via amygdala somatostatin interneurons.	Stujenske JM et al.	—	2022	→
Relations between family cohesion and adolescent-parent's neural synchrony in response to emotional stimulations.	Deng X et al.	—	2022	→
Sex Differences in the Alcohol-Mediated Modulation of BLA Network States.	DiLeo A et al.	—	2022	→
Stressed rats fail to exhibit avoidance reactions to innately aversive social calls.	Shukla A et al.	—	2022	→
Temporal dynamics of affect in the brain: Evidence from human imaging and animal models.	Puccetti NA et al.	—	2022	→
The olfactory bulb coordinates the ventral hippocampus-medial prefrontal cortex circuit during spatial working memory performance.	Salimi M et al.	—	2022	→
The role of gamma oscillations in central nervous system diseases: Mechanism and treatment.	Guan A et al.	—	2022	→
Theta coupling within the medial prefrontal cortex regulates fear extinction and renewal.	Wang C et al.	—	2022	→
A Decision Architecture for Safety Computations.	Tashjian SM et al.	—	2021	→
Age- and sex-specific fear conditioning deficits in mice lacking Pcdh10, an Autism Associated Gene.	Ferri SL et al.	—	2021	→
Alterations of amygdala-prefrontal cortical coupling and attention deficit/hyperactivity disorder-like behaviors induced by neonatal habenula lesion: normalization by Ecklonia stolonifera extract and its active compound fucosterol.	Kim YJ et al.	—	2021	→
Altered theta and beta oscillatory synchrony in a genetic mouse model of pathological anxiety.	Cruces-Solis H et al.	—	2021	→
Bidirectional Influence of Limbic GIRK Channel Activation on Innate Avoidance Behavior.	Vo BN et al.	—	2021	→
Effects of Acute Stress on the Oscillatory Activity of the Hippocampus-Amygdala-Prefrontal Cortex Network.	Merino E et al.	—	2021	→
Enhanced synchronization between prelimbic and infralimbic cortices during fear extinction learning.	Watanabe M et al.	—	2021	→
Frontostriatal Projections Regulate Innate Avoidance Behavior.	Loewke AC et al.	—	2021	→
Functional and directed connectivity of the cortico-limbic network in mice in vivo.	Khastkhodaei Z et al.	—	2021	→
Gamma Oscillations in the Basolateral Amygdala: Localization, Microcircuitry, and Behavioral Correlates.	Headley DB et al.	—	2021	→
Individual Differences in Conditioned Fear and Extinction in Female Rats.	Tryon SC et al.	—	2021	→
Information transmission in mPFC-BLA network during exploratory behavior in the open field.	Bao X et al.	—	2021	→
Lack of redundancy between electrophysiological measures of long-range neuronal communication.	Strahnen D et al.	—	2021	→
Neural correlates and determinants of approach-avoidance conflict in the prelimbic prefrontal cortex.	Fernandez-Leon JA et al.	—	2021	→
Neuronal oscillations and the mouse prefrontal cortex.	Jung F et al.	—	2021	→
Prefrontal Theta Oscillations Are Modulated by Estradiol Status During Fear Recall and Extinction Recall.	Bierwirth P et al.	—	2021	→
Salient safety conditioning improves novel discrimination learning.	Nahmoud I et al.	—	2021	→
Temporal structure of brain oscillations predicts learned nocebo responses to pain.	Thomaidou MA et al.	—	2021	→
The Basolateral Amygdala Mediates the Role of Rapid Eye Movement Sleep in Integrating Fear Memory Responses.	Machida M et al.	—	2021	→
The Benefits of Music Listening for Induced State Anxiety: Behavioral and Physiological Evidence.	Huang B et al.	—	2021	→
The Neurometabolic Basis of Mood Instability: The Parvalbumin Interneuron Link-A Systematic Review and Meta-Analysis.	Pinna A et al.	—	2021	→
Theta-gamma coupling during REM sleep depends on breathing rate.	Hammer M et al.	—	2021	→
Theta oscillations synchronize human medial prefrontal cortex and amygdala during fear learning.	Chen S et al.	—	2021	→
Theta-Range Oscillations in Stress-Induced Mental Disorders as an Oscillotherapeutic Target.	Okonogi T et al.	—	2021	→
Unsupervised Methods for Detection of Neural States: Case Study of Hippocampal-Amygdala Interactions.	Cocina F et al.	—	2021	→
A bird's-eye view of brain activity in socially interacting mice through mobile edge computing (MEC).	Kim J et al.	—	2020	→
Adverse caregiving in infancy blunts neural processing of the mother.	Opendak M et al.	—	2020	→
A Role of Low-Density Lipoprotein Receptor-Related Protein 4 (LRP4) in Astrocytic Aβ Clearance.	Zhang H et al.	—	2020	→
Assessing the Effects of Continuous Theta Burst Stimulation Over the Dorsolateral Prefrontal Cortex on Human Cognition: A Systematic Review.	Ngetich R et al.	—	2020	→
Cross-Frequency Phase-Amplitude Coupling between Hippocampal Theta and Gamma Oscillations during Recall Destabilizes Memory and Renders It Susceptible to Reconsolidation Disruption.	Radiske A et al.	—	2020	→
Cross-Frequency Power-Power Coupling Analysis: A Useful Cross-Frequency Measure to Classify ICA-Decomposed EEG.	Thammasan N et al.	—	2020	→
Decoding the circuitry of consciousness: From local microcircuits to brain-scale networks.	Modolo J et al.	—	2020	→
Disinhibition-assisted long-term potentiation in the prefrontal-amygdala pathway via suppression of somatostatin-expressing interneurons.	Ito W et al.	—	2020	→
Experience-dependent resonance in amygdalo-cortical circuits supports fear memory retrieval following extinction.	Ozawa M et al.	—	2020	→
Extinction learning alters the neural representation of conditioned fear.	Graner JL et al.	—	2020	→
Fear Learning Enhances Prefrontal Cortical Suppression of Auditory Thalamic Inputs to the Amygdala in Adults, but Not Adolescents.	Ferrara NC et al.	—	2020	→
From Conditioning to Emotion: Translating Animal Models of Learning to Human Psychopathology.	Heller AS	—	2020	→
Hippocampal gamma oscillations by sucrose instrumental memory retrieval in rats across sleep/wake cycle.	Padovani L et al.	—	2020	→
Implanting and Recycling Neuropixels Probes for Recordings in Freely Moving Mice.	Juavinett AL et al.	—	2020	→
Know safety, no fear.	Sangha S et al.	—	2020	→
Mesoscopic-scale functional networks in the primate amygdala.	Morrow JK et al.	—	2020	→
Mind the Gate to Improve Comorbid Cognitive Impairments in Epilepsy.	Maguire J	—	2020	→
Prefrontal - subthalamic pathway supports action selection in a spatial working memory task.	Heikenfeld C et al.	—	2020	→
Representation of probabilistic outcomes during risky decision-making.	Castegnetti G et al.	—	2020	→
Reward-related dynamical coupling between basolateral amygdala and nucleus accumbens.	Hsu CC et al.	—	2020	→
Structural correlates of emotional response to electrical stimulation of the amygdala in subjects with PTSD.	Avecillas-Chasin JM et al.	—	2020	→
Targeting the glutamatergic system to counteract organophosphate poisoning: A novel therapeutic strategy.	Aroniadou-Anderjaska V et al.	—	2020	→
The integration of Gaussian noise by long-range amygdala inputs in frontal circuit promotes fear learning in mice.	Aime M et al.	—	2020	→
The role of mPFC and MTL neurons in human choice under goal-conflict.	Gazit T et al.	—	2020	→
Theta Oscillations Through Hippocampal/Prefrontal Pathway: Importance in Cognitive Performances.	Soltani Zangbar H et al.	—	2020	→
White Matter Plasticity in Anxiety: Disruption of Neural Network Synchronization During Threat-Safety Discrimination.	Liu J et al.	—	2020	→
A Brief Review of the EEG Literature on Mindfulness and Fear Extinction and its Potential Implications for Posttraumatic Stress Symptoms (PTSS).	Kummar AS et al.	—	2019	→
Allergen-induced anxiety-like behavior is associated with disruption of medial prefrontal cortex - amygdala circuit.	Dehdar K et al.	—	2019	→
Amygdala ensembles encode behavioral states.	Gründemann J et al.	—	2019	→
Beyond Emotions: Oscillations of the Amygdala and Their Implications for Electrical Neuromodulation.	Schönfeld LM et al.	—	2019	→
Chronically implanted Neuropixels probes enable high-yield recordings in freely moving mice.	Juavinett AL et al.	—	2019	→
Closed-loop control of gamma oscillations in the amygdala demonstrates their role in spatial memory consolidation.	Kanta V et al.	—	2019	→
Effect of prenatal stress on neural oscillations in developing hippocampal formation.	Zhang H et al.	—	2019	→
Embracing Complexity in Defensive Networks.	Headley DB et al.	—	2019	→
Emergent decision-making behaviour and rhythm generation in a computational model of the ventromedial nucleus of the hypothalamus.	MacGregor DJ et al.	—	2019	→
Gamma Oscillations in the Basolateral Amygdala: Biophysical Mechanisms and Computational Consequences.	Feng F et al.	—	2019	→
High-precision magnetoencephalography for reconstructing amygdalar and hippocampal oscillations during prediction of safety and threat.	Tzovara A et al.	—	2019	→
Hippocampal network oscillations at the interplay between innate anxiety and learned fear.	Çalışkan G et al.	—	2019	→
Insoluble Aβ overexpression in an <i>App</i> knock-in mouse model alters microstructure and gamma oscillations in the prefrontal cortex, affecting anxiety-related behaviours.	Pervolaraki E et al.	—	2019	→
Interhemispheric Connectivity Potentiates the Basolateral Amygdalae and Regulates Social Interaction and Memory.	Huang TN et al.	—	2019	→
<i>Scn2a</i> haploinsufficient mice display a spectrum of phenotypes affecting anxiety, sociability, memory flexibility and ampakine CX516 rescues their hyperactivity.	Tatsukawa T et al.	—	2019	→
Making translation work: Harmonizing cross-species methodology in the behavioural neuroscience of Pavlovian fear conditioning.	Haaker J et al.	—	2019	→
Multiplexing of Theta and Alpha Rhythms in the Amygdala-Hippocampal Circuit Supports Pattern Separation of Emotional Information.	Zheng J et al.	—	2019	→
Neuromodulation in circuits of aversive emotional learning.	Likhtik E et al.	—	2019	→
New Insights from 22-kHz Ultrasonic Vocalizations to Characterize Fear Responses: Relationship with Respiration and Brain Oscillatory Dynamics.	Dupin M et al.	—	2019	→
Perineuronal Nets, Inhibitory Interneurons, and Anxiety-Related Ventral Hippocampal Neuronal Oscillations Are Altered by Early Life Adversity.	Murthy S et al.	—	2019	→
Sex differences in fear extinction.	Velasco ER et al.	—	2019	→
Ultrasonic Vocalizations Emitted during Defensive Behavior Alter the Influence of the Respiratory Rhythm on Brain Oscillatory Dynamics in the Fear Circuit of Rats.	Carney RSE	—	2019	→
Valence coding in amygdala circuits.	Pignatelli M et al.	—	2019	→
Attentional set to safety recruits the ventral medial prefrontal cortex.	Yao S et al.	—	2018	→
Basal Forebrain and Brainstem Cholinergic Neurons Differentially Impact Amygdala Circuits and Learning-Related Behavior.	Aitta-Aho T et al.	—	2018	→
Coherent Activity between the Prelimbic and Auditory Cortex in the Slow-Gamma Band Underlies Fear Discrimination.	Concina G et al.	—	2018	→
Dynamic ErbB4 Activity in Hippocampal-Prefrontal Synchrony and Top-Down Attention in Rodents.	Tan Z et al.	—	2018	→
Early life trauma increases threat response of peri-weaning rats, reduction of axo-somatic synapses formed by parvalbumin cells and perineuronal net in the basolateral nucleus of amygdala.	Santiago AN et al.	—	2018	→
Effects of bright light exposure on human fear conditioning, extinction, and associated prefrontal activation.	Yoshiike T et al.	—	2018	→
GABAergic interneurons: The orchestra or the conductor in fear learning and memory?	Lucas EK et al.	—	2018	→
Hippocampal network oscillations as mediators of behavioural metaplasticity: Insights from emotional learning.	Çalışkan G et al.	—	2018	→
Input Convergence, Synaptic Plasticity and Functional Coupling Across Hippocampal-Prefrontal-Thalamic Circuits.	Bueno-Junior LS et al.	—	2018	→
Limited prefrontal cortical regulation over the basolateral amygdala in adolescent rats.	Selleck RA et al.	—	2018	→
Mixed selectivity encoding and action selection in the prefrontal cortex during threat assessment.	Grunfeld IS et al.	—	2018	→
Neural Coding of Appetitive Food Experiences in the Amygdala.	Liu J et al.	—	2018	→
Neural oscillations in the infralimbic cortex after electrical stimulation of the amygdala. Relevance to acute stress processing.	Luque-García A et al.	—	2018	→
Neural Oscillatory Correlates for Conditioning and Extinction of Fear.	Trenado C et al.	—	2018	→
Neuronal coding mechanisms mediating fear behavior.	Rozeske RR et al.	—	2018	→
Optogenetic fMRI and electrophysiological identification of region-specific connectivity between the cerebellar cortex and forebrain.	Choe KY et al.	—	2018	→
Oscillations Synchronize Amygdala-to-Prefrontal Primate Circuits during Aversive Learning.	Taub AH et al.	—	2018	→
Oscillatory Synchronous Inhibition in the Basolateral Amygdala and its Primary Dependence on NR2A-containing NMDA Receptors.	Aroniadou-Anderjaska V et al.	—	2018	→
Reciprocal amygdala-prefrontal interactions in learning.	Yizhar O et al.	—	2018	→
Respiration-Entrained Brain Rhythms Are Global but Often Overlooked.	Tort ABL et al.	—	2018	→
Strengthened functional connectivity among LFPs in rat medial prefrontal cortex during anxiety.	Lu J et al.	—	2018	→
Temporal coupling of field potentials and action potentials in the neocortex.	Watson BO et al.	—	2018	→
Vigilance-Associated Gamma Oscillations Coordinate the Ensemble Activity of Basolateral Amygdala Neurons.	Amir A et al.	—	2018	→
A Consistent Definition of Phase Resetting Using Hilbert Transform.	Oprisan SA	—	2017	→
A generalized phase resetting method for phase-locked modes prediction.	Oprisan SA et al.	—	2017	→
Amygdala inputs to prefrontal cortex guide behavior amid conflicting cues of reward and punishment.	Burgos-Robles A et al.	—	2017	→
Automated approach to detecting behavioral states using EEG-DABS.	Loris ZB et al.	—	2017	→
Cellular and oscillatory substrates of fear extinction learning.	Davis P et al.	—	2017	→
Dissecting the Function of Hippocampal Oscillations in a Human Anxiety Model.	Khemka S et al.	—	2017	→
Distribution, physiology and pharmacology of relaxin-3/RXFP3 systems in brain.	Ma S et al.	—	2017	→
Hippocampal-prefrontal theta-gamma coupling during performance of a spatial working memory task.	Tamura M et al.	—	2017	→
Involvement of serotonin 2A receptor activation in modulating medial prefrontal cortex and amygdala neuronal activation during novelty-exposure.	Hervig ME et al.	—	2017	→
Magnetoencephalography study of different relationships among low- and high-frequency-band neural activities during the induction of peaceful and fearful audiovisual modalities among males and females.	Yang CY et al.	—	2017	→
Melatonin ameliorates amygdala-dependent emotional memory deficits in Tg2576 mice by up-regulating the CREB/c-Fos pathway.	Peng C et al.	—	2017	→
Novelty and fear conditioning induced gene expression in high and low states of anxiety.	Donley MP et al.	—	2017	→
Prior Learning of Relevant Nonaversive Information Is a Boundary Condition for Avoidance Memory Reconsolidation in the Rat Hippocampus.	Radiske A et al.	—	2017	→
Reversible Inactivation of the Higher Order Auditory Cortex during Fear Memory Consolidation Prevents Memory-Related Activity in the Basolateral Amygdala during Remote Memory Retrieval.	Cambiaghi M et al.	—	2017	→
Synaptic Plasticity, Engrams, and Network Oscillations in Amygdala Circuits for Storage and Retrieval of Emotional Memories.	Bocchio M et al.	—	2017	→
Temporal coupling of field potentials and action potentials in the neocortex	Watson BO et al.	—	2017	—
The role of gamma interbrain synchrony in social coordination when humans face territorial threats.	Mu Y et al.	—	2017	→
4-Hz oscillations synchronize prefrontal-amygdala circuits during fear behavior.	Karalis N et al.	—	2016	→
Amygdala fMRI Signal as a Predictor of Reaction Time.	Riedel P et al.	—	2016	→
Direct Ventral Hippocampal-Prefrontal Input Is Required for Anxiety-Related Neural Activity and Behavior.	Padilla-Coreano N et al.	—	2016	→
Disruption of Network Synchrony and Cognitive Dysfunction After Traumatic Brain Injury.	Wolf JA et al.	—	2016	→
Low- and high-gamma oscillations deviate in opposite directions from zero-phase synchrony in the limbic corticostriatal loop.	Catanese J et al.	—	2016	→
On cross-frequency phase-phase coupling between theta and gamma oscillations in the hippocampus.	Scheffer-Teixeira R et al.	—	2016	→
Prefrontal neuronal assemblies temporally control fear behaviour.	Dejean C et al.	—	2016	→
The basolateral amygdala γ-aminobutyric acidergic system in health and disease.	Prager EM et al.	—	2016	→
Amygdala-prefrontal interactions in (mal)adaptive learning.	Likhtik E et al.	—	2015	→
Ascending control of arousal and motivation: role of nucleus incertus and its peptide neuromodulators in behavioural responses to stress.	Ma S et al.	—	2015	→
Fear renewal preferentially activates ventral hippocampal neurons projecting to both amygdala and prefrontal cortex in rats.	Jin J et al.	—	2015	→
Limbic Encephalitis: Potential Impact of Adaptive Autoimmune Inflammation on Neuronal Circuits of the Amygdala.	Melzer N et al.	—	2015	→
Long-range neural synchrony in behavior.	Harris AZ et al.	—	2015	→
Neuronal Circuits for Fear Expression and Recovery: Recent Advances and Potential Therapeutic Strategies.	Dejean C et al.	—	2015	→
Observation of Distressed Conspecific as a Model of Emotional Trauma Generates Silent Synapses in the Prefrontal-Amygdala Pathway and Enhances Fear Learning, but Ketamine Abolishes those Effects.	Ito W et al.	—	2015	→
Representations of Value in the Brain: An Embarrassment of Riches?	Stott JJ et al.	—	2015	→
Resolving the neural circuits of anxiety.	Calhoon GG et al.	—	2015	→
The nature of individual differences in inhibited temperament and risk for psychiatric disease: A review and meta-analysis.	Clauss JA et al.	—	2015	→
What does gamma coherence tell us about inter-regional neural communication?	Buzsáki G et al.	—	2015	→
Oscillatory substrates of fear and safety.	Bocchio M et al.	—	2014	→