Human single-neuron activity is modulated by intracranial theta burst stimulation of the basolateral amygdala

The amygdala is a highly connected cluster of nuclei with input from multiple sensory modalities and vast projections to distributed cortical and subcortical regions involved in autonomic regulation and cognition (Kim et al., 2011; McDonald and Mott, 2017; Roy et al., 2009; Stein et al., 2007). Numerous studies have described the amygdala’s capacity to facilitate the encoding of long-lasting emotional memories (Dolcos et al., 2004; Hermans et al., 2014; LaBar and Cabeza, 2006; Phelps, 2006; Phelps, 2004; Phelps and LeDoux, 2005; Qasim et al., 2023; Richardson et al., 2004; Roozendaal et al., 2009; Schwabe et al., 2013; Zheng et al., 2017). Specific oscillatory dynamics in the amygdalohippocampal circuit are increasingly understood to be essential in prioritizing the encoding of these salient memories (Qasim et al., 2023; Zheng et al., 2019; Zheng et al., 2017).

Recently, direct electrical stimulation of the basolateral complex of the amygdala (BLA) in humans has revealed a more generalized ability to enhance declarative memory irrespective of the emotional valence (Inman et al., 2018), likely by promoting synaptic plasticity-related processes underlying memory consolidation in the hippocampus and medial temporal lobe (McGaugh, 2013; McGaugh, 2004; McGaugh et al., 2002; Roesler et al., 2021). These pro-memory effects were achieved with rhythmic theta burst stimulation (TBS), which is known to induce long-term potentiation (LTP), a key mechanism in memory formation (Larson and Munkácsy, 2015). Emerging evidence suggests that intracranial TBS may also enhance memory specificity (Titiz et al., 2017), evoke theta-frequency oscillations (Solomon et al., 2021), and facilitate short-term plasticity in local field potential recordings (Herrero et al., 2021; Huang et al., 2024). However, the extent to which exogenous TBS modulates activity at the single-cell level and whether this modulation is associated with memory performance remains poorly understood.

Here, we address this knowledge gap by conducting simultaneous microelectrode recordings from prefrontal and medial temporal structures during a memory task in which intracranial TBS was applied to the BLA. To characterize neuronal modulation, we contrasted the trial-averaged, peri-stimulation firing rates across stim and no-stim conditions; neurons were further classified based on their location, baseline activity, and direction of effect (enhancement vs. suppression). We predicted that intracranial TBS of the BLA would modulate spiking activity within highly connected regions (e.g. hippocampus) and improve memory task performance.

We recorded single-unit activity from 23 patients (n=30 sessions) with medically refractory epilepsy as they completed a visual recognition memory task (see Supplementary file 1 for patient demographics). During the encoding session of the experiment, each patient received either 80 or 160 trials of bipolar intracranial TBS to a contiguous pair of macroelectrode contacts in the BLA (see Figure 1—figure supplement 1 for anatomical localization of stimulated contacts). An equal number of ‘no-stimulation’ trials were randomly interspersed to evaluate the effect of stimulation on memory performance and control for neuronal modulation resulting from experimental stimuli (e.g. image presentation). In total, we isolated 203 putative neurons from 68 bundles of 8 microwires each, distributed among recording sites in the hippocampus (HIP, n=95 units), orbitofrontal cortex (OFC, n=44), amygdala (AMY, n=39), and anterior cingulate cortex (ACC, n=25) (Figure 1; see also Figure 1—figure supplement 2 and Figure 1—figure supplement 3); a subset of these units (n=47, 23.2%) was excluded from subsequent analyses because low baseline firing rates (<0.1 Hz) limited the ability to robustly detect modulation.

Figure 1 with 3 supplements see all

Download asset Open asset

TBS of the BLA modulates widely distributed populations of neurons

We hypothesized that BLA stimulation would modulate neuronal activity in the sampled regions, given the amygdala’s well-established connectivity to the HIP, OFC, and ACC (Roy et al., 2009). To test this hypothesis, we quantified spike counts across trials within peri-stimulation epochs (1 s pre-trial ISI, 1 s after image onset, 1 s during stimulation/after image offset, and 1 s post-stimulation) and used Wilcoxon signed-rank tests to compare the spike counts against a null distribution generated by shuffling epoch labels. We performed two firing rate contrasts across trials (pre-trial ISI vs. during stimulation, pre-trial ISI vs. post-stimulation) within two distinct conditions (stim, no-stim); an additional contrast of the pre-trial ISI vs. image onset epochs was included to evaluate the sensitivity of neurons to task image presentations (Figure 2A).

Figure 2 with 1 supplement see all

Download asset Open asset

BLA TBS modulated firing rates in 30.1% of all recorded units, a significantly higher proportion than the 15.4% responsive to no-stim (image only) trials (one-sided Fisher’s exact test, OR = 2.37, p<0.001; Figure 3A). Across all regions sampled, we observed units modulated by the stim and no-stim conditions. Units in HIP (OR = 2.07, p=0.044), OFC (OR = 5.09, p=0.040), and AMY (OR = 3.33, p=0.042) were most sensitive to stimulation; we did not observe a difference in the proportions of units within the ACC responsive to the stim vs. no-stim conditions (OR = 1.00, p=0.661; Figure 3B). Only 9.0% of units responded to both the stim and no-stim conditions, despite approximately half of the stim-modulated units (representing 14.7% of all units) exhibiting a change in firing rate associated with image onset (Figure 3D). This result suggests that the units modulated by stimulation are largely distinct from those responsive to image offset during trials without stimulation. Stimulation, however, did not appear to alter the rhythmicity in neuronal firing, as measured by spiking autocorrelograms (Figure 3—figure supplement 1).

Figure 3 with 4 supplements see all

Download asset Open asset

Because neuronal firing properties vary across cell types (Barthó et al., 2004; Keller et al., 2010; Peyrache et al., 2012; Le Van Quyen et al., 2008), we also tested whether baseline (pre-trial ISI) firing rates predicted a unit’s response to stimulation, suggestive of selective engagement of specific neuron populations. Stimulation-modulated units exhibited significantly higher baseline firing rates compared to unaffected units (U(N_{Stim, Mod} = 47, N_{Stim, NS} = 109)=3450.50, p<0.001). No difference in baseline firing rate was observed among units modulated in the no-stim condition, compared to those that were unaffected (U(N_{No-Stim, Mod} = 24, N_{No-Stim, NS} = 132)=1964.00, p=0.062) (Figure 3C). The median (Q1, Q3) baseline firing rates for modulated units in the stim and no-stim conditions were 1.77 Hz (0.95 Hz, 5.39 Hz) and 1.53 Hz (0.72 Hz, 5.41 Hz), respectively.

In a subset of experimental sessions (n=7), we explored the effects of different stimulation parameters on neuronal modulation within an experimental session; more specifically, we employed a lower stimulation amplitude (0.5 mA vs. 1.0 mA) and varied pulse frequency (33 Hz vs. 50 Hz and 33 Hz vs. 80 Hz). Neither the amplitude of stimulation (OR = 1.69, p=0.302, n=30) nor pulse frequency (33 vs. 80 Hz; OR = 0.00, p=1.000, n=1; 50 vs. 80 Hz; OR = 1.40, p=0.758, n=6) significantly altered the proportion of modulated units (Figure 3—figure supplement 2).

Our exploratory analyses of pseudo-population activity revealed interesting temporal dynamics associated with image presentation and the delivery of stimulation. More specifically, we observed variation among the first three principal components across both stim and no-stim trials associated with image presentation (t = –3 to t=0) and robust shifts in coactivity modes associated with stimulation (t=0 to t=1) (Figure 3F). Further characterization of these dynamics suggests that TBS of the BLA primarily resulted in transient changes to firing rate coactivity within hippocampal neurons and was present regardless of the neuron’s laterality to stimulation (Figure 3—figure supplement 3). Taken together, these analyses reveal global structure in the state space of responses to BLA stimulation within hippocampal circuits.

Finally, we performed three supplementary analyses to evaluate the robustness of our approach to detecting firing rate modulation: a sensitivity analysis assessing the proportion of modulated units at different firing rate thresholds for inclusion/exclusion, a data dropout analysis designed to control for the possibility that nonphysiological stimulation artifacts may preclude the detection of temporally adjacent spiking, and a synthetic detection probability analysis. These results recapitulate our observation that units with higher baseline firing are most likely to exhibit modulation (though the probability of detecting modulation is lower for sparsely active neurons) and suggest that suppression in firing rate is not solely attributable to amplifier saturation following stimulation (Figure 2—figure supplement 1).

TBS modulates excitatory and inhibitory neurons equally

Using k-means clustering, we grouped neurons into two distinct clusters based on waveform morphology, representing neurons that were presumed to be excitatory (E) and inhibitory (I) (Figure 4B). Inhibitory (fast-spiking) neurons exhibited shorter waveform valley-to-peak width (VP) and peak half-width (PHW), compared with excitatory (regular-spiking) neurons (I cluster centroid: VP = 0.50 ms, PHW = 0.51 ms; E cluster centroid: VP = 0.32 ms, PHW = 0.31 ms) and greater baseline firing rates (U(N_I = 23, N_E = 133)=1074.50, p=0.023) (Figure 4D). Although we observed a much greater proportion of excitatory vs. inhibitory neurons (E: 85.3%, I: 14.7%), stimulation appeared to affect excitatory and inhibitory neurons equally, suggesting that one cell type is not preferentially activated over another (Figure 4E).

Figure 4

Download asset Open asset

Association between neuronal modulation and memory performance is unclear

Next, we investigated the link between stimulation and performance during the visual recognition memory task. To this end, we first used a linear mixed-effects model to examine the effect of condition (stim, no-stim) on memory performance (d’) across trials in each session, with individual sessions treated as a random effect (intercept). Experiment type was also included as a fixed effect since data were aggregated across four highly similar experiments with minor differences in the content of visual stimuli, number of trials, stimulation parameters, and testing intervals. We did not observe an overall effect of memory enhancement (p>0.05) when controlling for subject-level variability (Figure 5A). The lack of a memory enhancement effect may be associated with high hit rates limiting sensitivity (mean ± SD) (75.7% ± 13.5% for no-stim trials, 75.0% ± 14.3% for stim trials) and considerable variability among false alarm rates across participants (17.9% ± 17.4%, range 0–70%; Figure 5B).

Figure 5 with 1 supplement see all

Download asset Open asset

At the level of individual sessions, we observed enhanced memory (Δd’ >+0.5) in 36.7%, impaired memory (Δd’<–0.5) in 20.0%, and negligible change (–0.5 ≤ Δd’≤0.5) in 43.3% when comparing performance between the stim and no-stim conditions; a threshold of Δd’±0.5 was chosen for this classification based on the defined range of a ‘medium effect’ for Cohen’s d. To test our hypothesis that neuronal modulation would be associated with changes in memory performance, we combined the sessions that resulted in either memory enhancement or impairment and contrasted the proportion of modulated units across regions sampled. We did not, however, observe a meaningful difference in the proportion of modulated units when grouped by behavioral outcome (all contrasts p>0.05) (Figure 5C).

Proximity to white matter has been shown to influence the effects of stimulation on behavior and the strength of evoked responses (Mankin et al., 2021; Mohan et al., 2020; Paulk et al., 2022). Across all stimulated contacts, we observed only small differences in the proximity of stimulation contacts to white matter (median = 4.6 mm, range = 1.5–8.0 mm), likely because the chosen target (i.e. BLA) has several nearby white matter structures (e.g. stria terminalis). Nonetheless, we performed a linear regression between the proximity to white matter and the stimulation-induced effect on behavior (stimulation vs. no-stimulation d’ difference), the results of which indicate no clear association (p>0.05; see Figure 5—figure supplement 1).

TBS is an efficient and validated paradigm for inducing LTP in neural circuits (Larson and Munkácsy, 2015). Additionally, intracranial TBS was recently shown to promote region-specific short-term plasticity (Herrero et al., 2021; Huang et al., 2024) and entrain frequency-matched oscillations (Solomon et al., 2021). At present, however, there is an incomplete understanding of how these population-level responses to stimulation relate to a modulation in the activity of individual neurons, which are thought to be the substrate of memory encoding and retrieval (Hebb, 1949). Here, we address this knowledge gap by characterizing neuronal firing recorded from microelectrodes in humans undergoing intracranial TBS of the BLA. Our experimental design focused explicitly on stimulation of the BLA, given our prior work that seeks to understand amygdala-mediated memory enhancement in humans (Inman et al., 2023; Inman et al., 2020; Inman et al., 2018).

We observed neurons distributed throughout the hippocampus, orbitofrontal cortex, anterior cingulate cortex, and amygdala responsive to direct electrical TBS. The effect of TBS on firing rate was heterogeneous with respect to onset, duration, and valence. Previous work characterizing local field potential responses to intracranial TBS observed similarly bidirectional effects throughout the brain suggestive of short-term plasticity (Huang et al., 2024). Few studies, however, have characterized the impact of exogenous stimulation on the spiking activity of individual neurons, none of which involve intracranial TBS. One study reported a long-lasting reduction in neural excitability among parietal neurons, with variable onset time and recovery following continuous transcranial TBS in nonhuman primates (Romero et al., 2022). In a similar vein, it was recently shown that human neurons are largely suppressed by single-pulse electrical stimulation (Cowan et al., 2024; van der Plas et al., 2024). Other emerging evidence suggests that transcranial direct current stimulation may entrain the rhythm rather than rate of neuronal spiking (Krause et al., 2019) and that stimulation-evoked modulation of spiking may meaningfully impact behavioral performance on cognitive tasks (Fehring et al., 2024). An alternative approach has focused on the delivery of spatially selective microstimulation resembling the extracellular currents that normally modulate neuronal activity—this methodology has been used to bidirectionally drive neuronal firing in human temporal cortex (Youssef et al., 2023) and enhance memory specificity for images following stimulation (Titiz et al., 2017).

Although subsets of neurons from each region we sampled were responsive to stimulation, we observed the greatest difference in the proportion of modulated units across conditions in the hippocampus, orbitofrontal cortex, and amygdala. This regional selectivity is to be expected, given that numerous studies have characterized how structural, functional, and effective connectivity among brain regions predicts the effects of stimulation (Fox et al., 2020; Huang et al., 2024; Huang et al., 2019; Keller et al., 2018; Keller et al., 2011; Solomon et al., 2018; Stiso et al., 2019). We also observed that units with greater baseline activity were most likely to exhibit modulated firing rates following stimulation. Other studies have identified firing patterns and waveform properties that differ between inhibitory and excitatory neurons in humans (Barthó et al., 2004; Keller et al., 2010; Peyrache et al., 2012; Le Van Quyen et al., 2008). For example, baseline firing rate disambiguates ‘regular-spiking’ and ‘fast-spiking’ neurons, which are presumed to represent pyramidal cells and interneurons, respectively. To test these hypotheses directly, we clustered neurons into presumed excitatory and inhibitory neurons based on waveform morphology. In doing so, we observed ~85% excitatory and ~15% inhibitory neurons, which is very similar to what has been reported previously in human intracranial recordings (Cowan et al., 2024; Peyrache et al., 2012). Interestingly, stimulation appeared to modulate approximately the same proportion of neurons for each cell type (~30%), despite the differently sized groups. Recent reports, however, have suggested that the extent to which electrical fields entrain neuronal spiking, particularly with respect to phase-locking, may be specific to distinct classes of cells (Lee et al., 2024).

Modulation in neuronal activity was defined by contrasting firing rates before, during, and after TBS across trials. In doing so, we were able to characterize coarse differences in activity indicative of enhancement or suppression. This approach, however, did not allow for analysis of more subtle, nuanced effects such as entrainment of spiking to individual bursts or pulses of TBS. Characterizations of rhythmicity in firing were challenging, given that most of the neurons we identified exhibit sparse activity with low baseline firing rates, and stimulation often resulted in further suppression of spiking.

Although stimulation artifacts resulted in amplitude threshold crossings that may be spuriously interpreted as a neuronal spike, we implemented several methods to mitigate the influence of nonphysiological activity. First, the characteristics of each unit (e.g. waveform shape) were manually inspected during spike sorting and further quantified using several quality control metrics (e.g. interspike intervals); stimulation resulted in a stereotyped response that was easily detectable and removed from subsequent analyses. Additionally, we tested for modulation both during stimulation and in the post-stimulation epochs—a period in which no artifact was present. Contrary to what would be expected if stimulation artifacts were falsely elevating firing rates, we observed predominantly suppression during stimulation and enhancement post-stimulation.

Since we collected our microelectrode recordings in the context of a visual recognition memory task, we tested whether stimulation resulted in a change in memory performance. Although we hypothesized that stimulation would improve memory performance, we did not identify an apparent stimulation-related memory enhancement when controlling for individual differences, in contrast to our prior work (Inman et al., 2018). The absence of such an effect may be, at least in part, attributable to the considerable variability that we observed in this cohort; indeed, baseline memory performance and individual differences have been shown to account for a substantial portion of the variability in this amygdala-mediated memory enhancement effect (Hollearn et al., 2025).

Several studies on rats have demonstrated that brief electrical stimulation of the BLA can prioritize the consolidation of specific memories (Bass et al., 2014; Bass et al., 2012; Bass and Manns, 2015; Manns and Bass, 2016). These pro-memory effects emerged ~24 hr post-encoding and appear to be hippocampal-dependent (Bass et al., 2014), despite not resulting in a net change in the firing rates of hippocampal pyramidal neurons; instead, BLA stimulation resulted in brief periods of spike-field and field-field synchrony within CA3–CA1 in the low-gamma frequency range (30–55 Hz), which may facilitate spike-timing-dependent plasticity in recently active neurons (Bass and Manns, 2015).

The present study did not investigate interactions between spiking activity and local field potentials because neuronal spiking was sparse at baseline and often further suppressed by stimulation; only a very small proportion of the total number of trials across all neurons exhibited ≥10 spikes in both the 1 s pre- and post-stimulation epochs (~2.5%). Although certain metrics are not biased by sample size (e.g. pairwise phase consistency), low spike counts can dramatically affect variance and, therefore, result in unstable estimates (Vinck et al., 2011).

How exactly the activity of single neurons is aggregated to produce local field potentials, which in turn interact with neuronal ensembles distributed throughout the brain, remains an active area of research (Herreras, 2016; Kajikawa and Schroeder, 2011; Manning et al., 2009; Teleńczuk et al., 2020). One recent study that leveraged closed-loop stimulation targeting memory consolidation during sleep observed neuronal spiking with greater phase-locking to medial temporal lobe slow-wave activity following stimulation (Geva-Sagiv et al., 2023); neuronal phase-locking, particularly to hippocampal theta oscillations, has long been associated with robust memory encoding and retrieval (Jacobs et al., 2007; Rutishauser et al., 2010; Schonhaut et al., 2024; Yoo et al., 2021). Further characterization of these spike-field interactions and refinement of closed-loop stimulation methods may provide a means for precisely modulating neuronal dynamics, for example, by entraining neuronal spiking that is phase-aligned to endogenous hippocampal theta oscillations to selectively enhance the encoding or retrieval of memories (Hasselmo, 2005; Hasselmo et al., 2002; Hasselmo and Stern, 2014; Siegle and Wilson, 2014).

Finally, we performed an exploratory analysis of neuronal pseudo-population activity, which suggests that hippocampal neurons exhibit robust changes in firing rate coactivity in response to BLA stimulation. Related research has similarly described how BLA stimulation can induce synchronous firing of hippocampal neurons, which has memory-enhancing effects (Bass and Manns, 2015). Other studies have leveraged similar, low-dimensional representations of population dynamics to describe how coordinated neural activity facilitates inferential reasoning and memory retrieval within the medial frontal cortex and hippocampus, respectively (Courellis et al., 2024; Minxha et al., 2020). Thus, a greater understanding of how neuronal coactivity may be precisely modulated by stimulation may help to refine therapeutic interventions targeting complex cognition and computation.

Custom Python analysis scripts used in the manuscript are publicly available on GitHub (https://github.com/Justin-Campbell/BLAESUnits copy archived at Campbell, 2025). The dataset analyzed in the study is available in an Open Science Foundation (OSF) repository under a Creative Commons Attribution 4.0 International license and can be accessed at: https://osf.io/y5j28/.

1. 1. Campbell J

   (2025) Open Science Framework

   ID y5j28. Data for "Human single-neuron activity is modulated by intracranial theta burst stimulation of the basolateral amygdala".

   https://osf.io/y5j28/

1. 1. Barthó P
   2. Hirase H
   3. Monconduit L
   4. Zugaro M
   5. Harris KD
   6. Buzsáki G

   (2004) Characterization of neocortical principal cells and interneurons by network interactions and extracellular features

   Journal of Neurophysiology 92:600–608.

   https://doi.org/10.1152/jn.01170.2003

   • PubMed
   • Google Scholar
2. 1. Bass DI
   2. Partain KN
   3. Manns JR

   (2012) Event-specific enhancement of memory via brief electrical stimulation to the basolateral complex of the amygdala in rats

   Behavioral Neuroscience 126:204–208.

   https://doi.org/10.1037/a0026462

   • PubMed
   • Google Scholar
3. 1. Bass DI
   2. Nizam ZG
   3. Partain KN
   4. Wang A
   5. Manns JR

   (2014) Amygdala-mediated enhancement of memory for specific events depends on the hippocampus

   Neurobiology of Learning and Memory 107:37–41.

   https://doi.org/10.1016/j.nlm.2013.10.020

   • PubMed
   • Google Scholar
4. 1. Bass DI
   2. Manns JR

   (2015) Memory-enhancing amygdala stimulation elicits gamma synchrony in the hippocampus

   Behavioral Neuroscience 129:244–256.

   https://doi.org/10.1037/bne0000052

   • PubMed
   • Google Scholar
5. Software

   1. Campbell J

   (2025) BLAESUnits, version swh:1:rev:1d176770e7c93f4fed9e63f7420ffed523781a4a

   Software Heritage.

   https://archive.softwareheritage.org/swh:1:dir:78d101977a559abc058b434e7838b4526e9ccc0a;origin=https://github.com/Justin-Campbell/BLAESUnits;visit=swh:1:snp:1ad336011f85a50e8f0e4678421634ab93159df0;anchor=swh:1:rev:1d176770e7c93f4fed9e63f7420ffed523781a4a
6. 1. Courellis HS
   2. Minxha J
   3. Cardenas AR
   4. Kimmel DL
   5. Reed CM
   6. Valiante TA
   7. Salzman CD
   8. Mamelak AN
   9. Fusi S
   10. Rutishauser U

   (2024) Abstract representations emerge in human hippocampal neurons during inference

   Nature 632:841–849.

   https://doi.org/10.1038/s41586-024-07799-x

   • PubMed
   • Google Scholar
7. Preprint

   1. Cowan RL
   2. Davis TS
   3. Merricks EM
   4. Kundu B
   5. Shofty B
   6. Rahimpour S
   7. Schevon CA
   8. Rolston JD
   9. Smith EH

   (2024) Cell-type specific responses to single-pulse electrical stimulation of the human brain

   bioRxiv.

   https://doi.org/10.1101/2024.11.24.625077

   • Google Scholar
8. 1. Davis TS
   2. Caston RM
   3. Philip B
   4. Charlebois CM
   5. Anderson DN
   6. Weaver KE
   7. Smith EH
   8. Rolston JD

   (2021) LeGUI: A fast and accurate graphical user interface for automated detection and anatomical localization of intracranial electrodes

   Frontiers in Neuroscience 15:769872.

   https://doi.org/10.3389/fnins.2021.769872

   • PubMed
   • Google Scholar
9. 1. Dolcos F
   2. LaBar KS
   3. Cabeza R

   (2004) Interaction between the amygdala and the medial temporal lobe memory system predicts better memory for emotional events

   Neuron 42:855–863.

   https://doi.org/10.1016/s0896-6273(04)00289-2

   • PubMed
   • Google Scholar
10. 1. Fehring DJ
    2. Yokoo S
    3. Abe H
    4. Buckley MJ
    5. Miyamoto K
    6. Jaberzadeh S
    7. Yamamori T
    8. Tanaka K
    9. Rosa MGP
    10. Mansouri FA

    (2024) Direct current stimulation modulates prefrontal cell activity and behaviour without inducing seizure-like firing

    Brain 147:3751–3763.

    https://doi.org/10.1093/brain/awae273

    • PubMed
    • Google Scholar
11. 1. Fox KCR
    2. Shi L
    3. Baek S
    4. Raccah O
    5. Foster BL
    6. Saha S
    7. Margulies DS
    8. Kucyi A
    9. Parvizi J

    (2020) Intrinsic network architecture predicts the effects elicited by intracranial electrical stimulation of the human brain

    Nature Human Behaviour 4:1039–1052.

    https://doi.org/10.1038/s41562-020-0910-1

    • PubMed
    • Google Scholar
12. 1. Geva-Sagiv M
    2. Mankin EA
    3. Eliashiv D
    4. Epstein S
    5. Cherry N
    6. Kalender G
    7. Tchemodanov N
    8. Nir Y
    9. Fried I

    (2023) Augmenting hippocampal-prefrontal neuronal synchrony during sleep enhances memory consolidation in humans

    Nature Neuroscience 26:1100–1110.

    https://doi.org/10.1038/s41593-023-01324-5

    • PubMed
    • Google Scholar
13. 1. Hanslmayr S
    2. Staresina BP
    3. Bowman H

    (2016) Oscillations and episodic memory: addressing the synchronization/desynchronization conundrum

    Trends in Neurosciences 39:16–25.

    https://doi.org/10.1016/j.tins.2015.11.004

    • PubMed
    • Google Scholar
14. 1. Hasselmo ME
    2. Bodelón C
    3. Wyble BP

    (2002) A proposed function for hippocampal theta rhythm: separate phases of encoding and retrieval enhance reversal of prior learning

    Neural Computation 14:793–817.

    https://doi.org/10.1162/089976602317318965

    • PubMed
    • Google Scholar
15. 1. Hasselmo ME

    (2005) What is the function of hippocampal theta rhythm?--Linking behavioral data to phasic properties of field potential and unit recording data

    Hippocampus 15:936–949.

    https://doi.org/10.1002/hipo.20116

    • PubMed
    • Google Scholar
16. 1. Hasselmo ME
    2. Stern CE

    (2014) Theta rhythm and the encoding and retrieval of space and time

    NeuroImage 85 Pt 2:656–666.

    https://doi.org/10.1016/j.neuroimage.2013.06.022

    • PubMed
    • Google Scholar
17. Book

    1. Hebb DO

    (1949) The Organization of Behavior; A Neuropsychological Theory

    John Wiley and Sons, Inc.

    https://doi.org/10.1002/sce.37303405110

    • Google Scholar
18. 1. Hermans EJ
    2. Battaglia FP
    3. Atsak P
    4. de Voogd LD
    5. Fernández G
    6. Roozendaal B

    (2014) How the amygdala affects emotional memory by altering brain network properties

    Neurobiology of Learning and Memory 112:2–16.

    https://doi.org/10.1016/j.nlm.2014.02.005

    • PubMed
    • Google Scholar
19. 1. Herreras O

    (2016) Local field potentials: myths and misunderstandings

    Frontiers in Neural Circuits 10:101.

    https://doi.org/10.3389/fncir.2016.00101

    • PubMed
    • Google Scholar
20. 1. Herrero JL
    2. Smith A
    3. Mishra A
    4. Markowitz N
    5. Mehta AD
    6. Bickel S

    (2021) Inducing neuroplasticity through intracranial θ-burst stimulation in the human sensorimotor cortex

    Journal of Neurophysiology 126:1723–1739.

    https://doi.org/10.1152/jn.00320.2021

    • PubMed
    • Google Scholar
21. 1. Hollearn MK
    2. Manns JR
    3. Blanpain LT
    4. Hamann SB
    5. Bijanki K
    6. Gross RE
    7. Drane DL
    8. Campbell JM
    9. Wahlstrom KL
    10. Light GF
    11. Tasevac A
    12. Demarest P
    13. Brunner P
    14. Willie JT
    15. Inman CS

    (2025) Exploring individual differences in amygdala-mediated memory modulation

    Cognitive, Affective, & Behavioral Neuroscience 25:188–209.

    https://doi.org/10.3758/s13415-024-01250-4

    • Google Scholar
22. 1. Huang Y
    2. Hajnal B
    3. Entz L
    4. Fabó D
    5. Herrero JL
    6. Mehta AD
    7. Keller CJ

    (2019) Intracortical dynamics underlying repetitive stimulation predicts changes in network connectivity

    The Journal of Neuroscience 39:6122–6135.

    https://doi.org/10.1523/JNEUROSCI.0535-19.2019

    • PubMed
    • Google Scholar
23. 1. Huang Y
    2. Zelmann R
    3. Hadar P
    4. Dezha-Peralta J
    5. Richardson RM
    6. Williams ZM
    7. Cash SS
    8. Keller CJ
    9. Paulk AC

    (2024) Theta-burst direct electrical stimulation remodels human brain networks

    Nature Communications 15:6982.

    https://doi.org/10.1038/s41467-024-51443-1

    • Google Scholar
24. 1. Inman CS
    2. Manns JR
    3. Bijanki KR
    4. Bass DI
    5. Hamann S
    6. Drane DL
    7. Fasano RE
    8. Kovach CK
    9. Gross RE
    10. Willie JT

    (2018) Direct electrical stimulation of the amygdala enhances declarative memory in humans

    PNAS 115:98–103.

    https://doi.org/10.1073/pnas.1714058114

    • Google Scholar
25. 1. Inman CS
    2. Bijanki KR
    3. Bass DI
    4. Gross RE
    5. Hamann S
    6. Willie JT

    (2020) Human amygdala stimulation effects on emotion physiology and emotional experience

    Neuropsychologia 145:106722.

    https://doi.org/10.1016/j.neuropsychologia.2018.03.019

    • PubMed
    • Google Scholar
26. 1. Inman CS
    2. Hollearn MK
    3. Augustin L
    4. Campbell JM
    5. Olson KL
    6. Wahlstrom KL

    (2023) Discovering how the amygdala shapes human behavior: From lesion studies to neuromodulation

    Neuron 111:3906–3910.

    https://doi.org/10.1016/j.neuron.2023.09.040

    • PubMed
    • Google Scholar
27. 1. Jacobs J
    2. Kahana MJ
    3. Ekstrom AD
    4. Fried I

    (2007) Brain oscillations control timing of single-neuron activity in humans

    The Journal of Neuroscience 27:3839–3844.

    https://doi.org/10.1523/JNEUROSCI.4636-06.2007

    • PubMed
    • Google Scholar
28. 1. Kajikawa Y
    2. Schroeder CE

    (2011) How local is the local field potential?

    Neuron 72:847–858.

    https://doi.org/10.1016/j.neuron.2011.09.029

    • PubMed
    • Google Scholar
29. 1. Keller CJ
    2. Truccolo W
    3. Gale JT
    4. Eskandar E
    5. Thesen T
    6. Carlson C
    7. Devinsky O
    8. Kuzniecky R
    9. Doyle WK
    10. Madsen JR
    11. Schomer DL
    12. Mehta AD
    13. Brown EN
    14. Hochberg LR
    15. Ulbert I
    16. Halgren E
    17. Cash SS

    (2010) Heterogeneous neuronal firing patterns during interictal epileptiform discharges in the human cortex

    Brain 133:1668–1681.

    https://doi.org/10.1093/brain/awq112

    • PubMed
    • Google Scholar
30. 1. Keller CJ
    2. Bickel S
    3. Entz L
    4. Ulbert I
    5. Milham MP
    6. Kelly C
    7. Mehta AD

    (2011) Intrinsic functional architecture predicts electrically evoked responses in the human brain

    PNAS 108:10308–10313.

    https://doi.org/10.1073/pnas.1019750108

    • PubMed
    • Google Scholar
31. 1. Keller CJ
    2. Huang Y
    3. Herrero JL
    4. Fini ME
    5. Du V
    6. Lado FA
    7. Honey CJ
    8. Mehta AD

    (2018) Induction and quantification of excitability changes in human cortical networks

    The Journal of Neuroscience 38:5384–5398.

    https://doi.org/10.1523/JNEUROSCI.1088-17.2018

    • PubMed
    • Google Scholar
32. 1. Kim MJ
    2. Loucks RA
    3. Palmer AL
    4. Brown AC
    5. Solomon KM
    6. Marchante AN
    7. Whalen PJ

    (2011) The structural and functional connectivity of the amygdala: from normal emotion to pathological anxiety

    Behavioural Brain Research 223:403–410.

    https://doi.org/10.1016/j.bbr.2011.04.025

    • PubMed
    • Google Scholar
33. 1. Krause MR
    2. Vieira PG
    3. Csorba BA
    4. Pilly PK
    5. Pack CC

    (2019) Transcranial alternating current stimulation entrains single-neuron activity in the primate brain

    PNAS 116:5747–5755.

    https://doi.org/10.1073/pnas.1815958116

    • PubMed
    • Google Scholar
34. 1. LaBar KS
    2. Cabeza R

    (2006) Cognitive neuroscience of emotional memory

    Nature Reviews. Neuroscience 7:54–64.

    https://doi.org/10.1038/nrn1825

    • PubMed
    • Google Scholar
35. 1. Larson J
    2. Munkácsy E

    (2015) Theta-burst LTP

    Brain Research 1621:38–50.

    https://doi.org/10.1016/j.brainres.2014.10.034

    • PubMed
    • Google Scholar
36. 1. Lee SY
    2. Kozalakis K
    3. Baftizadeh F
    4. Campagnola L
    5. Jarsky T
    6. Koch C
    7. Anastassiou CA

    (2024) Cell-class-specific electric field entrainment of neural activity

    Neuron 112:2614–2630.

    https://doi.org/10.1016/j.neuron.2024.05.009

    • PubMed
    • Google Scholar
37. 1. Le Van Quyen M
    2. Bragin A
    3. Staba R
    4. Crépon B
    5. Wilson CL
    6. Engel J Jr

    (2008) Cell type-specific firing during ripple oscillations in the hippocampal formation of humans

    The Journal of Neuroscience 28:6104–6110.

    https://doi.org/10.1523/JNEUROSCI.0437-08.2008

    • PubMed
    • Google Scholar
38. 1. Mankin EA
    2. Aghajan ZM
    3. Schuette P
    4. Tran ME
    5. Tchemodanov N
    6. Titiz A
    7. Kalender G
    8. Eliashiv D
    9. Stern J
    10. Weiss SA
    11. Kirsch D
    12. Knowlton B
    13. Fried I
    14. Suthana N

    (2021) Stimulation of the right entorhinal white matter enhances visual memory encoding in humans

    Brain Stimulation 14:131–140.

    https://doi.org/10.1016/j.brs.2020.11.015

    • PubMed
    • Google Scholar
39. 1. Manning JR
    2. Jacobs J
    3. Fried I
    4. Kahana MJ

    (2009) Broadband shifts in local field potential power spectra are correlated with single-neuron spiking in humans

    The Journal of Neuroscience 29:13613–13620.

    https://doi.org/10.1523/JNEUROSCI.2041-09.2009

    • PubMed
    • Google Scholar
40. 1. Manns JR
    2. Bass DI

    (2016) The amygdala and prioritization of declarative memories

    Current Directions in Psychological Science 25:261–265.

    https://doi.org/10.1177/0963721416654456

    • PubMed
    • Google Scholar
41. 1. McDonald AJ
    2. Mott DD

    (2017) Functional neuroanatomy of amygdalohippocampal interconnections and their role in learning and memory

    Journal of Neuroscience Research 95:797–820.

    https://doi.org/10.1002/jnr.23709

    • PubMed
    • Google Scholar
42. 1. McGaugh JL
    2. McIntyre CK
    3. Power AE

    (2002) Amygdala modulation of memory consolidation: interaction with other brain systems

    Neurobiology of Learning and Memory 78:539–552.

    https://doi.org/10.1006/nlme.2002.4082

    • PubMed
    • Google Scholar
43. 1. McGaugh JL

    (2004) The amygdala modulates the consolidation of memories of emotionally arousing experiences

    Annual Review of Neuroscience 27:1–28.

    https://doi.org/10.1146/annurev.neuro.27.070203.144157

    • PubMed
    • Google Scholar
44. 1. McGaugh JL

    (2013) Making lasting memories: remembering the significant

    PNAS 110 Suppl 2:10402–10407.

    https://doi.org/10.1073/pnas.1301209110

    • PubMed
    • Google Scholar
45. 1. Mercier MR
    2. Dubarry AS
    3. Tadel F
    4. Avanzini P
    5. Axmacher N
    6. Cellier D
    7. Vecchio MD
    8. Hamilton LS
    9. Hermes D
    10. Kahana MJ
    11. Knight RT
    12. Llorens A
    13. Megevand P
    14. Melloni L
    15. Miller KJ
    16. Piai V
    17. Puce A
    18. Ramsey NF
    19. Schwiedrzik CM
    20. Smith SE
    21. Stolk A
    22. Swann NC
    23. Vansteensel MJ
    24. Voytek B
    25. Wang L
    26. Lachaux JP
    27. Oostenveld R

    (2022) Advances in human intracranial electroencephalography research, guidelines and good practices

    NeuroImage 260:119438.

    https://doi.org/10.1016/j.neuroimage.2022.119438

    • PubMed
    • Google Scholar
46. 1. Minxha J
    2. Adolphs R
    3. Fusi S
    4. Mamelak AN
    5. Rutishauser U

    (2020) Flexible recruitment of memory-based choice representations by the human medial frontal cortex

    Science 368:eaba3313.

    https://doi.org/10.1126/science.aba3313

    • PubMed
    • Google Scholar
47. 1. Mohan UR
    2. Watrous AJ
    3. Miller JF
    4. Lega BC
    5. Sperling MR
    6. Worrell GA
    7. Gross RE
    8. Zaghloul KA
    9. Jobst BC
    10. Davis KA
    11. Sheth SA
    12. Stein JM
    13. Das SR
    14. Gorniak R
    15. Wanda PA
    16. Rizzuto DS
    17. Kahana MJ
    18. Jacobs J

    (2020) The effects of direct brain stimulation in humans depend on frequency, amplitude, and white-matter proximity

    Brain Stimulation 13:1183–1195.

    https://doi.org/10.1016/j.brs.2020.05.009

    • PubMed
    • Google Scholar
48. 1. Paulk AC
    2. Zelmann R
    3. Crocker B
    4. Widge AS
    5. Dougherty DD
    6. Eskandar EN
    7. Weisholtz DS
    8. Richardson RM
    9. Cosgrove GR
    10. Williams ZM
    11. Cash SS

    (2022) Local and distant cortical responses to single pulse intracranial stimulation in the human brain are differentially modulated by specific stimulation parameters

    Brain Stimulation 15:491–508.

    https://doi.org/10.1016/j.brs.2022.02.017

    • PubMed
    • Google Scholar
49. Preprint

    1. Pedregosa F
    2. Varoquaux G
    3. Gramfort A
    4. Michel V
    5. Thirion B
    6. Grisel O
    7. Blondel M
    8. Müller A
    9. Nothman J
    10. Louppe G
    11. Prettenhofer P
    12. Weiss R
    13. Dubourg V
    14. Vanderplas J
    15. Passos A
    16. Cournapeau D
    17. Brucher M
    18. Perrot M
    19. Duchesnay É

    (2012) Scikit-Learn: machine learning in python

    arXiv.

    https://doi.org/10.48550/arXiv.1201.0490

    • Google Scholar
50. 1. Peyrache A
    2. Dehghani N
    3. Eskandar EN
    4. Madsen JR
    5. Anderson WS
    6. Donoghue JA
    7. Hochberg LR
    8. Halgren E
    9. Cash SS
    10. Destexhe A

    (2012) Spatiotemporal dynamics of neocortical excitation and inhibition during human sleep

    PNAS 109:1731–1736.

    https://doi.org/10.1073/pnas.1109895109

    • Google Scholar
51. 1. Phelps EA

    (2004) Human emotion and memory: interactions of the amygdala and hippocampal complex

    Current Opinion in Neurobiology 14:198–202.

    https://doi.org/10.1016/j.conb.2004.03.015

    • PubMed
    • Google Scholar
52. 1. Phelps EA
    2. LeDoux JE

    (2005) Contributions of the amygdala to emotion processing: from animal models to human behavior

    Neuron 48:175–187.

    https://doi.org/10.1016/j.neuron.2005.09.025

    • PubMed
    • Google Scholar
53. 1. Phelps EA

    (2006) Emotion and cognition: insights from studies of the human amygdala

    Annual Review of Psychology 57:27–53.

    https://doi.org/10.1146/annurev.psych.56.091103.070234

    • PubMed
    • Google Scholar
54. 1. Qasim SE
    2. Mohan UR
    3. Stein JM
    4. Jacobs J

    (2023) Neuronal activity in the human amygdala and hippocampus enhances emotional memory encoding

    Nature Human Behaviour 7:754–764.

    https://doi.org/10.1038/s41562-022-01502-8

    • PubMed
    • Google Scholar
55. 1. Richardson MP
    2. Strange BA
    3. Dolan RJ

    (2004) Encoding of emotional memories depends on amygdala and hippocampus and their interactions

    Nature Neuroscience 7:278–285.

    https://doi.org/10.1038/nn1190

    • PubMed
    • Google Scholar
56. 1. Roesler R
    2. Parent MB
    3. LaLumiere RT
    4. McIntyre CK

    (2021) Amygdala-hippocampal interactions in synaptic plasticity and memory formation

    Neurobiology of Learning and Memory 184:107490.

    https://doi.org/10.1016/j.nlm.2021.107490

    • PubMed
    • Google Scholar
57. 1. Romero MC
    2. Merken L
    3. Janssen P
    4. Davare M

    (2022) Neural effects of continuous theta-burst stimulation in macaque parietal neurons

    eLife 11:e65536.

    https://doi.org/10.7554/eLife.65536

    • PubMed
    • Google Scholar
58. 1. Roozendaal B
    2. McEwen BS
    3. Chattarji S

    (2009) Stress, memory and the amygdala

    Nature Reviews. Neuroscience 10:423–433.

    https://doi.org/10.1038/nrn2651

    • PubMed
    • Google Scholar
59. 1. Roy AK
    2. Shehzad Z
    3. Margulies DS
    4. Kelly AMC
    5. Uddin LQ
    6. Gotimer K
    7. Biswal BB
    8. Castellanos FX
    9. Milham MP

    (2009) Functional connectivity of the human amygdala using resting state fMRI

    NeuroImage 45:614–626.

    https://doi.org/10.1016/j.neuroimage.2008.11.030

    • PubMed
    • Google Scholar
60. 1. Rutishauser U
    2. Ross IB
    3. Mamelak AN
    4. Schuman EM

    (2010) Human memory strength is predicted by theta-frequency phase-locking of single neurons

    Nature 464:903–907.

    https://doi.org/10.1038/nature08860

    • PubMed
    • Google Scholar
61. 1. Schonhaut DR
    2. Rao AM
    3. Ramayya AG
    4. Solomon EA
    5. Herweg NA
    6. Fried I
    7. Kahana MJ

    (2024) MTL neurons phase-lock to human hippocampal theta

    eLife 13:e85753.

    https://doi.org/10.7554/eLife.85753

    • PubMed
    • Google Scholar
62. 1. Schwabe L
    2. Tegenthoff M
    3. Höffken O
    4. Wolf OT

    (2013) Mineralocorticoid receptor blockade prevents stress-induced modulation of multiple memory systems in the human brain

    Biological Psychiatry 74:801–808.

    https://doi.org/10.1016/j.biopsych.2013.06.001

    • PubMed
    • Google Scholar
63. Conference

    1. Seabold S
    2. Perktold J

    (2010) Statsmodels: econometric and statistical modeling with python

    Python in Science Conference.

    https://doi.org/10.25080/Majora-92bf1922-011

    • Google Scholar
64. 1. Shoham S
    2. Fellows MR
    3. Normann RA

    (2003) Robust, automatic spike sorting using mixtures of multivariate t-distributions

    Journal of Neuroscience Methods 127:111–122.

    https://doi.org/10.1016/s0165-0270(03)00120-1

    • PubMed
    • Google Scholar
65. 1. Siegle JH
    2. Wilson MA

    (2014) Enhancement of encoding and retrieval functions through theta phase-specific manipulation of hippocampus

    eLife 3:e03061.

    https://doi.org/10.7554/eLife.03061

    • PubMed
    • Google Scholar
66. 1. Solomon EA
    2. Kragel JE
    3. Gross R
    4. Lega B
    5. Sperling MR
    6. Worrell G
    7. Sheth SA
    8. Zaghloul KA
    9. Jobst BC
    10. Stein JM
    11. Das S
    12. Gorniak R
    13. Inman CS
    14. Seger S
    15. Rizzuto DS
    16. Kahana MJ

    (2018) Medial temporal lobe functional connectivity predicts stimulation-induced theta power

    Nature Communications 9:4437.

    https://doi.org/10.1038/s41467-018-06876-w

    • PubMed
    • Google Scholar
67. 1. Solomon EA
    2. Sperling MR
    3. Sharan AD
    4. Wanda PA
    5. Levy DF
    6. Lyalenko A
    7. Pedisich I
    8. Rizzuto DS
    9. Kahana MJ

    (2021) Theta-burst stimulation entrains frequency-specific oscillatory responses

    Brain Stimulation 14:1271–1284.

    https://doi.org/10.1016/j.brs.2021.08.014

    • PubMed
    • Google Scholar
68. 1. Stein JL
    2. Wiedholz LM
    3. Bassett DS
    4. Weinberger DR
    5. Zink CF
    6. Mattay VS
    7. Meyer-Lindenberg A

    (2007) A validated network of effective amygdala connectivity

    NeuroImage 36:736–745.

    https://doi.org/10.1016/j.neuroimage.2007.03.022

    • PubMed
    • Google Scholar
69. 1. Stiso J
    2. Khambhati AN
    3. Menara T
    4. Kahn AE
    5. Stein JM
    6. Das SR
    7. Gorniak R
    8. Tracy J
    9. Litt B
    10. Davis KA
    11. Pasqualetti F
    12. Lucas TH
    13. Bassett DS

    (2019) White matter network architecture guides direct electrical stimulation through optimal state transitions

    Cell Reports 28:2554–2566.

    https://doi.org/10.1016/j.celrep.2019.08.008

    • PubMed
    • Google Scholar
70. 1. Suppa A
    2. Huang YZ
    3. Funke K
    4. Ridding MC
    5. Cheeran B
    6. Di Lazzaro V
    7. Ziemann U
    8. Rothwell JC

    (2016) Ten years of theta burst stimulation in humans: established knowledge, unknowns and prospects

    Brain Stimulation 9:323–335.

    https://doi.org/10.1016/j.brs.2016.01.006

    • PubMed
    • Google Scholar
71. 1. Teleńczuk M
    2. Teleńczuk B
    3. Destexhe A

    (2020) Modelling unitary fields and the single-neuron contribution to local field potentials in the hippocampus

    The Journal of Physiology 598:3957–3972.

    https://doi.org/10.1113/JP279452

    • PubMed
    • Google Scholar
72. 1. Titiz AS
    2. Hill MRH
    3. Mankin EA
    4. M Aghajan Z
    5. Eliashiv D
    6. Tchemodanov N
    7. Maoz U
    8. Stern J
    9. Tran ME
    10. Schuette P
    11. Behnke E
    12. Suthana NA
    13. Fried I

    (2017) Theta-burst microstimulation in the human entorhinal area improves memory specificity

    eLife 6:e29515.

    https://doi.org/10.7554/eLife.29515

    • PubMed
    • Google Scholar
73. Preprint

    1. van der Plas M
    2. Roux F
    3. Chelvarajah R
    4. Sawlani V
    5. Staresina B
    6. Wimber M
    7. Rollings DT
    8. Hanslmayr S

    (2024) Characterizing neuronal and population responses to electrical stimulation in the human hippocampo-cortical network

    bioRxiv.

    https://doi.org/10.1101/2024.11.28.625915

    • Google Scholar
74. 1. Vinck M
    2. Oostenveld R
    3. van Wingerden M
    4. Battaglia F
    5. Pennartz CMA

    (2011) An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias

    NeuroImage 55:1548–1565.

    https://doi.org/10.1016/j.neuroimage.2011.01.055

    • PubMed
    • Google Scholar
75. 1. Virtanen P
    2. Gommers R
    3. Oliphant TE
    4. Haberland M
    5. Reddy T
    6. Cournapeau D
    7. Burovski E
    8. Peterson P
    9. Weckesser W
    10. Bright J
    11. van der Walt SJ
    12. Brett M
    13. Wilson J
    14. Millman KJ
    15. Mayorov N
    16. Nelson ARJ
    17. Jones E
    18. Kern R
    19. Larson E
    20. Carey CJ
    21. Polat İ
    22. Feng Y
    23. Moore EW
    24. VanderPlas J
    25. Laxalde D
    26. Perktold J
    27. Cimrman R
    28. Henriksen I
    29. Quintero EA
    30. Harris CR
    31. Archibald AM
    32. Ribeiro AH
    33. Pedregosa F
    34. van Mulbregt P
    35. Vijaykumar A
    36. Bardelli AP
    37. Rothberg A
    38. Hilboll A
    39. Kloeckner A
    40. Scopatz A
    41. Lee A
    42. Rokem A
    43. Woods CN
    44. Fulton C
    45. Masson C
    46. Häggström C
    47. Fitzgerald C
    48. Nicholson DA
    49. Hagen DR
    50. Pasechnik DV
    51. Olivetti E
    52. Martin E
    53. Wieser E
    54. Silva F
    55. Lenders F
    56. Wilhelm F
    57. Young G
    58. Price GA
    59. Ingold GL
    60. Allen GE
    61. Lee GR
    62. Audren H
    63. Probst I
    64. Dietrich JP
    65. Silterra J
    66. Webber JT
    67. Slavič J
    68. Nothman J
    69. Buchner J
    70. Kulick J
    71. Schönberger JL
    72. de Miranda Cardoso JV
    73. Reimer J
    74. Harrington J
    75. Rodríguez JLC
    76. Nunez-Iglesias J
    77. Kuczynski J
    78. Tritz K
    79. Thoma M
    80. Newville M
    81. Kümmerer M
    82. Bolingbroke M
    83. Tartre M
    84. Pak M
    85. Smith NJ
    86. Nowaczyk N
    87. Shebanov N
    88. Pavlyk O
    89. Brodtkorb PA
    90. Lee P
    91. McGibbon RT
    92. Feldbauer R
    93. Lewis S
    94. Tygier S
    95. Sievert S
    96. Vigna S
    97. Peterson S
    98. More S
    99. Pudlik T
    100. Oshima T
    101. Pingel TJ
    102. Robitaille TP
    103. Spura T
    104. Jones TR
    105. Cera T
    106. Leslie T
    107. Zito T
    108. Krauss T
    109. Upadhyay U
    110. Halchenko YO
    111. Vázquez-Baeza Y
    112. SciPy 1.0 Contributors

    (2020) SciPy 1.0: fundamental algorithms for scientific computing in Python

    Nature Methods 17:261–272.

    https://doi.org/10.1038/s41592-019-0686-2

    • PubMed
    • Google Scholar
76. 1. Yoo HB
    2. Umbach G
    3. Lega B

    (2021) Neurons in the human medial temporal lobe track multiple temporal contexts during episodic memory processing

    NeuroImage 245:118689.

    https://doi.org/10.1016/j.neuroimage.2021.118689

    • PubMed
    • Google Scholar
77. 1. Youssef D
    2. Wittig JH
    3. Jackson S
    4. Inati SK
    5. Zaghloul KA

    (2023) Neuronal spiking responses to direct electrical microstimulation in the human cortex

    The Journal of Neuroscience 43:4448–4460.

    https://doi.org/10.1523/JNEUROSCI.1666-22.2023

    • PubMed
    • Google Scholar
78. 1. Zheng J
    2. Anderson KL
    3. Leal SL
    4. Shestyuk A
    5. Gulsen G
    6. Mnatsakanyan L
    7. Vadera S
    8. Hsu FPK
    9. Yassa MA
    10. Knight RT
    11. Lin JJ

    (2017) Amygdala-hippocampal dynamics during salient information processing

    Nature Communications 8:14413.

    https://doi.org/10.1038/ncomms14413

    • PubMed
    • Google Scholar
79. 1. Zheng J
    2. Stevenson RF
    3. Mander BA
    4. Mnatsakanyan L
    5. Hsu FPK
    6. Vadera S
    7. Knight RT
    8. Yassa MA
    9. Lin JJ

    (2019) Multiplexing of theta and alpha rhythms in the amygdala-hippocampal circuit supports pattern separation of emotional information

    Neuron 102:887–898.

    https://doi.org/10.1016/j.neuron.2019.03.025

    • PubMed
    • Google Scholar

Author details

1. Justin M Campbell

   Interdepartmental Program in Neuroscience, University of Utah, Salt Lake City, United States

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   justin.campbell@hsc.utah.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8685-2081

2. Rhiannon L Cowan

   Department of Neurosurgery, University of Utah, Salt Lake City, United States

   Contribution

   Conceptualization, Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8397-960X

3. Krista L Wahlstrom

   Department of Psychology, University of Utah, Salt Lake City, United States

   Contribution

   Investigation, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

4. Martina K Hollearn

   Department of Psychology, University of Utah, Salt Lake City, United States

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

5. Dylan Jensen

   Interdepartmental Program in Neuroscience, University of Utah, Salt Lake City, United States

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

6. Tyler Davis

   Department of Neurosurgery, University of Utah, Salt Lake City, United States

   Contribution

   Software, Writing – review and editing

   Competing interests

   No competing interests declared

7. Shervin Rahimpour

   Department of Neurosurgery, University of Utah, Salt Lake City, United States

   Contribution

   Resources, Writing – review and editing

   Competing interests

   No competing interests declared

8. Ben Shofty

   Department of Neurosurgery, University of Utah, Salt Lake City, United States

   Contribution

   Resources, Writing – review and editing

   Competing interests

   No competing interests declared

9. Amir Arain

   Department of Neurology, University of Utah, Salt Lake City, United States

   Contribution

   Resources, Writing – review and editing

   Competing interests

   No competing interests declared

10. John D Rolston

    Department of Neurosurgery, Brigham and Women’s Hospital, Boston, United States

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

11. Stephan Hamann

    Department of Psychology, Emory University, Atlanta, United States

    Contribution

    Writing – review and editing

    Competing interests

    No competing interests declared

12. Shuo Wang

    Department of Radiology, Washington University School of Medicine, St. Louis, United States

    Contribution

    Resources, Writing – review and editing

    Competing interests

    Reviewing editor, eLife

    "This ORCID iD identifies the author of this article:" 0000-0003-2562-0225

13. Lawrence N Eisenman

    Department of Neurology, Washington University School of Medicine, St. Louis, United States

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

14. James Swift

    1. Department of Neurological Surgery, Washington University School of Medicine, St. Louis, United States
    2. National Center for Adaptive Neurotechnologies, St. Louis, United States

    Contribution

    Software, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

15. Tao Xie

    1. Department of Neurological Surgery, Washington University School of Medicine, St. Louis, United States
    2. National Center for Adaptive Neurotechnologies, St. Louis, United States

    Contribution

    Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

16. Peter Brunner

    1. Department of Neurological Surgery, Washington University School of Medicine, St. Louis, United States
    2. National Center for Adaptive Neurotechnologies, St. Louis, United States

    Contribution

    Software, Supervision, Investigation

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-2588-2754

17. Joseph Manns

    Department of Psychology, Emory University, Atlanta, United States

    Contribution

    Conceptualization, Supervision, Project administration, Writing – review and editing

    Competing interests

    No competing interests declared

18. Cory Inman

    1. Interdepartmental Program in Neuroscience, University of Utah, Salt Lake City, United States
    2. Department of Psychology, University of Utah, Salt Lake City, United States

    Contribution

    Resources, Supervision, Funding acquisition, Project administration, Writing – review and editing

    Contributed equally with

    Elliot H Smith and Jon Timothy Willie

    Competing interests

    No competing interests declared

19. Elliot H Smith

    Department of Neurosurgery, University of Utah, Salt Lake City, United States

    Contribution

    Conceptualization, Resources, Supervision, Writing – review and editing

    Contributed equally with

    Cory Inman and Jon Timothy Willie

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-4323-4643

20. Jon Timothy Willie

    1. Department of Neurological Surgery, Washington University School of Medicine, St. Louis, United States
    2. National Center for Adaptive Neurotechnologies, St. Louis, United States

    Contribution

    Resources, Supervision, Funding acquisition, Investigation, Project administration, Writing – review and editing

    Contributed equally with

    Cory Inman and Elliot H Smith

    Competing interests

    No competing interests declared

• 2,158

  views

• 29

  downloads

• 0

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.