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

Nature Human Behaviour volume 7, pages 754–764 (2023)Cite this article

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Abstract

Emotional events comprise our strongest and most valuable memories. Here we examined how the brain prioritizes emotional information for storage using direct brain recording and deep brain stimulation. First, 148 participants undergoing intracranial electroencephalographic (iEEG) recording performed an episodic memory task. Participants were most successful at remembering emotionally arousing stimuli. High-frequency activity (HFA), a correlate of neuronal spiking activity, increased in both the hippocampus and the amygdala when participants successfully encoded emotional stimuli. Next, in a subset of participants (N = 19), we show that applying high-frequency electrical stimulation to the hippocampus selectively diminished memory for emotional stimuli and specifically decreased HFA. Finally, we show that individuals with depression (N = 19) also exhibit diminished emotion-mediated memory and HFA. By demonstrating how direct stimulation and symptoms of depression unlink HFA, emotion and memory, we show the causal and translational potential of neural activity in the amygdalohippocampal circuit for prioritizing emotionally arousing memories.

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Fig. 1: Emotional features of stimuli in a verbal free recall task influence recall performance.
Fig. 2: High-frequency activity predicts successful emotional memory encoding in the hippocampus and amygdala.
Fig. 3: Direct stimulation of the hippocampus during encoding impairs emotion-mediated memory and decreases HFA.
Fig. 4: Participants with depression exhibit diminished arousal-mediated memory and HFA.

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Data availability

The raw electrophysiological data used in this study are available at http://memory.psych.upenn.edu/RAM. Word valence and arousal ratings are available at http://saifmohammad.com/WebPages/lexicons.html34 and http://crr.ugent.be/archives/100373. The CIT168 atlas is available at https://osf.io/r2hvk/74.

Code availability

Custom analysis and modelling code is available at https://github.com/seqasim/NHB_EmotionMemory_Models.

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Acknowledgements

We are grateful to the patients for participating in our study. This work was supported by the National Science Foundation (NSF) and National Institute of Health (NIH) grants U01-NS113198 and R01-MH104606 (to J.J.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank M. Hermiller and L. Kunz for helpful comments and suggestions. We thank M. Kahana for help with data collection.

Author information

Authors and Affiliations

  1. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Salman E. Qasim

  2. Surgical Neurology Branch, NINDS, National Institutes of Health, Bethesda, MD, USA

    Uma R. Mohan

  3. Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA

    Joel M. Stein

  4. Department of Biomedical Engineering, Columbia University, New York, NY, USA

    Joshua Jacobs

  5. Department of Neurological Surgery, Columbia University, New York, NY, USA

    Joshua Jacobs

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  1. Salman E. Qasim
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Contributions

S.E.Q. conceived the study; S.E.Q. and U.R.M. analysed the data; J.M.S. processed neuroimaging data; all authors interpreted the results, and S.E.Q. and J.J. wrote the manuscript.

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Correspondence to Salman E. Qasim or Joshua Jacobs.

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Extended data

Extended Data Fig. 1 Emotional context modulates recall dynamics.

A) Conditional response probability based on valence, averaged across sessions for each participant (n=147). The height of each bar depicts the probability, averaged across participants, of making a transition to a particular valence word (denoted by the color of the bar) as a function of the just recalled word’s valence (denoted by the x-axis label). Error bars denote standard deviation. T-statistics denote the relative proportion of within-valence transitions versus across-valence transitions, across participants. The largest t-statistic is bolded, denoting the relative prevalence of neutral-neutral transitions. B) Conditional response probability based on arousal, averaged across sessions for each participant (n=140). The height of each bar depicts the probability, averaged across participants, of making a transition to a particular arousal word (denoted by the color of the bar) as a function of the just recalled word’s arousal (denoted by the x-axis label). Error bars denote standard deviation. T-statistics denote the relative proportion of within-arousal transitions versus across-arousal transitions, across participants. The largest t-statistic is bolded, denoting the relative prevalence of arousing-arousing transitions. Related to Fig. 1.

Extended Data Fig. 2 Segmentation of electrodes to different amygdala nuclei.

Count of electrodes categorized to different amygdala nuclei on the basis of post-implant imaging. BLN = basolateral nuclei, ATA = amygdala transition areas, AAA = anterior amygdala area, CMN = cortical and medial nuclei, CEN = central nucleus, AMY = could not be localized to specific subregion. Related to Fig. 2.

Extended Data Fig. 3 Memory-related power changes are not due to changes in spectra characteristics.

A) Power spectra slope across the entire session for both remembered (dark shade) and forgotten (light shade) trials in both hippocampus (purple) and amygdala (orange) for all participants (n=147). Asterisk denote significant difference (t(3397)= 4.4, p= 1.1 x 10−5, Cohen’s d= 0.14, CI= [0.03, 0.09], two-sided t-test). Error bars denote standard deviation. B) Peak frequency across the entire session for both remembered and forgotten trials in both hippocampus and amygdala for all participants (n=147). Asterisk denote significant difference (t(3262)= -7.6, p= 4.3 x 10−14, Cohen’s d= -0.24, CI= [-2.2, 1.3], two-sided t-test). Error bars denote standard deviation. C) Peak height across the entire session for both remembered and forgotten trials in both hippocampus and amygdala for all participants (n=147). Asterisk denote significant difference (t(3030)= 4.6, p= 4 x 10−6, d= 0.15, CI= [0.01, 0.03], two-sided t-test). Error bars denote standard deviation. Related to Fig. 2.

Extended Data Fig. 4 Word-level SME for high arousal and low arousal words averaged across the population.

A) Heatmaps of hippocampal power (z-scored within session) for specific words from the task wordpool, averaged across sessions and participants. Words were selected from the 30 words with the highest arousal ratings (left) or lowest arousal ratings (right). Warm colors indicate higher values while cool colors indicate lower values. Above each heatmap is the averaged z-scored power across the words in the heatmap. B) Same as panel A), but for amygdalar power. Related to Fig. 2.

Extended Data Fig. 5 Regional differences in the relationship between neuronal activity, memory and valence.

A) Probability of recall as a function of HFA (z-scored) in the hippocampus and amygdala, binned by valence and split by hemisphere, fit by a logistic regression model (solid line). Shading indicates standard deviation of bootstrapped model fits. B) Probability of recall as a function of HFA (z-scored), binned by valence and split by region, fit by a logistic regression model (solid line). Shading indicates standard deviation of bootstrapped model fits. C) Probability of recall as a function of HFA (z-scored) in the hippocampus, binned by valence and split by longitudinal axis position, fit by a logistic regression model (solid line). Shading indicates standard deviation of bootstrapped model fits. Related to Fig. 2.

Extended Data Fig. 6 Hippocampal and amygdalar spectrogram depicting difference in power between remembered and forgotten trials across all electrodes.

A) Median z-scored spectrogram for hippocampal (left) and amygdalar (right) electrodes showing difference between remembered and forgotten words. Warm colors indicate an increase in power during encoding of remembered words, while cool colors indicate a decrease in power. B) Median HFA difference between remembered and forgotten words across all electrodes in the hippocampus (left) and amygdala (right), split by binned arousal rating. Horizontal bars indicate significant clusters of time-points when comparing remembered and forgotten high arousal words (t(1)’s > 2.5, p’s < 0.05, Cohen’s d’s > 0.1, two-sided cluster-based permutation test). Related to Fig. 2.

Extended Data Fig. 7 Location of stimulation electrodes.

Hippocampal electrodes (purple), amygdala electrodes (orange) and nonhippocampal MTL electrodes (teal) where direct stimulation was applied. Black electrodes were used for recording, only. Related to Fig. 3.

Extended Data Fig. 8 Stimulation does not impair early-position words more than late-position words.

Probability of recall as a function of serial position for both the stimulation off (black) and on (yellow) conditions, in participants who underwent hippocampal stimulation, fit by a logistic regression model (solid line). Shading indicates standard deviation of bootstrapped model fits. Related to Fig. 3.

Extended Data Fig. 9 Hippocampal stimulation selectively decreases HFA.

Change in hippocampal power (post–pre) when stimulation was applied to the hippocampus (left, averaged across n=16 electrodes) and nearby control regions (right, averaged across n=8 electrodes), compared between stimulation (dark) and no stimulation (light) conditions. Frequency bands are defined as follows: theta (2–8 Hz), alpha (8–13 Hz), beta (13–30 Hz) and HFA (30–128 Hz). Error bars denote standard deviation. Related to Fig. 3.

Extended Data Fig. 10 Depression reverses HFA-memory relationship for negative words.

A) Probability of recall as a function of valence for both depressed and non-depressed participants, fit by a logistic regression model (solid line). Shading indicates standard deviation of bootstrapped model fits. B) Probability of recall as a function of HFA (z-scored), binned by valence and depression level, fit by a logistic regression model (solid line). Shading indicates standard deviation of bootstrapped model fits. Related to Fig. 4.

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Qasim, S.E., Mohan, U.R., Stein, J.M. et al. Neuronal activity in the human amygdala and hippocampus enhances emotional memory encoding. Nat Hum Behav 7, 754–764 (2023). https://doi.org/10.1038/s41562-022-01502-8

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