ArticleBrain stimulation
Identifying the neurophysiological effects of memory-enhancing amygdala stimulation using interpretable machine learning.
Article in Brain stimulation. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Computational Insights Into Smart Bioelectronics in Digital Health Care (2020-2024): Topic Modeling Study.JMIR medical informatics · 2026Review
- Impacts of early life adversity on the neurocircuitry of emotional memory in children.Development and psychopathology · 2025Article
- Visualizing functional network connectivity differences using an explainable machine-learning method.Physiological measurement · 2025Article
- The Neurostimulationist will see you now: prescribing direct electrical stimulation therapies for the human brain in epilepsy and beyond.Frontiers in human neuroscience · 2024Review
- The amygdala mediates the facilitating influence of emotions on memory through multiple interacting mechanisms.Neurobiology of stress · 2023Review
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Authors and funding
9 authors.
Funding
Abstract
backgroundDirect electrical stimulation of the amygdala can enhance declarative memory for specific events. An unanswered question is what underlying neurophysiological changes are induced by amygdala stimulation.
objectiveTo leverage interpretable machine learning to identify the neurophysiological processes underlying amygdala-mediated memory, and to develop more efficient neuromodulation technologies.
methodPatients with treatment-resistant epilepsy and depth electrodes placed in the hippocampus and amygdala performed a recognition memory task for neutral images of objects. During the encoding phase, 160 images were shown to patients. Half of the images were followed by brief low-amplitude amygdala stimulation. For local field potentials (LFPs) recorded from key medial temporal lobe structures, feature vectors were calculated by taking the average spectral power in canonical frequency bands, before and after stimulation, to train a logistic regression classification model with elastic net regularization to differentiate brain states.
resultsClassifying the neural states at the time of encoding based on images subsequently remembered versus not-remembered showed that theta and slow-gamma power in the hippocampus were the most important features predicting subsequent memory performance. Classifying the post-image neural states at the time of encoding based on stimulated versus unstimulated trials showed that amygdala stimulation led to increased gamma power in the hippocampus.
conclusionAmygdala stimulation induced pro-memory states in the hippocampus to enhance subsequent memory performance. Interpretable machine learning provides an effective tool for investigating the neurophysiological effects of brain stimulation.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.