Mohammad S E Sendi1, Cory S Inman2, Kelly R Bijanki3, Lou Blanpain4, James K Park5, Stephan Hamann6, Robert E Gross7, Jon T Willie8, Babak Mahmoudi9. 1. Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, 313 Ferst Dr NW, Atlanta, 30332, GA, USA; Department of Electrical and Computer Engineering at Georgia Institute of Technology, 777 Atlantic Dr, Atlanta, GA, 30313, USA. 2. Department of Psychology at University of Utah, 380 1530 E, Salt Lake City, UT, 84112, United States. 3. Department of Neurosurgery at Baylor College of Medicine, 7200 Cambridge St 9th Floor, Houston, TX, 77030, USA. 4. Neuroscience Graduate Program at Emory University, 1462 Clifton Rd. Suite 314, Atlanta, GA, 30322, USA. 5. Department of Neurosurgery at Emory University, 100 Woodruff Circle, Atlanta, GA, 30322, USA. 6. Department of Psychology at Emory University, 36 Eagle Row, Atlanta, GA, 3032, USA. 7. Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, 313 Ferst Dr NW, Atlanta, 30332, GA, USA; Department of Neurosurgery at Emory University, 100 Woodruff Circle, Atlanta, GA, 30322, USA; Department of Neurology at Emory University, 12 Executive Park Dr NE, Atlanta, GA, 30322, USA. 8. Department of Neurology at Washington University School of Medicine in Saint Louis, 660 S. Euclid Avenue Campus Box 8057 St, Louis, MO, 63110, USA. 9. Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, 313 Ferst Dr NW, Atlanta, 30332, GA, USA; Department of Biomedical Informatics at Emory University, 100 Woodruff Circle, Atlanta, GA, 30322, USA. Electronic address: b.mahmoudi@emory.edu.
Abstract
BACKGROUND: Direct 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. OBJECTIVE: To leverage interpretable machine learning to identify the neurophysiological processes underlying amygdala-mediated memory, and to develop more efficient neuromodulation technologies. METHOD: Patients 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. RESULTS: Classifying 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. CONCLUSION: Amygdala 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.
BACKGROUND: Direct 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. OBJECTIVE: To leverage interpretable machine learning to identify the neurophysiological processes underlying amygdala-mediated memory, and to develop more efficient neuromodulation technologies. METHOD: Patients 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. RESULTS: Classifying 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. CONCLUSION: Amygdala 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.
Authors: Alexander R Backus; Jan-Mathijs Schoffelen; Szabolcs Szebényi; Simon Hanslmayr; Christian F Doeller Journal: Curr Biol Date: 2016-01-28 Impact factor: 10.834
Authors: David I Bass; Zainab G Nizam; Kristin N Partain; Arick Wang; Joseph R Manns Journal: Neurobiol Learn Mem Date: 2013-11-08 Impact factor: 2.877
Authors: E A Solomon; J E Kragel; R Gross; B Lega; M R Sperling; G Worrell; S A Sheth; K A Zaghloul; B C Jobst; J M Stein; S Das; R Gorniak; C S Inman; S Seger; D S Rizzuto; M J Kahana Journal: Nat Commun Date: 2018-10-25 Impact factor: 14.919