Decoding Features of Real-world Navigation from Neural Data via RNS
Abstract number :
1.35
Submission category :
11. Behavior/Neuropsychology/Language / 11A. Adult
Year :
2022
Submission ID :
2203947
Source :
www.aesnet.org
Presentation date :
12/3/2022 12:00:00 PM
Published date :
Nov 22, 2022, 05:22 AM
Authors :
Kathryn Graves, BA, MS, MPhil – Yale University; Tyler Gray, BA – Yale University; Ariadne Letrou, BS – Yale University; Imran Quraishi, MD, PhD – Yale University; Nicholas Turk-Browne, PhD – Yale University
This abstract is a recipient of the Young Investigator Award
This abstract has been invited to present during the Genetics & Behavior/Neuropsychology/Language platform session
Rationale: Temporal lobe epilepsy affects the hippocampus, a brain system critical for memory and spatial navigation. Despite the large literature on animal models and virtual navigation in humans, how this region supports real-world behavior remains unclear. Addressing this extant gap is critical for developing treatments for diseases that affect the hippocampus. Here, we explore what information about human navigation can be extracted from ensemble-level neural signals in the hippocampus during real-world ambulation. By measuring spectral features (e.g., theta oscillations) during walking along a linear track, we sought to decode the navigator’s heading direction and physical location in space. That is, we analyzed the extent to which oscillatory power in these regions (a) was modulated by the direction patients walked and (b) could be used to reconstruct their spatial position.
Methods: Twelve epilepsy patients with implanted Responsive Neurostimulation (RNS) devices who had contacts in the hippocampus were recruited to participate in this study. We used a custom telemetry apparatus to interface with their neural recordings and synchronize with our task. Patients alternated between walking and standing along a linear track in one-minute intervals. Musical cues indicated whether to walk or stand, with one song playing during the walking epochs and a different song playing during the standing epochs. We first investigated directional coding by performing binary classification of the local field potential (LFP) during movement in Direction A versus Direction B on the track. We then sought to establish evidence of place coding activity from the LFP data. To this end, we used an Inverted Encoding Model (IEM) on the neural time series for each hippocampal contact to reconstruct the paths each patient walked during a held out trial.
Results: Classification of movement in Direction A versus Direction B in the hippocampus was significantly above chance (p = 0.007) (Figure 1). This effect was specific to the hippocampus, as we could not significantly classify direction from non-hippocampal contacts (p > 0.05). It was also possible to reconstruct (in held-out data) the track position from a larger number of hippocampal contacts than would be expected by chance (p=0.01). Figure 2 shows an example of a true path and reconstructed path from a hippocampal contact.
Conclusions: Via this rare data collection opportunity, we found evidence of hippocampal heading-direction and place coding during real-world navigation. These findings represent a significant translation between the decades of animal literature and what little is known of naturalistic navigation in humans, with implications for understanding neuropsychiatric comorbidities in epilepsy and other neurologic disorders. We further show that navigational coding can be identified from sparsely sampled LFPs without the need for single unit data, enabling future experiments of memory and navigation that have not previously been possible.
Funding: National Institutes of Health (NIH) Grant R01MH069456 (to N. B. Turk-Browne), Swebilius Foundation Grant (to I. H. Quraishi), NIH Grant 1F99NS125835-01 (to K. N. Graves), and Swebilius Foundation Grant (to K. N. Graves)
Behavior