Analysis of an open source, closed-loop, realtime system for hippocampal sharp-wave ripple disruption

Abstract

Transient neural activity pervades hippocampal electrophysiological activity. During more quiescent states, brief ≈100 ms periods comprising large ≈150-250 Hz oscillations known as sharp-wave ripples (SWR) which co-occur with ensemble bursts of spiking activity, are regularly found in local field potentials recorded from area CA1. SWRs and their concomitant neural activity are thought to be important for memory consolidation, recall, and memory-guided decision making. Temporallyselective manipulations of hippocampal neural activity upon online hippocampal SWR detection have been used as causal evidence of the importance of SWR for mnemonic process as evinced by behavioral and/or physiological changes. However, though this approach is becoming more wide spread, the performance trade-offs involved in building a SWR detection and disruption system have not been explored, limiting the design and interpretation of SWR detection experiments. We present an open source, plug-and-play, online ripple detection system with a detailed performance characterization. Our system has been constructed to interface with an open source software platform, Trodes, and two hardware acquisition platforms, Open Ephys and SpikeGadgets. We show that our in vivo results -approximately 80% detection latencies falling in between ≈20-66 ms with ≈2 ms closed-loop latencies while maintaining <10 false detections per minute -are dependent upon both algorithmic trade-offs and acquisition hardware. We discuss strategies to improve detection accuracy and potential limitations of online ripple disruptions. By characterizing this system in detail, we present a template for analyzing other closed-loop neural detection and perturbation systems. Thus, we anticipate our modular, open source, realtime system will facilitate a wide range of carefully-designed causal closed-loop neuroscience experiments.

Deep learning-based feature extraction for prediction and interpretation of sharp-wave ripples in the rodent hippocampus

“…Moreover, with the advent of ultra-dense recordings, the need for automatic identification is pressing. In spite of recent advances ( Dutta et al, 2019 ; Hagen et al, 2021 ), current solutions still require improvement to capture the complexity of SWR events across hippocampal layers.…”

Deep learning based feature extraction for prediction and interpretation of sharp-wave ripples

“…Remarkably, the filter exhibited larger variability across sessions. Our CNN performed similar to a filter-based optimized algorithm (F1=0.66 ± 0.11) (Dutta et al, 2019), but significantly better than RippleNET, a recurrent network designed to detect SWR mostly during periods of immobility (F1=0.31 ± 0.21; p<0.00001 one-way ANOVA for comparisons with both CNN12 and CNN32) (Hagen et al, 2021). This supports similar operation of CNN as compared with the gold standard in conditions when optimized detection was possible.…”

“…Moreover, with the advent of ultra-dense recordings, the need for automatic identification is pressing. In spite of recent advances (Dutta et al, 2019; Hagen et al, 2021), current solutions still fall short in capturing the complexity of SWR events across hippocampal layers.…”

An open-source, ready-to-use and validated ripple detector plugin for the Open Ephys GUI

“…Sharp wave-ripples – electrophysiological oscillations in the local field potential (LFP) of the hippocampal CA1 region – are thought to coordinate the repetitive reactivation of memory-related ensembles and contribute to the integration and stabilization of memory representations (Buzsáki, 2015). Regarding their oscillatory features, most SWR events last from 50 to 150 ms, and their frequency components are within the 100-250 Hz range (Buzsáki, 2015; Dutta, Ackermann and Kemere, 2019; Fernández-Ruiz et al, 2019; Adamantidis, Herrera and Gent, 2019). Studies that investigate the causal link between SWRs, learning, and memory consolidation during sleep have usually employed closed-loop, real-time ripple disruption experiments using electrical stimulation (Girardeau et al, 2009; Ego-Stengel and Wilson, 2010; Maingret et al, 2016) or, more recently, optogenetic techniques (Kovács et al, 2016; Norimoto et al, 2018; Oliva et al, 2020).…”

“…Valuable efforts have been applied to provide a real-time ripple detector whose closed-loop, system-level performance is known so that investigators can be aware of the limitations and possibilities offered by such a tool (Dutta, Ackermann and Kemere, 2019). Nevertheless, the scientific community still lacks open-source, publicly available ripple detection modules already integrated with widely used electrophysiological recording platforms.…”