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Reliable Analysis of Single-Unit Recordings from the Human Brain under Noisy Conditions: Tracking Neurons over Hours

Author

Listed:
  • Johannes Niediek
  • Jan Boström
  • Christian E Elger
  • Florian Mormann

Abstract

Recording extracellulary from neurons in the brains of animals in vivo is among the most established experimental techniques in neuroscience, and has recently become feasible in humans. Many interesting scientific questions can be addressed only when extracellular recordings last several hours, and when individual neurons are tracked throughout the entire recording. Such questions regard, for example, neuronal mechanisms of learning and memory consolidation, and the generation of epileptic seizures. Several difficulties have so far limited the use of extracellular multi-hour recordings in neuroscience: Datasets become huge, and data are necessarily noisy in clinical recording environments. No methods for spike sorting of such recordings have been available. Spike sorting refers to the process of identifying the contributions of several neurons to the signal recorded in one electrode. To overcome these difficulties, we developed Combinato: a complete data-analysis framework for spike sorting in noisy recordings lasting twelve hours or more. Our framework includes software for artifact rejection, automatic spike sorting, manual optimization, and efficient visualization of results. Our completely automatic framework excels at two tasks: It outperforms existing methods when tested on simulated and real data, and it enables researchers to analyze multi-hour recordings. We evaluated our methods on both short and multi-hour simulated datasets. To evaluate the performance of our methods in an actual neuroscientific experiment, we used data from from neurosurgical patients, recorded in order to identify visually responsive neurons in the medial temporal lobe. These neurons responded to the semantic content, rather than to visual features, of a given stimulus. To test our methods with multi-hour recordings, we made use of neurons in the human medial temporal lobe that respond selectively to the same stimulus in the evening and next morning.

Suggested Citation

  • Johannes Niediek & Jan Boström & Christian E Elger & Florian Mormann, 2016. "Reliable Analysis of Single-Unit Recordings from the Human Brain under Noisy Conditions: Tracking Neurons over Hours," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-26, December.
  • Handle: RePEc:plo:pone00:0166598
    DOI: 10.1371/journal.pone.0166598
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    Citations

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    Cited by:

    1. Gray Umbach & Ryan Tan & Joshua Jacobs & Brad E. Pfeiffer & Bradley Lega, 2022. "Flexibility of functional neuronal assemblies supports human memory," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    2. Luca D. Kolibius & Frederic Roux & George Parish & Marije Wal & Mircea Plas & Ramesh Chelvarajah & Vijay Sawlani & David T. Rollings & Johannes D. Lang & Stephanie Gollwitzer & Katrin Walther & Rüdige, 2023. "Hippocampal neurons code individual episodic memories in humans," Nature Human Behaviour, Nature, vol. 7(11), pages 1968-1979, November.
    3. Marcel Bausch & Johannes Niediek & Thomas P. Reber & Sina Mackay & Jan Boström & Christian E. Elger & Florian Mormann, 2021. "Concept neurons in the human medial temporal lobe flexibly represent abstract relations between concepts," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    4. Annika Hagemann & Jens Wilting & Bita Samimizad & Florian Mormann & Viola Priesemann, 2021. "Assessing criticality in pre-seizure single-neuron activity of human epileptic cortex," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-18, March.
    5. Sina Mackay & Thomas P. Reber & Marcel Bausch & Jan Boström & Christian E. Elger & Florian Mormann, 2024. "Concept and location neurons in the human brain provide the ‘what’ and ‘where’ in memory formation," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    6. Sara Mahallati & James C Bezdek & Milos R Popovic & Taufik A Valiante, 2019. "Cluster tendency assessment in neuronal spike data," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-29, November.

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