Advertisement

Surgical and Electrophysiological Techniques for Single-Neuron Recordings in Human Epilepsy Patients

  • Juri Minxha
  • Adam N. Mamelak
  • Ueli Rutishauser
Protocol
Part of the Neuromethods book series (NM, volume 134)

Abstract

Extracellular recordings of single-neuron activity in awake behaving animals are one of the principal techniques used to decipher the neuronal basis of behavior. While only routinely possible in animals, rare clinical procedures make it possible to perform such recordings in awake human beings. Such human single-neuron recordings have started to reveal insights into the neural mechanisms of learning, memory, cognition, attention, and decision-making in humans. Here, we describe in detail the methods we developed to perform such recordings in patients undergoing invasive monitoring for localization of epileptic seizures. We describe three aspects: the neurosurgical procedure to implant depth electrodes with embedded microwires, electrophysiological methods to perform experiments in the clinical settings, and data processing steps to isolate single neurons. Together, this chapter provides a comprehensive overview of the methods needed to perform single-neuron recordings in humans during psychophysical tasks.

Keywords

Single-neuron recordings Human intracranial Functional neurosurgery Stereotactic Electrophysiology Spike sorting 

References

  1. 1.
    Mullin JP, Shriver M, Alomar S, Najm I, Bulacio J, Chauvel P, Gonzalez-Martinez J (2016) Is SEEG safe? A systematic review and meta-analysis of stereo-electroencephalography-related complications. Epilepsia 57(3):386–401CrossRefPubMedGoogle Scholar
  2. 2.
    Fried I, Rutishauser U, Cerf M, Kreiman G (2014) Single neuron studies of the human brain: probing cognition. MIT Press, BostonCrossRefGoogle Scholar
  3. 3.
    Fried I, Wilson CL, Maidment NT, Engel J, Behnke E, Fields TA, MacDonald KA, Morrow JW, Ackerson L (1999) Cerebral microdialysis combined with single-neuron and electroencephalographic recording in neurosurgical patients—technical note. J Neurosurg 91(4):697–705CrossRefPubMedGoogle Scholar
  4. 4.
    Schmidt RF, Wu C, Lang MJ, Soni P, Williams KA Jr, Boorman DW, Evans JJ, Sperling MR, Sharan AD (2016) Complications of subdural and depth electrodes in 269 patients undergoing 317 procedures for invasive monitoring in epilepsy. Epilepsia 57(10):1697–1708CrossRefPubMedGoogle Scholar
  5. 5.
    Hefft S, Brandt A, Zwick S, von Elverfeldt D, Mader I, Cordeiro J, Trippel M, Blumberg J, Schulze-Bonhage A (2013) Safety of hybrid electrodes for single-neuron recordings in humans. Neurosurgery 73(1):78–85; discussion 85CrossRefPubMedGoogle Scholar
  6. 6.
    Mehta AD, Labar D, Dean A, Harden C, Hosain S, Pak J, Marks D, Schwartz TH (2005) Frameless stereotactic placement of depth electrodes in epilepsy surgery. J Neurosurg 102(6):1040–1045CrossRefPubMedGoogle Scholar
  7. 7.
    Misra A, Burke JF, Ramayya AG, Jacobs J, Sperling MR, Moxon KA, Kahana MJ, Evans JJ, Sharan AD (2014) Methods for implantation of micro-wire bundles and optimization of single/multi-unit recordings from human mesial temporal lobe. J Neural Eng 11(2):026013CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Mamelak A (2014) Ethical and practical considerations for human microelectrode recording studies. In: Fried I et al (eds) Single neuron studies of the human brain. MIT Press, BostonGoogle Scholar
  9. 9.
    Pelli DG (1997) The VideoToolbox software for visual psychophysics: transforming numbers into movies. Spat Vis 10:437–442CrossRefPubMedGoogle Scholar
  10. 10.
    Brainard DH (1997) The psychophysics toolbox. Spat Vis 10:433–436CrossRefPubMedGoogle Scholar
  11. 11.
    Cornelissen FW, Peters EM, Palmer J (2002) The Eyelink Toolbox: eye tracking with MATLAB and the Psychophysics Toolbox. Behav Res Methods Instrum Comput 34(4):613–617CrossRefPubMedGoogle Scholar
  12. 12.
    Rutishauser U, Kotowicz A, Laurent G (2013) A method for closed-loop presentation of sensory stimuli conditional on the internal brain-state of awake animals. J Neurosci Methods 215(1):139–155CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Gibson S, Judy JW, Markovic D (2012) Spike sorting the first step in decoding the brain. IEEE Signal Process Mag 29(1):124–143CrossRefGoogle Scholar
  14. 14.
    Lewicki MS (1998) A review of methods for spike sorting: the detection and classification of neural action potentials. Network 9(4):R53–R78CrossRefPubMedGoogle Scholar
  15. 15.
    Rutishauser U, Cerf M, Kreiman G (2014) Data analysis techniques for human microwire recordings: spike detection and sorting, decoding, relation between neurons and local field potential. In: Fried I et al (eds) Single neuron studies of the human brain. MIT Press, Boston, pp 59–98Google Scholar
  16. 16.
    Quiroga RQ (2009) What is the real shape of extracellular spikes? J Neurosci Methods 177(1):194–198CrossRefGoogle Scholar
  17. 17.
    Bankman IN, Johnson KO, Schneider W (1993) Optimal detection, classification, and superposition resolution in neural waveform recordings. IEEE Trans Biomed Eng 40(8):836–841CrossRefPubMedGoogle Scholar
  18. 18.
    Rutishauser U, Schuman EM, Mamelak AN (2006) Online detection and sorting of extracellularly recorded action potentials in human medial temporal lobe recordings, in vivo. J Neurosci Methods 154(1):204–224CrossRefPubMedGoogle Scholar
  19. 19.
    Quiroga RQ, Nadasdy Z, Ben-Shaul Y (2004) Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. Neural Comput 16(8):1661–1687CrossRefPubMedGoogle Scholar
  20. 20.
    Rossant C, Kadir SN, Goodman DF, Schulman J, Hunter ML, Saleem AB, Grosmark A, Belluscio M, Denfield GH, Ecker AS, Tolias AS, Solomon S, Buzsaki G, Carandini M, Harris KD (2016) Spike sorting for large, dense electrode arrays. Nat Neurosci 19(4):634–641CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Rutishauser U, Ross IB, Mamelak AN, Schuman EM (2010) Human memory strength is predicted by theta-frequency phase-locking of single neurons. Nature 464(7290):903–907CrossRefPubMedGoogle Scholar
  22. 22.
    Kaminski J, Sullivan S, Chung JM, Ross IB, Mamelak AN, Rutishauser U (2017) Persistently active neurons in human medial frontal and medial temporal lobe support working memory. Nat Neurosci 20(4):590–601CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Pouzat C, Mazor O, Laurent G (2002) Using noise signature to optimize spike-sorting and to assess neuronal classification quality. J Neurosci Methods 122(1):43–57CrossRefPubMedGoogle Scholar
  24. 24.
    Rutishauser U, Ye S, Koroma M, Tudusciuc O, Ross IB, Chung JM, Mamelak AN (2015) Representation of retrieval confidence by single neurons in the human medial temporal lobe. Nat Neurosci 18(7):1041–1050CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Hill DN, Mehta SB, Kleinfeld D (2011) Quality metrics to accompany spike sorting of extracellular signals. J Neurosci 31(24):8699–8705CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Schmitzer-Torbert N, Jackson J, Henze D, Harris K, Redish A (2005) Quantitative measures of cluster quality for use in extracellular recordings. Neuroscience 131(1):1–11CrossRefPubMedGoogle Scholar
  27. 27.
    Minxha J, Mosher C, Morrow JK, Mamelak AN, Adolphs R, Gothard KM, Rutishauser U (2017) Fixations gate species-specific responses to free viewing of faces in the human and macaque amygdala. Cell Rep 18(4):878–891CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Adolphs R (2010) What does the amygdala contribute to social cognition? Ann N Y Acad Sci 1191(1):42–61CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Paton JJ, Belova MA, Morrison SE, Salzman CD (2006) The primate amygdala represents the positive and negative value of visual stimuli during learning. Nature 439(7078):865–870CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Tsao DY, Freiwald WA, Tootell RB, Livingstone MS (2006) A cortical region consisting entirely of face-selective cells. Science 311(5761):670–674CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Rutishauser U, Tudusciuc O, Wang S, Mamelak AN, Ross IB, Adolphs R (2013) Single-neuron correlates of atypical face processing in autism. Neuron 80(4):887–899CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Rutishauser U, Tudusciuc O, Neumann D, Mamelak AN, Heller AC, Ross IB, Philpott L, Sutherling WW, Adolphs R (2011) Single-unit responses selective for whole faces in the human amygdala. Curr Biol 21(19):1654–1660CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Rutishauser U, Mamelak AN, Adolphs R (2015) The primate amygdala in social perception—insights from electrophysiological recordings and stimulation. Trends Neurosci 38(5):295–306CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Adolphs R, Gosselin F, Buchanan TW, Tranel D, Schyns P, Damasio AR (2005) A mechanism for impaired fear recognition after amygdala damage. Nature 433(7021):68–72CrossRefPubMedGoogle Scholar
  35. 35.
    Dal Monte O, Costa VD, Noble PL, Murray EA, Averbeck BB (2014) Amygdala lesions in rhesus macaques decrease attention to threat. Nat Commun 6:10161–10161CrossRefGoogle Scholar
  36. 36.
    Adolphs R, Tranel D, Damasio AR (1998) The human amygdala in social judgment. Nature 393(6684):470–474CrossRefPubMedGoogle Scholar
  37. 37.
    Tyszka, MJ, Pauli, WM (2016) A high resolution in vivo MRI atlas of the adult human amygdaloid complex. Submitted for publicationGoogle Scholar
  38. 38.
    Mormann F, Kornblith S, Quiroga RQ, Kraskov A, Cerf M, Fried I, Koch C (2008) Latency and selectivity of single neurons indicate hierarchical processing in the human medial temporal lobe. J Neurosci 28(36):8865–8872CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Harris KD, Henze DA, Csicsvari J, Hirase H, Buzsaki G (2000) Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements. J Neurophysiol 84(1):401–414PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2018

Authors and Affiliations

  • Juri Minxha
    • 1
    • 2
  • Adam N. Mamelak
    • 2
  • Ueli Rutishauser
    • 1
    • 2
    • 3
  1. 1.Computation and Neural Systems ProgramCalifornia Institute of TechnologyPasadenaUSA
  2. 2.Department of NeurosurgeryCedars-Sinai Medical CenterLos AngelesUSA
  3. 3.Department of NeurologyCedars-Sinai Medical CenterLos AngelesUSA

Personalised recommendations