Single-neuron representations of odours in the human brain – Nature
Gottfried, J. A. Central mechanisms of odour object perception. Nat. Rev. Neurosci. 11, 628–641 (2010).
Google Scholar
McGann, J. P. Poor human olfaction is a 19th-century myth. Science 356, eaam7263 (2017).
Google Scholar
Buck, L. & Axel, R. A novel multigene family may encode odorant receptors: a molecular basis for odor recognition. Cell 65, 175–187 (1991).
Google Scholar
Murthy, V. N. Olfactory maps in the brain. Annu. Rev. Neurosci. 34, 233–258 (2011).
Google Scholar
Wilson, D. A., Chapuis, J. & Sullivan, R. M. in Handbook of Olfaction and Gustation Ch. 10 (ed. Doty, R.) 209–224 (John Wiley & Sons, 2015); https://doi.org/10.1002/9781118971758.ch10.
Gretenkord, S. et al. Coordinated electrical activity in the olfactory bulb gates the oscillatory entrainment of entorhinal networks in neonatal mice. PLOS Biol. 17, e2006994 (2019).
Google Scholar
Sosulski, D. L., Bloom, M. L., Cutforth, T., Axel, R. & Datta, S. R. Distinct representations of olfactory information in different cortical centres. Nature 472, 213–216 (2011).
Google Scholar
Echevarria-Cooper, S. L. et al. Mapping the microstructure and striae of the human olfactory tract with diffusion MRI. J. Neurosci. 42, 58–68 (2022).
Google Scholar
Bekkers, J. M. & Suzuki, N. Neurons and circuits for odor processing in the piriform cortex. Trends Neurosci. 36, 429–438 (2013).
Google Scholar
Stettler, D. D. & Axel, R. Representations of odor in the piriform cortex. Neuron 63, 854–864 (2009).
Google Scholar
Howard, J. D., Plailly, J., Grueschow, M., Haynes, J.-D. & Gottfried, J. A. Odor quality coding and categorization in human posterior piriform cortex. Nat. Neurosci. 12, 932–938 (2009).
Google Scholar
Gottfried, J. A., Winston, J. S. & Dolan, R. J. Dissociable codes of odor quality and odorant structure in human piriform cortex. Neuron 49, 467–479 (2006).
Google Scholar
Poellinger, A. et al. Activation and habituation in olfaction—an fMRI study. NeuroImage 13, 547–560 (2001).
Google Scholar
Jiang, H. et al. Theta oscillations rapidly convey odor-specific content in human piriform cortex. Neuron 94, 207–219 (2017).
Google Scholar
Poo, C., Agarwal, G., Bonacchi, N. & Mainen, Z. F. Spatial maps in piriform cortex during olfactory navigation. Nature 601, 595–599 (2022).
Google Scholar
Poo, C. & Isaacson, J. S. Odor representations in olfactory cortex: “sparse” coding, global inhibition, and oscillations. Neuron 62, 850–861 (2009).
Google Scholar
Zhan, C. & Luo, M. Diverse patterns of odor representation by neurons in the anterior piriform cortex of awake mice. J. Neurosci. 30, 16662–16672 (2010).
Google Scholar
Roland, B., Deneux, T., Franks, K. M., Bathellier, B. & Fleischmann, A. Odor identity coding by distributed ensembles of neurons in the mouse olfactory cortex. eLife 6, e26337 (2017).
Google Scholar
Blazing, R. M. & Franks, K. M. Odor coding in piriform cortex: mechanistic insights into distributed coding. Curr. Opin. Neurobiol. 64, 96–102 (2020).
Google Scholar
Pashkovski, S. L. et al. Structure and flexibility in cortical representations of odour space. Nature 583, 253–258 (2020).
Google Scholar
Haddad, R. et al. Olfactory cortical neurons read out a relative time code in the olfactory bulb. Nat. Neurosci. 16, 949–957 (2013).
Google Scholar
Xu, W. & Wilson, D. A. Odor-evoked activity in the mouse lateral entorhinal cortex. Neuroscience 223, 12–20 (2012).
Google Scholar
Cain, D. P. & Bindra, D. Responses of amygdala single units to odors in the rat. Exp. Neurol. 35, 98–110 (1972).
Google Scholar
Kupers, R. et al. Neural correlates of olfactory processing in congenital blindness. Neuropsychologia 49, 2037–2044 (2011).
Google Scholar
Kjelvik, G., Evensmoen, H. R., Brezova, V. & Håberg, A. K. The human brain representation of odor identification. J. Neurophysiol. 108, 645–657 (2012).
Google Scholar
Bitterman, Y., Mukamel, R., Malach, R., Fried, I. & Nelken, I. Ultra-fine frequency tuning revealed in single neurons of human auditory cortex. Nature 451, 197–201 (2008).
Google Scholar
Reber, T. P. et al. Representation of abstract semantic knowledge in populations of human single neurons in the medial temporal lobe. PLOS Biol. 17, e3000290 (2019).
Google Scholar
Rutishauser, U., Reddy, L., Mormann, F. & Sarnthein, J. The architecture of human memory: insights from human single-neuron recordings. J. Neurosci. 41, 883–890 (2021).
Google Scholar
Halgren, E., Babb, T. L., Rausch, R. & Crandall, P. H. Neurons in the human basolateral amygdala and hippocampal formation do not respond to odors. Neurosci. Lett. 4, 331–335 (1977).
Google Scholar
Fontanini, A., Spano, P. & Bower, J. M. Ketamine-xylazine-induced slow (J. Neurosci. 23, 7993–8001 (2003).
Google Scholar
Sobel, N. et al. Sniffing and smelling: separate subsystems in the human olfactory cortex. Nature 392, 282–286 (1998).
Google Scholar
Zelano, C. et al. Nasal respiration entrains human limbic oscillations and modulates cognitive function. J. Neurosci. 36, 12448–12467 (2016).
Google Scholar
Meyers, E. The neural decoding toolbox. Front. Neuroinformatics 7, 8 (2013).
Vinje, W. E. & Gallant, J. L. Sparse coding and decorrelation in primary visual cortex during natural vision. Science 287, 1273–1276 (2000).
Google Scholar
Pedreira, C., Martinez, J., Ison, M. J. & Quian Quiroga, R. How many neurons can we see with current spike sorting algorithms? J. Neurosci. Methods 211, 58–65 (2012).
Google Scholar
Lindquist, K. A., Satpute, A. B., Wager, T. D., Weber, J. & Barrett, L. F. The brain basis of positive and negative affect: evidence from a meta-analysis of the human neuroimaging literature. Cereb. Cortex 26, 1910–1922 (2016).
Google Scholar
Winston, J. S., Gottfried, J. A., Kilner, J. M. & Dolan, R. J. Integrated neural representations of odor intensity and affective valence in human amygdala. J. Neurosci. 25, 8903–8907 (2005).
Google Scholar
Pignatelli, M. & Beyeler, A. Valence coding in amygdala circuits. Curr. Opin. Behav. Sci. 26, 97–106 (2019).
Google Scholar
Toet, A. et al. The relation between valence and arousal in subjective odor experience. Chemosens. Percept. 13, 141–151 (2020).
Google Scholar
Eichenbaum, H., Morton, T. H., Potter, H. & Corkin, S. Selective olfactory deficits in case H.M. Brain 106, 459–472 (1983).
Google Scholar
Quiroga, R. Q., Reddy, L., Kreiman, G., Koch, C. & Fried, I. Invariant visual representation by single neurons in the human brain. Nature 435, 1102–1107 (2005).
Google Scholar
Quiroga, R. Q., Kraskov, A., Koch, C. & Fried, I. Explicit encoding of multimodal percepts by single neurons in the human brain. Curr. Biol. 19, 1308–1313 (2009).
Google Scholar
Mignot, C., Schunke, A., Sinding, C. & Hummel, T. Olfactory adaptation: recordings from the human olfactory epithelium. Eur. Arch. Otorhinolaryngol. 279, 3503–3510 (2022).
Google Scholar
Wilson, D. A. Habituation of odor responses in the rat anterior piriform cortex. J. Neurophysiol. 79, 1425–1440 (1998).
Google Scholar
Sobel, N. et al. Time course of odorant-induced activation in the human primary olfactory cortex. J. Neurophysiol. 83, 537–551 (2000).
Google Scholar
Pedreira, C. et al. Responses of human medial temporal lobe neurons are modulated by stimulus repetition. J. Neurophysiol. 103, 97–107 (2010).
Google Scholar
Jacobson, G. A., Rupprecht, P. & Friedrich, R. W. Experience-dependent plasticity of odor representations in the telencephalon of zebrafish. Curr. Biol. 28, 1–14 (2018).
Google Scholar
Franks, K. M. et al. Recurrent circuitry dynamically shapes the activation of piriform cortex. Neuron 72, 49–56 (2011).
Google Scholar
Iravani, B., Arshamian, A., Ohla, K., Wilson, D. A. & Lundström, J. N. Non-invasive recording from the human olfactory bulb. Nat. Commun. 11, 648 (2020).
Google Scholar
Garavan, H., Pendergrass, J. C., Ross, T. J., Stein, E. A. & Risinger, R. C. Amygdala response to both positively and negatively valenced stimuli. NeuroReport 12, 2779 (2001).
Google Scholar
Jin, J., Zelano, C., Gottfried, J. A. & Mohanty, A. Human amygdala represents the complete spectrum of subjective valence. J. Neurosci. 35, 15145–15156 (2015).
Google Scholar
Anderson, A. K. et al. Dissociated neural representations of intensity and valence in human olfaction. Nat. Neurosci. 6, 196–202 (2003).
Google Scholar
Doty, R. L. Olfactory dysfunction in neurodegenerative diseases: is there a common pathological substrate? Lancet Neurol. 16, 478–488 (2017).
Google Scholar
Poo, C. & Isaacson, J. S. A major role for intracortical circuits in the strength and tuning of odor-evoked excitation in olfactory cortex. Neuron 72, 41–48 (2011).
Google Scholar
Mandairon, N. et al. Context-driven activation of odor representations in the absence of olfactory stimuli in the olfactory bulb and piriform cortex. Front. Behav. Neurosci. 8, 138 (2014).
Google Scholar
Schulze, P., Bestgen, A.-K., Lech, R. K., Kuchinke, L. & Suchan, B. Preprocessing of emotional visual information in the human piriform cortex. Sci. Rep. 7, 9191 (2017).
Google Scholar
Djordjevic, J. et al. A rose by any other name: would it smell as sweet? J. Neurophysiol. 99, 386–393 (2008).
Google Scholar
Bensafi, M. et al. Olfactomotor activity during imagery mimics that during perception. Nat. Neurosci. 6, 1142–1144 (2003).
Google Scholar
Herz, R. S. Verbal coding in olfactory versus nonolfactory cognition. Mem. Cognit. 28, 957–964 (2000).
Google Scholar
Young, B. D. Olfactory imagery: is exactly what it smells like. Philos. Stud. 177, 3303–3327 (2020).
Google Scholar
Topalovic, U. et al. A wearable platform for closed-loop stimulation and recording of single-neuron and local field potential activity in freely moving humans. Nat. Neurosci. 26, 517–527 (2023).
Google Scholar
Tay, A. S.-M. S., Caravan, B. & Mamelak, A. N. in Intracranial EEG: A Guide for Cognitive Neuroscientists (ed. Axmacher, N.) 671–682 (Springer, 2023); https://doi.org/10.1007/978-3-031-20910-9_42.
Niediek, J., Boström, J., Elger, C. E. & Mormann, F. Reliable analysis of single-unit recordings from the human brain under noisy conditions: tracking neurons over hours. PLoS ONE 11, e0166598 (2016).
Google Scholar
Dehnen, G. et al. Duplicate detection of spike events: a relevant problem in human single-unit recordings. Brain Sci. 11, 761 (2021).
Google Scholar
Davis, T. S. et al. LeGUI: a fast and accurate graphical user interface for automated detection and anatomical localization of intracranial electrodes. Front. Neurosci. 15, 769872 (2021).
Google Scholar
Oostenveld, R., Fries, P., Maris, E. & Schoffelen, J.-M. FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput. Intell. Neurosci. 2011, 156869 (2011).
Google Scholar
Noto, T., Zhou, G., Schuele, S., Templer, J. & Zelano, C. Automated analysis of breathing waveforms using BreathMetrics: a respiratory signal processing toolbox. Chem. Senses 43, 583–597 (2018).
Google Scholar
Tiihonen, M., Jacobsen, T., Haumann, N. T., Saarikallio, S. & Brattico, E. I know what I like when I see it: likability is distinct from pleasantness since early stages of multimodal emotion evaluation. PLoS ONE 17, e0274556 (2022).
Google Scholar
Shuman, V., Sander, D. & Scherer, K. Levels of Valence. Front. Psychol. 4, (2013).
Brainard, D. H. The Psychophysics Toolbox. Spat. Vis. 10, 433–436 (1997).
Google Scholar
Pelli, D. G. The VideoToolbox software for visual psychophysics: transforming numbers into movies. Spat. Vis. 10, 437–442 (1997).
Google Scholar
Kleiner, M. et al. What’s new in psychtoolbox-3. Perception 36, 1–16 (2007).
Mormann, F. et al. Latency and selectivity of single neurons indicate hierarchical processing in the human medial temporal lobe. J. Neurosci. 28, 8865–8872 (2008).
Google Scholar
Reber, T. P. et al. Single-neuron mechanisms of neural adaptation in the human temporal lobe. Nat. Commun. 14, 2496 (2023).
Google Scholar
Rolls, E. T. & Tovee, M. J. Sparseness of the neuronal representation of stimuli in the primate temporal visual cortex. J. Neurophysiol. 73, 713–726 (1995).
Google Scholar
Treves, A. & Rolls, E. T. What determines the capacity of autoassociative memories in the brain? Netw. Comput. Neural Syst. 2, 371–397 (1991).
Google Scholar
Aarts, E., Verhage, M., Veenvliet, J. V., Dolan, C. V. & van der Sluis, S. A solution to dependency: using multilevel analysis to accommodate nested data. Nat. Neurosci. 17, 491–496 (2014).
Google Scholar
Barr, D. J., Levy, R., Scheepers, C. & Tily, H. J. Random effects structure for confirmatory hypothesis testing: keep it maximal. J. Mem. Lang. 68, 255–278 (2013).
Google Scholar