Delta activity encodes taste information in the human brain

Neuroimage. 2018 Nov 1:181:471-479. doi: 10.1016/j.neuroimage.2018.07.034. Epub 2018 Jul 20.

Abstract

The categorization of food via sensing nutrients or toxins is crucial to the survival of any organism. On ingestion, rapid responses within the gustatory system are required to identify the oral stimulus to guide immediate behavior (swallowing or expulsion). The way in which the human brain accomplishes this task has so far remained unclear. Using multivariate analysis of 64-channel scalp EEG recordings obtained from 16 volunteers during tasting salty, sweet, sour, or bitter solutions, we found that activity in the delta-frequency range (1-4 Hz; delta power and phase) has information about taste identity in the human brain, with discriminable response patterns at the single-trial level within 130 ms of tasting. Importantly, the latencies of these response patterns predicted the point in time at which participants indicated detection of a taste by pressing a button. Furthermore, taste pattern discrimination was independent of motor-related activation and encoded taste identity rather than other taste features such as intensity and valence. On comparison with our previous findings from a delayed taste-discrimination task (Crouzet et al., 2015), taste-specific neural representations emerged earlier during this speeded taste-detection task, suggesting a goal-dependent flexibility in gustatory response coding. Together, these findings provide the first evidence of a role of delta activity in taste-information coding in humans. Crucially, these neuronal response patterns can be linked to the speed of simple gustatory perceptual decisions - a vital performance index of nutrient sensing.

Keywords: Delta; EEG; Gustation; MVPA; Taste quality.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Cerebral Cortex / physiology*
  • Delta Rhythm / physiology*
  • Discrimination, Psychological / physiology*
  • Electroencephalography / methods*
  • Female
  • Humans
  • Male
  • Pattern Recognition, Automated*
  • Support Vector Machine*
  • Taste Perception / physiology*
  • Time Factors
  • Young Adult