Taste Quality Decoding Parallels Taste Sensations

Abstract

In most species, the sense of taste is key in the distinction of potentially nutritious and harmful food constituents and thereby in the acceptance (or rejection) of food. Taste quality is encoded by specialized receptors on the tongue, which detect chemicals corresponding to each of the basic tastes (sweet, salty, sour, bitter, and savory [1]), before taste quality information is transmitted via segregated neuronal fibers [2], distributed coding across neuronal fibers [3], or dynamic firing patterns [4] to the gustatory cortex in the insula. In rodents, both hardwired coding by labeled lines [2] and flexible, learning-dependent representations [5] and broadly tuned neurons [6] seem to coexist. It is currently unknown how, when, and where taste quality representations are established in the cortex and whether these representations are used for perceptual decisions. Here, we show that neuronal response patterns allow to decode which of four tastants (salty, sweet, sour, and bitter) participants tasted in a given trial by using time-resolved multivariate pattern analyses of large-scale electrophysiological brain responses. The onset of this prediction coincided with the earliest taste-evoked responses originating from the insula and opercular cortices, indicating that quality is among the first attributes of a taste represented in the central gustatory system. These response patterns correlated with perceptual decisions of taste quality: tastes that participants discriminated less accurately also evoked less discriminated brain response patterns. The results therefore provide the first evidence for a link between taste-related decision-making and the predictive value of these brain response patterns.

Single-trial prediction of reaction time variability from MEG brain activity

“…For all of the time windows, we determined the significance by both a two-tailed t -test and a group-level permutation test (see Methods). For multiple comparison corrections, a time-cluster-based approach was applied in which a cluster of time points was significant after the correction only when it had more than five consecutively significant time points23 (see Supplementary Methods: Time-cluster-based approach for multiple comparison correction). The rows of red dots and green horizontal lines below each panel in Fig.…”

Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion

“…In another study, Douglas et al [25] stated that EEG data can outperform fMRI data in decoding the belief decision. Moreover, decoding the taste categories was recently performed by Crouzet et al [26] using EEG. The above mentioned studies suggest the potential of EEG for decoding brain activity.…”

“…This restriction may be overcome in the future with better machine for the collection of data. In case of EEG, this research area is still progressing and only few studies have been reported in past years [24, 26] which shows lack of research in this area. The same is stated by Agrawal et al, in an article “EEG and NIRS offer portable solutions but with their signal quality no substantial results for predicting brain activity have been reported” [49].…”

“…Functional MRI is widely used in research and is considered to be a better modality for brain decoding, as it can extract more information from specific regions of the brain compared to EEG. In EEG, the quality of data is considered to be vulnerable compared to the quality of fMRI data so it is difficult to extract more information from a specific brain region using EEG [26]. Hence EEG is not a popular modality to decode brain activity from specific brain regions.…”

“…Hence EEG is not a popular modality to decode brain activity from specific brain regions. Some new studies have used multivariate pattern analysis (MVPA) to decode data on brain activity which are acquired from the whole brain instead of from a particular brain region [11, 26, 50–52]. For decoding the brain activity patterns, MVPA is an emerging technique and has proven as a highly useful technique for decoding of different patterns of brain activity [11, 53, 54].…”