Magnetoencephalography Responses to Unpredictable and Predictable Rare Somatosensory Stimuli in Healthy Adult Humans

Abstract

Mismatch brain responses to unpredicted rare stimuli are suggested to be a neural indicator of prediction error, but this has rarely been studied in the somatosensory modality. Here, we investigated how the brain responds to unpredictable and predictable rare events. Magnetoencephalography responses were measured in adults frequently presented with somatosensory stimuli (FRE) that were occasionally replaced by two consecutively presented rare stimuli [unpredictable rare stimulus (UR) and predictable rare stimulus (PR); p = 0.1 for each]. The FRE and PR were electrical stimulations administered to either the little finger or the forefinger in a counterbalanced manner between the two conditions. The UR was a simultaneous electrical stimulation to both the forefinger and the little finger (for a smaller subgroup, the UR and FRE were counterbalanced for the stimulus properties). The grand-averaged responses were characterized by two main components: one at 30–100 ms (M55) and the other at 130–230 ms (M150) latency. Source-level analysis was conducted for the primary somatosensory cortex (SI) and the secondary somatosensory cortex (SII). The M55 responses were larger for the UR and PR than for the FRE in both the SI and the SII areas and were larger for the UR than for the PR. For M150, both investigated areas showed increased activity for the UR and the PR compared to the FRE. Interestingly, although the UR was larger in stimulus energy (stimulation of two fingers at the same time) and had a larger prediction error potential than the PR, the M150 responses to these two rare stimuli did not differ in source strength in either the SI or the SII area. The results suggest that M55, but not M150, can possibly be associated with prediction error signals. These findings highlight the need for disentangling prediction error and rareness-related effects in future studies investigating prediction error signals.

Somatosensory Deviance Detection ERPs and Their Relationship to Analogous Auditory ERPs and Interoceptive Accuracy

“…In addition to location deviance sMMR (e.g., Akatsuka et al, 2007; Chen, Hämmerer, D’Ostilio, et al, 2014; Hautasaari et al, 2019; Kekoni et al, 1997; Naeije et al, 2016, 2018; Restuccia et al, 2007; Restuccia et al, 2009; Shen, Smyk, et al, 2018; Shen, Weiss, et al, 2018; Shinozaki et al, 1998; Strömmer et al, 2014; Strömmer et al, 2017; Tarkka et al, 2016; Xu et al, 2021), a somatosensory mismatch response has been demonstrated in an ignore condition by changes in various other stimulus properties, such as duration (Butler et al, 2011; Chen, Hämmerer, D’Ostilio, et al, 2014, Chen et al, 2018; Spackman et al, 2007; Spackman et al, 2010), vibratory frequency (Kekoni et al, 1997; Spackman et al, 2007), and a within-pair inter-stimulus interval of stimulus pairs (Akatsuka et al, 2005) that are not related to spatial location or the somatotopic organization of SI. Therefore, the present findings concerning the differences between MMR to somatosensory location deviance (existing) and intensity deviance (no significant response found) should not be explained only by the difference of how these stimulus features recruit the cortical somatotopy of fingers.…”

“…This is important because the physical features of the stimuli most likely significantly affect the responses. Quite surprisingly, most previous studies have not taken into account this either in the study design or in the analysis (see, however, Xu et al, 2021; Ostwald et al, 2012 for somatosensory deviance detection responses).…”

“…It can be speculated that one possible explanation for why the sMMR was elicited to location but not to intensity deviance conditions might be due to the somatotopic organization of the primary somatosensory cortex (SI), which means that adjacent locations on the skin activate adjacent neurons in the neural tissue (Penfield & Boldrey, 1937). The somatotopic organization of SI could have an impact, especially on location deviance sMMR, because it is proposed to be generated by the neural activity in the somatosensory cortices (e.g., Butler et al, 2011;Chen et al, 2010;Naeije et al, 2016Naeije et al, , 2018Spackman et al, 2010;Xu et al, 2021), and the somatotopy contributes to the neural processing of the spatial location of the somatosensory stimulus (Penfield & Boldrey, 1937). It seems that even if the intensity of the somatosensory stimulus is also encoded mostly in the SI, the somatosensory intensity encoding is not based on the same kind of representational map as somatotopic maps for somatosensory location encoding (e.g., Bensmaia, 2008;Muniak et al, 2007).…”

“…When the input does not match the prediction, an error occurs, and the error signal is projected upwards in the hierarchical neural network to update the predictive model (Friston, 2005). Sensory ERPs, especially the MMN, is suggested to reflect prediction error (auditory: e.g., Wacongne et al, 2011; Chennu et al, 2013; for reviews, see e.g., Carbajal & Malmierca, 2018; Denham & Winkler, 2020; visual: e.g., Nirenberg et al, 2010; for a review, see Stefanics et al, 2014; somatosensory: e.g., Naeije et al, 2018, Xu et al, 2021).…”