pain, which may increase spontaneously. (c) Brain features and ratings are recorded separately. In this design, the rating of pain or somatosensory experience is conducted outside a scanner. Association between the individual ratings (e.g. pain) and their brain features (e.g. grey matter volume of the insula), which are collected separately, can be investigated by correlational analysis.
4.1
An overview of brain regions associated with motor control. The figure only displays the relative position and size of the brain regions, not depicting the anatomical details.
4.2
Sensorimotor control of the basal ganglia and the cerebellum. (a) The basal ganglia consist of a direct and an indirect pathway of motor control. In both pathways, the striatum is activated by the cortex, which forms a loop of control with the thalamus (the grey arrow). In the direct pathway (the solid black arrow), the activation of the striatum inhibits the activity of the internal segment of the globus pallidus (GPi) and the substantia nigra (SNr), which further inhibits thalamic functions. Therefore, cortical activation is associated with an increased thalamic activity via the direct pathway. In the indirect pathway (the black dashed line), the activation of the striatum inhibits the activity of the external segment of the globus pallidus (GPe), which further inhibits the activity of the subthalamic nucleus (STN). Notably, the activity of the STN further activates GPi/SNr, which decreases thalamic activity. Therefore, cortical activation is associated with a decrease in thalamic activity via the indirect pathway. (b) The cerebellum plays a key role as an internal model of motor learning. A forward model predicts the sensory outcomes when motor commands are executed. It adjusts sensorimotor processing via feedback of the predicted sensory outcomes. An inverse model calculates the motor commands that would produce the sensory outcomes from desired actions. According to Wolpert et al. (2001), both models are crucial to motor control.
4.3
Experimental design for neuroimaging of the brain mechanisms of chewing. (a) The basic concept of study design. The study consists of multiple blocks of a chewing task and a baseline (no-chewing) block. (b) Variations of the study design. Different studies may differ in the number of blocks of tasks and the definition of the baseline block (e.g. resting or clenching the teeth). The variations lead to a different interpretation of imaging results.
4.4
Brain activation associated with chewing and clenching. Source: Lin (2018). Reproduced with permission of John Wiley and Sons.
4.5
Experimental design for neuroimaging of the brain mechanisms of swallowing. (a) Study design of the swallowing tasks, including the water swallowing task and the saliva swallowing task. (b) Examples of the study design for investigating brain mechanisms of swallowing. An overt swallowing task (with either water or saliva swallowing) is associated with the execution of swallowing movement. A covert swallowing task is associated with the motor planning of swallowing.
4.6
Brain activation associated with water (the upper panel) and saliva (the lower panel) swallowing. Source: Sörös et al. (2009). Reproduced with permission of John Wiley & Sons, Inc.
5.1
Processing of oral somatosensory information. Information from individual sensory modalities is transduced via peripheral receptors at the level of somatosensation and integrated to form a holistic perceptual experience (e.g. oral stereognosis) at the level of somatoperception. Information is further integrated, with knowledge and affective–motivational experience, to form a feeling of intraoral condition at the level of somatorepresentation.
5.2
Experimental methods of investigating oral mechanoreceptors. (a) Recording signals from periodontal mechanoreceptors. Source: Trulsson (2006). Reproduced with permission of John Wiley and Sons. (b) The food splitting task. Source: Grigoriadis et al. (2017) with permission of Springer Nature under the terms of the Creative Commons CC BY 4.0 License.
5.3
Brain activation associated with gustation at the insula. A consistent pattern of brain activation is identified in the insular cortex for studies focusing on the quality, intensity and affective value of taste stimuli, respectively. Source: Yeung et al. (2018). Reproduced with permission of Elsevier.
5.4
Basic concepts of perceptual processing. (a) Top-down processing highlights the neural processing of intrinsic (personal) factors, such as one’s goal planning, on the formation of perceptual experience. The bottom-up processing highlights the neural processing of extrinsic (environmental) factors, such as the physical features of stimuli, on the formation of perceptual experience. (b) In predictive coding, the sensory inputs that we receive from the real world are compared with our prediction of the sensory outcomes. A mismatch (i.e. ‘prediction error‘) occurs when our prediction does not fit the outcome we perceive. The prediction error is associated with attentional bias and learning. For example, we may pay more attention to an unexpected event compared to an expected one.
5.5
Experimental design of manipulating threat value associated with pain. (a) The presence of noxious stimuli is associated with visual cues, which predict low-intensity stimuli (i.e. the square) constantly or predict high-or low-intensity stimuli (i.e. the circle). The latter evokes higher anxiety related to pain due to the increased uncertainty (i.e. the stimulus intensity is less predictable). Moreover, the same low-intensity stimuli would be perceived as more painful in the high-uncertainty condition (i.e. the condition predicted by a circle). (b) The threat value of pain is associated with the severity of noxious stimuli. Subjects receive different instructions regarding the severity (e.g. may cause tissue damage or not) of noxious stimuli, which are delivered at different sites (grey and black). The detection threshold of pain, i.e. the intensity that subjects feel painful and non-painful for equal times, is determined. When subjects feel a stronger severity of the stimuli (i.e. feeling more threatened), they would report higher anxiety towards the stimuli. Moreover, the same stimuli (tuned at the detection threshold) would be perceived as painful more likely in the more threatening condition (i.e. the condition with more severity) compared to the condition they regard as less threatening.
6.1
Brain activation associated with pain processing. (a) The brain regions commonly reported in functional magnetic resonance imaging (fMRI) studies of noxious stimuli. The figure only displays the relative position and size of the brain regions, not depicting the anatomical details. Note that the insular cortex (bounded by a dashed line) is covered by the frontal, parietal and temporal operculum. (b) Metaanalytical findings of the brain activation of experimental orofacial pain in healthy subjects. Increased activation is consistently found in the posterior mid-cingulate cortex (pMCC), the PPC, the insula, the thalamus, the S1 and the S2. Decreased activation is consistently found in the primary motor cortex (M1) and the S1. Source: Ayoub et al. (2018). Reproduced with permission of Elsevier.
6.2
Functional networks associated with chronic pain. Source: Davis et al. (2017) with permission of Springer Nature under the terms of the Creative Commons CC BY 4.0 License.
6.3
Mechanisms of peripheral and central sensitization. (a) In the normal status, signals induced by noxious stimuli and non-noxious (e.g. tactile) stimuli are transduced via the pathway of nociceptive and tactile processing, respectively. (b) In peripheral sensitization, neurons would show increased responsiveness to noxious stimuli. For example, the inflammation at the
pain, which may increase spontaneously. (c) Brain features and ratings are recorded separately. In this design, the rating of pain or somatosensory experience is conducted outside a scanner. Association between the individual ratings (e.g. pain) and their brain features (e.g. grey matter volume of the insula), which are collected separately, can be investigated by correlational analysis.
