in the left panel. The result can be thresholded according to intensity (i.e. the t-score). For example, only the voxels with a t-score >6 are preserved after thresholding, as shown in the middle panel. The result can be thresholded according to the size of a cluster of voxels. For example, only the clusters with a size larger than 100 voxels will be preserved after thresholding, as shown in the right panel.
Experimental design of functional neuroimaging research. (a) Under the assumption of pure insertion, the difference of brain activation between two experimental conditions only reflects the mental function contrasted by the conditions (e.g. perception of pain intensity). However, the contrast may be associated with more than one mental function (e.g. perception of pain intensity and attention to noxious stimuli). (b) A factorial design helps to delineate the association between two mental functions. For example, the light grey area denotes the effect of increased pain on brain activation, and the dark grey area denotes the effect of increased attention. (c) A conjunction design focuses on the pattern of brain activation common to two experimental conditions (e.g. a clenching task and a chewing task). The activation may reflect the brain mechanisms of a mental function common to both task conditions.
Conceptual differences between functional specialization, functional segregation and functional integration. (a) Functional specialization highlights the association between a brain region and a specific mental function. For example, activation at the occipital lobe is considered mainly for the processing of visual perception. (b) Functional segregation highlights that a mental function is associated with multiple brain regions that are functionally connected within a module. For example, visual cognition is associated with the module consisting of the yellow regions, and motor control is associated with the module consisting of the blue regions. (c) Functional integration highlights the pattern of global communication between multiple brain regions. For example, individual variability in mental functions may be associated with the efficiency of how information is distributed in a network.
Analysis of resting-state functional connectivity. (a) The spontaneous blood-oxygen-level-dependent (BOLD) activity is acquired using resting-state functional magnetic resonance imaging. Subjects fix their eyesight on a crosshair without additional external stimuli. (b) Brain images are segmented into multiple regions according to a brain atlas. (c) To each brain region, the mean BOLD time series is extracted by averaging the time series from all the voxels within a region. (d) Association between the regional time series is quantified with correlation coefficients. (e) The correlation coefficient represents the strength of the connection between each pair of brain regions. (f) In the seed-based approach, a brain region of interest (i.e. the ‘seed’ region) is pre-selected. Functional connectivity is calculated between the seed region and all the other voxels to explore the brain regions that have a strong connection with the seed region.
Analysis of structural connectivity. (a) In deterministic tractography, each voxel is assigned with a single direction, which reflects the principal direction of diffusivity. A continuous streamline is formed by tracking the direction of voxels. (b) Probabilistic tractography assumes that there exists an uncertainty of the direction within each voxel. In the right panel, the probabilistic distribution of the directions is estimated for each voxel. A higher probability of a ‘leftward’ direction can be identified. In the right panel, the intensity of a voxel represents the frequency that a streamline passes that voxel. For example, more streamlines pass the yellow voxel (here four out of seven streamlines) compared to the red voxel (here one out of seven streamlines). (c) Structural covariance quantifies the strength of association of a brain feature between different brain regions across subjects. For example, the cortical thickness of six brain regions is assessed for eight subjects. The right panel reveals the association between regions 2 and 6, as quantified by the correlation coefficient of the cortical thickness between the regions.
Graph-based analysis of brain connectivity. (a) The pattern of functional and structural connections between brain regions can be translated from the ‘brain space’ to the ‘network space’ with applications of graph theory. In a graph, the nodes represent brain regions and the links represent the functional and structural connectivity between regions, which can be quantified by the correlation coefficient between blood-oxygen-level-dependent (BOLD) time series and the streamlines identified by tractography, respectively. (b) In the network analysis, the global metrics quantify the degree of integration of a network. For example, characteristic path length can be calculated by finding the shortest path length between a pair of nodes, such as the path A–B–D (but not A–B–E–D) between the nodes A and D. (c) The local metrics quantify the degree of segregation of a network. For example, the clustering coefficient is used to quantify the fraction of the triangular architecture in the whole network (e.g. A–B–C and E–G–H), which represents a pattern of clustered nodes. Notably, a small-world network offers a balance between the efficiency of global and local communication. A highly regular network (i.e. the middle-right panel) and a highly random network (i.e. the middle-left panel) may suffer from a lower global and local efficiency, respectively.
3.1
Methods of the assessment of oral cutting ability. (a) The sieving method quantifies the proportion of the chewed food (e.g. peanuts) with different particle sizes, using multiple sieves with different pore sizes (e.g. from the diameter of 355–3500 µm). The total weight of food particles that pass through a sieve is plotted against the pore size of the sieve. A smaller median particle size (e.g. the grey curve) represents better performance in cutting. Source: Chia-Shu Lin. (b) A test gummy jelly is customized with a standardized size and shape. The chewed fragments are collected and photographed. Colour and morphological features (e.g. the area and perimeter) of each fragment, which reflect individual cutting ability, are assessed by analyzing the image. Source: Salazar et al. (2020). Reproduced with permission of Elsevier.
3.2
Methods of the assessment of oral mixing ability. (a) In the two-colour chewing gum test, the degree of mixing food can be assessed by the colour hue of chewing gum with different colours. For example, if a piece of red and a piece of yellow gums are well mixed, the resulting bolus would in orange homogenously. The hue of the bolus can be quantified by imaging analysis. A smaller standard deviation of hue represents a greater homogeneity of colour mixing, i.e. a better mixing ability. (b) The degree of mixing is assessed according to the pattern of spatial clusters. A piece of juice chew with red and white portions was chewed by a subject for 20 strokes and collected, as shown in the left panel. The degree of clustering is assessed based on the analysis of variogram, which reflects how fine the clusters of different colours are. A pattern with finer clusters (e.g. the case in the lower-right panel) reflects better mixing ability. Source: Lo et al. (2020). Reproduced with permission of John Wiley and Sons.
3.3
Experimental design of pain/somatosensory experience. (a) Blood-oxygen-level-dependent (BOLD) signals are recorded concurrently with discrete ratings of stimulation. In this design, noxious stimuli with high and low intensities are followed by a rating phase (‘?’), which requires subjects to rate the pain intensity they perceive for the stimuli. Brain activation associated with pain can be contrasted by the phases that subjects feel strong vs. mild pain, according to their ratings (i.e. the black bars). (b) BOLD signals are recorded concurrently with continuous ratings of spontaneous pain. Patients with chronic pain continuously
in the left panel. The result can be thresholded according to intensity (i.e. the t-score). For example, only the voxels with a t-score >6 are preserved after thresholding, as shown in the middle panel. The result can be thresholded according to the size of a cluster of voxels. For example, only the clusters with a size larger than 100 voxels will be preserved after thresholding, as shown in the right panel.
