Edited by: P. Hemachandra Reddy, Texas Tech University, USA
Reviewed by: Koteswara Rao Valasani, The University of Kansas, USA; Ramesh Kandimalla, Emory University, USA
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Neuroimaging studies have consistently identified brain activation in the motor area and the cerebellum during chewing. In this study, we further investigated the structural and functional brain signature associated with masticatory performance, which is a widely used index for evaluating overall masticatory function in the elderly. Twenty-five healthy elderly participants underwent oral examinations, masticatory performance tests, and behavioral assessments, including the Cognitive Abilities Screening Instrument and the short-form Geriatric Depression Scale. Masticatory performance was assessed with the validated colorimetric method, using color-changeable chewing gum. T1-weighted structural magnetic resonance imaging (MRI) and resting-state function MRI were performed. We analyzed alterations in gray matter volume (GMV) using voxel-based morphometry and resting-state functional connectivity (rsFC) between brain regions using the seed-based method. The structural and functional MRI analyses revealed the following findings: (1) the GMV change in the premotor cortex was positively correlated with masticatory performance. (2) The rsFC between the cerebellum and the premotor cortex was positively correlated with masticatory performance. (3) The GMV changes in the dorsolateral prefrontal cortex (DLPFC), as well as the rsFC between the cerebellum and the DLPFC, were positively correlated with masticatory performance. The findings showed that in the premotor cortex, a reduction of GMV and rsFC would reflect declined masticatory performance. The positive correlation between DLPFC connectivity and masticatory performance implies that masticatory ability is associated with cognitive function in the elderly. Our findings highlighted the role of the central nervous system in masticatory performance and increased our understanding of the structural and functional brain signature underlying individual variations in masticatory performance in the elderly.
Masticatory performance is a widely used objective measure of the clinical ability for food comminution (The Academy of Prosthodontics,
Neuroimaging studies have shown that chewing is predominantly associated with brain activity of the motor area and the cerebellum (Onozuka et al.,
The current study aimed to investigate the structural and functional brain signature of masticatory performance in the healthy elderly population. Here, we hypothesized that the motor area and cerebellum, which are the core regions of the cortico-cerebellar network of motor control and mastication (Quintero et al.,
Twenty-seven elderly participants, who were 55 years of age or older and able to communicate with the experimenters, were recruited. The following exclusion criteria were applied: (1) a history of major physical or psychiatric disorders, including epilepsy, major depression, schizophrenia, or neurovascular diseases, (2) a history of brain injury or brain surgery, and (3) the inability to undergo an MRI due to physical or psychological contraindications. The participants were recruited via advertisements at local community centers and at the Taipei Veterans General Hospital. The study was approved by the Institutional Review Board of the National Yang Ming University and the Taipei Veterans General Hospital, Taiwan. Written informed consent was provided by all participants before the start of the experiment. One participant was excluded due to a failure of imaging acquisition, and one participant was excluded due to severe deterioration in masticatory performance (see
Mean | SD | Max | Min | |||
---|---|---|---|---|---|---|
Gender | Female | 17 | ||||
Male | 8 | |||||
Age |
64.2 | 6.3 | 74 | 55 | ||
Education | University/college | 7 | ||||
Professional school | 3 | |||||
High school | 10 | |||||
Elementary school | 5 | |||||
CASI |
95.3 | 4.7 | 100 | 84 | ||
GDS | 1.6 | 1.8 | 6 | 0 | ||
Prosthesis type | No prosthesis | 7 | ||||
Removable denture | 5 | |||||
Fixed prosthesis | 13 | |||||
Eichner Index | A | 15 | ||||
B | 8 | |||||
C | 2 | |||||
MPI |
70.3 | 3.3 | 75.6 | 63.5 |
Before the MRI scan, we used the Cognitive Abilities Screening Instrument (CASI) (Teng et al.,
Here, we used the colorimetric method based on color-changeable chewing gum (Masticatory Performance Evaluating Gum XYLITOL, Lotte Co. Ltd., Tokyo, Japan) to assess the participants’ masticatory performance (Hayakawa et al.,
Here, 80.1 ± 0.7, −21.0 ± 1.3, and 40.2 ± 0.9 were mean ± SD of the L*, a*, and b* values, respectively, obtained from five pieces of the gum before chewing. The ΔE value, which denotes the color change from before to after chewing, is used as the masticatory performance index (MPI) (Hama et al.,
Because the selection of regions is subjective, we performed two analyses to investigate the inter-rater reliability and the intra-rater test–retest reliability of the procedure. The analyses were performed using IBM SPSS Statistics 20. For the inter-rater reliability, two experimenters (Chia-Shu Lin and Hsien-Wei Ko) independently analyzed the MPI. Agreement of the two series of results was evaluated using the intra-class correlation coefficient (ICC) model, which assesses the absolute agreement of rating between two raters based on a two-factor mixed model. The analysis revealed high inter-rater reliability (averaged ICC = 0.978). For the test–retest reliability, one experimenter (Chia-Shu Lin) analyzed the MPI twice, with a break of 5 days between analyses. The test–retest reliability of the two series of results was evaluated using the ICC model, which assesses the consistency of rating between two time points based on a two-factor mixed model The analysis revealed high test–retest reliability (averaged ICC = 0.986).
The oral examinations were performed by a dentist (Chia-Shu Lin). For all participants, the Eichner Index (Eichner,
T1-weighted MRI and resting-state functional MRI data were acquired at the MRI Laboratory of National Yang-Ming University using a 3-Tesla Siemens MRI scanner (Siemens Magnetom Tim Trio, Erlangen, Germany). The total scan time was approximately 11.5 min. For all 25 participants, high-resolution T1 structural images were acquired in the sagittal plane using a high-resolution sequence [(TR) = 2530 ms, (TE) = 3.02 ms, matrix size = 256 × 256 × 192, voxel size = 1 mm × 1 mm × 1 mm]. Because of busy personal schedules, five participants only received the T1 scan. Twenty participants also underwent a resting-state functional MRI (fMRI) scan using a gradient echo EPI (Echo Planar Imaging) T2* weighted sequence [(TR) = 2000 ms, (TE) = 20 ms, matrix size = 64 × 64 × 40, voxel size = 3.4 mm × 3.4 mm × 3.4 mm, 183 volumes in total]. During scanning, the participants were instructed to relax, remain awake, and keep their eyes open and fixed on a cross symbol on the screen.
We applied voxel-based morphometry (VBM) to quantify GMV for individual participants, using the DARTEL (Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra) package (Ashburner,
Pre-processing of the resting-state fMRI data was performed using the Data Processing Assistant for Resting-State fMRI (Chao-Gan and Yu-Feng,
The pre-processed GM images were analyzed using general linear models (GLMs) to assess the association between GMV and the MPI for each brain voxel. The MPI, age, gender, and total brain volume (TBV) were included as the covariates of the model. The last three covariates were considered as the nuisance factors that may influence individual GMV (Barnes et al.,
We first restricted the VBM analyses to the regions of interest (ROIs) in the motor area and cerebellum, with small volume correction. The ROIs were defined according to a previous study on the age-related brain mechanisms of mastication and aging (Onozuka et al.,
Regions of interest | MNI coordinates |
||
---|---|---|---|
Sensorimotor cortex | 2 | −13 | 76 |
Supplementary motor area | 62 | −3 | 14 |
Cerebellum | 24 | −64 | −30 |
Brain region |
Side | Cluster size (voxels) | MNI coordinates |
||||
---|---|---|---|---|---|---|---|
Premotor cortex | R | 81 | 4.0 | <0.001 | 24 | −7 | 66 |
Dorsolateral prefrontal cortex | L | 37 | 3.9 | <0.001 | −24 | 21 | 51 |
Cuneus | R | 36 | 3.7 | <0.001 | 8 | −75 | 23 |
Insula | L | 27 | 3.7 | <0.001 | −45 | −1 | 9 |
Inferior temporal gyrus | R | 41 | 3.6 | <0.001 | 35 | −1 | −44 |
Precentral gyrus | R | 27 | 3.3 | <0.001 | 47 | 0 | 48 |
The VBM analyses revealed that GMV of the premotor cortex and the precentral gyrus was positively correlated with the MPI (Tables
Additionally, we found that GMV of the dorsolateral prefrontal cortex (DLPFC) was positively correlated with the MPI. Due to its role in cognitive processing and motor control (Rowe et al.,
The demographic, behavioral, and clinical profiles of the study group are shown in Table
Tables
Table
Brain region |
Side | Cluster size (voxels) | MNI coordinates |
||||
---|---|---|---|---|---|---|---|
Middle temporal gyrus | R | 93 | 4.6 | <0.001 | 64 | −30 | −14 |
Cerebellum crus I | R | 116 | 4.0 | <0.001 | 42 | −60 | −42 |
Superior parietal lobe | L | 23 | 3.6 | <0.001 | −12 | −68 | 64 |
Lateral occipital lobe | R | 11 | 3.5 | <0.001 | 36 | −82 | −14 |
Cerebellum crus I | L | 36 | 3.5 | <0.001 | −34 | −64 | −38 |
Cerebellum lobule VIIb | L | 8 | 3.4 | <0.001 | −42 | −60 | −56 |
Cerebellum lobule VIIb | R | 5 | 3.4 | <0.001 | 16 | −74 | −54 |
Cerebellum crus I | L | 81 | 4.4 | <0.001 | −38 | −82 | −32 |
Cerebellum crus II | L | 29 | 3.9 | <0.001 | −46 | −78 | −44 |
Cerebellum lobule VI | L | 43 | 3.9 | <0.001 | −22 | −60 | −16 |
Frontal pole | R | 22 | 3.8 | <0.001 | 12 | 50 | 50 |
Cerebellum lobule VIIbs | R | 7 | 3.7 | <0.001 | 10 | −80 | −52 |
Inferior temporal gyrus | L | 14 | 3.5 | <0.001 | −42 | −10 | −36 |
Orbitofrontal cortex | L | 11 | 3.5 | <0.001 | −14 | 6 | −22 |
Fusiform gyrus | L | 17 | 3.4 | <0.001 | −22 | −78 | −16 |
Visual cortex | L | 5 | 3.4 | <0.001 | −46 | −80 | 4 |
Temporal pole | L | 5 | 3.3 | <0.001 | −26 | 12 | −36 |
Previous studies have identified brain activation in the cerebellum and the motor cortex when participants are chewing (Onozuka et al., Change in GMV in the premotor cortex and the precentral gyrus is positively correlated with masticatory performance (Tables Change in rsFC between the cerebellum and the premotor cortex is positively correlated with masticatory performance (Table Gray matter volume changes in the DLPFC, as well as the rsFC between the cerebellum and the DLPFC, were positively correlated with masticatory performance (Table
The VBM analyses revealed that GMV of the premotor cortex and precentral gyrus (close to the premotor cortex) is positively associated with masticatory performance (Tables
In contrast, we did not find significant results in the M1. The M1 initiates and maintains voluntary movement, and therefore, its activation has been observed during chewing (Onozuka et al.,
The SBFC analyses revealed that the connectivity between the cerebellum and the premotor cortex were positively correlated with masticatory performance (Table
While we found that the cerebellum-motor rsFC was correlated with masticatory performance, the cerebellar GMV
The relationship between mastication and cognitive change has been a topic of much debate [for a review see (Weijenberg et al.,
To our knowledge, this is the first study to investigate the structural and functional brain signature related to masticatory performance in the elderly. Due to the sampling and experimental methods, our findings should be considered with the following limitations:
We focused on elderly individuals who were physically and mentally healthy. Therefore, the correlation between the GMV change and masticatory performance is limited to healthy aging. The interplay between aging and pathological factors related to motor control (e.g., frailty) remains unclear. We used masticatory performance as a clinical index for the overall ability to masticate. It should be noted that masticatory performance can be influenced by many factors, including saliva flow rate, occlusal force, and oral stereognosis (Ikebe et al., The current study only performed correlation analyses for investigating different brain regions’ GMVs and connectivity associated with masticatory performance. The statistically significant correlation cannot be inferred to be a cause-effect link. For example, it is uncertain whether the reduced cerebellar GMV leads to reduced masticatory function or vice versa. A longitudinal study that evaluates an interventional effect (e.g., denture wearing) on masticatory performance would be helpful to clarify the cause-effect link.
Clinically, our findings suggest that in addition to oral conditions (e.g., the number of functional teeth), age-related changes in the central nervous system also contribute to masticatory performance. Therefore, regarding prosthodontic treatment, special care is needed for elderly patients who show signs of cortico-cerebellar deficits. Clinically, our findings can be explained in two ways. On the one hand, because the premotor cortex is critical to motor control, a greater degree of premotor GMV suggests that the individual has a greater capability to control mastication. On the other hand, from the perspective of brain plasticity, the variation in GMV may be associated with the degrees of neurogenesis and synaptogenesis, which are reshaped by long-term skill learning (Zatorre et al.,
C-SL, S-YW, and C-YW conceived and designed the research. C-SL (predominantly) and H-WK executed the experiment. C-SL (predominantly) and H-WK analyzed the data. C-SL (predominantly), S-YW, and C-YW wrote the paper. C-SL, S-YW, C-YW, and H-WK finalized the paper.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
C-SL was funded by Ministry of Science and Technology of Taiwan (103-2314-B-010-025-MY3). This work was supported in part by the 3T MRI Core Facility at National Yang-Ming University.