Food Hardness Modulates Behavior, Cognition, and Brain Activation: A Systematic Review of Animal and Human Studies

Food hardness is one of the dietary features that may impact brain functions. We performed a systematic review to evaluate the effect of food hardness (hard food versus soft food diet) on behavior, cognition, and brain activation in animals and humans (PROSPERO ID: CRD42021254204). The search was conducted on 29 June 2022 using Medline (Ovid), Embase, and Web of Science databases. Data were extracted, tabulated by food hardness as an intervention, and summarized by qualitative synthesis. The SYRCLE and JBI tools were used to assess the risk of bias (RoB) of individual studies. Of the 5427 studies identified, 18 animal studies and 6 human studies met the inclusion criteria and were included. The RoB assessment indicated that 61% of animal studies had unclear risks, 11% had moderate risks, and 28% had low risks. All human studies were deemed to have a low risk of bias. The majority (48%) of the animal studies showed that a hard food diet improved behavioral task performance compared to soft food diets (8%). However, 44% of studies also showed no differential effects of food hardness on behavioral tests. It was also evident that certain regions of the brain were activated in response to changes in food hardness in humans, with a positive association between chewing hard food, cognition performance, and brain function. However, variations in the methodologies of the included studies hindered the meta-analysis execution. In conclusion, our findings highlight the beneficial effects of dietary food hardness on behavior, cognition, and brain function in both animals and humans, however, this effect may depend on several factors that require further understanding of the causality.

Brain activity and human unilateral chewing: an FMRI study

Brain mechanisms underlying mastication have been studied in non-human mammals but less so in humans. We used functional magnetic resonance imaging (fMRI) to evaluate brain activity in humans during gum chewing. Chewing was associated with activations in the cerebellum, motor cortex and caudate, cingulate, and brainstem. We also divided the 25-second chew-blocks into 5 segments of equal 5-second durations and evaluated activations within and between each of the 5 segments. This analysis revealed activation clusters unique to the initial segment, which may indicate brain regions involved with initiating chewing. Several clusters were uniquely activated during the last segment as well, which may represent brain regions involved with anticipatory or motor events associated with the end of the chew-block. In conclusion, this study provided evidence for specific brain areas associated with chewing in humans and demonstrated that brain activation patterns may dynamically change over the course of chewing sequences.

Reverse cycle chewing before and after orthodontic-surgical correction in class III patients

OBJECTIVE

The aim of this study was to investigate the prevalence of reverse-sequence chewing cycles in skeletal class III patients before and after orthodontic-surgical therapy to evaluate whether the occlusal and skeletal correction is followed by a functional improvement.

STUDY DESIGN

Twenty skeletal class III patients (11 males and 9 females, 22.7 ± 3.0 years old) were recruited for this study. All patients received orthodontic and surgical treatment. Chewing cycles were recorded with a kinesiograph before (T0) and after (T1) therapy.

RESULTS

A significant decrease in the number of reverse chewing cycles after surgical correction was exhibited in all recordings, when chewing either soft or hard boluses, on both the right and the left side.

CONCLUSIONS

Evaluation of the prevalence of reverse chewing cycles could be considered an indicator of functional adaptation after therapy and a method for the early detection of nonresponding patients who may require further consideration using a different approach.

Improving the sensitivity of EEG-fMRI studies of epileptic activity by modelling eye blinks, swallowing and other video-EEG detected physiological confounds

RATIONALE

To improve the sensitivity and specificity of simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) it is prudent to devise modelling strategies explaining the residual variance. The purpose of this study is to investigate the potential value of including additional regressors for physiological activities, derived from video-EEG, in the modelling of haemodynamic patterns linked to interictal epileptiform discharges (IEDs) using simultaneously recorded video-EEG-fMRI.

METHODS

Ten patients with IED (focal epilepsy: 6, idiopathic generalized epilepsy (IGE):4) were studied. BOLD-sensitive fMRI images were acquired on a 3T MRI scanner. 64-channel EEG was recorded using MR-compatible system. A custom made, dual-video-camera system synchronised with EEG was used to record video simultaneously. IEDs and physiological activities were identified and labelled on video-EEG using Brain Analyzer2. fMRI time-series data were pre-processed and analysed using SPM5 software. Two general linear models (GLM) were created; GLM1: IEDs were convolved with the canonical haemodynamic response function and its derivatives. Realignment parameters and pulse regressors were included in the design matrix as confounds, GLM2: GLM1 and additional regressors identified on video-EEG including: eye blinks, hand or foot movement, chewing and swallowing were also included in the design matrix. SPM [F] maps (p<0.05, corrected for family wise error and p<0.001, uncorrected) were generated for both models. We compared the resulting blood oxygen level dependent (BOLD) maps for cluster size, statistical significance and degree of concordance with the irritative zone.

RESULTS

BOLD changes relating to physiological activities were generally seen in expected brain areas. In patients with focal epilepsy, the extent and Z-score of the IED-related global maximum BOLD clusters increased in 4/6 patients and additional IED-related BOLD clusters were observed in 3/6 patients for GLM2. Also, the degree of concordance of IED-related maps with irritative zone improved for one patient for GLM2 and was unchanged for the other cases. In patients with IGE, the size and statistical significance for global maximum and other BOLD clusters increased in 2/4 patients. We conclude that the inclusion of additional regressors, derived from video based information, in the design matrix explains a greater amount of variance and can reveal additional IED-related BOLD clusters which may be part of the epileptic networks.