Montreal Cognitive Assessment and the Clock Drawing Test to Identify MCI and Predict Dementia in Isolated REM Sleep Behavior Disorder

Near-infrared brain functional characteristics of mild cognitive impairment with sleep disorders

BACKGROUND

Mild cognitive impairment (MCI) has a high risk of progression to Alzheimer's disease. The disease is often accompanied by sleep disorders, and whether sleep disorders have an effect on brain function in patients with MCI is unclear.

AIM

To explore the near-infrared brain function characteristics of MCI with sleep disorders.

METHODS

A total of 120 patients with MCI (MCI group) and 50 healthy subjects (control group) were selected. All subjects underwent the functional near-infrared spectroscopy test. Collect baseline data, Mini-Mental State Examination, Montreal Cognitive Assessment scale, fatigue severity scale (FSS) score, sleep parameter, and oxyhemoglobin (Oxy-Hb) concentration and peak time of functional near-infrared spectroscopy test during the task period. The relationship between Oxy-Hb concentration and related indexes was analyzed by Pearson or Spearmen correlation.

RESULTS

Compared with the control group, the FSS score of the MCI group was higher (t = 11.310), and the scores of Pittsburgh sleep quality index, sleep time, sleep efficiency, nocturnal sleep disturbance, and daytime dysfunction were higher (Z = -10.518, -10.368, -9.035, -10.661, -10.088). Subjective sleep quality and total sleep time scores were lower (Z = -11.592, -9.924). The sleep efficiency of the MCI group was lower, and the awakening frequency, rem sleep latency period, total sleep time, and oxygen desaturation index were higher (t = 5.969, 5.829, 2.887, 3.003, 5.937). The Oxy-Hb concentration at T0, T1, and T2 in the MCI group was lower (t = 14.940, 11.280, 5.721), and the peak time was higher (t = 18.800, 13.350, 9.827). In MCI patients, the concentration of Oxy-Hb during T0 was negatively correlated with the scores of Pittsburgh sleep quality index, sleep time, total sleep time, and sleep efficiency (r = -0.611, -0.388, -0.563, -0.356). It was positively correlated with sleep efficiency and total sleep time (r = 0.754, 0.650), and negatively correlated with oxygen desaturation index (r = -0.561) and FSS score (r = -0.526). All comparisons were P < 0.05.

CONCLUSION

Patients with MCI and sleep disorders have lower near-infrared brain function than normal people, which is related to sleep quality. Clinically, a comprehensive assessment of the near-infrared brain function of patients should be carried out to guide targeted treatment and improve curative effect.

Exploring the link between tooth loss, cognitive function, and brain wellness in the context of healthy aging

AIMS

The aim of this study was to evaluate the utility of using MRI-derived tooth count, an indirect and nonspecific indicator of oral/periodontal health, and brain age gap (BAG), an MRI-based measure of premature brain aging, in predicting cognition in a population of otherwise healthy adults.

METHODS

This retrospective study utilized data from 329 participants from the University of South Carolina's Aging Brain Cohort Repository. Participants underwent neuropsychological testing including the Montreal Cognitive Assessment (MoCA), completed an oral/periodontal health questionnaire, and submitted to high-resolution structural MRI imaging. The study compared variability on cognitive scores (MoCA) accounted for by MRI-derived BAG, MRI-derived total tooth count, and self-reported oral/periodontal health.

RESULTS

We report a significant positive correlation between the total number of teeth and MoCA total scores after controlling for age, sex, and race, indicating a robust relationship between tooth count and cognition, r(208) = .233, p < .001. In a subsample of participants identified as being at risk for MCI (MoCA <= 25, N = 36) inclusion of MRI-based tooth count resulted in an R2 change of .192 (H0 = 0.138 → H1 = 0.330), F(1,31) = 8.86, p = .006. Notably, inclusion of BAG, a valid and reliable measure of overall brain health, did not significantly improve prediction of MoCA scores in similar linear regression models.

CONCLUSIONS

Our data support the idea that inclusion of MRI-based total tooth count may enhance the ability to predict clinically meaningful differences in cognitive abilities in healthy adults. This study contributes to the growing body of evidence linking oral/periodontal health with cognitive function.