Correlation of muscle strength, information processing speed and cognitive function in the elderly with cognitive impairment--evidence from EEG

Abstract

Objective: This study investigates the interplay between muscle strength, information processing speed, EEG-specific biomarkers, and cognitive function in elderly individuals with cognitive impairments, emphasizing the mediating roles of information processing speed and EEG-specific biomarkers.

Method: A cross-sectional study design was employed to recruit 151 elderly participants. The participants underwent grip strength and 30-s sit-to-stand tests to assess muscle strength, completed the Trail Making Test part A (TMT-A) and the Symbol Digit Modality Test (SDMT) to evaluate information processing speed, and utilized the Montreal Cognitive Assessment (MOCA) to gauge cognitive function. Additionally, EEG signals were recorded for 5 min to capture neural activity.

Results: The difference in information processing speed among elderly individuals with varying degrees of cognitive impairment was statistically significant (p < 0.001). A significant negative correlation was observed between the MoCA score and the time consumption of TMT-A (r = -0.402, p < 0.01), and a significant positive correlation was found between the MoCA score and the SDMT score (r = 0.609, p < 0.01). Grip strength was negatively correlated with the time consumption of TMT-A (r = -0.336, p < 0.01) and positively correlated with the SDMT score (r = 0.336, p < 0.01). A significant negative correlation was found between the 30-s sit-to-stand test and the time consumption of TMT-A (r = -0.273, p < 0.01), and a significant positive correlation was observed between the 30-s sit-to-stand test and the SDMT score (r = 0.372, p < 0.01). Additionally, we observed that the α1 power value indicators were significantly correlated with the MoCA score, the time consumption of TMT-A, and the SDMT score (all p < 0.01). The α1 power values at F7 + F8 and T5 + T6 were identified as sensitive EEG indicators for muscle strength and information processing speed. The EEG-specific indicators (B = 0.019, 95% CI: 0.003, 0.047) and information processing speed (B = 0.137, 95% CI: 0.096, 0.292) were found to partially mediate the relationship between grip strength and MoCA scores, with information processing speed exerting a stronger mediating effect.

Conclusion: Specific patterns were observed in the EEG of elderly individuals with cognitive impairments, which could objectively assess the risk of cognitive decline in this population. Muscle strength, information processing speed, and EEG-specific biomarkers were closely associated with cognitive function in elderly individuals. The potential pathway of interaction-muscle strength → EEG-specific biomarkers → information processing speed → cognitive function-provides valuable insights into advancing the field of cognitive research in the elderly.

Keywords: EEG; cognitive impairment; information processing speed; muscle strength; the elderly.

Figure 1

Flowchart of participants recruitment.

Figure 2

Flowchart of test process.

Figure 3

Relationship between information processing speed and MoCA score in the elderly with cognitive impairment (A1,A2); Relationship between muscle strength and information processing speed in the elderly with cognitive impairment (B1–B4).

Figure 4

The correlation between EEG indicators and MoCA, TMT-A, and SDMT. Subkey01, MoCA; Subkey02, TMT-A; Subkey03, SDMT.

Figure 5

Potential pathways for muscle strength, information processing speed, and EEG-specific indicators.

Figure 6

Pathway construction of muscle strength, information processing speed, EEG-specific indicators, and cognitive function; (A) Mediation Model 1 of grip strength, information processing speed, and cognitive function; (B) Mediation Model 2 of 30-second sit-to-stand, information processing speed, and cognitive function; (C) Structural equation Model 3 with EEG-specific indicators and information processing speed as serial mediators.