Work

Recognition of mild cognitive impairment in older adults using a polynomial regression model based on prefrontal cortex hemoglobin oxygenation

Year: 2024

Type: article

Abstract: AIM: This study employed a three-minute game-based intelligence test (GBIT) to create a hemoglobin polynomial regression model for early identification of mild cognitive impairment (MCI) in older adults. METHODS: Hb) and deoxyhemoglobin (HHb). Data sequences underwent a fitting procedure using a transformed cubic polynomial function. The transformed coefficients were used as predictors of a logistic regression model to recognize MCI in older adults. RESULTS: This study confirmed the relationship between age and cognitive performance. The findings demonstrate that the NIRS cubic polynomial function trends during the GBIT test showed significant changes in older adults, increasing with age. Logistic regression analysis identified age and the orientation (coefficient a) of HHb as the main factors for recognizing MCI. The model achieved an overall precision of 83.33 % (sensitivity = 75.00 %; specificity = 84.68 %) with the formula: ln (Odds [MCI]) = -1.64 + 0.57 × HHb_a + 1.40 × age. CONCLUSIONS: NIRS hemoglobin response characteristics during GBIT may serve as an efficient indicator of MCI in older adults. These findings may advance the field of cognitive health evaluation, resulting in earlier detection of cognitive deterioration in older adults.

Source: Experimental Gerontology

DOI: 10.1016/j.exger.2024.112637

Authors: Mao Tso-Yen, Huang Chun-Feng, Lo Hong-Wa, Liu Ying-Fang, Hsu Wei-Hsun, Hwang Shinn-Jang

Institutions: Chaoyang University of Technology, National Yang Ming Chiao Tung University, En Chu Kong Hospital, Hsin Sheng College of Medical Care and Management

Language: English

Topic: Dementia and Cognitive Impairment Research

Subfield: Psychiatry and Mental health

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,990