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Updated: Jul 6, 2026

Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach
Published on: February 14, 2014
Reassessing metrics: The limitations of brain function as indicators of brain health
Umema Zafar1, Syed Hamid Habib2, Muhammad Omar Malik3
1Umema Zafar, MBBS, MPhil, PhD. Associate Professor, Physiology Department, HBS Medical and Dental College, Islamabad, Pakistan.
Objective:
This study aimed to evaluate brain health using cognition, vision, hearing, emotional well-being, aging, and motor function as predictors, with blood neurofilament light (NfL), neuron-specific enolase (NSE), and phosphorylated tau (pTau) as response variables for detecting chronic brain injury.
Methodology:
This is a predictive cross-sectional modelling study carried out in various settings across Peshawar, Pakistan in which a total of 75 healthy adults were included from May 2022 to February 2023. Various tests were performed including visual fields, audiometry, dynamometry, Mini-Mental State Examination (MMSE), and Trait Emotional Intelligence Questionnaire (TEIQue-SF). Serum levels of NfL, pTau, and NSE were measured via ELISA and multiple linear regression models were developed. These biomarkers were selected based on a preliminary scoping review.
Results:
Statistically significant discrepancies were present between predicted and actual biomarker values (NfL: actual 0.026±0.011 ng/ml vs. predicted 0.001±0.000 ng/ml, p<0.001; NSE: actual 10.46±1.52 ng/ml vs. predicted 4.127±1.8 ng/ml, p<0.001; p Tau: actual 4.48±3.23 ng/ml vs. predicted 1.027±0.85 ng/ml, p<0.001), indicating poor prediction model performance. The k-fold cross-validation confirmed this, with over 2% deviation in all subsets.
Conclusion:
Brain health prediction models based solely on neuro-biomarkers and brain function parameters may not generalize well to healthy populations, indicating the need for additional factors influencing brain health.

