Screening tools for subjective cognitive decline and mild cognitive impairment based on task-state prefrontal functional connectivity: a functional near-infrared spectroscopy study

Abstract

Background

Subjective cognitive decline (SCD) and mild cognitive impairment (MCI) carry the risk of progression to dementia, and accurate screening methods for these conditions are urgently needed. Studies have suggested the potential ability of functional near-infrared spectroscopy (fNIRS) to identify MCI and SCD. The present fNIRS study aimed to develop an early screening method for SCD and MCI based on activated prefrontal functional connectivity (FC) during the performance of cognitive scales and subject-wise cross-validation via machine learning.

Methods

Activated prefrontal FC data measured by fNIRS were collected from 55 normal controls, 80 SCD patients, and 111 MCI patients. Differences in FC were analyzed among the groups, and FC strength and cognitive scale performance were extracted as features to build classification and predictive models through machine learning. Model performance was assessed based on accuracy, specificity, sensitivity, and area under the curve (AUC) with 95 % confidence interval (CI) values.

Results

Statistical analysis revealed a trend toward more impaired prefrontal FC with declining cognitive function. Prediction models were built by combining features of prefrontal FC and cognitive scale performance and applying machine learning models, The models showed generally satisfactory abilities to differentiate among the three groups, especially those employing linear discriminant analysis, logistic regression, and support vector machine. Accuracies of 92.0 % for MCI vs. NC, 80.0 % for MCI vs. SCD, and 76.1 % for SCD vs. NC were achieved, and the highest AUC values were 97.0 % (95 % CI: 94.6 %-99.3 %) for MCI vs. NC, 87.0 % (95 % CI: 81.5 %-92.5 %) for MCI vs. SCD, and 79.2 % (95 % CI: 71.0 %-87.3 %) for SCD vs. NC.

Conclusion

The developed screening method based on fNIRS and machine learning has the potential to predict early-stage cognitive impairment based on prefrontal FC data collected during cognitive scale-induced activation.