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This research aims to evaluate the effectiveness of frontal brain entropy metrics derived from fNIRS as predictors of brain age in major depressive disorder.

May 6, 2026Open Access

Frontal Cortex Entropy Derived From Resting‐State fNIRS for Brain Age Prediction in Major Depressive Disorder

Key Points

This research aims to evaluate the effectiveness of frontal brain entropy metrics derived from fNIRS as predictors of brain age in major depressive disorder.
Resting-state fNIRS data were collected from the frontal cortex of 49 healthy controls and 35 patients with major depressive disorder.
Static and dynamic brain entropy metrics were calculated using permutation entropy from time-series signals of hemoglobin.
Support vector regression was utilized to assess brain age predictions based on these brain entropy features.
Brain age prediction models achieved outstanding performance, with some combinations reaching an area under the ROC curve of 1.00.
Both static and dynamic brain entropy metrics effectively differentiated between major depressive disorder and healthy controls.
Frontal brain entropy metrics indicate a potential connection to accelerated brain aging in patients with major depressive disorder.

Abstract

Objective: This study investigated the potential of frontal brain entropy (BEN) metrics derived from resting-state functional near-infrared spectroscopy (rs-fNIRS) as neurophysiological biomarkers for predicting brain age in patients with major depressive disorder (MDD). Methods: Rs-fNIRS data (26 channels) were acquired from the frontal cortex of 49 healthy controls (HCs) and 35 MDD patients. Time-series signals for oxyhemoglobin (HbO), deoxyhemoglobin (HbR), and total hemoglobin (HbT) were extracted. Static BEN was computed as permutation entropy (PE) using the full time series for each channel, while dynamic BEN was derived by calculating PE within consecutive time windows. Support vector regression (SVR) was applied to predict brain age in the HC group using static and dynamic BEN features. The optimal model was then used to predict brain age in the MDD group. Finally, receiver operating characteristic (ROC) curve analysis was conducted to evaluate the discriminative capacity of BEN and brain age metrics for distinguishing MDD from HC. Results: < 0.001). Moreover, both group-discriminative BEN and brain age metrics derived from static and dynamic BEN achieved outstanding diagnostic performance, with several combinations reaching an area under the ROC curve of 1.00 in distinguishing MDD from HC. Conclusions: Frontal BEN derived from rs-fNIRS represents a potential neurophysiological biomarker of accelerated brain aging in patients with MDD. Furthermore, brain age estimated from cerebrovascular hemodynamic complexity demonstrates high discriminative ability for identifying MDD. Collectively, these findings suggest that frontal neurovascular complexity metrics may serve as both diagnostic markers for MDD and quantitative indicators of pathological aging progression.

Frontal Cortex Entropy Derived From Resting‐State fNIRS for Brain Age Prediction in Major Depressive Disorder

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