Effective Connectivity Analysis of the Brain Network in Drivers during Actual Driving Using Near-Infrared Spectroscopy
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Summary
This study investigates how cognitive workload modulates the effective connectivity (EC) of brain networks during actual driving. While driving is a complex activity requiring high-level cognitive functions, previous research has largely focused on simulated environments or functional connectivity, which lacks directional causal information. This research aims to assess changes in EC among the prefrontal cortex (PFC), motor-related areas (MA), and vision-related areas (VA) across resting, simple-driving, and car-following states. The authors hypothesize that increased cognitive workload strengthens EC, but excessive workload may weaken it. Twelve young, right-handed male participants underwent an actual driving experiment on a 1400-meter annular route. Brain hemodynamic activity was recorded using functional near-infrared spectroscopy (fNIRS) with 16 channels positioned over bilateral PFC, MA, and VA regions. The experiment consisted of three 5-minute sessions: resting, simple driving (task_1), and car-following (task_2). Data were pre-processed using band-pass filtering and wavelet transform to extract wavelet amplitude (WA) signals. Conditional Granger Causality (CGC) analysis was employed to evaluate directional causal interactions between brain regions, determining connection existence and strength. Subjective cognitive workload was assessed using the NASA Task Load Index (NASA-TLX). Results indicated that subjective cognitive workload and hemodynamic activity levels increased linearly with task complexity. WA values in bilateral PFC, MA, and VA were significantly higher during driving tasks compared to rest. EC analysis revealed that connection strength among PFC, MA, and VA increased from the resting state to the simple-driving state. However, during the car-following task, connection strength relatively decreased compared to simple driving. Specifically, connections from bilateral PFC to VA and from VA to left MA appeared during simple driving but were lost or weakened during car-following. In terms of causal flow, the PFC acted as a causal target, while MA and VA served as causal sources during simple driving. Notably, the left MA shifted from a causal source to a causal target during the car-following task. The findings demonstrate that moderate cognitive workload strengthens the effective connectivity of the brain network, facilitating the integration of visual, motor, and executive functions. However, superfluous cognitive workload, such as that imposed by car-following, can weaken these connections and alter causal roles, particularly in the left motor area. This suggests that while the brain network adapts to increased demand, excessive load may disrupt optimal functional coupling. The study highlights the utility of fNIRS and CGC in analyzing real-world driving scenarios, providing insights into how cognitive demands influence neural dynamics and potentially impacting driver safety assessments.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | canonical_url | — | — | 1 | 2026-08-09 |
| extract | success | cached | — | — | 3 | 2026-08-10 |
| clean | success | clean | — | — | 1 | 2026-08-09 |
| chunk | success | chunk | — | — | 1 | 2026-08-09 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-08-09 |
| enrich | success | semantic_scholar | — | — | 1 | 2026-08-09 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.6-27b-nvidia | summ-v5 | 2 | 2026-08-10 |
| tag | success | vector_similarity | — | — | 10 | 2026-08-11 |
| verify | partial | — | — | — | 2 | 2026-08-10 |
Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified_with_issues.
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- Empirical Findings: physiological data
- Theoretical Contribution: theory or model, computational model