Analysis of functional network asymmetry in major depressive disorder under four fNIRS tasks
Jianli Yang1, Shuwei Ma2, Aobo Cheng3
1College of Electronic and Information Engineering, Hebei University, Baoding 071002, China; Key Laboratory of Digital Medical Engineering of Heibei Province, Baoding 071002, China.
Background:
Research in functional asymmetry of Major Depressive Disorder (MDD) under different tasks is crucial for clinical diagnose.
Methods:
Fifty individuals with MDD and twenty healthy controls (HCS) were recruited for hemodynamic data collection under four fNIRS tasks (Emotional picture, Verbal fluency, Fingering and Negative emotional picture description task). Integral values and functional connectivity strength were employed to probe neural activation and functional connectivity in frontal and temporal lobes in MDD. Following, asymmetry characteristic of the frontal cortex between MDD and HCS under four tasks were carefully analyzed and compared.
Results:
Individuals with MDD demonstrated heightened connectivity between the frontal and right temporal lobes and reduced connectivity between the frontal and left temporal lobes compared to HCS in all tasks. Additionally, MDD exhibited attenuated activation in the left frontal lobes and exaggerated activation in the right frontal lobes, diverging from HCS. Furthermore, the disparities in left-right asymmetry characteristic of frontal cortex activation between MDD and HCS were more pronounced during the combined task.
Limitations:
Further research is required to grasp the neurophysiological mechanisms governing left-right asymmetry across various tasks and the influence of task-induced brain fatigue on cerebral cortex hemodynamics in MDD.
Conclusion:
The left-right asymmetry feature provides valuable neurophysiological insights for diagnosing MDD clinically. Variations in activation patterns and functional connectivity features between MDD and HCS are closely tied to the task chosen. Thus, in clinical practice, carefully selecting appropriate fNIRS tasks and relevant features can significantly improve the diagnostic accuracy of MDD.
