Constrained principal component analysis reveals functionally connected load-dependent networks involved in multiple...

Area of Science:

• Neuroscience
• Cognitive Science
• Computational Neuroscience

Background:

• Working memory is crucial for cognitive tasks.
• Understanding the temporal dynamics of brain networks in working memory is essential.

Purpose of the Study:

• To investigate the temporal progression of functionally connected brain networks during a verbal working memory task.
• To determine how varying memory load affects these network dynamics.

Main Methods:

• Constrained Principal Component Analysis (CPCA) with a finite impulse response (FIR) basis set was employed.
• Analysis focused on a multistage verbal working memory trial with manipulated memory load.
• Four functional components were extracted and analyzed for load sensitivity and temporal characteristics.

Main Results:

• All four extracted components demonstrated statistically significant sensitivity to memory load.
• Two components (1 and 4) exhibited sustained activity for approximately 3 seconds.
• Sustained activity networks involved increased activation in dorsal anterior cingulate cortex, right dorsolateral prefrontal cortex, and left supramarginal gyrus, with decreased activation in auditory and default network regions.
• Non-sustained networks showed increased activation in occipital cortex, dorsal anterior cingulate cortex, sensori-motor regions, and superior parietal cortex.

Conclusions:

• The findings suggest that distinct functional networks contribute differentially over the course of a working memory trial.
• Sustained-peak components are likely involved in both encoding and the delay period of working memory.
• This study offers novel insights into the dynamic interplay of brain regions supporting verbal working memory.