Characterizing load-dependent changes in whole-brain activity patterns during an extended N-back task

Working memory (WM) is a core cognitive function that supports goal-directed behavior by temporarily maintaining and manipulating information. One of the most widely used paradigms for investigating WM function is the N-back task, and numerous neuroimaging studies have examined load-dependent neural responses using a variety of analytical approaches. However, most previous studies have focused on low-to- moderate load ranges (primarily 0–3-back), and it remains unclear how whole-brain activity patterns reconfigure across a broader range of WM demands, including conditions approaching capacity limits. In the present study, we investigated behavioral performance and whole-brain activity patterns across an extended N-back task ranging from 0-back to 7-back. Behavioral analyses revealed that discrimination sensitivity (d′) decreased nonlinearly with increasing WM load, whereas reaction time (RT) exhibited an inverted-U pattern, peaking at intermediate load conditions. To characterize load- dependent whole-brain activity patterns, we computed relative activation maps by subtracting the participant-wise mean activation map across all conditions from each condition-specific activation map. Spatial similarity analyses with the Yeo 7-network templates revealed that low-load conditions showed relatively high similarity to default mode network (DMN)-related patterns. Similarity to the dorsal attention network (DAN) and frontoparietal network (FPN) was maximal at intermediate load levels, indicating load-dependent changes in network similarity profiles. High-load conditions were characterized by partial re-emergence of DMN-related patterns, accompanied by reduced DAN/FPN similarity. In addition, semantic similarity analysis using Neurosynth- derived semantic maps revealed relatively high similarity to default mode-related and self-referential representations under low-load conditions. Intermediate-load conditions showed strong correspondence with working memory- and executive control-related representations, whereas high-load conditions exhibited increased similarity to salience-, aversive/interoceptive-, and inhibitory-control-related representations. Together, these findings suggest that increasing WM load is associated not merely with stronger activation, but with changes in whole-brain activity patterns accompanied by nonlinear changes in network similarity profiles across levels of cognitive demand. Furthermore, the relative activation map-based whole-brain pattern analysis used in this study may provide a useful approach for evaluating changes in whole-brain state representations associated with cognitive load.