Distributed brain network mediating the dynamic shifts of priorities in abstract working memory
Poster No:
870
Submission Type:
Late-Breaking Abstract Submission
Authors:
Mengya Zhang1, Qing Yu2
Institutions:
1The Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Science, Shanghai, Shanghai, 2Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, Shanghai
First Author:
Mengya Zhang
The Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Science
Shanghai, Shanghai
The Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Science
Shanghai, Shanghai
Co-Author:
Qing Yu
Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences
Shanghai, Shanghai
Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences
Shanghai, Shanghai
Introduction:
The advantage of working memory (WM) as the workspace for complex cognition is marked by the ability to hold more than a single item simultaneously and prioritize corresponding item according to demands(Yu et al., 2020). Although research has recently shown higher-level task information recruit association areas such as frontal cortex for maintenance (Muhle-Karbe et al., 2017; Woolgar et al., 2015) and posterior regions for implementation (Zhang & Yu, 2024), how the brain maintains multiple pieces of abstract task information and switches between them to guide behaviour remains unknown. To address these questions, building on our previously tested WM paradigm(Zhang & Yu, 2024) and leveraging fMRI with multivariate pattern analyses, the study examined the neural substrates of remembered task goals with distinct functional priorities and the neural process underpinning the back-and-forth switches based on task demands.
Methods:
Twenty-five (mean age = 24.2 years; age range = 21 to 30 years; 17 females) participants performed a delayed continuous response task (Fig. 1A), where they maintained a task goal and subsequently a specific visual stimulus to be manipulated according to the former during response phase. Critically, the experiment had a block design (18 trials), at the beginning of which 2 out of 4 possible goals were selected and randomly associated with 2 shapes, which were used to signify the currently relevant goal on each trial for the remainder of the block. This design allowed us to decern the neural code of prioritized memory from that of unprioritized item concurrently held in WM for future use (Muhle-Karbe et al., 2021) . Whole-brain searchlight-based decoding was conducted to examine the neural substrates of prioritized and unprioritized goals. To understand their differential relations to behaviour, we employed an analysis associating performance with representational strengths on both trial- and block-wise timescales. Lastly, to illustrate the neural processes of priority switching, we explored how neural traces fluctuated from previous to next trials in the case of switch (cued goals were different), compared to non-switch (cued goals were same).
Results:
Significant decoding of prioritized goals were found in a highly distributed manner across the cortex during both goal maintenance and implementation (Delay 1 & 2), including but not limited to medial and lateral sections of prefrontal cortex (PFC, Fig. 1B). In contrast, unprioritized goals were only observed in several temporo-occipital visual-related regions (Figure 1C). Whole-brain behavioural correlation analysis further pointed out the functional divergence: while representations of prioritized goals scaled positively with performance on both trial- and block-wise timescales, unprioritized goals interfered with current trials, but facilitate behaviour over the course of a block, highlighting that the unselected item was held in a state that both minimizes disruption to current demand and benefits prolonged maintenance (for later use). Hippocampus (HPC) and entorhinal cortex played a central part in this balancing role. Finally, ventromedial PFC (vmPFC) was identified as a critical region in flexibly shifting between the two concurrently held goals.
Conclusions:
Our results demonstrated that abstract task goals concurrently held in WM are encoded differently based on task demands. This distinction is partially achieved by spatial separation. Furthermore, HPC is theorized to play a key role in balancing the prolonged maintenance of item not in immediate use and its interference on current behaviour. Dynamic shifts of priorities rely on a network centered on vmPFC and specific regions, depending on the direction of switching.
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making 2
Learning and Memory:
Working Memory 1
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Multivariate Approaches
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Cognition
FUNCTIONAL MRI
Memory
MRI
Multivariate
1|2Indicates the priority used for review
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2. Muhle-Karbe, P. S., Myers, N. E., & Stokes, M. G. (2021). A hierarchy of functional states in working memory. The Journal of Neuroscience, 41(20), 4461–4475.
3. Woolgar, A., Afshar, S., Williams, M. A., & Rich, A. N. (2015). Flexible coding of task rules in frontoparietal cortex: An adaptive system for flexible cognitive control. Journal of Cognitive Neuroscience, 27(10), 1895–1911.
4. Yu, Q., Teng, C., & Postle, B. R. (2020). Different states of priority recruit different neural representations in visual working memory. PLoS Biology, 18(6), 1–21.
5. Zhang, M., & Yu, Q. (2024). The representation of abstract goals in working memory is supported by task-congruent neural geometry. PLOS Biology, 22(12), e3002461.