2026 ECNP Congress

EP02-2009

Impact of parietal and frontal cortical volume on individual differences in visual working memory capacity

Tanu Majumder — M. Hettwer, C. Barnes-Scheufler, A. Reif, R.A. Bittner · Universitätsmedizin Frankfurt am Main, Klinik für Psychiatrie- Psychosomatik und Psychotherapie, Heinrich-Hoffmann-Straße 10 D-60528 Frankfurt am Main, Germany

2026 ECNP Congress

Background

Working memory (WM) is a core cognitive ability crucial for higher-level cognitive abilities including reasoning and learning, which has limited capacity. Working memory capacity (WMC) is a trait with high intra-individual stability and with strong links to high-level cognitive measures including global fluid intelligence, abstract reasoning, language abilities, mathematics, and overall scholastic performance. Prior research in functional neuroimaging has implicated a fronto-parietal network in supporting WM, with parietal regions mainly supporting the storage of information and frontal regions linked to cognitive control [1]. Furthermore, network-level meta-analytic evidence shows that WM encoding engages fronto-parietal regions, including the intraparietal sulcus (IPS), superior parietal lobule (SPL), inferior frontal junction (IFJ), precentral gyrus (PCG), medial prefrontal cortex (mPFC), and supplementary motor area (SMA) [2]. Although structural brain measures such as cortical thickness and gray matter volume have been linked to WM performance [3], these findings have emphasized global or network-level effects rather than variability within specific regions. Consequently, the extent to which structural variability in key fronto-parietal subregions contributes to individual differences in WMC remains unclear, despite evidences that differences in cortical anatomy provides a stable basis for variability in cognitive abilities. Therefore, we aim to examine whether WMC is associated with structural variability in predefined fronto-parietal regions.

Methods

We will examine 95 healthy adults (18-40 years) using a visual change detection task to estimate working memory capacity. Capacity will be quantified using Pashler’s K with the maximum value (K_max) used as the primary behavioural measure. 3T structural MRI data will be processed using FreeSurfer to extract cortical thickness and volume from regions of interest derived from a meta-analysis of WM fMRI studies [2], including bilateral IPS, SPL, IFJ, SMA, PCG, and mPFC. Associations between WMC and brain structure will be tested using correlation and multiple regression models controlling for age, sex, and total intracranial volume, using the false discovery rate to correct for multiple comparisons.

Results

We expect a positive association of cortical thickness and cortical volume in parietal regions (IPS, SPL) with WMC, consistent with evidence linking posterior parietal cortex to capacity limits in visual working memory [4]. Structural variation in frontal regions (IFJ, SMA) is also expected to predict performance, showing individual differences in control-related processes [1]. Furthermore, we expect that parietal structure will explain variance in WMC beyond frontal regions, consistent with its central functional role for WMC [5].

Conclusions

This study will provide a test of the structure-behaviour relationship in WM, using a behavioural measure well established in cognitive neuroscience. By integrating parietal and frontal regions within a single framework, we aim to clarify how different components of the fronto-parietal network contribute to individual differences in WMC, extending prior functional findings to brain structure. Identifying region-specific anatomical markersof WMC may support future research aimed at understanding cognitive variability across individuals, including in clinical populations where working memory deficits are common, and may help inform targeted interventions including training approaches.

References

  1. Nee, D.E., Brown, J.W., Askren, M.K., Berman, M.G., Demiralp, E., Krawitz, A., Jonides, J., 2013. A meta-analysis of executive components of working memory. Cereb. Cortex 23 (2), 264–282. https://doi.org/10.1093/cercor/bhs007
  2. Kim, H., 2019. Neural activity during working memory encoding, maintenance, and retrieval: a network-based model and meta-analysis. Hum. Brain Mapp. 40 (17), 4912–4933. https://doi.org/10.1002/hbm.24747
  3. Machizawa, M.G., Driver, J., Watanabe, T., 2020. Gray matter volume in different cortical structures dissociably relates to individual differences in capacity and precision of visual working memory. Cereb. Cortex 30 (9), 4759–4770. https://doi.org/10.1093/cercor/bhaa046
  4. Todd, J.J., Marois, R., 2004. Capacity limit of visual short-term memory in human posterior parietal cortex. Nature 428 (6984), 751–754. https://doi.org/10.1038/nature02466
  5. Todd, J.J., Marois, R., 2005. Posterior parietal cortex activity predicts individual differences in visual short-term memory capacity. Cogn. Affect. Behav. Neurosci. 5 (2), 144–155. https://doi.org/10.3758/CABN.5.2.144