Multiscale connectivity framework for working memory network in paediatric acute lymphoblastic leukaemia survivors.

Overview

1. Department of Radiology, St.Jude Children’s Research Hospital, Memphis, TN 38105, USA
2. Department of Psychology and Biobehavioral Sciences, St.Jude Children’s Research Hospital, Memphis, TN 38105, USA
3. Department of Biostatistics, St.Jude Children’s Research Hospital, Memphis, TN 38105, USA

Abstract

Working memory impairments are a common late effect in survivors of childhood acute lymphoblastic leukaemia, yet the structural network substrates of these difficulties remain poorly defined. Existing connectomic studies often rely on whole-brain parcellations, overlooking working memory-associated circuitry and multiscale organization. We developed a multiscale structural connectivity framework to investigate working memory-associated networks using diffusion MRI and performed a cross-sectional study with 70 acute lymphoblastic leukaemia survivors and 70 age and sex matched healthy controls. Working memory-relevant regions were identified based on functional activation patterns, and structural connectomes were constructed at two spatial scales: a fine-scale 76-node network and a coarser 24-node network derived from spatially contiguous, architecturally and functionally coherent regional groupings, as defined in the multimodal parcellation atlas of Human Connectome Project. Graph theoretical metrics, clustering coefficient, Eigenvector centrality, local assortativity and participation coefficient were computed to assess local network topology. Group comparisons were conducted with false discovery rate correction for multiple comparisons. Compared to healthy controls, survivors exhibited marked topological shifts. Specifically, clustering and assortativity were increased in the caudate, putamen and thalamus but decreased in the frontoparietal cortex. In contrast, centrality and participation showed the opposite pattern, signalling subcortical segregation and cortical hyperintegration. These effects were consistent across both spatial scales. Additional findings included scale-specific effects unique to the fine scale, as well as heterogeneous fine-scale patterns that resolved into consistent regional changes at the coarse scale. All effects remained significant after false discovery rate correction, highlighting the robustness of the network reorganization. Our framework combining a targeted working memory network with multiscale connectomic analysis proves its worth by revealing structural changes of working memory circuitry in survivors compared to healthy controls. The results show a broad reorganization, with weakened cortical networks and strengthened subcortical circuits, possibly as a form of compensation. These insights sharpen our understanding of treatment-related structural network alterations and point to new targets for future studies of cognitive outcomes and rehabilitation.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• humanconnectome.org/study/hcp-lifespan-development, at Human Connectome Project; found in “Data availability”

Data availability

The imaging datasets analysed in this study were obtained from the St. Jude Total Therapy Study 16 cohort and the Human Connectome Project—Development (HCP-D). Access to Total Therapy Study 16 data is restricted and may be requested from St. Jude Children’s Research Hospital, subject to appropriate approvals. The HCP-D dataset is publicly accessible at https://www.humanconnectome.org/study/hcp-lifespan-development/document/hcp-development-20-release. All diffusion MRI preprocessing and structural connectivity reconstruction were performed using publicly available software, including FSL (https://fsl.fmrib.ox.ac.uk/fsl/docs/diffusion/index.html), MRtrix3 (https://www.mrtrix.org/), ANTs (https://github.com/ANTsX/ANTs) and FreeSurfer (https://github.com/freesurfer/freesurfer). Graph-theoretical analyses were conducted using Python libraries such as Nilearn (https://github.com/nilearn/nilearn) and NetworkX (https://github.com/networkx/networkx). Statistical analyses were performed in R (https://www.r-project.org/). Network visualizations and figure generation were produced using Nilearn and Connectome Workbench (https://github.com/Washington-University/workbench). Custom analysis scripts used in this study are available at https://github.com/rajikha/MultiscaleConnectivityFramework.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 5 keywords, 2 funders, 58 references.

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This paper

Raja, R., Glass, J. O., Song, R., Jacola, L. M., Patni, T., Li, Y., & Reddick, W. E. (2026). Multiscale connectivity framework for working memory network in paediatric acute lymphoblastic leukaemia survivors. Brain communications, 8(2), fcag137. https://doi.org/10.1093/braincomms/fcag137

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