Structural connectivity-based predictors of cognitive impairment in stroke patients attributable to aging

PLoS One. 2023 Apr 14;18(4):e0280892. doi: 10.1371/journal.pone.0280892. eCollection 2023.

Abstract

Despite the rising global burden of stroke and its socio-economic implications, the neuroimaging predictors of subsequent cognitive impairment are still poorly understood. We address this issue by studying the relationship of white matter integrity assessed within ten days after stroke and patients' cognitive status one year after the attack. Using diffusion-weighted imaging, we apply the Tract-Based Spatial Statistics analysis and construct individual structural connectivity matrices by employing deterministic tractography. We further quantify the graph-theoretical properties of individual networks. The Tract-Based Spatial Statistic did identify lower fractional anisotropy as a predictor of cognitive status, although this effect was mostly attributable to the age-related white matter integrity decline. We further observed the effect of age propagating into other levels of analysis. Specifically, in the structural connectivity approach we identified pairs of regions significantly correlated with clinical scales, namely memory, attention, and visuospatial functions. However, none of them persisted after the age correction. Finally, the graph-theoretical measures appeared to be more robust towards the effect of age, but still were not sensitive enough to capture a relationship with clinical scales. In conclusion, the effect of age is a dominant confounder especially in older cohorts, and unless appropriately addressed, may falsely drive the results of the predictive modelling.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Aging
  • Cognitive Dysfunction* / diagnostic imaging
  • Cognitive Dysfunction* / etiology
  • Cognitive Dysfunction* / psychology
  • Diffusion Magnetic Resonance Imaging
  • Diffusion Tensor Imaging / methods
  • Humans
  • Stroke* / complications
  • Stroke* / diagnostic imaging
  • White Matter* / diagnostic imaging

Grants and funding

This study was supported by the Institute of Computer Science of the Czech Academy of Sciences in the form of long-term strategic development financing to BB, JK, and JH [RVO:67985807], the Ministry of Health Czech Republic in the form of a grant to BB and JH [(National Institute of Mental Health (NIMH), IN: 00023752], the Czech Technical University Internal Grant Agency in the form of a grant to BB [SGS19/169/OHK3/3T/13], Czech Health Research Council Project in the form of a grant to BB, JH, DK, JO, LS, PJi, and PM [NV17-28427A], the Motol University Hospital, Prague in the form of a grant to VM, VŠ, AO, MV, PJ, AT, and PM [00064203], and the Czech Academy of Sciences Strategy AV21 Research Programmes “Hopes and Risks of the Digital Age” and “Breakthrough Technologies for the Future – Sensing, Digitisation, Artificial Intelligence and Quantum Technologies” in the form of a grant to JH. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.