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. 2021 May 11;118(19):e2018784118.
doi: 10.1073/pnas.2018784118.

Cognitive impairment after focal brain lesions is better predicted by damage to structural than functional network hubs

Justin Reber  1   2 , Kai Hwang  3 , Mark Bowren  2   3 , Joel Bruss  2 , Pratik Mukherjee  4 , Daniel Tranel  2 , Aaron D Boes  5   2   6
Affiliations

Cognitive impairment after focal brain lesions is better predicted by damage to structural than functional network hubs

Justin Reber et al. Proc Natl Acad Sci U S A. .

Abstract

Hubs are highly connected brain regions important for coordinating processing in brain networks. It is unclear, however, which measures of network "hubness" are most useful in identifying brain regions critical to human cognition. We tested how closely two measures of hubness-edge density and participation coefficient, derived from white and gray matter, respectively-were associated with general cognitive impairment after brain damage in two large cohorts of patients with focal brain lesions (N = 402 and 102, respectively) using cognitive tests spanning multiple cognitive domains. Lesions disrupting white matter regions with high edge density were associated with cognitive impairment, whereas lesions damaging gray matter regions with high participation coefficient had a weaker, less consistent association with cognitive outcomes. Similar results were observed with six other gray matter hubness measures. This suggests that damage to densely connected white matter regions is more cognitively impairing than similar damage to gray matter hubs, helping to explain interindividual differences in cognitive outcomes after brain damage.

Keywords: brain networks; edge density; functional connectivity; participation coefficient; structural connectivity.

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Conflict of interest statement

The authors declare no competing interest.

Figures

Fig. 1.
Fig. 1.
Lesion overlaps and network measures. Overlap map of all participants’ lesions in (A) the Iowa cohort. The distribution of edge density and participation coefficient are represented in B and C, respectively. Validation analyses were carried out in the WU cohort (D). The images are presented in radiological orientation.
Fig. 2.
Fig. 2.
Lesion load calculation pipeline. Lesions were traced on the patient’s structural MRI, then transformed to a template brain. The resulting lesions were then overlaid on the edge density and participation coefficient maps, which were derived from healthy subjects, and the sum of all voxels within the lesion volume was calculated.
See this image and copyright information in PMC

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