4.0 Article

A novel stroke lesion network mapping approach: improved accuracy yet still low deficit prediction

期刊

BRAIN COMMUNICATIONS
卷 3, 期 4, 页码 -

出版社

OXFORD UNIV PRESS
DOI: 10.1093/braincomms/fcab259

关键词

stroke; functional connectivity; behaviour; resting-state networks

资金

  1. Flagship ERA-NET Joint Transnational Call [ANR-17-HBPR-0001]
  2. Italian Ministero dell'Istruzione-Ministero dell'Universita e della Ricerca (MIUR)-Departments of Excellence Italian Ministry of Research [MART_ECCELLENZA18_01]
  3. Fondazione Cassa di Risparmio di Padova e Rovigo (CARIPARO)-Ricerca Scientifica di Eccellenza [55403]
  4. Italian Ministero della Salute [RF-2008-12366899, RF2019-12369300]
  5. Celeghin Foundation Padova [CUP C94I20000420007]
  6. BIAL foundation grant [361/18]
  7. Horizon 2020 European School of Network Neuroscience-European School of Network Neuroscience (euSNN) [869505]
  8. Horizon 2020 research and innovation programme
  9. Visionary Nature Based Actions For Heath, Wellbeing & Resilience in Cities (VARCITIES) [869505]
  10. European Union [818521]
  11. Agence Nationale de la Recherche (ANR) [ANR-17-HBPR-0001] Funding Source: Agence Nationale de la Recherche (ANR)

向作者/读者索取更多资源

The new methodology of lesion network mapping in stroke improves the estimation of functional brain network disconnected, but falls short in predicting neurological impairment post-stroke. Despite yielding networks with higher anatomical specificity, the approach does not perform better than the standard method in predicting behavioral deficits.
Pini et al. developed a new methodology for 'lesion network mapping' in stroke. This new methodology enabled a more accurate estimation of functional brain network disconnected in stroke. However, the method did not predict well neurological impairment limiting its application to clinical questions. Lesion network mapping estimates functional network abnormalities caused by a focal brain lesion. The method requires embedding the volume of the lesion into a normative functional connectome and using the average functional magnetic resonance imaging signal from that volume to compute the temporal correlation with all other brain locations. Lesion network mapping yields a map of potentially functionally disconnected regions. Although promising, this approach does not predict behavioural deficits well. We modified lesion network mapping by using the first principal component of the functional magnetic resonance imaging signal computed from the voxels within the lesioned area for temporal correlation. We measured potential improvements in connectivity strength, anatomical specificity of the lesioned network and behavioural prediction in a large cohort of first-time stroke patients at 2-weeks post-injury (n = 123). This principal component functional disconnection approach localized mainly cortical voxels of high signal-to-noise; and it yielded networks with higher anatomical specificity, and stronger behavioural correlation than the standard method. However, when examined with a rigorous leave-one-out machine learning approach, principal component functional disconnection approach did not perform better than the standard lesion network mapping in predicting neurological deficits. In summary, even though our novel method improves the specificity of disconnected networks and correlates with behavioural deficits post-stroke, it does not improve clinical prediction. Further work is needed to capture the complex adjustment of functional networks produced by focal damage in relation to behaviour.

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