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Arnoux, Audrey; Toba, Monica N.; Duering, Marco; Diouf, Momar; Daouk, Joel; Constans, Jean-Marc; Puy, Laurent; Barbay, Melanie; Godefroy, Olivier (2018): Is VLSM a valid tool for determining the functional anatomy of the brain? Usefulness of additional Bayesian network analysis. In: Neuropsychologia, Vol. 121: pp. 69-78
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Abstract

Objectives: The ability of voxel-based lesion-symptom mapping (VLSM) to define the functional anatomy of the human brain has not been fully assessed. With a view to assessing VLSM's validity, the present study analyzed the technique's ability to determine the known clinical-anatomic correlates of hemiparesis in stroke patients. Design: Lesions (damaged in at least 5 patients) associated with transformed limb motor score (after adjustment on lesion volume) at 6 months were examined in 272 patients using VLSM. The value of additional multivariable linear, logistic and Bayesian analyses was examined. Results: We first checked that motor hemiparesis was fully accounted for by corticospinal tract (CST) lesions (sensitivity = 100%;p = 0.0001). Conventional VLSM analysis flagged up 2 regions corresponding to the CST, but also 8 regions located outside the CST. All 10 brain regions achieving statistical significance in the VLSM analysis were submitted to 3 additional analyses. The backward linear regression analysis selected 5 regions, one only corresponding to the CST (R-2 : 0.03, p = 0.0008). The logistic regression analysis selected correctly the CST (OR: 2.39, 95%CI: 1.44-3.96;0.001). The Bayesian network analysis selected regions including the CST (in 92% of 3000 bootstrap replications) and identified the source of multicollinearity. These lesions evaluated by structural equation modeling resulted in an excellent fit (p-value = 0.228, chi/df = 1.19, RMSEA = 0.032, CFI = 0.999). Analyses of confusion factors showed that conventional VLSM analyses were strongly influenced by lesion frequency (R-2 = 0.377;p = 0.0001) and multicollinearity. Conclusions: Conventional VLSM analyses are sensitive but weakened by a type I error due to the combined effects of multicollinearity and lesion frequency. We demonstrate that the addition of a Bayesian network analysis, and to a lesser extent of logistic regression, controlled for this type I error and constituted a reliable means of defining the functional anatomy of the motor system in stroke patients.