Open AccessThe first step in modern lesion-deficit analysis: Figure 1
Author(s) -
Parashkev Nachev
Publication year - 2014
Publication title -
brain
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.142
H-Index - 336
eISSN - 1460-2156
pISSN - 0006-8950
DOI - 10.1093/brain/awu275
Subject(s) - univariate , inference , voxel , context (archaeology) , computer science , multivariate statistics , artificial intelligence , lesion , multivariate analysis , identification (biology) , spatial contextual awareness , obstacle , psychology , pattern recognition (psychology) , machine learning , history , biology , psychiatry , botany , archaeology
Sir,I am grateful to Karnath and his colleague (Karnath and Smith, 2014) for a sophisticated commentary on our recent study (Mah et al. , 2014); nonetheless, four aspects of their analysis may cause some readers to misapprehend our conclusions in a way that will tend to perpetuate the errors it was our original aim to correct.First, the principal reason for changing to multivariate inference is not the complex distributed functional architecture of the brain but the complex distributed structural architecture of lesions. Just as mass-univariate inference has not been an obstacle to discovering functional networks with functional MRI, so it would not have been a (major) obstacle to discovering such networks with lesions if lesions had the spatial properties of blood oxygen level-dependent. Multivariate inference in the context of lesion-mapping is not an extension to the conventional voxel-wise mass-univariate method (i.e. voxel-based lesion–symptom mapping), mainly for those who wish to examine networks …