2005
DOI: 10.1016/j.bandl.2004.06.108
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Analyzing aphasia data in a multidimensional symptom space

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Cited by 17 publications
(7 citation statements)
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“…In reality, most individual cases of aphasia are the consequence of varying degrees of damage to multiple independent but interacting language subsystems with distinct but overlapping neural substrates. Accordingly, each case of aphasia should be characterized not as one of a number of discrete types, but rather as a point in a multidimensional symptom space [ 7 11 ]. The specific dimensions defining this space should, as far as possible, reflect functions that have been empirically demonstrated to be neurally and functionally distinct.…”
Section: Introductionmentioning
confidence: 99%
“…In reality, most individual cases of aphasia are the consequence of varying degrees of damage to multiple independent but interacting language subsystems with distinct but overlapping neural substrates. Accordingly, each case of aphasia should be characterized not as one of a number of discrete types, but rather as a point in a multidimensional symptom space [ 7 11 ]. The specific dimensions defining this space should, as far as possible, reflect functions that have been empirically demonstrated to be neurally and functionally distinct.…”
Section: Introductionmentioning
confidence: 99%
“…Traditional lesion-mapping methods risk losing important information: patient groups typically comprise a broad range of lesioned areas and behavioral deficits, but are grouped either within a pre-specified lesion region of interest, or within narrowly defined behavioral categories for evaluation purposes. In addition, clinical measures can be very broad, and measure a conglomerate of neuroanatomically distinct abilities (Bates, Saygin, Moineau, Marangolo, & Pizzamiglio, 2005). The VLSM technique (Bates et al, 2003) is an important advancement because it preserves continuous lesion and behavioral information; no categorization based on lesion site, clinical diagnosis, or behavioral performance is necessary.…”
mentioning
confidence: 99%
“…Moreover, the reciprocal transformation allowed us to explicitly model potential ceiling effects in the data, revealing more subtle effects later in development. In general, continuous regressors should reveal more subtle effects than group analyses (Bates, Saygin, Moineau, Marangolo & Pizzamiglio, 2005; Zangl, Klarman, Thal, Fernald & Bates, 2005). We ran three types of linear regressions on the transformed data: (1) normal ordinary least squares linear regression; (2) robust regression (resilient to outliers with high leverage values); and (3) linear regression with robust standard errors (i.e.…”
Section: Resultsmentioning
confidence: 99%