Subjective experience of inner speech in aphasia: Preliminary behavioral relationships and neural correlates

Abstract

Many individuals with aphasia describe anomia with comments like “I know it but I can’t say it.” The exact meaning of such phrases is unclear. We hypothesize that at least two discrete experiences exist: the sense of (1) knowing a concept, but failing to find the right word, and (2) saying the correct word internally but not aloud (successful inner speech, sIS). We propose that sIS reflects successful lexical access; subsequent overt anomia indicates post-lexical output deficits. In this pilot study, we probed the subjective experience of anomia in 37 persons with aphasia. Self-reported sIS related to aphasia severity and phonological output deficits. In multivariate lesion-symptom mapping, sIS was associated with dorsal stream lesions, particularly in ventral sensorimotor cortex. These preliminary results suggest that people with aphasia can often provide meaningful insights about their experience of anomia and that reports of sIS relate to specific lesion locations and language deficits.

An empirical evaluation of multivariate lesion behaviour mapping using support vector regression

“…Our results resolve the controversy on multiple comparisons in SVR-LSM (Zhang et al, 2014;Mirman et al, 2015;Fama et al, 2017;Griffis et al, 2017). They…”

“…A third issue of discussion relates to the way statistical inference is computed in MLBM. Until now, the most often used multivariate method is based on support vector regression (SVR-LSM; Zhang et al, 2014, Mirman et al, 2015Fama et al, 2017;Griffis et al, 2017;Ghaleh et al, 2018;Wiesen et al, submitted). SVR-LSM has several advantages over other multivariate methods: the analysis can be performed voxel-wise on a whole brain-level, and continuous behavioural variables can be modelled.…”

“…However, there is a dissent on the practically highly relevant question if correction for multiple comparisons as an additional step in SVR-LSM is required. Fama et al (2017) argued that "because SVR-LSM considers all voxels simultaneously in a single regression model, correction for multiple comparisons is not required". Further, Gaonkar et al, (2013) postulated that the "interdependence [of the parameters in a SVR model] has the potential to alleviate multiple comparisons problems" when used to assess voxel-wise significance in multivariate imaging analyses.…”

The network underlying human higher-order motor control: Insights from machine learning-based lesion-behaviour mapping in apraxia

“…This method is an extension of support vector machines (Cortes and Vapnik, 1995). SVR is able to model continuous variables and has been successfully implemented in SVR-based lesion symptom mapping (SVR-LSM) to map lesion-behaviour relationships with high resolution on a whole-brain voxel-level (Zhang et al, 2014;Mirman et al, 2015b;Fama et al, 2017;Griffis et al, 2017;Ghaleh et al, 2018;Chen et al, 2018;Sperber et al, in press). Most importantly, SVR-LSM was validated in a set of simulation studies for simple brain networks (Zhang et al, 2014).…”

Self‐reported inner speech use in university students

“…Brain‐imaging studies suggest that the most prominent neural correlates of inner speech are the left inferior frontal gyrus, Wernicke's area, the supplementary motor area, insula, left superior parietal lobe, and right posterior cerebellar cortex (Alderson‐Day et al, ; Fama, Hayward, Snider, Friedman, & Turkeltaub, ; Geva et al, ; Hurlburt, Alderson‐Day, Kühn, & Fernyhough, ; Perrone‐Bertolotti et al, ).…”