"Can touch this": Cross-modal shape categorization performance is associated with microstructural characteristics of white matter association pathways

Abstract

Previous studies on visuo-haptic shape processing provide evidence that visually learned shape information can transfer to the haptic domain. In particular, recent neuroimaging studies have shown that visually learned novel objects that were haptically tested recruited parts of the ventral pathway from early visual cortex to the temporal lobe. Interestingly, in such tasks considerable individual variation in cross-modal transfer performance was observed. Here, we investigate whether this individual variation may be reflected in microstructural characteristics of white-matter (WM) pathways. We first trained participants on a fine-grained categorization task of novel shapes in the visual domain, followed by a haptic categorization test. We then correlated visual training-performance and haptic test-performance, as well as performance on a symbol-coding task requiring visuo-motor dexterity with microstructural properties of WM bundles potentially involved in visuo-haptic processing (the inferior longitudinal fasciculus [ILF], the fronto-temporal part of the superior longitudinal fasciculus [SLF_ft ] and the vertical occipital fasciculus [VOF]). Behavioral results showed that haptic categorization performance was good on average but exhibited large inter-individual variability. Haptic performance also was correlated with performance in the symbol-coding task. WM analyses showed that fast visual learners exhibited higher fractional anisotropy (FA) in left SLF_ft and left VOF. Importantly, haptic test-performance (and symbol-coding performance) correlated with FA in ILF and with axial diffusivity in SLF_ft . These findings provide clear evidence that individual variation in visuo-haptic performance can be linked to microstructural characteristics of WM pathways. Hum Brain Mapp 38:842-854, 2017. © 2016 Wiley Periodicals, Inc.

Keywords: DTI; haptics; individual variability; multisensory processing; shape processing; tractography.

Figure 1

Reference objects A and B denote the representative objects of each of the two categories. The remaining objects were then vertex‐morphed linearly from A to B. The number below each object specifies the percentage of B (e.g., object 6 means that this object contains 94% of A and 6% of B). The objects in the upper row were used as stimuli for the visual training, whereas the stimuli in the lower row were used for haptic testing. Note that test objects were never shown during the training session. [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 2

Structure of the experiments. Every block started with presenting the two reference objects one after the other (each reference was presented for 6 s in the visual training block and for 10 s in the haptic testing block). This was followed by a randomized presentation of the morphed objects (visual objects were presented for 3 s while haptic objects were presented for 6 s). For each object, participants had to report either category A or B as a response. During (visual) training, feedback was provided after the response. During (haptic) testing no feedback was given. [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 3

Structure of the coding task. Stimuli consisted of an array of digit‐symbol pairs (nine simple shapes each paired with a single digit, one through nine). Stimuli were shown all the time above the blanks, which participants were required to correctly fill in within two minutes. A short practice session was provided, followed by the real task according to the experimental specifications in the KWAIS‐IV manual.

Figure 4

Histogram of accuracy in the haptic task for all 37 participants sorted from low to high performance, showing clear individual variance.

Figure 5

Example of left ILF, SLF_ft and VOF extracted in a representative subject. [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 6

Scatter plots for the significant correlations between performance measures and the WM microstructural metrics of the tracts of interest. [Color figure can be viewed at http://wileyonlinelibrary.com]