Gray Matter Increase Induced by Practice Correlates with Task-Specific Activation: A Combined Functional and Morphometric Magnetic Resonance Imaging Study

Abstract

The neurophysiological basis of practice-induced gray matter increase is unclear. To study the relationship of practice-induced gray matter changes and neural activation, we conducted a combined longitudinal functional and morphometric (voxel-based morphometry) magnetic resonance imaging (MRI) study on mirror reading. Compared with normal reading, mirror reading resulted in an activation of the dorsolateral occipital cortex, medial occipital cortex, superior parietal cortex, medial and dorsolateral prefrontal cortex, as well as anterior insula and cerebellum. Daily practice of 15 min for 2 weeks resulted in an increased performance of mirror reading. After correction for pure performance effects, we found a practice-related decrease of activation at the right superior parietal cortex and increase of activation at the right dorsal occipital cortex. The longitudinal voxel-based morphometry analysis yielded an increase of gray matter in the right dorsolateral occipital cortex that corresponded to the peak of mirror-reading-specific activation. This confirms that short-term gray matter signal increase corresponds to task-specific processing. We speculate that practice-related gray matter signal changes in MRI are primarily related to synaptic remodeling within specific processing areas.

Network-level structural covariance in the developing brain

“…Previous studies have documented age-dependent patterns of regional cortical thickness in healthy children (18,50), and structural changes have been associated with practice and learning (51)(52)(53) as well as with aging and disease (54,55). A comprehensive, datadriven approach to human connectomic mapping (56) may provide a framework for understanding normal childhood development as well as neurodevelopmental (31) and age-related disorders (14) that impact network function.…”

Neuroanatomy Predicts Individual Risk Attitudes

“…Similar to risk preferences, cortical neuroanatomy is relatively stable over time in healthy midlife adults, yet susceptible to localized and specific short-term (hours to weeks) changes following experimental manipulations (Draganski et al, 2004;Ilg et al, 2008;Sagi et al, 2012). For example, local volume of GM and WM have been shown to change over a period of weeks following motor or visual training (Draganski et al, 2004;Ilg et al, 2008;Scholz et al, 2009;Kühn et al, 2014), and WM changes in hippocampal regions have even been observed following a few hours of perceptual learning in the visual domain (Sagi et al, 2012).…”

“…For example, local volume of GM and WM have been shown to change over a period of weeks following motor or visual training (Draganski et al, 2004;Ilg et al, 2008;Scholz et al, 2009;Kühn et al, 2014), and WM changes in hippocampal regions have even been observed following a few hours of perceptual learning in the visual domain (Sagi et al, 2012). Nevertheless, a recent study found that enduring longitudinal cognitive (navigational) changes are associated with enduring changes in hippocampal neuroanatomy, in a longer time scale of up to 4 years (Woollett and Maguire, 2011).…”

“…Activation levels in a number of these regions have been shown to reflect the idiosyncratic valuations individuals place on a host of rewards (Bartra et al, 2013), including immediate and delayed monetary rewards (Kable and Glimcher, 2007), food and drink rewards (O'Doherty et al, 2002;Plassmann et al, 2007;Chib et al, 2009;Hare et al, 2009;Levy and Glimcher, 2011), and social rewards (Izuma et al, 2008). Indeed, the activity patterns in these areas can be used to predict much about an individual's preferences.…”

Strategy‐based reasoning training modulates cortical thickness and resting‐state functional connectivity in adults with chronic traumatic brain injury

“…MRI‐based morphometry has enabled us to noninvasively and quantitatively assess training‐induced structural changes in the healthy adult brain (Best, Chiu, Liang Hsu, Nagamatsu, & Liu‐Ambrose, 2015; Bezzola, Merillat, Gaser, & Jancke, 2011; Draganski et al., 2004, 2006; Engvig et al., 2010; Ilg et al., 2008; Kwok et al., 2011; Landi, Baguear, & Della‐Maggiore, 2011; Schmidt‐Wilcke, Rosengarth, Luerding, Bogdahn, & Greenlee, 2010; Takeuchi et al., 2011, 2014; Taubert et al., 2010; Woollett & Maguire, 2011). For example, Engvig et al.…”