Cognitive learning is associated with gray matter changes in healthy human individuals: A tensor-based morphometry study
ABSTRACT Longitudinal voxel-based morphometry studies have demonstrated morphological changes in cortical structures following motor and cognitive learning. In this study, we applied, for the first time, tensor-based morphometry (TBM) to assess the short-term structural brain gray matter (GM) changes associated with cognitive learning in healthy subjects. Using a 3 T scanner, a 3D T1-weighted sequence was acquired from 32 students at baseline and after two weeks. Students were separated into two groups: 13 defined as "students in cognitive training", who underwent a two-week cognitive learning period, and 19 "students not in cognitive training", who were not involved in any teaching activity. GM changes were assessed using TBM and statistical parametric mapping. Baseline regional GM volume did not differ between the two groups. At follow up, compared to "students not in cognitive training", the "students in cognitive training" had a significant GM volume increase in the dorsomedial frontal cortex, the orbitofrontal cortex, and the precuneus (p<0.001). These results suggest that cognitive learning results in short-term structural GM changes of neuronal networks of the human brain, which are known to be involved in cognition. This may have important implications for the development of rehabilitation strategies in patients with neurological diseases.
- SourceAvailable from: Eugenio Abela
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- "The method applied here has been successfully used in previous longitudinal morphological studies, yielding pathophysiologically plausible results in a wide variety of neurological conditions and experimental settings (e.g. Agosta et al. 2009; Brambati et al. 2009; Ceccarelli et al. 2009; Kipps et al. 2005; Tao et al. 2009; Filippi et al. 2010; Farbota et al. 2012). However, to the best of our knowledge, this is the first study to report such effects in human cortical ischaemic stroke. "
ABSTRACT: Preclinical studies using animal models have shown that grey matter plasticity in both perilesional and distant neural networks contributes to behavioural recovery of sensorimotor functions after ischaemic cortical stroke. Whether such morphological changes can be detected after human cortical stroke is not yet known, but this would be essential to better understand post-stroke brain architecture and its impact on recovery. Using serial behavioural and high-resolution magnetic resonanc"/>e imaging (MRI) measurements, we tracked recovery of dexterous hand function in 28 patients with ischaemic stroke involving the primary sensorimotor cortices. We were able to classify three recovery subgroups (fast, slow, and poor) using response feature analysis of individual recovery curves. To detect areas with significant longitudinal grey matter vol-ume (GMV) change, we performed tensor-based mor-phometry of MRI data acquired in the subacute phase, i.e. after the stage compromised by acute oedema and inflam-mation. We found significant GMV expansion in the per-ilesional premotor cortex, ipsilesional mediodorsal thalamus, and caudate nucleus, and GMV contraction in the contralesional cerebellum. According to an interaction model, patients with fast recovery had more perilesional than subcortical expansion, whereas the contrary was true for patients with impaired recovery. Also, there were sig-nificant voxel-wise correlations between motor perfor-mance and ipsilesional GMV contraction in the posterior parietal lobes and expansion in dorsolateral prefrontal cortex. In sum, perilesional GMV expansion is associated with successful recovery after cortical stroke, possibly reflecting the restructuring of local cortical networks. Distant changes within the prefrontal-striato-thalamic net-work are related to impaired recovery, probably indicating higher demands on cognitive control of motor behaviour.Brain Structure and Function 06/2014; DOI:10.1007/s00429-014-0804-y · 4.57 Impact Factor
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- "Effects were reported for cluster of voxels exceeding a cluster size threshold of p < 0.05 family wise error (FWE) corrected for multiple comparisons in the context of Gaussian random field theory and a voxel-level threshold of p < 0.001 (uncorrected). The rationale for choosing this statistical threshold was motivated by the fact that a lot of the published morphometric studies investigating learning-induced GM plasticity used a comparable threshold approach (e.g., Ceccarelli et al., 2009; Taubert et al., 2010; Bezzola et al., 2011; Hoekzema et al, 2011). Hence, we wanted to be consistent with the literature to ensure comparability across studies. "
ABSTRACT: Long-term motor skill learning has been consistently shown to result in functional as well as structural changes in the adult human brain. However, the effect of short learning periods on brain structure is not well understood. In the present study, subjects performed a sequential pinch force task (SPFT) for 20 min on 5 consecutive days. Changes in brain structure were evaluated with anatomical magnetic resonance imaging (MRI) scans acquired on the first and last day of motor skill learning. Behaviorally, the SPFT resulted in sequence-specific learning with the trained (right) hand. Structural gray matter (GM) alterations in left M1, right ventral premotor cortex (PMC) and right dorsolateral prefrontal cortex (DLPFC) correlated with performance improvements in the SPFT. More specifically we found that subjects with strong sequence-specific performance improvements in the SPFT also had larger increases in GM volume in the respective brain areas. On the other hand, subjects with small behavioral gains either showed no change or even a decrease in GM volume during the time course of learning. Furthermore, cerebellar GM volume before motor skill learning predicted (A) individual learning-related changes in the SPFT and (B) the amount of structural changes in left M1, right ventral PMC and DLPFC. In summary, we provide novel evidence that short-term motor skill learning is associated with learning-related structural brain alterations. Additionally, we showed that practicing a motor skill is not exclusively accompanied by increased GM volume. Instead, bidirectional structural alterations explained the variability of the individual learning success.Frontiers in Systems Neuroscience 05/2012; 6:37. DOI:10.3389/fnsys.2012.00037
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- "Poldrack, 2000). For example, a number of studies have demonstrated changes in gray-and/or white matter structure (Draganski et al., 2006; Ceccarelli et al., 2009; Lövdén et al., 2010b; Takeuchi et al., 2010; Garavan et al., 2000), and in the density of dopamine receptors (McNab et al., 2009). Interestingly, one study demonstrated a correspondence between regions that were activated during the trained task (i.e., mirror reading), regions that showed practice-related activation increases, and regions that showed changes of gray matter volume (Ilg et al., 2008). "
ABSTRACT: DEVELOPMENTAL TRAINING STUDIES ARE IMPORTANT TO INCREASE OUR UNDERSTANDING OF THE POTENTIAL OF THE DEVELOPING BRAIN BY PROVIDING ANSWERS TO QUESTIONS SUCH AS: "Which functions can and which functions cannot be improved as a result of practice?," "Is there a specific period during which training has more impact?," and "Is it always advantageous to train a particular function?"In addition, neuroimaging methods provide valuable information about the underlying mechanisms that drive cognitive plasticity. In this review, we describe how neuroscientific studies of training effects inform us about the possibilities of the developing brain, pointing out that childhood is a special period during which training may have different effects. We conclude that there is much complexity in interpreting training effects in children. Depending on the type of training and the level of maturation of the individual, training may influence developmental trajectories in different ways. We propose that the immature brain structure might set limits on how much can be achieved with training, but that the immaturity can also have advantages, in terms of flexibility for learning.Frontiers in Human Neuroscience 04/2012; 6:76. DOI:10.3389/fnhum.2012.00076 · 2.90 Impact Factor