Diffusion tensor imaging metrics of the corpus callosum in relation to bimanual coordination: Effect of task complexity and sensory feedback

Gooijers, Jolien; Caeyenberghs, Karen; Sisti, Helene M.; Geurts, Monique; Heitger, Marcus; Leemans, Alexander; Swinnen, Stephan P.

doi:10.1002/hbm.21429

Cited by 61 publications

(51 citation statements)

References 79 publications

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“…Lower FAvalues are found in the anterior and middle regions compared to the posterior regions (Sullivan et al 2001;Chepuri et al 2002;Pfefferbaum and Sullivan 2003;Hasan et al 2005Hasan et al , 2009. Consistent with this, we also found that the parietal and occipital regions of the CC had higher FA values compared to anterior and middle regions Gooijers et al 2011). Regional differences of the FA have been attributed to varying degrees of permeability of the myelin sheaths, changes in orientation of axons, differences in axon-packing density, and Bimanual learning and corpus callosum DTI www.learnmem.org thickness of myelin and axonal caliber, where a higher FA value is indicative of more densely packed CC fibers (Chepuri et al 2002;Hofer and Frahm 2006).…”

Section: Discussionsupporting

confidence: 89%

“…Significant correlations were detected for the most difficult frequency ratios after training and not at initiation of practice or for the intrinsically favorable isofrequency rhythms, adding specificity to the obtained effect. These data are novel in that previous studies examining the role of the CC in bimanual tasks primarily looked at single session motor performance instead of the learning-related dynamics (Johansen-Berg et al 2007;Bonzano et al 2008;Muetzel et al 2008;Gooijers et al 2011). Here, we compared correlation coefficients at pre-and post-test and made use of a partial correlation analysis to investigate the association between CC structural integrity and bimanual coordination learning, as discussed further below.…”

Section: Discussionmentioning

confidence: 99%

“…While several studies have revealed the importance of the CC in bimanual coordination, findings on the specificity of CC subregions for predicting bimanual performance are mixed (Sullivan et al 2001;Johansen-Berg et al 2007;Bonzano et al 2008;Muetzel et al 2008;Gooijers et al 2011). For instance, asynchronous bimanual finger-thumb opposition movements have been correlated with structural organization of the midbody (Johansen-Berg et al 2007), alternate finger tapping with the splenium (Sullivan et al 2001;Muetzel et al 2008) and performance on a repetitive finger tapping task with fractional anisotropy (FA) of anterior regions (Bonzano et al 2008).…”

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confidence: 99%

“…Here, we used a tractography-based parcellation method that approximates the heterogeneous anatomic and functional distribution of information transmitted along the CC (Huang et al 2005;Hofer and Frahm 2006;Caeyenberghs et al 2011;Gooijers et al 2011). We partitioned the CC into seven subregions based on its connections to the (1) prefrontal regions, (2) premotor and supplementary motor areas, (3) primary motor cortex, (4) primary sensory cortex, and (5) occipital, (6) parietal, and (7) temporal cortex, as we have done previously Gooijers et al 2011).…”

mentioning

confidence: 99%

“…We partitioned the CC into seven subregions based on its connections to the (1) prefrontal regions, (2) premotor and supplementary motor areas, (3) primary motor cortex, (4) primary sensory cortex, and (5) occipital, (6) parietal, and (7) temporal cortex, as we have done previously Gooijers et al 2011). We investigated the organization of the seven CC subregions in healthy young adults in relation to performance on a bimanual visuomotor task, which is a computerized version of the "Etch-a-sketch" device (for earlier versions, see Preilowski 1972;Jeeves et al 1988;Mueller et al 2009).…”

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confidence: 99%

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Microstructural organization of corpus callosum projections to prefrontal cortex predicts bimanual motor learning

Sisti¹,

Geurts²,

Gooijers³

et al. 2012

Learn. Mem.

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The corpus callosum (CC) is the largest white matter tract in the brain. It enables interhemispheric communication, particularly with respect to bimanual coordination. Here, we use diffusion tensor imaging (DTI) in healthy humans to determine the extent to which structural organization of subregions within the CC would predict how well subjects learn a novel bimanual task. A single DTI scan was taken prior to training. Participants then practiced a bimanual visuomotor task over the course of 2 wk, consisting of multiple coordination patterns. Findings revealed that the predictive power of fractional anisotropy (FA) was a function of CC subregion and practice. That is, FA of the anterior CC, which projects to the prefrontal cortex, predicted bimanual learning rather than the middle CC regions, which connect primary motor cortex. This correlation was specific in that FA correlated significantly with performance of the most difficult frequency ratios tested and not the innately preferred, isochronous frequency ratio. Moreover, the effect was only evident after training and not at initiation of practice. This is the first DTI study in healthy adults which demonstrates that white matter organization of the interhemispheric connections between the prefrontal structures is strongly correlated with motor learning capability.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Microstructural organization of corpus callosum projections to prefrontal cortex predicts bimanual motor learning

Sisti¹,

Geurts²,

Gooijers³

et al. 2012

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Aging effects on the resting state motor network and interlimb coordination

Solesio-Jofre

Serbruyns

Woolley

et al. 2014

Human Brain Mapping

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Both increases and decreases in resting state functional connectivity have been previously observed within the motor network during aging. Moreover, the relationship between altered functional connectivity and age-related declines in bimanual coordination remains unclear. Here, we explored the developmental dynamics of the resting brain within a task-specific motor network in a sample of 128 healthy participants, aged 18-80 years. We found that age-related increases in functional connectivity between interhemispheric dorsal and ventral premotor areas were associated with poorer performance on a novel bimanual visuomotor task. Additionally, a control analysis performed on the default mode network confirmed that our age-related increases in functional connectivity were specific to the motor system. Our findings suggest that increases in functional connectivity within the resting state motor network with aging reflect a loss of functional specialization that may not only occur in the active brain but also in the resting brain.

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A combined diffusion‐weighted and electroencephalography study on age‐related differences in connectivity in the motor network during bimanual performance

Babaeeghazvini

Rueda‐Delgado

Adab

et al. 2018

Human Brain Mapping

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We studied the relationship between age‐related differences in inter‐ and intra‐hemispheric structural and functional connectivity in the bilateral motor network. Our focus was on the correlation between connectivity and declined motor performance in older adults. Structural and functional connectivity were estimated using diffusion weighted imaging and resting‐state electro‐encephalography, respectively. A total of 48 young and older healthy participants were measured. In addition, motor performances were assessed using bimanual coordination tasks. To pre‐select regions‐of‐interest (ROIs), a neural model was adopted that accounts for intra‐hemispheric functional connectivity between dorsal premotor area (PMd) and primary motor cortex (M1) and inter‐hemispheric connections between left and right M1 (M1L and M1R). Functional connectivity was determined via the weighted phase‐lag index (wPLI) in the source‐reconstructed beta activity during rest. We quantified structural connectivity using kurtosis anisotropy (KA) values of tracts derived from diffusion tensor‐based fiber tractography between the aforementioned areas. In the group of older adults, wPLI values between M1L–M1R were negatively associated with the quality of bimanual motor performance. The additional association between wPLI values of PMdL––M1L and PMdR–M1L supports that functional connectivity with the left hemisphere mediated (bimanual) motor control in older adults. The correlational analysis between the selected structural and functional connections revealed a strong association between wPLI values in the left intra‐hemispheric PMdL–M1L pathway and KA values in M1L–M1R and PMdR–M1L pathways in the group of older adults. This suggests that weaker structural connections in older adults correlate with stronger functional connectivity and, hence, poorer motor performance.

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Diffusion tensor imaging metrics of the corpus callosum in relation to bimanual coordination: Effect of task complexity and sensory feedback

Cited by 61 publications

References 79 publications

Microstructural organization of corpus callosum projections to prefrontal cortex predicts bimanual motor learning

Microstructural organization of corpus callosum projections to prefrontal cortex predicts bimanual motor learning

Aging effects on the resting state motor network and interlimb coordination

A combined diffusion‐weighted and electroencephalography study on age‐related differences in connectivity in the motor network during bimanual performance

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