Article

The handyman's brain: A neuroimaging meta-analysis describing the similarities and differences between grip type and pattern in humans

NeuroImage (Impact Factor: 6.36). 06/2014; 102. DOI: 10.1016/j.neuroimage.2014.05.064
Source: PubMed

ABSTRACT

Background:
Handgrip is a ubiquitous human movement that was critical in our evolution. However, the differences in brain activity between grip type (i.e. power or precision) and pattern (i.e. dynamic or static) are not fully understood. In order to address this, we performed Activation Likelihood Estimation (ALE) analysis between grip type and grip pattern using functional magnetic resonance imaging (fMRI) data. ALE provides a probabilistic summary of the BOLD response in hundreds of subjects, which is often beyond the scope of a single fMRI experiment.

Methods:
We collected data from 28 functional magnetic resonance data sets, which included a total of 398 male and female subjects. Using ALE, we analyzed the BOLD response during power, precision, static and dynamic grip in a range of forces and age in right handed healthy individuals without physical impairment, cardiovascular or neurological dysfunction using a variety of grip tools, feedback and experimental training.

Results:
Power grip generates unique activation in the postcentral gyrus (areas 1 and 3b) and precision grip generates unique activation in the supplementary motor area (SMA, area 6) and precentral gyrus (area 4a). Dynamic handgrip generates unique activation in the precentral gyrus (area 4p) and SMA (area 6) and of particular interest, both dynamic and static grip share activation in the area 2 of the postcentral gyrus, an area implicated in the evolution of handgrip. According to effect size analysis, precision and dynamic grip generates stronger activity than power and static, respectively.

Conclusion:
Our study demonstrates specific differences between grip type and pattern. However, there was a large degree of overlap in the pre and postcentral gyrus, SMA and areas of the frontal-parietal-cerebellar network, which indicates that other mechanisms are potentially involved in regulating handgrip. Further, our study provides empirically based regions of interest, which can be downloaded here within, that can be used to more effectively study power grip in a range of populations and conditions.

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Available from: Michael King, Dec 05, 2014
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    • "Also, it is important to stress that this study investigated the visuomotor network in the context of a power grip dynamic task, where all fingers participated in performing the task (as opposed to a precision task where two fingers perform the task or to a finger tapping task). This is important as it has been shown that there are differences and specific activations between the two tasks [e.g.,Khorrami et al., 2011;King et al., 2014]. Overall, the current study offers a substantial characterisation of visuomotor processing engaged by a dynamic power grip task executed with the DH and NDH and, at the same time, illustrates a clear differentiation between qualitative and quantitative approaches to fMRI analysis. "
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    ABSTRACT: Motor fMRI studies, comparing dominant (DH) and nondominant (NDH) hand activations have reported mixed findings, especially for the extent of ipsilateral (IL) activations and their relationship with task complexity. To date, no study has directly compared DH and NDH activations using an event-related visually guided dynamic power-grip paradigm with parametric (three) forces (GF) in healthy right-handed subjects. We implemented a hierarchical statistical approach aimed to: (i) identify the main effect networks engaged when using either hand; (ii) characterise DH/NDH responses at different GFs; (iii) assess contralateral (CL)/IL-specific and hemisphere-specific activations. Beyond confirming previously reported results, this study demonstrated that increasing GF has an effect on motor response that is contextualised also by the use of DH or NDH. Linear analysis revealed increased activations in sensorimotor areas, with additional increased recruitments of subcortical and cerebellar areas when using the NDH. When looking at CL/IL-specific activations, CL sensorimotor areas and IL cerebellum were activated with both hands. When performing the task with the NDH, several areas were also recruited including the CL cerebellum. Finally, there were hand-side-independent activations of nonmotor-specific areas in the right and left hemispheres, with the right hemisphere being involved more extensively in sensori-motor integration through associative areas while the left hemisphere showing greater activation at higher GF. This study shows that the functional networks subtending DH/NDH power-grip visuomotor functions are qualitatively and quantitatively distinct and this should be taken into consideration when performing fMRI studies, particularly when planning interventions in patients with specific impairments. Hum Brain Mapp, 2015. © 2015 Wiley Periodicals, Inc.
    Full-text · Article · Sep 2015 · Human Brain Mapping
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    [Show abstract] [Hide abstract]
    ABSTRACT: Background: Handgrip is a ubiquitous human movement that was critical in our evolution. However, the differences in brain activity between grip type (i.e. power or precision) and pattern (i.e. dynamic or static) are not fully understood. In order to address this, we performed Activation Likelihood Estimation (ALE) analysis between grip type and grip pattern using functional magnetic resonance imaging (fMRI) data. ALE provides a probabilistic summary of the BOLD response in hundreds of subjects, which is often beyond the scope of a single fMRI experiment. Methods: We collected data from 28 functional magnetic resonance data sets, which included a total of 398 male and female subjects. Using ALE, we analyzed the BOLD response during power, precision, static and dynamic grip in a range of forces and age in right handed healthy individuals without physical impairment, cardiovascular or neurological dysfunction using a variety of grip tools, feedback and experimental training. Results: Power grip generates unique activation in the postcentral gyrus (areas 1 and 3b) and precision grip generates unique activation in the supplementary motor area (SMA, area 6) and precentral gyrus (area 4a). Dynamic handgrip generates unique activation in the precentral gyrus (area 4p) and SMA (area 6) and of particular interest, both dynamic and static grip share activation in the area 2 of the postcentral gyrus, an area implicated in the evolution of handgrip. According to effect size analysis, precision and dynamic grip generates stronger activity than power and static, respectively. Conclusion: Our study demonstrates specific differences between grip type and pattern. However, there was a large degree of overlap in the pre and postcentral gyrus, SMA and areas of the frontal-parietal-cerebellar network, which indicates that other mechanisms are potentially involved in regulating handgrip. Further, our study provides empirically based regions of interest, which can be downloaded here within, that can be used to more effectively study power grip in a range of populations and conditions.
    Full-text · Article · Jun 2014 · NeuroImage
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    ABSTRACT: Previous studies have used fMRI to address the relationship between grip force (GF) applied to an object and BOLD response. However, whilst the majority of these studies showed a linear relationship between GF and neural activity in the contralateral M1 and ipsilateral cerebellum, animal studies have suggested the presence of non-linear components in the GF-neural activity relationship. Here, we present a methodology for assessing non-linearities in the BOLD response to different GF levels, within primary motor as well as sensory and cognitive areas and the cerebellum. To be sensitive to complex forms, we designed a feasible grip task with five GF targets using an event-related visually guided paradigm and studied a cohort of 13 healthy volunteers. Polynomial functions of increasing order were fitted to the data. (1) activated motor areas irrespective of GF; (2) positive higher-order responses in and outside M1, involving premotor, sensory and visual areas and cerebellum; (3) negative correlations with GF, predominantly involving the visual domain. Overall, our results suggest that there are physiologically consistent behaviour patterns in cerebral and cerebellar cortices; for example, we observed the presence of a second-order effect in sensorimotor areas, consistent with an optimum metabolic response at intermediate GF levels, while higher-order behaviour was found in associative and cognitive areas. At higher GF levels, sensory-related cortical areas showed reduced activation, interpretable as a redistribution of the neural activity for more demanding tasks. These results have the potential of opening new avenues for investigating pathological mechanisms of neurological diseases.
    No preview · Article · Apr 2015 · Brain Structure and Function