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      Time-Dependent Cortical Activation in Voluntary Muscle Contraction

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          Abstract

          This study was to characterize dynamic source strength changes estimated from high-density scalp electroencephalogram (EEG) at different phases of a submaximal voluntary muscle contraction. Eight healthy volunteers performed isometric handgrip contractions of the right arm at 20% maximal intensity. Signals of the handgrip force, electromyography (EMG) from the finger flexor and extensor muscles and 64-channel EEG were acquired simultaneously. Sources of the EEG were analyzed at 19 time points across preparation, execution and sustaining phases of the handgrip. A 3-layer boundary element model (BEM) based on the MNI (Montréal Neurological Institute) brain MRI was used to overlay the sources. A distributed current density model, LORETA L1 norm method was applied to the data that had been processed by independent component analysis (ICA). Statistical analysis based on a mixed-effects polynomial regression model showed a significant and consistent time-dependent non-linear source strength change pattern in different phases of the handgrip. The source strength increased at the preparation phase, peaked at the force onset time and decreased in the sustaining phase. There was no significant difference in the changing pattern of the source strength among Brodmann’s areas 1, 2, 3, 4, and 6. These results show, for the first time, a high time resolution increasing-and-decreasing pattern of activation among the sensorimotor regions with the highest activity occurs at the muscle activity onset. The similarity in the source strength time courses among the cortical centers examined suggests a synchronized parallel function in controlling the motor activity.

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          Most cited references48

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          Neuronal population coding of movement direction.

          Although individual neurons in the arm area of the primate motor cortex are only broadly tuned to a particular direction in three-dimensional space, the animal can very precisely control the movement of its arm. The direction of movement was found to be uniquely predicted by the action of a population of motor cortical neurons. When individual cells were represented as vectors that make weighted contributions along the axis of their preferred direction (according to changes in their activity during the movement under consideration) the resulting vector sum of all cell vectors (population vector) was in a direction congruent with the direction of movement. This population vector can be monitored during various tasks, and similar measures in other neuronal populations could be of heuristic value where there is a neural representation of variables with vectorial attributes.
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            A standardized boundary element method volume conductor model.

            We used a 3-compartment boundary element method (BEM) model from an averaged magnetic resonance image (MRI) data set (Montreal Neurological Institute) in order to provide simple access to realistically shaped volume conductor models for source reconstruction, as compared to individually derived models. The electrode positions were transformed into the model's coordinate system, and the best fit dipole results were transformed back to the original coordinate system. The localization accuracy of the new approach was tested in a comparison with simulated data and with individual BEM models of epileptic spike data from several patients. The standard BEM model consisted of a total of 4770 nodes, which describe the smoothed cortical envelope, the outside of the skull, and the outside of the skin. The electrode positions were transformed to the model coordinate system by using 3-5 fiducials (nasion, left and right preauricular points, vertex, and inion). The transformation consisted of an averaged scaling factor and a rigid transformation (translation and rotation). The potential values at the transformed electrode positions were calculated by linear interpolation from the stored transfer matrix of the outer BEM compartment triangle net. After source reconstruction the best fit dipole results were transformed back into the original coordinate system by applying the inverse of the first transformation matrix. Test-dipoles at random locations and with random orientations inside of a highly refined reference BEM model were used to simulate noise-free data. Source reconstruction results using a spherical and the standardized BEM volume conductor model were compared to the known dipole positions. Spherical head models resulted in mislocation errors at the base of the brain. The standardized BEM model was applied to averaged and unaveraged epileptic spike data from 7 patients. Source reconstruction results were compared to those achieved by 3 spherical shell models and individual BEM models derived from the individual MRI data sets. Similar errors to that evident with simulations were noted with spherical head models. Standardized and individualized BEM models were comparable. This new approach to head modeling performed significantly better than a simple spherical shell approximation, especially in basal brain areas, including the temporal lobe. By using a standardized head for the BEM setup, it offered an easier and faster access to realistically shaped volume conductor models as compared to deriving specific models from individual 3-dimensional MRI data.
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              Selectivity for the shape, size, and orientation of objects for grasping in neurons of monkey parietal area AIP.

              In this study, we mainly investigated the visual selectivity of hand-manipulation-related neurons in the anterior intraparietal area (area AIP) while the animal was grasping or fixating on three-dimensional (3D) objects of different geometric shapes, sizes, and orientations. We studied the activity of 132 task-related neurons during the hand-manipulation tasks in the light and in the dark, as well as during object fixation. Seventy-seven percent (101/132) of the hand-manipulation-related neurons were visually responsive, showing either lesser activity during manipulation in the dark than during that in the light (visual-motor neurons) or no activation in the dark (visual-dominant neurons). Of these visually responsive neurons, more than half (n = 66) responded during the object-fixation task (object-type). Among these, 55 were tested for their shape selectivity during the object-fixation task, and many (n = 25) were highly selective, preferring one particular shape of the six different shapes presented (ring, cube, cylinder, cone, sphere, and square plate). For 28 moderately selective object-type neurons, we performed multidimensional scaling (MDS) to examine how the neurons encode the similarity of objects. The results suggest that some moderately selective neurons responded preferentially to common geometric features shared by similar objects (flat, round, elongated, etc.). Moderately selective nonobject-type visually responsive neurons, which did not respond during object fixation, were found by MDS to be more closely related to the handgrip than to the object shape. We found a similar selectivity for handgrip in motor-dominant neurons that did not show any visual response. With regard to the size of the objects, 16 of 26 object-type neurons tested were selective for both size and shape, whereas 9 object-type neurons were selective for shape but not for size. Seven of 12 nonobject-type and all (8/8) of the motor-dominant neurons examined were selective for size, and almost all of them were also selective for objects. Many hand-manipulation-related neurons that preferred the plate and/or ring were selective for the orientation of the objects (17/20). These results suggest that the visual responses of object-type neurons represent the shape, size, and/or orientation of 3D objects, whereas those of the nonobject-type neurons probably represent the shape of the handgrip, grip size, or hand-orientation. The activity of motor-dominant neurons was also, in part, likely to represent these parameters of hand movement. This suggests that the dorsal visual pathway is concerned with the aspect of form, orientation, and/or size perception that is relevant for the visual control of movements.
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                Author and article information

                Journal
                Open Neuroimag J
                TONIJ
                The Open Neuroimaging Journal
                Bentham Open
                1874-4400
                23 December 2011
                2011
                : 5
                : 232-239
                Affiliations
                [1 ]Departments of Biomedical Engineering, The Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195, USA
                [2 ]Departments of Physical Medicine and Rehabilitation, The Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195, USA
                [3 ]Departments of Quantitative Health Sciences, The Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195, USA
                [4 ]Department of Chemical and Biomedical Engineering, Cleveland State University, Cleveland, Ohio 44115, USA
                [5 ]Department of Health and Kinesiology, University of Texas at San Antonio, San Antonio, TX 78249, USA
                Author notes
                [* ]Address correspondence to this author at the Department of Biomedical Engineering, The Cleveland Clinic, 9500 Euclid Avenue, ND20, Cleveland, OH 44195, USA; Tel: (216) 4459336; Fax: (216) 444-9198; E-mail: yueg@ 123456ccf.org
                Article
                TONIJ-5-232
                10.2174/1874440001105010232
                3256579
                22253665
                205c55f8-4074-4215-ad06-4c38504592b8
                © Yang et al.; Licensee Bentham Open.

                This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0/), which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

                History
                : 6 November 2010
                : 10 May 2011
                : 27 June 2011
                Categories
                Article

                Neurosciences
                current density reconstruction,electroencephalography (eeg),eeg source,voluntary muscle contraction.,brain,handgrip force

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