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      Motor versus body awareness: Voxel-based lesion analysis in anosognosia for hemiplegia and somatoparaphrenia following right hemisphere stroke

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      Cortex
      Elsevier BV

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          Abstract

          Anosognosia for hemiplegia (AHP) is informative about the neurocognitive basis of motor awareness. However, it is frequently associated with concomitant symptoms, such as hemispatial neglect and disturbances in the sense of body ownership (DSO). Although double dissociations between these symptoms have been reported, there is ongoing debate about whether they are manifestations of independent abnormalities, or a single neurocognitive deficit. We aimed to investigate the specificity of lesions associated with AHP by surpassing four, existing methodological limitations: (a) recruit a relatively large sample of patients (total N = 70) in a multi-centre study; (b) identify lesions associated with AHP in grey and white matter using voxel-based methods; (c) take into account the duration of AHP and concomitant neglect symptoms; and (d) compare lesions against a control hemiplegic group, patients suffering from AHP and DSO, and a few, rare patients with selective DSO. Results indicated that acute AHP is associated with a wide network, mainly including: (1) the Rolandic operculum, (2) the insula and (3) the superior temporal gyri. Subcortically, damage mainly involved the basal ganglia and white matter, mostly the superior corona radiate, arcuate fasciculus and the part of the ventral, superior longitudinal fasciculus. Persistent symptoms were linked with wider damage involving fronto-temporal cortex and long white matter tracts. A shift in the latero-medial direction (mainly involving the basal ganglia and surrounding white matter) emerged when DSO was taken accounted for. These results suggest that while bodily awareness is processed by areas widely distributed across the brain, intact subcortical structures and white matter tracts may be necessary to support basic feelings of owning and controlling contralateral body parts. An accurate and 'up-to-date' awareness of our motor abilities, however, may rely also on intact processing in cortical areas which presumably allow higher-order inferences about the current state of the body.

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          Most cited references80

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          The assessment and analysis of handedness: The Edinburgh inventory

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            Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain.

            An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute (MNI) (D. L. Collins et al., 1998, Trans. Med. Imag. 17, 463-468) was performed. The MNI single-subject main sulci were first delineated and further used as landmarks for the 3D definition of 45 anatomical volumes of interest (AVOI) in each hemisphere. This procedure was performed using a dedicated software which allowed a 3D following of the sulci course on the edited brain. Regions of interest were then drawn manually with the same software every 2 mm on the axial slices of the high-resolution MNI single subject. The 90 AVOI were reconstructed and assigned a label. Using this parcellation method, three procedures to perform the automated anatomical labeling of functional studies are proposed: (1) labeling of an extremum defined by a set of coordinates, (2) percentage of voxels belonging to each of the AVOI intersected by a sphere centered by a set of coordinates, and (3) percentage of voxels belonging to each of the AVOI intersected by an activated cluster. An interface with the Statistical Parametric Mapping package (SPM, J. Ashburner and K. J. Friston, 1999, Hum. Brain Mapp. 7, 254-266) is provided as a freeware to researchers of the neuroimaging community. We believe that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain in which deformations are well known. However, this tool does not alleviate the need for more sophisticated labeling strategies based on anatomical or cytoarchitectonic probabilistic maps.
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              Neural signatures of body ownership: a sensory network for bodily self-consciousness.

              Body ownership refers to the special perceptual status of one's own body, which makes bodily sensations seem unique to oneself. We studied the neural correlates of body ownership by controlling whether an external object was accepted as part of the body or not. In the rubber hand illusion (RHI), correlated visuotactile stimulation causes a fake hand to be perceived as part of one's own body. In the present study, we distinguished between the causes (i.e., multisensory stimulation) and the effect (i.e., the feeling of ownership) of the RHI. Participants watched a right or a left rubber hand being touched either synchronously or asynchronously with respect to their own unseen right hand. A quantifiable correlate of the RHI is a shift in the perceived position of the subject's hand toward the rubber hand. We used positron emission tomography to identify brain areas whose activity correlated with this proprioceptive measure of body ownership. Body ownership was related to activity in the right posterior insula and the right frontal operculum. Conversely, when the rubber hand was not attributed to the self, activity was observed in the contralateral parietal cortex, particularly the somatosensory cortex. These structures form a network that plays a fundamental role in linking current sensory stimuli to one's own body and thus also in self-consciousness.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Cortex
                Cortex
                Elsevier BV
                00109452
                October 2016
                October 2016
                : 83
                : 62-77
                Article
                10.1016/j.cortex.2016.07.001
                27494375
                4f8be445-8d93-49f3-93b7-1190712b25a4
                © 2016

                https://www.elsevier.com/tdm/userlicense/1.0/

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