15
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Motor Imagery Impairment in Postacute Stroke Patients

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Not much is known about how well stroke patients are able to perform motor imagery (MI) and which MI abilities are preserved after stroke. We therefore applied three different MI tasks (one mental chronometry task, one mental rotation task, and one EEG-based neurofeedback task) to a sample of postacute stroke patients ( n = 20) and age-matched healthy controls ( n = 20) for addressing the following questions: First, which of the MI tasks indicate impairment in stroke patients and are impairments restricted to the paretic side? Second, is there a relationship between MI impairment and sensory loss or paresis severity? And third, do the results of the different MI tasks converge? Significant differences between the stroke and control groups were found in all three MI tasks. However, only the mental chronometry task and EEG analysis revealed paresis side-specific effects. Moreover, sensitivity loss contributed to a performance drop in the mental rotation task. The findings indicate that although MI abilities may be impaired after stroke, most patients retain their ability for MI EEG-based neurofeedback. Interestingly, performance in the different MI measures did not strongly correlate, neither in stroke patients nor in healthy controls. We conclude that one MI measure is not sufficient to fully assess an individual's MI abilities.

          Related collections

          Most cited references48

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Dopaminergic foundations of schizotypy as measured by the German version of the Oxford-Liverpool Inventory of Feelings and Experiences (O-LIFE)—a suitable endophenotype of schizophrenia

          The concept of schizotypy or “psychosis proneness” captures individual differences in perceptual, cognitive, and affective experiences that may relate to a range of psychotic disorders. The concept is an important way to assess the contribution of pre-existing psychological and genetically based biological features to the development of illnesses such as schizophrenia (so called endophenotypes). The Oxford-Liverpool Inventory of Feelings and Experiences (O-LIFE) is a widely used multi-dimensional measure of the construct and consists of four scales which mirror several groups of psychotic symptoms: Unusual Experiences (UnEx; positive symptoms), Cognitive Disorganization (CogDis; cognitive symptoms), Introvertive Anhedonia (IntAn; negative symptoms), and Impulsive Nonconformity (ImpNon; impulsive and antisocial symptoms). For the purpose of evaluating the suitability of schizotypy as an endophenotype of schizophrenia the current version of the O-LIFE was translated into German: its psychometric properties (including re-test reliability and construct validity) were examined in a large sample (n > 1200) and compared to those of the English original. The German version was both highly reliable and consistent with the original. The study aimed to show that schizotypy as measured by the O-LIFE can indeed be regarded as an endophenotype of schizophrenia in terms of genetic associations regarding relevant dopamine-related candidate polymorphisms of schizotypy [i.e., Val158Met-polymorphism of the COMT gene, uVNTR of the MAOA gene, Taq1A-polymorphism of the DRD2 gene, VNTR of the SLC6A3 (DAT) gene]. We also wanted to compare the genetic associations of the O-LIFE to those published using other operationalizations of schizotypy. Our results show a large number of significant associations and borderline-significant trends between the O-LIFE sub-scales and a range of genes, thereby supporting using the O-LIFE in the search for endophenotypic markers.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Brain-computer interfaces for communication and rehabilitation.

            Brain-computer interfaces (BCIs) use brain activity to control external devices, thereby enabling severely disabled patients to interact with the environment. A variety of invasive and noninvasive techniques for controlling BCIs have been explored, most notably EEG, and more recently, near-infrared spectroscopy. Assistive BCIs are designed to enable paralyzed patients to communicate or control external robotic devices, such as prosthetics; rehabilitative BCIs are designed to facilitate recovery of neural function. In this Review, we provide an overview of the development of BCIs and the current technology available before discussing experimental and clinical studies of BCIs. We first consider the use of BCIs for communication in patients who are paralyzed, particularly those with locked-in syndrome or complete locked-in syndrome as a result of amyotrophic lateral sclerosis. We then discuss the use of BCIs for motor rehabilitation after severe stroke and spinal cord injury. We also describe the possible neurophysiological and learning mechanisms that underlie the clinical efficacy of BCIs.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms.

              One of the most popular feature extraction algorithms for brain-computer interfaces (BCI) is common spatial patterns (CSPs). Despite its known efficiency and widespread use, CSP is also known to be very sensitive to noise and prone to overfitting. To address this issue, it has been recently proposed to regularize CSP. In this paper, we present a simple and unifying theoretical framework to design such a regularized CSP (RCSP). We then present a review of existing RCSP algorithms and describe how to cast them in this framework. We also propose four new RCSP algorithms. Finally, we compare the performances of 11 different RCSP (including the four new ones and the original CSP), on electroencephalography data from 17 subjects, from BCI competition datasets. Results showed that the best RCSP methods can outperform CSP by nearly 10% in median classification accuracy and lead to more neurophysiologically relevant spatial filters. They also enable us to perform efficient subject-to-subject transfer. Overall, the best RCSP algorithms were CSP with Tikhonov regularization and weighted Tikhonov regularization, both proposed in this paper.
                Bookmark

                Author and article information

                Journal
                Neural Plast
                Neural Plast
                NP
                Neural Plasticity
                Hindawi
                2090-5904
                1687-5443
                2017
                28 March 2017
                : 2017
                : 4653256
                Affiliations
                1Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany
                2Kliniken Schmieder Allensbach, Allensbach, Germany
                3Kliniken Schmieder Konstanz, Konstanz, Germany
                4Research Center Neurosensory Science, University of Oldenburg, Oldenburg, Germany
                5Cluster of Excellence Hearing4All, University of Oldenburg, Oldenburg, Germany
                Author notes

                Academic Editor: Malgorzata Kossut

                Author information
                http://orcid.org/0000-0001-9392-1244
                http://orcid.org/0000-0001-8814-5535
                http://orcid.org/0000-0001-9426-1559
                Article
                10.1155/2017/4653256
                5387846
                28458926
                047bce00-ec3e-4bdf-86e9-cb2c4901459d
                Copyright © 2017 Niclas Braun et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 9 December 2016
                : 14 February 2017
                Categories
                Research Article

                Neurosciences
                Neurosciences

                Comments

                Comment on this article