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      Changes in brain activity following intensive voice treatment in children with cerebral palsy : Changes in Brain Activity Following Intensive Voice Treatment in Children With Cerebral Palsy

      , , , , , ,
      Human Brain Mapping
      Wiley-Blackwell

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          Abstract

          Eight children (3 females; 8–16 years) with motor speech disorders secondary to cerebral palsy underwent 4 weeks of an intensive neuroplasticity‐principled voice treatment protocol, LSVT LOUD ® , followed by a structured 12‐week maintenance program. Children were asked to overtly produce phonation (ah) at conversational loudness, cued‐phonation at perceived twice‐conversational loudness, a series of single words, and a prosodic imitation task while being scanned using fMRI, immediately pre‐ and post‐treatment and 12 weeks following a maintenance program. Eight age‐ and sex‐matched controls were scanned at each of the same three time points. Based on the speech and language literature, 16 bilateral regions of interest were selected a priori to detect potential neural changes following treatment. Reduced neural activity in the motor areas (decreased motor system effort) before and immediately after treatment, and increased activity in the anterior cingulate gyrus after treatment (increased contribution of decision making processes) were observed in the group with cerebral palsy compared to the control group. Using graphical models, post‐treatment changes in connectivity were observed between the left supramarginal gyrus and the right supramarginal gyrus and the left precentral gyrus for the children with cerebral palsy, suggesting LSVT LOUD enhanced contributions of the feedback system in the speech production network instead of high reliance on feedforward control system and the somatosensory target map for regulating vocal effort. Network pruning indicates greater processing efficiency and the recruitment of the auditory and somatosensory feedback control systems following intensive treatment. Hum Brain Mapp 38:4413–4429, 2017 . © 2017 Wiley Periodicals, Inc.

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          There is great interest in estimating brain "networks" from FMRI data. This is often attempted by identifying a set of functional "nodes" (e.g., spatial ROIs or ICA maps) and then conducting a connectivity analysis between the nodes, based on the FMRI timeseries associated with the nodes. Analysis methods range from very simple measures that consider just two nodes at a time (e.g., correlation between two nodes' timeseries) to sophisticated approaches that consider all nodes simultaneously and estimate one global network model (e.g., Bayes net models). Many different methods are being used in the literature, but almost none has been carefully validated or compared for use on FMRI timeseries data. In this work we generate rich, realistic simulated FMRI data for a wide range of underlying networks, experimental protocols and problematic confounds in the data, in order to compare different connectivity estimation approaches. Our results show that in general correlation-based approaches can be quite successful, methods based on higher-order statistics are less sensitive, and lag-based approaches perform very poorly. More specifically: there are several methods that can give high sensitivity to network connection detection on good quality FMRI data, in particular, partial correlation, regularised inverse covariance estimation and several Bayes net methods; however, accurate estimation of connection directionality is more difficult to achieve, though Patel's τ can be reasonably successful. With respect to the various confounds added to the data, the most striking result was that the use of functionally inaccurate ROIs (when defining the network nodes and extracting their associated timeseries) is extremely damaging to network estimation; hence, results derived from inappropriate ROI definition (such as via structural atlases) should be regarded with great caution. Copyright © 2010 Elsevier Inc. All rights reserved.
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            Principles of experience-dependent neural plasticity: implications for rehabilitation after brain damage.

            This paper reviews 10 principles of experience-dependent neural plasticity and considerations in applying them to the damaged brain. Neuroscience research using a variety of models of learning, neurological disease, and trauma are reviewed from the perspective of basic neuroscientists but in a manner intended to be useful for the development of more effective clinical rehabilitation interventions. Neural plasticity is believed to be the basis for both learning in the intact brain and relearning in the damaged brain that occurs through physical rehabilitation. Neuroscience research has made significant advances in understanding experience-dependent neural plasticity, and these findings are beginning to be integrated with research on the degenerative and regenerative effects of brain damage. The qualities and constraints of experience-dependent neural plasticity are likely to be of major relevance to rehabilitation efforts in humans with brain damage. However, some research topics need much more attention in order to enhance the translation of this area of neuroscience to clinical research and practice. The growing understanding of the nature of brain plasticity raises optimism that this knowledge can be capitalized upon to improve rehabilitation efforts and to optimize functional outcome.
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              An update on the prevalence of cerebral palsy: a systematic review and meta-analysis.

              The aim of this study was to provide a comprehensive update on (1) the overall prevalence of cerebral palsy (CP); (2) the prevalence of CP in relation to birthweight; and (3) the prevalence of CP in relation to gestational age. A systematic review and meta-analysis was conducted and reported, based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) statement. Population-based studies on the prevalence of CP in children born in 1985 or after were selected. Statistical analysis was carried out using computer package R, version 2.14. A total of 49 studies were selected for this review. The pooled overall prevalence of CP was 2.11 per 1000 live births (95% confidence interval [CI] 1.98-2.25). The prevalence of CP stratified by gestational age group showed the highest pooled prevalence to be in children weighing 1000 to 1499g at birth (59.18 per 1000 live births; 95% CI 53.06-66.01), although there was no significant difference on pairwise meta-regression with children weighing less than 1000g. The prevalence of CP expressed by gestational age was highest in children born before 28 weeks' gestation (111.80 per 1000 live births; 95% CI 69.53-179.78; p<0.0327). The overall prevalence of CP has remained constant in recent years despite increased survival of at-risk preterm infants. © The Authors. Developmental Medicine & Child Neurology © 2013 Mac Keith Press.
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                Author and article information

                Journal
                Human Brain Mapping
                Hum. Brain Mapp.
                Wiley-Blackwell
                10659471
                September 2017
                September 05 2017
                : 38
                : 9
                : 4413-4429
                Article
                10.1002/hbm.23669
                6867062
                28580693
                b6ed98f6-3c79-4782-929f-86adda771d84
                © 2017

                http://doi.wiley.com/10.1002/tdm_license_1.1

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