Comorbid motor deficits in a clinical sample of children with specific language impairment

The aim of the present study was to compare the motor function of a clinical sample of children with specific language impairment (SLI) to a language-matched comparison group that had not been referred for SLI assessment. A typical language comparison group with similar nonverbal IQ was also included. There were approximately 35 children in each group, aged 9- to 10-years-old, and the children completed a range of standardised language, motor and literacy measures. The results showed that the SLI group scored significantly lower than the language-matched and typical language comparison groups on all of the motor and literacy measures. We conclude that language factors alone are insufficient to explain the extensive comorbid motor and literacy deficits shown by the children with SLI in this study. We suggest that the clinical diagnosis of SLI may be influenced by the presence of additional developmental difficulties, which should be made explicit in assessment procedures, and that intervention strategies which address the broad range of difficulties experienced by children with a clinical diagnosis of SLI, should be prioritised.

Synthesis of Walking Sounds for Alleviating Gait Disturbances in Parkinson’s Disease

Managing gait disturbances in people with Parkinson’s disease is a pressing challenge, as symptoms can contribute to injury and morbidity through an increased risk of falls. While drug-based interventions have limited efficacy in alleviating gait impairments, certain non-pharmacological methods, such as cueing, can also induce transient improvements to gait. The approach adopted here is to use computationally-generated sounds to help guide and improve walking actions. The first method described uses recordings of force data taken from the steps of a healthy adult which in turn were used to synthesize realistic gravel-footstep sounds that represented different spatio-temporal parameters of gait, such as step duration and step length. The second method described involves a novel method of sonifying, in real time, the swing phase of gait using real-time motion-capture data to control a sound synthesis engine. Both approaches explore how simple but rich auditory representations of action based events can be used by people with Parkinson’s to guide and improve the quality of their walking, reducing the risk of falls and injury. Studies with Parkinson’s disease patients are reported which show positive results for both techniques in reducing step length variability. Potential future directions for how these sound approaches can be used to manage gait disturbances in Parkinson’s are also discussed.

Motor deficits in children with autism spectrum disorder: A cross-syndrome study

Recent research suggests that children with autism spectrum disorder (ASD) experience some level of motor difficulty, and that this may be associated with social communication skills. However, other studies show that children with language impairments, but without the social communication problems, are at risk of motor difficulties as well. The aim of the present study was to determine if children with ASD have syndrome specific motor deficits in comparison to children with specific language impairment (SLI). We used an independent groups design with three groups of children (8-10 years old) matched on age and nonverbal IQ; an ASD group, an SLI group, and a typically developing (TD) group. All of the children completed an individually administered, standardized motor assessment battery. We found that the TD group demonstrated significantly better motor skills than either the ASD or SLI groups. Detailed analyses of the motor subtests revealed that the ASD and SLI groups had very similar motor profiles across a range of fine and gross motor skills, with one exception. We conclude that children with ASD, and SLI, are at risk of clinically significant motor deficits. However, future behavioural and neurological studies of motor skills in children with ASD should include an SLI comparison group in order to identify possible autism specific deficits.

Urocortin – From Parkinson’s disease to the skeleton

Urocortin (Ucn 1), a 40 amino acid long peptide related to corticotropin releasing factor (CRF) was discovered 19 years ago, based on its sequence homology to the parent molecule. Its existence was inferred in the CNS because of anatomical and pharmacological discrepancies between CRF and its two receptor subtypes. Although originally found in the brain, where it has opposing actions to CRF and therefore confers stress-coping mechanisms, Ucn 1 has subsequently been found throughout the periphery including heart, lung, skin, and immune cells. It is now well established that this small peptide is involved in a multitude of physiological and pathophysiological processes, due to its receptor subtype distribution and promiscuity in second messenger signalling pathways. As a result of extensive studies in this field, there are now well over one thousand peer reviewed publications involving Ucn 1. In this review, we intend to highlight some of the less well known actions of Ucn 1 and in particular its role in neuronal cell protection and maintenance of the skeletal system, both by conventional methods of reviewing the literature and using bioinformatics, to highlight further associations between Ucn 1 and disease conditions. Understanding how Ucn 1 works in these tissues, will help to unravel its role in normal and pathophysiological processes. This would ultimately allow the generation of putative medical interventions for the alleviation of important diseases such as Parkinson's disease, arthritis, and osteoporosis.

Compensatory Arm Reactions in Individuals with Parkinson’s Disease

This study examined how perturbation-evoked compensatory arm reactions in individuals with Parkinson’s disease (PD) are influenced by explicit verbal instruction. Ten individuals with PD and 15 older adults without PD responded to surface translations with or without specific instruction to reach for and grasp the handrail. Electromyographic (EMG) and kinematic recordings were taken from the reaching arm. Results showed that individuals with and without PD benefitted similarly from explicit instruction. Explicit instruction resulted in earlier (p=0.005) and larger (p<0.001) medial deltoid EMG responses in comparison to no specific instructions. Compensatory arm reactions also occurred with a higher peak medio-lateral wrist velocity (p<0.001) and higher peak shoulder abduction angular velocity (p<0.001) with explicit instruction. Explicit instruction positively influenced compensatory arm reactions in individuals with and without PD. Future research is needed to determine whether the benefits of instruction persist over time and translate to a loss of balance in real life.

Detection of Parkinson’s disease tremor-onset with a frequency analysing artificial neural network

Deep Brain Stimulator devices are becoming widely used for therapeutic benefits in movement disorders such as Parkinson's disease. Prolonging the battery life span of such devices could dramatically reduce the risks and accumulative costs associated with surgical replacement. This paper demonstrates how an artificial neural network can be trained using pre-processing frequency analysis of deep brain electrode recordings to detect the onset of tremor in Parkinsonian patients. Implementing this solution into an 'intelligent' neurostimulator device will remove the need for continuous stimulation currently used, and open up the possibility of demand-driven stimulation. Such a methodology could potentially decrease the power consumption of a deep brain pulse generator.

Identification of Parkinson’s disease tremor onset using artificial neural networks

In this paper we consider the possibility of using an artificial neural network to accurately identify the onset of Parkinson’s Disease tremors in human subjects. Data for the network is obtained by means of deep brain implantation in the human brain. Results presented have been obtained from a practical study (i.e. real not simulated data) but should be regarded as initial trials to be discussed further. It can be seen that a tuned artificial neural network can act as an extremely effective predictor in these circumstances.