Can spatial filtering separate voluntary and involuntary components in children with dyskinetic cerebral palsy?

The design of myocontrolled devices faces particular challenges in children with dyskinetic cerebral palsy because the electromyographic signal for control contains both voluntary and involuntary components. We hypothesized that voluntary and involuntary components of movements would be uncorrelated and thus detectable as different synergistic patterns of muscle activity, and that removal of the involuntary components would improve online EMG-based control. Therefore, we performed a synergy-based decomposition of EMG-guided movements, and evaluated which components were most controllable using a Fitts' Law task. Similarly, we also tested which muscles were most controllable. We then tested whether removing the uncontrollable components or muscles improved overall function in terms of movement time, success rate, and throughput. We found that removal of less controllable components or muscles did not improve EMG control performance, and in many cases worsened performance. These results suggest that abnormal movement in dyskinetic CP is consistent with a pervasive distortion of voluntary movement rather than a superposition of separable voluntary and involuntary components of movement

Association of SNPs in EGR3 and ARC with schizophrenia supports a biological pathway for schizophrenia risk

We have previously hypothesized a biological pathway of activity-dependent synaptic plasticity proteins that addresses the dual genetic and environmental contributions to schizophrenia. Accordingly, variations in the immediate early gene EGR3, and its target ARC, should influence schizophrenia susceptibility. We used a pooled Next-Generation Sequencing approach to identify variants across these genes in U.S. populations of European (EU) and African (AA) descent. Three EGR3 and one ARC SNP were selected and genotyped for validation, and three SNPs were tested for association in a replication cohort. In the EU group of 386 schizophrenia cases and 150 controls EGR3 SNP rs1877670 and ARC SNP rs35900184 showed significant associations (p = 0.0078 and p = 0.0275, respectively). In the AA group of 185 cases and 50 controls, only the ARC SNP revealed significant association (p = 0.0448). The ARC SNP did not show association in the Han Chinese (CH) population. However, combining the EU, AA, and CH groups revealed a highly significant association of ARC SNP rs35900184 (p = 2.353 x 10(-7); OR [95% CI] = 1.54 [1.310-1.820]). These findings support previously reported associations between EGR3 and schizophrenia. Moreover, this is the first report associating an ARC SNP with schizophrenia and supports recent large-scale GWAS findings implicating the ARC complex in schizophrenia risk. These results support the need for further investigation of the proposed pathway of environmentally responsive, synaptic plasticity-related, schizophrenia genes

Effect of target distance on controllability for myocontrol

Myocontrol holds great promise because it has the potential to provide flexible and accurate prosthetic control that approaches the quality of normal movement. Speed and accuracy are important factors to consider when applying myoelectric signals to external devices. Fitts's law can be used to assess the speed-accuracy trade-off. We hypothesized that speed is affected not only by accuracy as prescribed by Fitts's law, but also by target distances independent of target size. A total of 12 healthy adult subjects were studied. Subjects controlled the vertical movement of a computer cursor by contracting their dominant first dorsal interosseus muscle to reach targets as quickly as possible. We manipulated twenty-five different target distances in order to obtain five indices of difficulty, as defined by Fitts's law, combined with five target widths. We tested the relationship between movement time and the index of difficulty as predicted by Fitts's law among different combinations of target distance and widths. Results showed a significant linear regression for all conditions, with the exception of a significantly longer movement time than predicted for targets close to the start point. Movements to these targets showed significantly higher relative variance during stabilization, higher overshoot, and lower success. Therefore, we found that with comparable index of difficulty, small distance movements had a higher variability, slower movement, and higher rates of error compared to larger distance movements. Our results are consistent with our hypothesis that low muscle activation required for short distances results in higher variability and low controllability in reaching the target as required by the task demand. Neurophysiological mechanisms underlying the violation of the Fitts's law relationship are discussed. These results have significance for myocontrol applications, and we suggest that such applications require control signals with sufficient recruitment to reduce variability at lower levels of muscle activation

Validation Phase: Comparison of Pooled Sample NGS with Individual Genotyping Results and Genetic Association Analyses in EU cohort.

<p><i>145 cases</i>, <i>150 controls; 195 males</i>, <i>100 females</i>.</p><p>Pooled EU.</p

<i>ARC</i> SNP Association Analysis in All Populations Combined.

<p><i>1062 cases</i>, <i>691 controls; 1015 males</i>, <i>735 females</i>.</p><p>All Samples Combined <i>ARC</i>.</p

Replication of <i>ARC</i> SNP Association with Schizophrenia in Han Chinese Population.

<p><i>491 cases</i>, <i>491 controls; 525 males</i>, <i>457 females</i>.</p><p>Replication CH <i>ARC</i>.</p

Replication Phase: Genetic Association Between Schizophrenia and SNPs in <i>EGR3</i> and <i>ARC</i> in AA Population.

<p><i>185 cases</i>, <i>50 controls; 127 males</i>, <i>108 females</i>.</p><p>Replication AA.</p

Validation Phase: Comparison of Pooled Sample NGS with Individual Genotyping Results and Genetic Association Analyses in AA cohort.

<p><i>42 cases</i>, <i>50 controls; 49 males</i>, <i>43 females</i>.</p><p>Pooled AA.</p

Minor Allele Frequency Differences in EU and AA.

<p>Results of polymorphisms identified by NGS are mapped to the reference human genome used by the 1000 Genomes project (hg18). Vertical lines indicate the Δ MAF (difference between the minor allele frequency in cases versus controls) for each base pair of the genome region investigated. Higher peaks indicate a greater difference in prevalence of that variation between cases and controls, and thus a higher potential for association with schizophrenia risk. The red graph indicates data for Whites and the Blue graph indicates data for African Americans. (A) Δ MAF map for the <i>EGR3</i> locus, hg18 coordinates chr8:22,591,791–22,612,066. (B) Δ MAF map for the <i>ARC</i> locus, hg18 coordinates chr8:143,682,474–143,697,026</p