Optimising classification of proximal arm strength impairment in wheelchair rugby: a proof of concept study

This study examined the relationship between proximal arm strength and mobility performance in wheelchair rugby (WR) athletes and examined whether a valid structure for classifying arm strength impairment could be determined. Fifty-seven trained WR athletes with strength impaired arms and no trunk function performed six upper body isometric strength tests and three 10 m sprints in their rugby wheelchair. All strength measures correlated with 2 m and 10 m sprint times (r ≥ -0.43; p ≤ 0.0005) and were entered into k-means cluster analyses with 4-clusters (to mirror the current International Wheelchair Rugby Federation [IWRF] system) and 3-clusters. The 3-cluster structure provided a more valid structure than both the 4-cluster and existing IWRF system, as evidenced by clearer differences in strength (Effect sizes [ES] ≥ 1.0) and performance (ES ≥ 1.1) between adjacent clusters and stronger mean silhouette coefficient (0.64). Subsequently, the 3-cluster structure for classifying proximal arm strength impairment would result in less overlap between athletes from adjacent classes and reduce the likelihood of athletes being disadvantaged due to their impairment. This study demonstrated that the current battery of isometric strength tests and cluster analyses could facilitate the evidence-based development of classifying proximal arm strength impairment in WR

Additional file 2: of Recently evolved human-specific methylated regions are enriched in schizophrenia signals

Annotation of all DMRs with schizophrenia-associated SNPs. This file contains annotation of all the human-lineage specific DMRs that are associated with schizophrenia markers. Details of the various markers present within each DMR is provided, along with the marker with most significant p-value. (XLSX 263 kb

Additional file 1: of Recently evolved human-specific methylated regions are enriched in schizophrenia signals

Additional Method, Figures and Tables (DOCX 475 kb

Decreased brain activation in cuneus/precuneus in bipolar disorder cases (N = 85) compared to healthy controls (N = 121) for the Negative Faces > Shapes contrast.

<p>Color bar indicates z values. Coordinates are given in MNI space. X = 4 (sagittal view), Z = 22 (transversal view). Abbreviations: R, right; A, anterior.</p

Significant clusters at whole-brain level for diagnostic category and polygenic risk score analyses, corrected for sex and age.

<p>*Remains significant after Bonferroni correction (8 independent tests)</p><p>^#P < 0.05 with IQ and education in model</p><p>Abbrevations: Pos, Positive; Neg, Negative; HC, healthy controls; BD, bipolar disorder; PGRS, polygenic risk score; L, left; R, right. ‘+’, positively associated; ‘-’, negatively associated.</p><p>Coordinates are given in MNS space.</p><p>Significant clusters at whole-brain level for diagnostic category and polygenic risk score analyses, corrected for sex and age.</p

Demographic data and clinical characterization of individuals participating in a faces matching functional MRI study.

<p>Abbreviations: BD, bipolar disorder; HC, healthy controls; SD, standard deviation; WASI, Wechsler Abbreviated Scale of Intelligence; IDS, Inventory of Depressive Symptoms; YMRS, Young Mania Rating Scale; PANSS P score, Positive and Negative Syndrome Scale positive subscale; GAF-S, Global Assessment of Functioning–symptom score; GAF-F, Global Assessment of Functioning–function score; BD PGRS, bipolar disorder polygenic risk score; ms, milliseconds.</p><p>BD PGRS values are reported as z-scores (with SD in brackets).</p><p>Complete behavioral data (response times and accuracy rates per condition) were available for 80/85 BD and 119/121 HC. For the remaining individuals (5 BD, 2 HC), an accuracy rate for each session (i.e. a combined rate for negative faces and shapes, and for positive faces and shapes) was available and was used to confirm that the participants paid attention to the task (accuracy rate: 97.4% and 96.0%, respectively).</p><p>^a Mean age at fMRI scanning. Age range was 18 to 63.</p><p>^b IDS score at scanning was available for 60/85 individuals (70.6%).</p><p>^c YMRS score at scanning was available for 69/85 individuals (81.2%).</p><p>^d PANSS P score at scanning was available for 38/85 individuals (44.7%).</p><p>^e Last six months</p><p>Demographic data and clinical characterization of individuals participating in a faces matching functional MRI study.</p

Bipolar disorder polygenic risk score is positively correlated with BOLD activation in the right inferior frontal gyrus in the entire sample (N = 206) including bipolar disorder cases and healthy controls for the Negative Faces > Shapes contrast.

<p>Color bar indicates z values. Coordinates are given in MNI space. Y = 18 (coronal view), Z = 12 (transversal view). Abbreviations: R, right; A, anterior.</p

Segmental duplication (SD): Partial least square regression (PLSR) coefficients with multiple covariates.

<p>Segmental duplication (SD): Partial least square regression (PLSR) coefficients with multiple covariates.</p

Probing the Association between Early Evolutionary Markers and Schizophrenia

<div><p>Schizophrenia is suggested to be a by-product of the evolution in humans, a compromise for our language, creative thinking and cognitive abilities, and thus, essentially, a human disorder. The time of its origin during the course of human evolution remains unclear. Here we investigate several markers of early human evolution and their relationship to the genetic risk of schizophrenia. We tested the schizophrenia evolutionary hypothesis by analyzing genome-wide association studies of schizophrenia and other human phenotypes in a statistical framework suited for polygenic architectures. We analyzed evolutionary proxy measures: human accelerated regions, segmental duplications, and ohnologs, representing various time periods of human evolution for overlap with the human genomic loci associated with schizophrenia. Polygenic enrichment plots suggest a higher prevalence of schizophrenia associations in human accelerated regions, segmental duplications and ohnologs. However, the enrichment is mostly accounted for by linkage disequilibrium, especially with functional elements like introns and untranslated regions. Our results did not provide clear evidence that markers of early human evolution are more likely associated with schizophrenia. While SNPs associated with schizophrenia are enriched in HAR, Ohno and SD regions, the enrichment seems to be mediated by affiliation to known genomic enrichment categories. Taken together with previous results, these findings suggest that schizophrenia risk may have mainly developed more recently in human evolution.</p></div