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Heritability and polygenic predictionIn the EUR sample, the SNP-based heritability (h 2 SNP ) (that is, the proportion of variance in liability attributable to all measured SNPs)
Background
Intraindividual variability in reaction time (RT) has received extensive discussion as an indicator of cognitive performance, a putative intermediate phenotype of many clinical disorders, and a possible trans-diagnostic phenotype that may elucidate shared risk factors for mechanisms of psychiatric illnesses.
Scope and Methodology
Using the examples of attention deficit hyperactivity disorder (ADHD) and autism spectrum disorders (ASD), we discuss RT variability. We first present a new meta-analysis of RT variability in ASD with and without comorbid ADHD. We then discuss potential mechanisms that may account for RT variability and statistical models that disentangle the cognitive processes affecting RTs. We then report a second meta-analysis comparing ADHD and non-ADHD children on diffusion model parameters. We consider how findings inform the search for neural correlates of RT variability.
Findings
Results suggest that RT variability is increased in ASD only when children with comorbid ADHD are included in the sample. Furthermore, RT variability in ADHD is explained by moderate to large increases (d = 0.63–0.99) in the ex-Gaussian parameter τ and the diffusion parameter drift rate, as well as by smaller differences (d = 0.32) in the diffusion parameter of nondecision time. The former may suggest problems in state regulation or arousal and difficulty detecting signal from noise, whereas the latter may reflect contributions from deficits in motor organization or output. The neuroimaging literature converges with this multicomponent interpretation and also highlights the role of top-down control circuits.
Conclusion
We underscore the importance of considering the interactions between top-down control, state regulation (e.g. arousal), and motor preparation when interpreting RT variability and conclude that decomposition of the RT signal provides superior interpretive power and suggests mechanisms convergent with those implicated using other cognitive paradigms. We conclude with specific recommendations for the field for next steps in the study of RT variability in neurodevelopmental disorders.
Schizophrenia and bipolar disorder are two distinct diagnoses that share symptomology. Understanding the genetic factors contributing to the shared and disorder-specific symptoms will be crucial for improving diagnosis and treatment. In genetic data consisting of 53,555 cases (20,129 bipolar disorder [BD], 33,426 schizophrenia [SCZ]) and 54,065 controls, we identified 114 genome-wide significant loci implicating synaptic and neuronal pathways shared between disorders. Comparing SCZ to BD (23,585 SCZ, 15,270 BD) identified four genomic regions including one with disorder-independent causal variants and potassium ion response genes as contributing to differences in biology between the disorders. Polygenic risk score (PRS) analyses identified several significant correlations within case-only phenotypes including SCZ PRS with psychotic features and age of onset in BD. For the first time, we discover specific loci that distinguish between BD and SCZ and identify polygenic components underlying multiple symptom dimensions. These results point to the utility of genetics to inform symptomology and potential treatment.
The electroencephalographic response to transcranial magnetic stimulation (TMS) recently has been established as a direct parameter of motor cortex excitability. Its N100 component was suggested to reflect an inhibitory response. We investigated influences of cerebral maturation on TMS-evoked N100 in 6- to 10-year-old healthy children. We used a forewarned reaction time (contingent negative variation) task to test the effects of response preparation and sensory attention on N100 amplitude. Single-pulse TMS of motor cortex at 105% motor threshold intensity evoked N100 amplitudes of more than 100 microV in resting children (visible in single trials), which correlated negatively with age and positively with absolute stimulation intensity. During late contingent negative variation, which involves preactivation of the cortical structures necessary for a fast response, N100 amplitude was significantly reduced. We conclude that (1) N100 amplitude reduction during late contingent negative variation provides further evidence that TMS-evoked N100 reflects inhibitory processes, (2) response preparation and attention modulate N100, and (3) TMS-evoked N100 undergoes maturational changes and could serve to test cortical integrity and inhibitory function in children. Parallels between the inhibitory N100 after TMS (provoking massive synchronous excitation) and the inhibitory wave component of epileptic spike wave complexes are suggested.
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