Involvement of the cortico-basal ganglia-thalamocortical loop in developmental stuttering

Stuttering is a complex neurodevelopmental disorder that has to date eluded a clear explication of its pathophysiological bases. In this review, we utilize the Directions Into Velocities of Articulators (DIVA) neurocomputational modeling framework to mechanistically interpret relevant findings from the behavioral and neurological literatures on stuttering. Within this theoretical framework, we propose that the primary impairment underlying stuttering behavior is malfunction in the cortico-basal ganglia-thalamocortical (hereafter, cortico-BG) loop that is responsible for initiating speech motor programs. This theoretical perspective predicts three possible loci of impaired neural processing within the cortico-BG loop that could lead to stuttering behaviors: impairment within the basal ganglia proper; impairment of axonal projections between cerebral cortex, basal ganglia, and thalamus; and impairment in cortical processing. These theoretical perspectives are presented in detail, followed by a review of empirical data that make reference to these three possibilities. We also highlight any differences that are present in the literature based on examining adults versus children, which give important insights into potential core deficits associated with stuttering versus compensatory changes that occur in the brain as a result of having stuttered for many years in the case of adults who stutter. We conclude with outstanding questions in the field and promising areas for future studies that have the potential to further advance mechanistic understanding of neural deficits underlying persistent developmental stuttering.R01 DC007683 - NIDCD NIH HHS; R01 DC011277 - NIDCD NIH HHSPublished versio

The development of rapid online control in children with and without Developmental Coordination Disorder

The online control of manual actions is critical for the development of functional skills in children, not the least because demands on behaviour and complexity of the environment increase with age. When unexpected changes occur during the course of action, rapid online corrections are necessary to ensure that movement parameters (like force and timing) can be quickly updated. Developmentally, the motor network supporting online control is thought to mature rapidly over childhood; however, cross-sectional research suggests that the trajectory of change is not linear because the mode of control undergoes reorganisation during middle childhood. At the same time, development of frontal executive systems (particularly inhibition) may influence the way children enlist motor functions like online (predictive) control. Maturational theories that once considered these systems to be unitary in their development are now being challenged by a more parsimonious neuro-behavioural hypothesis—interactive specialization; this suggests behaviour can be strengthened and supported by the interaction of separate but overlapping neural networks. growing body of research indicates that online control processes may be disrupted for children with motor coordination problems (aka Developmental Coordination Disorder; DCD). As well, it has been widely reported that these children show problems related to executive function including tasks that involve response inhibition. It is argued here that deficits in predictive online control may be exacerbated under task conditions that require concurrent inhibitory control as when one is required to withhold a response to a compelling cue and move to an alternate location. However, there is not a clear picture of developmental change in the ability to couple motor and executive systems, nor of differences in growth patterns between typically developing children (TDC) and children with DCD. The purpose of my research was to address this knowledge gap by conducting cross-sectional and longitudinal studies of development to examine the unfolding interaction between online and executive systems in healthy and atypically developing children. Specifically, I examined how TDC and DCD groups corrected their arm movement mid-flight during a step-perturbation paradigm, and how a concurrent inhibitory load constrained their responses to a target shift

Summaries of plenary, symposia, and oral sessions at the XXII World Congress of Psychiatric Genetics, Copenhagen, Denmark, 12-16 October 2014

The XXII World Congress of Psychiatric Genetics, sponsored by the International Society of Psychiatric Genetics, took place in Copenhagen, Denmark, on 12-16 October 2014. A total of 883 participants gathered to discuss the latest findings in the field. The following report was written by student and postdoctoral attendees. Each was assigned one or more sessions as a rapporteur. This manuscript represents topics covered in most, but not all of the oral presentations during the conference, and contains some of the major notable new findings reported

Longitudinal Analysis of Risk Factors Affecting Reading Trajectories in Children Diagnosed with Pediatric Brain Tumors

Prior research suggests aggressive cancer treatments contribute to cognitive impairments in children diagnosed with pediatric brain tumors. The literature also suggests that younger age at diagnosis (AAD) and treatment may result in disrupted cognitive trajectories due to limited brain plasticity. In line with this research, we hypothesized an interaction between radiation therapy (RT) and young AAD of brain tumors, where young AAD and RT results in lower standard scores on the WRAT-R Reading Comprehension Subtest. Analyses included archival data; the sample consists of 134 children diagnosed with pediatric brain tumors with multiple assessments resulting in 487 cases for analysis. Participants were diagnosed with mixed tumor types and locations. A two level multilevel model was used to analyze reading trajectories while taking into account AAD, time since diagnosis, socioeconomic status (SES), and RT. Results detected a positive interaction between AAD and RT (γ =2.08, p=.02). For participants with RT, younger AAD was associated with lower reading scores, whereas AAD had no effect for participants without RT. Results also detected a negative interaction between radiation and time (γ =-2.29, p=.00) indicating that children treated with RT have reading scores that decrease over time. These data suggested that children diagnosed with pediatric brain tumors treated with RT are at higher risk of reading impairment as reflected in their reading scores

Machine learning in the social and health sciences

The uptake of machine learning (ML) approaches in the social and health sciences has been rather slow, and research using ML for social and health research questions remains fragmented. This may be due to the separate development of research in the computational/data versus social and health sciences as well as a lack of accessible overviews and adequate training in ML techniques for non data science researchers. This paper provides a meta-mapping of research questions in the social and health sciences to appropriate ML approaches, by incorporating the necessary requirements to statistical analysis in these disciplines. We map the established classification into description, prediction, and causal inference to common research goals, such as estimating prevalence of adverse health or social outcomes, predicting the risk of an event, and identifying risk factors or causes of adverse outcomes. This meta-mapping aims at overcoming disciplinary barriers and starting a fluid dialogue between researchers from the social and health sciences and methodologically trained researchers. Such mapping may also help to fully exploit the benefits of ML while considering domain-specific aspects relevant to the social and health sciences, and hopefully contribute to the acceleration of the uptake of ML applications to advance both basic and applied social and health sciences research

When change is the only constant:The promise of longitudinal neuroimaging in understanding social anxiety disorder

Longitudinal studies offer a unique window into developmental change. Yet, most of what we know about the pathophysiology of psychiatric disorders is based on cross-sectional work. Here, we highlight the importance of adopting a longitudinal approach in order to make progress into the identification of neurobiological mechanisms of social anxiety disorder (SAD). Using examples, we illustrate how longitudinal data can uniquely inform SAD etiology and timing of interventions. The brain’s inherently adaptive quality requires that we model risk correlates of disorders as dynamic in their expression. Developmental theories regarding timing of environmental events, cascading effects and (mal)adaptations of the developing brain will be crucial components of comprehensive, integrative models of SAD. We close by discussing analytical considerations in working with longitudinal, developmental data

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction.

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome