Although dyslexia is characterized by a deficit in phonological representations, the nature of this deficit is debated. Previously, it was shown that adults with dyslexia respond differently to online manipulations of auditory feedback. In the present study, we found that individual differences in reading and reading-related skills within a group of 30 children (10-13 years old) with dyslexia were associated with the response to altered feedback. The fractional anisotropy of the arcuate fasciculus/superior longitudinal fasciculus was not directly related to the response to altered feedback. This study corroborates that speech perception-production communication is important for phonological representations and reading.

a Purpose: The purpose of this study was to examine whether developmental dyslexia (DD) is characterized by deficiencies in speech sensory and motor feedforward and feedback mechanisms, which are involved in the modulation of phonological representations. Method: A total of 42 adult native speakers of Dutch (22 adults with DD; 20 participants who were typically reading controls) were asked to produce /bep/ while the first formant (F1) of the /e/ was not altered (baseline), increased (ramp), held at maximal perturbation (hold), and not altered again (after-effect). The F1 of the produced utterance was measured for each trial and used for statistical analyses. The measured F1s produced during each phase were entered in a linear mixed-effects model. Results:Participants with DD adapted more strongly during the ramp phase and returned to baseline to a lesser extent when feedback was back to normal (after-effect phase) when compared with the typically reading group. In this study, a faster deviation from baseline during the ramp phase, a stronger adaptation response during the hold phase, and a slower return to baseline during the after-effect phase were associated with poorer reading and phonological abilities. Conclusion:The data of the current study are consistent with the notion that the phonological deficit in DD is associated with a weaker sensorimotor magnet for phonological representations.

Studies of the role of phonological representations in learning to read have almost exclusively focused on speech perception. In the current study, we examined links between sensorimotor control of speech, reading, and reading-related abilities. We studied two languages, English and Dutch, which vary in the regularity of their spelling-to-sound mappings. There were 236 American and Dutch children, 4 to 8 years old, who performed an altered auditory feedback task in which the first formant of the /ɛ/ vowel was altered. A stronger response to altered feedback for literate relative to preliterate children was observed, and this was particularly the case for the Dutch children. Moreover, the magnitude of the responses was related to precursors of reading in preliterate children and to reading skill in literate children. We propose that these findings could be related to changes in children's speech production skills that facilitate the integration of orthographic and phonemic information.

Background Accurately predicting which patients with chronic heart failure (CHF) are particularly vulnerable for adverse outcomes is of crucial importance to support clinical decision making. The goal of the current study was to examine the predictive value on long term heart failure (HF) hospitalisation and all-cause mortality in CHF patients, by exploring and exploiting machine learning (ML) and traditional statistical techniques on a Dutch health insurance claims database. Methods Our study population consisted of 25,776 patients with a CHF diagnosis code between 2012 and 2014 and one year and three years follow-up HF hospitalisation (1446 and 3220 patients respectively) and all-cause mortality (2434 and 7882 patients respectively) were measured from 2015 to 2018. The area under the receiver operating characteristic (ROC) curve (AUC) was calculated after modelling the data using Logistic Regression, Random Forest, Elastic Net regression and Neural Networks. Results AUC rates ranged from 0.710 to 0.732 for 1-year HF hospitalisation, 0.705–0.733 for 3-years HF hospitalisation, 0.765–0.787 for 1-year mortality and 0.764–0.791 for 3-years mortality. Elastic Net performed best for all endpoints. Differences between techniques were small and only statistically significant between Elastic Net and Logistic Regression compared with Random Forest for 3-years HF hospitalisation. Conclusion In this study based on a health insurance claims database we found clear predictive value for predicting long-term HF hospitalisation and mortality of CHF patients by using ML techniques compared to traditional statistics.

Decades of research have led to several competing theories regarding the neural contributors to impaired reading. But how can we know which theory (or theories) identifies the types of markers that indeed differentiate between individuals with reading disabilities (RD) and their typically developing (TD) peers? To answer this question, we propose a new analytical tool for theory evaluation and comparison, grounded in the Bayesian latent-mixture modeling framework. We start by constructing a series of latent-mixture classification models, each reflecting one existing theoretical claim regarding the neurofunctional markers of RD (highlighting network-level differences in either mean activation, inter-subject heterogeneity, inter-region variability, or connectivity). Then, we run each model on fMRI data alone (i.e., while models are blind to participants’ behavioral status), which enables us to interpret the fit between a model’s classification of participants and their behavioral (known) RD/TD status as an estimate of its explanatory power. Results from n=127 adolescents and young adults (RD: n=59; TD: n=68) show that models based on network-level differences in mean activation and heterogeneity failed to differentiate between TD and RD individuals. In contrast, classifications based on variability and connectivity were significantly associated with participants’ behavioral status. These findings suggest that differences in inter-region variability and connectivity may be better network-level markers of RD than mean activation or heterogeneity (at least in some populations and tasks). More broadly, the results demonstrate the promise of latent-mixture modeling as a theory-driven tool for evaluating different theoretical claims regarding neural contributors to language disorders and other cognitive traits.