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Background: Transcranial magnetic stimulation (TMS) treatments have shown promise in improving arm recovery in stroke patients. Currently, little is known about patients' experiences with repetitive TMS treatment, and this lack of knowledge may affect optimal implementation in clinical practice. The aim of this explorative study was to gain insight in the perceived effects and experiences of the design and delivery of a rTMS treatment for upper limb recovery from the perspectives of stroke patients., Methods: This qualitative study was conducted as part of a randomized controlled trial (RCT) in a specialized rehabilitation center. Data were collected through face-to-face semi-structured interviews with 13 stroke patients who completed a 10-day rTMS intervention for upper limb recovery. The interviews were recorded, transcribed verbatim and analyzed using thematic analysis., Results: The major themes that emerged from the patients' feedback were the following: positive experiences of the treatment (experienced physical effects, comfort, therapeutic relationship, receiving information, learning about the brain, no burden of added rTMS treatment session, no unpleasant aspects), concerns (effects of stimulation of the brain, equipment, logistics), general experience of recovery, experienced psychological effects (grateful, sense of purpose, recovery as extra motivation to exercise, disappointment and hope of group allocation), and motivation to participate (personal benefit and cognitions, altruism). Important components related to the positive experience of the design and delivery of the treatment included comfort (i.e. moment of relaxation) and the sensation of a painless treatment without side-effects. Key concerns included uncertainty and anxiety about possible negative consequences and group allocation., Conclusions: This study demonstrates that rTMS is well accepted by stroke patients with an upper limb paresis. Besides the expectation of a therapeutic benefit, the patients reported various psychological effects. Positive experiences, such as the provision of a short moment of relaxation each day, could have practical implications for clinical stroke rehabilitation settings aimed at improving patient satisfaction. Explanation about and feedback from routine motor recovery progression monitoring at fixed times post-stroke is also valued by patients. Negative emotions may be limited or avoided by transparent and recurrent information delivery in future trials.

Objective: Recovery of the paretic arm post-stroke can be assessed using observational and self-reported measures. The aim of this study was to determine whether the correspondence (match) or non-correspondence (mismatch) between observational and self-reported improvements in upper limb capacity are significantly different at 0-3 months compared with 3-6 months post-stroke., Methods: A total of 159 patients with ischaemic stroke with upper limb paresis were included in the study. Recovery of arm capacity was measured with observational (Action Research Arm Test; ARAT) and self-reported measures (Motor Activity Log Quality of Movement; MAL-QOM and Stroke Impact Scale Hand; SIS-Hand) at 0-3 and 3-6 months post-stroke. The proportion of matches was defined (contingency tables and Fisher's exact test) and compared across the different time-windows using McNemar's test., Results: The proportion of matches was not significantly different at 0-3 months compared with 3-6 months post-stroke for the ARAT vs MAL-QOM and SIS-Hand (all p > 0.05). In case of mismatches, patients' self-reports were more often pessimistic (86%) in the first 3 months post-stroke compared with the subsequent 3 months (39%)., Conclusion: The match between observational and self-reported measures of upper limb capacity is not dependent on the timing of assessment post-stroke. Assessment of both observational and self-reported measures may help to recognize possible over- or under-estimation of improvement in upper limb capacity post-stroke.

Children's ability to relate number to a continuous quantity abstraction visualized as a number line is widely accepted to be predictive of mathematics achievement. However, a debate has emerged with respect to how children's placements are distributed on this number line across development. In the current study, different models were applied to children's longitudinal number placement data to get more insight into the development of number line representations in kindergarten and early primary school years. In addition, longitudinal developmental relations between number line placements and mathematical achievement, measured with a national test of mathematics, were investigated using cross-lagged panel modeling. A group of 442 children participated in a 3-year longitudinal study (ages 5-8 years) in which they completed a number-to-position task every 6 months. Individual number line placements were fitted to various models, of which a one-anchor power model provided the best fit for many of the placements at a younger age (5 or 6 years) and a two-anchor power model provided better fit for many of the children at an older age (7 or 8 years). The number of children who made linear placements also grew with age. Cross-lagged panel analyses indicated that the best fit was provided with a model in which number line acuity and mathematics performance were mutually predictive of each other rather than models in which one ability predicted the other in a non-reciprocal way. This indicates that number line acuity should not be seen as a predictor of math but that both skills influence each other during the developmental process., (Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.)

Symbolic (i.e., with Arabic numerals) approximate arithmetic with large numerosities is an important predictor of mathematics. It was previously evidenced to onset before formal schooling at the kindergarten age (Gilmore et al., 2007) and was assumed to map onto pre-existing nonsymbolic (i.e., abstract magnitudes) representations. With a longitudinal study (Experiment 1), we show, for the first time, that nonsymbolic and symbolic arithmetic demonstrate different developmental trajectories. In contrast to Gilmore et al.'s (2007) findings, Experiment 1 showed that symbolic arithmetic onsets in grade 1, with the start of formal schooling, not earlier. Gilmore et al. (2007) had examined English-speaking children, whereas we assessed a large Dutch-speaking sample. The Dutch language for numbers can be cognitively more demanding, for example, due to the inversion property in numbers above 20. Thus, for instance, the number 48 is named in Dutch "achtenveertig" (eight and forty) instead of "forty eight." To examine the effect of the language of numbers, we conducted a cross-cultural study with English- and Dutch-speaking children that had similar SES and math achievement skills (Experiment 2). Results demonstrated that Dutch-speaking kindergarteners lagged behind English-speaking children in symbolic arithmetic, not nonsymbolic and demonstrated a working memory overload in symbolic arithmetic, not nonsymbolic. Also, we show for the first time that the ability to name two-digit numbers highly correlates with symbolic approximate arithmetic not nonsymbolic. Our experiments empirically demonstrate that the symbolic number system is modulated more by development and education than the nonsymbolic system. Also, in contrast to the nonsymbolic system, the symbolic system is modulated by language.

How do kindergarteners solve different single-digit addition problem formats? We administered problems that differed solely on the basis of two dimensions: response type (approximate or exact), and stimulus type (nonsymbolic, i.e., dots, or symbolic, i.e., Arabic numbers). We examined how performance differs across these dimensions, and which cognitive mechanism (mental model, transcoding, or phonological storage) underlies performance in each problem format with respect to working memory (WM) resources and mental number line representations. As expected, nonsymbolic problem formats were easier than symbolic ones. The visuospatial sketchpad was the primary predictor of nonsymbolic addition. Symbolic problem formats were harder because they either required the storage and manipulation of quantitative symbols phonologically or taxed more WM resources than their nonsymbolic counterparts. In symbolic addition, WM and mental number line results showed that when an approximate response was needed, children transcoded the information to the nonsymbolic code. When an exact response was needed, however, they phonologically stored numerical information in the symbolic code. Lastly, we found that more accurate symbolic mental number line representations were related to better performance in exact addition problem formats, not the approximate ones. This study extends our understanding of the cognitive processes underlying children's simple addition skills.