Emotion dysregulation and integration of emotion-related brain networks affect intraindividual change in ADHD severity throughout late adolescence

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The Contribution of Cognitive Factors to Individual Differences in Understanding Noise-Vocoded Speech in Young and Older Adults

Noise-vocoded speech is commonly used to simulate the sensation after cochlear implantation as it consists of spectrally degraded speech. High individual variability exists in learning to understand both noise-vocoded speech and speech perceived through a cochlear implant (CI). This variability is partly ascribed to differing cognitive abilities like working memory, verbal skills or attention. Although clinically highly relevant, up to now, no consensus has been achieved about which cognitive factors exactly predict the intelligibility of speech in noise-vocoded situations in healthy subjects or in patients after cochlear implantation. We aimed to establish a test battery that can be used to predict speech understanding in patients prior to receiving a CI. Young and old healthy listeners completed a noise-vocoded speech test in addition to cognitive tests tapping on verbal memory, working memory, lexicon and retrieval skills as well as cognitive flexibility and attention. Partial-least-squares analysis revealed that six variables were important to significantly predict vocoded-speech performance. These were the ability to perceive visually degraded speech tested by the Text Reception Threshold, vocabulary size assessed with the Multiple Choice Word Test, working memory gauged with the Operation Span Test, verbal learning and recall of the Verbal Learning and Retention Test and task switching abilities tested by the Comprehensive Trail-Making Test. Thus, these cognitive abilities explain individual differences in noise-vocoded speech understanding and should be considered when aiming to predict hearing-aid outcome

Enhancing precision in human neuroscience

Human neuroscience has always been pushing the boundary of what is measurable. During the last decade, concerns about statistical power and replicability - in science in general, but also specifically in human neuroscience - have fueled an extensive debate. One important insight from this discourse is the need for larger samples, which naturally increases statistical power. An alternative is to increase the precision of measurements, which is the focus of this review. This option is often overlooked, even though statistical power benefits from increasing precision as much as from increasing sample size. Nonetheless, precision has always been at the heart of good scientific practice in human neuroscience, with researchers relying on lab traditions or rules of thumb to ensure sufficient precision for their studies. In this review, we encourage a more systematic approach to precision. We start by introducing measurement precision and its importance for well-powered studies in human neuroscience. Then, determinants for precision in a range of neuroscientific methods (MRI, M/EEG, EDA, Eye-Tracking, and Endocrinology) are elaborated. We end by discussing how a more systematic evaluation of precision and the application of respective insights can lead to an increase in reproducibility in human neuroscience

The effect of 10 Hz repetitive transcranial magnetic stimulation of posterior parietal cortex on visual attention.

Repetitive transcranial magnetic stimulation (rTMS) of the posterior parietal cortex (PPC) at frequencies lower than 5 Hz transiently inhibits the stimulated area. In healthy participants, such a protocol can induce a transient attentional bias to the visual hemifield ipsilateral to the stimulated hemisphere. This bias might be due to a relatively less active stimulated hemisphere and a relatively more active unstimulated hemisphere. In a previous study, Jin and Hilgetag (2008) tried to switch the attention bias from the hemifield ipsilateral to the hemifield contralateral to the stimulated hemisphere by applying high frequency rTMS. High frequency rTMS has been shown to excite, rather than inhibit, the stimulated brain area. However, the bias to the ipsilateral hemifield was still present. The participants' performance decreased when stimuli were presented in the hemifield contralateral to the stimulation site. In the present study we tested if this unexpected result was related to the fact that participants were passively resting during stimulation rather than performing a task. Using a fully crossed factorial design, we compared the effects of high frequency rTMS applied during a visual detection task and high frequency rTMS during passive rest on the subsequent offline performance in the same detection task. Our results were mixed. After sham stimulation, performance was better after rest than after task. After active 10 Hz rTMS, participants' performance was overall better after task than after rest. However, this effect did not reach statistical significance. The comparison of performance after rTMS with task and performance after sham stimulation with task showed that 10 Hz stimulation significantly improved performance in the whole visual field. Thus, although we found a trend to better performance after rTMS with task than after rTMS during rest, we could not reject the hypothesis that high frequency rTMS with task and high frequency rTMS during rest equally affect performance

Correction for time-on-task.

<p>Conditional response accuracy (CRA) for the stimulation and the test phase. The last row contains the difference between the performance in the stimulation and the test phase, that is, the correction factor for time-on-task. L, R and B denote left, right and bilateral Gabors.</p><p>Correction for time-on-task.</p

Response measures.

<p>Dependent measures analyzed in the study.</p><p>Response measures.</p

Corrected conditional response accuracies (CORR) after 10 Hz rTMS.

<p>Stars indicate significant differences after correcting for time on task as indicated by one-tailed paired t-tests with <i>p</i> < 0.05 after correcting for multiple comparisons.</p

Results.

<p>Summary of percent correct response accuracy (RA), conditional response accuracy (CRA), response omissions (RO), erroneous unilateral responses to bilateral Gabors after correcting for time-on-task (ERR) and performance corrected for the time spent of task (CORR). Note that for the task, CORR is identical to CRA, since the time-on-task correction was applied only to the rest data. Reaction times (RT) are listed in milliseconds (ms).</p><p>Results.</p