Lutz Jäncke

Laterality indices (LIs) quantify the left-right asymmetry of brain and behavioural variables and provide a measure that is statistically convenient and seemingly easy to interpret. Substantial variability in how structural and functional asymmetries are recorded, calculated, and reported, however, suggest little agreement on the conditions required for its valid assessment. The present study aimed for consensus on general aspects in this context of laterality research, and more specifically within a particular method or technique (i.e., dichotic listening, visual half-field technique, performance asymmetries, preference bias reports, electrophysiological recording, functional MRI, structural MRI, and functional transcranial Doppler sonography). Experts in laterality research were invited to participate in an online Delphi survey to evaluate consensus and stimulate discussion. In Round 0, 106 experts generated 453 statements on what they considered good practice in their field of expertise. Statements were organised into a 295-statement survey that the experts then were asked, in Round 1, to independently assess for importance and support, which further reduced the survey to 241 statements that were presented again to the experts in Round 2. Based on the Round 2 input, we present a set of critically reviewed key recommendations to record, assess, and report laterality research for various methods.

Editorial: Longitudinal aging research: Cognition, behavior and neuroscience

Tinnitus refers to the conscious perception of sound for which no external auditory stimulus can be identified. Tinnitus prevalence is relatively high, with estimates ranging from 10 to 15% of the population. This disease is, for many affected subjects, highly annoying. Unfortunately, most of the currently applied treatments are more or less ineffective. Thus, tinnitus is mostly chronically manifested. These treatment failures may be due to our limited understanding of the neural underpinnings. Therefore, well-conducted neuroscience studies with tinnitus patients are urgently needed. The authors of this paper examined global MEG-based resting-state functional connectivity of neural oscillations in a relatively large of individuals with chronic tinnitus (n=61). The tinnitus patients were additionally divided into those suffering from impaired hearing and those with no hearing loss. The authors examined the hypothesis that distinct neural oscillatory networks might be involved in generating and controlling tinnitus symptoms. The main finding of their study is that tinnitus symptoms are associated with either increased or decreased global functional connectivity measures in different frequency bands. Tinnitus severity was also significantly related to altered global functional connectivities in both auditory and nonauditory regions. The authors propose that these findings might be used as candidate biomarkers for monitoring tinnitus treatments. Importantly, however, this study enhances the knowledge about the neural mechanisms underlying the mysterious tinnitus problem.

With the worldwide increase in people speaking more than one language, a better understanding of the behavioural and neural mechanisms governing lexical selection, lexical access in multiple languages and code switching has attracted widespread interest from several disciplines. Previous studies documented higher costs when processing a non-native (L2) than a native (L1) language or when switching from L2 to L1. However, studies on auditory language reception are still scarce and did not take into account the degree of switching experience. Accordingly, in the present study, we combined behavioural and electrophysiological measurements to assess lexical access in L1 and L2 as well as code switching in professional simultaneous interpreters, trainee interpreters, foreign language teachers and Anglistics students, while the participants performed a bilingual auditory lexical decision task. The purpose of this study was to expand the knowledge on code switching in auditory language processing and examine whether the degree of simultaneous interpretation experience might reduce switching costs. As a main result, we revealed that L2 compared to L1 trials, as well as switch compared to non-switch trials, generally resulted in lower accuracies, longer reaction times and increased N400 amplitudes in all groups of participants. Otherwise, we did not reveal any influence of switching direction and interpretation expertise on N400 parameters. Taken together, these results suggest that a late age of L2 acquisition leads to switching costs, irrespective of proficiency level. Furthermore, we provided first evidence that simultaneous interpretation training does not diminish switching costs, at least when focusing on lexical access.

We analysed a dataset comprising 118 subjects who were scanned three times (at baseline, 1-year follow-up, and 7-year follow-up) using structural magnetic resonance imaging (MRI) over the course of 7 years. We aimed to examine whether it is possible to identify individual subjects based on a restricted number of neuroanatomical features measured 7 years previously. We used FreeSurfer to compute 15 standard brain measures (total intracranial volume [ICV], total cortical thickness [CT], total cortical surface area [CA], cortical grey matter [CoGM], cerebral white matter [CeWM], cerebellar cortex [CBGM], cerebellar white matter [CBWM], subcortical volumes [thalamus, putamen, pallidum, caudatus, hippocampus, amygdala and accumbens] and brain stem volume). We used linear discriminant analysis (LDA), random forest machine learning (RF) and a newly developed rule-based identification approach (RBIA) for the identification process. Using RBIA, different sets of neuroanatomical features (ranging from 2 to 14) obtained at baseline were combined by if-then rules and compared to the same set of neuroanatomical features derived from the 7-year follow-up measurement. We achieved excellent identification results with LDA, while the identification results for RF were very good but not perfect. The RBIA produced the best results, achieving perfect participant identification for some four-feature sets. The identification results improved substantially when using larger feature sets, with 14 neuroanatomical features providing perfect identification. Thus, this study shows again that the human brain is highly individual in terms of neuroanatomical features. These results are discussed in the context of the current literature on brain plasticity and the scientific attempts to develop brain-fingerprinting techniques.

Mathematical ability is shaped by a complex interaction between genetic and environmental factors. Currently, it is assumed that the genetic variance explains about 60% of the mathematical ability. However, it is currently unclear how a particular gene might influence those brain areas that are known to be involved in controlling mathematical abilities. In this study, the authors linked a particular gene (ROBO1) to mathematical abilities and neuroanatomical features of the brain. They examined the genetic influence on cerebral cortical volume of 3–6-year-old children without mathematical training to predict mathematical ability in school at 7–9 years of age. They identified that the ROBO1 gene, which is known to regulate prenatal growth of cerebral cortical layers, is associated with the volume of the right parietal cortex, a key region for mathematical operations. Interestingly, Individual volume differences in this region explained up to 20% of the behavioral variance in mathematical ability. This extraordinary study suggests that a fundamental genetic component of mathematical ability is rooted in the early development of the parietal cortex, which is modulated by a particular gene influence.

Some believe that the human brain is simply a blown-up primate brain. But is this really the case? The authors of this extraordinary study are answering this question by comparing brain sizes and several morphometric brain measures obtained from 14 primate species. Based on these data, the authors computed various neuroanatomical measures and conducted network analysis using graph-theoretical approaches. Their analysis revealed remarkable volumetric scaling relationships, pointing toward a restriction of macroscale connectivity in bigger brains. A major finding is that with increasing brain size across the different species, cortical surface area outpaces white matter volume. Thus, larger brains showed reduced overall connectedness accompanied by sparser long-range connectivity. These constraints on white matter connectivity are in addition associated with longer communication paths, higher local network clustering, and higher levels of asymmetry in connectivity patterns between homologous areas across the left and right hemispheres. These analyses uncovered a remarkable allometric scaling principle, which partly is also observed in the human brain {1,2}.