Native language differences in the structural connectome of the human brain

Investigating attention toward pain-related cues in an Arabic-speaking population with and without chronic pain

There is some evidence for attentional biases in individuals with chronic pain (CP). Cultural and linguistic differences might affect the manifestation of these processes across populations. However, such attentional biases have not been explored in the Arabic-speaking population. The current study investigated these attentional biases and possible associations with resilience. Two matched groups of Arabic-speaking participants with (58) and without (58) CP were recruited from Jordan and the United Kingdom. They completed emotionally modified versions of the Posner cueing and Stroop tasks, alongside questionnaires. Significant group differences were found for the Posner task, with the CP group exhibiting disengagement revealed by the inhibition of return (IOR) effect for sensory pain-related cues compared to delayed disengagement for the other cue types. The control group showed IOR across cue types. No group differences were found on the Stroop task. The CP group had lower resilience scores than healthy controls, and resilience moderated performance on the Posner task. The study provides preliminary evidence about the attentional processes in the Arabic population; the speed of disengagement is affected in the CP group with early disengagement for sensory pain-related information compared to affect pain and neutral stimuli. Furthermore, resilience levels in the CP and control group moderated the performance on the Posner task, suggesting that it influences attentional allocation. This study can help in understanding how the phenomenon of attention bias intertwines with the cultural and linguistic factors. Future research should further explore attentional dynamics across different time points in this population and the modulatory effect of resilience.

White matter plasticity during second language learning within and across hemispheres

Adult second language (L2) learning is a challenging enterprise inducing neuroplastic changes in the human brain. However, it remains unclear how the structural language connectome and its subnetworks change during adult L2 learning. The current study investigated longitudinal changes in white matter (WM) language networks in each hemisphere, as well as their interconnection, in a large group of Arabic-speaking adults who learned German intensively for 6 mo. We found a significant increase in WM-connectivity within bilateral temporal-parietal semantic and phonological subnetworks and right temporal-frontal pathways mainly in the second half of the learning period. At the same time, WM-connectivity between the two hemispheres decreased significantly. Crucially, these changes in WM-connectivity are correlated with L2 performance. The observed changes in subnetworks of the two hemispheres suggest a network reconfiguration due to lexical learning. The reduced interhemispheric connectivity may indicate a key role of the corpus callosum in L2 learning by reducing the inhibition of the language-dominant left hemisphere. Our study highlights the dynamic changes within and across hemispheres in adult language-related networks driven by L2 learning.

Prediction underlying comprehension of human motion: an analysis of Deaf signer and non-signer EEG in response to visual stimuli

Introduction

Sensory inference and top-down predictive processing, reflected in human neural activity, play a critical role in higher-order cognitive processes, such as language comprehension. However, the neurobiological bases of predictive processing in higher-order cognitive processes are not well-understood.

Methods

This study used electroencephalography (EEG) to track participants' cortical dynamics in response to Austrian Sign Language and reversed sign language videos, measuring neural coherence to optical flow in the visual signal. We then used machine learning to assess entropy-based relevance of specific frequencies and regions of interest to brain state classification accuracy.

Results

EEG features highly relevant for classification were distributed across language processing-related regions in Deaf signers (frontal cortex and left hemisphere), while in non-signers such features were concentrated in visual and spatial processing regions.

Discussion

The results highlight functional significance of predictive processing time windows for sign language comprehension and biological motion processing, and the role of long-term experience (learning) in minimizing prediction error.