Autistic children often exhibit atypical brain lateralization of language processing, but it is unclear what aspects of language contribute to this phenomenon. This study employed functional near-infrared spectroscopy to measure hemispheric lateralization by estimating hemodynamic responses associated with processing linguistic and non-linguistic auditory stimuli. The study involved a group of autistic children (N = 20, mean age = 5.8 years) and a comparison group of nonautistic peers (N = 20, mean age = 6.5 years). The children were presented with stimuli with systematically decreasing linguistic relevance: naturalistic native speech, meaningless native speech with scrambled word order, nonnative speech, and music. The results revealed that both groups showed left lateralization in the temporal lobe when listening to naturalistic native speech. However, the distinction emerged between autism and nonautistic in terms of processing the linguistic hierarchy. Specifically, the nonautistic comparison group demonstrated a systematic reduction in left lateralization as linguistic relevance decreased. In contrast, the autism group displayed no such pattern and showed no lateralization when listening to scrambled native speech accompanied by enhanced response in the right hemisphere. These results provide evidence of atypical neural specialization for spoken language in preschool- and school-age autistic children and shed new light on the underlying linguistic correlates contributing to such atypicality at the sublexical level.

Harmonization and aggregation of heterogeneous data from Human Biomonitoring (HBM) studies is critical to enhance the reliability of conclusions and move towards FAIR (i.e., Findable, Accessible, Interoperable, Reusable) data. We introduce the HBM Data Toolkit developed by the Flemish Institute for Technological Research (Vlaamse Instelling voor Technologisch Onderzoek - VITO) with the primary goal of optimizing data integrity and interoperability, key steps towards FAIR, while using flexible templates and ensuring data confidentiality. The HBM Data Toolkit was built in 2023-2024 and made available for stakeholders (via https://hbm.vito.be/tools) within the Partnership for the Assessment of Risks from Chemicals (PARC eu-parc.eu). The toolkit consists of 4 modules including data harmonization, data validation, derived variables, and summary statistics calculation. A Python package was created to interpret the templates, making validation and transformation possible. Using Pyodide and WebAssembly, the toolkit runs entirely in the web browser, enabling secure, local execution of Python code without uploading any data. In the validation module, input files in common format (i.e., Excel) were used to configure data templates, aligning with standards and formats as specified under the HBM4EU project (hbm4eu.eu) and PARC. The HBM Data Toolkit allows harmonized data storage in the Personal Exposure and Health (PEH) data platform. Formatted and validated HBM data were made compatible with the Monte Carlo Risk Assessment (MCRA) platform. In the derived variables calculation module, the toolkit also allows users to calculate imputed censored data and standardize/normalize the biomarker data. Furthermore, summary statistics (e.g., geometric mean, percentiles) can be calculated and further visualized in the European HBM dashboard and integrated into the Information Platform for Chemical Monitoring (IPCHEM). In conclusion, the current toolkit proves effective in advancing data quality, harmonization, and aggregation in HBM studies. With local execution, user-friendly codebooks, and standardized schemas, it supports a unified framework that enables consistent analysis and interpretation across diverse studies and datasets.

SignificanceInterhemispheric imbalance after stroke impedes motor recovery. Although repetitive transcranial magnetic stimulation (rTMS) shows therapeutic potential, the neural mechanisms underlying its effects—particularly on cortical excitability and interhemispheric interaction—remain unclear. Understanding these mechanisms is essential for optimizing personalized neuromodulation strategies in stroke rehabilitation.AimTo explore how low-frequency rTMS facilitates motor recovery post-stroke, especially in regulating interhemispheric inhibition (IHI), functional connectivity, and cortical excitability, we used a multimodal approach that included paired-pulse TMS, functional near-infrared spectroscopy (fNIRS), and motor outcomes.ApproachA total of 27 patients with stroke were randomized to receive 2 weeks of either real (n=14) or sham (n=13) 1 Hz rTMS over the contralesional primary motor cortex (M1). Paired-pulse TMS was used to measure IHI and laterality quotient (LQ), whereas fNIRS was used to measure resting-state functional connectivity (RSFC). Clinical scales were also used to assess behavioral-level motor functioning.ResultsCompared with the sham group, rTMS significantly modulated IHI, as evidenced by a reduction in inhibitory drive from the stimulated contralesional M1 to ipsilesional M1 (mean difference = 15.57%, p=0.048). In parallel, RSFC analysis revealed decreased connectivity from contralesional M1 to ipsilesional premotor and supplementary motor areas [F(2,50)=7.704, p=0.006, false discovery rate–corrected]. Between-group comparisons further showed greater LQ improvements in the rTMS group than in the sham group. Changes in selected neurophysiological measures were significantly correlated with improvements in upper limb motor function.ConclusionsWe showed that low-frequency rTMS promotes motor recovery after stroke by rebalancing cortical excitability and reducing maladaptive connectivity patterns. The integration of TMS and fNIRS provides converging evidence for rTMS-induced cortical plasticity and highlights the potential of these tools for guiding personalized neuromodulation strategies in stroke rehabilitation.

Pitch is a fundamental element in music. While most previous studies on musical pitch have focused on musicians, our understanding of musical pitch perception in non-musicians is still limited. This study aimed to explore how Mandarin-speaking listeners who did not receive musical training perceive and categorize musical pitch. Two experiments were conducted in the study. In Experiment 1, participants were asked to discriminate musical tone pairs with different intervals. The results showed that the nearer apart the tones were, the more difficult it was to distinguish. Among adjacent note pairs at major 2nd pitch distance, the A4–B4 pair was perceived as the easiest to differentiate, while the C4–D4 pair was found to be the most difficult. In Experiment 2, participants completed a tone discrimination and identification task with the C4–D4 and A4–B4 musical tone continua as stimuli. The results revealed that the C4–D4 tone continuum elicited stronger categorical perception than the A4–B4 continuum, although the C4–D4 pair was previously found to be more difficult to distinguish in Experiment 1, suggesting a complex interaction between pitch perception and categorization processing. Together, these two experiments revealed the cognitive mechanism underlying musical pitch perception in ordinary populations and provided insights into future musical pitch training strategies.