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Roos, N. M.

Publications and source records attributed to Roos, N. M..

2 recordsLinked to original sources

Microstructure of white matter association tracts in the temporal lobe predicts the timing of electrophysiological activity and behaviour in picture naming

White matter (WM) microstructure has been mainly associated with accuracy of picture naming. However, little is known about how WM properties relate to the timing of picture naming and its underlying electrophysiological activity. Here, we linked diffusion-weighted, electrophysiological, and behavioural data, using time-based measures to conceptually connect WM deterioration to alterations in conduction speed and precision. Our sample included individuals of varying age, with or without stroke. We correlated fractional anisotropy (FA) of bilateral WM tracts traversing the temporal lobe with mean response times (RTs) and response time variability (coefficient of variation, CV) in picture naming, and with the onset latency of neuronal activity, represented by the electrophysiological P2 component time-locked to picture onset. We selected tracts showing associations with P2 latency and naming performance separately, on which we conducted path analyses examining the relationship between structure, neuronal activity and behaviour. Depending on the tract, higher FA was associated with faster and/or more consistent naming, suggesting that ventral tracts differentially contribute to naming speed and consistency. Importantly, path modelling for the right inferior fronto-occipital fasciculus (IFOF) revealed that FA predicted P2 latency, which in turn predicted RTs, possibly reflecting a structure-activity-behaviour relationship in lexical-semantic processing. P2 source reconstruction demonstrated anatomical proximity to posterior segments of the right IFOF, supporting these findings. Overall, this study provides a proof of concept for modelling structure-activity-behaviour relationships in language research, while highlighting the need for elaboration of such relations at a more mechanistic level.

neuroscience↗

The Concise Language Paradigm in left and right hemisphere stroke: a longitudinal source-localized EEG study

The mechanisms underlying language recovery after stroke remain poorly understood. Most previous studies have either focused on language comprehension or production and/or relied on hemodynamic measures, with limited complementary electrophysiological evidence. Here, we combined the Concise Language Paradigm (CLaP), assessing comprehension and production, with individualized electroencephalography (EEG) source reconstruction to investigate post-stroke language function and changes in hemispheric dominance over time. In two sessions, we tested stroke-free controls, individuals with left hemisphere (LH) or right hemisphere (RH) stroke. Participants with stroke were assessed two and seven months post-stroke. EEG data were analyzed using event-related potentials and time-frequency representations, followed by source reconstruction based on state-of-the-art individual MRI-derived forward models. Laterality indices were calculated to assess changes in hemispheric dominance between sessions. Behaviorally, the LH group showed low performance at two months followed by overall improvement, whereas the RH group mainly improved in functional communication. EEG activity was significantly modulated by task condition across comprehension and production contrasts. Source-level results in controls were consistent with established language-related networks, serving as regions of interest for the stroke analyses. In the stroke groups, hemispheric dominance differed between LH and RH stroke participants, with LH participants showing systematic shifts from left- to right-hemisphere dominance between sessions, if not already right-hemisphere dominant at two months. Our study provides evidence on comprehension and production performance after stroke using a single paradigm. These findings demonstrate dynamic changes in hemispheric dominance during post-stroke recovery, ultimately highlighting the value of individualized EEG source reconstruction to investigate post-stroke plasticity.

neuroscience↗