ORCID Profile
0000-0002-3111-943X
Current Organisation
University of Helsinki
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Publisher: Cold Spring Harbor Laboratory
Date: 12-2022
DOI: 10.1101/2022.11.30.518501
Abstract: Significant advances in non-linear dynamics and computational modeling have opened up the possibility of studying how whole-brain dynamics may be impacted by brain injury. Importantly, by looking at the local level of synchronization, it is possible to obtain a comprehensive characterization of the spatiotemporal patterns affected at different spatial scales. In the current study, we applied the turbulent dynamics framework to investigate the temporal evolution in whole-brain dynamics using an open access resting state fMRI dataset from a cohort of moderate-to-severe traumatic brain injury (TBI) patients and healthy controls (HC). We first examined how several measures related to turbulent dynamics differ between HCs and TBI patients at 3-, 6- and 12-months post-injury. We found a significant reduction in these empirical measures after TBI, with the largest change at 6-months post-injury. Next, we built a Hopf whole-brain model with coupled oscillators and conducted in silico perturbations to investigate the mechanistic principles underlying the reduced turbulent dynamics found in the empirical data. This revealed a shift to lower coupling parameters in the TBI dataset and, critically, decreased susceptibility and information encoding capability. These findings confirm the potential of the turbulent framework to characterize whole-brain dynamics after TBI and suggest a mechanistic link between structural disconnection and impaired information processing. Whole-brain turbulent dynamics capture longitudinal changes after TBI during one-year recovery period TBI patients show partial recovery of resting state network dynamics at large spatial scales Whole-brain computational modeling provides a mechanistic link between structural disconnection and recovery
Publisher: MDPI AG
Date: 14-04-2022
DOI: 10.3390/JCM11082184
Abstract: Background: Traumatic brain injury (TBI) is a common and devastating neurological condition, associated often with poor functional outcome and deficits in executive function. Due to the neuropathology of TBI, neuroimaging plays a crucial role in its assessment, and while diffusion MRI has been proposed as a sensitive biomarker, longitudinal studies evaluating treatment-related diffusion MRI changes are scarce. Recent evidence suggests that neurological music therapy can improve executive functions in patients with TBI and that these effects are underpinned by neuroplasticity changes in the brain. However, studies evaluating music therapy induced structural connectome changes in patients with TBI are lacking. Design: Single-blind crossover (AB/BA) randomized controlled trial (NCT01956136). Objective: Here, we report secondary outcomes of the trial and set out to assess the effect of neurological music therapy on structural white matter connectome changes and their association with improved execute function in patients with TBI. Methods: Using an AB/BA design, 25 patients with moderate or severe TBI were randomized to receive a 3-month neurological music therapy intervention either during the first (AB, n = 16) or second (BA, n = 9) half of a 6-month follow-up period. Neuropsychological testing and diffusion MRI scans were performed at baseline and at the 3-month and 6-month stage. Findings: Compared to the control group, the music therapy group increased quantitative anisotropy (QA) in the right dorsal pathways (arcuate fasciculus, superior longitudinal fasciculus) and in the corpus callosum and the right frontal aslant tract, thalamic radiation and corticostriatal tracts. The mean increased QA in this network of results correlated with improved executive function. Conclusions: This study shows that music therapy can induce structural white matter neuroplasticity in the post-TBI brain that underpins improved executive function.
Publisher: ExLing Society
Date: 12-2022
Publisher: Oxford University Press (OUP)
Date: 04-01-2022
DOI: 10.1093/BRAINCOMMS/FCAC001
Abstract: A classical observation in neurology is that aphasic stroke patients with impairments in speech production can nonetheless sing the same utterances. This preserved ability suggests a distinctive neural architecture for singing that could contribute to speech recovery. However, to date, these structural correlates remain unknown. Here, we combined a multivariate lesion–symptom mapping and voxel-based morphometry approach to analyse the relationship between lesion patterns and grey matter volume and production rate in speech and singing tasks. Lesion patterns for spontaneous speech and cued repetition extended into frontal, temporal and parietal areas typically reported within the speech production network. Impairment in spontaneous singing was associated with damage to the left anterior–posterior superior and middle temporal gyri. Preservation of grey matter volume in the same regions where damage led to poor speech and singing production supported better performance in these tasks. When iding the patients into fluent and dysfluent singers based on the singing performance from demographically matched controls, we found that the preservation of the left middle temporal gyrus was related to better spontaneous singing. These findings provide insights into the structural correlates of singing in chronic aphasia and may serve as biomarkers to predict treatment response in clinical trials using singing-based interventions for speech rehabilitation.
Publisher: Walter de Gruyter GmbH
Date: 05-2022
Abstract: This study explores three potential factors that influence Chinese L2 learners’ classifier use in a classroom setting: L1 background, task, and classifier type. We developed a picture-prompted test, including composition, free cloze, and multiple-choice cloze questions to elicit the use of classifiers. Participants were 50 Chinese L2 learners from Arabic, English, and Japanese L1 backgrounds. Although Japanese L1 participants performed numerically better than their Arabic and English counterparts, statistical analysis suggests that L1 was not a significant predictor of test performance. The composition task was shown to be conducive to the use of test-taking strategies, and it revealed a higher classifier accuracy than the more constrained multiple-choice task. Meanwhile, there was an interaction between L1 and task, suggesting that L1 influence may be conditioned by task type. Moreover, our logistic model predicts different levels of accuracy for classifier use by type, which potentially suggests a developmental path of acquiring classifiers licensed by the most prominent noun feature they are associated with, with shape being the earliest, followed by animate, inanimate, and concept.
Publisher: Elsevier BV
Date: 10-2023
Publisher: Elsevier BV
Date: 12-2022
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Jiahuan Zhang.