Biology subjects

Ondobaka, S.

Publications and source records attributed to Ondobaka, S..

3 recordsLinked to original sources

bioRxiv2026

Theta phase and theta-gamma coupling organise the spoken language network

Speech production requires rapid coordination of conceptual and lexical processes across distributed cortical networks, yet the neurophysiological mechanisms enabling this coordination remain poorly understood. Oscillatory coupling has emerged as a candidate mechanism for coordinating neural activity across spatial scales. Here, we used whole-head magnetoencephalography during overt picture naming to test how phase and phase-amplitude coupling organise neural dynamics preceding articulation. We show that theta (4-8 Hz) phase coupling increases within two functionally distinct networks: a ventral occipito-temporal network supporting object recognition and a medial fronto-temporal network supporting semantic-lexical retrieval. These networks converged in the right fusiform gyrus beyond chance levels, identifying a candidate integration hub. In parallel, whole-brain analysis of theta-gamma (4-8 Hz, 40-100 Hz) phase-amplitude coupling revealed selective increases in the left inferior frontal and fusiform gyri during picture naming relative to control. Mixed-effects modelling further showed that coupling in the left fusiform correlates with trial-level response times during naming but not control trials. Together, these findings reveal the oscillatory mechanisms that implement known functional specialisation in the spoken language network; theta phase coupling coordinates distributed recognition and retrieval streams, while theta-gamma coupling modulates local computations within core word production nodes. By defining an oscillatory framework for real-time speech production, this work advances mechanistic understanding of the spoken language network and identifies frequency- and region-specific targets for neuromodulation of language production disorders. HighlightsO_LIDistinct oscillatory mechanisms support distributed and local language production processes C_LIO_LITheta phase-coupling distinguishes object recognition and word retrieval networks C_LIO_LITheta-gamma coupling increases locally in core language regions C_LIO_LIIncreases in theta-gamma coupling scale with trial-by-trial naming speed C_LI

neuroscience↗

bioRxiv2021

Lesion site and therapy time predict responses to a therapy for anomia after stroke: a prognostic model development study

BACKGROUNDStroke is a leading cause of disability, and language impairments (aphasia) after stroke are both common and particularly feared. Most stroke survivors with aphasia exhibit anomia (difficulties with naming common objects), but while many therapeutic interventions for anomia have been proposed, treatment effects are typically much larger in some patients than others. Here, we asked whether that variation might be more systematic, and even predictable, than previously thought. METHODS18 patients, each at least 6 months after left hemisphere stroke, engaged in a computerised treatment for their anomia over a 6 week period. Using only: (a) the patients initial accuracy when naming (to-be) trained items; (b) the hours of therapy that they devoted to the therapy; and (c) whole-brain lesion location data, derived from structural MRI; we developed Partial Least Squares regression models to predict the patients improvements on treated items, and tested them in cross-validation. RESULTSSomewhat surprisingly, the best model included only lesion location data and the hours of therapy undertaken. In cross-validation, this model significantly out-performed the null model, in which the prediction for each patient was simply the mean treatment effect of the group. This model also made promisingly accurate predictions in absolute terms: the correlation between empirical and predicted treatment response was 0.62 (95%CI: 0.27, 0.95). DISCUSSIONOur results indicate that individuals variation in response to anomia treatment are, at least somewhat, systematic and predictable, from the interaction between where and how much lesion damage they have suffered, and the time they devoted to the therapy.

neuroscience↗

bioRxiv2019

Inter-hemispheric inhibition in stroke survivors is related to fatigue and cortical excitability

ObjectivePersistent post-stroke fatigue is a major debilitating condition that has been linked to low corticomotor excitability and aberrant attention, both phenomena that are associated with the inter-hemispheric inhibition balance in the brain. In this study, we examined the relationship between inter-hemispheric inhibitory effective connectivity, motor cortex excitability and chronic persistence of post-stroke fatigue.\n\nMethodsWe tested eighteen non-depressed stroke survivors with minimal motoric and cognitive impairments using spectral dynamic causal modelling (spDCM) of resting state magnetic resonance imaging (rs-fMRI) and transcranial magnetic stimulation (TMS) measures of cortical excitability. We also assessed the levels of non-exercise induced, persistent fatigue using Fatigue Severity Scale (FSS) - a self-report questionnaire which has been widely applied and validated across different conditions. To understand neural effective connectivity mechanisms involved in fatigue and corticomotor excitability we examined the balance in inhibitory connectivity between homologue regions in M1, anterior insula, caudate and thalamus of the resting brain.\n\nResultsInter-hemispheric inhibition balance between left and right M1 accounted for 67% of variability in the reported fatigue (R=.82, p<0.001). Inter-hemispheric inhibition balance in M1 also accounted for 54% of variability in the corticomotor excitability characterised by individual resting motor thresholds (R=.74, p<0.001), a measure that has been associated with subjective fatigue reports. Other examined inter-hemispheric connections did not show significant relationships with either fatigue or cortical excitability measures.\n\nConclusionOur findings suggest that the balance in inter-hemispheric effective connectivity between primary motor regions is involved in regulation of corticomotor excitability and could explain subjective post-stroke fatigue.

neuroscience↗