Biology subjects

Verheyden, G.

Publications and source records attributed to Verheyden, G..

3 recordsLinked to original sources

bioRxiv2024

EEG Responses to Upper Limb Pinprick Stimulation in Acute and Early Subacute Motor and Sensorimotor Stroke

BackgroundElectroencephalography (EEG) during pinprick stimulation has the potential to unveil neural mechanisms underlying sensorimotor impairments post-stroke. This study explored event-related peak pinprick amplitude and oscillatory responses in healthy controls, in people with motor and sensorimotor in acute and early subacute stroke, their relationship and to what extent EEG somatosensory responses can predict sensorimotor impairment. MethodsIn this study, involving 26 individuals, 10 people with a (sub-)acute sensorimotor stroke, 6 people with a (sub)acute motor stroke and 10 age-matched controls, pinpricks were applied to the dorsa of the impaired hand to collect somatosensory evoked potentials. Time(-frequency) analyses of somatosensory evoked potential (SEP) data at electrodes C3 and C4 explored peak pinprick amplitude and oscillatory responses across the three groups. Also, in stroke, (sensori-)motor impairments were assessed at baseline Fugl Meyer Assessment Upper Extremity (FMA-UE) and Erasmus modified Nottingham Sensory Assessment (EmNSA) at baseline and 7 to 14 days later including Fugl Meyer Assessment Upper Extremity (FMA-UE) and Erasmus modified Nottingham Sensory Assessment (EmNSA). Mixed model analyses were used to address objectives. ResultsIt was demonstrated that increased beta desynchronization magnitude correlated with milder motor impairments (R2 =0.213), whereas increased beta resynchronization and delta power were associated to milder somatosensory impairment (R2 =0.550). At the second session, larger peak-to-peak SEP amplitude and beta band resynchronization at baseline were related to greater improvements in EMNSA and FMA-UE score, respectively, in sensorimotor stroke group. ConclusionsThese findings highlight the potential of EEG combined with somatosensory stimuli to differentiate between sensorimotor and motor impairments in stroke, offering preliminary insights into both diagnostic and prognostic aspects of upper limb recovery.

neuroscience↗

bioRxiv2022

The differential effect of age on upper limb sensory processing, proprioception and motor function

Sensory processing consists in the integration and interpretation of somatosensory information. It builds upon proprioception but is a distinct function requiring complex processing by the brain over time. Currently little is known about the effect of aging on sensory processing ability, nor the influence of other covariates such as motor function, proprioception, or cognition. In this study, we measured upper limb passive and active sensory processing, motor function, proprioception, and cognition in 40 healthy younger adults and 54 older adults. We analyzed age differences across all measures and evaluated the influence of covariates on sensory processing through regression. Our results showed larger effect sizes for age differences in sensory processing (r=0.38) compared to motor function (r=0.18-0.22) and proprioception (r=0.10-0.27), but smaller than for cognition (r=0.56-0.63). Aside from age, we found no evidence that sensory processing performance was related to motor function or proprioception, but active sensory processing was related to cognition ({beta}=0.30-0.42). In conclusion, sensory processing showed an age-related decline, while some proprioceptive and motor abilities were preserved across age.

neuroscience↗

bioRxiv2021

Multimodal lesion network mapping to predict sensorimotor behavior in stroke patients

Lesion network mapping (LNM) has proved to be a successful technique to map symptoms to brain networks after acquired brain injury. Beyond the characteristics of a lesion, such as its etiology, size or location, LNM has shown that common symptoms in patients after injury may reflect the effects of their lesions on the same circuits, thereby linking symptoms to specific brain networks. Here, we extend LNM to its multimodal form, using a combination of functional and structural connectivity maps drawn from data from 1000 healthy participants in the Human Connectome Project. We applied the multimodal LNM to a cohort of 54 stroke patients with the aim of predicting sensorimotor behavior, as assessed through a combination of motor and sensory tests. Test scores were predicted using a Canonical Correlation Analysis with multimodal brain maps as independent variables, and cross-validation strategies were employed to overcome overfitting. The results obtained led us to draw three conclusions. First, the multimodal analysis reveals how functional connectivity maps contribute more than structural connectivity maps in the optimal prediction of sensorimotor behavior. Second, the maximal association solution between the behavioral outcome and multimodal lesion connectivity maps suggests an equal contribution of sensory and motor coefficients, in contrast to the unimodal analyses where the sensory contribution dominates in both structural and functional maps. Finally, when looking at each modality individually, the performance of the structural connectivity maps strongly depends on whether sensorimotor performance was corrected for lesion size, thereby eliminating the effect of larger lesions that produce more severe sensorimotor dysfunction. By contrast, the maps of functional connectivity performed similarly irrespective of any correction for lesion size. Overall, these results support the extension of LNM to its multimodal form, highlighting the synergistic and additive nature of different types of imaging modalities, and the influence of their corresponding brain networks on behavioral performance after acquired brain injury.

neuroscience↗