Biology subjects

Feldheim, J.

Publications and source records attributed to Feldheim, J..

4 recordsLinked to original sources

bioRxiv2023

Cortical high gamma power relates to movement speed and is disproportionately reduced after stroke

Motor cortical high-gamma oscillations (60 to 90 Hz) occur at the onset of movement and are spatially focused over the contralateral primary motor cortex. Although high-gamma oscillations are widely recognized for their significance in human motor control, their precise function on a cortical level remains elusive. Importantly, their relevance in human stroke pathophysiology is unknown. Understanding the neurophysiological processes of motor coding could be an important step in improving motor recovery after stroke. We recorded magnetoencephalography data during a thumb movement speed task in 14 chronic stroke survivors, 15 age-matched control participants and 29 healthy young participants. Motor cortical high-gamma oscillations showed a strong relation with movement speed as trials with higher movement speed were associated with greater high-gamma power. Stroke survivors showed reduced cortical high-gamma power, surpassing the effect attributable to decreased movement speed in these participants. Even though motor skill acquisition was evident in all groups, it was not linked to high-gamma power. Our study is, to our knowledge, the first to quantify high-gamma oscillations after stroke, revealing a reduction in movement-related high-gamma power. Moreover, we provide strong evidence for a pivotal role of motor cortical high-gamma oscillations in encoding movement speed.

neuroscience↗

bioRxiv2023

Lesion aware automated processing pipeline for multimodal neuroimaging stroke data and The Virtual Brain (TVB)

BackgroundProcessing stroke magnetic resonance imaging (MRI) brain data can be susceptible to lesion-based abnormalities. In this study we developed and validated the Lesion Aware automated Processing Pipeline (LeAPP) that incorporates mitigation measures, improving volumetric and connectomics outputs compared to current standards in automated MRI processing pipelines. MethodsBuilding upon the Human Connectome Project (HCP) minimal processing pipeline, we introduced correction measures, such as cost-function masking and virtual brain transplant, and extended functional and diffusion processing to match acquisition protocols often found in a clinical context. A total of 51 participants (36 stroke patients (65.7{+/-}12.96 years, 18 female) and 15 healthy controls (69.2{+/-}7.4 years, 7 female)) were processed across four time points for patients (3-5, 30-40, 85-95, 340-380 days after stroke onset) and one time point for controls. Artificially lesioned brains (N=82), derived from healthy brains and informed by real stroke lesions were created, thus generating ground-truth data for validation. The processing pipeline and validation framework are available as containerized open-source software. Reconstruction quality has been quantified on whole brain level and for lesion affected and unaffected regions-of-interest (ROIs) using metrics like dice score, volume difference and center-of-gravity distance. Global and local level connectome reconstruction was assessed using node strength, node centrality and clustering coefficient. ResultsThe new pipeline LeAPP provides close reconstructions of the ground truth. Deviations in reconstructed averaged whole brain node strength and all ROI based volume and connectome metrics were significantly reduced compared to the HCP pipeline without stroke specific mitigation measures. ConclusionsLeAPP improves reconstruction quality of multimodal MRI processing for brain parcellation and structural connectome estimation significantly over the non-adapted HCP in the presence of lesions and provides a robust framework for diffusion and functional image processing of clinical stroke data. This novel open-source automated processing pipeline contributes to a development towards reproducible research. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=190 HEIGHT=200 SRC="FIGDIR/small/555078v1_ufig1.gif" ALT="Figure 1"> View larger version (85K): org.highwire.dtl.DTLVardef@ca58aborg.highwire.dtl.DTLVardef@8342f4org.highwire.dtl.DTLVardef@1a0d4b9org.highwire.dtl.DTLVardef@1edf1cf_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2023

Ventral premotor cortex influences spinal cord activation during force generation

Force generation is a crucial element of dexterity and a highly relevant skill of the human motor system. How cerebral and spinal components interact and how spinal activation is influenced by cerebral primary motor and premotor areas is poorly understood. Here we conducted combined cortico-spinal functional MRI during a simple visually guided isometric force generation task in a group of 20 healthy young subjects. Activation was localized in the ipsilateral cervical spinal cord and contralateral primary motor and premotor areas. The main finding is that spinal activation was influenced by ventral premotor cortex activation. Spinal activation was furthermore significantly correlated with primary motor cortex activation while increasing target forces led to an increase in the amount of activation. These data indicate that human premotor areas such as the ventral premotor cortex might be functionally connected to the lower cervical spinal cord contributing to distal upper limb functions, a finding which extends our understanding about human motor function beyond the animal literature.

neuroscience↗

bioRxiv2019

Crossmodal Pattern Discrimination in Humans and Robots: A Visuo-Tactile Case Study

The quality of crossmodal perception hinges on two factors: The accuracy of the independent unimodal perception and the ability to integrate information from different sensory systems. In humans, the ability for cognitively demanding crossmodal perception diminishes from young to old age.\n\nTo research to which degree impediments of these two abilities contribute to the age-related decline and to evaluate how this might apply to artificial systems, we replicate a medical study on visuo-tactile crossmodal pattern discrimination utilizing state-of-the-art tactile sensing technology and artificial neural networks. We explore the perception of each modality in isolation as well as the crossmodal integration.\n\nWe show that in an artificial system the integration of complex high-level unimodal features outperforms the comparison of independent unimodal classifications at low stimulus intensities where errors frequently occur. In comparison to humans, the artificial system outperforms older participants in the unimodal as well as the crossmodal condition. However, compared to younger participants, the artificial system performs worse at low stimulus intensities. Younger participants seem to employ more efficient crossmodal integration mechanisms than modelled in the proposed artificial neural networks.\n\nOur work creates a bridge between neurological research and embodied artificial neurocognitive systems and demonstrates how collaborative research might help to derive hypotheses from the allied field. Our results indicate that empirically-derived neurocognitive models can inform the design of future neurocomputational architectures. For crossmodal processing, sensory integration on lower hierarchical levels, as suggested for efficient processing in the human brain, seems to improve the performance of artificial neural networks.

neuroscience↗