Ultra-high field spatio-temporal characterisation of sensorimotor processing in the neonatal brain
Poster No:
944
Submission Type:
Abstract Submission
Authors:
Lixuan Zhu1,2, Jucha Willers Moore1, Philippa Bridgen3,4, Pierluigi Di Cio3,4, Lucy Billimoria3,4, Ines Tomazinho1,4, Shaihan Malik1,5, Jo Hajnal1,5, Dafnis Batalle1,2, Tomoki Arichi1,3,4,5
Institutions:
1Early Life Imaging Research Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 2Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom, 3London Collaborative Ultra field System, King’s College London, London, United Kingdom, 4Guys and St Thomas’ NHS Foundation Trust, Kings College London, London, United Kingdom, 5Imaging Physics and Engineering Research Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
First Author:
Lixuan Zhu
Early Life Imaging Research Department, School of Biomedical Engineering and Imaging Sciences|Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London
King's College London, London, United Kingdom|London, United Kingdom
Early Life Imaging Research Department, School of Biomedical Engineering and Imaging Sciences|Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London
King's College London, London, United Kingdom|London, United Kingdom
Co-Author(s):
Jucha Willers Moore
Early Life Imaging Research Department, School of Biomedical Engineering and Imaging Sciences
King's College London, London, United Kingdom
Early Life Imaging Research Department, School of Biomedical Engineering and Imaging Sciences
King's College London, London, United Kingdom
Philippa Bridgen
London Collaborative Ultra field System, King’s College London|Guys and St Thomas’ NHS Foundation Trust, Kings College London
London, United Kingdom|London, United Kingdom
London Collaborative Ultra field System, King’s College London|Guys and St Thomas’ NHS Foundation Trust, Kings College London
London, United Kingdom|London, United Kingdom
Pierluigi Di Cio
London Collaborative Ultra field System, King’s College London|Guys and St Thomas’ NHS Foundation Trust, Kings College London
London, United Kingdom|London, United Kingdom
London Collaborative Ultra field System, King’s College London|Guys and St Thomas’ NHS Foundation Trust, Kings College London
London, United Kingdom|London, United Kingdom
Lucy Billimoria
London Collaborative Ultra field System, King’s College London|Guys and St Thomas’ NHS Foundation Trust, Kings College London
London, United Kingdom|London, United Kingdom
London Collaborative Ultra field System, King’s College London|Guys and St Thomas’ NHS Foundation Trust, Kings College London
London, United Kingdom|London, United Kingdom
Ines Tomazinho
Early Life Imaging Research Department, School of Biomedical Engineering and Imaging Sciences|Guys and St Thomas’ NHS Foundation Trust, Kings College London
King's College London, London, United Kingdom|London, United Kingdom
Early Life Imaging Research Department, School of Biomedical Engineering and Imaging Sciences|Guys and St Thomas’ NHS Foundation Trust, Kings College London
King's College London, London, United Kingdom|London, United Kingdom
Shaihan Malik
Early Life Imaging Research Department, School of Biomedical Engineering and Imaging Sciences|Imaging Physics and Engineering Research Department, School of Biomedical Engineering and Imaging Sciences
King's College London, London, United Kingdom|King's College London, London, United Kingdom
Early Life Imaging Research Department, School of Biomedical Engineering and Imaging Sciences|Imaging Physics and Engineering Research Department, School of Biomedical Engineering and Imaging Sciences
King's College London, London, United Kingdom|King's College London, London, United Kingdom
Jo Hajnal
Early Life Imaging Research Department, School of Biomedical Engineering and Imaging Sciences|Imaging Physics and Engineering Research Department, School of Biomedical Engineering and Imaging Sciences
King's College London, London, United Kingdom|King's College London, London, United Kingdom
Early Life Imaging Research Department, School of Biomedical Engineering and Imaging Sciences|Imaging Physics and Engineering Research Department, School of Biomedical Engineering and Imaging Sciences
King's College London, London, United Kingdom|King's College London, London, United Kingdom
Dafnis Batalle
Early Life Imaging Research Department, School of Biomedical Engineering and Imaging Sciences|Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry
King's College London, London, United Kingdom|Psychology and Neuroscience, King’s College London, London, United Kingdom
Early Life Imaging Research Department, School of Biomedical Engineering and Imaging Sciences|Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry
King's College London, London, United Kingdom|Psychology and Neuroscience, King’s College London, London, United Kingdom
Tomoki Arichi
Early Life Imaging Research Department, School of Biomedical Engineering and Imaging Sciences|London Collaborative Ultra field System, King’s College London|Guys and St Thomas’ NHS Foundation Trust, Kings College London|Imaging Physics and Engineering Research Department, School of Biomedical Engineering and Imaging Sciences
King's College London, London, United Kingdom|London, United Kingdom|London, United Kingdom|King's College London, London, United Kingdom
Early Life Imaging Research Department, School of Biomedical Engineering and Imaging Sciences|London Collaborative Ultra field System, King’s College London|Guys and St Thomas’ NHS Foundation Trust, Kings College London|Imaging Physics and Engineering Research Department, School of Biomedical Engineering and Imaging Sciences
King's College London, London, United Kingdom|London, United Kingdom|London, United Kingdom|King's College London, London, United Kingdom
Introduction:
Sensorimotor processing spans a brain-wide network involving distinct but interconnected regions representing the complex interplay between sensory input and motor outputs (Keysers et al., 2010; Saby et al., 2015). Emerging evidence has shown that the hierarchy of this sensorimotor integration emerges during the perinatal period (Whitehead et al., 2019). Ultra-high field (7T) fMRI markedly increase signal-to-noise ratio (SNR) enabling high spatial resolution studies, thus allowing unprecedented exploration of this sensorimotor processing during this critical developmental period.
Here, we aimed to use high resolution 7T fMRI to characterise the spatio-temporal features of processing across sensorimotor brain regions in neonates following passive movement stimulation, to gain new insights into the emergence of early sensorimotor network activity.
Here, we aimed to use high resolution 7T fMRI to characterise the spatio-temporal features of processing across sensorimotor brain regions in neonates following passive movement stimulation, to gain new insights into the emergence of early sensorimotor network activity.
Methods:
This study received approval from the NHS Research Ethics Committee (19/LO/1384), and written informed consent was obtained from parents of all participants.
We recruited 8 full-term neonates (median postmenstrual age (PMA) at scan: 38.4 weeks; range: 35.7–40.4; 6 females) who underwent scanning during natural sleep. Data were acquired under optimised safety protocols using a Siemens 7T system with a Nova Medical 1TX-32RX head coil (Bridgen et al., 2023). fMRI data was acquired with a gradient-echo echo-planar imaging (EPI) sequence with the following parameters: isotropic resolution of 0.8 mm, TR/TE = 2660/48 ms, 25 slices, and a scan duration of 11:05 mins over a redistricted field of view (FoV) including the primary motor (M1) and somatosensory (S1) cortices in both hemispheres (Willers Moore et al. 2024).
To provide sensorimotor stimulation, we used a custom-designed robotic device (Allievi et al., 2013) that delivered passive extension and flexion to the right wrist (26.6 s on/off blocks). fMRI data underwent preprocessing (including motion correction, distortion correction and spatial smoothing) (Jucha Willers Moore et al., 2024) and alignment to an age-specific brain template followed by temporal concatenation independent component analysis (ICA) using FSL Melodic (Beckmann et al., 2005; Jenkinson et al., 2012). Subject-specific spatial maps for the 5 components identified to have spatiotemporal features consistent with sensorimotor activity and their corresponding time series were characterised using dual regression (Nickerson et al., 2017).
We recruited 8 full-term neonates (median postmenstrual age (PMA) at scan: 38.4 weeks; range: 35.7–40.4; 6 females) who underwent scanning during natural sleep. Data were acquired under optimised safety protocols using a Siemens 7T system with a Nova Medical 1TX-32RX head coil (Bridgen et al., 2023). fMRI data was acquired with a gradient-echo echo-planar imaging (EPI) sequence with the following parameters: isotropic resolution of 0.8 mm, TR/TE = 2660/48 ms, 25 slices, and a scan duration of 11:05 mins over a redistricted field of view (FoV) including the primary motor (M1) and somatosensory (S1) cortices in both hemispheres (Willers Moore et al. 2024).
To provide sensorimotor stimulation, we used a custom-designed robotic device (Allievi et al., 2013) that delivered passive extension and flexion to the right wrist (26.6 s on/off blocks). fMRI data underwent preprocessing (including motion correction, distortion correction and spatial smoothing) (Jucha Willers Moore et al., 2024) and alignment to an age-specific brain template followed by temporal concatenation independent component analysis (ICA) using FSL Melodic (Beckmann et al., 2005; Jenkinson et al., 2012). Subject-specific spatial maps for the 5 components identified to have spatiotemporal features consistent with sensorimotor activity and their corresponding time series were characterised using dual regression (Nickerson et al., 2017).
Results:
Clear patterns of activation were identified across the sensorimotor network in both hemispheres related to stimulation. Figure 1 illustrates the spatial distribution of the identified sensorimotor networks, including robust activation within the primary somatosensory (S1) and motor cortices (M1). Figure 2 presents the temporal profiles of these activations, derived from the time series of the five sensorimotor-related networks. Notably, the left and right somatosensory cortices have a single peak and shorter response profile compared to the combined network encompassing S1 and M1 which shows a broader response with two peaks, potentially indicating a sequential activation pattern and hierarchical processing with secondary involvement of M1.
Conclusions:
Our findings suggest that neonatal sensorimotor processing involves a temporal hierarchy in the neonatal period, with somatosensory regions responding earlier than motor regions, consistent with prior EEG work (Whitehead et al., 2019). The higher sensitivity and spatial resolution of 7T fMRI allowed us to delineate differences in activity across sensorimotor regions following passive movement stimulation, offering novel insights into the functional maturation of sensorimotor pathways in the neonatal brain. Future studies with larger cohorts will further validate these findings and expand our understanding of early sensorimotor processing through taking advantage of the higher spatial resolution to explore the specific responses across cortical depths.
Lifespan Development:
Early life, Adolescence, Aging 1
Novel Imaging Acquisition Methods:
BOLD fMRI 2
Keywords:
Cortex
Development
FUNCTIONAL MRI
MRI
PEDIATRIC
Somatosensory
Touch
1|2Indicates the priority used for review
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Beckmann, C. F., DeLuca, M., Devlin, J. T., & Smith, S. M. (2005). Investigations into resting-state connectivity using independent component analysis. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1457), 1001–1013. https://doi.org/10.1098/rstb.2005.1634
Bridgen, P., Tomi-Tricot, R., Uus, A., Cromb, D., Quirke, M., Almalbis, J., Bonse, B., De la Fuente Botella, M., Maggioni, A., Cio, P. Di, Cawley, P., Casella, C., Dokumaci, A. S., Thomson, A. R., Willers Moore, J., Bridglal, D., Saravia, J., Finck, T., Price, A. N., … Arichi, T. (2023). High resolution and contrast 7 tesla MR brain imaging of the neonate. Frontiers in Radiology, 3. https://doi.org/10.3389/fradi.2023.1327075
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