Biology subjects

Slater, R.

Publications and source records attributed to Slater, R..

6 recordsLinked to original sources

bioRxiv2023

Sensory event-related potential morphology predicts age in premature infants

Preterm infants undergo substantial neurosensory development in the first weeks after birth. Infants born prematurely are more likely to have long-term adverse neurological outcomes and early detection of abnormal brain development is essential for timely interventions. We investigated whether sensory-evoked cortical potentials could be used to accurately estimate the age of an infant. Such a model could be used to identify infants who deviate from normal neurodevelopment by comparing the brain age to the infants postmenstrual age (PMA). Infants aged between 28- and 40-weeks PMA from a training and test sample (consisting of 101 and 65 recording sessions in 82 and 14 infants, respectively) received trains of approximately 10 visual and 10 tactile stimuli (interstimulus interval approximately 10 seconds). PMA could be predicted accurately from the magnitude of the evoked responses (training set mean absolute error (MAE and 95% confidence intervals): 1.41 [1.14; 1.74] weeks, p = 0.0001; test set MAE: 1.55 [1.21; 1.95] weeks, p = 0.0002. Moreover, we show with two examples that brain age, and the deviations between brain age and PMA, may be biologically and clinically meaningful. By firstly demonstrating that brain age is correlated with a measure known to relate to maturity of the nervous system (based on animal and human literature, the magnitude of reflex withdrawal is used) and secondly by linking brain age to long-term neurological outcomes, we show that brain age deviations are related to biologically meaningful individual differences in the rate of functional nervous system maturation rather than noise generated by the model. In summary, we demonstrate that sensory-evoked potentials are predictive of age in premature infants. It takes less than 5 minutes to collect the stimulus electroencephalographic data required for our model, hence, increasing its potential utility in the busy neonatal care unit. This model could be used to detect abnormal development of infants response to sensory stimuli in their environment and may be predictive of later life abnormal neurodevelopmental outcome.

neuroscience↗

bioRxiv2023

Brain age as an estimator of neurodevelopmental outcome: A deep learning approach for neonatal cot-side monitoring

The preterm neonate can experience stressors that affect the rate of brain maturation and lead to long-term neurodevelopmental deficits. However, some neonates who are born early follow normal developmental trajectories. Extraction of data from electroencephalography (EEG) signals can be used to calculate the neonates brain age which can be compared to their true age. Discrepancies between true age and brain age (the brain age delta) can then be used to quantify maturational deviation, which has been shown to correlate with long-term abnormal neurodevelopmental outcomes. Nevertheless, current brain age models that are based on traditional analytical techniques are less suited to clinical cot-side monitoring due to their dependency on long-duration EEG recordings, the need to record activity across multiple EEG channels, and the manual calculation of predefined EEG features which is time-consuming and may not fully capture the wealth of information in the EEG signal. In this study, we propose an alternative deep-learning approach to determine brain age, which operates directly on the EEG, using a Convolutional Neural Network (CNN) block based on the Inception architecture (called Sinc). Using this deep-learning approach on a dataset of preterm infants with normal neurodevelopmental outcomes (where we assume brain age = postmenstrual age), we can calculate infant brain age with a Mean Absolute Error (MAE) of 0.78 weeks (equivalent to a brain age estimation error for the infant within +/- 5.5 days of their true age). Importantly, this level of accuracy can be achieved by recording only 20 minutes of EEG activity from a single channel. This compares favourably to the degree of accuracy that can be achieved using traditional methods that require long duration recordings (typically >2 hours of EEG activity) recorded from a higher density 8-electrode montage (MAE = 0.73 weeks). Importantly, the deep learning models brain age deltas also distinguish between neonates with normal and severely abnormal outcomes (Normal MAE = 0.71 weeks, severely abnormal MAE = 1.27 weeks, p=0.02, one-way ANOVA), making it highly suited for potential clinical applications. Lastly, in an independent dataset collected at an independent site, we demonstrate the models generalisability in age prediction, as accurate age predictions were also observed (MAE of 0.97 weeks). HighlightsO_LIPreterm stress exposure leads to long-term neurodevelopmental deficits C_LIO_LIDeficits are quantifiable using EEG-based brain age prediction errors C_LIO_LIOur deep-learning solution for brain age prediction outperforms previous approaches C_LIO_LIPredictions are achieved with only 20 mins EEG and a single bipolar channel C_LIO_LIPrediction errors correlate with long-term Bayley scale neurodevelopmental outcomes C_LI

neuroscience↗

bioRxiv2022

Concurrent mapping of brain ontogeny and phylogeny within a common connectivity space

Developmental and evolutionary effects on brain organisation are complex, yet linked, as evidenced by the striking correspondence in cortical expansion changes. However, it is still not possible to study concurrently the ontogeny and phylogeny of cortical areal connections, which is arguably more relevant to brain function than allometric changes. Here, we propose a novel framework that allows the integration of connectivity maps from humans (adults and neonates) and non-human primates (macaques) onto a common space. We use white matter bundles to anchor the definition of the common space and employ the uniqueness of the areal connection patterns to these bundles to probe areal specialisation. This enables us to quantitatively study divergences and similarities in cortical connectivity over both evolutionary and developmental scales. It further allows us to map brain maturation trajectories, including the effect of premature birth, and to translate cortical atlases between diverse brains.

neuroscience↗

bioRxiv2021

The Forget-Me-Not dHCP study: 7 Tesla high resolution diffusion imaging in the unfixed post-mortem neonatal brain

Diffusion MRI of the neonatal brain allows investigation of the organisational structure of maturing fibres during brain development. Post-mortem imaging has the potential to achieve high resolution by using long scan times, enabling precise assessment of small structures. The Forget-Me-Not study, part of the Developing Human Connectome Project (dHCP), aims to acquire and publicly distribute high-resolution diffusion MRI data for unfixed post-mortem neonatal brain at 7T with a custom-built head coil. This paper describes how the study addressed logistical, technical and ethical challenges relating to recruitment pipeline, care pathway, tissue preservation, scan setup and protocol optimisation. Results from the first subject recruited to the study demonstrate high-quality diffusion MRI data. Preliminary voxel-wise and tractography-based analyses are presented for the cortical plate, subplate and white matter pathways, with comparison to age-matched in vivo dHCP data. These results demonstrate that high quality post-mortem data can be acquired and provide a sensitive means to explore the developing human brain, as well as altered diffusion properties consistent with post-mortem changes, at high resolution.

bioengineering↗

bioRxiv2020

Functional and diffusion MRI reveal the functional and structural basis of infants' noxious-evoked brain activity

Understanding the neurophysiology underlying pain perception in infants is central to improving early life pain management. In this multimodal MRI study, we use resting-state functional and white matter diffusion MRI to investigate individual variability in infants noxious-evoked brain activity. In an 18-infant nociception-paradigm dataset, we show it is possible to predict infants cerebral haemodynamic responses to experimental noxious stimulation using their resting-state activity across nine networks from a separate stimulus-free scan. In an independent 215-infant Developing Human Connectome Project dataset, we use this resting-state-based prediction model to generate noxious responses. We identify a significant correlation between these predicted noxious responses and infants white matter mean diffusivity, and this relationship is subsequently confirmed within our nociception-paradigm dataset. These findings reveal that a newborn infants pain-related brain activity is tightly coupled to both their spontaneous resting-state activity and underlying white matter microstructure. This work provides proof-of-concept that knowledge of an infants functional and structural brain architecture could be used to predict pain responses, informing infant pain management strategies and facilitating evidence-based personalisation of care.

neuroscience↗

bioRxiv2020

Inferring the infant pain experience: a translational fMRI-based signature approach

BackgroundIn the absence of verbal communication it is challenging to infer an individuals sensory and emotional experience. In adults, fMRI has been used to develop multivariate brain activity signatures, which reliably capture elements of human pain experience. We translate whole-brain fMRI signatures that encode pain perception in adults to the newborn infant brain, to advance understanding of functional brain development and pain perception in early life. MethodsA cohort of adults (n=10; mean age=28.3 years) and 2 cohorts of healthy infants (Cohort A: n=15; Cohort B: n=22; mean postnatal age=3 days) were stimulated with low intensity nociceptive stimuli (64-512mN) during acquisition of functional MRI data. fMRI pain signatures were applied directly to the adult data and transformed such that they could be applied to the infant brain. In each cohort, we assessed the concordance of the signatures with the brain responses using cosine-similarity scores, and we assessed stimulus intensity encoding of the signature responses using Spearman rank correlation. Brain activity in pro-pain and anti-pain brain regions were also examined. FindingsThe Neurologic Pain Signature (NPS), which reflects aspects of nociceptive pain experience, was activated in both the adults and infants, and reliably encoded stimulus intensity. However, the Stimulus Intensity Independent Pain Signature (SIIPS1), which reflects higher-level cognitive modulation of nociceptive input, was only expressed in adults. Pro-pain brain regions showed similar activation patterns in adults and infants, whereas, anti-pain brain regions exhibited divergent responses. InterpretationBasic intensity encoding of nociceptive information is similar in adults and infants. However, translation of adult brain signatures into infants reveals significant differences in infant cerebral processing of nociceptive information, which may reflect their lack of expectation, motivation and contextualisation. This study expands the use of brain activity pain signatures to non-verbal patients and provides a potential approach to assess analgesic interventions in infancy. FundingThis work was funded by Wellcome (Senior Research Fellowship awarded to Prof. Rebeccah Slater) and SSNAP "Support for Sick and Newborn Infants and their Parents" Medical Research Fund (University of Oxford Excellence Fellowship awarded to Dr Eugene Duff). Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed for research articles published prior to March 2020 using terms including fMRI, infant or neonate, and pain or nociception in the title or abstract. Due to the relatively new emergence of this field, and the experimental and analytical challenges involved in studying cerebral processing of pain in the MRI environment in healthy newborn infants, only five fMRI studies have examined infant brain responses to nociceptive input. In a foundational pilot study, Williams et al., applied an experimental noxious stimulus to a single infant, evoking widespread brain activity that included several brain regions involved in pain processing in adults. Goksan et al., subsequently performed an observational cohort study and used regional analyses to compare active brain regions in infants (n=10) and adults (n=10), concluding that the evoked patterns of brain activity were broadly similar in infants and adults. Further follow-up analysis in the infant cohort revealed that the functional connectivity of brain regions involved in descending pain modulation influences the magnitude of pain-related brain activity. Two further studies focused on methodological advances, providing evidence-based recommendations for fMRI acquisition parameters and image processing in order to maximise the quality of infant data, and these methods have been implemented in this study. Added value of this studyThis study translates validated adult pain fMRI brain signatures to a nonverbal patient population in which the assessment and management of pain presents a significant clinical challenge. Application of fMRI brain signatures to newborn infants expands on previous fMRI studies that provided only qualitative evidence that noxious stimulation commonly activates brain regions in the adult and infant brain. Here we demonstrate that the basic encoding of the sensory discriminative aspects of pain, as represented by the Neurologic Pain Signature (NPS), occurs in both adults and infants, whereas higher-level cognitive modulation of pain, represented by the Stimulus Intensity Independent Pain Signature (SIIPS1) is only present in adults and not observed in infants. The differences in how the immature infant brain processes pain, relative to the mature adult brain, are likely to reflect differences in their expectation, motivation and contextualisation of external events rather than differences in their core nociceptive cerebral processing of pain. This work allows us to use quantitative fMRI observations to make stronger inferences related to pain experience in nonverbal infants. Implications of all the available evidenceBehavioural pain scores used in neonatal clinical care offer limited sensitivity and specificity to pain. Neonatal clinical trials that use these scores as outcome measures frequently report a lack of efficacy of common analgesic interventions, resulting in few evidence-based drugs for treating pain. The value of using brain-based neuroimaging markers of pain as a means of providing objective evidence of analgesic efficacy in early proof of concept studies is well recognised in adults, even in the absence of behavioural pain modulation. Similarly, in infants EEG-based measures of noxious-evoked brain activity have been used as outcome measures in clinical trials of analgesics to overcome some of the inherent limitations of using behavioural observations to quantify analgesic efficacy. Considering the successful translation of the Neurologic Pain Signature (NPS) and its sensitivity to analgesic modulation in adults, this novel methodology represents an objective brain-based fMRI approach that could be used to advance the discovery and assessment of analgesic interventions in infancy.

neuroscience↗