Simultaneous EEG-fMRI

Combining information from EEG (left) and BOLD contrast fMRI (right) provides information about brain activity with both high temporal sensitivity and spatial specificity. Figures reproduced from Arichi et al. 2017 Elife

Working with our collaborators at UCL (led by Dr Lorenzo Fabrizi) we are collecting simultaneous EEG and fMRI data from neonates. We used this methodology to study spontaneous bursting neuronal activity which is a hallmark of the developing brain and thought to be crucial for establishing neural circuits. For the first time in preterm human infants, we identified the source of the most common type of this activity in the late preterm period (posterior temporal delta brushes) as the insula, thus identifying it as a key hub in the developing human brain. This was the first description of simultaneous EEG-fMRI and demonstration of the link between electrical neural activity and the fMRI haemodynamic response in human infancy. The work was recognised by a Merit Abstract Award (Organisation of Human Brain Mapping) and significant media attention (ie, FT, Independent).

Please also refer to the article published in Elife in 2017:

This work is funded by a Clinician Scientist Fellowship from the Medical Research Council (MRC)

Sensori-motor system Development

The preterm period (equivalent to the third trimester of human gestation) is a key period for the establishment of the sensori-motor system in humans. Working with Professor David Edwards (King’s College London) and Professor Etienne Burdet (Human Robotics Group, Imperial College London) we have developed and used a number of MR compatible robotic devices to systematically study functional responses across the preterm period and following brain injury. Characterising these processes will be crucial to understanding the pathophysiology that underlies cerebral palsy in children.

A review of our methods can be found here:

MR compatible robotic devices for providing a somatosensory stimulus to the mouth, wrist and ankles

Examples include:

  1. fMRI responses to hand movement in preterm and term infants:
  2. Characterisation of the neonatal HRF following somatosensory stimulation:
  3. fMRI responses to olfactory stimuli:
  4. A novel MR compatible device for controlled stimulation of the wrist:
  5. Characterisation of altered connectivity following focal brain injury in preterm infants:
  6. Systematic study of the evolution of sensori-motor responses across the preterm period:
  7. Characterisation of the sensorimotor homunculus in preterm infants:
  8. Development of a novel MR compatible device for measuring grasp in infants:
Maturation of brain responses to passive motor stimulation of the right wrist across the preterm period. As infants mature in the period equivalent to the third trimester, the spatial distribution of the response can be seen to rapidly increase with increasing involvement of associative processing regions (figure from: Allievi, Arichi et al. Cereb Cor 2016)

Neural correlates of infant learning

Infants are exposed to many different types of sensory information from very early in development. Animal studies suggest that such patterns of environmental stimulation may play a key role in establishing and consolidating patterns of functional connectivity. However, to encode the complexity of their environment, newborn babies must be capable of processing multi-modal sensory information so that they can interpret the relationship between different stimulus types. Working with Professor Bill Fifer (Columbia University) and using custom engineering techniques we are studying whether neonates can learn an association between two simple sensory stimuli.

The results of this work confirming that associative learning from external sensory experience can shape activity in the developing cortex has been published in Cerebral Cortex:

Dall’Orso et al. Cortical Processing of Multimodal Sensory Learning in human neonates. Cerebral Cortex 2020

This work is funded by a Clinician Scientist Fellowship from the Medical Research Council (MRC)

Excitatory-Inhibitory imbalance in infants at risk of Neurodevelopmetal disorders

Animal models suggest that an imbalance between excitatory and inhibitory neural activity underlies neurodevelopmental disorders such as autism. The aim of this project is to understand if the balance between excitatory (glutamate) and inhibitory (Gamma Amino-Butyric Acid (GABA)) neurotransmitter levels is altered in newborn infants who have a high familial risk of developing neurodevelopmental disorders. To achieve this, we are working with Dr Enrico de Vita and Professor Grainne McAlonan and will use Magnetic Resonance Spectroscopy (MRS) at both 3T and ultra high-field 7T to compare GABA and glutamate levels between groups of control and high risk infants. This new knowledge may help to identify which infants will develop difficulties later in childhood and provide a crucial foundation onto which new therapeutic strategies can be identified and tested.

Neonatal GABA+ and Glx edited spectra from the anterior cingulate cortex (A) and thalamus (D) are plotted in black, with double Gaussian fitted model in red. Corresponding group averages from the ACC (B) and thalamus (E) in black, with the standard deviation in dark grey and the 95% confidence interval in light grey. GABA+% fit error (C, F) showing the difference in GABA+ fitting between single Gaussian (SG) and the double Gaussian (DG) models. (Figure reproduced from Yanez Lopez et al. Neuroimage 2021)

A paper describing the optimised acquisition and analysis pipline developed for applying the HERMES sequence with neonates has been published in Neuroimage:

Yanez Lopez et al. Simultaneous quantification of GABA, Glx and GSH in the neonatal human brain using magnetic resonance spectroscopy. Neuroimage 2021; 233: 117930

The data associated with this manuscript can be provided upon completion of a user agreement, please contact us via email: or

This work is funded by a grant from Action Medical Research.

An MR compatible Virtual Reality System

The MRI scanner environment is noisy and claustrophobic, making it challenging for many subjects and impeding imaging of vulnerable populations. Traditional interventions to alleviate anxiety (patient education/preparation, simple audio and video entertainment etc.) provide only limited relief. We are aiming to replace the visual scene with appropriate engaging content which could substantially mitigate claustrophobia and to make the visual experience congruent with physical sensations (noise, vibration, table movement) which could further reduce stress by achieving an integrated experience. Although Virtual Reality (VR) technology could provide a means to do this, in typical applications, immersion is strongly enhanced by user control of the perceived environment through tracking head motion, which is clearly undesirable in MRI. We are therefore developing a fully MRI compatible VR system for brain imaging applications, using gaze tracking for interactive control.

A paper describing the design and features of the VR system has been published in Scientific Reports in 2021:

Qian et al. An eye tracking based virtual reality system for use inside magnetic resonance imaging systems. Scientific Reports 2021; 11: 16301

A video showing the system in action can be seen here


Brain Development in Down Syndrome

Down Syndrome is the most common genetic developmental disorder in humans. It is associated with varying degrees of intellectual disability and delays in speech, memory and learning. As both length and quality of life are improving for individuals with Down Syndrome, it is vital that we can understand the biological substrates of the associated cognitive difficulties so that we can hopefully develop treatments in the future. We are working with Dr Ana Baburamani andProfessor Mary Rutherford to acquire fetal and neonatal MR images from individuals with a diagnosis of Down Syndrome.

A review of this work and the associated video podcast:,

This work is funded by a grant from Great Ormond Street Sparks.

MAVEHA – Movement Assessment for Very Early Health Assessment

Babies make frequent but seemingly non goal-orientated spontaneous movements throughout the day. Although evidence suggests that with training, it is possible to identify which babies are at high risk of developing cerebral palsy by carefully watching these movements, this process has never been automated. Detailed measures of the movements themselves (such as how fast or smooth the movements are or which parts of the body are involved) and how this changes during the first few months have also never been done. Working with colleagues at Imperial College London led by Dr Bernhard Kainz in the department of computing, we are aiming to combine precise motion tracking methods (3D video and electromagnetic tracking) with machine learning methods to develop a new method which can automatically assess a baby’s movements and help predict which babies may have difficulties later in life.

A paper presented at CVPR 2021 describing a novel deep learning based method to estimate pose in infants from video data is available here:

Schmidtke et al. Unsupervised Human Pose Estimation through Transforming Shape Templates Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 2484-2494

This work is funded by a grant from the EPSRC