Developing multidimensional metrics for evaluating paediatric neurodevelopmental disorders

Healthy brain functioning depends on efficient communication of information between brain regions, forming complex networks. By quantifying synchronisation between brain regions, a functionally connected brain network can be articulated. In neurodevelopmental disorders, where diagnosis is based on measures of behaviour and tasks, a measure of the underlying biological mechanisms holds promise as a potential clinical tool. Graph theory provides a tool for investigating the neural correlates of neuropsychiatric disorders, where there is disruption of efficient communication within and between brain networks. This research aimed to use recent conceptualisation of graph theory, along with measures of behaviour and cognitive functioning, to increase understanding of the neurobiological risk factors of atypical development. Using magnetoencephalography to investigate frequency-specific temporal dynamics at rest, the research aimed to identify potential biological markers derived from sensor-level whole-brain functional connectivity. Whilst graph theory has proved valuable for insight into network efficiency, its application is hampered by two limitations. First, its measures have hardly been validated in MEG studies, and second, graph measures have been shown to depend on methodological assumptions that restrict direct network comparisons. The first experimental study (Chapter 3) addressed the first limitation by examining the reproducibility of graph-based functional connectivity and network parameters in healthy adult volunteers. Subsequent chapters addressed the second limitation through adapted minimum spanning tree (a network analysis approach that allows for unbiased group comparisons) along with graph network tools that had been shown in Chapter 3 to be highly reproducible. Network topologies were modelled in healthy development (Chapter 4), and atypical neurodevelopment (Chapters 5 and 6). The results provided support to the proposition that measures of network organisation, derived from sensor-space MEG data, offer insights helping to unravel the biological basis of typical brain maturation and neurodevelopmental conditions, with the possibility of future clinical utility.

Dissociating the spatio-temporal characteristics of cortical neuronal activity associated with human volitional swallowing in the healthy adult brain

Human swallowing represents a complex highly coordinated sensorimotor function whose functional neuroanatomy remains incompletely understood. Specifically, previous studies have failed to delineate the temporo-spatial sequence of those cerebral loci active during the differing phases of swallowing. We therefore sought to define the temporal characteristics of cortical activity associated with human swallowing behaviour using a novel application of magnetoencephalography (MEG). In healthy volunteers (n = 8, aged 28-45), 151-channel whole cortex MEG was recorded during the conditions of oral water infusion, volitional wet swallowing (5 ml bolus), tongue thrust or rest. Each condition lasted for 5 s and was repeated 20 times. Synthetic aperture magnetometry (SAM) analysis was performed on each active epoch and compared to rest. Temporal sequencing of brain activations utilised time-frequency wavelet plots of regions selected using virtual electrodes. Following SAM analysis, water infusion preferentially activated the caudolateral sensorimotor cortex, whereas during volitional swallowing and tongue movement, the superior sensorimotor cortex was more strongly active. Time-frequency wavelet analysis indicated that sensory input from the tongue simultaneously activated caudolateral sensorimotor and primary gustatory cortex, which appeared to prime the superior sensory and motor cortical areas, involved in the volitional phase of swallowing. Our data support the existence of a temporal synchrony across the whole cortical swallowing network, with sensory input from the tongue being critical. Thus, the ability to non-invasively image this network, with intra-individual and high temporal resolution, provides new insights into the brain processing of human swallowing. © 2004 Elsevier Inc. All rights reserved.