The usefulness of spatial pattern analysis in understanding the pathogenesis of neurodegenerative disorders, with particular reference to plaque formation in Alzheimer's disease

Discrete, microscopic lesions are developed in the brain in a number of neurodegenerative diseases. These lesions may not be randomly distributed in the tissue but exhibit a spatial pattern, i.e., a departure from randomness towards regularlity or clustering. The spatial pattern of a lesion may reflect its development in relation to other brain lesions or to neuroanatomical structures. Hence, a study of spatial pattern may help to elucidate the pathogenesis of a lesion. A number of statistical methods can be used to study the spatial patterns of brain lesions. They range from simple tests of whether the distribution of a lesion departs from random to more complex methods which can detect clustering and the size, distribution and spacing of clusters. This paper reviews the uses and limitations of these methods as applied to neurodegenerative disorders, and in particular to senile plaque formation in Alzheimer's disease.

Atypical connectivity in autistic spectrum disorders

Autism is a developmental disorder that is currently defined in terms of a triad of impairments in social interaction, communication, and behavioural flexibility. Psychological models have focussed on deficits in high level social and cognitive processes, such as ‘weak central coherence’ and deficits in ‘theory of mind’. Converging evidence from different fields of neuroscience research indicates that the underlying neural dysfunction is associated with atypical patterns of cortical connectivity (Rippon et al., 2007). This arises very early in development and results in sensory, perceptual and cognitive deficits at a much earlier and more fundamental level than previously suggested, but with cascading effects on higher level psychological and social processes. Earlier research in this sphere has focussed mainly on patterns of underconnectivity in distributed cortical networks underpinning process such as language and executive function. (Just et al., 2007). Such research mainly utilises imaging techniques with high spatial resolution. This paper focuses on evidence associated with local over-connectivity, evident in more low level and transitory processes and hence more easily measurable with techniques with high temporal resolution, such as MEG and EEG. Results are described which provide evidence of such local over-connectivity, characterised by atypical results in the gamma frequency range (Brown et al., 2005) together with discussions about the future directions of such research and its implications for remediation.

Development of a passive MEG stimulus for measurement of the binaural masking level difference

The ability to hear a target signal over background noise is an important aspect of efficient hearing in everyday situations. This mechanism depends on binaural hearing whenever there are differences in the inter-aural timing of inputs from the noise and the signal. Impairments in binaural hearing may underlie some auditory processing disorders, for example temporal-lobe epilepsies. The binaural masking level difference (BMLD) measures the advantage in detecting a tone whose inter-aural phase differs from that of the masking noise. BMLD’s are typically estimated psychophysically, but this is challenging in children or those with cognitive impairments. The aim of this doctorate is to design a passive measure of BMLD using magnetoencephalography (MEG) and test this in adults, children and patients with different types of epilepsy. The stimulus consists of Gaussian background noise with 500-Hz tones presented binaurally either in-phase or 180° out-of-phase between the ears. Source modelling provides the N1m amplitude for the in-phase and out-of-phase tones, representing the extent of signal perception over background noise. The passive BMLD stimulus is successfully used as a measure of binaural hearing capabilities in participants who would otherwise be unable to undertake a psychophysical task.

Work disability and depressive disorders:impact on the European population

OBJECTIVE: Our aim was to study the impact of depressive disorders on work disability to discover the determinants of depression for work disability in the European countries. DESIGN: The sample was composed of 31,126 individuals from 29 countries included in the 2002 World Health Survey of the World Health Organization. National representative samples of countries from all regions of Europe and with different levels of economic development and health coverage were selected. RESULTS: Estimates of people not working because of ill health did not differ among European countries in relation to levels of economic development or health coverage. Significant determinants of people with diagnosis of depression not working because of ill health (reference category) versus working were age (odds ratio = 0.97), female sex (odds ratio = 1.71), education (odds ratio = 1.11), marital status (being unmarried indicating less probability), lowest income level, and comorbidity with angina pectoris (odds ratio = 0.51). Moreover, according to previous studies, we found some determinants (comorbidity with other diseases, young age, and unemployment) impacting on health status. CONCLUSIONS: Depression is a substantial cause of work disability and it is a complex phenomenon that involves many variables. Investigation into this relationship should improve, focusing on the role of determinants.

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.