Biochemical correlates of cognition: exploring the relationships between blood, brain and behaviour

This multi-modal investigation aimed to refine analytic tools including proton magnetic resonance spectroscopy (1H-MRS) and fatty acid gas chromatography-mass spectrometry (GC-MS) analysis, for use with adult and paediatric populations, to investigate potential biochemical underpinnings of cognition (Chapter 1). Essential fatty acids (EFAs) are vital for the normal development and function of neural cells. There is increasing evidence of behavioural impairments arising from dietary deprivation of EFAs and their long-chain fatty acid metabolites (Chapter 2). Paediatric liver disease was used as a deficiency model to examine the relationships between EFA status and cognitive outcomes. Age-appropriate Wechsler assessments measured Full-scale IQ (FSIQ) and Information Processing Speed (IPS) in clinical and healthy cohorts; GC-MS quantified surrogate markers of EFA status in erythrocyte membranes; and 1H-MRS quantified neurometabolite markers of neuronal viability and function in cortical tissue (Chapter 3). Post-transplant children with early-onset liver disease demonstrated specific deficits in IPS compared to age-matched acute liver failure transplant patients and sibling controls, suggesting that the time-course of the illness is a key factor (Chapter 4). No signs of EFA deficiency were observed in the clinical cohort, suggesting that EFA metabolism was not significantly impacted by liver disease. A strong, negative correlation was observed between omega-6 fatty acids and FSIQ, independent of disease diagnosis (Chapter 5). In a study of healthy adults, effect sizes for the relationship between 1H-MRS- detectable neurometabolites and cognition fell within the range of previous work, but were not statistically significant. Based on these findings, recommendations are made emphasising the need for hypothesis-driven enquiry and greater subtlety of data analysis (Chapter 6). Consistency of metabolite values between paediatric clinical cohorts and controls indicate normal neurodevelopment, but the lack of normative, age-matched data makes it difficult to assess the true strength of liver disease-associated metabolite changes (Chapter 7). Converging methods offer a challenging but promising and novel approach to exploring brain-behaviour relationships from micro- to macroscopic levels of analysis (Chapter 8).

Cognitive-behavioural phenotype in a group of girls from 1.2 to 12 years old with the Incontinentia Pigmenti syndrome:recommendations for clinical management

Incontinentia Pigmenti (IP, OMIM#308300) is a rare X-linked genomic disorder (about 1,400 cases) that affects the neuroectodermal tissue and Central Nervous System (CNS). The objective of this study was to describe the cognitive-behavioural profile in children in order to plan a clinical intervention to improve their quality of life. A total of 14 girls (age range: from 1 year and 2 months to 12 years and 10 months) with IP and the IKBKG/NEMO gene deletion were submitted to a cognitive assessment including intelligence scales, language and visuo-spatial competence tests, learning ability tests, and a behavioural assessment. Five girls had severe to mild intellectual deficiencies and the remaining nine had a normal neurodevelopment. Four girls were of school age and two of these showed no intellectual disability, but had specific disabilities in calculation and arithmetic reasoning. This is the first description of the cognitive-behavioural profile in relation to developmental age. We stress the importance of an early assessment of learning abilities in individuals with IP without intellectual deficiencies to prevent the onset of any such deficit.

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.