title: Discerning nonlinear brain dynamics from EEG
title: an application to autistic spectrum disorder in young children
titleType: Subtitle
Creators: name: Ticcinelli, Valentina
Publisher: name: Lancaster University
Year: 2018

description: A challenging goal in neuroscience is that of identifying specific brain
      patterns characterising autistic spectrum disorder (ASD). Genetic studies,
      together with investigations based on magnetic resonance imaging (MRI) and
      functional MRI, support the idea that distinctive structural features
      could exist in the ASD brain. In the developing brains of babies and small
      children, structural differences could provide the basis for different
      brain connectivity, giving rise to macroscopic effects detectable by e.g.
      electroencephalography (EEG). A significant body of research has already
      been conducted in this direction, mainly computing spectral power and
      coherence. Perhaps due to methodological limitations, together with high
      variability within and between the cohorts investigated, results have not
      been in complete agreement, and it is therefore still the case that the
      diagnosis of ASD is based on behavioural tests and interviews.
      
      This thesis describes a step-by-step characterisation and comparison of
      brain dynamics from ASD and neurotypical subjects, based on the analysis
      of multi-probe EEG time-series from male children aged 3-5 years. The
      methods applied are all ones that take explicit account of the
      intrinsically non-linear, open, and time-variable nature of the system.
      
      Time-frequency representations were first computed from the time-series to
      evaluate the spectral power and to categorise the ranges encompassing
      different activities as low-frequency (LF, 0.8-3.5 Hz),
      mid-range-frequency (MF, 3.5-12 Hz) or high-frequency (HF, 12-48 Hz). The
      spatial pathways for the propagation of neuronal activity were then
      investigated by calculation of wavelet phase coherence. Finally, deeper
      insight into brain connectivity was achieved by computation of the
      dynamical cross-frequency coupling between triplets of spatially
      distributed phases. In doing so, dynamical Bayesian inference was used to
      find the coupling parameters between the oscillators in the
      spatially-distributed network. The sets of parameters extracted by this
      means allowed evaluation of the strength of particular coupling components
      of the triplet LF, MF→HF, and enabled reconstruction of the coupling
      functions. By investigation of the form of the coupling functions, the
      thesis goes beyond conventional measures like the directionality and
      strength of an interaction, and reveals subtler features of the underlying
      mechanism.
      
      The measured power distributions highlight differences between ASD and
      typically developing children in the preferential frequency range for
      local synchronisation of neuronal activity: the relative power is
      generally higher at LF and HF, and lower at MF, in the ASD case. The phase
      coherence maps from ASD subjects also exhibited differences, with lower
      connectivity at LF and MF in the frontal and fronto-occipital pairs, and
      higher coherence at high frequencies for central links. There was higher
      inter-subject variability in a comparison of the forms of coupling
      functions in the ASD group; and a weaker coupling in their theta-gamma
      range, which can be linked with the cognitive features of the disorder.
      
      In conclusion, the approach developed in this thesis gave promising
      preliminary results, suggesting that a biomarker for ASD could be defined
      in terms of the described patterns of functional and effective
      connectivity computed from EEG measurements.