Neurophysiology

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Research outputs from the Neurophysiology department at the RD&E.

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    Neurodevelopmental outcomes in children and adults with Fetal Valproate Spectrum Disorder: A contribution from the ConcePTION project
    (elsevier, 2023-09-01) Bluett-Duncan, M.; Astill, D.; Charbak, R.; Clayton-Smith, J.; Cole, S.; Cook, P. A.; Cozens, J.; Keely, K.; Morris, J.; Mukherjee, R.; Murphy, E.; Turnpenny, P.; Williams, J.; Wood, A. G.; Yates, L. M.; Bromley, R. L.
    AIM: To describe the neurodevelopmental phenotype of older children and adults with a diagnosis of Fetal Valproate Spectrum Disorder (FVSD). METHODS: In this cross-sectional study, 90 caregivers were recruited and completed a series of questionnaires regarding the neurodevelopmental outcomes of 146 individuals aged 7-37 years (M = 18.1), including individuals with a formal diagnosis of FVSD (n = 99), individuals exposed to Valproate but without an FVSD diagnosis (n = 24), and individuals not exposed to Valproate (N = 23). The mean dose of valproate exposure for individuals with an FVSD diagnosis was 1470 mg/day. RESULTS: Individuals with a diagnosis FVSD showed significantly higher levels of moderate (43.4%) and severe (14.4%) cognitive impairment than other groups (p = 0.003), high levels of required formal educational support (77.6%), and poorer academic competence than individuals not exposed to Valproate (p = 0.001). Overall psychosocial problems (p = 0.02), internalising problems (p = 0.05) and attention problems (p = 0.001), but not externalising problems, were elevated in individuals with a diagnosis of FVSD. Rates of neurodevelopmental disorders, particularly autistic spectrum conditions (62.9%) and sensory problems (80.6%) are particularly central to the FVSD phenotype. There was no evidence of a statistical dose-dependent effect, possibly due to the high mean dose of exposure having a uniformly negative impact across the sample. Individuals with FVSD had required a significant number of health and child development services. INTERPRETATION: Children and young adults with a diagnosis of FVSD are at an increased risk of a range of altered neurodevelopmental outcomes, highlighting the need for a multidisciplinary approach to clinical management across the lifespan.
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    Kernel-based Nonlinear Manifold Learning for EEG-based Functional Connectivity Analysis and Channel Selection with Application to Alzheimer's Disease
    (elsevier, 2023-07-01) Gunawardena, R.; Sarrigiannis, P. G.; Blackburn, D. J.; He, F.
    Dynamical, causal, and cross-frequency coupling analysis using the electroencephalogram (EEG) has gained significant attention for diagnosing and characterizing neurological disorders. Selecting important EEG channels is crucial for reducing computational complexity in implementing these methods and improving classification accuracy. In neuroscience, measures of (dis) similarity between EEG channels are often used as functional connectivity (FC) features, and important channels are selected via feature selection. Developing a generic measure of (dis) similarity is important for FC analysis and channel selection. In this study, learning of (dis) similarity information within the EEG is achieved using kernel-based nonlinear manifold learning. The focus is on FC changes and, thereby, EEG channel selection. Isomap and Gaussian Process Latent Variable Model (Isomap-GPLVM) are employed for this purpose. The resulting kernel (dis) similarity matrix is used as a novel measure of linear and nonlinear FC between EEG channels. The analysis of EEG from healthy controls (HC) and patients with mild to moderate Alzheimer's disease (AD) are presented as a case study. Classification results are compared with other commonly used FC measures. Our analysis shows significant differences in FC between bipolar channels of the occipital region and other regions (i.e. parietal, centro-parietal, and fronto-central) between AD and HC groups. Furthermore, our results indicate that FC changes between channels along the fronto-parietal region and the rest of the EEG are important in diagnosing AD. Our results and its relation to functional networks are consistent with those obtained from previous studies using fMRI, resting-state fMRI and EEG.
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    Cross-Frequency Multilayer Network Analysis with Bispectrum-based Functional Connectivity: A Study of Alzheimer's Disease
    (elsevier, 2023-06-01) Klepl, D.; He, F.; Wu, M.; Blackburn, D. J.; Sarrigiannis, P. G.
    Alzheimer's disease (AD) is a neurodegenerative disorder known to affect functional connectivity (FC) across many brain regions. Linear FC measures have been applied to study the differences in AD by splitting neurophysiological signals, such as electroencephalography (EEG) recordings, into discrete frequency bands and analysing them in isolation from each other. We address this limitation by quantifying cross-frequency FC in addition to the traditional within-band approach. Cross-bispectrum, a higher-order spectral analysis approach, is used to measure the nonlinear FC and is compared with the cross-spectrum, which only measures the linear FC within bands. This work reports the reconstruction of a cross-frequency FC network where each frequency band is treated as a layer in a multilayer network with both inter- and intra-layer edges. Cross-bispectrum detects cross-frequency differences, mainly increased FC in AD cases in δ-θ coupling. Overall, increased strength of low-frequency coupling and decreased level of high-frequency coupling is observed in AD cases in comparison to healthy controls (HC). We demonstrate that a graph-theoretic analysis of cross-frequency brain networks is crucial to obtain a more detailed insight into their structure and function. Vulnerability analysis reveals that the integration and segregation properties of networks are enabled by different frequency couplings in AD networks compared to HCs. Finally, we use the reconstructed networks for classification. The extra cross-frequency coupling information can improve the classification performance significantly, suggesting an important role of cross-frequency FC. The results highlight the importance of studying nonlinearity and including cross-frequency FC in characterising AD.