Brain network analyses in clinical neuroscience

Network analyses are now considered fundamental for understanding brain function. Nonetheless neuroimaging characterisations of connectivity are just emerging in clinical neuroscience. Here, we briefly outline the concepts underlying structural, functional and effective connectivity, and discuss some cutting-edge approaches to the quantitative assessment of brain architecture and dynamics. As illustrated by recent evidence, comprehensive and integrative network analyses offer the potential for refining pathophysiological concepts and therapeutic strategies in neurological and psychiatric conditions across the lifespan

Frontoparietal connectivity, Sensory Features, and Anxiety in Children and Adolescents with Autism Spectrum Disorder

Objectives Because atypical global neural connectivity has been documented in autistic youth, but only limited data are available regarding the association between generalized anxiety disorder (GAD), sensory features (SF), and neural connectivity between frontal and parietal brain regions, these links were investigated in a sample of male autistic children and adolescents.
 Methods Forty-one autistic males aged between 6 and 18 years and their mothers were recruited as volunteer participants from Queensland, Australia. Participants underwent 3 min of eyes-closed and 3 min of eyes-opened electroencephalography (EEG) under resting conditions. EEG connectivity was investigated using Granger causality between frontal and parietal regions in alpha (8–13 Hz) and beta (13–30 Hz) bands.
 Results There was a significant (p 
 Conclusions Reduced frontoparietal connectivity in association with higher anxiety and SF may demonstrate reduced relaxation due to greater sensitivity to sensory input

EEG-based functional connectivity analysis of brain abnormalities: A systematic review study

Several imaging modalities and many signal recording techniques have been used to study the brain activities. Significant advancements in medical device technologies like electroencephalographs have provided conditions for recording neural information with high temporal resolution. These recordings can be used to calculate the connections between different brain areas. It has been proved that brain abnormalities affect the brain activity in different brain regions and the connectivity patterns between them are changed as a result. This paper studies the electroencephalogram (EEG) functional connectivity methods and investigates the impacts of brain abnormalities on brain functional connectivities. The effects of different brain abnormalities including stroke, depression, emotional disorders, epilepsy, attention deficit hyperactivity disorder (ADHD), autism, and Alzheimer's disease on functional connectivity of the EEG recordings have been explored in this study. The EEG-based metrics and network properties of different brain abnormalities have been discussed to present a comparison of the connectivities affected by each abnormality. Also, the effects of therapy and medical intake on the EEG functional connectivity network of each abnormality have been reviewed.This research received no external funding

Atypical Cortical Connectivity in Autism Spectrum Disorder (ASD) as Measured by Magnetoencephalography (MEG)

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition, characterised by impairments in social interaction and communication, the presence of repetitive behaviours, and multisensory hyper- and hypo-sensitives. This thesis utilised magnetoencephalography, in combination with robust analysis techniques, to investigate the neural basis of ASD. Based on previous research, it was hypothesised that cortical activity in ASD would be associated with disruptions to oscillatory synchronisation during sensory processing, as well as during high-level perspective-taking. More specifically, a novel framework was introduced, based on local gamma-band dysregulation, global hypoconnectivity and deficient predictive-coding. To test this framework, data were collected from adolescents diagnosed with ASD and age-matched controls. Using a visual grating stimulus, it was found that in primary visual cortex, ASD participants had reduced coupling between the phase of alpha oscillations and the amplitude of gamma oscillations (i.e. phase amplitude coupling), suggesting dysregulated visual gamma in ASD. These findings were based on a robust analysis pipeline outlined in Chapter 2. Next, directed connectivity in the visual system was quantified using Granger causality. Compared with controls, ASD participants showed reductions in feedback connectivity, mediated by alpha oscillations, but no differences in inter-regional feedforward connectivity, mediated by gamma oscillations. In the auditory domain, it was found that ASD participants had reduced steady-state responses at 40Hz, in terms of oscillatory power and inter-trial coherence, again suggesting dysregulated gamma. Investigating predictive-coding theories of ASD using an auditory oddball paradigm, it was found that evoked responses to the omission of an expected tone were reduced for ASD participants. Finally, we found reductions in theta-band oscillatory power and connectivity for ASD participants, during embodied perspective-taking. Overall, these findings fit the proposed framework, and demonstrate that cortical activity in ASD is characterised by disruptions to oscillatory synchronisation, at the local and global scales, during both sensory processing and higher-level perspective-taking

Discerning nonlinear brain dynamics from EEG:an application to autistic spectrum disorder in young children

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

EEG-Based Effective Connectivity Analysis for Attention Deficit Hyperactivity Disorder Detection Using Color-Coded Granger-Causality Images and Custom Convolutional Neural Network

Background: Attention deficit hyperactivity disorder (ADHD) is prevalent worldwide, affecting approximately 8-12% of children. Early detection and effective treatment of ADHD are crucial for improving academic, social, and emotional outcomes. Despite numerous studies on ADHD detection, existing models still lack accuracy distinguishing between ADHD and healthy control (HC) children.Methods: This study introduces an innovative methodology that utilizes granger causality (GC), a well-established brain connectivity analysis technique, to reduce the required EEG electrodes. We computed GC indexes (GCI) for the entire brain and specific brain regions, known as regional GCI, across different frequency bands. Subsequently, these GCIs were transformed into color-coded images and fed into a custom-developed 11-layer convolutional neural network.Results: The proposed model is evaluated through a five-fold cross-validation, achieving the highest accuracy of 99.80% in the gamma frequency band for the entire brain and an accuracy of 98.50% in distinguishing the theta frequency band of the right hemisphere of ADHD and HC children by only using eight electrodes.Conclusion: The proposed framework provides a powerful automated tool for accurately classifying ADHD and HC children. The study’s outcome demonstrates that the innovative proposed methodology utilizing GCI and a custom-developed convolutional neural network can significantly improve ADHD detection accuracy, improving affected children’s overall quality of life

Learning and comparing functional connectomes across subjects

Functional connectomes capture brain interactions via synchronized fluctuations in the functional magnetic resonance imaging signal. If measured during rest, they map the intrinsic functional architecture of the brain. With task-driven experiments they represent integration mechanisms between specialized brain areas. Analyzing their variability across subjects and conditions can reveal markers of brain pathologies and mechanisms underlying cognition. Methods of estimating functional connectomes from the imaging signal have undergone rapid developments and the literature is full of diverse strategies for comparing them. This review aims to clarify links across functional-connectivity methods as well as to expose different steps to perform a group study of functional connectomes

Unsupervised Manifold Learning using High-order Morphological Brain Networks derived from T1-w MRI for Autism Diagnosis

Brain disorders, such as Autism Spectrum Disorder (ASD), alter brain functional (from fMRI) and structural (from diffusion MRI) connectivities at multiple levels and in varying degrees. While unraveling such alterations have been the focus of a large number of studies, morphological brain connectivity has been out of the research scope. In particular, shape-to-shape relationships across brain regions of interest (ROIs) were rarely investigated. As such, the use of networks based on morphological brain data in neurological disorder diagnosis, while leveraging the advent of machine learning, could complement our knowledge on brain wiring alterations in unprecedented ways. In this paper, we use conventional T1-weighted MRI to define morphological brain networks (MBNs), each quantifying shape relationship between different cortical regions for a specific cortical attribute at both low-order and high-order levels. While typical brain connectomes investigate the relationship between two ROIs, we propose high-order MBN which better captures brain complex interactions by modeling the morphological relationship between pairs of ROIs. For ASD identification, we present a connectomic manifold learning framework, which learns multiple kernels to estimate a similarity measure between ASD and normal controls (NC) connectional features, to perform dimensionality reduction for clustering ASD and NC subjects. We benchmark our ASD identification method against both supervised and unsupervised state-of-the-art methods, while depicting the most discriminative high- and low-order relationships between morphological regions in the left and right hemispheres