Sofia Ira Ktena

Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex

ABSTRACT The macro‐connectome elucidates the pathways through which brain regions are structurally connected or functionally coupled to perform a specific cognitive task. It embodies the notion of representing and understanding all connections within the brain as a network, while the subdivision of the brain into interacting functional units is inherent in its architecture. As a result, the definition of network nodes is one of the most critical steps in connectivity network analysis. Although brain atlases obtained from cytoarchitecture or anatomy have long been used for this task, connectivity‐driven methods have arisen only recently, aiming to delineate more homogeneous and functionally coherent regions. This study provides a systematic comparison between anatomical, connectivity‐driven and random parcellation methods proposed in the thriving field of brain parcellation. Using resting‐state functional MRI data from the Human Connectome Project and a plethora of quantitative evaluation techniques investigated in the literature, we evaluate 10 subject‐level and 24 groupwise parcellation methods at different resolutions. We assess the accuracy of parcellations from four different aspects: (1) reproducibility across different acquisitions and groups, (2) fidelity to the underlying connectivity data, (3) agreement with fMRI task activation, myelin maps, and cytoarchitectural areas, and (4) network analysis. This extensive evaluation of different parcellations generated at the subject and group level highlights the strengths and shortcomings of the various methods and aims to provide a guideline for the choice of parcellation technique and resolution according to the task at hand. The results obtained in this study suggest that there is no optimal method able to address all the challenges faced in this endeavour simultaneously. HIGHLIGHTSA systematic comparison of state‐of‐the‐art parcellation methods is provided.10 subject‐ and 24 group‐level methods are evaluated using publicly available data.Experiments consist of quantitative assessments of parcellations at varying scales.Several criteria are simultaneously considered to evaluate parcellations.Results suggest that there is no optimal method able to address all the challenges.

A virtual reality platform for safe evaluation and training of natural gaze-based wheelchair driving

The importance of ensuring user safety throughout the training and evaluation process of brain-machine interfaces is not to be neglected. In this study, a virtual reality software system was built with the intention to create a safe environment, where the performance of wheelchair control interfaces could be tested and compared. We use this to evaluate our eye tracking input methodology, a promising solution for hands-free wheelchair navigation, because of the abundance of control commands that it offers and its intuitive nature. Natural eye movements have long been considered to reflect cognitive processes and are highly correlated with user intentions. Therefore, the sequence of gaze locations during navigation is recorded and analyzed, in order to search and unveil patterns in saccadic movements. Moreover, this study compares different eye-based solutions that have previously been implemented, and proposes a new, more natural approach. The preliminary results on N = 6 healthy subjects indicate that the proposed free-view solution leads to 18.4% faster completion of the task (440 sec) benchmarked against a naive free-view approach.

Spectral Graph Convolutions for Population-Based Disease Prediction

Exploiting the wealth of imaging and non-imaging information for disease prediction tasks requires models capable of representing, at the same time, individual features as well as data associations between subjects from potentially large populations. Graphs provide a natural framework for such tasks, yet previous graph-based approaches focus on pairwise similarities without modelling the subjects’ individual characteristics and features. On the other hand, relying solely on subject-specific imaging feature vectors fails to model the interaction and similarity between subjects, which can reduce performance. In this paper, we introduce the novel concept of Graph Convolutional Networks (GCN) for brain analysis in populations, combining imaging and non-imaging data. We represent populations as a sparse graph where its vertices are associated with image-based feature vectors and the edges encode phenotypic information. This structure was used to train a GCN model on partially labelled graphs, aiming to infer the classes of unlabelled nodes from the node features and pairwise associations between subjects. We demonstrate the potential of the method on the challenging ADNI and ABIDE databases, as a proof of concept of the benefit from integrating contextual information in classification tasks. This has a clear impact on the quality of the predictions, leading to 69.5% accuracy for ABIDE (outperforming the current state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion, significantly outperforming standard linear classifiers where only individual features are considered.

Distance Metric Learning Using Graph Convolutional Networks: Application to Functional Brain Networks

Evaluating similarity between graphs is of major importance in several computer vision and pattern recognition problems, where graph representations are often used to model objects or interactions between elements. The choice of a distance or similarity metric is, however, not trivial and can be highly dependent on the application at hand. In this work, we propose a novel metric learning method to evaluate distance between graphs that leverages the power of convolutional neural networks, while exploiting concepts from spectral graph theory to allow these operations on irregular graphs. We demonstrate the potential of our method in the field of connectomics, where neuronal pathways or functional connections between brain regions are commonly modelled as graphs. In this problem, the definition of an appropriate graph similarity function is critical to unveil patterns of disruptions associated with certain brain disorders. Experimental results on the ABIDE dataset show that our method can learn a graph similarity metric tailored for a clinical application, improving the performance of a simple k-nn classifier by 11.9% compared to a traditional distance metric.

Metric learning with spectral graph convolutions on brain connectivity networks.

ABSTRACT Graph representations are often used to model structured data at an individual or population level and have numerous applications in pattern recognition problems. In the field of neuroscience, where such representations are commonly used to model structural or functional connectivity between a set of brain regions, graphs have proven to be of great importance. This is mainly due to the capability of revealing patterns related to brain development and disease, which were previously unknown. Evaluating similarity between these brain connectivity networks in a manner that accounts for the graph structure and is tailored for a particular application is, however, non‐trivial. Most existing methods fail to accommodate the graph structure, discarding information that could be beneficial for further classification or regression analyses based on these similarities. We propose to learn a graph similarity metric using a siamese graph convolutional neural network (s‐GCN) in a supervised setting. The proposed framework takes into consideration the graph structure for the evaluation of similarity between a pair of graphs, by employing spectral graph convolutions that allow the generalisation of traditional convolutions to irregular graphs and operates in the graph spectral domain. We apply the proposed model on two datasets: the challenging ABIDE database, which comprises functional MRI data of 403 patients with autism spectrum disorder (ASD) and 468 healthy controls aggregated from multiple acquisition sites, and a set of 2500 subjects from UK Biobank. We demonstrate the performance of the method for the tasks of classification between matching and non‐matching graphs, as well as individual subject classification and manifold learning, showing that it leads to significantly improved results compared to traditional methods. HighlightsMetric learning approach for similarity estimation between brain connectivity graphs.The method employs spectral graph convolutions to learn localised feature maps.Quantitative and qualitative evaluation on ABIDE and UK Biobank databases.Global loss function leads to improved results on heterogeneous datasets.

Exploring heritability of functional brain networks with inexact graph matching

Data-driven brain parcellations aim to provide a more accurate representation of an individuals functional connectivity, since they are able to capture individual variability that arises due to development or disease. This renders comparisons between the emerging brain connectivity networks more challenging, since correspondences between their elements are not preserved. Unveiling these correspondences is of major importance to keep track of local functional connectivity changes. We propose a novel method based on graph edit distance for the comparison of brain graphs directly in their domain, that can accurately reflect similarities between individual networks while providing the network element correspondences. This method is validated on a dataset of 116 twin subjects provided by the Human Connectome Project.

A flexible graphical model for multi-modal parcellation of the cortex

&NA; Advances in neuroimaging have provided a tremendous amount of in‐vivo information on the brains organisation. Its anatomy and cortical organisation can be investigated from the point of view of several imaging modalities, many of which have been studied for mapping functionally specialised cortical areas. There is strong evidence that a single modality is not sufficient to fully identify the brains cortical organisation. Combining multiple modalities in the same parcellation task has the potential to provide more accurate and robust subdivisions of the cortex. Nonetheless, existing brain parcellation methods are typically developed and tested on single modalities using a specific type of information. In this paper, we propose Graph‐based Multi‐modal Parcellation (GraMPa), an iterative framework designed to handle the large variety of available input modalities to tackle the multi‐modal parcellation task. At each iteration, we compute a set of parcellations from different modalities and fuse them based on their local reliabilities. The fused parcellation is used to initialise the next iteration, forcing the parcellations to converge towards a set of mutually informed modality specific parcellations, where correspondences are established. We explore two different multi‐modal configurations for group‐wise parcellation using resting‐state fMRI, diffusion MRI tractography, myelin maps and task fMRI. Quantitative and qualitative results on the Human Connectome Project database show that integrating multi‐modal information yields a stronger agreement with well established atlases and more robust connectivity networks that provide a better representation of the population. Graphical abstract Figure. No caption available. HighlightsAn iterative graphbased cortex parcellation method for multimodal data.A flexible approach to integrate different modalities and exploit their reliabilities.Grouplevel parcellations computed for two different multimodal configurations.Coarse modality specific parcellations computed for quantitative evaluations.Extensive quantitative and qualitative evaluation using a broad set of criteria.

Graph Saliency Maps Through Spectral Convolutional Networks: Application to Sex Classification with Brain Connectivity

Graph convolutional networks (GCNs) allow to apply traditional convolution operations in non-Euclidean domains, where data are commonly modelled as irregular graphs. Medical imaging and, in particular, neuroscience studies often rely on such graph representations, with brain connectivity networks being a characteristic example, while ultimately seeking the locus of phenotypic or disease-related differences in the brain. These regions of interest (ROIs) are, then, considered to be closely associated with function and/or behaviour. Driven by this, we explore GCNs for the task of ROI identification and propose a visual attribution method based on class activation mapping. By undertaking a sex classification task as proof of concept, we show that this method can be used to identify salient nodes (brain regions) without prior node labels. Based on experiments conducted on neuroimaging data of more than 5000 participants from UK Biobank, we demonstrate the robustness of the proposed method in highlighting reproducible regions across individuals. We further evaluate the neurobiological relevance of the identified regions based on evidence from large-scale UK Biobank studies.

Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer’s disease

HighlightsFirst application of graph convolutional networks for brain analysis in populations.Graph based population model that leverages imaging and non‐imaging data.Experiments on two large and challenging databases: ABIDE and ADNI.Extensive evaluation of all the main components of the method.State of the art performance on both databases. Graphical abstract Figure. No caption available. ABSTRACT Graphs are widely used as a natural framework that captures interactions between individual elements represented as nodes in a graph. In medical applications, specifically, nodes can represent individuals within a potentially large population (patients or healthy controls) accompanied by a set of features, while the graph edges incorporate associations between subjects in an intuitive manner. This representation allows to incorporate the wealth of imaging and non‐imaging information as well as individual subject features simultaneously in disease classification tasks. Previous graph‐based approaches for supervised or unsupervised learning in the context of disease prediction solely focus on pairwise similarities between subjects, disregarding individual characteristics and features, or rather rely on subject‐specific imaging feature vectors and fail to model interactions between them. In this paper, we present a thorough evaluation of a generic framework that leverages both imaging and non‐imaging information and can be used for brain analysis in large populations. This framework exploits Graph Convolutional Networks (GCNs) and involves representing populations as a sparse graph, where its nodes are associated with imaging‐based feature vectors, while phenotypic information is integrated as edge weights. The extensive evaluation explores the effect of each individual component of this framework on disease prediction performance and further compares it to different baselines. The framework performance is tested on two large datasets with diverse underlying data, ABIDE and ADNI, for the prediction of Autism Spectrum Disorder and conversion to Alzheimers disease, respectively. Our analysis shows that our novel framework can improve over state‐of‐the‐art results on both databases, with 70.4% classification accuracy for ABIDE and 80.0% for ADNI.