Yulia Dodonova

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In the recent years there have been a number of studies that applied deep learning algorithms to neuroimaging data. Pipelines used in those studies mostly require multiple processing steps for feature extraction, although modern advancements in deep learning for image classification can provide a powerful framework for automatic feature generation and more(More)
This paper aims to tackle the problem of brain network classification with machine learning algorithms using spectra of networks' matrices. Two approaches are discussed: first, linear and tree-based models are trained on the vectors of sorted eigenvalues of the adjacency matrix, the Laplacian matrix and the normalized Laplacian; next, SVM classifier is(More)
We consider a task of classifying normal and pathological brain networks. These networks (called connectomes) represent macroscale connections between predefined brain regions, hence, the nodes of connectomes are uniquely labeled and the set of labels (brain regions) is the same across different brains. We make use of this property and hypothesize that(More)
The present study aimed to investigate the relationship between the speed of emotional information processing and emotional intelligence (EI). To evaluate individual differences in the speed of emotional information processing, a recognition memory task consisted of two subtests similar in design but differing in the emotionality of the stimuli. The first(More)
In this paper, we tackle a problem of predicting phenotypes from structural connectomes. We propose that normalized Laplacian spectra can capture structural properties of brain networks, and hence graph spectral distributions are useful for a task of connectome-based classification. We introduce a kernel that is based on earth mover’s distance (EMD) between(More)
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