Abstract
In this study, we focused on the verification of suitable aggregation operators enabling accurate differentiation of selected neurophysiological features extracted from resting-state electroencephalographic recordings of patients who were diagnosed with schizophrenia (SZ) or healthy controls (HC). We built the Choquet integral-based operators using traditional classification results as an input to the procedure of establishing the fuzzy measure densities. The dataset applied in the study was a collection of variables characterizing the organization of the neural networks computed using the minimum spanning tree (MST) algorithms obtained from signal-spaced functional connectivity indicators and calculated separately for predefined frequency bands using classical linear Granger causality (GC) measure. In the series of numerical experiments, we reported the results of classification obtained using numerous generalizations of the Choquet integral and other aggregation functions, which were tested to find the most appropriate ones. The obtained results demonstrate that the classification accuracy can be increased by 1.81% using the extended versions of the Choquet integral called in the literature, namely, generalized Choquet integral or pre-aggregation operators.
Highlights
Mental illnesses are usually long-lasting conditions associated with great psychological suffering, the substantially limited possibility of independent functioning, and social development
Studies based on electroencephalography (EEG) and functional magnetic resonance imaging (MRI) revealed abnormalities in the functional connectivity of the brain, which were correlated with the clinical picture of the SZ (Skudlarski et al, 2010; Uhlhaas, 2013; Krukow et al, 2018)
We described particular classifiers that were considered in the series of numerical experiments and determined their accuracy
Summary
Mental illnesses are usually long-lasting conditions associated with great psychological suffering, the substantially limited possibility of independent functioning, and social development. Social, and economic burdens associated with severe mental illness prompt researchers to search for new therapies and to develop accurate methods of differential diagnosis, which should be based on objective, biological markers (Pantelis et al, 2009). Studies based on electroencephalography (EEG) and functional MRI (fMRI) revealed abnormalities in the functional connectivity of the brain, which were correlated with the clinical picture of the SZ (Skudlarski et al, 2010; Uhlhaas, 2013; Krukow et al, 2018). To understand the systemic level of the brain organization and to explain neurophysiological processes such as disconnectivity syndrome in the SZ, researchers started to analyze the brain as a complex network (van den Heuvel and Sporns, 2013). Aberrant functional networks in the SZ were linked with cognitive impairments (Sheffield et al, 2015; Krukow et al, 2020) and the duration of the illness (Jonak et al, 2019)
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