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    Characterization and Features of Neural Oscillations in Mental Disorders
    (Springer Science+Business Media, 2025) Hirano, Yoji; Atagun, Murat Ilhan
    Viewing psychiatric disorders or symptoms as outcomes of brain dysfunction emphasizes the necessity of objectively assessing brain function for a deeper understanding of their pathophysiology. Moreover, to measure the brain’s functional abnormalities underlying the dynamically changing mental states and symptoms, EEG and MEG with high temporal resolution are optimal. Research employing EEG/MEG to investigate brain function in relation to psychiatric disorders via classical task-evoked ERP/ERF studies has produced substantial findings. Nevertheless, recent progress in equipment digitalization and analytical methodology development has facilitated the quantification of neural oscillations, inherent rhythmic periodic activities critical for sustaining complex brain functions. It is evident that these oscillations play a role in the pathophysiology of psychiatric disorders, such as schizophrenia and bipolar disorder. In this chapter, we provide an overview of previous research using EEG/MEG in major psychiatric disorders, along with the latest findings and future prospects. © 2025 Springer Nature Switzerland AG.
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    Methods for Measuring Neural Oscillations in Mental Disorders
    (Springer Science+Business Media, 2025) Atagun, Murat Ilhan; Tamura, Shunsuke; Hirano, Yoji
    Electroencephalography (EEG) and magnetoencephalography (MEG) are neurophysiological methods for recording brain electrical signals. The signals consisting of excitatory post-synaptic potentials and fields summate and propagate through brain surface and cranium and thereby captured by the EEG and MEG. The signal gets contribution from several sources and become convoluted by detached inputs. The signal could be altered by inputs according to the network and task demands. Cognitive, sensory, or motor tasks invoke associated neural networks. Stimulation characteristics determine analysis strategies. Decomposition of the EEG data has three phases including (i) preprocessing, (ii) processing, and (iii) post-processing. Within the recent years, deep learning and classification systems are added into EEG data analysis. In this chapter, we aimed to introduce methods for experimental and analytical procedures of EEG research. © 2025 Springer Nature Switzerland AG.