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Neurocognitive Mechanisms for Detecting Early Phase of Depressive Disorder: Analysis of Event-Related Potentials in Human Brain
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Author(s): Shashikanta Tarai (National Institute of Technology Raipur, India)
Copyright: 2019
Pages: 34
Source title:
Early Detection of Neurological Disorders Using Machine Learning Systems
Source Author(s)/Editor(s): Sudip Paul (North-Eastern Hill University Shillong, India), Pallab Bhattacharya (National Institute of Pharmaceutical Education and Research (NIPER) Ahmedabad, India)and Arindam Bit (National Institute of Technology Raipur, India)
DOI: 10.4018/978-1-5225-8567-1.ch010
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Abstract
This chapter discusses neurocognitive mechanisms in terms of latency and amplitudes of EEG signals in depression that are presented in the form of event-related potentials (ERPs). Reviewing the available literature on depression, this chapter classifies early P100, ERN, N100, N170, P200, N200, and late P300 ERP components in frontal, mid-frontal, temporal, and parietal lobes. Using auditory oddball paradigm, most of the studies testing depressive patients have found robust P300 amplitude reduction. Proposing EEG methods and summarizing behavioral, neuroanatomical, and electrophysiological findings, this chapter discusses how the different tasks, paradigms, and stimuli contribute to the cohesiveness of neural signatures and psychobiological markers for identifying the patients with depression. Existing research gaps are directed to conduct ERP studies following go/no-go, flanker interference, and Stroop tasks on global and local attentional stimuli associated with happy and sad emotions to examine anterior cingulate cortex (ACC) dysfunction in depression.
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