Single trial variability in neural activity during a working memory task reveals multiple distinct information processing sequences

• Published in: NeuroImage (Orlando, Fla.) Fla.), 2023-04, Vol.269, p.119895-119895, Article 119895
• Main Authors: Nakuci, Johan, Covey, Thomas J., Shucard, Janet L., Shucard, David W., Muldoon, Sarah F.
• Format: Article
• Language: English
• Subjects: Accuracy; Brain Mapping; Buttons; Clustering; Cognition - physiology; Cognitive ability; Decision making; Decomposition; EEG; Electrodes; Electroencephalography; Electroencephalography - methods; Event-related potentials; Evoked Potentials - physiology; Humans; Information processing; Memory; Memory, Short-Term - physiology; Mental task performance; Neural networks; Short term memory; Single trial variability; Working memory
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2023.119895

•Traditional analysis assumes variability in brain activity represents noise.•We develop a method to cluster multiple trials of a stimulus dependent task.•We show that trials of the same task can be classified into three waveforms.•Variability arises from the presence of multiple ERP subtypes.•ERP subtypes can be linked to behavioral outcomes. Successful encoding, maintenance, and retrieval of information stored in working memory requires persistent coordination of activity among multiple brain regions. It is generally assumed that the pattern of such coordinated activity remains consistent for a given task. Thus, to separate this task-relevant signal from noise, multiple trials of the same task are completed, and the neural response is averaged across trials to generate an event-related potential (ERP). However, from trial to trial, the neuronal activity recorded with electroencephalogram (EEG) is actually spatially and temporally diverse, conflicting with the assumption of a single pattern of activity for a given task. Here, we show that variability in neuronal activity among single time-locked trials arises from the presence of multiple forms of stimulus dependent synchronized activity (i.e., distinct ERPs). We develop a data-driven classification method based on community detection to identify three discrete spatio-temporal clusters, or subtypes, of trials with different patterns of activation that are further associated with differences in decision-making processes. These results demonstrate that differences in the patterns of neural activity during working memory tasks represent fluctuations in the engagement of distinct brain networks and cognitive processes, suggesting that the brain can choose from multiple mechanisms to perform a given task.