Neural Interactions in a Spatially-Distributed Cortical Network During Perceptual Decision-Making

Abstract

Behavioral experiments evidence that attention is not maintained at a constant level, but fluctuates with time. Recent studies associate such fluctuations with dynamics of attention-related cortical networks, however the exact mechanism remains unclear. To address this issue, we consider functional neuronal interactions during the accomplishment of a reaction time (RT) task which requires sustained attention. The participants are subjected to a binary classification of a large number of presented ambiguous visual stimuli with different degrees of ambiguity. Generally, high ambiguity causes high RT and vice versa. However, we demonstrate that RT fluctuates even when the stimulus ambiguity remains unchanged. The analysis of neuronal activity reveals that the subject's behavioral response is preceded by the formation of a distributed functional network in the β-frequency band. This network is characterized by high connectivity in the frontal cortex and supposed to subserve a decision-making process. We show that neither the network structure nor the duration of its formation depend on RT and stimulus ambiguity. In turn, RT is related to the moment of time when the β-band functional network emerges. We hypothesize that RT is affected by the processes preceding the decision-making stage, e.g., encoding visual sensory information and extracting decision-relevant features from raw sensory information.

Dissociating Cognitive Processes During Ambiguous Information Processing in Perceptual Decision-Making

“…The authors concluded that the evidence accumulation process began after early visual perception and lasted 290–440 ms depending on the strength of the evidence. In our recent study (Maksimenko et al, 2019 ), we considered the decision-making stage of the ambiguous stimuli classification task and observed that the emergence of a large-scale frontoparietal network in the β-band preceded the perceptual decisions. We reported that neither the network structure nor the duration of its formation depended on the stimulus ambiguity.…”

“…On the contrary, when the stimulus ambiguity is high, the observer experiences difficulty in making a decision and hence takes more time to accumulate the evidence. Following Philiastides and Sajda ( 2005 ) and Maksimenko et al ( 2019 ), we analyzed the event-related spectral perturbations (ERSP) during the sensory processing (0.5 s post-stimulus onset) and response formation (0.3 s before response) stages. We demonstrated that increasing ambiguity resulted in a higher frontal θ-band power for 0.15 s post-stimulus onset.…”

High-level stimulus template modulates neuronal response at the earlier processing stages

“…This effect implies the reduced neural response to repeated compared with unrepeated stimuli (Henson and Rugg, 2003) and relates to the low-level (Vogels, 2016;Vinken et al, 2018) and the high-level (Gilbert and Li, 2013;Schwiedrzik and Freiwald, 2017) predictions. According to our previous works (Maksimenko et al, 2019, HA stimuli interpretation takes longer RT than the interpretation of LA ones. The LA and HA Necker cubes have almost the same morphology, and we assume their similar processing on low levels.…”

Functional Near-Infrared Spectroscopy for the Classification of Motor-Related Brain Activity on the Sensor-Level

“…The proposed system can serve as a sensor of motor activity to be used for neurorehabilitation after severe brain injuries, including traumas and strokes. different brain areas (e.g., event-related synchronization/desynchronization) on a sensor level provide important information about the current state of the nervous system and cognitive brain ability [7][8][9][10]. Particular brain states are associated with motor brain activity during either real or imaginary movement.Revealing specific features of spatial brain cortex activity related to real motions and motor imagery of different limbs can be essential not only for basic research in neuroscience, but also for applications in medicine to improve the quality of life of post-traumatic and post-stroke patients using brain-computer interfaces (BCI) for rehabilitation [11][12][13] or to control prostheses and exoskeletons [14].…”

“…The proposed system can serve as a sensor of motor activity to be used for neurorehabilitation after severe brain injuries, including traumas and strokes. different brain areas (e.g., event-related synchronization/desynchronization) on a sensor level provide important information about the current state of the nervous system and cognitive brain ability [7][8][9][10]. Particular brain states are associated with motor brain activity during either real or imaginary movement.…”