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Makaila Banks, Abhishek Dedhe, Tanique McDonald, Brianna Carr, Marc Mancarella, Jackie Hembrook-Short, Farran Briggs; Neuronal Mechanisms of Attention Measured Through Multi-unit Recordings in LGN and V1. Journal of Vision 2019;19(10):271a. doi: https://doi.org/10.1167/19.10.271a.
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Understanding the neural mechanisms of attention has numerous applications to healthcare, education, and workplace efficiency. Despite its interconnected circuitry throughout the brain, the neurophysiological basis of attention is poorly understood. Previous research has shown that attention has an impact on the activity of single neurons in the visual thalamus (LGN) and primary visual cortex (V1). Our lab followed up on these findings by recording multi-unit activity from neuronal populations in the LGN and V1 while rhesus macaque monkeys were performing covert visual spatial attention tasks. Monkeys were trained to perform a contrast-change detection task and two versions of a discrimination task. All tasks required monkeys to focus on a central fixation dot that cued them to attend toward or away from a drifting grating stimulus placed in the lower hemifield and overlapping the receptive fields of recorded neurons. Monkeys indicated whether or not (contrast-change detection) and which direction (contrast, orientation, or color change discrimination) the grating stimulus changed, receiving a juice reward for correct answers. While monkeys performed these tasks, multi-unit activity was recorded in the LGN and in V1. V1 recording contacts were assigned to supragranular (SG), granular (G) and infragranular (IG) layers. Multi-unit recordings were sorted and analyzed using custom code and attention index (AI) values computed per contact as the difference divided by the sum of multi-unit firing rate across attention conditions. Comparison of the distributions of AI values across structures and layers revealed net suppressive effects of attention, supporting our hypothesis that attention facilitates a minority of feature-selective neurons in early visual structures. Further analysis of the tuning data for each multi-unit could reveal relationships between neuronal feature selectivity and attentional modulation across tasks.
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