Cell ReportsNeurosciencePeer-reviewed journal article
MEG frequency tagging reveals a grid-like code during covert attentional movements
Giuliano GiariiD, Lorenzo VignaliiD, Yangwen XuiD, Roberto BottiniiD
Abstract
Grid cells in the entorhinal cortex map regular environments with a 60{degrees} periodicity, encoding body- and eye-movements direction during spatial exploration. Recent evidence in monkeys suggests that grid cells firing is elicited by spatial attention even without eye movements. We investigated whether movements of covert attention can elicit grid-like responses in humans by concurrently recording MEG and eye-tracker. To obtain a measure of grid-like signal non-invasively, we developed a new paradigm based on frequency tagging. While keeping central fixation, participants were presented visually with sequences of linearly-spaced trajectories (15{degrees} or 30{degrees} in different conditions) formed by static lines or moving dots in separate sessions. Trajectories appeared sequentially on the screen at a fixed rate (6 Hz), allowing different spatial periodicities (e.g., 90{degrees}, 60{degrees}, 45{degrees}) to have corresponding temporal periodicities (e.g., 1, 1.5, 2 Hz), thus resulting in distinct spectral responses in the MEG signal. Analyses of inter-trial coherence evidenced a higher steady-state response for the frequency corresponding to 60{degrees} periodicity compared to control periodicities. This effect was localized in medial-temporal sources and not in control regions. Moreover, in a control experiment using a recurrent sequence of letters featuring the same temporal periodicity but lacking spatial structure, the 60{degrees} effect did not emerge, suggesting its dependency on spatial movements of attention. We report the first evidence, in humans, that grid-like signals in the medial-temporal lobe can be elicited by covert attentional movements. Moreover, we propose a new neuroimaging paradigm based on frequency tagging to study grid-like activity non-invasively.
- organism: human
- assay: electronic health records
- research focus: cell type annotation
- approach: statistical model
- Citations
- 10
- FWCI
- 1.49
- Field percentile
- top 19%
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