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Shelepenkov, D.

Publications and source records attributed to Shelepenkov, D..

2 recordsLinked to original sources

Low-Frequency activity shapes fine-scale information routing in the early visual cortex

Visual processing requires flexible routing of task-relevant information across cortical hierarchies. One proposed mechanism is nested oscillatory activity, in which low-frequency rhythms dynamically modulate local excitability and inter-areal communication. However, the specific predictions of this framework have not been tested during active stimulus processing at fine spatial and temporal scales. Here, we reanalyzed local field potentials (LFPs) and multi-unit activity (MUA) recorded from V1 and V4 in macaque monkeys performing a figure-ground segregation task. We show that alpha-band activity in V1 carries information about the position of the figure and the orientation of the stimulus with fine spatial specificity within a transient post-stimulus window, a period that coincides with the emergence of figure-ground modulation and the dominant V4[->]V1 feedback. During this window, both local spiking activity and inter-areal coupling between V1 and V4 depended on alpha amplitude and on the instantaneous V1-V4 phase difference, indicating that low-frequency synchronization shapes effective communication between cortical populations. Together, these findings support the view that alpha-band activity reflect fine-scale information routing during visual processing through coordinated modulation of local excitability and hierarchical feedback interactions.

neuroscience↗

Dynamic and task-dependent decoding of the human attentional spotlight from MEG

Attention is a fundamental mechanism enabling the brain to overcome its limited capacity for parallel processing. In non-human primates, invasive electrophysiology has shown that attentional selection operates rhythmically, primarily within the alpha ([~]8-12 Hz) and theta ([~]4-5 Hz) bands. Whether such finely resolved control signals can be captured non-invasively in humans, and how they adapt to changing task demands, remains unclear. Using high-precision magnetoencephalography (MEG) combined with machine learning, we decoded the spatial locus of covert attention in humans performing three variants of a spatial cueing task that manipulated cue validity as well invalid trial switching rules. Spatial attention could be decoded from whole-brain MEG activity at both static and time-resolved scales, with accuracies significantly above chance (N = 30). Decoding performance decreased as cue validity was reduced, indicating that task structure shapes attentional engagement. Analysis of decoding trajectories revealed rhythmic fluctuations at [~]8-12 Hz across all tasks, demonstrating alpha-band sampling of attention. Pre-target attention became increasingly focused on the cued side, especially in the 100% Valid condition, consistent with proactive orienting. Furthermore, individual and task-specific differences in decoding strength correlated with task-variations in behavioral performance, linking the accuracy of neural attention codes to both discrimination accuracy and reaction time. These findings demonstrate that MEG can non-invasively capture dynamic, task-dependent fluctuations in spatial attention that parallel those observed in non-human primates. They reveal that attentional demands reshape the neural code for attention, modulate rhythmic sampling, and influence behavioral efficiency. This work bridges invasive primate and non-invasive human research and establishes MEG-based decoding of attention as a promising tool for mechanistic and clinical applications, including neurofeedback and attention-related interventions.

neuroscience↗