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Trajkovic, J.

Publications and source records attributed to Trajkovic, J..

3 recordsLinked to original sources

Top-down and bottom-up interactions rely on nested brain oscillations.

Adaptive visual processing is enabled through the dynamic interplay between top-down and bottom-up (feedback/feedforward) information exchange, presumably propagated through brain oscillations. Here we causally tested for the oscillatory mechanisms governing this interaction in the visual system. Using concurrent TMS-EEG, we emulated top-down signals by a single TMS-pulse over the Frontal Eye Field (right-FEF), while manipulating the strength of sensory input through the presentation of moving concentric gratings (compared to a control-TMS site). FEF-TMS without sensory input led to a top-down controlled occipital phase-realignment, alongside higher fronto-occipital phase-connectivity, in the alpha/beta-band. Sensory input in the absence of FEF-TMS increased occipital gamma activity. Crucially, testing the interaction between top-down and bottom-up processes (FEF-TMS during sensory input) revealed an increased nesting of the bottom-up gamma activity in the alpha/beta-band cycles. This establishes a causal link between phase-to-power coupling and top-down modulation of feedforward signals, providing novel mechanistic insights into how attention interacts with sensory input at the neural level, shaping rhythmic sampling.

neuroscience↗

Strengthening connectivity between premotor and motor cortex increases inter-areal communication in the human brain

The ventral premotor cortex (PMv) is an important component of cortico-cortical pathways mediating prefrontal control over primary motor cortex (M1) function. Paired associative stimulation (ccPAS) is known to change PMv influence over M1 in humans, which manifests differently depending on the behavioural context. Here we show that these changes in influence are functionally linked to PMv-M1 phase synchrony changes induced by repeated paired stimulation of the two areas. PMv-to-M1 ccPAS leads to increased phase synchrony in alpha and beta bands while reversed order M1-to-PMv ccPAS leads to decreased theta phase synchrony. These changes are visible at rest but they are predictive of changes in oscillatory power in the same frequencies during movement execution and inhibition, respectively. The results unveil a link between the physiology of the motor network and the resonant frequencies mediating its interactions, and provide a putative mechanism underpinning the relationship between synaptic efficacy and brain oscillations.

neuroscience↗

Developmental changes in individual alpha frequency: Recording EEG data during public engagement events

Statistical power in cognitive neuroimaging experiments is often very low. Low sample size can reduce the likelihood of detecting real effects (false negatives) and increase the risk of detecting non-existing effects by chance (false positives). Here we document our experience of leveraging a relatively unexplored method of collecting a large sample size for simple electroencephalography (EEG) studies: by recording EEG in the community during public engagement and outreach events. We collected data from 346 participants (189 females, age range 6-76 years) over 6 days, totalling 29 hours, at local science festivals. Alpha activity (6-15 Hz) was filtered from 30 seconds of signal, recorded from a single electrode placed between the occipital midline (Oz) and inion (Iz) while participants rested with their eyes closed. A total of 289 good quality datasets were obtained. Using this community-based approach, we were able to replicate controlled, lab-based findings: IAF increased during childhood, reaching a peak frequency of 10.28 Hz at 28.1 years old, and slowed again in middle and older age. Total alpha power decreased linearly, but the aperiodic-adjusted alpha power did not change over the lifespan. Aperiodic slopes and intercepts were highest in the youngest participants. There were no associations between these EEG indexes and self-reported fatigue, measured by the Multidimensional Fatigue Inventory. Finally, we present a set of important considerations for researchers who wish to collect EEG data within public engagement and outreach environments.

neuroscience↗