Biology subjects

Catal, Y.

Publications and source records attributed to Catal, Y..

3 recordsLinked to original sources

bioRxiv2025

How Intrinsic Neural Timescales Relate To Event-Related Activity - Key Role For Intracolumnar Connections

The relationship of the brains intrinsic neural timescales (INTs) during the resting state with event-related activity in response to external stimuli remains poorly understood. Here, we bridge this gap by combining computational modeling with magnetoencephalography (MEG) data to investigate the relation of intrinsic neuronal timescales (INT) with task-related activity, e.g., event-related fields (ERFs). Using the Jansen-Rit model, we first show that intracolumnar (and thus intra-regional) excitatory and inhibitory connections (rather than inter-regional feedback, feedforward and lateral connections between the columns of different regions) strongly influence both resting state INTs and task-related ERFs. Secondly, our results demonstrate a positive relationship between the magnitude of event-related fields (mERFs) and INTs, observed in both model simulations and empirical MEG data collected during an emotional face recognition task. Thirdly, modeling shows that the positive relationship of mERF and INT depends on intracolumnar connections through observing that the correlation between them disappears for fixed values of intracolumnar connections. Together, these findings highlight the importance of intracolumnar connections as a shared biological mechanism underlying both the resting-states INTs and the task-states event-related activity including their interplay.

neuroscience↗

bioRxiv2023

Intrinsic neural timescales attenuate information transfer along the uni-transmodal hierarchy

The brains intrinsic timescales are organized in a hierarchy with shorter timescales in sensory regions and longer ones in associative regions. This timescale hierarchy overlaps with the timing demands of sensory information. Our question was how does this timescale hierarchy affect information transfer. We used a model of the timescale hierarchy based on connected excitatory and inhibitory populations across the cortex. We found that a hierarchy of information transfer follows the hierarchy of timescales with higher information transfer in sensory areas while it is lower in associative regions. Probing the effect of changes in timescale hierarchy on information transfer, we changed various model parameters which all, through, the loss of hierarchy, induced increased information transfer. Finally, the steepness of the timescale hierarchy relates negatively to total information transfer. Human MEG data confirmed our results. In sum, we demonstrate a key role of the brains timescale hierarchy in mediating information transfer.

neuroscience↗

bioRxiv2022

The Intrinsic Hierarchy of Self - Converging Topography and Dynamics

The brain can be characterized by an intrinsic hierarchy in its topography which, as recently shown for the uni-transmodal distinction of core and periphery, converges with its dynamics. Does such intrinsic hierarchical organization in both topography and dynamic also apply to the brains inner core itself and its higher-order cognitive functions like self? Applying multiple fMRI data sets, we show how the recently established three-layer topography of self (internal, external, mental) is already present during the resting state and carried over to task states including both task-specific and -unspecific effects. Moreover, the topographic hierarchy converges with corresponding dynamic changes (measured by power-law exponent, autocorrelation window, median frequency, sample entropy, complexity) during both rest and task states. Finally, analogous to the topographic hierarchy, we also demonstrate hierarchy among the different dynamic measures themselves according to background and foreground. Finally, we show task-specific- and un-specific effects in the hierarchies of both dynamics and topography. Together, we demonstrate the existence of an intrinsic topographic hierarchy of self and its convergence with dynamics.

neuroscience↗