The brain shows a topographical hierarchy along the lines of lower- and higher-order networks. The exact temporal dynamics characterization of this lower-higher-order topography at rest and its impact on task states remains unclear, though. Using 2 functional magnetic resonance imaging data sets, we investigate lower- and higher-order networks in terms of the signal compressibility, operationalized by Lempel–Ziv complexity (LZC). As we assume that this degree of complexity is related to the slow–fast frequency balance, we also compute the median frequency (MF), an estimation of frequency distribution. We demonstrate (i) topographical differences at rest between higher- and lower-order networks, showing lower LZC and MF in the former; (ii) task-related and task-specific changes in LZC and MF in both lower- and higher-order networks; (iii) hierarchical relationship between LZC and MF, as MF at rest correlates with LZC rest–task change along the lines of lower- and higher-order networks; and (iv) causal and nonlinear relation between LZC at rest and LZC during task, with MF at rest acting as mediator. Together, results show that the topographical hierarchy of lower- and higher-order networks converges with their temporal hierarchy, with these neural dynamics at rest shaping their range of complexity during task states in a nonlinear way.

The human brain's cerebral cortex exhibits a topographic division into higher‐order transmodal core and lower‐order unimodal periphery regions. While timescales between the core and periphery region diverge, features of their power spectra, especially scale‐free dynamics during resting‐state and their mdulation in task states, remain unclear. To answer this question, we investigated the ~1/f‐like pink noise manifestation of scale‐free dynamics in the core‐periphery topography during rest and task states applying infra‐slow inter‐trial intervals up to 1 min falling inside the BOLD's infra‐slow frequency band. The results demonstrate (1) higher resting‐state power‐law exponent (PLE) in the core compared to the periphery region; (2) significant PLE increases in task across the core and periphery regions; and (3) task‐related PLE increases likely followed the task's atypically low event rates, namely the task's periodicity (inter‐trial interval = 52–60 s; 0.016–0.019 Hz). A computational model and a replication dataset that used similar infra‐slow inter‐trial intervals provide further support for our main findings. Altogether, the results show that scale‐free dynamics differentiate core and periphery regions in the resting‐state and mediate task‐related effects.

The brain is continuously bombarded by external stimuli, which are processed in different input systems. The intrinsic features of these sensory input systems remain yet unclear. Investigating topography and dynamics of input systems is the goal of our study in order to better understand the intrinsic features that shape their neural processing. Using a functional magnetic resonance imaging dataset, we measured neural topography and dynamics of the input systems during rest and task states. Neural dynamics were probed by scale-free activity, measured with the power-law exponent (PLE), as well as by order/disorder as measured with sample entropy (SampEn). Our main findings during both rest and task states are: 1) differences in neural dynamics (PLE, SampEn) between regions within each of the three sensory input systems 2) differences in topography and dynamics among the three input systems; 3) PLE and SampEn correlate and, as demonstrated in simulation, show non-linear relationship in the critical range of PLE; 4) scale-free activity during rest mediates the transition of SampEn from rest to task as probed in a mediation model. We conclude that the sensory input systems are characterized by their intrinsic topographic and dynamic organization which, through scale-free activity, modulates their input processing.