Non-equilibrium dynamics and entropy production in the human brain

Living systems operate out of thermodynamic equilibrium at small scales, consuming energy and producing entropy in the environment in order to perform molecular and cellular functions. However, it remains unclear whether non-equilibrium dynamics manifest at macroscopic scales, and if so, how such dynamics support higher-order biological functions. Here we present a framework to probe for non-equilibrium dynamics by quantifying entropy production in macroscopic systems. We apply our method to the human brain, an organ whose immense metabolic consumption drives a diverse range of cognitive functions. Using whole-brain imaging data, we demonstrate that the brain fundamentally operates out of equilibrium at large scales. Moreover, we find that the brain produces more entropy -- operating further from equilibrium -- when performing physically and cognitively demanding tasks. By simulating an Ising model, we show that macroscopic non-equilibrium dynamics can arise from asymmetries in the interactions at the microscale. Together, these results suggest that non-equilibrium dynamics are vital for cognition, and provide a general tool for quantifying the non-equilibrium nature of macroscopic systems.Comment: 18 pages, 14 figure

Multiscale and multimodal network dynamics underpinning working memory

Working memory (WM) allows information to be stored and manipulated over short time scales. Performance on WM tasks is thought to be supported by the frontoparietal system (FPS), the default mode system (DMS), and interactions between them. Yet little is known about how these systems and their interactions relate to individual differences in WM performance. We address this gap in knowledge using functional MRI data acquired during the performance of a 2-back WM task, as well as diffusion tensor imaging data collected in the same individuals. We show that the strength of functional interactions between the FPS and DMS during task engagement is inversely correlated with WM performance, and that this strength is modulated by the activation of FPS regions but not DMS regions. Next, we use a clustering algorithm to identify two distinct subnetworks of the FPS, and find that these subnetworks display distinguishable patterns of gene expression. Activity in one subnetwork is positively associated with the strength of FPS-DMS functional interactions, while activity in the second subnetwork is negatively associated. Further, the pattern of structural linkages of these subnetworks explains their differential capacity to influence the strength of FPS-DMS functional interactions. To determine whether these observations could provide a mechanistic account of large-scale neural underpinnings of WM, we build a computational model of the system composed of coupled oscillators. Modulating the amplitude of the subnetworks in the model causes the expected change in the strength of FPS-DMS functional interactions, thereby offering support for a mechanism in which subnetwork activity tunes functional interactions. Broadly, our study presents a holistic account of how regional activity, functional interactions, and structural linkages together support individual differences in WM in humans

Development of structural correlations and synchronization from adaptive rewiring in networks of Kuramoto oscillators

L.P. acknowledges support from the National Science Foundation Graduate Research Fellowship Program. J.K. acknowledges support from the National Science Foundation Graduate Research Fellowship Program and NIH T32-EB020087, PD: Felix W. Wehrli. D.S.B. also acknowledges support from the John D. and Catherine T. MacArthur Foundation, the Alfred P. Sloan Foundation, and the National Science Foundation (BCS-1441502, CAREER PHY-1554488, BCS-1631550, and CNS-1626008). We also thank two anonymous reviewers whose comments greatly improved the quality of this work. The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies.Peer reviewedPublisher PD

Properties of Ly-alpha emitters around the radio galaxy MRC 0316-257

Observations of the radio galaxy MRC 0316-257 at z=3.13 and the surrounding field are presented. Using narrow- and broad-band imaging obtained with the VLT, 77 candidate Ly-alpha emitters with a rest-frame equivalent width of > 15 A were selected in a ~7'x7' field around the radio galaxy. Spectroscopy of 40 candidate emitters resulted in the discovery of 33 emission line galaxies of which 31 are Ly-alpha emitters with redshifts similar to that of the radio galaxy, while the remaining two galaxies turned out to be [OII] emitters. The Ly-alpha profiles have widths (FWHM) in the range of 120-800 km/s, with a median of 260 km/s. Where the signal-to-noise was large enough, the Ly-alpha profiles were found to be asymmetric, with apparent absorption troughs blueward of the profile peaks, indicative of absorption along the line of sight of an HI mass of at least 2x10^2 - 5x10^4 M_sun. The properties of the Ly-alpha galaxies (faint, blue and small) are consistent with young star forming galaxies which are still nearly dust free. The volume density of Ly-alpha emitting galaxies in the field around MRC 0316-257 is a factor of 3.3+0.5-0.4 larger compared with the density of field Ly-alpha emitters at that redshift. The velocity distribution of the spectroscopically confirmed emitters has a FWHM of 1510 km/s, which is substantially smaller than the width of the narrow-band filter (FWHM ~ 3500 km/s). The peak of the velocity distribution is located within 200 km/s of the redshift of the radio galaxy. We conclude that the confirmed Ly-alpha emitters are members of a protocluster of galaxies at z~3.13. The size of the protocluster is larger than 3.3x3.3 Mpc^2. The mass of this structure is estimated to be > 3-6x10^14 M_sun and could be the progenitor of a cluster of galaxies similar to e.g. the Virgo cluster. (Abridged)Comment: 23 Pages, including 20 PostScript figures. Publiced in Astronomy & Astrophysics. v2: typo fixed and Journal reference adde

Relations between large-scale brain connectivity and effects of regional stimulation depend on collective dynamical state

International audienceAt the macroscale, the brain operates as a network of interconnected neuronal populations, which display coordinated rhythmic dynamics that support interareal communication. Understanding how stimulation of different brain areas impacts such activity is important for gaining basic insights into brain function and for further developing therapeutic neurmodulation. However, the complexity of brain structure and dynamics hinders predictions regarding the downstream effects of focal stimulation. More specifically, little is known about how the collective oscillatory regime of brain network activity-in concert with network structure-affects the outcomes of perturbations. Here, we combine human connectome data and biophysical modeling to begin filling these gaps. By tuning parameters that control collective system dynamics, we identify distinct states of simulated brain activity and investigate how the distributed effects of stimulation manifest at different dynamical working points. When baseline oscillations are weak, the stimulated area exhibits enhanced power and frequency, and due to network interactions, activity in this excited frequency band propagates to nearby regions. Notably, beyond these linear effects, we further find that focal stimulation causes more distributed modifications to interareal coherence in a band containing regions' baseline oscillation frequencies. Importantly, depending on the dynamical state of the system, these broadband effects can be better predicted by functional rather than structural connectivity, emphasizing a complex interplay between anatomical organization, dynamics, and response to perturbation. In contrast, when the network operates in a regime of strong regional oscillations, stimulation causes only slight shifts in power and frequency, and structural connectivity becomes most predictive of stimulation-induced changes in network activity patterns. In sum, this work builds upon and extends previous computational studies investigating the impacts of stimulation, and underscores the fact that both the stimulation site, and, crucially, the regime of brain network dynamics, can influence the network-wide responses to local perturbations