Dynamic reconfiguration of human brain networks during learning

Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes -- flexibility and selection -- must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we explore the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance.Comment: Main Text: 19 pages, 4 figures Supplementary Materials: 34 pages, 4 figures, 3 table

Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches

In the past two decades, functional Magnetic Resonance Imaging has been used to relate neuronal network activity to cognitive processing and behaviour. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this work, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area

Learning and comparing functional connectomes across subjects

Functional connectomes capture brain interactions via synchronized fluctuations in the functional magnetic resonance imaging signal. If measured during rest, they map the intrinsic functional architecture of the brain. With task-driven experiments they represent integration mechanisms between specialized brain areas. Analyzing their variability across subjects and conditions can reveal markers of brain pathologies and mechanisms underlying cognition. Methods of estimating functional connectomes from the imaging signal have undergone rapid developments and the literature is full of diverse strategies for comparing them. This review aims to clarify links across functional-connectivity methods as well as to expose different steps to perform a group study of functional connectomes

The specificity and robustness of long-distance connections in weighted, interareal connectomes

Brain areas' functional repertoires are shaped by their incoming and outgoing structural connections. In empirically measured networks, most connections are short, reflecting spatial and energetic constraints. Nonetheless, a small number of connections span long distances, consistent with the notion that the functionality of these connections must outweigh their cost. While the precise function of these long-distance connections is not known, the leading hypothesis is that they act to reduce the topological distance between brain areas and facilitate efficient interareal communication. However, this hypothesis implies a non-specificity of long-distance connections that we contend is unlikely. Instead, we propose that long-distance connections serve to diversify brain areas' inputs and outputs, thereby promoting complex dynamics. Through analysis of five interareal network datasets, we show that long-distance connections play only minor roles in reducing average interareal topological distance. In contrast, areas' long-distance and short-range neighbors exhibit marked differences in their connectivity profiles, suggesting that long-distance connections enhance dissimilarity between regional inputs and outputs. Next, we show that -- in isolation -- areas' long-distance connectivity profiles exhibit non-random levels of similarity, suggesting that the communication pathways formed by long connections exhibit redundancies that may serve to promote robustness. Finally, we use a linearization of Wilson-Cowan dynamics to simulate the covariance structure of neural activity and show that in the absence of long-distance connections, a common measure of functional diversity decreases. Collectively, our findings suggest that long-distance connections are necessary for supporting diverse and complex brain dynamics.Comment: 18 pages, 8 figure

High-Field Functional MRI from the Perspective of Single Vessels in Rats and Humans

Functional MRI (fMRI) has been employed to map brain activity and connectivity based on the neurovascular coupled hemodynamic signal. However, in most cases of fMRI studies, the cerebral vascular hemodynamic signal has been imaged in a spatially smoothed manner due to the limit of spatial resolution. There is a need to improve the spatiotemporal resolution of fMRI to map dynamic signal from individual venule or individual arteriole directly. Here, the thesis aims to provide a vascular-specific view of hemodynamic response during active state or resting state. To better characterize the temporal features of task-related fMRI signal from different vascular compartments, we implemented a line-scanning method to acquire vessel-specific blood-oxygen-level-dependent (BOLD) / cerebral-blood-volume (CBV) fMRI signal at 100-ms temporal resolution with sensory or optogenetic stimulation. Furthermore, we extended the line-scanning method with multi-echo scheme to provide vessel-specific fMRI with the higher contrast-to-noise ratio (CNR), which allowed us to directly map the distinct evoked hemodynamic signal from arterioles and venules at different echo time (TE) from 3 ms to 30 ms. The line-scanning fMRI methods acquire single k-space line per TR under a reshuffled k space acquisition scheme which has the limitation of sampling the fMRI signal in real-time for resting-state fMRI studies. To overcome this, we implemented a balanced Steady-state free precession (SSFP) to map task-related and resting-state fMRI (rsfMRI) with high spatial resolution in anesthetized rats. We reveal venule-dominated functional connectivity for BOLD fMRI and arteriole-dominated functional connectivity for CBV fMRI. The BOLD signal from individual venules and CBV signal from individual arterioles show correlations at an ultra-slow frequency (< 0.1 Hz), which are correlated with the intracellular calcium signal measured in neighboring neurons. In complementary data from awake human subjects, the BOLD signal is spatially correlated among sulcus veins and specified intracortical veins of the visual cortex at similar ultra-slow rhythms. This work provides a high-resolution fMRI approach to resolve brain activation and functional connectivity at the level of single vessels, which opened a new avenue to investigate brian functional connectivity at the scale of vessels