A methodological framework for inverse-modeling of propagating cortical activity using MEG/EEG

FreeSurfer is a suite of tools for the analysis of neuroimaging data that provides an array of algorithms to quantify the functional, connectional and structural properties of the human brain. It has evolved from a package primarily aimed at generating surface representations of the cerebral cortex into one that automatically creates models of most macroscopically visible structures in the human brain given any reasonable T1-weighted input image. It is freely available, runs on a wide variety of hardware and software platforms, and is open source. Copyright © 2012 Elsevier Inc. All rights reserved.

Many complex networks have a small-world topology characterized by dense local clustering or cliquishness of connections between neighboring nodes yet a short path length between any (distant) pair of nodes due to the existence of relatively few long-range connections. This is an attractive model for the organization of brain anatomical and functional networks because a small-world topology can support both segregated/specialized and distributed/integrated information processing. Moreover, small-world networks are economical, tending to minimize wiring costs while supporting high dynamical complexity. The authors introduce some of the key mathematical concepts in graph theory required for small-world analysis and review how these methods have been applied to quantification of cortical connectivity matrices derived from anatomical tract-tracing studies in the macaque monkey and the cat. The evolution of small-world networks is discussed in terms of a selection pressure to deliver cost-effective information-processing systems. The authors illustrate how these techniques and concepts are increasingly being applied to the analysis of human brain functional networks derived from electroencephalography/magnetoencephalography and fMRI experiments. Finally, the authors consider the relevance of small-world models for understanding the emergence of complex behaviors and the resilience of brain systems to pathological attack by disease or aberrant development. They conclude that small-world models provide a powerful and versatile approach to understanding the structure and function of human brain systems.

The traditional belief is that the event-related alpha response can solely be described in terms of suppression or event-related desynchronization (ERD). Recent research, however, has shown that under certain conditions alpha responds reliably with an increase in amplitudes (event-related synchronization or ERS). ERS is elicited in situations, where subjects withhold or control the execution of a response and is obtained over sites that probably are under, or exert top-down control. Thus, we assume that alpha ERS reflects top-down, inhibitory control processes. This assumption leads over to the timing aspect of our hypothesis. By the very nature of an oscillation, rhythmic amplitude changes reflect rhythmic changes in excitation of a population of neurons. Thus, the time and direction of a change - described by phase - is functionally related to the timing of neuronal activation processes. A variety of findings supports this view and shows, e.g., that alpha phase coherence increases between task-relevant sites and that phase lag lies within a time range that is consistent with neuronal transmission speed. Another implication is that phase reset will be a powerful mechanism for the event-related timing of cortical processes. Empirical evidence suggests that the extent of phase locking is a functionally sensitive measure that is related to cognitive performance. Our general conclusion is that alpha ERS plays an active role for the inhibitory control and timing of cortical processing whereas ERD reflects the gradual release of inhibition associated with the emergence of complex spreading activation processes.