Recently, the big research push in the neurosciences has been to characterize the complete wiring diagram of the brain – to describe the Connectome.  Though a lofty goal that will likely span another decade or more of work, no one doubts the enormous contribution that it will provide to the understanding of brain functioning.  However, even with the connectome in hand, researchers will still be at a loss to explain the generation, coordination, and consequences of dynamic rhythmic processes that characterize so much of the cognitive neuroscience literature.  To bridge this explanatory gap, Dr. Nancy Kopell, a professor of mathematics and statistics at Boston University, has suggested a new large-scale research undertaking aimed at characterizing the dynamic interaction of brain regions – to describe the “Dynome”.

Dr. Kopell’s work mainly focuses on mathematically characterizing the types of dynamics interactions that take place between cells, within networks, and between brain regions, as well as constructing biophysical models of how these dynamics may occur.  Her research, and that of her contemporaries, has demonstrated that dynamic brain rhythms can be involved in phenomena such as network synchronization, attentional gain, and recruiting brain regions per cognitive demands.  For example, the well-known pyramidal interneuronal gamma (PING) mechanism leading to higher frequency gamma rhythms relies on feedback inhibition.  This inhibition leads to a winner-take-all type scenario whereby the most activated cells end up suppressing more weakly activated ones, and potentially serves as a mechanism of focused attention.

Unfortunately, a difficulty that arises, even within a type brain rhythm, is that the generative mechanisms and modulation of rhythms can vary widely by brain region, cortical layer, and even cell type.  Thus, it becomes critical to outline not only the anatomical structure of a region, as connectomics is attempting, but also the cellular physiology, functional synaptic connectivity, and neuromodulatory profiles present.  Fortunately, technologies that have been emerging over the last few years in experimental neuroscience seem well-suited to provide answers to these exact types of questions.  Specifically, high density electrode recordings, optogenetic manipulation, and large scale 3D imaging of neuronal activity have allowed more in-depth analysis of network activity, small circuit functioning, and cell-type specific physiology.  Additionally, new data analytic techniques are providing ways to characterize activity and understand correlations, while data-driven computational models allow researchers to test potential generative mechanisms.

The neuronal heterogeneity involved in implementing these brain rhythms might seem intimidating, but it is likely that it is necessary for the emergence of many cognitive phenomena.  For example, if the same rhythms are being differentially generated in two brain regions, then the similar operating frequency will facilitate stronger interactions, while the dissimilar implementation may mean distinct computations are being performed.  Moreover, the heterogeneity allows these various regions to be differentially regulated by the same neuromodulators, possibly leading to the diverse set of changes that occur across different cognitive states.  Perhaps the most compelling evidence for this is in the effectiveness of deep brain stimulation in treating various mental illnesses.  Cognitive and behavioral abnormalities exhibited by patients with Parkinson’s disease, depression, or obsessive compulsive disorder all experience dramatic improvements simply by a perturbation of the underlying pathological network dynamics.

Importantly, elucidation of the Dynome can and should occur in concert with that of the Connectome.  The highly plastic nature of the brain means that connections constantly change according to the dynamic activity present.  With that in mind, it seems a static Connectome can never completely capture its architecture.  However, this type of broad framework, large-scale synthesis, and open-ended goals may allow neuroscience to continue its rapid progression towards explaining brain functioning.  As data is continually compiled across various levels of brain functioning, the contributions of both connectomics and dynomics will both be needed to move us from genes, to physiology, to network dynamics and interactions, eventually to cognition and pathology.

To hear more about Dr. Kopell’s specific contributions to the dynome, please attend her talk on Tuesday, January 31 at 4pm in the CNCB Marilyn G. Farquhar Seminar Room. To learn more about her lab and for a list of recent publications, please visit her website: http://math.bu.edu/people/nk/

Ryan Golden is a first-year in the Neurosciences Graduate Program, and is currently rotating in Maxim Bazhenov’s lab.  His interests broadly fall under computational and theoretical neuroscience, and is specifically interested in the biophysical implementation and modulation of reinforcement learning.

 

 

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