Bu Researchers Find Evidence of Persistent Neural Networks in Long-Term Intracranial EEG Recordings

Newswise — Over the past two decades, the increased ability to analyze relationships among neural structures has provided novel insights into brain function. Most network approaches, how-ever, focus on static representations of the brain’s physical or statistical connectivity.

In an article to be published in the November 2, 2011 issue of The Journal of Neuroscience (31(44):15757–15767; DOI:10.1523/JNEUROSCI.2287-11.2011), a team of researchers at Boston University, Massachusetts General Hospital and Harvard Medical School present evidence that a dynamic, metastable frequency-band-dependent scaffold of brain function-al connectivity exists from which transient activity emerges and recedes.

To arrive at these findings, the researchers examined functional connectivity networks de-duced from continuous long-term invasive brain voltage recordings to determine how brain functional networks evolve spontaneously over long epochs of continuous time.

“For a population of six human patients, we identified a persistent pattern of connections that form a frequency-band-dependent network template and a set of core connections that appear frequently and together,” said Mark A. Kramer, assistant professor of mathematics and statistics at Boston University and the study’s principal investigator.

Kramer and his colleagues found these structures to be robust, regardless of cognitive state, supporting the existence of a metastable frequency-band-dependent scaffold of brain connectivity.

These findings help characterize the nature of coordinated neuronal activity, typically as-sociated with neuronal rhythms across functionally distinct brain areas. Recent advances have allowed the study of neuronal coordination in large networks of interacting elements from single neurons to neuronal populations. A complete characterization of the structure and function of human brain networks promises important insights for understanding normal and pathological brain activity.

In the article, the researchers describe a functional network analysis of invasive brain volt-age recordings obtained from six patients with epilepsy. They found the networks to exhib-it striking variability from moment to moment, yet persistent templates emerged through-out. These network templates appeared on a relatively short timescale, were independent of brain state, and consisted of common “core” links that tended to appear together. These results suggest that brain voltage activity may evolve through transient states that manifest with moment-to-moment variability, but maintain an underlying, recurrent core structure.

Although the research included only patients with pharmacologically resistant epilepsy, similar network characteristics and stabilities were observed in all of the patients exam-ined, even though they differed in the etiology of their epilepsy, medication regimen, age, sex, and other clinical features. In addition, similar results have been found in less invasive recordings in patients without epilepsy.

While the generalizability of these results to other subject populations requires further re-search, the specifics of a subject’s functional connectivity networks may also be of consid-erable clinical utility. The template networks may provide insight into pathological altera-tions observed in the resting state. This research demonstrates that for invasive brain volt-age data, even relatively short-duration recordings during unconstrained spontaneous ac-tivity sufficiently capture persistent template structure such that exhaustive long-term data acquisition may not be necessary.

How the topological characteristics of these template networks—and fluctuations from these templates—relate to pathological processes, such as seizure initiation and spread, may provide additional information for surgical treatment of epilepsy. Understanding per-sistent functional network structure—in individual patients and large populations—may permit new insights into the characterization of both healthy and diseased brain states.

About Boston University—Founded in 1839, Boston University is an internationally recognized private research university with more than 30,000 students participating in undergraduate, graduate, and professional programs. As Boston University’s largest aca-demic division, the College and Graduate School of Arts & Sciences is the heart of the BU experience with a global reach that enhances the University’s reputation for teaching and research.

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Contact information for the authors:

Correspondence should be addressed to

Mark A. Kramer, assistant professorDepartment of Mathematics and StatisticsBoston University111 Cummington StreetBoston, MA 02215E-mail: [email protected]