Postdoctoral Research: -- Coming soon --

PhD Research: Circadian rhythms and epileptogenesis

In the late 1800s, British neurologist Sir William R. Gowers classified epileptic ‘fits’ according to their tendency to occur at different times of day. Throughout the following century, studies have clearly established that epileptic seizures occur in a circadian pattern, with peak seizure times varying according to the epilepsy syndrome. More recently, studies have suggested a further link between circadian rhythms and epilepsy: specifically, a number of studies have provided evidence that circadian regulation may become fundamentally altered in epilepsy. Our recent animal models have shown that circadian rhythms of hippocampal neural activity become permanently altered following brain injury. This alteration occurs almost immediately following injury, prior to the emergence of spontaneous seizures, and persists throughout the development of chronic epilepsy (Figure 1). Based on these findings, we are investigating the possibility that epilepsy may be in part a circadian disorder, with dysfunctional circadian regulation contributing to the emergence of epileptic seizures.

Our studies seek to answer the following questions:

  • How are circadian rhythms altered in both animal and human epilepsy?
  • Which circadian centers in the brain are injured during epilepsy, and can this damage account for the observed circadian dysfunction?
  • Does altered circadian regulation precipitate epileptic seizures and related cognitive disorders?
  • Can corrective electrical or optogenetic stimulation, modulation on slow (circadian) time scales, be used to treat epilepsy?

Dr. Carney’s laboratory is investigating these questions using a variety of approaches. At the core, these studies are driven by our specialization in long-term in vivo recordings. Circadian datasets have been collected that contain >1 month of continuous electrical recording, sampled at 12 kHz and from 32 channels across bilateral hippocampi. This totals approximately 10k hours or 3TB of data per animal. The high levels of spatial and temporal resolution, and long time-spans, are ideal for analysis of slow dynamical processes. Analysis of this data is driven by advanced signal processing techniques; we have developed specialized algorithms for rhythm extraction and spike sorting designed specifically to work with very large circadian data sets. This analysis will lead to precise characterization of neural processes underlying circadian dysfunction.

This work is complemented by two additional techniques: computer modeling and high angular resolution MRI imaging. For the modeling approach, we are incorporating realistic circadian drivers into detailed biophysical (Hodgkin Huxley-type) computer models of the hippocampal neural networks in order to quantify how circadian alterations arise and how they can contribute to seizure generation. Ultimately, these models will also be used to test the applicability of circadian control strategies for epilepsy therapy. Thus far, this modeling work has yielded specific predictions as to which circadian centers in the brain may be damaged so as to produce this circadian dysfunction. This relates directly to our structural analysis of circadian relay centers: we are presently performing in vivo high-angular resolution MRI diffusion tensor imaging of circadian centers in the brain and fiber tracking of relevant white matter tracks that are responsible for conveying circadian information. This structural analysis will enable the identification of physical damage to key circadian centers that could explain the origins of circadian dysfunction in epilepsy and that will guide studies to test the effectiveness of corrective circadian stimulation.


Figure 1: Circadian modulation of hippocampal gamma rhythm. (A) Extraction of gamma-frequency rhythms using the intrinsic mode function (IMF) from CA1 EEG recordings, as obtained by empirical mode decomposition. (B) Long-term tracking of IMF amplitude shows phase shift post status epilepticus (day 10) that emerges during the latent period and persists throughout the emergence of chronic spontaneous seizures (SS).

Project Team

  • David A. Stanley, Graduate Student
  • Dr. Mansi B. Parekh, Post Doctoral Associate
  • Dr. Junli Zhou, Post Doctoral Associate
  • Dr. Paul R. Carney, Professor
  • Dr. William L. Ditto, Professor (University of Hawaiʻi at Mānoa)
  • Dr. Thomas H. Mareci, Professor
  • Dr. Sachin S. Talathi, Assistant Professor


  • National Institutes of Health (NIH)
  • Office of Naval Research (ONR)
  • National Science and Engineering Research Council (NSERC) of Canada
  • Wilder Center of Excellence for Epilepsy Research

Last updated November 2013