White Matter Network Architecture Guides Direct Electrical Stimulation through Optimal State Transitions
Jennifer Stiso,
Ankit N. Khambhati,
Tommaso Menara,
Ari E. Kahn,
Joel M. Stein,
Sandihitsu R. Das,
Richard Gorniak,
Joseph Tracy,
Brian Litt,
Kathryn A. Davis,
Fabio Pasqualetti,
Timothy H. Lucas,
Danielle S. Bassett
Affiliations
Jennifer Stiso
Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
Ankit N. Khambhati
Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
Tommaso Menara
Department of Mechanical Engineering, University of California, Riverside, Riverside, CA 92521, USA
Ari E. Kahn
Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
Joel M. Stein
Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
Sandihitsu R. Das
Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
Richard Gorniak
Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, PA 19107, USA
Joseph Tracy
Department of Neurology, Thomas Jefferson University Hospital, Philadelphia, PA 19107, USA
Brian Litt
Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
Kathryn A. Davis
Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
Fabio Pasqualetti
Department of Mechanical Engineering, University of California, Riverside, Riverside, CA 92521, USA
Timothy H. Lucas
Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
Danielle S. Bassett
Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Physics and Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Corresponding author
Summary: Optimizing direct electrical stimulation for the treatment of neurological disease remains difficult due to an incomplete understanding of its physical propagation through brain tissue. Here, we use network control theory to predict how stimulation spreads through white matter to influence spatially distributed dynamics. We test the theory’s predictions using a unique dataset comprising diffusion weighted imaging and electrocorticography in epilepsy patients undergoing grid stimulation. We find statistically significant shared variance between the predicted activity state transitions and the observed activity state transitions. We then use an optimal control framework to posit testable hypotheses regarding which brain states and structural properties will efficiently improve memory encoding when stimulated. Our work quantifies the role that white matter architecture plays in guiding the dynamics of direct electrical stimulation and offers empirical support for the utility of network control theory in explaining the brain’s response to stimulation. : Stiso et al. report evidence that network control theory can explain the propagation of electrical stimulation through the human brain and quantify how white matter connectivity is crucial for driving spatially distributed changes in activity. Furthermore, they use network control theory to predict stimulation outcome in specific cognitive contexts. Keywords: brain stimulation, network control theory, brain network
