Research Topics
 Mark A KramerSummaryAffiliation: Boston University Country: USA Publications
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Publications
 Human seizures selfterminate across spatial scales via a critical transitionMark A Kramer
Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA
Proc Natl Acad Sci U S A 109:2111621. 2012..This description constrains the specific biophysical mechanisms underlying seizure termination, suggests a dynamical understanding of status epilepticus, and demonstrates an accessible system for studying critical transitions in nature...  Emergence of persistent networks in longterm intracranial EEG recordingsMark A Kramer
Department of Mathematics and Statistics, Boston University, Boston, Massachusetts 02215, USA
J Neurosci 31:1575767. 2011..These results suggest that a metastable, frequencybanddependent scaffold of brain connectivity exists from which transient activity emerges and recedes...  Network inference with confidence from multivariate time seriesMark A Kramer
Department of Mathematics and Statistics, Boston University, Boston, Massachusetts 02215, USA
Phys Rev E Stat Nonlin Soft Matter Phys 79:061916. 2009..We demonstrate that the procedure is accurate and robust in both the determination of edges and the reporting of uncertainty associated with that determination...  Rhythm generation through period concatenation in rat somatosensory cortexMark A Kramer
Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, United States of America
PLoS Comput Biol 4:e1000169. 2008..We conclude that neural activity in the superficial and deep cortical layers may temporally combine to generate a slower oscillation...  Coalescence and fragmentation of cortical networks during focal seizuresMark A Kramer
Department of Mathematics and Statistics, Boston University, 111 Cummington Street, Boston, MA 02215, USA
J Neurosci 30:1007685. 2010..These results suggest that, at the macroscopic spatial scale, epilepsy is not so much a manifestation of hypersynchrony but instead of network reorganization...  Emergent network topology at seizure onset in humansMark A Kramer
Center for BioDynamics, 111 Cummington Street, Boston University, Boston, MA 02215, USA
Epilepsy Res 79:17386. 2008..Using these measures, we can identify spatially localized brain regions that may facilitate seizures and may be potential targets for focal therapies...  Sharp edge artifacts and spurious coupling in EEG frequency comodulation measuresMark A Kramer
Department of Mathematics and Statistics and Center for BioDynamics, Boston University, Boston, MA 02215, USA
J Neurosci Methods 170:3527. 2008..In this short communication, we describe how abrupt increases or decreases in voltage data may produce spurious coupling in these measures and suggest techniques to detect these effects...  Emergence of stable functional networks in longterm human electroencephalographyCatherine J Chu
Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
J Neurosci 32:270313. 2012....  A procedure for testing acrosscondition rhythmic spikefield association changeKyle Q Lepage
Boston University, Department of Mathematics and Statistics, Boston, MA, USA
J Neurosci Methods 213:4362. 2013....  Epilepsy as a disorder of cortical network organizationMark A Kramer
Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA
Neuroscientist 18:36072. 2012..Although the characteristics of functional networks that support the epileptic seizure remain an area of active research, the prevailing trends point to a complex set of network dynamics between, before, and during seizures...  Inferring evoked brain connectivity through adaptive perturbationKyle Q Lepage
Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA
J Comput Neurosci 34:30318. 2013..The proposed method demonstrates improved accuracy compared to network inference based on passive observation of node dynamics and an increased rate of convergence relative to network estimation employing a naïve stimulation strategy...  Network analysis: applications for the developing brainCatherine J ChuShore
Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
J Child Neurol 26:488500. 2011....  The dependence of spike field coherence on expected intensityKyle Q Lepage
Department of Mathematics and Statistics, Boston University, Boston, MA 15213, USA
Neural Comput 23:220941. 2011..Hence, intensity field coherence is a rateindependent measure and a candidate on which to base the appropriate statistical inference of spike field synchrony...  Drawing inferences from Fano factor calculationsUri T Eden
Department of Mathematics and Statistics, Boston University, 111 Cummington St, Boston, MA 02215, USA
J Neurosci Methods 190:14952. 2010..The analysis provides a simple method to determine how close to 1 the computed Fano factor should be and to formally test whether the observed variability in the spiking is likely to arise in data generated by a Poisson process...  Some sampling properties of common phase estimatorsKyle Q Lepage
Department of Mathematics, Boston University, Boston, MA 02446, USA
Neural Comput 25:90121. 2013..This analysis suggests how prior knowledge about a rhythmic signal can be used to improve the accuracy of phase estimates...  Dynamic crossfrequency couplings of local field potential oscillations in rat striatum and hippocampus during performance of a Tmaze taskAdriano B L Tort
Department of Mathematics and Center for BioDynamics, Boston University, Boston, MA 02215, USA
Proc Natl Acad Sci U S A 105:2051722. 2008..Crossfrequency coupling of multiple neuronal rhythms could be a general mechanism used by the brain to perform networklevel dynamical computations underlying voluntary behavior...  A unified approach to linking experimental, statistical and computational analysis of spike train dataLiang Meng
Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, United States of America
PLoS ONE 9:e85269. 2014..This approach  linking statistical, computational, and experimental neuroscience  provides an effective technique to constrain detailed biophysical models to specific mechanisms consistent with observed spike train data. ..  A showcase of torus canards in neuronal burstersJohn Burke
Department of Mathematics and Statistics, Center for BioDynamics, Boston University, Boston, MA, 02215, USA
J Math Neurosci 2:3. 2012..Based on these examples, as well as on emerging theory, we propose that torus canards are a common dynamic phenomenon separating the regimes of spiking and bursting activity...  Distributed control in a meanfield cortical network model: implications for seizure suppressionShinung Ching
Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
Phys Rev E Stat Nonlin Soft Matter Phys 86:021920. 2012..By introducing a meanfield model of neuronal interactions we are able to identify limitations in network controllability based on physiological constraints that suggest the need for more nuanced network control strategies...  A sequential Monte Carlo approach to estimate biophysical neural models from spikesLiang Meng
Department of Mathematics and Statistics, Boston University, Boston, MA, USA
J Neural Eng 8:065006. 2011..We also address the issues of model identification and misspecification, and show that accurate estimates of model parameters and hidden variables are possible given only spike time data...  New dynamics in cerebellar Purkinje cells: torus canardsMark A Kramer
Department of Mathematics and Statistics, Boston University, Boston, Massachusetts 02215, USA
Phys Rev Lett 101:068103. 2008..We propose that the system exhibits a dynamical phenomenon new to realistic, biophysical applications: torus canards...  Introduction to focus issue: rhythms and dynamic transitions in neurological disease: modeling, computation, and experimentTasso J Kaper
Department of Mathematics and Statistics, Boston University, Boston, Massachusetts 02215, USA
Chaos 23:046001. 2013..This focus issue brings together articles presenting modeling, computational, analytical, and experimental perspectives about rhythms and dynamic transitions between them that are associated to various diseases. ..  An elementary model of torus canardsG Nicholas Benes
Department of Mathematics and Statistics, Center for BioDynamics, Boston University, Boston, Massachusetts 02215, USA
Chaos 21:023131. 2011..The results of this elementary model provide insight into the torus canards observed in a higherdimensional neuroscience model...  Are different rhythms good for different functions?Nancy Kopell
Department of Mathematics and Statistics, Boston University Boston, MA, USA
Front Hum Neurosci 4:187. 2010..We suggest that diverse rhythms, or variations of a rhythm, can support different components of a cognitive act, with multiple rhythms potentially playing multiple roles...  Synchronization measures of bursting data: application to the electrocorticogram of an auditory eventrelated experimentMark A Kramer
Program in Applied Science and Technology, University of California, Berkeley, California 94720 1708, USA
Phys Rev E Stat Nonlin Soft Matter Phys 70:011914. 2004..We apply the synchronization measure to human electrocorticogram data collected during an auditory eventrelated potential experiment. The results suggest a crude model of cortical connectivity...  Bifurcation control of a seizing human cortexMark A Kramer
Program in Applied Science and Technology, University of California, Berkeley, CA 94720, USA
Phys Rev E Stat Nonlin Soft Matter Phys 73:041928. 2006..We show how bifurcations induced by the linear controller alter those present in the original dynamics...  Pathological pattern formation and cortical propagation of epileptic seizuresMark A Kramer
Program in Applied Science and Technology, University of California, Berkeley, CA 94720 1740, USA
J R Soc Interface 2:11327. 2005..This suggests that seizing activity on the human cortex may be understood as an example of pathological pattern formation. Included is a discussion of the applications and limitations of these results...  Mechanisms of seizure propagation in a cortical modelMark A Kramer
Applied Science and Technology Graduate Group, University of California, Berkeley, CA 94720 1740, USA
J Comput Neurosci 22:6380. 2007..We compare the model results with the disinhibition and 4AP models of epilepsy and suggest how the model may guide the development of new anticonvulsant therapies...  Synchronization measures of the scalp electroencephalogram can discriminate healthy from Alzheimer's subjectsMark A Kramer
Graduate Group in Applied Science and Technology, University of California, Berkeley, Berkeley, CA, USA
Int J Neural Syst 17:619. 2007....  Quantitative approximation of the cortical surface potential from EEG and ECoG measurementsMark A Kramer
Graduate Group in Applied Science and Technology, University of California, Berkeley, CA 94720 1708, USA
IEEE Trans Biomed Eng 51:135865. 2004..The inclusion of the biharmonic term, the extension to other geometries, and the application to electrocorticogram measurements are discussed...