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Lainscsek C, Salami P, Carvalho VR, Mendes EMAM, Fan M, Cash SS, Sejnowski TJ. Network-motif delay differential analysis of brain activity during seizures. CHAOS (WOODBURY, N.Y.) 2023; 33:123136. [PMID: 38156987 PMCID: PMC10757649 DOI: 10.1063/5.0165904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 11/28/2023] [Indexed: 01/03/2024]
Abstract
Delay Differential Analysis (DDA) is a nonlinear method for analyzing time series based on principles from nonlinear dynamical systems. DDA is extended here to incorporate network aspects to improve the dynamical characterization of complex systems. To demonstrate its effectiveness, DDA with network capabilities was first applied to the well-known Rössler system under different parameter regimes and noise conditions. Network-motif DDA, based on cortical regions, was then applied to invasive intracranial electroencephalographic data from drug-resistant epilepsy patients undergoing presurgical monitoring. The directional network motifs between brain areas that emerge from this analysis change dramatically before, during, and after seizures. Neural systems provide a rich source of complex data, arising from varying internal states generated by network interactions.
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Affiliation(s)
| | - Pariya Salami
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA
| | | | - Eduardo M. A. M. Mendes
- Laboratório de Modelagem, Análise e Controle de Sistemas Não Lineares, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, 31270-901 Belo Horizonte, MG, Brazil
| | - Miaolin Fan
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA
| | - Sydney S. Cash
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA
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Scheid BH, Ashourvan A, Stiso J, Davis KA, Mikhail F, Pasqualetti F, Litt B, Bassett DS. Time-evolving controllability of effective connectivity networks during seizure progression. Proc Natl Acad Sci U S A 2021; 118:e2006436118. [PMID: 33495341 PMCID: PMC7865160 DOI: 10.1073/pnas.2006436118] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Over one third of the estimated 3 million people with epilepsy in the United States are medication resistant. Responsive neurostimulation from chronically implanted electrodes provides a promising treatment alternative to resective surgery. However, determining optimal personalized stimulation parameters, including when and where to intervene to guarantee a positive patient outcome, is a major open challenge. Network neuroscience and control theory offer useful tools that may guide improvements in parameter selection for control of anomalous neural activity. Here we use a method to characterize dynamic controllability across consecutive effective connectivity (EC) networks based on regularized partial correlations between implanted electrodes during the onset, propagation, and termination regimes of 34 seizures. We estimate regularized partial correlation adjacency matrices from 1-s time windows of intracranial electrocorticography recordings using the Graphical Least Absolute Shrinkage and Selection Operator (GLASSO). Average and modal controllability metrics calculated from each resulting EC network track the time-varying controllability of the brain on an evolving landscape of conditionally dependent network interactions. We show that average controllability increases throughout a seizure and is negatively correlated with modal controllability throughout. Our results support the hypothesis that the energy required to drive the brain to a seizure-free state from an ictal state is smallest during seizure onset, yet we find that applying control energy at electrodes in the seizure onset zone may not always be energetically favorable. Our work suggests that a low-complexity model of time-evolving controllability may offer insights for developing and improving control strategies targeting seizure suppression.
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Affiliation(s)
- Brittany H Scheid
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
| | - Arian Ashourvan
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
| | - Jennifer Stiso
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA 19104
| | - Kathryn A Davis
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104
| | - Fadi Mikhail
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, CA 92521
| | - Brian Litt
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104;
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA 19104
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Santa Fe Institute, Santa Fe, NM 87501
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Karunakaran KD, Peng K, Berry D, Green S, Labadie R, Kussman B, Borsook D. NIRS measures in pain and analgesia: Fundamentals, features, and function. Neurosci Biobehav Rev 2020; 120:335-353. [PMID: 33159918 DOI: 10.1016/j.neubiorev.2020.10.023] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 09/28/2020] [Accepted: 10/19/2020] [Indexed: 02/06/2023]
Abstract
Current pain assessment techniques based only on clinical evaluation and self-reports are not objective and may lead to inadequate treatment. Having a functional biomarker will add to the clinical fidelity, diagnosis, and perhaps improve treatment efficacy in patients. While many approaches have been deployed in pain biomarker discovery, functional near-infrared spectroscopy (fNIRS) is a technology that allows for non-invasive measurement of cortical hemodynamics. The utility of fNIRS is especially attractive given its ability to detect specific changes in the somatosensory and high-order cortices as well as its ability to measure (1) brain function similar to functional magnetic resonance imaging, (2) graded responses to noxious and innocuous stimuli, (3) analgesia, and (4) nociception under anesthesia. In this review, we evaluate the utility of fNIRS in nociception/pain with particular focus on its sensitivity and specificity, methodological advantages and limitations, and the current and potential applications in various pain conditions. Everything considered, fNIRS technology could enhance our ability to evaluate evoked and persistent pain across different age groups and clinical populations.
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Affiliation(s)
- Keerthana Deepti Karunakaran
- Center for Pain and the Brain, Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Harvard Medical School, United States.
| | - Ke Peng
- Center for Pain and the Brain, Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Harvard Medical School, United States; Département en Neuroscience, Centre de Recherche du CHUM, l'Université de Montréal Montreal, QC, Canada
| | - Delany Berry
- Center for Pain and the Brain, Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Harvard Medical School, United States
| | - Stephen Green
- Center for Pain and the Brain, Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Harvard Medical School, United States
| | - Robert Labadie
- Center for Pain and the Brain, Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Harvard Medical School, United States
| | - Barry Kussman
- Division of Cardiac Anesthesia, Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Harvard Medical School, United States
| | - David Borsook
- Center for Pain and the Brain, Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Harvard Medical School, United States.
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DAS A, Cash SS, Sejnowski TJ. Heterogeneity of Preictal Dynamics in Human Epileptic Seizures. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:52738-52748. [PMID: 32411567 PMCID: PMC7224217 DOI: 10.1109/access.2020.2981017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
It is generally understood that there is a preictal phase in the development of a seizure and this precictal period is the basis for seizure prediction attempts. The focus of this study is the preictal global spatiotemporal dynamics and its intra-patient variability. We analyzed preictal broadband brain connectivity from human electrocorticography (ECoG) recordings of 185 seizures (which included 116 clinical seizures) collected from 12 patients. ECoG electrodes record from only a part of the cortex, leaving large regions of the brain unobserved. Brain connectivity was therefore estimated using the sparse-plus-latent-regularized precision matrix (SLRPM) method, which calculates connectivity from partial correlations of the conditional statistics of the observed regions given the unobserved latent regions. Brain connectivity was quantified using eigenvector centrality (EC), from which a degree of heterogeneity was calculated for the preictal periods of all seizures in each patient. Results from the SLRPM method are compared to those from the sparse-regularized precision matrix (SRPM) and correlation methods, which do not account for the unobserved inputs when estimating brain connectivity. The degree of heterogeneity estimated by the SLRPM method is higher than those estimated by the SRPM and correlation methods for the preictal periods in most patients. These results reveal substantial heterogeneity or desynchronization among brain areas in the preictal period of human epileptic seizures. Furthermore, the SLRPM method identifies more onset channels from the preictal active electrodes compared to the SRPM and correlation methods. Finally, the correlation between the degree of heterogeneity and seizure severity of patients for SLRPM and SRPM methods were lower than that obtained from the correlation method. These results support recent findings suggesting that inhibitory neurons can have anti-seizure effects by inducing variability or heterogeneity across seizures. Understanding how this variability is linked to seizure initiation may lead to better predictions and controlling therapies.
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Affiliation(s)
- Anup DAS
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA 94305 USA
| | - Sydney S Cash
- Massachusetts General Hospital, Boston, MA 02114 USA
| | - Terrence J Sejnowski
- Division of Biological Sciences and Institute of Neural Computation, University of California, San Diego, La Jolla, CA 92093 USA
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