A Blind Module Identification Approach for Predicting Effective Connectivity Within Brain Dynamical Subnetworks

Fadi N. Karameh, Ziad Nahas

Research output: Contribution to journalArticle

Abstract

Model-based network discovery measures, such as the brain effective connectivity, require fitting of generative process models to measurements obtained from key areas across the network. For distributed dynamic phenomena, such as generalized seizures and slow-wave sleep, studying effective connectivity from real-time recordings is significantly complicated since (i) outputs from only a subnetwork can be practically measured, and (ii) exogenous subnetwork inputs are unobservable. Model fitting, therefore, constitutes a challenging blind module identification or model inversion problem for finding both the parameters and the many unknown inputs of the subnetwork. We herein propose a novel estimation framework for identifying nonlinear dynamic subnetworks in the case of slowly-varying, otherwise unknown local inputs. Starting with approximate predictions obtained using Cubature Kalman filtering, residuals of local output predictions are utilized to improve upon local input estimates. The algorithm performance is tested on both simulated and clinical EEG of induced seizures under electroconvulsive therapy (ECT). For the simulated network, the algorithm significantly boosted the estimation accuracy for inputs and connections from noisy EEG. For the clinical data, the algorithm predicted increased subnetwork inputs during the pre-stimulus anesthesia condition. Importantly, it predicted an increased frontocentral connectivity during the generalized seizure that is commensurate with electrode placement and that corroborates the clinical hypothesis of increased frontal focality of therapeutic ECT seizures. The proposed framework can be extended to account for several input configurations and can in principle be applied to study effective connectivity within brain subnetworks defined at the microscale (cortical lamina interaction) or at the macroscale (sensory integration).

LanguageEnglish (US)
Pages28-65
Number of pages38
JournalBrain Topography
Volume32
Issue number1
DOIs
StatePublished - Jan 30 2019

Fingerprint

Seizures
Electroconvulsive Therapy
Brain
Electroencephalography
Nonlinear Dynamics
Sleep
Electrodes
Anesthesia
Therapeutics

Keywords

  • Blind deconvolution
  • Brain subnetworks
  • Effective connectivity
  • Kalman filtering
  • Model inversion
  • Neuronal modeling

PubMed: MeSH publication types

  • Journal Article

Cite this

A Blind Module Identification Approach for Predicting Effective Connectivity Within Brain Dynamical Subnetworks. / Karameh, Fadi N.; Nahas, Ziad.

In: Brain Topography, Vol. 32, No. 1, 30.01.2019, p. 28-65.

Research output: Contribution to journalArticle

@article{31ec80cf2ad9464d8b7ec9373cca1837,
title = "A Blind Module Identification Approach for Predicting Effective Connectivity Within Brain Dynamical Subnetworks",
abstract = "Model-based network discovery measures, such as the brain effective connectivity, require fitting of generative process models to measurements obtained from key areas across the network. For distributed dynamic phenomena, such as generalized seizures and slow-wave sleep, studying effective connectivity from real-time recordings is significantly complicated since (i) outputs from only a subnetwork can be practically measured, and (ii) exogenous subnetwork inputs are unobservable. Model fitting, therefore, constitutes a challenging blind module identification or model inversion problem for finding both the parameters and the many unknown inputs of the subnetwork. We herein propose a novel estimation framework for identifying nonlinear dynamic subnetworks in the case of slowly-varying, otherwise unknown local inputs. Starting with approximate predictions obtained using Cubature Kalman filtering, residuals of local output predictions are utilized to improve upon local input estimates. The algorithm performance is tested on both simulated and clinical EEG of induced seizures under electroconvulsive therapy (ECT). For the simulated network, the algorithm significantly boosted the estimation accuracy for inputs and connections from noisy EEG. For the clinical data, the algorithm predicted increased subnetwork inputs during the pre-stimulus anesthesia condition. Importantly, it predicted an increased frontocentral connectivity during the generalized seizure that is commensurate with electrode placement and that corroborates the clinical hypothesis of increased frontal focality of therapeutic ECT seizures. The proposed framework can be extended to account for several input configurations and can in principle be applied to study effective connectivity within brain subnetworks defined at the microscale (cortical lamina interaction) or at the macroscale (sensory integration).",
keywords = "Blind deconvolution, Brain subnetworks, Effective connectivity, Kalman filtering, Model inversion, Neuronal modeling",
author = "Karameh, {Fadi N.} and Ziad Nahas",
year = "2019",
month = "1",
day = "30",
doi = "10.1007/s10548-018-0666-3",
language = "English (US)",
volume = "32",
pages = "28--65",
journal = "Brain Topography",
issn = "0896-0267",
publisher = "Kluwer Academic/Human Sciences Press Inc.",
number = "1",

}

TY - JOUR

T1 - A Blind Module Identification Approach for Predicting Effective Connectivity Within Brain Dynamical Subnetworks

AU - Karameh, Fadi N.

AU - Nahas, Ziad

PY - 2019/1/30

Y1 - 2019/1/30

N2 - Model-based network discovery measures, such as the brain effective connectivity, require fitting of generative process models to measurements obtained from key areas across the network. For distributed dynamic phenomena, such as generalized seizures and slow-wave sleep, studying effective connectivity from real-time recordings is significantly complicated since (i) outputs from only a subnetwork can be practically measured, and (ii) exogenous subnetwork inputs are unobservable. Model fitting, therefore, constitutes a challenging blind module identification or model inversion problem for finding both the parameters and the many unknown inputs of the subnetwork. We herein propose a novel estimation framework for identifying nonlinear dynamic subnetworks in the case of slowly-varying, otherwise unknown local inputs. Starting with approximate predictions obtained using Cubature Kalman filtering, residuals of local output predictions are utilized to improve upon local input estimates. The algorithm performance is tested on both simulated and clinical EEG of induced seizures under electroconvulsive therapy (ECT). For the simulated network, the algorithm significantly boosted the estimation accuracy for inputs and connections from noisy EEG. For the clinical data, the algorithm predicted increased subnetwork inputs during the pre-stimulus anesthesia condition. Importantly, it predicted an increased frontocentral connectivity during the generalized seizure that is commensurate with electrode placement and that corroborates the clinical hypothesis of increased frontal focality of therapeutic ECT seizures. The proposed framework can be extended to account for several input configurations and can in principle be applied to study effective connectivity within brain subnetworks defined at the microscale (cortical lamina interaction) or at the macroscale (sensory integration).

AB - Model-based network discovery measures, such as the brain effective connectivity, require fitting of generative process models to measurements obtained from key areas across the network. For distributed dynamic phenomena, such as generalized seizures and slow-wave sleep, studying effective connectivity from real-time recordings is significantly complicated since (i) outputs from only a subnetwork can be practically measured, and (ii) exogenous subnetwork inputs are unobservable. Model fitting, therefore, constitutes a challenging blind module identification or model inversion problem for finding both the parameters and the many unknown inputs of the subnetwork. We herein propose a novel estimation framework for identifying nonlinear dynamic subnetworks in the case of slowly-varying, otherwise unknown local inputs. Starting with approximate predictions obtained using Cubature Kalman filtering, residuals of local output predictions are utilized to improve upon local input estimates. The algorithm performance is tested on both simulated and clinical EEG of induced seizures under electroconvulsive therapy (ECT). For the simulated network, the algorithm significantly boosted the estimation accuracy for inputs and connections from noisy EEG. For the clinical data, the algorithm predicted increased subnetwork inputs during the pre-stimulus anesthesia condition. Importantly, it predicted an increased frontocentral connectivity during the generalized seizure that is commensurate with electrode placement and that corroborates the clinical hypothesis of increased frontal focality of therapeutic ECT seizures. The proposed framework can be extended to account for several input configurations and can in principle be applied to study effective connectivity within brain subnetworks defined at the microscale (cortical lamina interaction) or at the macroscale (sensory integration).

KW - Blind deconvolution

KW - Brain subnetworks

KW - Effective connectivity

KW - Kalman filtering

KW - Model inversion

KW - Neuronal modeling

UR - http://www.scopus.com/inward/record.url?scp=85051647305&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85051647305&partnerID=8YFLogxK

U2 - 10.1007/s10548-018-0666-3

DO - 10.1007/s10548-018-0666-3

M3 - Article

VL - 32

SP - 28

EP - 65

JO - Brain Topography

T2 - Brain Topography

JF - Brain Topography

SN - 0896-0267

IS - 1

ER -