Deep brain stimulation for neurodegenerative disease

Moran, Rosalyn J.

doi:10.1016/bs.pbr.2015.07.002

Cited by 8 publications

(3 citation statements)

References 76 publications

(84 reference statements)

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“…epileptic seizures, or the symptoms of Parkinson's disease)? This question has been addressed in the setting of Parkinson's disease [66]; however, there is a long road ahead before these models are sufficiently well developed and validated to be used in the treatment of neurological disorders. In a research context, an avenue receiving much attention is how to validate theories of brain function based…”

Section: Discussionmentioning

confidence: 99%

“…For instance, would it be possible to use DCM as a part of biological control system, to suppress or prevent unwanted activity in a diseased brain (e.g., epileptic seizures, or the symptoms of Parkinson's disease)? This question has been addressed in the setting of Parkinson's disease (61); however, there is a long road ahead before these models are sufficiently well developed and validated to be used in the treatment of neurological disorders. In a research context, an avenue receiving much attention is how to validate theories of brain function based on predictive coding and more generally the Bayesian brain hypothesisand in particular, how to identify the mechanisms of information transfer between layers of cortex (24,25,62).…”

Section: Discussionmentioning

confidence: 99%

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Structure learning in coupled dynamical systems and dynamic causal modelling

Jafarian

Zeidman

Friston

2019

Phil. Trans. R. Soc. A.

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Identifying a coupled dynamical system out of many plausible candidates, each of which could serve as the underlying generator of some observed measurements, is a profoundly ill-posed problem that commonly arises when modelling real world phenomena. In this review, we detail a set of statistical procedures for inferring the structure of nonlinear coupled dynamical systems (structure learning), which has proved useful in neuroscience research. A key focus here is the comparison of competing models of network architecturesand implicit coupling functionsin terms of their Bayesian model evidence. These methods are collectively referred to as dynamic casual modelling (DCM). We focus on a relatively new approach that is proving remarkably useful; namely, Bayesian model reduction (BMR), which enables rapid evaluation and comparison of models that differ in their network architecture. We illustrate the usefulness of these techniques through modelling neurovascular coupling (cellular pathways linking neuronal and vascular systems), whose function is an active focus of research in neurobiology and the imaging of coupled neuronal systems.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Structure learning in coupled dynamical systems and dynamic causal modelling

Jafarian

Zeidman

Friston

2019

Phil. Trans. R. Soc. A.

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“…We used a conductance based neuronal mass model as shown in Figure 2 (known as “CMM_NMDA” model in SPM12) (Moran, 2015, Moran et al, 2013, Shaw et al, 2017). The conductance based model represents the actvity of cortical columns based on the interactions of four neuronal populations: excitatory spiny stellate cells, superfical pyramidal cells, inhibitory interneurons, and deep pryramidal cells as shown in Figure 2.…”

Section: Methodsmentioning

confidence: 99%

Neurochemistry-enriched dynamic causal models of magnetoencephalography, using magnetic resonance spectroscopy

Jafarian

Hughes

Adams

et al. 2022

Preprint

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We present a hierarchical (i.e., empirical) Bayesian framework for testing hypotheses about synaptic neurotransmission, based on the integration of ultra-high field magnetic resonance spectroscopy (7T-MRS) and magnetoencephalography. A first level dynamic causal modelling of cortical microcircuits is used to infer the connectivity parameters of a generative model of neurophysiological observations. At the second level, 7T- magnetic resonance spectroscopy (MRS) estimates of regional neurotransmitter concentration supply empirical priors on synaptic connectivity. We compare the evidence for alternative empirical priors, defined by monotonic functions of spectroscopic estimates, on subsets of synaptic connections. For efficiency and reproducibility, we used Bayesian model reduction, parametric empirical Bayes and variational Bayesian inversion. In particular, we used Bayesian model reduction to compare models of how spectroscopic neurotransmitter measures inform estimates of synaptic connectivity. This identifies the synaptic connections that are influenced by neurotransmitter levels, as measured by 7T-MRS . We demonstrate the method using resting-state magnetoencephalography (i.e., task-free recording) and 7T-MRS data from healthy adults. We validate the analysis by split-sampling of the magnetoencephalography dataset. Our results confirm the hypotheses that GABA concentration influences local recurrent inhibitory intrinsic connectivity in deep and superficial cortical layers, while glutamate influences the excitatory connections between superficial and deep layers and connections from superficial to inhibitory interneurons. The method is suitable for applications with magnetoencephalography or electroencephalography, and is well-suited to evince the mechanisms of neurological and psychiatric disorders, including responses to psychopharmacological interventions.

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Modeling Deep Brain Stimulation for Parkinson's Disease

Lowery

2017

Computational Models of Brain and Behavior

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Deep brain stimulation for neurodegenerative disease

Cited by 8 publications

References 76 publications

Structure learning in coupled dynamical systems and dynamic causal modelling

Structure learning in coupled dynamical systems and dynamic causal modelling

Neurochemistry-enriched dynamic causal models of magnetoencephalography, using magnetic resonance spectroscopy

Modeling Deep Brain Stimulation for Parkinson's Disease

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