Computational modeling of neurostimulation in brain diseases

Wang, Yujiang; Hutchings, Frances; Kaiser, Marcus

doi:10.1016/bs.pbr.2015.06.012

Cited by 33 publications

(29 citation statements)

References 225 publications

(247 reference statements)

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“…Treatment parameters may be tuned to biological rhythms (50,67,(77)(78)(79) or respond directly to fluctuating conditions within each patient (80)(81)(82). To investigate temporal fluctuations within each patient and determine how treatments interact with these changes, researchers may draw inspiration from spatiotemporal analyses in other fields, such as ecology (83), genetics (84), and engineering (85,86), as well as develop new techniques that address specific dataanalytical challenges.…”

Section: Discussionmentioning

confidence: 99%

Seizure pathways change on circadian and slower timescales in individual patients with focal epilepsy

Schroeder

Diehl

Chowdhury

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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Personalized medicine requires that treatments adapt to not only the patient but also changing factors within each individual. Although epilepsy is a dynamic disorder characterized by pathological fluctuations in brain state, surprisingly little is known about whether and how seizures vary in the same patient. We quantitatively compared within-patient seizure network evolutions using intracranial electroencephalographic (iEEG) recordings of over 500 seizures from 31 patients with focal epilepsy (mean 16.5 seizures per patient). In all patients, we found variability in seizure paths through the space of possible network dynamics. Seizures with similar pathways tended to occur closer together in time, and a simple model suggested that seizure pathways change on circadian and/or slower timescales in the majority of patients. These temporal relationships occurred independent of whether the patient underwent antiepileptic medication reduction. Our results suggest that various modulatory processes, operating at different timescales, shape within-patient seizure evolutions, leading to variable seizure pathways that may require tailored treatment approaches.

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Section: Discussionmentioning

confidence: 99%

Seizure pathways change on circadian and slower timescales in individual patients with focal epilepsy

Schroeder

Diehl

Chowdhury

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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“…Such questions are of interest for all brain stimulations techniques, not only from a basic science viewpoint, but also from a technological one, as their answer is essential to things such as device development and optimization. For instance, the mechanisms through which DBS exerts its effects, for example, in the treatment for Parkinson's disease, have been investigated both computationally and experimentally (Saenger et al, 2017;Santaniello et al, 2015;Wang, Hutchins, & Kaiser, 2015). While DBS's basic physics is known by construction, it has been suggested to improve motor symptoms by activating efferent fibres (Hashimoto, Elder, Okun, Patrick, & Vitek, 2003), by modifying of oscillatory activity (Vitek, 2008), or by decoupling oscillations within the basal ganglia (Moran, Stein, Tischler, & Bar-Gad, 2012).…”

Section: Neurofeedback's Mechanismsmentioning

confidence: 99%

Neurofeedback: Principles, appraisal, and outstanding issues

Papo

2019

Eur J of Neuroscience

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Neurofeedback is a form of brain training in which subjects are fed back information about some measure of their brain activity which they are instructed to modify in a way thought to be functionally advantageous. Over the last 20 years, neurofeedback has been used to treat various neurological and psychiatric conditions, and to improve cognitive function in various contexts. However, in spite of a growing popularity, neurofeedback protocols typically make (often covert) assumptions on what aspects of brain activity to target, where in the brain to act and how, which have far‐reaching implications for the assessment of its potential and efficacy. Here we critically examine some conceptual and methodological issues associated with the way neurofeedback's general objectives and neural targets are defined. The neural mechanisms through which neurofeedback may act at various spatial and temporal scales, and the way its efficacy is appraised are reviewed, and the extent to which neurofeedback may be used to control functional brain activity discussed. Finally, it is proposed that gauging neurofeedback's potential, as well as assessing and improving its efficacy will require better understanding of various fundamental aspects of brain dynamics and a more precise definition of functional brain activity and brain‐behaviour relationships.

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“…This in silico testbed can be systematically probed to study microcircuit dynamics, information flow and biophysical mechanisms with a level of resolution and precision not available experimentally. Unraveling the non-intuitive multiscale interactions occurring in M1 circuits will help us understand disease and develop new pharmacological and neurostimulation treatments for motor disorders 101,100,97,36,7,138,50,12,119 , and improve decoding methods for brain-machine interfaces 22,124,35,67 .…”

Section: Implications For Experimental Research and Therapeuticsmentioning

confidence: 99%

Multiscale dynamics and information flow in a data-driven model of the primary motor cortex microcircuit

Durá-Bernal

Neymotin

Suter

et al. 2017

Preprint

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We developed a biologically detailed multiscale model of mouse primary motor cortex (M1) microcircuits, incorporating data from several recent experimental studies. The model simulates at scale a cylindrical volume with a diameter of 300 m and cortical depth 1350 m of M1. It includes over 10,000 cells distributed across cortical layers based on measured cell densities, with close to 30 million synaptic connections. Neuron models were optimized to reproduce electrophysiological properties of major classes of M1 neurons. Layer 5 corticospinal and corticostriatal neuron morphologies with 700+ compartments reproduced cell 3D reconstructions, and their ionic channel distributions were optimized within experimental constraints to reproduce in vitro recordings. The network was driven by the main long-range inputs to M1: posterior nucleus (PO) and ventrolateral (VL) thalamus PO, primary and secondary somatosensory cortices (S1, S2), contralateral M1, secondary motor cortex (M2), and orbital cortex (OC). The network local and long-range connections depended on pre-and post-synaptic cell class and cortical depth. Data was based on optogenetic circuit mapping studies which determined that connection strengths vary within layer as a function of the neuron's cortical depth. The synaptic input distribution across cell dendritic trees -likely to subserve important neural coding functions -was also mapped using optogenetic methods and incorporated into the model. We employed the model to study the effect on M1 of increased activity from each of the long-range inputs, and of different levels of H-current in pyramidal tract-projecting (PT) corticospinal neurons. Microcircuit dynamics and information flow were quantified using firing rates, oscillations, and information transfer measures (Spectral Granger causality). We evaluated the response to short pulses and long time-varying activity arising from the different long-range inputs. We also studied the interaction between two simultaneous long-range inputs. Simulation results support the hypothesis that M1 operates along a continuum of modes characterized by the degree of activation of intratelencephalic (IT), corticothalamic (CT) and PT neurons. The particular subset of M1 neurons targeted by different long-range inputs and the level of H-current in PT neurons regulated the different modes. Downregulation of H-current facilitated synaptic integration of inputs and increased PT output activity. Therefore, VL, cM1 and M2 inputs with downregulated H-current promoted an PT-predominant mode; whereas PO, S11 . CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/201707 doi: bioRxiv preprint first posted online Oct. 11, 2017; and S2 inputs with upregulated H-current favored an IT-predominant mode, but a mixed mode where upper layer IT cells in turn activated PT cells if H-c...

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Computational modeling of neurostimulation in brain diseases

Cited by 33 publications

References 225 publications

Seizure pathways change on circadian and slower timescales in individual patients with focal epilepsy

Seizure pathways change on circadian and slower timescales in individual patients with focal epilepsy

Neurofeedback: Principles, appraisal, and outstanding issues

Multiscale dynamics and information flow in a data-driven model of the primary motor cortex microcircuit

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