Combining EEG and MRI data to personalize neurostimulation for focal epilepsy; an open-source software implementation

Beumer, Steven; Paulides, Margarethus M.; Klooster, Debby; Boon, Paul; Mestrom, Rmc Rob

doi:10.1016/j.brs.2021.10.294

Cited by 2 publications

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“…In the same vein, Reihart and Nguyen demonstrated fast working-memory performance improvement in elders by applying HD-tACS with frequencies tuned in an individualized fashion ( Reinhart and Nguyen, 2019 ). Nowadays, the importance of personalization is further emphasized in the clinical usage of personalized neurostimulation for epilepsy ( Beumer et al, 2021 ), sensorimotor disorders ( Gupta et al, 2023 ), cognitive function ( Albizu et al, 2023 ), and dysfunction ( Hunold et al, 2022 ; Reinhart, 2022 ; Aiello et al, 2023 ), and several other applications. In fact, researchers are actively exploring tailored approaches that leverage computational models, machine learning, and Bayesian optimization algorithms to optimize stimulation parameters and enhance treatment outcomes.…”

Section: Evidence In Support Of Personalized Interventions/treatmentsmentioning

confidence: 99%

“…Other endeavors relied on the understanding that combining meaningful biomarkers of both anatomy and function can maximize the chances of successful motor recovery by personalized therapy in stroke rehabilitation ( Ovadia-Caro et al, 2019 ). Building on this foundation, Beumer et al (2021) developed a user-friendly software tool that streamlined research workflows and facilitated the analysis of personalized stimulation approaches. By integrating EEG source localization and tDCS optimization techniques, their tool maximized the overlap between the induced electric field and the intended stimulation target, particularly in cases of focal epilepsy.…”

Section: State-of-the-art In Personalized Neurostimulationmentioning

confidence: 99%

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Personalized strategies of neurostimulation: from static biomarkers to dynamic closed-loop assessment of neural function

Carè,

Chiappalone,

Cota

2024

Front. Neurosci.

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Despite considerable advancement of first choice treatment (pharmacological, physical therapy, etc.) over many decades, neurological disorders still represent a major portion of the worldwide disease burden. Particularly concerning, the trend is that this scenario will worsen given an ever expanding and aging population. The many different methods of brain stimulation (electrical, magnetic, etc.) are, on the other hand, one of the most promising alternatives to mitigate the suffering of patients and families when conventional treatment fall short of delivering efficacious treatment. With applications in virtually all neurological conditions, neurostimulation has seen considerable success in providing relief of symptoms. On the other hand, a large variability of therapeutic outcomes has also been observed, particularly in the usage of non-invasive brain stimulation (NIBS) modalities. Borrowing inspiration and concepts from its pharmacological counterpart and empowered by unprecedented neurotechnological advancement, the neurostimulation field has seen in recent years a widespread of methods aimed at the personalization of its parameters, based on biomarkers of the individuals being treated. The rationale is that, by taking into account important factors influencing the outcome, personalized stimulation can yield a much-improved therapy. Here, we review the literature to delineate the state-of-the-art of personalized stimulation, while also considering the important aspects of the type of informing parameter (anatomy, function, hybrid), invasiveness, and level of development (pre-clinical experimentation versus clinical trials). Moreover, by reviewing relevant literature on closed loop neuroengineering solutions in general and on activity dependent stimulation method in particular, we put forward the idea that improved personalization may be achieved when the method is able to track in real time brain dynamics and adjust its stimulation parameters accordingly. We conclude that such approaches have great potential of promoting the recovery of lost functions and enhance the quality of life for patients.

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Section: Evidence In Support Of Personalized Interventions/treatmentsmentioning

confidence: 99%

Section: State-of-the-art In Personalized Neurostimulationmentioning

confidence: 99%

Personalized strategies of neurostimulation: from static biomarkers to dynamic closed-loop assessment of neural function

Carè,

Chiappalone,

Cota

2024

Front. Neurosci.

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Augmenting cognitive work: a review of cognitive enhancement methods and applications for operational domains

Marois

Lafond²

2022

Cogn Tech Work

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Combining EEG and MRI data to personalize neurostimulation for focal epilepsy; an open-source software implementation

Cited by 2 publications

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Personalized strategies of neurostimulation: from static biomarkers to dynamic closed-loop assessment of neural function

Personalized strategies of neurostimulation: from static biomarkers to dynamic closed-loop assessment of neural function

Augmenting cognitive work: a review of cognitive enhancement methods and applications for operational domains

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