Characterizing and predicting cortical evoked responses to direct electrical stimulation of the human brain

Deciphering the Physical Characteristics of Ophthalmic Filters Used in Optometric Vision Therapy

BACKGROUND

This paper aimed to measure and characterize eleven monochromatic filters and twenty-two combinations used empirically to treat patients with visual dysfunctions to propose enhanced protocols based on solid evidence. Their wavelength, transmittance, and relative sensitivity were defined on the retinal cone cells.

METHODS

A double-beam UV-VIS-NIR spectrophotometer, VARIAN brand, Cary 5000 model, owned by the National Center of Metrology, with high precision and accuracy, was used to characterize all filters. Filters were purchased from Optomatters Corporation, Belgium.

RESULTS

When two or three filters are combined, their transmittance and relative sensitivity on the retinal cone cells decrease regardless of wavelength. As a result, the efficiency of combined filters may decrease during treatments. Additionally, most filters and combinations, regardless of the wavelength, transmit a considerable percentage of light from the red spectrum. A depressant is the best monochromatic filter, and Upsilon-Neurasthenic is the strongest combination to stimulate blue cone cells. In contrast, Stimulant and Delta-Theta are best for red and green cone cells. Mu-Delta and Mu-Theta can be interchangeable, as well as Alpha-Delta and Alpha-Theta.

CONCLUSIONS

Results suggest that the current phototherapy treatment protocol must be deeply revised, and the number of filters and combinations should be reduced to reduce costs and time and boost efficiency.

Predictive modeling of evoked intracranial EEG response to medial temporal lobe stimulation in patients with epilepsy

Despite promising advancements, closed-loop neurostimulation for drug-resistant epilepsy (DRE) still relies on manual tuning and produces variable outcomes, while automated predictable algorithms remain an aspiration. As a fundamental step towards addressing this gap, here we study predictive dynamical models of human intracranial EEG (iEEG) response under parametrically rich neurostimulation. Using data from n = 13 DRE patients, we find that stimulation-triggered switched-linear models with ~300 ms of causal historical dependence best explain evoked iEEG dynamics. These models are highly consistent across different stimulation amplitudes and frequencies, allowing for learning a generalizable model from abundant STIM OFF and limited STIM ON data. Further, evoked iEEG in nearly all subjects exhibited a distance-dependent pattern, whereby stimulation directly impacts the actuation site and nearby regions (≲ 20 mm), affects medium-distance regions (20 ~ 100 mm) through network interactions, and hardly reaches more distal areas (≳ 100 mm). Peak network interaction occurs at 60 ~ 80 mm from the stimulation site. Due to their predictive accuracy and mechanistic interpretability, these models hold significant potential for model-based seizure forecasting and closed-loop neurostimulation design.

Band power modulation through intracranial EEG stimulation and its cross-session consistency

OBJECTIVE

Direct electrical stimulation of the brain through intracranial electrodes is currently used to probe the epileptic brain as part of pre-surgical evaluation, and it is also being considered for therapeutic treatments through neuromodulation. In order to effectively modulate neural activity, a given neuromodulation design must elicit similar responses throughout the course of treatment. However, it is unknown whether intracranial electrical stimulation responses are consistent across sessions. The objective of this study was to investigate the within-subject, cross-session consistency of the electrophysiological effect of electrical stimulation delivered through intracranial electroencephalography (iEEG).

APPROACH

We analysed data from 79 epilepsy patients implanted with iEEG who underwent brain stimulation as part of a memory experiment. We quantified the effect of stimulation in terms of band power modulation and compared this effect from session to session. As a reference, we made the same measurements during baseline periods.

MAIN RESULTS

In most sessions, the effect of stimulation on band power could not be distinguished from baseline fluctuations of band power. Stimulation effect was consistent in a third of the session pairs, while the rest had a consistency measure not exceeding the baseline standards. Cross-session consistency was highly correlated with the degree of band power increase, and it also tended to be higher when the baseline conditions were more similar between sessions.

SIGNIFICANCE

These findings can inform our practices for designing neuromodulation with greater efficacy when using direct electrical brain stimulation as a therapeutic treatment.