A machine-learning algorithm correctly classifies cortical evoked potentials from both visual stimulation and electrical stimulation of the optic nerve

Abstract

Objective. Optic nerve’s intraneural stimulation is an emerging neuroprosthetic approach to provide artificial vision to totally blind patients. An open question is the possibility to evoke individual non-overlapping phosphenes via selective intraneural optic nerve stimulation. To begin answering this question, first, we aim at showing in preclinical experiments with animals that each intraneural electrode could evoke a distinguishable activity pattern in the primary visual cortex. Approach. We performed both patterned visual stimulation and patterned electrical stimulation in healthy rabbits while recording evoked cortical activity with an electrocorticogram array in the primary visual cortex. Electrical stimulation was delivered to the optic nerve with the intraneural array OpticSELINE. We used a support vector machine algorithm paired to a linear regression model to classify cortical responses originating from visual stimuli located in different portions of the visual field and electrical stimuli from the different electrodes of the OpticSELINE. Main results. Cortical activity induced by visual and electrical stimulation could be classified with nearly 100% accuracy relative to the specific location in the visual field or electrode in the array from which it originated. For visual stimulation, the accuracy increased with the separation of the stimuli and reached 100% for separation higher than 7°. For electrical stimulation, at low current amplitudes, the accuracy increased with the distance between electrodes, while at higher current amplitudes, the accuracy was nearly 100% already for the shortest separation. Significance. Optic nerve’s intraneural stimulation with the OpticSELINE induced discernible cortical activity patterns. These results represent a necessary condition for an optic nerve prosthesis to deliver vision with non-overlapping phosphene. However, clinical investigations will be required to assess the translation of these results into perceptual phenomena.

Three-dimensional multilayer concentric bipolar electrodes enhance the selectivity of optic nerve stimulation

“…The dimensionality reduction and clustering of the EEPs were automatic and relatively assumption-free [31], enabling a straightforward classification of significant EEP without defining a priori punctual features, such as latency or amplitude. Indeed, we already showed that punctual features appeared to be not informative enough for prediction models [17]. Hence, this classification allowed us to compare how significant electrically evoked responses spread across the cortex with changing stimulus parameters and electrode configuration.…”

“…Animal experiments were performed according to the authorisation GE519 approved by the Département de l’Emploi, des Affaires Sociales et de la Santé, Direction Générale de la Santé of the Republique et Canton de Genève (Switzerland), as previously described [17]. Two female Chinchilla Bastard rabbits (>16 weeks, >2.5 kg) were premedicated 30 min before the transfer to the surgical room with an intramuscular injection of xylazine (3 mg kg -1 ; Rompun®20 mg ml -1 , 0.15 ml kg -1 ), ketamine (25 mg kg -1 ; Ketanarkon ® 100 mg ml -1 , 0.25 ml kg -1 ) and buprenorphine (0.03 mg kg -1 ; Temgesic® 0.3 mg ml -1 , 0.1 ml kg -1 ).…”

“…The Flat-OpticSELINE [17] is a modified version of the OpticSELINE [16] without protruding wings. Therefore, it is equivalent to a transverse intrafascicular multichannel electrode (TIME) array.…”

“…Hence, we validated this technology in the context of intraneural optic nerve stimulation. Our research group recently proposed a new intraneural electrode array (OpticSELINE) for artificial vision via optic nerve electrical stimulation [16,17]. However, the selective activation of a few optic nerve fibres by each electrode was still an open challenge.…”

The role of the visual field size in artificial vision

“…Essentially, artificial vision is a rudimentary form of vision caused by the artificial activation of neurons in the visual system by physical forces such as mechanical, chemical or electrical stimuli. For example, visual prostheses electrically stimulate visual neurons upstream to the damaged location, such as the retina, the optic nerve, the lateral geniculate nucleus or the cortex [5,[12][13][14][15][16][17][18][19][20][21]. The artificial stimulation of visual neurons induces the perception of bright dots in the visual space (called phosphenes).…”