Cargando…

Decoding Neural Activity in Sulcal and White Matter Areas of the Brain to Accurately Predict Individual Finger Movement and Tactile Stimuli of the Human Hand

Millions of people worldwide suffer motor or sensory impairment due to stroke, spinal cord injury, multiple sclerosis, traumatic brain injury, diabetes, and motor neuron diseases such as ALS (amyotrophic lateral sclerosis). A brain-computer interface (BCI), which links the brain directly to a comput...

Descripción completa

Detalles Bibliográficos
Autores principales: Bouton, Chad, Bhagat, Nikunj, Chandrasekaran, Santosh, Herrero, Jose, Markowitz, Noah, Espinal, Elizabeth, Kim, Joo-won, Ramdeo, Richard, Xu, Junqian, Glasser, Matthew F., Bickel, Stephan, Mehta, Ashesh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8415782/
https://www.ncbi.nlm.nih.gov/pubmed/34483823
http://dx.doi.org/10.3389/fnins.2021.699631
_version_ 1783748036986404864
author Bouton, Chad
Bhagat, Nikunj
Chandrasekaran, Santosh
Herrero, Jose
Markowitz, Noah
Espinal, Elizabeth
Kim, Joo-won
Ramdeo, Richard
Xu, Junqian
Glasser, Matthew F.
Bickel, Stephan
Mehta, Ashesh
author_facet Bouton, Chad
Bhagat, Nikunj
Chandrasekaran, Santosh
Herrero, Jose
Markowitz, Noah
Espinal, Elizabeth
Kim, Joo-won
Ramdeo, Richard
Xu, Junqian
Glasser, Matthew F.
Bickel, Stephan
Mehta, Ashesh
author_sort Bouton, Chad
collection PubMed
description Millions of people worldwide suffer motor or sensory impairment due to stroke, spinal cord injury, multiple sclerosis, traumatic brain injury, diabetes, and motor neuron diseases such as ALS (amyotrophic lateral sclerosis). A brain-computer interface (BCI), which links the brain directly to a computer, offers a new way to study the brain and potentially restore impairments in patients living with these debilitating conditions. One of the challenges currently facing BCI technology, however, is to minimize surgical risk while maintaining efficacy. Minimally invasive techniques, such as stereoelectroencephalography (SEEG) have become more widely used in clinical applications in epilepsy patients since they can lead to fewer complications. SEEG depth electrodes also give access to sulcal and white matter areas of the brain but have not been widely studied in brain-computer interfaces. Here we show the first demonstration of decoding sulcal and subcortical activity related to both movement and tactile sensation in the human hand. Furthermore, we have compared decoding performance in SEEG-based depth recordings versus those obtained with electrocorticography electrodes (ECoG) placed on gyri. Initial poor decoding performance and the observation that most neural modulation patterns varied in amplitude trial-to-trial and were transient (significantly shorter than the sustained finger movements studied), led to the development of a feature selection method based on a repeatability metric using temporal correlation. An algorithm based on temporal correlation was developed to isolate features that consistently repeated (required for accurate decoding) and possessed information content related to movement or touch-related stimuli. We subsequently used these features, along with deep learning methods, to automatically classify various motor and sensory events for individual fingers with high accuracy. Repeating features were found in sulcal, gyral, and white matter areas and were predominantly phasic or phasic-tonic across a wide frequency range for both HD (high density) ECoG and SEEG recordings. These findings motivated the use of long short-term memory (LSTM) recurrent neural networks (RNNs) which are well-suited to handling transient input features. Combining temporal correlation-based feature selection with LSTM yielded decoding accuracies of up to 92.04 ± 1.51% for hand movements, up to 91.69 ± 0.49% for individual finger movements, and up to 83.49 ± 0.72% for focal tactile stimuli to individual finger pads while using a relatively small number of SEEG electrodes. These findings may lead to a new class of minimally invasive brain-computer interface systems in the future, increasing its applicability to a wide variety of conditions.
format Online
Article
Text
id pubmed-8415782
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-84157822021-09-04 Decoding Neural Activity in Sulcal and White Matter Areas of the Brain to Accurately Predict Individual Finger Movement and Tactile Stimuli of the Human Hand Bouton, Chad Bhagat, Nikunj Chandrasekaran, Santosh Herrero, Jose Markowitz, Noah Espinal, Elizabeth Kim, Joo-won Ramdeo, Richard Xu, Junqian Glasser, Matthew F. Bickel, Stephan Mehta, Ashesh Front Neurosci Neuroscience Millions of people worldwide suffer motor or sensory impairment due to stroke, spinal cord injury, multiple sclerosis, traumatic brain injury, diabetes, and motor neuron diseases such as ALS (amyotrophic lateral sclerosis). A brain-computer interface (BCI), which links the brain directly to a computer, offers a new way to study the brain and potentially restore impairments in patients living with these debilitating conditions. One of the challenges currently facing BCI technology, however, is to minimize surgical risk while maintaining efficacy. Minimally invasive techniques, such as stereoelectroencephalography (SEEG) have become more widely used in clinical applications in epilepsy patients since they can lead to fewer complications. SEEG depth electrodes also give access to sulcal and white matter areas of the brain but have not been widely studied in brain-computer interfaces. Here we show the first demonstration of decoding sulcal and subcortical activity related to both movement and tactile sensation in the human hand. Furthermore, we have compared decoding performance in SEEG-based depth recordings versus those obtained with electrocorticography electrodes (ECoG) placed on gyri. Initial poor decoding performance and the observation that most neural modulation patterns varied in amplitude trial-to-trial and were transient (significantly shorter than the sustained finger movements studied), led to the development of a feature selection method based on a repeatability metric using temporal correlation. An algorithm based on temporal correlation was developed to isolate features that consistently repeated (required for accurate decoding) and possessed information content related to movement or touch-related stimuli. We subsequently used these features, along with deep learning methods, to automatically classify various motor and sensory events for individual fingers with high accuracy. Repeating features were found in sulcal, gyral, and white matter areas and were predominantly phasic or phasic-tonic across a wide frequency range for both HD (high density) ECoG and SEEG recordings. These findings motivated the use of long short-term memory (LSTM) recurrent neural networks (RNNs) which are well-suited to handling transient input features. Combining temporal correlation-based feature selection with LSTM yielded decoding accuracies of up to 92.04 ± 1.51% for hand movements, up to 91.69 ± 0.49% for individual finger movements, and up to 83.49 ± 0.72% for focal tactile stimuli to individual finger pads while using a relatively small number of SEEG electrodes. These findings may lead to a new class of minimally invasive brain-computer interface systems in the future, increasing its applicability to a wide variety of conditions. Frontiers Media S.A. 2021-08-17 /pmc/articles/PMC8415782/ /pubmed/34483823 http://dx.doi.org/10.3389/fnins.2021.699631 Text en Copyright © 2021 Bouton, Bhagat, Chandrasekaran, Herrero, Markowitz, Espinal, Kim, Ramdeo, Xu, Glasser, Bickel and Mehta. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Bouton, Chad
Bhagat, Nikunj
Chandrasekaran, Santosh
Herrero, Jose
Markowitz, Noah
Espinal, Elizabeth
Kim, Joo-won
Ramdeo, Richard
Xu, Junqian
Glasser, Matthew F.
Bickel, Stephan
Mehta, Ashesh
Decoding Neural Activity in Sulcal and White Matter Areas of the Brain to Accurately Predict Individual Finger Movement and Tactile Stimuli of the Human Hand
title Decoding Neural Activity in Sulcal and White Matter Areas of the Brain to Accurately Predict Individual Finger Movement and Tactile Stimuli of the Human Hand
title_full Decoding Neural Activity in Sulcal and White Matter Areas of the Brain to Accurately Predict Individual Finger Movement and Tactile Stimuli of the Human Hand
title_fullStr Decoding Neural Activity in Sulcal and White Matter Areas of the Brain to Accurately Predict Individual Finger Movement and Tactile Stimuli of the Human Hand
title_full_unstemmed Decoding Neural Activity in Sulcal and White Matter Areas of the Brain to Accurately Predict Individual Finger Movement and Tactile Stimuli of the Human Hand
title_short Decoding Neural Activity in Sulcal and White Matter Areas of the Brain to Accurately Predict Individual Finger Movement and Tactile Stimuli of the Human Hand
title_sort decoding neural activity in sulcal and white matter areas of the brain to accurately predict individual finger movement and tactile stimuli of the human hand
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8415782/
https://www.ncbi.nlm.nih.gov/pubmed/34483823
http://dx.doi.org/10.3389/fnins.2021.699631
work_keys_str_mv AT boutonchad decodingneuralactivityinsulcalandwhitematterareasofthebraintoaccuratelypredictindividualfingermovementandtactilestimuliofthehumanhand
AT bhagatnikunj decodingneuralactivityinsulcalandwhitematterareasofthebraintoaccuratelypredictindividualfingermovementandtactilestimuliofthehumanhand
AT chandrasekaransantosh decodingneuralactivityinsulcalandwhitematterareasofthebraintoaccuratelypredictindividualfingermovementandtactilestimuliofthehumanhand
AT herrerojose decodingneuralactivityinsulcalandwhitematterareasofthebraintoaccuratelypredictindividualfingermovementandtactilestimuliofthehumanhand
AT markowitznoah decodingneuralactivityinsulcalandwhitematterareasofthebraintoaccuratelypredictindividualfingermovementandtactilestimuliofthehumanhand
AT espinalelizabeth decodingneuralactivityinsulcalandwhitematterareasofthebraintoaccuratelypredictindividualfingermovementandtactilestimuliofthehumanhand
AT kimjoowon decodingneuralactivityinsulcalandwhitematterareasofthebraintoaccuratelypredictindividualfingermovementandtactilestimuliofthehumanhand
AT ramdeorichard decodingneuralactivityinsulcalandwhitematterareasofthebraintoaccuratelypredictindividualfingermovementandtactilestimuliofthehumanhand
AT xujunqian decodingneuralactivityinsulcalandwhitematterareasofthebraintoaccuratelypredictindividualfingermovementandtactilestimuliofthehumanhand
AT glassermatthewf decodingneuralactivityinsulcalandwhitematterareasofthebraintoaccuratelypredictindividualfingermovementandtactilestimuliofthehumanhand
AT bickelstephan decodingneuralactivityinsulcalandwhitematterareasofthebraintoaccuratelypredictindividualfingermovementandtactilestimuliofthehumanhand
AT mehtaashesh decodingneuralactivityinsulcalandwhitematterareasofthebraintoaccuratelypredictindividualfingermovementandtactilestimuliofthehumanhand