Periodic Reporting for period 5 - Brain3.0 (Invasive cognitive brain computer interfaces to enhance and restore attention: proof of concept and underlying cortical mechanisms.)
Reporting period: 2022-10-01 to 2023-09-30
The goal of brain-machine interfaces (BMIs) is to establish a direct communication pathway between the brain and external devices, typically computers or prosthetic devices. BMIs aim to interpret neural signals and translate them into meaningful outputs, allowing individuals to interact with technology or control external devices using their thoughts. The key objectives of brain-machine interfaces include: Restoring Functionality, Assisting People with Disabilities, Neuroprosthetics, Enhancing Human Performance, Research and Understanding, Communication for Locked-In Patients, Advancing Brain-Computer Interaction, Exploring Human-Machine Integration.
While significant progress has been made in the field of brain-machine interfaces, there are still many challenges to overcome, including improving the precision of neural decoding and addressing specific cognitive functions. The BRAIN 3.0 bridges several of these gaps and achieved several of the objectives of BMIs.
Specifically, the goal of the BRAIN 3.0 project is to:
1 Enhance the precision of the neural decoding of cognitive functions that are not directly observable outside of specific types of behaviors.
2 Provide a better understanding of the attentional function and its interaction with other cognitive functions.
3 Achieve a closed-loop invasive brain-machine interfaces to enhance the attentional function.
4 Achieve a closed-loop non-invasive BMIs to enhance the attentional function.
5 Provide a better understanding of how closed-loop BMIs affect the brain both at the neuronal and at the network levels.
The BRAIN 3.0 project thus both contributes to fundamental knowledge on the attentional function and how closed loop BMIs interfere with this core cognitive function and to innovative health perspectives on the restoration of the attentional function.
The overall long-term aim of the project targets applications in normal healthy subjects or subjects with mild to severe attentional deficits via a novel specific class of BMIs termed cognitive BMIs.
While significant progress has been made in the field of brain-machine interfaces, there are still many challenges to overcome, including improving the precision of neural decoding and addressing specific cognitive functions. The BRAIN 3.0 bridges several of these gaps and achieved several of the objectives of BMIs.
Specifically, the goal of the BRAIN 3.0 project is to:
1 Enhance the precision of the neural decoding of cognitive functions that are not directly observable outside of specific types of behaviors.
2 Provide a better understanding of the attentional function and its interaction with other cognitive functions.
3 Achieve a closed-loop invasive brain-machine interfaces to enhance the attentional function.
4 Achieve a closed-loop non-invasive BMIs to enhance the attentional function.
5 Provide a better understanding of how closed-loop BMIs affect the brain both at the neuronal and at the network levels.
The BRAIN 3.0 project thus both contributes to fundamental knowledge on the attentional function and how closed loop BMIs interfere with this core cognitive function and to innovative health perspectives on the restoration of the attentional function.
The overall long-term aim of the project targets applications in normal healthy subjects or subjects with mild to severe attentional deficits via a novel specific class of BMIs termed cognitive BMIs.
The BRAIN 3.0 project results cover two main topics. First, the project provides enhanced precision of the neural decoding of cognitive functions that are generated by the brain but not directly observable outside of specific types of behaviors. Second, the project achieves both invasive and non-invasive closed-loop cognitive brain-machine interfaces to enhance the attentional function and provide a better understanding of how closed-loop BMIs affect the brain both at the neuronal and at the network levels.
In a first set of studies, we identify intracortical electrophysiological functional markers of spatial attention at multiple temporal and spatial scales. We demonstrate a high spatio-temporal resolution prefrontal population real-time decoding of the covert attentional spotlight (1° of spatial resolution, 50 ms of temporal resolution), from intra-cortical multi-unit neuronal activity (Gaillard et al., Nature Communications, 2020). We expand these findings to local field potentials (LFPs), as an intermediate step to identify the EEG markers of attention (de Sousa et al., NeuroImage, 2021). We show that spatial-attention related information that can be extracted from prefrontal cortex varies with time at very slow times scales of circa 5 to 7 cycles per hour (Gaillard et al., BioRxiv, 2021). We further apply a combination of machine learning approaches and supervised dimensionality reduction techniques in order to dissociate, at the single trial level, spatial attention information from information related to sustained attention in the task (Amengual et al., Nature Communications, 2022). All of these studies provided the precise identification of invasive attentional markers that can be used to drive cognitive brain machine interfaces. These studies opened the way to a fine characterization of the neural bases of attention and the attentional function. We also show that attention is fully multiplexed with other task related variables (Amengual et al., BioRxiv, 2022; Mouille et al., BioRxiv, 2023).
We also demonstrate the feasibility to track covert spatial attention localization using non-invasive fMRI BOLD signals, with an unprecedented spatial resolution (Loriette et al., 2021). We also improve on the state of the art of decoding spatial attention information from EEG signals and we show that this information fluctuates at a rhythm of 5-7 cycles per hour similarly to what has been described on invasive signals (Dali et al., FENS, 2022).
In a second set of studies, we develop invasive ephys attention-based neurofeedback. We develop an intracortical real-time neurofeedback protocol in which the goal of the subject is to modulate attention-related brain activity in order to maximize reward outcome in the trial. We show complex changes in how spatial attention information coding interacts with sustained attention signals (Sutter et al., ongoing). We also develop a functional magnetic resonance imaging (fMRI) real-time neurofeedback protocol. We show that this neurofeedback enhances sustained attention to the task (Dali et al., NeuroFrance, 2023). In addition, we report specific changes in the spatial attentional cortical network manifesting in an enhancement in the fronto-parietal top-down attentional control network as well as in the reward network and a disconnection between the fronto-parietal and the visual networks both during the task and at rest.
This work has been regularly presented at international conferences as posters and/or talks, as well as at invited conferences. In addition, this work has been published in high-impact and well recognized peer-reviewed scientific journals and made available open access. Other works have been will/be published as accessible reprints and are being submitted for evaluation in peer-reviewed journals. Three PhD dissertations have been finalized during the course of the project. Outcomes from BRAIN 3.0 have been systematically shared on the BenhamedLab twitter/X accounts, on Benhamedlab.org as well as a dedicated website.
In a first set of studies, we identify intracortical electrophysiological functional markers of spatial attention at multiple temporal and spatial scales. We demonstrate a high spatio-temporal resolution prefrontal population real-time decoding of the covert attentional spotlight (1° of spatial resolution, 50 ms of temporal resolution), from intra-cortical multi-unit neuronal activity (Gaillard et al., Nature Communications, 2020). We expand these findings to local field potentials (LFPs), as an intermediate step to identify the EEG markers of attention (de Sousa et al., NeuroImage, 2021). We show that spatial-attention related information that can be extracted from prefrontal cortex varies with time at very slow times scales of circa 5 to 7 cycles per hour (Gaillard et al., BioRxiv, 2021). We further apply a combination of machine learning approaches and supervised dimensionality reduction techniques in order to dissociate, at the single trial level, spatial attention information from information related to sustained attention in the task (Amengual et al., Nature Communications, 2022). All of these studies provided the precise identification of invasive attentional markers that can be used to drive cognitive brain machine interfaces. These studies opened the way to a fine characterization of the neural bases of attention and the attentional function. We also show that attention is fully multiplexed with other task related variables (Amengual et al., BioRxiv, 2022; Mouille et al., BioRxiv, 2023).
We also demonstrate the feasibility to track covert spatial attention localization using non-invasive fMRI BOLD signals, with an unprecedented spatial resolution (Loriette et al., 2021). We also improve on the state of the art of decoding spatial attention information from EEG signals and we show that this information fluctuates at a rhythm of 5-7 cycles per hour similarly to what has been described on invasive signals (Dali et al., FENS, 2022).
In a second set of studies, we develop invasive ephys attention-based neurofeedback. We develop an intracortical real-time neurofeedback protocol in which the goal of the subject is to modulate attention-related brain activity in order to maximize reward outcome in the trial. We show complex changes in how spatial attention information coding interacts with sustained attention signals (Sutter et al., ongoing). We also develop a functional magnetic resonance imaging (fMRI) real-time neurofeedback protocol. We show that this neurofeedback enhances sustained attention to the task (Dali et al., NeuroFrance, 2023). In addition, we report specific changes in the spatial attentional cortical network manifesting in an enhancement in the fronto-parietal top-down attentional control network as well as in the reward network and a disconnection between the fronto-parietal and the visual networks both during the task and at rest.
This work has been regularly presented at international conferences as posters and/or talks, as well as at invited conferences. In addition, this work has been published in high-impact and well recognized peer-reviewed scientific journals and made available open access. Other works have been will/be published as accessible reprints and are being submitted for evaluation in peer-reviewed journals. Three PhD dissertations have been finalized during the course of the project. Outcomes from BRAIN 3.0 have been systematically shared on the BenhamedLab twitter/X accounts, on Benhamedlab.org as well as a dedicated website.
A lot of energy and resources are deployed worldwide to the development of motor brain machine interfaces or interfaces specifically dedicated to allow communication in non-communicative patients. In comparison, cognitive brain machine interfaces targeted to specific cognitive functions are still in their infancy. The Brain 3.0 project has contributed in this respect in a unique way. We demonstrate in a reliable and reproducible manner that covert cognitive mental content can be tracked in real-time with high informational precision. In addition, we identify multiple neuronal markers associated with the attentional function (and hence its deficits) all of which can be used as a target for neurofeedback and cognitive brain machine interface applications both invasively and non-invasively. This is a major achievement in the field.
Based on these findings, we further implemented neurofeedback protocols driven by covert attention-related information and we show that such protocols have both an effect on behavioral attentional performance and we describe associated brain changes both at the mesoscopic level of neuronal population and at the macroscopic network level. This is setting the stage to novel cognitive brain-machine interfaces to enhance and restore attention.
Based on these findings, we further implemented neurofeedback protocols driven by covert attention-related information and we show that such protocols have both an effect on behavioral attentional performance and we describe associated brain changes both at the mesoscopic level of neuronal population and at the macroscopic network level. This is setting the stage to novel cognitive brain-machine interfaces to enhance and restore attention.