Biology subjects

Ramsey, N. F.

Publications and source records attributed to Ramsey, N. F..

9 recordsLinked to original sources

bioRxiv2025

Across-speaker articulatory reconstruction from sensorimotor cortex for generalizable brain-computer interfaces

ObjectiveSpeech brain-computer interfaces (BCIs) can restore speech features like articulatory movements from brain activity. However, for individuals with vocal tract paralysis, lack of articulatory movements can pose a challenge for speech BCI development. To address this challenge, our study aims at extracting generalizable articulatory features from a group of native Dutch speakers and reconstructing these features from brain data of a separate group of able-bodied individuals. ApproachWe applied a tensor component analysis (TCA) model to extract generalisable articulatory features from a publicly available articulatory movement dataset. To reconstruct articulatory features from the brain, we analyzed data of three able-bodied participants P1, P2 and P3 with high-density electrocorticography (HD-ECoG) electrode arrays implanted over the sensorimotor cortex. For each participant, a separate TCA model was applied to extract neural features. A gradient boosting regression model was used to reconstruct articulatory features from neural features. Reconstruction performance was measured as Pearsons correlation coefficient (PCC) between the reconstructed and the generalizable articulatory features. ResultsThe extracted articulatory features showed even contributions across participants, indicating that these features captured generalizable articulatory kinematic patterns. By using these features, we were able to reconstruct articulatory features from brain data. PCC between the reconstructed and original articulatory features were significantly above chance for all three participants, with mean PCCs of 0.80, 0.75 and 0.76 for P1, P2 and P3 respectively. SignificanceWith the rapid development of speech BCI, our research demonstrates that speech-related articulatory features can be restored from HD-ECoG signal using generalizable articulatory features derived from able-bodied individuals. With the potential to reconstruct audio or speech-related facial movements from the reconstructed articulatory features, our framework may provide a new way for developing speech BCIs for people unable to produce mouth movements.

neuroscience↗

bioRxiv2025

Optimal positioning and size of high-density electrocorticography grids for speech brain-computer interfaces

Speech-based brain-computer interfaces (BCIs) can offer an intuitive means of communication for those who have lost the ability to speak due to paralysis. Significant progress has been made in classifying individual words from high numbers of electrocorticographic (ECoG) electrodes on the sensorimotor cortex (SMC). As implantations of larger grids with more ECoG electrodes are associated with higher surgical risk, we here examined whether confined electrode configurations can match the classification accuracy of larger grids. To this end, we analyzed data from eight able-bodied participants with high-density ECoG grids (64 to 128 electrodes) who performed a task that involved speaking 12 Dutch words. Word pronunciation was associated with changes in high frequency band activity in two SMC foci, one in the ventral SMC and another in the dorsal SMC. Using a combinatorics approach, we found that a smaller, rectangular, configuration with a surface area of 325 mm2 to 561 mm2 (32 electrodes) could achieve a word classification accuracy similar to that of the larger grids: 76{+/-}16% versus 75{+/-}17% across participants, respectively (practical chance level 16.7%). The best configurations were oriented vertically and centered on the central sulcus. These findings indicate that a 32-electrode ECoG grid placed optimally can be sufficient for achieving high word classification accuracy on a closed set of words. We conclude that targeted placement of small ECoG grids can reduce surgical demands on end users and justify energy- and complexity-efficient designs of fully implantable BCI devices for individuals with severe paralysis.

neuroscience↗

bioRxiv2022

Direct Speech Reconstruction from Sensorimotor Brain Activity with Optimized Deep Learning Models

Development of brain-computer interface (BCI) technology is key for enabling communication in individuals who have lost the faculty of speech due to severe motor paralysis. A BCI control strategy that is gaining attention employs speech decoding from neural data. Recent studies have shown that a combination of direct neural recordings and advanced computational models can provide promising results. Understanding which decoding strategies deliver best and directly applicable results is crucial for advancing the field. In this paper, we optimized and validated a decoding approach based on speech reconstruction directly from high-density electrocorticography recordings from sensorimotor cortex during a speech production task. We show that 1) dedicated machine learning optimization of reconstruction models is key for achieving the best reconstruction performance; 2) individual word decoding in reconstructed speech achieves 92-100% accuracy (chance level is 8%); 3) direct reconstruction from sensorimotor brain activity produces intelligible speech. These results underline the need for model optimization in achieving best speech decoding results and highlight the potential that reconstruction-based speech decoding from sensorimotor cortex can offer for development of next-generation BCI technology for communication.

neuroscience↗

bioRxiv2021

Task-independent acute effects of delta-9-tetrahydrocannabinol on human brain function and its relationship with cannabinoid receptor gene expression: a neuroimaging meta-regression analysis

BackgroundThe neurobiological mechanisms underlying the effects of delta-9-tetrahydrocannabinol (THC) remain unclear. Here, we examined the spatial acute effect of THC on human on regional brain activation or blood flow (hereafter called activation signal) in a core network of brain regions that subserve a multitude of processes. We also investigated whether the neuromodulatory effects of THC are related to the local expression of its key molecular target, cannabinoid-type-1 (CB1R) but not type-2 (CB2R) receptor. MethodsA systematic search was conducted of acute THC-challenge studies using fMRI, PET, and arterial spin labelling in accordance with established guidelines. Using pooled summary data from 372 participants, tested using a within-subject repeated measures design under experimental conditions, we investigated the effects of a single dose (6-42mg) of THC, compared to placebo, on brain signal. FindingsAs predicted, THC augmented the activation signal, relative to placebo, in the anterior cingulate, superior frontal cortices, middle temporal and middle and inferior occipital gyri, striatum, amygdala, thalamus, and cerebellum crus II and attenuated it in the middle temporal gyrus (spatially distinct from the cluster with THC-induced increase in activation signal), superior temporal gyrus, angular gyrus, precuneus, cuneus, inferior parietal lobule, and the cerebellum lobule IV/V. Using post-mortem gene expression data from an independent cohort from the Allen Human Brain atlas, we found a direct relationship between the magnitude of THC-induced brain signal change, indexed using pooled effect-size estimates, and CB1R gene expression, a proxy measure of CB1R protein distribution, but not CB2R expression. A dose-response relationship was observed with THC dose in certain brain regions. InterpretationThese meta-analytic findings shed new light on the localisation of the effects of THC in the human brain, suggesting that THC has neuromodulatory effects in regions central to many cognitive tasks and processes, with greater effects in regions with higher levels of CB1R expression.

neuroscience↗

bioRxiv2021

Open multimodal iEEG-fMRI dataset from naturalistic stimulation with a short audiovisual film

Intracranial human recordings are a valuable and rare resource that the whole neuroscience community can benefit from. Making such data available to the neuroscience community not only helps tackle the reproducibility issues in science, it also helps make more use of this valuable data. The latter is especially true for data collected using naturalistic tasks. Here, we describe a dataset collected from a large group of human subjects while they watched a short audiovisual film. The dataset is characterized by several unique features. First, it combines a large amount of intracranial data from 51 intracranial electroencephalography (iEEG) participants, who all did the same task. Second, the intracranial data are accompanied by fMRI recordings acquired for the same task in 30 functional magnetic resonance imaging (fMRI) participants. Third, the data were acquired using a rich audiovisual stimulus, for which we provide detailed speech and video annotations. This multimodal dataset can be used to address questions about neural mechanisms of multimodal perception and language comprehension as well as the nature of the neural signal acquired during the same task across brain recording modalities.

neuroscience↗

bioRxiv2021

Typical somatomotor physiology of the hand is preserved in a patient with an amputated arm

Electrophysiological signals in the human motor system may change in different ways after deafferentation, with some studies emphasizing reorganization while others propose retained physiology. Understanding whether motor electrophysiology is retained over longer periods of time can be invaluable for patients with paralysis (e.g. ALS or brainstem stroke) when signals from sensorimotor areas may be used for communication or control over neural prosthetic devices. In addition, a maintained electrophysiology can potentially benefit the treatment of phantom limb pains through prolonged use of these signals in a brain-machine interface (BCI). Here, we were presented with the unique opportunity to investigate the physiology of the sensorimotor cortex in a patient with an amputated arm using electrocorticographic (ECoG) measurements. While implanted with an ECoG grid for clinical evaluation of electrical stimulation for phantom limb pain, the patient performed attempted finger movements with the contralateral (lost) hand and executed finger movements with the ipsilateral (healthy) hand. The electrophysiology of the sensorimotor cortex contralateral to the amputated hand remained very similar to that of hand movement in healthy people, with a spatially focused increase of high-frequency band (65-175Hz; HFB) power over the hand region and a distributed decrease in low-frequency band (15-28Hz; LFB) power. The representation of the three different fingers (thumb, index and little) remained intact and HFB patterns could be decoded using support vector learning at single-trial classification accuracies of >90%, based on the first 1-3s of the HFB response. These results demonstrate that hand representations are largely retained in the motor cortex. The intact physiological response of the amputated hand, the high distinguishability of the fingers and fast temporal peak are encouraging for neural prosthetic devices that target the sensorimotor cortex.

neuroscience↗

bioRxiv2020

Functional MRI based simulations of ECoG grid configurations for optimal measurement of spatially distributed hand-gesture information

In electrocorticography (ECoG), the physical characteristics of the electrode grid determine which aspect of the neurophysiology is measured. For particular cases, the ECoG grid may be tailored to capture specific features, such as in the development and use of brain-computer-interfaces (BCI). Neural representations of hand movement are increasingly used to control ECoG based BCIs. However, it remains unclear which grid configurations are the most optimal to capture the dynamics of hand gesture information. Here, we investigate how the design and surgical placement of grids would affect the usability of ECoG measurements. High resolution 7T functional MRI was used as a proxy for neural activity in ten healthy participants to simulate various grid configurations, and evaluated the performance of each configuration for decoding hand gestures. The grid configurations varied in number of electrodes, electrode distance and electrode size. Optimal decoding of hand gestures occurred in grid configurations with a higher number of densely-packed, large-size, electrodes up to a grid of ~5x5 electrodes. When restricting the grid placement to a highly informative region of primary sensorimotor cortex, optimal parameters converged to about 3x3 electrodes, an inter-electrode distance of 8mm, and an electrode size of 3mm radius (performing at ~70% 3-class classification accuracy). Our approach might be used to identify the most informative region, find the optimal grid configuration and assist in positioning of the grid to achieve high BCI performance for the decoding of hand-gestures prior to surgical implantation.

bioengineering↗

bioRxiv2020

Moving in on human motor cortex. Characterizing the relationship between body parts with non-rigid population Response Fields.

For cortical motor activity, the relationships between different body part representations is unknown. Through reciprocal body part relationships, functionality of cortical motor areas with respect to whole body motor control can be characterized. In the current study, we investigate the relationship between body part representations within individual neuronal populations in motor cortices, following a 7 Tesla fMRI 18-body-part motor experiment in combination with our newly developed non-rigid population Response Field (pRF) model and graph theory. The non-rigid pRF metrics reveal somatotopic structures in all included motor cortices covering frontal, parietal, medial and insular cortices and that neuronal populations in primary sensorimotor cortex respond to fewer body parts than secondary motor cortices. Reciprocal body part relationships are estimated in terms of uniqueness, clique-formation, and importance. We report unique response profiles for the knee, a clique of body parts surrounding the ring finger, and a central role for the shoulder and wrist. These results reveal associations among body parts from the perspective of the central nervous system, while being in agreement with intuitive notions of body part usage.

neuroscience↗

bioRxiv2019

Putting the brain in a box: a toolbox for creating Cartesian Geometric Representations with Isometric Dimensions (Cgrids)

The folding of the human cortex complicates extraction of position information and recognition of patterns across the cortical surface. As straight lines correspond better to our intuitions in spatial orientation, we developed an approach for imposing Cartesian grids on portions of the cortical surface, which can then be represented in a rectangular matrix. These functions have been implemented in the Cgrid (Cartesian Geometric Representation with Isometric Dimensions) toolbox. Cgrids can be generated based on regions of interest, or combinations thereof, according to any one of the Freesurfers annotation schemes.\n\nThe toolbox was evaluated using the surface reconstructions of T1-weighted images of 30 subjects, and 17 different Cgrids that in combination covered nearly the entire surface area of the brain. The vast majority of Cgrids (94.6%) could be generated without issues.\n\nIn addition to facilitating spatial orientation and pattern recognition, the toolbox also allows detailed comparison between the left and right hemisphere, and performing surface based analysis using a volumetric data format. The output of the toolbox is fully compatible with most existing fMRI/MRI analyses packages, and is immediately suitable as input for second level analysis. The toolbox can be downloaded from: https://github.com/mathijsraemaekers/Cgrid-toolbox

neuroscience↗