Biology subjects

Ottenhoff, M. C.

Publications and source records attributed to Ottenhoff, M. C..

8 recordsLinked to original sources

bioRxiv2025

Insular error network enables self-correcting intracranial brain-computer interface

Error recognition is fundamental to adaptive behavior, enabling rapid compensatory action when outcomes deviate from expectations. Central to this function are neural circuits for performance monitoring, encoding cognitive signals that could support more reliable neural interfaces. Here, we recorded intracranial electroencephalography (iEEG) in epilepsy patients to enable a motor brain-computer interface (BCI) while sampling error-related activity across a distributed network. Our work reveals high-frequency population dynamics emerging in the anterior insula and propagating to the prefrontal cortex as the interface fails to follow the users intention. We identify spatially organized insular responses to error processing and movement feedback, highlighting it as a heterogeneous hub linking action and outcome. Real-time integration of error responses enables a self-correcting neural interface that enhances usability by reducing the need for manual user intervention. Together, our work demonstrates a human intracranial BCI harnessing insular brain activity, integrating cognitive processes directly into device control.

neuroscience↗

bioRxiv2025

Decoding continuous goal-directed movement from human brain-wide intracranial recordings.

Reaching out your hand is an effortless yet complex behavior that is indispensable in daily life. Restoring arm functionality is therefore rated a top priority by people with tetraplegia. Recently, neural correlates of movement have been observed and decoded beyond the motor cortex, but the degree and granularity of movement representation is not fully understood. Here, we explore the neural content of brain-wide movement-related neural activity by decoding neural correlates into 12 different kinematics of goal-directed reaching behavior. Eighteen participants implanted with stereotactic electroencephalography electrodes performed a gamified 3D goal-directed movement task. We demonstrate that continuous movement kinematics can be decoded from distributed recordings using low, mid and high frequency information in all participants using preferential subspace identification (PSID). The neural correlates of movement were distributed throughout the brain, including deeper structures such as the basal ganglia and insula. Moreover, we show that hand position could only be decoded using a goal-directed reference frame, indicating that widespread low-frequency activity is involved in higher-order processing of movements. Our results strengthen the evidence that widespread motor-related dynamics exist across numerous brain regions and can be used to continuously decode movement. The results may provide new opportunities for motor brain-computer interfaces for individuals with a compromised motor cortex, e.g. after stroke, or for control signals in adaptive closed-loop systems.

neuroscience↗

bioRxiv2024

Moving beyond the motor cortex: a brain-wide evaluation of target locations for intracranial speech neuroprostheses

Speech is the fastest and most natural form of communication, which can be impaired in certain disorders. Speech brain- computer interfaces (BCIs) offer a solution by decoding brain activity into speech. Current neuroprosthetic devices focus on the motor cortex, which might not be usable in all patient populations. Fortunately, many other brain regions have been associated with the speech production process. Here, we investigate which regions are potential (alternative) targets for a speech BCI across a brain-wide distribution within a single study. The distribution includes sulci and subcortical areas, sampled with both a high temporal and a high spatial resolution. Thirty participants were recorded with intracranial electroencephalography during speech production, resulting in 3249 recorded contacts across the brain. We trained machine learning models to continuously predict speech from a brain-wide global to a single-channel local scale. Within each scale we examined a variation of selected electrode contacts based on anatomical features within participants. We found significant speech detection in both gray and white matter tissue, no significant difference between gyri and sulci at any of the analysis scales and limited contribution from subcortical areas. The best potential targets in terms of decoding accuracy and consistency are located within the depth of and surrounding the lateral fissure bilaterally, such as the (sub)central sulcus, transverse temporal gyrus (Heschls gyrus), the supramarginal cortex and parts of the insula. These results highlight the potential benefits of extending beyond the motor cortex and reaching the sulcal depth for speech neuroprostheses.

bioengineering↗

bioRxiv2023

Global motor dynamics - Invariant neural representations of motor behavior in distributed brain-wide recordings

Neural activity is presumed to be correlated with motor behavior almost everywhere in the brain, implying that many different brain processes are involved in generating a behavioral output. Recent studies in multiple non-human species have observed pervasive brain-wide neural activity directly related to motor output. However, similar brain-wide investigations in humans with high-temporal resolution are lacking to date. Here, we recorded invasive data from brain-wide distributed electrodes in humans and reveal global neural dynamics that are predictive of movement across tasks and across participants. The dynamics are remarkably stable between participants with substantially varying electrode configurations and to loss of information. We demonstrate that these global neural dynamics are near brain-wide and present in all participants. Uncovering these global neural dynamics may allow for a more holistic and network-based perspective on motor-related neural activity.

neuroscience↗

bioRxiv2023

Decoding Arbitrary and Informed Decisions from Intracranial Recordings in Humans

Ideally, decisions are made based on prior knowledge, which allows for informed choices. Real life, however, often requires us to make decisions arbitrarily, without sufficient information. Decoding decision making processes from neural activity could allow for cognitive neuroprostheses and Brain-Computer Interfaces (BCIs) to support decision processes in rapid human-machine interactions, weigh decision-making confidence, and further enable neuromodulation protocols for the treatment of reward-related dysfunctions. To understand the differences between the decision-making processes in arbitrary and informed decisions, we recorded intracranial electroencephalography in a large number of cortical and subcortical areas from 5 patients during a categorization task. We demonstrate that individual decisions can be decoded from Local Field Potentials (LFPs) before motor response, in both arbitrary and informed conditions. Our analysis revealed dissimilar spatio-temporal patterns between arbitrary and informed decision-making, with arbitrary decisions being decodable in fewer brain regions and earlier in time compared to informed decisions.

neuroscience↗

bioRxiv2022

T-Rex: sTandalone Recorder of EXperiments; An easy and versatile neural recording platform

AO_SCPLOWBSTRACTC_SCPLOWRecording time in invasive neuroscientific empirical research is short and must be used as efficiently as possible. Time is often lost due to long setup times and errors by the researcher. Minimizing the number of manual actions reduces both and can be achieved by automating as much as possible. Importantly, automation should not reduce the flexibility of the system. Currently, recording setups are either custom-made by the researchers or provided as a module in comprehensive neuroscientific toolboxes, and no platforms exist focused explicitly on recording. Therefore, we developed a lightweight, flexible, platform- and measurement-independent recording system that can start and record experiments with a single press of a button. Data synchronization and recording are based on Lab Streaming Layer to ensure that all major programming languages and toolboxes can be used to develop and execute experiments. We have minimized the user restrictions as much as possible and imposed only two requirements on the experiment: The experiment should include a Lab Streaming Layer stream, and it should be able to run from a command line call. Further, we provided an easy-to-use interface that can be adjusted to specific measurement modalities, amplifiers, and participants. The presented system provides a new way of setting up and recording experiments for researchers and participants. Because of the automation and easy-to-use interface, the participant could even start and stop experiments by themselves, thus potentially providing data without the experimenters presence.

neuroscience↗

bioRxiv2022

Executed and imagined grasping movements can be decoded from lower dimensional representation of distributed non-motor brain areas.

Using brain activity directly as input for assistive tool control can circumvent muscular dysfunction and increase functional independence for physically impaired people. Most invasive motor decoding studies focus on decoding neural signals from the primary motor cortex, which provides a rich but superficial and spatially local signal. Initial non-primary motor cortex decoding endeavors have used distributed recordings to demonstrate decoding of motor activity by grouping electrodes in mesoscale brain regions. While these studies show that there is relevant and decodable movement related information outside the primary motor cortex, these methods are still exclusionary to other mesoscale areas, and do not capture the full informational content of the motor system. In this work, we recorded intracranial EEG of 8 epilepsy patients, including all electrode contacts except those contacts in or adjacent to the central sulcus. We show that executed and imagined movements can be decoded from non-motor areas; combining all non-motor contacts into a lower dimensional representation provides enough information for a Riemannian decoder to reach an area under the curve of 0.83 {+/-} 0.11. Additionally, by training our decoder on executed and testing on imagined movements, we demonstrate that between these two conditions there exists shared distributed information in the beta frequency range. By combining relevant information from all areas into a lower dimensional representation, the decoder was able to achieve high decoding results without information from the primary motor cortex. This representation makes the decoder more robust to perturbations, signal non-stationarities and neural tissue degradation. Our results indicate to look beyond the motor cortex and open up the way towards more robust and more versatile brain-computer interfaces.

neuroscience↗

bioRxiv2022

Speech Production in Intracranial Electroencephalography: iBIDS Dataset

Speech production is an intricate process involving a large number of muscles and cognitive processes. The neural processes underlying speech production are not completely understood. As speech is a uniquely human ability, it can not be investigated in animal models. High-fidelity human data can only be obtained in clinical settings and is therefore not easily available to all researchers. Here, we provide a dataset of 10 participants reading out individual words while we measured intracranial EEG from a total of 1103 electrodes. The data, with its high temporal resolution and coverage of a large variety of cortical and sub-cortical brain regions, can help in understanding the speech production process better. Simultaneously, the data can be used to test speech decoding and synthesis approaches from neural data to develop speech Brain-Computer Interfaces and speech neuroprostheses.

neuroscience↗