Biology subjects

Herff, C.

Publications and source records attributed to Herff, C..

17 recordsLinked to original sources

bioRxiv2026

High-Precision Event Synchronization for Chronic Deep Brain Stimulation Local Field Potential Recordings

BackgroundElectrophysiological recordings from chronically implanted Deep Brain Stimulation (DBS) electrodes can greatly advance understanding of disease and treatment mechanisms of motor and psychiatric disorders. The Medtronic Percept system allows for chronic recordings of local field potentials (LFP) from DBS target regions. However, these systems lack an inbuilt synchronization option to align LFP recordings to other recording modalities and consequently events in computerized tasks. ObjectiveWe propose and evaluate a synchronization method based on Transcutaneous Electrical Stimulation (TES) with low amplitudes to precisely align recorded LFP signals from the DBS electrodes to EEG recordings. MethodsThe TES-based synchronization approach was implemented and tested in 11 participants implanted with the Medtronic Percept for treatment of Parkinsons disease. ResultsThe proposed method provides high reliability, precise alignment and usability across all Medtronic Percept recording modes. Notably, the method enables recordings during adaptive DBS and with stimulation turned off. In this recording mode, LFP signals can be acquired from all recording contact pairs simultaneously, with a high signal-to-noise ratio. We provide detailed setup plans and share Python and Matlab scripts for signal alignment to enable easy application of our approach. ConclusionBy enabling reliable, well-aligned LFP recordings from all DBS contacts, our method provides a robust tool for studying neural dynamics and refining therapeutic interventions in diverse neurological conditions.

neuroscience↗

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 brain-wide signatures of uninformed choices for BCI assisted decision-making

Decision-making is an essential cognitive function. It can be impaired due to a number of neurological and psychiatric disorders as well as external factors such as time pressure or stress. To assist users during decision-making, we propose a decision-making brain computer interface (BCI) that can alert to uninformed decision-making, prompt additional information seeking and therefore improve decision-making quality. To this aim, we establish the feasibility of decoding uninformed decision-making from local field potentials recorded with implanted stereo-electroencephalography (sEEG) electrodes in 6 participants. We show that decoding of available information above chance level is possible for all participants, both after stimulus presentation, as well as before task response. Starting from stimulus onset, the temporal processing hierarchy of informed vs. uninformed decision-making spans from visual processing through hippocampal memory processes to frontal control network shifting. The anterior insula, known to be a decision-making hub, codes available information during the decision phase prior to button press. These results further elucidate the neural basis of coded information availability and confirm the feasibility of a decision-making BCI.

neuroscience↗

bioRxiv2025

Remote magnetomechanical neuromodulation uncovers a novel therapeutic mechanism for alleviating Parkinsonian symptoms in freely moving mice

To overcome the limitations of invasive neuromodulation systems, we introduce a wireless magnetomechanical approach for remote, minimally invasive deep brain stimulation (DBS) without chronically implanted electrodes. This method leverages biocompatible nanoscale magnetite nanodiscs (MNDs) with ground vortex magnetisation, which undergo in-plane transitions under low-frequency alternating magnetic fields, thereby generating localised piconewton-scale torques. These torques engage endogenous mechanosensory pathways to modulate neural activity, enabling reversible stimulation without the need for genetic modifications. Calcium imaging validated the rapid neuromodulatory effects of MNDs in vitro and ex vivo, which motivated the subsequent application of magnetomechanical DBS to the subthalamic nucleus in mice. We demonstrated the remote control of motor behaviour in wild-type mice and significant restoration of motor function in a severe hemiparkinsonian model. This study established the first wireless therapeutic magnetomechanical neuromodulation platform that leverages biocompatible nanomaterials and endogenous mechanosensory ion channels, representing a promising step toward untethered, clinically translatable neurotechnology.

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↗

bioRxiv2024

Whole-brain dynamics of articulatory, acoustic and semantic speech representations

Speech production is a complex process that traverses several representations, from the meaning of spoken words (semantic), through the movement of articulatory muscles (articulatory) and, finally, to the produced audio waveform (acoustic). In our study, we aimed to identify how these different representations of speech are spatially and temporally distributed throughout the depth of the brain. By considering multiple representations from the same exact data, we can limit potential con-founders to better understand the different aspects of speech production and acquire crucial complementary information for speech brain-computer interfaces (BCIs). Intracranial speech production data was collected of 15 participants, recorded from 1647 electrode contacts, while they overtly spoke 100 unique words. The electrodes were distributed across the entire brain, including sulci and subcortical areas. We found a bilateral spatial distribution for all three representations, although there was a stronger tuning in the left hemisphere with a more widespread and temporally dynamic distribution than in the right hemisphere. The articulatory and acoustic representations share a similar spatial distribution surrounding the Sylvian fissure, while the semantic representation appears to be widely distributed across the brain in a mostly distinct network. These results highlight the distributed nature of the speech production process and the potential of non-motor representations for speech BCIs.

neuroscience↗

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

The Nested Hierarchy of Overt, Mouthed, and Imagined Speech Activity Evident in Intracranial Recordings

Recent studies have demonstrated that it is possible to decode and synthesize various aspects of acoustic speech directly from intracranial measurements of electrophysiological brain activity. In order to continue progressing toward the development of a practical speech neuroprosthesis for the individuals with speech impairments, better understanding and modeling of imagined speech processes are required. The present study uses intracranial brain recordings from participants that performed a speaking task with trials consisting of overt, mouthed, and imagined speech, representing various degrees of decreasing behavioral output. Speech activity detection models are constructed using spatial, spectral, and temporal brain activity features, and the features and model performances are characterized and compared across the three degrees of behavioral output. The results indicate there is a hierarchy in which the relevant channels for the lower behavioral output modes form nested subsets of the relevant channels from the higher behavioral output modes. This provides important insights for the elusive goal of developing more effective imagined speech decoding models with respect to the better-established overt speech decoding counterparts.

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↗

bioRxiv2021

Rapid dynamic naturalistic monitoring of bradykinesia in Parkinson's disease using a wrist-worn accelerometer

IntroductionMotor fluctuations in Parkinsons disease are characterized by unpredictability in the timing and duration of dopaminergic therapeutic benefit on symptoms including bradykinesia and rigidity. These fluctuations significantly impair the quality of life of many Parkinsons patients. However, current clinical evaluation tools are not designed for the continuous, naturalistic (real-world) symptom monitoring needed to optimize clinical therapy to treat fluctuations. Although commercially available wearable motor monitoring, used over multiple days, can augment neurological decision making, the feasibility of rapid and dynamic detection of motor fluctuations is unclear. So far, applied wearable monitoring algorithms are trained on group data. Here, we investigate the influence of individual model training on short timescale classification of naturalistic bradykinesia fluctuations in Parkinsons patients using a single wrist-accelerometer. MethodsAs part of the Parkinson@Home study protocol, 20 Parkinson patients were recorded with bilateral wrist-accelerometers for a one hour OFF medication session and a one hour ON medication session during unconstrained activities in their own homes. Kinematic metrics were extracted from the accelerometer data from the bodyside with the largest unilateral bradykinesia fluctuations across medication states. The kinematic accelerometer features were compared over the whole one-hour recordings, and medication-state classification analyses were performed on one-minute segments of data. The influence of individual versus group model training, data window length, and total amount of training patients included in group model training on classification was analyzed. ResultsStatistically significant areas under the curves (AUCs) for medication induced bradykinesia fluctuation classification were seen in 85% of the Parkinson patients at the single minute timescale using the group models. Individually trained models performed at the same level as the group trained models (mean AUC both 0.70, +/- respectively 0.18 and 0.10) despite the small individual training dataset. AUCs of the group models improved as the length of the feature windows was increased to 300 seconds, and with additional training patient datasets. ConclusionMedication induced fluctuations in bradykinesia can be classified using wrist worn accelerometery at the time scale of a single minute. Rapid, naturalistic Parkinson motor monitoring has important clinical potential to evaluate dynamic symptomatic and therapeutic fluctuations and help tailor treatments on a fast timescale.

neuroscience↗

bioRxiv2020

Real-time Synthesis of Imagined Speech Processes from Minimally Invasive Recordings of Neural Activity

Speech neuroprosthetics aim to provide a natural communication channel to individuals who are unable to speak due to physical or neurological impairments. Real-time synthesis of acoustic speech directly from measured neural activity could enable natural conversations and significantly improve quality of life, particularly for individuals who have severely limited means of communication. Recent advances in decoding approaches have led to high quality reconstructions of acoustic speech from invasively measured neural activity. However, most prior research utilizes data collected during open-loop experiments of articulated speech, which neglects the critical human-in-the-loop aspect of a practical speech neuroprosthetic. Here we present an approach that synthesizes audible speech in real-time for both imagined and whispered speech conditions. Using a participant implanted with stereotactic depth electrodes, we were able to reliably generate audible speech in real-time. The decoding models rely predominately on frontal activity suggesting that speech processes have similar representations when vocalized, whispered, or imagined. Our real-time synthesis approach represents an essential step towards investigating how patients will learn to operate a closed-loop speech neuroprosthesis, as well as the development of techniques that incorporate co-adaptation of the user and system for optimized performance.

neuroscience↗

bioRxiv2019

Prefrontal high-gamma in ECoG tags periodicity of musical rhythms in perception and imagination

Rhythmic auditory stimuli are known to elicit matching activity patterns in neural populations. Furthermore, recent research has established the particular importance of high-gamma brain activity in auditory processing by showing its involvement in auditory phrase segmentation and envelope-tracking. Here, we use electrocorticographic (ECoG) recordings from eight human listeners, to see whether periodicities in high-gamma activity track the periodicities in the envelope of musical rhythms during rhythm perception and imagination. Rhythm imagination was elicited by instructing participants to imagine the rhythm to continue during pauses of several repetitions. To identify electrodes whose periodicities in high-gamma activity track the periodicities in the musical rhythms, we compute the correlation between the autocorrelations (ACC) of both the musical rhythms and the neural signals. A condition in which participants listened to white noise was used to establish a baseline. High-gamma autocorrelations in auditory areas in the superior temporal gyrus and in frontal areas on both hemispheres significantly matched the autocorrelation of the musical rhythms. Overall, numerous significant electrodes are observed on the right hemisphere. Of particular interest is a large cluster of electrodes in the right prefrontal cortex that is active during both rhythm perception and imagination. This indicates conscious processing of the rhythms structure as opposed to mere auditory phenomena. The ACC approach clearly highlights that high-gamma activity measured from cortical electrodes tracks both attended and imagined rhythms.

neuroscience↗

bioRxiv2018

Speech Synthesis from ECoG using Densely Connected 3D Convolutional Neural Networks

ObjectiveDirect synthesis of speech from neural signals could provide a fast and natural way of communication to people with neurological diseases. Invasively-measured brain activity (electrocorticography; ECoG) supplies the necessary temporal and spatial resolution to decode fast and complex processes such as speech production. A number of impressive advances in speech decoding using neural signals have been achieved in recent years, but the complex dynamics are still not fully understood. However, it is unlikely that simple linear models can capture the relation between neural activity and continuous spoken speech.\n\nApproachHere we show that deep neural networks can be used to map ECoG from speech production areas onto an intermediate representation of speech (logMel spectrogram). The proposed method uses a densely connected convolutional neural network topology which is well-suited to work with the small amount of data available from each participant.\n\nMain resultsIn a study with six participants, we achieved correlations up to r = 0.69 between the reconstructed and original logMel spectrograms. We transfered our prediction back into an audible waveform by applying a Wavenet vocoder. The vocoder was conditioned on logMel features that harnessed a much larger, pre-existing data corpus to provide the most natural acoustic output.\n\nSignificanceTo the best of our knowledge, this is the first time that high-quality speech has been reconstructed from neural recordings during speech production using deep neural networks.

neuroscience↗