Neural Signals Research Articles

Effects of transcranial magneto-acoustic stimulation on cognitive function and neural signal transmission in the hippocampal CA1 region of mice

As a new means of brain neuroregulation and research, transcranial magneto-acoustic stimulation (TMAS) uses the coupling effect of ultrasound and a static magnetic field to regulate neural activity in the corresponding brain areas. Calcium ions can promote the secretion of neurotransmitters and play a key role in the transmission of neural signals in brain cognition. In this study, to explore the effects of TMAS on cognitive function and neural signaling in the CA1 region of the hippocampus, TMAS was applied to male 2-month-old C57 mice with a magnetic field strength of 0.3 T and ultrasound intensity of 2.6 W/cm2. First, the efficiency of neural signaling in the CA1 region of the mouse hippocampus was detected by fiber photometry. Second, the effects of TMAS on cognitive function in mice were investigated through multiple behavioral experiments, including spatial learning and memory ability, anxiety and desire for novelty. The experimental results showed that TMAS could improve cognitive function in mice, and the efficiency of neural signaling in the CA1 area of the hippocampus was significantly increased during stimulation and maintained for one week after stimulation. In addition, the neural signaling efficiency in the CA1 area of the hippocampus increased in the open field (OF) experiment and recovered after one week, the neural signaling efficiency in the new object exploration (NOE) experiment was significantly enhanced, and the intensity slowed after one week. In conclusion, TMAS enhances cognitive performance and promotes neural signaling in the CA1 region of the mouse hippocampus.

Neural activity ramps in frontal cortex signal extended motivation during learning

Learning requires the ability to link actions to outcomes. How motivation facilitates learning is not well understood. We designed a behavioral task in which mice self-initiate trials to learn cue-reward contingencies and found that the anterior cingulate region of the prefrontal cortex (ACC) contains motivation-related signals to maximize rewards. In particular, we found that ACC neural activity was consistently tied to trial initiations where mice seek to leave unrewarded cues to reach reward-associated cues. Notably, this neural signal persisted over consecutive unrewarded cues until reward-associated cues were reached, and was required for learning. To determine how ACC inherits this motivational signal we performed projection-specific photometry recordings from several inputs to ACC during learning. In doing so, we identified a ramp in bulk neural activity in orbitofrontal cortex (OFC)-to-ACC projections as mice received unrewarded cues, which continued ramping across consecutive unrewarded cues, and finally peaked upon reaching a reward-associated cue, thus maintaining an extended motivational state. Cellular resolution imaging of OFC confirmed these neural correlates of motivation, and further delineated separate ensembles of neurons that sequentially tiled the ramp. Together, these results identify a mechanism by which OFC maps out task structure to convey an extended motivational state to ACC to facilitate goal-directed learning.

Neural activity ramps in frontal cortex signal extended motivation during learning.

Learning requires the ability to link actions to outcomes. How motivation facilitates learning is not well understood. We designed a behavioral task in which mice self-initiate trials to learn cue-reward contingencies and found that the anterior cingulate region of the prefrontal cortex (ACC) contains motivation-related signals to maximize rewards. In particular, we found that ACC neural activity was consistently tied to trial initiations where mice seek to leave unrewarded cues to reach reward-associated cues. Notably, this neural signal persisted over consecutive unrewarded cues until reward-associated cues were reached, and was required for learning. To determine how ACC inherits this motivational signal we performed projection-specific photometry recordings from several inputs to ACC during learning. In doing so, we identified a ramp in bulk neural activity in orbitofrontal cortex (OFC)-to-ACC projections as mice received unrewarded cues, which continued ramping across consecutive unrewarded cues, and finally peaked upon reaching a reward-associated cue, thus maintaining an extended motivational state. Cellular resolution imaging of OFC confirmed these neural correlates of motivation, and further delineated separate ensembles of neurons that sequentially tiled the ramp. Together, these results identify a mechanism by which OFC maps out task structure to convey an extended motivational state to ACC to facilitate goal-directed learning.

Effects of Different Cow-Milk Beta-Caseins on the Gut-Brain Axis: A Narrative Review of Preclinical, Animal, and Human Studies.

The gut and brain communicate through bidirectional neural, endocrine, and immune signals to coordinate central nervous system activity with gastrointestinal function. Dysregulated inflammation can promote immune cell activation and increase entero-endocrine signaling and intestinal permeability; hence, a functional gut-brain axis is necessary for a healthy digestive system. The consumption of milk products can lead to gut discomfort via effects on gastrointestinal tract function and the inflammatory state, which, in turn, affect the brain. A1 β-casein and A2 β-casein are major components of bovine-milk protein, and their digestion may result in different physiological effects following the consumption of milk products. Peptides derived from A1 β-casein, such as β-casomorphins, may increase gut dysfunction and inflammation, thereby modulating the availability of bioactive metabolites in the bloodstream and contribute to changes in cognitive function. This narrative review examines the functional interrelationships between the consumption of cow-milk-derived β-caseins and their effect on the brain, immune system, and the gut, which together comprise the gut-brain axis.

Interfacing Neuron-Motor Pathways with Stretchable and Biocompatible Electrode Arrays.

ConspectusIn the field of neuroscience, understanding the complex interactions within the intricate neuron-motor system depends crucially on the use of high-density, physiological multiple electrode arrays (MEAs). In the neuron-motor system, the transmission of biological signals primarily occurs through electrical and chemical signaling. Taking neurons for instance, when a neuron receives external stimuli, it generates an electrical signal known as the action potential. This action potential propagates along the neuron's axon and is transmitted to other neurons via synapses. At the synapse, chemical signals (neurotransmitters) are released, allowing the electrical signal to traverse the synaptic gap and influence the next neuron. MEAs can provide unparalleled insights into neural signal patterns when interfacing with the nerve systems through their excellent spatiotemporal resolution. However, the inherent differences in mechanical and chemical properties between these artificial devices and biological tissues can lead to serious complications after chronic implantation, such as body rejection, infection, tissue damage, or device malfunction. A promising strategy to enhance MEAs' biocompatibility involves minimizing their thickness, which aligns their bending stiffness with that of surrounding tissues, thereby minimizing damage over time. However, this solution has its limits; the resulting ultrathin devices, typically based on plastic films, lack the necessary stretchability, restricting their use to organs that neither stretch nor grow.For practical deployments, devices must exhibit certain levels of stretchability (ranging from 20 to 70%), tailored to the specific requirements of the target organs. In this Account, we outline recent advancements in developing stretchable MEAs that balance stretchability with sufficient electrical conductivity for effective use in physiological research, acting as sensors and stimulators. By concentrating on the neuron-motor pathways, we summarize how the stretchable MEAs meet various application needs and examine their effectiveness. We distinguish between on-skin and implantable uses, given their vastly different requirements. Within each application scope, we highlight cutting-edge technologies, evaluating their strengths and shortcomings. Recognizing that most current devices rely on elastic films with a Young's modulus value between ∼0.5 and 5 MPa, we delve into the potential for softer MEAs, particularly those using multifunctional hydrogels for an optimizing tissue-device interface and address the challenges in adapting such hydrogel-based MEAs for chronic implants. Additionally, transitioning soft MEAs from lab to fab involves connecting them to a rigid adapter and external machinery, highlighting a critical challenge at the soft-rigid interface due to strain concentration, especially in chronic studies subject to unforeseen mechanical strains. We discuss innovative solutions to this integration challenge, being optimistic that the development of durable, biocompatible, stretchable MEAs will significantly advance neuroscience and related fields.

Modeling and Analysis of Environmental Electromagnetic Interference in Multiple-Channel Neural Recording Systems for High Common-Mode Interference Rejection Performance.

Environmental electromagnetic interference (EMI) has always been a major interference source for multiple-channel neural recording systems, and little theoretical work has been attempted to address it. In this paper, equivalent circuit models are proposed to model both electromagnetic interference sources and neural signals in such systems, and analysis has been performed to generate the design guidelines for neural probes and the subsequent recording circuit towards higher common-mode interference (CMI) rejection performance while maintaining the recorded neural action potential (AP) signal quality. In vivo animal experiments with a configurable 32-channel neural recording system are carried out to validate the proposed models and design guidelines. The results show the power spectral density (PSD) of environmental 50 Hz EMI interference is reduced by three orders from 4.43 × 10-3 V2/Hz to 4.04 × 10-6 V2/Hz without affecting the recorded AP signal quality in an unshielded experiment environment.

A data compression algorithm with the improved SRLE for high-throughput neural signal acquisition device.

Multi-channel acquisition systems of brain neural signals can provide a powerful tool with a wide range of information for the clinical application of brain computer interfaces. High-throughput implantable systems are limited by size and power consumption, posing challenges to system design. To acquire more comprehensive neural signals and wirelessly transmit high-throughput brain neural signals, a FPGA-based acquisition system for multi-channel brain nerve signals has been developed. And the Bluetooth transmission with low-power technology are utilized. To wirelessly transmit large amount of data with limited Bluetooth bandwidth and improve the accuracy of neural signal decoding, an improved sharing run length encoding (SRLE) is proposed to compress the spike data of brain neural signal to improve the transmission efficiency of the system. The functional prototype has been developed, which consists of multi-channel data acquisition chips, FPGA main control module with the improved SRLE, a wireless data transmitter, a wireless data receiver and an upper computer. And the developed functional prototype was tested for spike detection of brain neural signal by animal experiments. From the animal experiments, it shows that the system can successfully collect and transmit brain nerve signals. And the improved SRLE algorithm has an excellent compression effect with the average compression rate of 5.94%, compared to the double run-length encoding, the FDR encoding, and the traditional run-length encoding. The developed system, incorporating the improved SRLE algorithm, is capable of wirelessly capturing spike signals with 1024 channels, thereby realizing the implantable systems of High-throughput brain neural signals.

Stability of ECoG high gamma signals during speech and implications for a speech BCI system in an individual with ALS: a year-long longitudinal study

Objective. Speech brain–computer interfaces (BCIs) have the potential to augment communication in individuals with impaired speech due to muscle weakness, for example in amyotrophic lateral sclerosis (ALS) and other neurological disorders. However, to achieve long-term, reliable use of a speech BCI, it is essential for speech-related neural signal changes to be stable over long periods of time. Here we study, for the first time, the stability of speech-related electrocorticographic (ECoG) signals recorded from a chronically implanted ECoG BCI over a 12 month period. Approach. ECoG signals were recorded by an ECoG array implanted over the ventral sensorimotor cortex in a clinical trial participant with ALS. Because ECoG-based speech decoding has most often relied on broadband high gamma (HG) signal changes relative to baseline (non-speech) conditions, we studied longitudinal changes of HG band power at baseline and during speech, and we compared these with residual high frequency noise levels at baseline. Stability was further assessed by longitudinal measurements of signal-to-noise ratio, activation ratio, and peak speech-related HG response magnitude (HG response peaks). Lastly, we analyzed the stability of the event-related HG power changes (HG responses) for individual syllables at each electrode. Main Results. We found that speech-related ECoG signal responses were stable over a range of syllables activating different articulators for the first year after implantation. Significance. Together, our results indicate that ECoG can be a stable recording modality for long-term speech BCI systems for those living with severe paralysis. Clinical Trial Information. ClinicalTrials.gov, registration number NCT03567213.

Neural Reinforcement Learning Signals Predict Recovery From Impulse Control Disorder Symptoms in Parkinson’s Disease

BackgroundImpulse control disorders (ICDs) in Parkinson’s disease are associated with a heavy burden on patients and caretakers. While recovery can occur, ICDs persist in many patients despite optimal management. The basis for this interindividual variability in recovery is unclear and poses a major challenge to personalized health care. MethodsWe adopted a computational psychiatry approach and leveraged the longitudinal, prospective Personalized Parkinson Project (136 people with Parkinson’s disease, within 5 years of diagnosis) to combine dopaminergic learning theory–informed functional magnetic resonance imaging with machine learning (at baseline) to predict ICD symptom recovery after 2 years of follow-up. We focused on change in Questionnaire for Impulsive-Compulsive Disorders in Parkinson’s Disease Rating Scale scores in the entire sample regardless of an ICD diagnosis. ResultsGreater reinforcement learning signals during gain trials but not loss trials at baseline, including those in the ventral striatum and medial prefrontal cortex, and the behavioral accuracy score measured while on medication were associated with greater recovery from impulse control symptoms 2 years later. These signals accounted for a unique proportion of the relevant variability over and above that explained by other known factors, such as decreases in dopamine agonist use. ConclusionsOur results provide a proof of principle for combining generative model–based inference of latent learning processes with machine learning–based predictive modeling of variability in clinical symptom recovery trajectories. We showed that reinforcement learning modeling parameters predicted recovery from ICD symptoms in Parkinson’s disease.

Functional ultrasound imaging and neuronal activity: how accurate is the spatiotemporal match?

Over the last decade, functional ultrasound (fUS) has risen as a critical tool in functional neuroimaging, leveraging hemodynamic changes to infer neural activity indirectly. Recent studies have established a strong correlation between neural spike rates (SR) and functional ultrasound signals. However, understanding their spatial distribution and variability across different brain areas is required to thoroughly interpret fUS signals. In this regard, we conducted simultaneous fUS imaging and Neuropixels recordings during stimulus-evoked activity in awake mice within three regions the visual pathway. Our findings indicate that the temporal dynamics of fUS and SR signals are linearly correlated, though the correlation coefficients vary among visual regions. Conversely, the spatial correlation between the two signals remains consistent across all regions with a spread of approximately 300 micrometers. Finally, we introduce a model that integrates the spatial and temporal components of the fUS signal, allowing for a more accurate interpretation of fUS images.

Robust gesture recognition based on attention-deep fast convolutional neural network and surface electromyographic signals.

The surface electromyographic (sEMG) signals reflect human motor intention and can be utilized for human-machine interfaces (HMI). Comparing to the sparse multi-channel (SMC) electrodes, the high-density (HD) electrodes have a large number of electrodes and compact space between electrodes, which can achieve more sEMG information and have the potential to achieve higher performance in myocontrol. However, when the HD electrodes grid shift or damage, it will affect gesture recognition and reduce recognition accuracy. To minimize the impact resulting from the electrodes shift and damage, we proposed an attention deep fast convolutional neural network (attention-DFCNN) model by utilizing the temporary and spatial characteristics of high-density surface electromyography (HD-sEMG) signals. Contrary to the previous methods, which are mostly base on sEMG temporal features, the attention-DFCNN model can improve the robustness and stability by combining the spatial and temporary features. The performance of the proposed model was compared with other classical method and deep learning methods. We used the dataset provided by The University Medical Center Göttingen. Seven able-bodied subjects and one amputee involved in this work. Each subject executed nine gestures under the electrodes shift (10 mm) and damage (6 channels). As for the electrodes shift 10 mm in four directions (inwards; onwards; upwards; downwards) on seven able-bodied subjects, without any pre-training, the average accuracy of attention-DFCNN (0.942 0.04) is significantly higher than LSDA (0.910 0.04, p < 0.01), CNN (0.920 0.05, p < 0.01), TCN (0.840 0.07, p < 0.01), LSTM (0.864 0.08, p < 0.01), attention-BiLSTM (0.852 0.07, p < 0.01), Transformer (0.903 0.07, p < 0.01) and Swin-Transformer (0.908 0.09, p < 0.01). The proposed attention-DFCNN algorithm and the way of combining the spatial and temporary features of sEMG signals can significantly improve the recognition rate when the HD electrodes grid shift or damage during wear.

Perceptual Foundation and Extension to Phase Tagging for Rapid Invisible Frequency Tagging (RIFT)

Abstract Recent years have seen the emergence of a visual stimulation protocol called Rapid Invisible Frequency Tagging (RIFT) in cognitive neuroscience. In RIFT experiments, visual stimuli are presented at a rapidly and sinusoidally oscillating luminance, using high refresh rate projection equipment. Such stimuli result in strong steady-state responses in visual cortex, measurable extracranially using EEG or MEG. The high signal-to-noise ratio of these neural signals, combined with the alleged invisibility of the manipulation, make RIFT a potentially promising technique to study the neural basis of visual processing. In this study, we set out to resolve two fundamental, yet still outstanding, issues regarding RIFT; as well as to open up a new avenue for taking RIFT beyond frequency tagging per se. First, we provide robust evidence that RIFT is indeed subjectively undetectable, going beyond previous anecdotal reports. Second, we demonstrate that full-amplitude luminance or contrast manipulation offer the best tagging results. Third and finally, we demonstrate that, in addition to frequency tagging, phase tagging can reliably be used in RIFT studies, opening up new avenues for constructing RIFT experiments. Together, this provides a solid foundation for using RIFT in visual cognitive neuroscience.

Low-intensity transcranial focused ultrasound suppresses pain by modulating pain-processing brain circuits

AbstractThere is an urgent and unmet clinical need to develop nonpharmacological interventions for chronic pain management because of the critical side effects of opioids. Low-intensity transcranial focused ultrasound (tFUS) is an emerging noninvasive neuromodulation technology with high spatial specificity and deep brain penetration. Here, we developed a tightly focused 128-element ultrasound transducer to specifically target small mouse brains using dynamic focus steering. We demonstrate that tFUS stimulation at pain-processing brain circuits can significantly alter pain-associated behaviors in mouse models in vivo. Our findings indicate that a single-session focused ultrasound stimulation to the primary somatosensory cortex (S1) significantly attenuates heat pain sensitivity in wild-type mice and modulates heat and mechanical hyperalgesia in a humanized mouse model of chronic pain in sickle cell disease. Results further revealed a sustained behavioral change associated with heat hypersensitivity by targeting deeper cortical structures (eg, insula) and multisession focused ultrasound stimulation to S1 and insula. Analyses of brain electrical rhythms through electroencephalography demonstrated a significant change in noxious heat hypersensitivity-related and chronic hyperalgesia–associated neural signals after focused ultrasound treatment. Validation of efficacy was carried out through control experiments, tuning ultrasound parameters, adjusting interexperiment intervals, and investigating effects on age, sex, and genotype in a head-fixed awake model. Importantly, tFUS was found to be safe, causing no adverse effects on motor function or the brain’s neuropathology. In conclusion, the validated proof-of-principle experimental evidence demonstrates the translational potential of novel focused ultrasound neuromodulation for next-generation pain treatment without adverse effects.

Epilepsy lesion localization method based on brain function network.

In the past, the localization of seizure onset zone (SOZ) primarily relied on traditional EEG signal analysis methods. However, due to their limited spatial and temporal resolution, accurately pinpointing neural activity was challenging, thereby restricting their clinical applicability. Compared with traditional EEG signals, SEEG signals have superior spatial and temporal resolution, and can more accurately record neural activity near epileptic foci, making them better suited for studying SOZ. In addition, the traditional EEG signal analysis methods still have limitations, mainly focusing on the analysis of local signal features, while ignoring the complexity and interconnection of the overall brain network. How to more accurately locate SOZ is still not well resolved. The purpose of this study is to develop an effective positioning method for more accurate positioning. To overcome these limitations, this study proposed a model integrating brain functional network analysis with nonlinear dynamics. We utilized weighted phase lag index (WPLI) to construct brain functional network, epilepic network connectivity strength (ENCS) as the feature, and introduced persistence entropy (PE) for feature fusion, subsequently employing support vector machine (SVM) classification. The proposed method was verified on the HUP-iEEG dataset, our solution identified the SOZ with 0.9440 accuracy, 0.9848 precision, 0.8974 recall rate, 0.9340 F1 score and 0.9697 area under the ROC curve across patients, which outperforms the existing approaches. It exhibits a 2.30 percentage point enhancement in localisation accuracy along with a 2.97 percentage points in AUC compared to others. Our method consider the interactions between nodes in brain network connections, as well as the inherent nonlinear and non-stationary properties of neural signals, to be more robust.

Intan Technologies integrated circuits can produce analog-to-digital conversion artifacts that affect neural signal acquisition

Objective. Intan Technologies’ integrated circuits (ICs) are valuable tools for neurophysiological data acquisition, providing signal amplification, filtering, and digitization from many channels (up to 64 channels/chip) at high sampling rates (up to 30 kSPS) within a compact package (⩽9× 7 mm). However, we found that the analog-to-digital converters (ADCs) in the Intan RHD2000 series ICs can produce artifacts in recorded signals. Here, we examine the effects of these ADC artifacts on neural signal quality and describe a method to detect them in recorded data. Approach. We identified two types of ADC artifacts produced by Intan ICs: 1) jumps, resulting from missing output codes, and 2) flatlines, resulting from overrepresented output codes. We identified ADC artifacts in neural recordings acquired with Intan RHD2000 ICs and tested the repeated performance of 17 ICs in vitro. With the on-chip digital-signal-processing disabled, we detected the ADC artifacts in each test recording by examining the distribution of unfiltered ADC output codes. Main Results. We found larger ADC artifacts in recordings using the Intan RHX data acquisition software versions 3.0–3.2, which did not run the necessary ADC calibration command when the inputs to the Intan recording controller were rescanned. This has been corrected in the Intan RHX software version 3.3. We found that the ADC calibration routine significantly reduced, but did not fully eliminate, the occurrence and size of ADC artifacts as compared with recordings acquired when the calibration routine was not run (p < 0.0001). When the ADC calibration routine was run, we found that the artifacts produced by each ADC were consistent over time, enabling us to sort ICs by performance. Significance. Our findings call attention to the importance of evaluating signal quality when acquiring electrophysiological data using Intan Technologies ICs and offer a method for detecting ADC artifacts in recorded data.

Stereoselective Bioconcentration and Neurotoxicity of Perfluoroethylcyclohexane Sulfonate in Marine Medaka.

Perfluoroethylcyclohexane sulfonate (PFECHS) is an emerging per- and polyfluoroalkyl substance used to replace perfluorooctane sulfonate (PFOS), mainly in aircraft hydraulic fluids. However, previous research indicates the potential neurotoxicity of this replacement chemical. In this study, marine medaka (Oryzias melastigma) was exposed to environmentally relevant concentrations of PFECHS (concentrations: 0, 0.08, 0.26, and 0.91 μg/L) from the embryonic stage for 90 days. After exposure, the brain and eyes of the medaka were collected to investigate the bioconcentration potential of PFECHS stereoisomers and their effects on the nervous systems. The determined bioconcentration factors (BCFs) of PFECHS ranged from 324 ± 97 to 435 ± 89 L/kg and from 454 ± 60 to 576 ± 86 L/kg in the brain and eyes of medaka, respectively. The BCFs of trans-PFECHS were higher than those of cis-PFECHS. PFECHS exposure significantly altered γ-aminobutyric acid (GABA) levels in the medaka brain and disrupted the GABAergic system, as revealed by proteomics, implying that PFECHS can disturb neural signal transduction like PFOS. PFECHS exposure resulted in significant alterations in multiple proteins associated with eye function in medaka. Abnormal locomotion was observed in PFECHS-exposed medaka larvae, which was rescued by adding exogenous GABA, suggesting the involvement of disrupted GABA signaling pathways in PFECHS neurotoxicity.

Shared neural codes of recognition memory

Recognition memory research has identified several electrophysiological indicators of successful memory retrieval, known as old-new effects. These effects have been observed in different sensory domains using various stimulus types, but little attention has been given to their similarity or distinctiveness and the underlying processes they may share. Here, a data-driven approach was taken to investigate the temporal evolution of shared information content between different memory conditions using openly available EEG data from healthy human participants of both sexes, taken from six experiments. A test dataset involving personally highly familiar and unfamiliar faces was used. The results show that neural signals of recognition memory for face stimuli were highly generalized starting from around 200 ms following stimulus onset. When training was performed on non-face datasets, an early (around 200–300 ms) to late (post-400 ms) differentiation was observed over most regions of interest. Successful cross-classification for non-face stimuli (music and object/scene associations) was most pronounced in late period. Additionally, a striking dissociation was observed between familiar and remembered objects, with shared signals present only in the late window for correctly remembered objects, while cross-classification for familiar objects was successful in the early period as well. These findings suggest that late neural signals of memory retrieval generalize across sensory modalities and stimulus types, and the dissociation between familiar and remembered objects may provide insight into the underlying processes.

AI-empowered neural processing for intelligent human-machine interface and biomedical devices

AI-empowered neural processing for intelligent human-machine interface and biomedical devices Jie Gu, Associate Professor from Northwestern University, examines AI-empowered neural processing for intelligent human-machine interface and biomedical devices. Most conventional wearable devices rely on motion detection or image classifications to capture users’ activities. However, they lack the ability to decode neural signals generated by the human body. Neural signals, such as EEG, ECG, and EMG, offer a rich amount of information on a person’s physiological and psychological activities. Recognition and use of such signals present many new opportunities for applications in medical and daily commercial usage. Recently, artificial intelligence (AI) has been applied to neural signal processing, leading to a new generation of intelligent human-machine interfaces and biomedical devices.

Heightened lateral habenula activity during stress produces brainwide and behavioral substrates of susceptibility.

Some individuals are susceptible to the experience of chronic stress and others are more resilient. While many brain regions implicated in learning are dysregulated after stress, little is known about whether and how neural teaching signals during stress differ between susceptible and resilient individuals. Here, we seek to determine if activity in the lateral habenula (LHb), which encodes a negative teaching signal, differs between susceptible and resilient mice during stress to produce different outcomes. After, but not before, chronic social defeat stress (CSDS), the LHb is active when susceptible mice are in the proximity of the aggressor strain. During stress itself, LHb activity is higher in susceptible mice during aggressor proximity, and activation of the LHb during stress biases mice towards susceptibility. This manipulation generates a persistent and widespread increase in the balance of subcortical versus cortical activity in susceptible mice. Taken together, our results indicate that heightened activity in the LHb during stress produces lasting brainwide and behavioral substrates of susceptibility.

Imagined speech event detection from electrocorticography and its transfer between speech modes and subjects

Speech brain-computer interfaces aim to support communication-impaired patients by translating neural signals into speech. While impressive progress was achieved in decoding performed, perceived and attempted speech, imagined speech remains elusive, mainly due to the absence of behavioral output. Nevertheless, imagined speech is advantageous since it does not depend on any articulator movements that might become impaired or even lost throughout the stages of a neurodegenerative disease. In this study, we analyzed electrocortigraphy data recorded from 16 participants in response to 3 speech modes: performed, perceived (listening), and imagined speech. We used a linear model to detect speech events and examined the contributions of each frequency band, from delta to high gamma, given the speech mode and electrode location. For imagined speech detection, we observed a strong contribution of gamma bands in the motor cortex, whereas lower frequencies were more prominent in the temporal lobe, in particular of the left hemisphere. Based on the similarities in frequency patterns, we were able to transfer models between speech modes and participants with similar electrode locations.