Behavior can be decoded across the cortex when individual differences are considered.

Group-level analyses have typically associated behavioral signatures with a constrained set of brain areas. Here we show that two behavioral metrics - reaction time (RT) and confidence - can be decoded across the cortex when each individual is considered separately. Subjects (N=50) completed a perceptual decision-making task with confidence. We built models decoding trial-level RT and confidence separately for each subject using the activation patterns in one brain area at a time after splitting the entire cortex into 200 regions of interest (ROIs). At the group level, we replicated previous results by showing that both RT and confidence could be decoded from a small number of ROIs (12.0% and 3.5%, respectively). Critically, at the level of the individual, both RT and confidence could be decoded from most brain regions even after Bonferroni correction (90.0% and 72.5%, respectively). Surprisingly, we observed that many brain regions exhibited opposite brain-behavior relationships across individuals, such that, for example, higher activations predicted fast RTs in some subjects but slow RTs in others. These results were further replicated in a second dataset. Lastly, we developed a simple test to determine the robustness of decoding performance, which showed that several hundred trials per subject are required for robust decoding. These results show that behavioral signatures can be decoded from a much broader range of cortical areas than previously recognized and suggest the need to study the brain-behavior relationship at both the group and the individual level.

Disruption of alpha oscillation propagation in patients with schizophrenia.

OBJECTIVE: Propagation of electroencephalogram (EEG) oscillations, often referred to as traveling waves, reflects the role of brain oscillations in neural information transmission. This propagation can be distorted by brain disorders such as schizophrenia that features disconnection of neural information transmission (i.e., disconnection syndrome). However, this possibility of the disruption of EEG oscillation propagation in patients with schizophrenia remains largely unexplored. METHODS: Using a publicly shared dataset (N = 19 and 24; patients with schizophrenia and healthy controls, respectively), we investigated EEG oscillation propagation by analyzing the local phase gradients (LPG) of alpha (8-12 Hz) oscillations in both healthy participants and patients with schizophrenia. RESULTS: Our results showed significant directionality in the propagation of alpha oscillations in healthy participants. Specifically, alpha oscillations propagated in an anterior-to-posterior direction along mid-line and a posterior-to-anterior direction laterally. In patients with schizophrenia, some of alpha oscillation propagation were notably disrupted, particularly in the central midline area where alpha oscillations propagated from anterior to posterior areas. CONCLUSION: Our finding lends support to the hypothesis of a disconnection syndrome in schizophrenia, underscoring a disruption in the anterior-to-posterior propagation of alpha oscillations. SIGNIFICANCE: This study identified disruption of alpha oscillation propagation observed in scalp EEG as a biomarker for schizophrenia.

A comprehensive dataset for home appliance control using ERP-based BCIs with the application of inter-subject transfer learning.

Brain-computer interfaces (BCIs) have a potential to revolutionize human-computer interaction by enabling direct links between the brain and computer systems. Recent studies are increasingly focusing on practical applications of BCIs-e.g., home appliance control just by thoughts. One of the non-invasive BCIs using electroencephalography (EEG) capitalizes on event-related potentials (ERPs) in response to target stimuli and have shown promise in controlling home appliance. In this paper, we present a comprehensive dataset of online ERP-based BCIs for controlling various home appliances in diverse stimulus presentation environments. We collected online BCI data from a total of 84 subjects among whom 60 subjects controlled three types of appliances (TV: 30, door lock: 15, and electric light: 15) with 4 functions per appliance, 14 subjects controlled a Bluetooth speaker with 6 functions via an LCD monitor, and 10 subjects controlled air conditioner with 4 functions via augmented reality (AR). Using the dataset, we aimed to address the issue of inter-subject variability in ERPs by employing the transfer learning in two different approaches. The first approach, "within-paradigm transfer learning," aimed to generalize the model within the same paradigm of stimulus presentation. The second approach, "cross-paradigm transfer learning," involved extending the model from a 4-class LCD environment to different paradigms. The results demonstrated that transfer learning can effectively enhance the generalizability of BCIs based on ERP across different subjects and environments.

Long-lasting forms of plasticity through patterned ultrasound-induced brainwave entrainment.

Achieving long-lasting neuronal modulation with low-intensity, low-frequency ultrasound is challenging. Here, we devised theta burst ultrasound stimulation (TBUS) with gamma bursts for brain entrainment and modulation of neuronal plasticity in the mouse motor cortex. We demonstrate that two types of TBUS, intermittent and continuous TBUS, induce bidirectional long-term potentiation or depression-like plasticity, respectively, as evidenced by changes in motor-evoked potentials. These effects depended on molecular pathways associated with long-term plasticity, including N-methyl-d-aspartate receptor and brain-derived neurotrophic factor/tropomyosin receptor kinase B activation, as well as de novo protein synthesis. Notably, bestrophin-1 and transient receptor potential ankyrin 1 play important roles in these enduring effects. Moreover, pretraining TBUS enhances the acquisition of previously unidentified motor skills. Our study unveils a promising protocol for ultrasound neuromodulation, enabling noninvasive and sustained modulation of brain function.

Brain-computer interface in critical care and rehabilitation.

This comprehensive review explores the broad landscape of brain-computer interface (BCI) technology and its potential use in intensive care units (ICUs), particularly for patients with motor impairments such as quadriplegia or severe brain injury. By employing brain signals from various sensing techniques, BCIs offer enhanced communication and motor rehabilitation strategies for patients. This review underscores the concept and efficacy of noninvasive, electroencephalogram-based BCIs in facilitating both communicative interactions and motor function recovery. Additionally, it highlights the current research gap in intuitive "stop" mechanisms within motor rehabilitation protocols, emphasizing the need for advancements that prioritize patient safety and individualized responsiveness. Furthermore, it advocates for more focused research that considers the unique requirements of ICU environments to address the challenges arising from patient variability, fatigue, and limited applicability of current BCI systems outside of experimental settings.

Differential Formation of Motor Cortical Dynamics during Movement Preparation According to the Predictability of Go Timing.

The motor cortex not only executes but also prepares movement, as motor cortical neurons exhibit preparatory activity that predicts upcoming movements. In movement preparation, animals adopt different strategies in response to uncertainties existing in nature such as the unknown timing of when a predator will attack-an environmental cue informing "go." However, how motor cortical neurons cope with such uncertainties is less understood. In this study, we aim to investigate whether and how preparatory activity is altered depending on the predictability of "go" timing. We analyze firing activities of the anterior lateral motor cortex in male mice during two auditory delayed-response tasks each with predictable or unpredictable go timing. When go timing is unpredictable, preparatory activities immediately reach and stay in a neural state capable of producing movement anytime to a sudden go cue. When go timing is predictable, preparation activity reaches the movement-producible state more gradually, to secure more accurate decisions. Surprisingly, this preparation process entails a longer reaction time. We find that as preparatory activity increases in accuracy, it takes longer for a neural state to transition from the end of preparation to the start of movement. Our results suggest that the motor cortex fine-tunes preparatory activity for more accurate movement using the predictability of go timing.

Multiple brain activation patterns for the same task.

Meaningful variation in internal states that impacts cognition and behavior remains challenging to discover and characterize. Here we leveraged trial-to-trial fluctuations in the brain-wide signal recorded using functional MRI to test if distinct sets of brain regions are activated on different trials when accomplishing the same task. Across three different perceptual decision-making experiments, we estimated the brain activations for each trial. We then clustered the trials based on their similarity using modularity-maximization, a data-driven classification method. In each experiment, we found multiple distinct but stable subtypes of trials, suggesting that the same task can be accomplished in the presence of widely varying brain activation patterns. Surprisingly, in all experiments, one of the subtypes exhibited strong activation in the default mode network, which is typically thought to decrease in activity during tasks that require externally focused attention. The remaining subtypes were characterized by activations in different task-positive areas. The default mode network subtype was characterized by behavioral signatures that were similar to the other subtypes exhibiting activation with task-positive regions. Finally, in a fourth experiment, we tested whether multiple activation patterns would also appear for a qualitatively different, working memory task. We again found multiple subtypes of trials with differential activation in frontoparietal control, dorsal attention, and ventral attention networks. Overall, these findings demonstrate that the same cognitive tasks are accomplished through multiple brain activation patterns.

Design of auditory P300-based brain-computer interfaces with a single auditory channel and no visual support.

Non-invasive brain-computer interfaces (BCIs) based on an event-related potential (ERP) component, P300, elicited via the oddball paradigm, have been extensively developed to enable device control and communication. While most P300-based BCIs employ visual stimuli in the oddball paradigm, auditory P300-based BCIs also need to be developed for users with unreliable gaze control or limited visual processing. Specifically, auditory BCIs without additional visual support or multi-channel sound sources can broaden the application areas of BCIs. This study aimed to design optimal stimuli for auditory BCIs among artificial (e.g., beep) and natural (e.g., human voice and animal sounds) sounds in such circumstances. In addition, it aimed to investigate differences between auditory and visual stimulations for online P300-based BCIs. As a result, natural sounds led to both higher online BCI performance and larger differences in ERP amplitudes between the target and non-target compared to artificial sounds. However, no single type of sound offered the best performance for all subjects; rather, each subject indicated different preferences between the human voice and animal sound. In line with previous reports, visual stimuli yielded higher BCI performance (average 77.56%) than auditory counterparts (average 54.67%). In addition, spatiotemporal patterns of the differences in ERP amplitudes between target and non-target were more dynamic with visual stimuli than with auditory stimuli. The results suggest that selecting a natural auditory stimulus optimal for individual users as well as making differences in ERP amplitudes between target and non-target stimuli more dynamic may further improve auditory P300-based BCIs. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-022-09901-3.

Bias in Prestimulus Motor Cortical Activity Determines Decision-making Error in Rodents.

Decision-making is a complex process that involves the integration and interpretation of sensory information to guide actions. The rodent motor cortex, which is generally involved in motor planning and execution, also plays a critical role in decision-making processes. In perceptual delayed-response tasks, the rodent motor cortex can represent sensory cues, as well as the decision of where to move. However, it remains unclear whether erroneous decisions arise from incorrect encoding of sensory information or improper utilization of the collected sensory information in the motor cortex. In this study, we analyzed the rodent anterior lateral motor cortex (ALM) while the mice performed perceptual delayed-response tasks. We divided population activities into sensory and choice signals to separately examine the encoding and utilization of sensory information. We found that the encoding of sensory information in the error trials was similar to that in the hit trials, whereas choice signals evolved differently between the error and hit trials. In error trials, choice signals displayed an offset in the opposite direction of instructed licking even before stimulus presentation, and this tendency gradually increased after stimulus onset, leading to incorrect licking. These findings suggest that decision errors are caused by biases in choice-related activities rather than by incorrect sensory encoding. Our study elaborates on the understanding of decision-making processes by providing neural substrates for erroneous decisions.

Quantifying the contribution of subject and group factors in brain activation.

Research in neuroscience often assumes universal neural mechanisms, but increasing evidence points toward sizeable individual differences in brain activations. What remains unclear is the extent of the idiosyncrasy and whether different types of analyses are associated with different levels of idiosyncrasy. Here we develop a new method for addressing these questions. The method consists of computing the within-subject reliability and subject-to-group similarity of brain activations and submitting these values to a computational model that quantifies the relative strength of group- and subject-level factors. We apply this method to a perceptual decision-making task (n = 50) and find that activations related to task, reaction time, and confidence are influenced equally strongly by group- and subject-level factors. Both group- and subject-level factors are dwarfed by a noise factor, though higher levels of smoothing increases their contributions relative to noise. Overall, our method allows for the quantification of group- and subject-level factors of brain activations and thus provides a more detailed understanding of the idiosyncrasy levels in brain activations.

Individual differences in gaze-cuing effect are associated with facial emotion recognition and social conformity.

Spontaneous gaze following and the concomitant joint attention enable us to share representations of the world with others, which forms a foundation of a broad range of social cognitive processes. Although this form of social orienting has long been suggested as a critical starting point for the development of social and communicative behavior, there is limited evidence directly linking it to higher-level social cognitive processes among healthy adults. Here, using a gaze-cuing paradigm, we examined whether individual differences in gaze following tendency predict higher-order social cognition and behavior among healthy adults. We found that individuals who showed greater gaze-cuing effect performed better in recognizing others' emotion and had greater tendency to conform with group opinion. These findings provide empirical evidence supporting the fundamental role of low-level socio-attentional processes in human sociality.

Robust and efficient representations of dynamic stimuli in hierarchical neural networks via temporal smoothing.

Introduction: Efficient coding that minimizes informational redundancy of neural representations is a widely accepted neural coding principle. Despite the benefit, maximizing efficiency in neural coding can make neural representation vulnerable to random noise. One way to achieve robustness against random noise is smoothening neural responses. However, it is not clear whether the smoothness of neural responses can hold robust neural representations when dynamic stimuli are processed through a hierarchical brain structure, in which not only random noise but also systematic error due to temporal lag can be induced. Methods: In the present study, we showed that smoothness via spatio-temporally efficient coding can achieve both efficiency and robustness by effectively dealing with noise and neural delay in the visual hierarchy when processing dynamic visual stimuli. Results: The simulation results demonstrated that a hierarchical neural network whose bidirectional synaptic connections were learned through spatio-temporally efficient coding with natural scenes could elicit neural responses to visual moving bars similar to those to static bars with the identical position and orientation, indicating robust neural responses against erroneous neural information. It implies that spatio-temporally efficient coding preserves the structure of visual environments locally in the neural responses of hierarchical structures. Discussion: The present results suggest the importance of a balance between efficiency and robustness in neural coding for visual processing of dynamic stimuli across hierarchical brain structures.

Modeling Long-term Spike Frequency Adaptation in SA-I Afferent Neurons Using an Izhikevich-based Biological Neuron Model.

To develop a biomimetic artificial tactile sensing system capable of detecting sustained mechanical touch, we propose a novel biological neuron model (BNM) for slowly adapting type I (SA-I) afferent neurons. The proposed BNM is designed by modifying the Izhikevich model to incorporate long-term spike frequency adaptation. Adjusting the parameters renders the Izhikevich model describing various neuronal firing patterns. We also search for optimal parameter values for the proposed BNM to describe firing patterns of biological SA-I afferent neurons in response to sustained pressure longer than 1-second. We obtain the firing data of SA-I afferent neurons for six different mechanical pressure ranging from 0.1 mN to 300 mN from the ex-vivo experiment on SA-I afferent neurons in rodents. Upon finding the optimal parameters, we generate spike trains using the proposed BNM and compare the resulting spike trains to those of biological SA-I afferent neurons using the spike distance metrics. We verify that the proposed BNM can generate spike trains showing long-term adaptation, which is not achievable by other conventional models. Our new model may offer an essential function to artificial tactile sensing technology to perceive sustained mechanical touch.

Cortical representation of musical pitch in event-related potentials.

Neural coding of auditory stimulus frequency is well-documented; however, the cortical signals and perceptual correlates of pitch have not yet been comprehensively investigated. This study examined the temporal patterns of event-related potentials (ERP) in response to single tones of pitch chroma, with an assumption that these patterns would be more prominent in musically-trained individuals than in non-musically-trained individuals. Participants with and without musical training (N = 20) were presented with seven notes on the C major scale (C4, D4, E4, F4, G4, A4, and B4), and whole-brain activities were recorded. A linear regression analysis between the ERP amplitude and the seven notes showed that the ERP amplitude increased or decreased as the frequency of the pitch increased. Remarkably, these linear correlations were anti-symmetric between the hemispheres. Specifically, we found that ERP amplitudes of the left and right frontotemporal areas decreased and increased, respectively, as the pitch frequency increased. Although linear slopes were significant in both groups, the musically-trained group exhibited marginally steeper slope, and their ERP amplitudes were most discriminant for frequency of tone of pitch at earlier latency than in the non-musically-trained group (~ 460 ms vs ~ 630 ms after stimulus onset). Thus, the ERP amplitudes in frontotemporal areas varied according to the pitch frequency, with the musically-trained participants demonstrating a wider range of amplitudes and inter-hemispheric anti-symmetric patterns. Our findings may provide new insights on cortical processing of musical pitch, revealing anti-symmetric processing of musical pitch between hemispheres, which appears to be more pronounced in musically-trained people. Supplementary Information: The online version contains supplementary material available at 10.1007/s13534-023-00274-y.

Breakdown of long-range spatial correlations of infraslow amplitude fluctuations of EEG oscillations in patients with current and past major depressive disorder.

Introduction: Identifying biomarkers for depression from brain activity is important for the diagnosis and treatment of depression disorders. We investigated spatial correlations of the amplitude fluctuations of electroencephalography (EEG) oscillations as a potential biomarker of depression. The amplitude fluctuations of EEG oscillations intrinsically reveal both temporal and spatial correlations, indicating rapid and functional organization of the brain networks. Amid these correlations, long-range temporal correlations are reportedly impaired in patients with depression, exhibiting amplitude fluctuations closer to a random process. Based on this occurrence, we hypothesized that the spatial correlations of amplitude fluctuations would also be altered by depression. Methods: In the present study, we extracted the amplitude fluctuations of EEG oscillations by filtering them through infraslow frequency band (0.05-0.1 Hz). Results: We found that the amplitude fluctuations of theta oscillations during eye-closed rest depicted lower levels of spatial correlation in patients with major depressive disorder (MDD) compared to control individuals. This breakdown of spatial correlations was most prominent in the left fronto - temporal network, specifically in patients with current MDD rather than in those with past MDD. We also found that the amplitude fluctuations of alpha oscillations during eye-open rest exhibited lower levels of spatial correlation in patients with past MDD compared to control individuals or patients with current MDD. Discussion: Our results suggest that breakdown of long-range spatial correlations may offer a biomarker for the diagnosis of depression (current MDD), as well as the tracking of the recovery from depression (past MDD).

Decoding Imagined Musical Pitch From Human Scalp Electroencephalograms.

Brain-computer interfaces (BCIs) can restore impaired cognitive functions in people with neurological disorders such as stroke. Musical ability is a cognitive function that is correlated with non-musical cognitive functions, and restoring it can enhance other cognitive functions. Pitch sense is the most relevant function to musical ability according to previous studies of amusia, and thus decoding pitch information is crucial for BCIs to be able to restore musical ability. This study evaluated the feasibility of decoding pitch imagery information directly from human electroencephalography (EEG). Twenty participants performed a random imagery task with seven musical pitches (C4-B4). We used two approaches to explore EEG features of pitch imagery: multiband spectral power at individual channels (IC) and differences between bilaterally symmetric channels (DC). The selected spectral power features revealed remarkable contrasts between left and right hemispheres, low- (< 13 Hz) and high-frequency ( 13 Hz) bands, and frontal and parietal areas. We classified two EEG feature sets, IC and DC, into seven pitch classes using five types of classifiers. The best classification performance for seven pitches was obtained using IC and multiclass Support Vector Machine with an average accuracy of 35.68±7.47% (max. 50%) and an information transfer rate (ITR) of 0.37±0.22 bits/sec. When grouping the pitches to vary the number of classes (K = 2-6), the ITR was similar across K and feature sets, suggesting the efficiency of DC. This study demonstrates for the first time the feasibility of decoding imagined musical pitch directly from human EEG.

Differential Encoding of Trace and Delay Fear Memory in the Entorhinal Cortex.

Trace fear conditioning is characterized by a stimulus-free trace interval (TI) between the conditioned stimulus (CS) and the unconditioned stimulus (US), which requires an array of brain structures to support the formation and storage of associative memory. The entorhinal cortex (EC) has been proposed to provide essential neural code for resolving temporal discontinuity in conjunction with the hippocampus. However, how the CS and TI are encoded at the neuronal level in the EC is not clear. In Exp. 1, we tested the effect of bilateral pre-training electrolytic lesions of EC on trace vs. delay fear conditioning using rats as subjects. We found that the lesions impaired the acquisition of trace but not delay fear conditioning confirming that EC is a critical brain area for trace fear memory formation. In Exp. 2, single-unit activities from EC were recorded during the pre-training baseline and post-training retention sessions following trace or delay conditioning. The recording results showed that a significant proportion of the EC neurons modulated their firing during TI after the trace conditioning, but not after the delay fear conditioning. Further analysis revealed that the majority of modulated units decreased the firing rate during the TI or the CS. Taken together, these results suggest that EC critically contributes to trace fear conditioning by modulating neuronal activity during the TI to facilitate the association between the CS and US across a temporal gap.

Effective Blue Light-Absorbing AuAg Nanoparticles in InP Quantum Dots-Based Color Conversion.

In typical color-by-blue mode-based quantum dot (QD) display devices, only part of the blue excitation light is absorbed by QD emitters, thus it is accompanied by the leakage of blue light through the devices. To address this issue, we offer, for the first time, the applicability of AuAg alloy nanoparticles (NPs) as effective blue light absorbers in InP QD-based color-by-blue platforms. For this, high-quality fluorescent green and red InP QDs with a double shell scheme of ZnSe/ZnS were synthesized and embedded in a transparent polymer film. Separately, a series of Au/Ag ratio-varied AuAg NPs with tunable plasmonic absorption peaks were synthesized. Among them, AuAg NPs possessing the most appropriate absorption peak with respect to spectral overlap with blue emission are chosen for the subsequent preparation of AuAg NP polymeric films with varied NP concentrations. A stack of AuAg NP polymeric film on top of InP QD film is then placed remotely on a blue light-emitting diode, successfully resulting in systematically progressive suppression of blue light leakage with increasing AuAg NP concentration. Furthermore, the beneficial function of the AuAg NP polymeric overlayer in mitigating undesirable QD excitation upon exposure to ambient lights was further examined.

In vivo direct imaging of neuronal activity at high temporospatial resolution.

There has been a long-standing demand for noninvasive neuroimaging methods that can detect neuronal activity at both high temporal and high spatial resolution. We present a two-dimensional fast line-scan approach that enables direct imaging of neuronal activity with millisecond precision while retaining the high spatial resolution of magnetic resonance imaging (MRI). This approach was demonstrated through in vivo mouse brain imaging at 9.4 tesla during electrical whisker-pad stimulation. In vivo spike recording and optogenetics confirmed the high correlation of the observed MRI signal with neural activity. It also captured the sequential and laminar-specific propagation of neuronal activity along the thalamocortical pathway. This high-resolution, direct imaging of neuronal activity will open up new avenues in brain science by providing a deeper understanding of the brain's functional organization, including the temporospatial dynamics of neural networks.

Edible algae (Ecklonia cava) bioprocessed with mycelia of shiitake (Lentinula edodes) mushrooms in liquid culture and its isolated fractions protect mice against allergic asthma.

BACKGROUND: Ecklonia cava is an edible marine brown alga harvested from the ocean that is widely consumed in Asian countries as a health-promoting medicinal food The objective of the present study is to evaluate the anti-asthma mechanism of a new functional food produced by bioprocessing edible algae Ecklonia cava and shiitake Lentinula edodes mushroom mycelia and isolated fractions. METHODS: We used as series of methods, including high performance liquid chromatography, gas chromatography, cell assays, and an in vivo mouse assay to evaluate the asthma-inhibitory effect of Ecklonia cava bioprocessed (fermented) with Lentinula edodes shiitake mushroom mycelium and its isolated fractions in mast cells and in orally fed mice. RESULTS: The treatments inhibited the degranulation of RBL-2H3 cells and immunoglobulin E (IgE) production, suggesting anti-asthma effects in vitro. The in vitro anti-asthma effects in cells were confirmed in mice following the induction of asthma by alumina and chicken egg ovalbumin (OVA). Oral administration of the bioprocessed Ecklonia cava and purified fractions suppressed the induction of asthma and was accompanied by the inhibition of inflammation- and immune-related substances, including eotaxin; thymic stromal lymphopoietin (TSLP); OVA-specific IgE; leukotriene C4 (LTC4); prostaglandin D2 (PGD2); and vascular cell adhesion molecule-1 (VCAM-1) in bronchoalveolar lavage fluid (BALF) and other fluids and organs. Th2 cytokines were reduced and Th1 cytokines were restored in serum, suggesting the asthma-induced inhibitory effect is regulated by the balance of the Th1/Th2 immune response. Serum levels of IL-10, a regulatory T cell (Treg) cytokine, were increased, further favoring reduced inflammation. Histology of lung tissues revealed that the treatment also reversed the thickening of the airway wall and the contraction and infiltration of bronchial and blood vessels and perialveolar inflammatory cells. The bioprocessed Ecklonia cava/mushroom mycelia new functional food showed the highest inhibition as compared with commercial algae and the fractions isolated from the bioprocessed product. CONCLUSIONS: The in vitro cell and in vivo mouse assays demonstrate the potential value of the new bioprocessed formulation as an anti-inflammatory and anti-allergic combination of natural compounds against allergic asthma and might also ameliorate allergic manifestations of foods, drugs, and viral infections.