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Background: A hyperactive default mode network (DMN) has been observed in people with major depressive disorder (MDD), and weak DMN suppression has been linked to depressive symptoms. However, whether dysregulation of the DMN contributes to blunted positive emotional experience in people with MDD is unclear. Methods: We recorded 128-channel electroencephalograms (EEGs) from 24 participants with MDD and 31 healthy controls in a resting state (RS) and an emotion-induction state (ES), in which participants engaged with emotionally positive pictures. We combined Granger causality analysis and data-driven decomposition to extract latent brain networks shared among states and groups, and we further evaluated their interactions across individuals. Results: We extracted 2 subnetworks. Subnetwork 1 represented a delta (8)-band (1~4 Hz) frontal network that was activated more in the ES than the RS (i.e., task-positive). Subnetwork 2 represented an alpha (a)-band (8~13 Hz) parietal network that was suppressed more in the ES than the RS (i.e., task-negative). These subnetworks were anticorrelated in both the healthy control and MDD groups, but with different sensitivities: for participants with MDD to achieve the same level of task-positive (subnetwork 1) activation as healthy controls, more suppression of task-negative (subnetwork 2) activation was necessary. Furthermore, the anticorrelation strength in participants with MDD correlated with the severity of 2 core MDD symptoms: anhedonia and rumination. Limitations: The sample size was small. Conclusion: Our findings revealed altered coordination between 2 functional networks in MDD and suggest that weak suppression of the task-negative [alpha]-band parietal network contributes to blunted positive emotional responses in adults with depression. The subnetworks identified here could be used for diagnosis or targeted for treatment in the future., Introduction Major depressive disorder (MDD) is characterized by a wide range of symptoms, including persistent and profound sadness, lack of mood reactivity, repetitive and passive thinking loops (depressive rumination), and [...]

Aims: Schizophrenia (SZ) is a brain disorder characterized by psychotic symptoms and cognitive dysfunction. Recently, irregularities in sharp‐wave ripples (SPW‐Rs) have been reported in SZ. As SPW‐Rs play a critical role in memory, their irregularities can cause psychotic symptoms and cognitive dysfunction in patients with SZ. In this study, we investigated the SPW‐Rs in human SZ. Methods: We measured whole‐brain activity using magnetoencephalography (MEG) in patients with SZ (n = 20) and sex‐ and age‐matched healthy participants (n = 20) during open‐eye rest. We identified SPW‐Rs and analyzed their occurrence and time‐frequency traits. Furthermore, we developed a novel multivariate analysis method, termed "ripple‐gedMEG" to extract the global features of SPW‐Rs. We also examined the association between SPW‐Rs and brain state transitions. The outcomes of these analyses were modeled to predict the positive and negative syndrome scale (PANSS) scores of SZ. Results: We found that SPW‐Rs in the SZ (1) occurred more frequently, (2) the delay of the coupling phase (3) appeared in different brain areas, (4) consisted of a less organized spatiotemporal pattern, and (5) were less involved in brain state transitions. Finally, some of the neural features associated with the SPW‐Rs were found to be PANSS‐positive, a pathological indicator of SZ. These results suggest that widespread but disorganized SPW‐Rs underlies the symptoms of SZ. Conclusion: We identified irregularities in SPW‐Rs in SZ and confirmed that their alternations were strongly associated with SZ neuropathology. These results suggest a new direction for human SZ research. [ABSTRACT FROM AUTHOR]

There has been tremendous progress in artificial neural networks (ANNs) over the past decade; however, the gap between ANNs and the biological brain as a learning device remains large. With the goal of closing this gap, this paper reviews learning mechanisms in the brain by focusing on three important issues in ANN research: efficiency, continuity, and generalization. We first discuss the method by which the brain utilizes a variety of self-organizing mechanisms to maximize learning efficiency, with a focus on the role of spontaneous activity of the brain in shaping synaptic connections to facilitate spatiotemporal learning and numerical processing. Then, we examined the neuronal mechanisms that enable lifelong continual learning, with a focus on memory replay during sleep and its implementation in brain-inspired ANNs. Finally, we explored the method by which the brain generalizes learned knowledge in new situations, particularly from the mathematical generalization perspective of topology. Besides a systematic comparison in learning mechanisms between the brain and ANNs, we propose "Mental Schema 2.0," a new computational property underlying the brain's unique learning ability that can be implemented in ANNs. [ABSTRACT FROM AUTHOR]

Creativity is defined by three key factors: novelty, feasibility and value. While many creativity tests focus primarily on novelty, they often neglect feasibility and value, thereby limiting their reflection of real‐world creativity. In this study, we employ GPT‐4, a large language model, to assess these three dimensions in a Japanese‐language Alternative Uses Test (AUT). Using a crowdsourced evaluation method, we acquire ground truth data for 30 question items and test various GPT prompt designs. Our findings show that asking for multiple responses in a single prompt, using an ‘explain first, rate later’ design, is both cost‐effective and accurate (r = .62, .59 and .33 for novelty, feasibility and value, respectively). Moreover, our method offers comparable accuracy to existing methods in assessing novelty, without the need for training data. We also evaluate additional models such as GPT‐4 Turbo, GPT‐4 Omni and Claude 3.5 Sonnet. Comparable performance across these models demonstrates the universal applicability of our prompt design. Our results contribute a straightforward platform for instant AUT evaluation and provide valuable ground truth data for future methodological research. [ABSTRACT FROM AUTHOR]

A new generalized multilinear regression model, termed the higher order partial least squares (HOPLS), is introduced with the aim to predict a tensor (multiway array) (\underline\bf Y) from a tensor (\underline\bf X) through projecting the data onto the latent space and performing regression on the corresponding latent variables. HOPLS differs substantially from other regression models in that it explains the data by a sum of orthogonal Tucker tensors, while the number of orthogonal loadings serves as a parameter to control model complexity and prevent overfitting. The low-dimensional latent space is optimized sequentially via a deflation operation, yielding the best joint subspace approximation for both (\underline\bf X) and (\underline\bf Y). Instead of decomposing (\underline\bf X) and (\underline\bf Y) individually, higher order singular value decomposition on a newly defined generalized cross-covariance tensor is employed to optimize the orthogonal loadings. A systematic comparison on both synthetic data and real-world decoding of 3D movement trajectories from electrocorticogram signals demonstrate the advantages of HOPLS over the existing methods in terms of better predictive ability, suitability to handle small sample sizes, and robustness to noise. [ABSTRACT FROM AUTHOR]

The acts of learning and memory are thought to emerge from the modifications of synaptic connections between neurons, as guided by sensory feedback during behavior. However, much is unknown about how such synaptic processes can sculpt and are sculpted by neuronal population dynamics and an interaction with the environment. Here, we embodied a simulated network, inspired by dissociated cortical neuronal cultures, with an artificial animal (an animat) through a sensory-motor loop consisting of structured stimuli, detailed activity metrics incorporating spatial information, and an adaptive training algorithm that takes advantage of spike timing dependent plasticity. By using our design, we demonstrated that the network was capable of learning associations between multiple sensory inputs and motor outputs, and the animat was able to adapt to a new sensory mapping to restore its goal behavior: move toward and stay within a user-defined area. We further showed that successful learning required proper selections of stimuli to encode sensory inputs and a variety of training stimuli with adaptive selection contingent on the animat's behavior. We also found that an individual network had the flexibility to achieve different multi-task goals, and the same goal behavior could be exhibited with different sets of network synaptic strengths. While lacking the characteristic layered structure of in vivo cortical tissue, the biologically inspired simulated networks could tune their activity in behaviorally relevant manners, demonstrating that leaky integrate-and- fire neural networks have an innate ability to process information. This closed-loop hybrid system is a useful tool to study the network properties intermediating synaptic plasticity and behavioral adaptation. The training algorithm provides a stepping stone towards designing future control systems, whether with artificial neural networks or biological animats themselves. [ABSTRACT FROM AUTHOR]

Mismatch negativity (MMN) is commonly recognized as a neural signal of prediction error evoked by deviants from the expected patterns of sensory input. Studies show that MMN diminishes when sequence patterns become more predictable over a longer timescale. This implies that MMN is composed of multiple subcomponents, each responding to different levels of temporal regularities. To probe the hypothesized subcomponents in MMN, we record human electroencephalography during an auditory local-global oddball paradigm where the tone-to-tone transition probability (local regularity) and the overall sequence probability (global regularity) are manipulated to control temporal predictabilities at two hierarchical levels. We find that the size of MMN is correlated with both probabilities and the spatiotemporal structure of MMN can be decomposed into two distinct subcomponents. Both subcomponents appear as negative waveforms, with one peaking early in the central-frontal area and the other late in a more frontal area. With a quantitative predictive coding model, we map the early and late subcomponents to the prediction errors that are tied to local and global regularities, respectively. Our study highlights the hierarchical complexity of MMN and offers an experimental and analytical platform for developing a multitiered neural marker applicable in clinical settings., Competing Interests: The authors declare no competing financial interests., (Copyright © 2024 Huang et al.)

Brain-machine interfaces (BMIs) employ the electrical activity generated by cortical neurons directly for controlling external devices and have been conceived as a means for restoring human cognitive or sensory-motor functions. The dominant approach in BMI research has been to decode motor variables based on single-unit activity (SUA). Unfortunately, this approach suffers from poor long-term stability and daily recalibration is normally required to maintain reliable performance. A possible alternative is BMIs based on electrocorticograms (ECoGs), which measure population activity and may provide more durable and stable recording. However, the level of long-term stability that ECoG-based decoding can offer remains unclear. Here we propose a novel ECoG-based decoding paradigm and show that we have successfully decoded hand positions and arm joint angles during an asynchronous food-reaching task in monkeys when explicit cues prompting the onset of movement were not required. Performance using our ECoG-based decoder was comparable to existing SUA-based systems while evincing far superior stability and durability. In addition, the same decoder could be used for months without any drift in accuracy or recalibration. These results were achieved by incorporating the spatio-spectro-temporal integration of activity across multiple cortical areas to compensate for the lower fidelity of ECoG signals. These results show the feasibility of high-performance, chronic and versatile ECoG-based neuroprosthetic devices for real-life applications. This new method provides a stable platform for investigating cortical correlates for understanding motor control, sensory perception, and high-level cognitive processes.

Here, we and others describe an unusual neurorobotic project, a merging of art and science called MEART, the semi-living artist. We built a pneumatically actuated robotic arm to create drawings, as controlled by a living network of neurons from rat cortex grown on a multi-electrode array (MEA). Such embodied cultured networks formed a real-time closed-loop system which could now behave and receive electrical stimulation as feedback on its behavior. We used MEART and simulated embodiments, or animats, to study the network mechanisms that produce adaptive, goal-directed behavior. This approach to neural interfacing will help instruct the design of other hybrid neural-robotic systems we call hybrots. The interfacing technologies and algorithms developed have potential applications in responsive deep brain stimulation systems and for motor prosthetics using sensory components. In a broader context, MEART educates the public about neuroscience, neural interfaces, and robotics. It has paved the way for critical discussions on the future of bio-art and of biotechnology.