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Emotion recognition based on physiological signals has been a hot topic and applied in many areas such as safe driving, health care and social security. In this paper, we present a comprehensive review on physiological signal-based emotion recognition, including emotion models, emotion elicitation methods, the published emotional physiological datasets, features, classifiers, and the whole framework for emotion recognition based on the physiological signals. A summary and comparation among the recent studies has been conducted, which reveals the current existing problems and the future work has been discussed.

EEG-based automatic emotion recognition can help brain-inspired robots in improving their interactions with humans. This paper presents a novel framework for emotion recognition using multi-channel electroencephalogram (EEG). The framework consists of a linear EEG mixing model and an emotion timing model. Our proposed framework considerably decomposes the EEG source signals from the collected EEG signals and improves classification accuracy by using the context correlations of the EEG feature sequences. Specially, Stack AutoEncoder (SAE) is used to build and solve the linear EEG mixing model and the emotion timing model is based on the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). The framework was implemented on the DEAP dataset for an emotion recognition experiment, where the mean accuracy of emotion recognition achieved 81.10% in valence and 74.38% in arousal, and the effectiveness of our framework was verified. Our framework exhibited a better performance in emotion recognition using multi-channel EEG than the compared conventional approaches in the experiments.

Neural communication networks form the fundamental basis for brain function. These communication networks are enabled by emitted ligands such as neurotransmitters, which activate receptor complexes to facilitate communication. Thus, neural communication is fundamentally dependent on the transcriptome. Here we develop NeuronChat, a method and package for the inference, visualization and analysis of neural-specific communication networks among pre-defined cell groups using single-cell expression data. We incorporate a manually curated molecular interaction database of neural signaling for both human and mouse, and benchmark NeuronChat on several published datasets to validate its ability in predicting neural connectivity. Then, we apply NeuronChat to three different neural tissue datasets to illustrate its functionalities in identifying interneural communication networks, revealing conserved or context-specific interactions across different biological contexts, and predicting communication pattern changes in diseased brains with autism spectrum disorder. Finally, we demonstrate NeuronChat can utilize spatial transcriptomics data to infer and visualize neural-specific cell-cell communication. Neurons communicate differently from non-neuronal cells. Here, authors present a method, NeuronChat, that utilizes scRNA-seq data and/or spatial transcriptomics to infer, visualize and analyze neural-specific cell-cell communication.

Emotion recognition and monitoring based on commonly used wearable devices can play an important role in psychological health monitoring and human-computer interaction. However, the existing methods cannot rely on the common smart bracelets or watches for emotion monitoring in daily life. To address this issue, our study proposes a method for emotional recognition using heart rate data from a wearable smart bracelet. A ‘neutral + target’ pair emotion stimulation experimental paradigm was presented, and a dataset of heart rate from 25 subjects was established, where neutral plus target emotion (neutral, happy, and sad) stimulation video pairs from China’s standard Emotional Video Stimuli materials (CEVS) were applied to the recruited subjects. Normalized features from the data of target emotions normalized by the baseline data of neutral mood were adopted. Emotion recognition experiment results approved the effectiveness of ‘neutral + target’ video pair simulation experimental paradigm, the baseline setting using neutral mood data, and the normalized features, as well as the classifiers of Adaboost and GBDT on this dataset. This method will promote the development of wearable consumer electronic devices for monitoring human emotional moods.

The hippocampal formation (HF) is well documented as having a feedforward, unidirectional circuit organization termed the trisynaptic pathway. This circuit organization exists along the septotemporal axis of the HF, but the circuit connectivity across septal to temporal regions is less well described. The emergence of viral genetic mapping techniques enhances our ability to determine the detailed complexity of HF circuitry. In earlier work, we mapped a subiculum (SUB) back projection to CA1 prompted by the discovery of theta wave back propagation from the SUB to CA1 and CA3. We reason that this circuitry may represent multiple extended noncanonical pathways involving the subicular complex and hippocampal subregions CA1 and CA3. In the present study, multiple retrograde viral tracing approaches produced robust mapping results, which supports this prediction. We find significant noncanonical synaptic inputs to dorsal hippocampal CA3 from ventral CA1 (vCA1), perirhinal cortex (Prh), and the subicular complex. Thus, CA1 inputs to CA3 run opposite the trisynaptic pathway and in a temporal to septal direction. Our retrograde viral tracing results are confirmed by anterograde-directed viral mapping of projections from input mapped regions to hippocampal dorsal CA3 (dCA3). We find that genetic inactivation of the projection of vCA1 to dCA3 impairs object-related spatial learning and memory but does not modulate anxiety-related behaviors. Our data provide a circuit foundation to explore novel functional roles contributed by these noncanonical hippocampal circuit connections to hippocampal circuit dynamics and learning and memory behaviors.

Sharing our feelings through content with images and short videos is one main way of expression on social networks. Visual content can affect people’s emotions, which makes the task of analyzing the sentimental information of visual content more and more concerned. Most of the current methods focus on how to improve the local emotional representations to get better performance of sentiment analysis and ignore the problem of how to perceive objects of different scales and different emotional intensity in complex scenes. In this paper, based on the alterable scale and multi-level local regional emotional affinity analysis under the global perspective, we propose a multi-level context pyramid network (MCPNet) for visual sentiment analysis by combining local and global representations to improve the classification performance. Firstly, Resnet101 is employed as backbone to obtain multi-level emotional representation representing different degrees of semantic information and detailed information. Next, the multi-scale adaptive context modules (MACM) are proposed to learn the sentiment correlation degree of different regions for different scale in the image, and to extract the multi-scale context features for each level deep representation. Finally, different levels of context features are combined to obtain the multi-cue sentimental feature for image sentiment classification. Extensive experimental results on seven commonly used visual sentiment datasets illustrate that our method outperforms the state-of-the-art methods, especially the accuracy on the FI dataset exceeds 90%.

The role of parvalbumin (PV)-positive interneurons in ocular dominance plasticity (ODP) has been a point of contention; here PV-positive cells are shown to initiate competitive periods of plasticity during the critical periods of eye development when ODP occurs, and transient reductions in inhibitory firing from PV-positive cells provides a return to normal firing rates in excitatory neurons, a key step in ODP progression. Influence of sensory experience on cortical microcircuitry It has long been known that early sensory experience can significantly affect the development and maturation of neural circuitry. One well-studied example of this is ocular dominance plasticity (ODP), in which loss of vision in one eye results in a reduction of cortical responsiveness to that eye. The mechanisms underlying the manifestation of ODP and the roles of inhibitory neurons in its expression have been a point of contention. Kuhlman et al . now show that parvalbumin-positive (PV + ) interneurons initiate competitive periods of plasticity during the critical periods of development when ODP occurs. Transient reductions in inhibitory firing from PV + cells provides for a return to normal firing rates in excitatory neurons, a key step in the progression of ODP in the adolescent cortex. Early sensory experience instructs the maturation of neural circuitry in the cortex 1 , 2 . This has been studied extensively in the primary visual cortex, in which loss of vision to one eye permanently degrades cortical responsiveness to that eye 3 , 4 , a phenomenon known as ocular dominance plasticity (ODP). Cortical inhibition mediates this process 4 , 5 , 6 , but the precise role of specific classes of inhibitory neurons in ODP is controversial. Here we report that evoked firing rates of binocular excitatory neurons in the primary visual cortex immediately drop by half when vision is restricted to one eye, but gradually return to normal over the following twenty-four hours, despite the fact that vision remains restricted to one eye. This restoration of binocular-like excitatory firing rates after monocular deprivation results from a rapid, although transient, reduction in the firing rates of fast-spiking, parvalbumin-positive (PV) interneurons, which in turn can be attributed to a decrease in local excitatory circuit input onto PV interneurons. This reduction in PV-cell-evoked responses after monocular lid suture is restricted to the critical period for ODP and appears to be necessary for subsequent shifts in excitatory ODP. Pharmacologically enhancing inhibition at the time of sight deprivation blocks ODP and, conversely, pharmacogenetic reduction of PV cell firing rates can extend the critical period for ODP. These findings define the microcircuit changes initiating competitive plasticity during critical periods of cortical development. Moreover, they show that the restoration of evoked firing rates of layer 2/3 pyramidal neurons by PV-specific disinhibition is a key step in the progression of ODP.

Loss of heterochromatin has been implicated as a cause of pre-mature aging and age-associated decline in organ functions in mammals; however, the specific cell types and gene loci affected by this type of epigenetic change have remained unclear. To address this knowledge gap, we probed chromatin accessibility at single-cell resolution in the brains, hearts, skeletal muscles, and bone marrows from young, middle-aged, and old mice, and assessed age-associated changes at 353,126 candidate cis -regulatory elements (cCREs) across 32 major cell types. Unexpectedly, we detected increased chromatin accessibility within specific heterochromatin domains in old mouse excitatory neurons. The gain of chromatin accessibility at these genomic loci was accompanied by the cell-type-specific loss of heterochromatin and activation of LINE1 elements. Immunostaining further confirmed the loss of the heterochromatin mark H3K9me3 in the excitatory neurons but not in inhibitory neurons or glial cells. Our results reveal the cell-type-specific changes in chromatin landscapes in old mice and shed light on the scope of heterochromatin loss in mammalian aging.

We show in this work that incorporating geometric features and geometry processing algorithms for mouse brain image registration broadens the applicability of registration algorithms and improves the registration accuracy of existing methods. We introduce the preprocessing and postprocessing steps in our proposed framework as RegBoost. We develop a method to align the axis of 3D image stacks by detecting the central planes that pass symmetrically through the image volumes. We then find geometric contours by defining external and internal structures to facilitate image correspondences. We establish Dirichlet boundary conditions at these correspondences and find the displacement map throughout the volume using Laplacian interpolation. We discuss the challenges in our standalone framework and demonstrate how our new approaches can improve the results of existing image registration methods. We expect our new approach and algorithms will have critical applications in brain mapping projects. [Display omitted] •Geometric features for registration improve upon existing registration results.•Using 3D over 2D operators ensures better results and continuities across 3D data.•Fully automated registration framework aligns input data to an annotated atlas.