Multi-Level Processes and Retina–Brain Pathways of Photic Regulation of Mood

Light exerts powerful biological effects on mood regulation. Whereas the source of photic information affecting mood is well established at least via intrinsically photosensitive retinal ganglion cells (ipRGCs) secreting the melanopsin photopigment, the precise circuits that mediate the impact of light on depressive behaviors are not well understood. This review proposes two distinct retina–brain pathways of light effects on mood: (i) a suprachiasmatic nucleus (SCN)-dependent pathway with light effect on mood via the synchronization of biological rhythms, and (ii) a SCN-independent pathway with light effects on mood through modulation of the homeostatic process of sleep, alertness and emotion regulation: (1) light directly inhibits brain areas promoting sleep such as the ventrolateral preoptic nucleus (VLPO), and activates numerous brain areas involved in alertness such as, monoaminergic areas, thalamic regions and hypothalamic regions including orexin areas; (2) moreover, light seems to modulate mood through orexin-, serotonin- and dopamine-dependent pathways; (3) in addition, light activates brain emotional processing areas including the amygdala, the nucleus accumbens, the perihabenular nucleus, the left hippocampus and pathways such as the retina–ventral lateral geniculate nucleus and intergeniculate leaflet–lateral habenula pathway. This work synthetizes new insights into the neural basis required for light influence mood

White Matter Brain Structure Predicts Language Performance and Learning Success

Individual differences in the ability to deal with language have long been discussed. The neural basis of these, however, is yet unknown. Here we investigated the relationship between long-range white matter connectivity of the brain, as revealed by diffusion tractography, and the ability to process syntactically complex sentences in the participants' native language as well as the improvement thereof by multi-day training. We identified specific network motifs that indeed related white matter tractography to individual language processing performance. First, for two such motifs, one in the left and one in the right hemisphere, their individual prevalence significantly predicted the individual language performance suggesting a predisposition for the individual ability to process syntactically complex sentences, which manifests itself in the white matter brain structure. Both motifs comprise a number of cortical regions, but seem to be dominated by areas known for the involvement in working memory rather than the classical language network itself. Second, we identified another left hemispheric network motif, whose change of prevalence over the training period significantly correlated with the individual change in performance, thus reflecting training induced white matter plasticity. This motif comprises diverse cortical areas including regions known for their involvement in language processing, working memory and motor functions. The present findings suggest that individual differences in language processing and learning can be explained, in part, by individual differences in the brain's white matter structure. Brain structure may be a crucial factor to be considered when discussing variations in human cognitive performance, more generally.

Dynamical Mechanism of Sampling-Based Probabilistic Inference under Probabilistic Population Codes

Animals make efficient probabilistic inferences based on uncertain and noisy information from the outside environment. It is known that probabilistic population codes, which have been proposed as a neural basis for encoding probability distributions, allow general neural networks (NNs) to perform near-optimal point estimation. However, the mechanism of sampling-based probabilistic inference has not been clarified. In this study, we trained two types of artificial NNs, feedforward NN (FFNN) and recurrent NN (RNN), to perform sampling-based probabilistic inference. Then we analyzed and compared their mechanisms of sampling. We found that sampling in RNN was performed by a mechanism that efficiently uses the properties of dynamical systems, unlike FFNN. In addition, we found that sampling in RNNs acted as an inductive bias, enabling a more accurate estimation than in maximum a posteriori estimation. These results provide important arguments for discussing the relationship between dynamical systems and information processing in NNs.

Neuromuscular Stimulation as an Intervention Tool for Recovery from Upper Limb Paresis after Stroke and the Neural Basis

Neuromodulators at the periphery, such as neuromuscular electrical stimulation (NMES), have been developed as add-on tools to regain upper extremity (UE) paresis after stroke, but this recovery has often been limited. To overcome these limits, novel strategies to enhance neural reorganization and functional recovery are needed. This review aims to discuss possible strategies for enhancing the benefits of NMES. To date, NMES studies have involved some therapeutic concerns that have been addressed under various conditions, such as the time of post-stroke and stroke severity and/or with heterogeneous stimulation parameters, such as target muscles, doses or durations of treatment and outcome measures. We began by identifying factors sensitive to NMES benefits among heterogeneous conditions and parameters, based on the “progress rate (PR)”, defined as the gains in UE function scores per intervention duration. Our analysis disclosed that the benefits might be affected by the target muscles, stroke severity and time period after stroke. Likewise, repetitive peripheral neuromuscular magnetic stimulation (rPMS) is expected to facilitate motor recovery, as already demonstrated by a successful study. In parallel, our efforts should be devoted to further understanding the precise neural mechanism of how neuromodulators make UE function recovery occur, thereby leading to overcoming the limits. In this study, we discuss the possible neural mechanisms.

Mid-level feature differences underlie early animacy and object size distinctions: Evidence from EEG decoding

Responses to visually-presented objects along the cortical surface of the human brain have a large-scale organization reflecting the broad categorical divisions of animacy and object size. Mounting evidence indicates that this topographical organization is driven by differences between objects in mid-level perceptual features. With regard to the timing of neural responses, images of objects quickly evoke neural responses with decodable information about animacy and object size, but are mid-level features sufficient to evoke these rapid neural responses? Or is slower iterative neural processing required to untangle information about animacy and object size from mid-level features? To answer this question, we used electroencephalography(EEG) to measure human neural responses to images of objects and their texform counterparts - unrecognizable images which preserve some mid-level feature information about texture and coarse form. We found that texform images evoked neural responses with early decodable information about both animacy and real-world size, as early as responses evoked by original images. Further, successful cross-decoding indicates that both texform and original images evoke information about animacy and size through a common underlying neural basis. Broadly, these results indicate that the visual system contains a mid-level feature bank carrying linearly decodable information on animacy and size, which can be rapidly activated without requiring explicit recognition or protracted temporal processing.

Brain 5-HT2A receptor binding and its neural network related to behavioral inhibition system

The tendency to avoid punishment, called behavioral inhibition system, is an essential aspect of motivational behavior. Behavioral inhibition system is related to negative affect, such as anxiety, depression and pain, but its neural basis has not yet been clarified. To clarify the association between individual variations in behavioral inhibition system and brain 5-HT2A receptor availability and specify which brain networks were involved in healthy male subjects, using [18F]altanserin positron emission tomography and resting-state functional magnetic resonance imaging. Behavioral inhibition system score negatively correlated with 5-HT2A receptor availability in anterior cingulate cortex. A statistical model indicated that the behavioral inhibition system score was associated with 5-HT2A receptor availability, which was mediated by the functional connectivity between anterior cingulate cortex and left middle frontal gyrus, both of which involved in the cognitive control of negative information processing. Individuals with high behavioral inhibition system displays low 5-HT2A receptor availability in anterior cingulate cortex and this cognitive control network links with prefrontal-cingulate integrity. These findings have implications for underlying the serotonergic basis of physiologies in aversion.

Human EEG recordings for 1,854 concepts presented in rapid serial visual presentation streams

The neural basis of object recognition and semantic knowledge has been extensively studied but the high dimensionality of object space makes it challenging to develop overarching theories on how the brain organises object knowledge. To help understand how the brain allows us to recognise, categorise, and represent objects and object categories, there is a growing interest in using large-scale image databases for neuroimaging experiments. In the current paper, we present THINGS-EEG, a dataset containing human electroencephalography responses from 50 subjects to 1,854 object concepts and 22,248 images in the THINGS stimulus set, a manually curated and high-quality image database that was specifically designed for studying human vision. The THINGS-EEG dataset provides neuroimaging recordings to a systematic collection of objects and concepts and can therefore support a wide array of research to understand visual object processing in the human brain.

Electrophysiological Correlates of Shyness Affected by Facial Attractiveness

Previous neurological studies of shyness have focused on the hemispheric asymmetry of alpha spectral power. To the best of our knowledge, few studies have focused on the interaction between different frequencies bands in the brain of shyness. Additionally, shy individuals are even shyer when confronted with a group of people they consider superior to them. This study aimed to reveal the neural basis of shy individuals using the delta-beta correlation. Further, it aimed to investigate the effect of evaluators’ facial attractiveness on the delta-beta correlation of shyness during the speech anticipation phase. We recorded electroencephalogram (EEG) activity of 94 participants during rest and anticipation of the public speaking phase. Moreover, during the speech anticipation phase, participants were presented with high or low facial attractiveness. The results showed that, as predicted, the delta-beta correlation in the frontal region was more robust for high shyness than for low shyness during the speech anticipation phase. However, no significant differences were observed in the delta-beta correlation during the baseline phase. Further exploration found that the delta-beta correlation was more robust for high facial attractiveness than low facial attractiveness in the high shyness group. However, no significant difference was found in the low-shyness group. This study suggests that a stronger delta-beta correlation might be the neural basis for shy individuals. Moreover, high facial attractiveness might enhance the delta-beta correlation of high shyness in anticipation of public speaking.