A neurofunctional signature of subjective disgust generalizes to oral distaste and socio-moral contexts.

While disgust originates in the hard-wired mammalian distaste response, the conscious experience of disgust in humans strongly depends on subjective appraisal and may even extend to socio-moral contexts. Here, in a series of studies, we combined functional magnetic resonance imaging with machine-learning-based predictive modelling to establish a comprehensive neurobiological model of subjective disgust. The developed neurofunctional signature accurately predicted momentary self-reported subjective disgust across discovery (n = 78) and pre-registered validation (n = 30) cohorts and generalized across core disgust (n = 34 and n = 26), gustatory distaste (n = 30) and socio-moral (unfair offers; n = 43) contexts. Disgust experience was encoded in distributed cortical and subcortical systems, and exhibited distinct and shared neural representations with subjective fear or negative affect in interoceptive-emotional awareness and conscious appraisal systems, while the signatures most accurately predicted the respective target experience. We provide an accurate functional magnetic resonance imaging signature for disgust with a high potential to resolve ongoing evolutionary debates.

Temporal dendritic heterogeneity incorporated with spiking neural networks for learning multi-timescale dynamics.

It is widely believed the brain-inspired spiking neural networks have the capability of processing temporal information owing to their dynamic attributes. However, how to understand what kind of mechanisms contributing to the learning ability and exploit the rich dynamic properties of spiking neural networks to satisfactorily solve complex temporal computing tasks in practice still remains to be explored. In this article, we identify the importance of capturing the multi-timescale components, based on which a multi-compartment spiking neural model with temporal dendritic heterogeneity, is proposed. The model enables multi-timescale dynamics by automatically learning heterogeneous timing factors on different dendritic branches. Two breakthroughs are made through extensive experiments: the working mechanism of the proposed model is revealed via an elaborated temporal spiking XOR problem to analyze the temporal feature integration at different levels; comprehensive performance benefits of the model over ordinary spiking neural networks are achieved on several temporal computing benchmarks for speech recognition, visual recognition, electroencephalogram signal recognition, and robot place recognition, which shows the best-reported accuracy and model compactness, promising robustness and generalization, and high execution efficiency on neuromorphic hardware. This work moves neuromorphic computing a significant step toward real-world applications by appropriately exploiting biological observations.

Brain-inspired multimodal hybrid neural network for robot place recognition.

Place recognition is an essential spatial intelligence capability for robots to understand and navigate the world. However, recognizing places in natural environments remains a challenging task for robots because of resource limitations and changing environments. In contrast, humans and animals can robustly and efficiently recognize hundreds of thousands of places in different conditions. Here, we report a brain-inspired general place recognition system, dubbed NeuroGPR, that enables robots to recognize places by mimicking the neural mechanism of multimodal sensing, encoding, and computing through a continuum of space and time. Our system consists of a multimodal hybrid neural network (MHNN) that encodes and integrates multimodal cues from both conventional and neuromorphic sensors. Specifically, to encode different sensory cues, we built various neural networks of spatial view cells, place cells, head direction cells, and time cells. To integrate these cues, we designed a multiscale liquid state machine that can process and fuse multimodal information effectively and asynchronously using diverse neuronal dynamics and bioinspired inhibitory circuits. We deployed the MHNN on Tianjic, a hybrid neuromorphic chip, and integrated it into a quadruped robot. Our results show that NeuroGPR achieves better performance compared with conventional and existing biologically inspired approaches, exhibiting robustness to diverse environmental uncertainty, including perceptual aliasing, motion blur, light, or weather changes. Running NeuroGPR as an overall multi-neural network workload on Tianjic showcases its advantages with 10.5 times lower latency and 43.6% lower power consumption than the commonly used mobile robot processor Jetson Xavier NX.

Fear of missing out (FOMO) associates with reduced cortical thickness in core regions of the posterior default mode network and higher levels of problematic smartphone and social media use.

BACKGROUND AND AIMS: Fear of missing out (FOMO) promotes the desire or urge to stay continuously connected with a social reference group and updated on their activities, which may result in escalating and potentially addictive smartphone and social media use. The present study aimed to determine whether the neurobiological basis of FOMO encompasses core regions of the reward circuitry or social brain, and associations with levels of problematic smartphone or social media use. METHODS: We capitalized on a dimensional neuroimaging approach to examine cortical thickness and subcortical volume associations in a sample of healthy young individuals (n = 167). Meta-analytic network and behavioral decoding analyses were employed to further characterize the identified regions. RESULTS: Higher levels of FOMO associated with lower cortical thickness in the right precuneus. In contrast, no associations between FOMO and variations in striatal morphology were observed. Meta-analytic decoding revealed that the identified precuneus region exhibited a strong functional interaction with the default mode network (DMN) engaged in social cognitive and self-referential domains. DISCUSSION AND CONCLUSIONS: Together the present findings suggest that individual variations in FOMO are associated with the brain structural architecture of the right precuneus, a core hub within a large-scale functional network resembling the DMN and involved in social and self-referential processes. FOMO may promote escalating social media and smartphone use via social and self-referential processes rather than reward-related processes per se.

Opposing associations of Internet Use Disorder symptom domains with structural and functional organization of the striatum: A dimensional neuroimaging approach.

Background: Accumulating evidence suggests brain structural and functional alterations in Internet Use Disorder (IUD). However, conclusions are strongly limited due to the retrospective case-control design of the studies, small samples, and the focus on general rather than symptom-specific approaches. Methods: We here employed a dimensional multi-methodical MRI-neuroimaging design in a final sample of n = 203 subjects to examine associations between levels of IUD and its symptom-dimensions (loss of control/time management, craving/social problems) with brain structure, resting state and task-based (pain empathy, affective go/no-go) brain function. Results: Although the present sample covered the entire range of IUD, including normal, problematic as well as pathological levels, general IUD symptom load was not associated with brain structural or functional alterations. However, the symptom-dimensions exhibited opposing associations with the intrinsic and structural organization of the brain, such that loss of control/time management exhibited negative associations with intrinsic striatal networks and hippocampal volume, while craving/social problems exhibited a positive association with intrinsic striatal networks and caudate volume. Conclusions: Our findings provided the first evidence for IUD symptom-domain specific associations with progressive alterations in the intrinsic structural and functional organization of the brain, particularly of striatal systems involved in reward, habitual and cognitive control processes.

Angiotensin Antagonist Inhibits Preferential Negative Memory Encoding via Decreasing Hippocampus Activation and Its Coupling With the Amygdala.

BACKGROUND: Exaggerated arousal and dysregulated emotion-memory interactions are key pathological dysregulations that accompany the development of posttraumatic stress disorder (PTSD). Current treatments for PTSD are of moderate efficacy, and preventing the dysregulations during exposure to threatening events may attenuate the development of PTSD symptomatology. METHODS: In a preregistered double-blind, between-subject, placebo-controlled pharmaco-functional magnetic resonance imaging design, this proof-of-concept study examined the potential of a single dose of the angiotensin II type 1 receptor antagonist losartan (LT) to attenuate the mnemonic advantage of threatening stimuli and the underlying neural mechanism via combining an emotional subsequent memory paradigm with LT (n = 29) or placebo (n = 30) and a surprise memory test after a 24-hour washout. RESULTS: LT generally improved memory performance and abolished emotional memory enhancement for negative but not positive material, while emotional experience during encoding remained intact. LT further suppressed hippocampus activity during encoding of subsequently remembered negative stimuli. At the network level, LT reduced coupling between the hippocampus and the basolateral amygdala during successful memory formation of negative stimuli. CONCLUSIONS: Our findings suggest that LT may have the potential to attenuate memory formation for negative but not positive information by decreasing hippocampus activity and its functional coupling strength with the amygdala. These findings suggest a promising potential of LT to prevent preferential encoding and remembering of negative events, a mechanism that could prevent the emotion-memory dysregulations underlying the development of PTSD symptomatology.

Neuromorphic computing chip with spatiotemporal elasticity for multi-intelligent-tasking robots.

Recent advances in artificial intelligence have enhanced the abilities of mobile robots in dealing with complex and dynamic scenarios. However, to enable computationally intensive algorithms to be executed locally in multitask robots with low latency and high efficiency, innovations in computing hardware are required. Here, we report TianjicX, a neuromorphic computing hardware that can support true concurrent execution of multiple cross-computing-paradigm neural network (NN) models with various coordination manners for robotics. With spatiotemporal elasticity, TianjicX can support adaptive allocation of computing resources and scheduling of execution time for each task. Key to this approach is a high-level model, "Rivulet," which bridges the gap between robotic-level requirements and hardware implementations. It abstracts the execution of NN tasks through distribution of static data and streaming of dynamic data to form the basic activity context, adopts time and space slices to achieve elastic resource allocation for each activity, and performs configurable hybrid synchronous-asynchronous grouping. Thereby, Rivulet is capable of supporting independent and interactive execution. Building on Rivulet with hardware design for realizing spatiotemporal elasticity, a 28-nanometer TianjicX neuromorphic chip with event-driven, high parallelism, low latency, and low power was developed. Using a single TianjicX chip and a specially developed compiler stack, we built a multi-intelligent-tasking mobile robot, Tianjicat, to perform a cat-and-mouse game. Multiple tasks, including sound recognition and tracking, object recognition, obstacle avoidance, and decision-making, can be concurrently executed. Compared with NVIDIA Jetson TX2, latency is substantially reduced by 79.09 times, and dynamic power is reduced by 50.66%.

Associations between levels of Internet Gaming Disorder symptoms and striatal morphology-replication and associations with social anxiety.

Background: Brain structural alterations of the striatum have been frequently observed in internet gaming disorder (IGD); however, the replicability of the results and the associations with social-affective dysregulations such as social anxiety remain to be determined. Methods: The present study combined a dimensional neuroimaging approach with both voxel-wise and data-driven multivariate approaches to (i) replicate our previous results on a negative association between IGD symptom load (assessed by the Internet Gaming Disorder Scale-Short Form) and striatal volume, (ii) extend these findings to female individuals, and (iii) employ multivariate and mediation models to determine common brain structural representations of IGD and social anxiety (assessed by the Liebowitz Social Anxiety Scale). Results: In line with the original study, the voxel-wise analyses revealed a negative association between IGD and volumes of the bilateral caudate. Going beyond the earlier study investigating only male participants, the present study demonstrates that the association in the right caudate was comparable in both the male and the female subsamples. Further examination using the multivariate approach revealed regionally different associations between IGD and social anxiety with striatal density representations in the dorsal striatum (caudate) and ventral striatum (nucleus accumbens). Higher levels of IGD were associated with higher social anxiety and the association was critically mediated by the multivariate neurostructural density variations of the striatum. Conclusions: Altered striatal volumes may represent a replicable and generalizable marker of IGD symptoms. However, exploratory multivariate analyses revealed more complex and regional specific associations between striatal density and IGD as well as social anxiety symptoms. Variations in both tendencies may share common structural brain representations, which mediate the association between increased IGD and social anxiety.

A Plane-Dependent Model of 3D Grid Cells for Representing Both 2D and 3D Spaces Under Various Navigation Modes.

Grid cells are crucial in path integration and representation of the external world. The spikes of grid cells spatially form clusters called grid fields, which encode important information about allocentric positions. To decode the information, studying the spatial structures of grid fields is a key task for both experimenters and theorists. Experiments reveal that grid fields form hexagonal lattice during planar navigation, and are anisotropic beyond planar navigation. During volumetric navigation, they lose global order but possess local order. How grid cells form different field structures behind these different navigation modes remains an open theoretical question. However, to date, few models connect to the latest discoveries and explain the formation of various grid field structures. To fill in this gap, we propose an interpretive plane-dependent model of three-dimensional (3D) grid cells for representing both two-dimensional (2D) and 3D space. The model first evaluates motion with respect to planes, such as the planes animals stand on and the tangent planes of the motion manifold. Projection of the motion onto the planes leads to anisotropy, and error in the perception of planes degrades grid field regularity. A training-free recurrent neural network (RNN) then maps the processed motion information to grid fields. We verify that our model can generate regular and anisotropic grid fields, as well as grid fields with merely local order; our model is also compatible with mode switching. Furthermore, simulations predict that the degradation of grid field regularity is inversely proportional to the interval between two consecutive perceptions of planes. In conclusion, our model is one of the few pioneers that address grid field structures in a general case. Compared to the other pioneer models, our theory argues that the anisotropy and loss of global order result from the uncertain perception of planes rather than insufficient training.

Large-scale reconfiguration of connectivity patterns among attentional networks during context-dependent adjustment of cognitive control.

The ability to adjust our behavior flexibly depending on situational demands and changes in the environment is an important characteristic of cognitive control. Previous studies have proved that this type of adaptive control plays a crucial role in selective attention, but have barely explored whether and how attentional networks support adaptive control. In the present study, a Stroop task with a different proportion of incongruent trials was used to investigate the brain activity and connectivity of six typical attentional control networks (i.e., the fronto-parietal network (FPN), cingulo-opercular network (CON), default mode network (DMN), dorsal attention network (DAN), and ventral attention network/salience network (VAN/SN)) in the environment with changing control demand. The behavioral analysis indicated a decreased Stroop interference (incongruent vs. congruent trial response time [RT]) with the increase in the proportion of incongruent trials within a block, indicating that cognitive control was improved there. The fMRI data revealed that the attenuate Stroop interference was accompanied by the activation of frontal and parietal regions, such as bilateral dorsolateral prefrontal cortex and anterior cingulate cortex. Crucially, the improved cognitive control induced by the increased proportion of incongruent trials was associated with the enhanced functional connectivity within the five networks, and a greater connection between CON with the DAN/SN, and between DMN with the CON/DAN/SN. Meanwhile, however, the functional coupling between the FPN and VAN was decreased. These results suggest that flexible regulations of cognitive control are implemented by the large-scale reconfiguration of connectivity patterns among the attentional networks.

Convergent cross-sectional and longitudinal evidence for gaming-cue specific posterior parietal dysregulations in early stages of internet gaming disorder.

Exaggerated reactivity to drug-cues and emotional dysregulations represent key symptoms of early stages of substance use disorders. The diagnostic criteria for (Internet) gaming disorder strongly resemble symptoms for substance-related addictions. However, previous cross-sections studies revealed inconsistent results with respect to neural cue reactivity and emotional dysregulations in these populations. To this end, the present fMRI study applied a combined cross-sectional and longitudinal design in regular online gamers (n = 37) and gaming-naïve controls (n = 67). To separate gaming-associated changes from predisposing factors, gaming-naive subjects were randomly assigned to 6 weeks of daily Internet gaming or a non-gaming condition. At baseline and after the training, subjects underwent an fMRI paradigm presenting gaming-related cues and non-gaming-related emotional stimuli. Cross-sectional comparisons revealed gaming-cue specific enhanced valence attribution and neural reactivity in a parietal network, including the posterior cingulate in regular gamers as compared to gaming naïve-controls. Longitudinal analysis revealed that 6 weeks of gaming elevated valence ratings as well as neural cue-reactivity in a similar parietal network, specifically the posterior cingulate in previously gaming-naïve controls. Together, the longitudinal design did not reveal supporting evidence for altered emotional processing of non-gaming associated stimuli in regular gamers whereas convergent evidence for increased emotional and neural reactivity to gaming-associated stimuli was observed. Findings suggest that exaggerated neural reactivity in posterior parietal regions engaged in default mode and automated information processing already occur during early stages of regular gaming and probably promote continued engagement in gaming behavior.

NeuroSLAM: a brain-inspired SLAM system for 3D environments.

Roboticists have long drawn inspiration from nature to develop navigation and simultaneous localization and mapping (SLAM) systems such as RatSLAM. Animals such as birds and bats possess superlative navigation capabilities, robustly navigating over large, three-dimensional environments, leveraging an internal neural representation of space combined with external sensory cues and self-motion cues. This paper presents a novel neuro-inspired 4DoF (degrees of freedom) SLAM system named NeuroSLAM, based upon computational models of 3D grid cells and multilayered head direction cells, integrated with a vision system that provides external visual cues and self-motion cues. NeuroSLAM's neural network activity drives the creation of a multilayered graphical experience map in a real time, enabling relocalization and loop closure through sequences of familiar local visual cues. A multilayered experience map relaxation algorithm is used to correct cumulative errors in path integration after loop closure. Using both synthetic and real-world datasets comprising complex, multilayered indoor and outdoor environments, we demonstrate NeuroSLAM consistently producing topologically correct three-dimensional maps.