Representations In Brain Research Articles

Unique contributions of perceptual and conceptual humanness to object representations in the human brain

The human brain is able to quickly and accurately identify objects in a dynamic visual world. Objects evoke different patterns of neural activity in the visual system, which reflect object category memberships. However, the underlying dimensions of object representations in the brain remain unclear. Recent research suggests that objects similarity to humans is one of the main dimensions used by the brain to organise objects, but the nature of the human-similarity features driving this organisation are still unknown. Here, we investigate the relative contributions of perceptual and conceptual features of humanness to the representational organisation of objects in the human visual system. We collected behavioural judgements of human-similarity of various objects, which were compared with time-resolved neuroimaging responses to the same objects. The behavioural judgement tasks targeted either perceptual or conceptual humanness features to determine their respective contribution to perceived human-similarity. Behavioural and neuroimaging data revealed significant and unique contributions of both perceptual and conceptual features of humanness, each explaining unique variance in neuroimaging data. Furthermore, our results showed distinct spatio-temporal dynamics in the processing of conceptual and perceptual humanness features, with later and more lateralised brain responses to conceptual features. This study highlights the critical importance of social requirements in information processing and organisation in the human brain.

Common and stimulus-type-specific brain representations of negative affect.

The brain contains both generalized and stimulus-type-specific representations of aversive events, but models of how these are integrated and related to subjective experience are lacking. We combined functional magnetic resonance imaging with predictive modeling to identify representations of generalized (common) and stimulus-type-specific negative affect across mechanical pain, thermal pain, aversive sounds and aversive images of four intensity levels each. This allowed us to examine how generalized and stimulus-specific representations jointly contribute to aversive experience. Stimulus-type-specific negative affect was largely encoded in early sensory pathways, whereas generalized negative affect was encoded in a distributed set of midline, forebrain, insular and somatosensory regions. All models specifically predicted negative affect rather than general salience or arousal and accurately predicted negative affect in independent samples, demonstrating robustness and generalizability. Common and stimulus-type-specific models were jointly important for predicting subjective experience. Together, these findings offer an integrated account of how negative affect is constructed in the brain and provide predictive neuromarkers for future studies.

Individual variability in brain representations of pain.

Characterizing cerebral contributions to individual variability in pain processing is crucial for personalized pain medicine, but has yet to be done. In the present study, we address this problem by identifying brain regions with high versus low interindividual variability in their relationship with pain. We trained idiographic pain-predictive models with 13 single-trial functional MRI datasets (n = 404, discovery set) and quantified voxel-level importance for individualized pain prediction. With 21 regions identified as important pain predictors, we examined the interindividual variability of local pain-predictive weights in these regions. Higher-order transmodal regions, such as ventromedial and ventrolateral prefrontal cortices, showed larger individual variability, whereas unimodal regions, such as somatomotor cortices, showed more stable pain representations across individuals. We replicated this result in an independent dataset (n = 124). Overall, our study identifies cerebral sources of individual differences in pain processing, providing potential targets for personalized assessment and treatment of pain.

Real-World Visual Experience Alters Baseline Brain Activity in the Resting State: A Longitudinal Study Using Expertise Model of Radiologists.

Visual experience modulates the intensity of evoked brain activity in response to training-related stimuli. Spontaneous fluctuations in the restful brain actively encode previous learning experience. However, few studies have considered how real-world visual experience alters the level of baseline brain activity in the resting state. This study aimed to investigate how short-term real-world visual experience modulates baseline neuronal activity in the resting state using the amplitude of low-frequency (<0.08 Hz) fluctuation (ALFF) and a visual expertise model of radiologists, who possess fine-level visual discrimination skill of homogeneous stimuli. In detail, a group of intern radiologists (n = 32) were recruited. The resting-state fMRI data and the behavioral data regarding their level of visual expertise in radiology and face recognition were collected before and after 1 month of training in the X-ray department in a local hospital. A machine learning analytical method, i.e., support vector machine, was used to identify subtle changes in the level of baseline brain activity. Our method led to a superb classification accuracy of 86.7% between conditions. The brain regions with highest discriminative power were the bilateral cingulate gyrus, the left superior frontal gyrus, the bilateral precentral gyrus, the bilateral superior parietal lobule, and the bilateral precuneus. To the best of our knowledge, this study is the first to investigate baseline neurodynamic alterations in response to real-world visual experience using longitudinal experimental design. These results suggest that real-world visual experience alters the resting-state brain representation in multidimensional neurobehavioral components, which are closely interrelated with high-order cognitive and low-order visual factors, i.e., attention control, working memory, memory, and visual processing. We propose that our findings are likely to help foster new insights into the neural mechanisms of visual expertise.

Spatiotemporal dynamics of odor representations in the human brain revealed by EEG decoding

How the human brain translates olfactory inputs into diverse perceptions, from pleasurable floral smells to sickening smells of decay, is one of the fundamental questions in olfaction. To examine how different aspects of olfactory perception emerge in space and time in the human brain, we performed time-resolved multivariate pattern analysis of scalp-recorded electroencephalogram responses to 10 perceptually diverse odors and associated the resulting decoding accuracies with perception and source activities. Mean decoding accuracies of odors exceeded the chance level 100 ms after odor onset and reached maxima at 350 ms. The result suggests that the neural representations of individual odors were maximally separated at 350 ms. Perceptual representations emerged following the decoding peak: unipolar unpleasantness (neutral to unpleasant) from 300 ms, and pleasantness (neutral to pleasant) and perceptual quality (applicability to verbal descriptors such as “fruity” or “flowery”) from 500 ms after odor onset, with all these perceptual representations reaching their maxima after 600 ms. A source estimation showed that the areas representing the odor information, estimated based on the decoding accuracies, were localized in and around the primary and secondary olfactory areas at 100 to 350 ms after odor onset. Odor representations then expanded into larger areas associated with emotional, semantic, and memory processing, with the activities of these later areas being significantly associated with perception. These results suggest that initial odor information coded in the olfactory areas (<350 ms) evolves into their perceptual realizations (300 to >600 ms) through computations in widely distributed cortical regions, with different perceptual aspects having different spatiotemporal dynamics.

Vigilance and Behavioral State-Dependent Modulation of Cortical Neuronal Activity throughout the Sleep/Wake Cycle.

GABAergic inhibitory neurons, through their molecular, anatomic, and physiological diversity, provide a substrate for the modulation of ongoing cortical circuit activity throughout the sleep/wake cycle. Here, we investigated neuronal activity dynamics of parvalbumin (PV), vasoactive intestinal polypeptide (VIP), and somatostatin (SST) neurons in naturally sleeping head-restrained mice at the level of layer 2/3 of the primary somatosensory barrel cortex of mice. Through calcium imaging and targeted single-unit loose-patch or whole-cell recordings, we found that PV action potential firing activity was largest during both rapid eye movement (REM) and nonrapid eye movement (NREM) sleep stages, that VIP neurons were most active during REM sleep, and that the overall activity of SST neurons remained stable throughout the sleep/wake cycle. Analysis of neuronal activity dynamics uncovered rapid decreases in PV cell firing at wake onset followed by a progressive recovery during wake. Simultaneous local field potential (LFP) recordings further revealed that except for SST neurons, a large proportion of neurons were modulated by ongoing delta and theta oscillations. During NREM sleep spindles, PV and SST activity increased and decreased, respectively. Finally, we uncovered the presence of whisking behavior in mice during REM sleep and show that the activity of VIP and SST is differentially modulated during awake and sleeping whisking bouts, which may provide a neuronal substrate for internal brain representations occurring during sleep.SIGNIFICANCE STATEMENT In the sensory cortex, the balance between excitation and inhibition is believed to be highly dynamic throughout the sleep/wake cycle, shaping the response of cortical circuits to external stimuli while allowing the formation of newly encoded memory. Using in vivo two-photon calcium imaging or targeted single-unit recordings combined with LFP recordings, we describe the vigilance state and whisking-behavior-dependent activity of excitatory pyramidal and inhibitory GABAergic neurons in the supragranular layers of mouse somatosensory cortex. Interneuronal activity was found to be differentially modulated by ongoing delta and theta waves, sleep spindles, and a novel type of whisking observed during REM sleep, potentially providing a neuronal substrate for internal brain representations occurring during sleep.

Multisensory mental representation in covid-19 patients and the possibility of long-lasting gustatory and olfactory dysfunction in the CNS.

Gustatory (GD) and olfactory (OD) dysfunctions are the most frequent neurological manifestations of COVID-19. We used mental imagery as an experimental psychological paradigm to access olfactory and gustatory brain representations in 80 Italian COVID-19 adult patients (68.75% reported both OD and GD). COVID-19 patients with OD + GD have a significantly and selectively decreased vividness of odor and taste imagery, indicating that COVID-19 has an effect on their chemosensory mental representations. OD + GD length and type influenced the status of mental chemosensory representations. OD + GD were become all COVID-19 negative at the time of testing. Data suggest that patients are not explicitly aware of long-term altered chemosensory processing. However, differences emerge when their chemosensory function is implicitly assessed using self-ratings. Among patients developing OD + GD, self-ratings of chemosensory function (taste, flavor) were significantly lower as compared to those who did not. At the level of mental representation, such differences can be further detected, in terms of a reduced ability to mentally activate an odor or taste mental image. Our study shows that COVID-19 infection not only frequently causes hyposmia and dysgeusia, but that may also alter the mental representations responsible for olfactory and gustatory perception.

Statistical Analysis of Multisensory and Text-Derived Representations on Concept Learning.

When learning concepts, cognitive psychology research has revealed that there are two types of concept representations in the human brain: language-derived codes and sensory-derived codes. For the objective of human-like artificial intelligence, we expect to provide multisensory and text-derived representations for concepts in AI systems. Psychologists and computer scientists have published lots of datasets for the two kinds of representations, but as far as we know, no systematic work exits to analyze them together. We do a statistical study on them in this work. We want to know if multisensory vectors and text-derived vectors reflect conceptual understanding and if they are complementary in terms of cognition. Four experiments are presented in this work, all focused on multisensory representations labeled by psychologists and text-derived representations generated by computer scientists for concept learning, and the results demonstrate that (1) for the same concept, both forms of representations can properly reflect the concept, but (2) the representational similarity analysis findings reveal that the two types of representations are significantly different, (3) as the concreteness of the concept grows larger, the multisensory representation of the concept becomes closer to human beings than the text-derived representation, and (4) we verified that combining the two improves the concept representation.

Aplikasi Pengenalan Identitas Gambar Dengan Menggunakan Metode Backpropagation

With today's technological developments, many types of identifier systems have been developed for the benefit of users of the technology. This recognition system is used for some objects or also on humans. Image recognition applications have great hopes for the future. Start with the introduction of sketch images or static photos in a certain format. Of course, this is supported by the development of science and technology in the fields of image processing, analysis, and image understanding. To recognize the identity of the image so far it is very rare, but in this study describes how to identify the identity of the image with human photos. The image (image) in question is an image or human image whose identity is not known. Currently, many manipulate images or photos with the same image. However, it is not known which one was manipulated and which one was genuine. Based on these problems, it is necessary to apply an identity recognition system so that it can be known which ones are manipulated and which ones are genuine, by applying the backpropagation method. According to previous research entitled facial recognition applications using the backpropagation algorithm and voila jones with the authors Sayuti Rahman, Emil Fitranhah, Andi Marwan Elhanafi stated that to be able to recognize a person's image with the backpropagation algorithm, one must go through several stages of training and testing stages. Image is a method found in artificial neural networks. Where the artificial neural network is an artificial representation of the human brain which always tries to simulate the learning process in the human brain. one of them is the backpropagation method, backpropagation is one of the artificial neural network models that has the ability to get a balance between the network's ability to recognize the patterns used during training and the network's ability to provide the correct response to input patterns that are similar (but not the same) with the same pattern. used during training.

Different computational relations in language are captured by distinct brain systems.

A critical way for humans to acquire information is through language, yet whether and how language experience drives specific neural semantic representations is still poorly understood. We considered statistical properties captured by 3 different computational principles of language (simple co-occurrence, network-(graph)-topological relations, and neural-network-vector-embedding relations) and tested the extent to which they can explain the neural patterns of semantic representations, measured by 2 functional magnetic resonance imaging experiments that shared common semantic processes. Distinct graph-topological word relations, and not simple co-occurrence or neural-network-vector-embedding relations, had unique explanatory power for the neural patterns in the anterior temporal lobe (capturing graph-common-neighbors), inferior frontal gyrus, and posterior middle/inferior temporal gyrus (capturing graph-shortest-path). These results were relatively specific to language: they were not explained by sensory-motor similarities and the same computational relations of visual objects (based on visual image database) showed effects in the visual cortex in the picture naming experiment. That is, different topological properties within language and the same topological computations (common-neighbors) for language and visual inputs are captured by different brain regions. These findings reveal the specific neural semantic representations along graph-topological properties of language, highlighting the information type-specific and statistical property-specific manner of semantic representations in the human brain.

Canonical finger-numeral configurations facilitate the processing of Arabic numerals in adults: An Event-Related Potential study

Various studies claim that early-learned, culture-typical (canonical) finger configurations used to communicate or represent numerosity, have stronger connections to numerical concepts stored in long-term memory than cultural-unfamiliar finger configurations, thereby allowing for faster access to their numerical meaning. The current study investigated whether presentation of canonical finger configurations gesturing numerosities 1–4 or 6–9 would facilitate young adults’ behavioral and neural processing of Arabic numerals. Thirty-one adults performed a number comparison task in which they had to decide whether simultaneously presented Arabic numerals and canonical or non-canonical finger configurations showed the same or a different numerosity, while measuring their performance and Event-Related Potentials (ERPs). The results showed faster responses when comparisons involved canonical (versus non-canonical) finger configurations, but only on numerosity-congruent trials where finger configuration and Arabic numeral matched in number identity. Canonical, and small-number finger configurations 1–4 in general (irrespective of their canonicity), also elicited enhanced amplitude of the early right-parietal P2p, and the later centro-parietal P3 on numerosity-congruent trials. We suggest these P2p and P3 findings respectively reflect facilitated numerical access and easier categorization of canonical finger-numeral configurations. The current results provide behavioral and neurophysiological evidence for the embodiment of culture-specific, canonical, finger-numeral configurations, and their link with other number representations in the adult brain, likely emerging from their more frequent use in daily life communication and/or in early childhood during number symbol acquisition.

Quantifying social semantics: An inclusive definition of socialness and ratings for 8388 English words

It has been proposed that social experience plays an important role in the grounding of concepts, and socialness has been proffered as a fundamental organisational principle underpinning semantic representation in the human brain. However, the empirical support for these hypotheses is limited by inconsistencies in the way socialness has been defined and measured. To further advance theory, the field must establish a clearer working definition, and research efforts could be facilitated by the availability of an extensive set of socialness ratings for individual concepts. Therefore, in the current work, we employed a novel and inclusive definition to test the extent to which socialness is reliably perceived as a broad construct, and we report socialness norms for over 8000 English words, including nouns, verbs, and adjectives. Our inclusive socialness measure shows good reliability and validity, and our analyses suggest that the socialness ratings capture aspects of word meaning which are distinct to those measured by other pertinent semantic constructs, including concreteness and emotional valence. Finally, in a series of regression analyses, we show for the first time that the socialness of a word's meaning explains unique variance in participant performance on lexical tasks. Our dataset of socialness norms has considerable item overlap with those used in both other lexical/semantic norms and in available behavioural mega-studies. They can help target testable predictions about brain and behaviour derived from multiple representation theories and neurobiological accounts of social semantics.

Automatic and effortful control of interference in working memory can be distinguished by unique behavioral and functional brain representations

Goal-irrelevant information in working memory (WM) may enter the focus of attention (FOA) during a task and cause proactive interference (PI). In the current study we used fMRI to test several hypotheses concerning the boundary conditions of PI in WM using a modified verbal 2-back task. Temporal distance between item and lure presentation was manipulated to evaluate potential differences among hypothesized states of FOA, short-term memory and long-term memory. PI was present for the most proximal 3-back lures but dissipated with lure distance along with increased activation in brain regions critical for memory recollection, such as right prefrontal cortex, parietal cortex, and hippocampus. Reduced PI and less IFG activation were also observed after repeated item presentation, supporting the notion that a rehearsed encoding of item-context information reduces the need for interference control. Moreover, a trial-by-trial approach revealed activity in ACC, insula, IFG, and parietal cortex with increasing lure trial interference regardless of distance. The current results are first evidence for an observable transition of cognitive control, to include MTL regions involved in recalling task-relevant information from outside the FOA when resolving PI in WM.

A Strain Rate-Dependent Constitutive Model for Göttingen Minipig Cerebral Arteries.

Computational simulations of traumatic brain injury (TBI) are commonly used to advance understanding of the injury-pathology relationship, tissue damage thresholds, and design of protective equipment such as helmets. Both human and animal TBI models have developed substantially over recent decades, partially due to the inclusion of more detailed brain geometry and representation of tissues like cerebral blood vessels. Explicit incorporation of vessels dramatically affects local strain and enables researchers to investigate TBI-induced damage to the vasculature. While some studies have indicated that cerebral arteries are rate-dependent, no published experimentally based, rate-sensitive constitutive models of cerebral arteries exist. In this work, we characterize the mechanical properties of axially failed porcine arteries, both quasi-statically (0.01 s-1) and at high rate (>100 s-1), and propose a rate-sensitive model to fit the data. We find that the quasi-static and high-rate stress-stretch curves become significantly different (p < 0.05) above a stretch of 1.23. We additionally find a significant change in both failure stretch and stress as a result of strain rate. The stress-stretch curve is then modeled as a Holzapfel-Gasser-Ogden material, with a Prony series added to capture the effects of viscoelasticity. Ultimately, this paper demonstrates that rate dependence should be considered in the material properties of cerebral arteries undergoing high strain-rate deformations and provides a ready-to-use model for finite element implementation.

The neural mechanism and transfer effect of creative cognitive reappraisal in negative emotion regulation

<p id=C8>Cognitive reappraisal plays an important role in regulating negative emotions. However, traditional cognitive reappraisal has some issues, such as a lower reconstruction degree and a lower emotion regulation effect. Considering these issues, we propose an effective emotion regulation strategy called creative cognitive reappraisal, which refers to a novel and unique interpretation that is highly creative and appropriate for current emotional stimuli. During the emotion regulation process, an individual suddenly experiences an Aha! moment when combining the interpretation with the stimulus situation. This study found a dramatic shift from negative to positive emotions and a long-lasting regulatory effect. According to imaging results, creative cognitive reappraisal was associated with the formation of a novel association in the hippocampus and positive emotional arousal in the amygdala. <p id=C9>However, the guided reappraisal used in this work was more akin to an understanding of reappraisal interpretation than active emotion regulation, which has lower ecological validity. Furthermore, it is difficult to generate high creative reappraisal on one’s own, making the application of creative cognitive reappraisal challenging. Thus, the goal of this project is to teach participants how to learn the method of creative cognitive reappraisal and then transfer and apply this strategy to specific negative emotion regulation in daily life, i.e., to establish a learn and then use emotion regulation mode that can truly play the role of creative cognitive reappraisal during emotion regulation. Furthermore, because teenagers have more serious mood problems but also have higher creativity, this project intends to apply a creative cognitive reappraisal strategy for emotion regulation to teenagers while also teaching them creative emotion regulation strategies to assist them in learning cognitive transformations and reconstructions. This method can teach teenagers how to use positive emotional regulation strategies to deal with negative life events while also preserving their creativity. <p id=C10>The specific research contents are as follows: 1) exploring the transfer effect of creative cognitive reappraisal in negative emotion regulation of college students and adolescents; 2) investigating changes in brain activation and representation patterns before and after creative cognitive reappraisal transfer; and 3) examining the similarities and differences in the neural mechanisms of creative cognitive reappraisal transfer between adolescents and adults. This project is an extension and expansion of the applicants existing work, and it would establish a theoretical foundation for verifying and promoting creative cognitive reappraisal as a learnable, usable and efficient emotional regulation strategy.

The spatiotemporal neural dynamics of object location representations in the human brain

To interact with objects in complex environments, we must know what they are and where they are in spite of challenging viewing conditions. Here, we investigated where, how and when representations of object location and category emerge in the human brain when objects appear on cluttered natural scene images using a combination of functional magnetic resonance imaging, electroencephalography and computational models. We found location representations to emerge along the ventral visual stream towards lateral occipital complex, mirrored by gradual emergence in deep neural networks. Time-resolved analysis suggested that computing object location representations involves recurrent processing in high-level visual cortex. Object category representations also emerged gradually along the ventral visual stream, with evidence for recurrent computations. These results resolve the spatiotemporal dynamics of the ventral visual stream that give rise to representations of where and what objects are present in a scene under challenging viewing conditions.

Analogy-Related Information Can Be Accessed by Simple Addition and Subtraction of fMRI Activation Patterns, Without Participants Performing any Analogy Task.

Analogical reasoning, for example, inferring that teacher is to chalk as mechanic is to wrench, plays a fundamental role in human cognition. However, whether brain activity patterns of individual words are encoded in a way that could facilitate analogical reasoning is unclear. Recent advances in computational linguistics have shown that information about analogical problems can be accessed by simple addition and subtraction of word embeddings (e.g., wrench = mechanic + chalk - teacher). Critically, this property emerges in artificial neural networks that were not trained to produce analogies but instead were trained to produce general-purpose semantic representations. Here, we test whether such emergent property can be observed in representations in human brains, as well as in artificial neural networks. fMRI activation patterns were recorded while participants viewed isolated words but did not perform analogical reasoning tasks. Analogy relations were constructed from word pairs that were categorically or thematically related, and we tested whether the predicted fMRI pattern calculated with simple arithmetic was more correlated with the pattern of the target word than other words. We observed that the predicted fMRI patterns contain information about not only the identity of the target word but also its category and theme (e.g., teaching-related). In summary, this study demonstrated that information about analogy questions can be reliably accessed with the addition and subtraction of fMRI patterns, and that, similar to word embeddings, this property holds for task-general patterns elicited when participants were not explicitly told to perform analogical reasoning.

Modelling brain representations of abstract concepts.

conceptual representations are critical for human cognition. Despite their importance, key properties of these representations remain poorly understood. Here, we used computational models of distributional semantics to predict multivariate fMRI activity patterns during the activation and contextualization of abstract concepts. We devised a task in which participants had to embed abstract nouns into a story that they developed around a given background context. We found that representations in inferior parietal cortex were predicted by concept similarities emerging in models of distributional semantics. By constructing different model families, we reveal the models’ learning trajectories and delineate how abstract and concrete training materials contribute to the formation of brain-like representations. These results inform theories about the format and emergence of abstract conceptual representations in the human brain.

Convolutional Neural Network Approach for Mobile Banking Fraudulent Transaction to Detect Financial Frauds

In the real world, the identification of financial fraud in compliance with IoT criteria is highly effective since financial fraud causes financial damage. Several forms of financial fraud are likely, but unauthorized usage of mobile payment by credit card no. or certificate no.is the most common scenario. Detection of financial crime is a growing environment in which the victims will keep ahead. However, intelligent fraud detection facets remain scientifically unsupported. Deep learning (DL)arises from the idea of a multi-type representation of the human brain that incorporates basic characteristics at the low level or high-level abstractions.Financial fraud was a big issue as forgers discovered new methods of stealing currency. Therefore, adaptive methods of identification of fraud against forgers are required. Thanks to their versatile nature to detect emergent financial transaction fraud, deep learning approaches were enticing candidates. In this article, we suggest an in-depth learning approach for adapting financial fraud through the use of convolution neural networks (CNN). With the fraudulent transactions dataset, we tested our model experimentally. The results of the analysis show which our methods detect transactional fraud appropriately.

Multiple representations of the body schema for the same body part

Purposeful motor actions depend on the brain's representation of the body, called the body schema, and disorders of the body schema have been reported to show motor deficits. The body schema has been assumed for almost a century to be a common body representation supporting all types of motor actions, and previous studies have considered only a single motor action. Although we often execute multiple motor actions, how the body schema operates during such actions is unknown. To address this issue, I developed a technique to measure the body schema during multiple motor actions. Participants made simultaneous eye and reach movements to the same location of 10 landmarks on their hand. By analyzing the internal configuration of the locations of these points for each of the eye and reach movements, I produced maps of the mental representation of hand shape. Despite these two movements being simultaneously directed to the same bodily location, the resulting hand map (i.e., a part of the body schema) was much more distorted for reach movements than for eye movements. Furthermore, the weighting of visual and proprioceptive bodily cues to build up this part of the body schema differed for each effector. These results demonstrate that the body schema is organized as multiple effector-specific body representations. I propose that the choice of effector toward one's body can determine which body representation in the brain is observed and that this visualization approach may offer a new way to understand patients' body schema.