Human EEG and artificial neural networks reveal disentangled representations of object real-world size in natural images.

Remarkably, human brains have the ability to accurately perceive and process the real-world size of objects, despite vast differences in distance and perspective. While previous studies have delved into this phenomenon, distinguishing this ability from other visual perceptions, like depth, has been challenging. Using the THINGS EEG2 dataset with high time-resolution human brain recordings and more ecologically valid naturalistic stimuli, our study uses an innovative approach to disentangle neural representations of object real-world size from retinal size and perceived real-world depth in a way that was not previously possible. Leveraging this state-of-the-art dataset, our EEG representational similarity results reveal a pure representation of object real-world size in human brains. We report a representational timeline of visual object processing: object real-world depth appeared first, then retinal size, and finally, real-world size. Additionally, we input both these naturalistic images and object-only images without natural background into artificial neural networks. Consistent with the human EEG findings, we also successfully disentangled representation of object real-world size from retinal size and real-world depth in all three types of artificial neural networks (visual-only ResNet, visual-language CLIP, and language-only Word2Vec). Moreover, our multi-modal representational comparison framework across human EEG and artificial neural networks reveals real-world size as a stable and higher-level dimension in object space incorporating both visual and semantic information. Our research provides a detailed and clear characterization of the object processing process, which offers further advances and insights into our understanding of object space and the construction of more brain-like visual models.

Dynamic saccade context triggers more stable object-location binding.

Our visual systems rapidly perceive and integrate information about object identities and locations. There is long-standing debate about if and how we achieve world-centered (spatiotopic) object representations across eye movements, with many studies reporting persistent retinotopic (eye-centered) effects even for higher level object-location binding. But these studies are generally conducted in fairly static experimental contexts. Might spatiotopic object-location binding only emerge in more dynamic saccade contexts? In the present study, we investigated this using the spatial congruency bias paradigm in healthy adults. In the static (single-saccade) context, we found purely retinotopic binding, as before. However, robust spatiotopic binding emerged in the dynamic saccade context (multiple frequent saccades and saccades during stimulus presentation). We further isolated specific factors that modulate retinotopic and spatiotopic binding. Our results provide strong evidence that dynamic saccade context can trigger more stable object-location binding in ecologically relevant spatiotopic coordinates, perhaps via a more flexible brain state that accommodates improved visual stability in the dynamic world. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Achieving more human brain-like vision via human EEG representational alignment.

Despite advancements in artificial intelligence, object recognition models still lag behind in emulating visual information processing in human brains. Recent studies have highlighted the potential of using neural data to mimic brain processing; however, these often rely on invasive neural recordings from non-human subjects, leaving a critical gap in understanding human visual perception. Addressing this gap, we present, for the first time, 'Re(presentational)Al(ignment)net', a vision model aligned with human brain activity based on non-invasive EEG, demonstrating a significantly higher similarity to human brain representations. Our innovative image-to-brain multi-layer encoding framework advances human neural alignment by optimizing multiple model layers and enabling the model to efficiently learn and mimic human brain's visual representational patterns across object categories and different modalities. Our findings suggest that ReAlnet represents a breakthrough in bridging the gap between artificial and human vision, and paving the way for more brain-like artificial intelligence systems.

Bridging the gap between EEG and DCNNs reveals a fatigue mechanism of facial repetition suppression.

Facial repetition suppression, a well-studied phenomenon characterized by decreased neural responses to repeated faces in visual cortices, remains a subject of ongoing debate regarding its underlying neural mechanisms. Our research harnesses advanced multivariate analysis techniques and the prowess of deep convolutional neural networks (DCNNs) in face recognition to bridge the gap between human electroencephalogram (EEG) data and DCNNs, especially in the context of facial repetition suppression. Our innovative reverse engineering approach, manipulating the neuronal activity in DCNNs and conducted representational comparisons between brain activations derived from human EEG and manipulated DCNN activations, provided insights into the underlying facial repetition suppression. Significantly, our findings advocate the fatigue mechanism as the dominant force behind the facial repetition suppression effect. Broadly, this integrative framework, bridging the human brain and DCNNs, offers a promising tool for simulating brain activity and making inferences regarding the neural mechanisms underpinning complex human behaviors.

Generate your neural signals from mine: individual-to-individual EEG converters.

Most models in cognitive and computational neuroscience trained on one subject do not generalize to other subjects due to individual differences. An ideal individual-to-individual neural converter is expected to generate real neural signals of one subject from those of another one, which can overcome the problem of individual differences for cognitive and computational models. In this study, we propose a novel individual-to-individual EEG converter, called EEG2EEG, inspired by generative models in computer vision. We applied THINGS EEG2 dataset to train and test 72 independent EEG2EEG models corresponding to 72 pairs across 9 subjects. Our results demonstrate that EEG2EEG is able to effectively learn the mapping of neural representations in EEG signals from one subject to another and achieve high conversion performance. Additionally, the generated EEG signals contain clearer representations of visual information than that can be obtained from real data. This method establishes a novel and state-of-the-art framework for neural conversion of EEG signals, which can realize a flexible and high-performance mapping from individual to individual and provide insight for both neural engineering and cognitive neuroscience.

Dynamic saccade context triggers more stable object-location binding.

Our visual systems rapidly perceive and integrate information about object identities and locations. There is long-standing debate about how we achieve world-centered (spatiotopic) object representations across eye movements, with many studies reporting persistent retinotopic (eye-centered) effects even for higher-level object-location binding. But these studies are generally conducted in fairly static experimental contexts. Might spatiotopic object-location binding only emerge in more dynamic saccade contexts? In the present study, we investigated this using the Spatial Congruency Bias paradigm in healthy adults. In the static (single saccade) context, we found purely retinotopic binding, as before. However, robust spatiotopic binding emerged in the dynamic (multiple frequent saccades) context. We further isolated specific factors that modulate retinotopic and spatiotopic binding. Our results provide strong evidence that dynamic saccade context can trigger more stable object-location binding in ecologically-relevant spatiotopic coordinates, perhaps via a more flexible brain state which accommodates improved visual stability in the dynamic world.

Parasitoid vectors a plant pathogen, potentially diminishing the benefits it confers as a biological control agent.

Huanglongbing (HLB) is a destructive disease of citrus primarily transmitted by the Asian citrus psyllid (ACP). Biocontrol of ACP is an environmentally sustainable alternative to chemicals. However, the risk of parasitoid rational application in ACP biocontrol has never been evaluated. Here we show, the dominant parasitoid of ACP, Tamarixia radiata, can acquire the HLB pathogen Candidatus Liberibacter asiaticus (CLas) and transmit it horizontally when probing ACP nymphs. If these ACP nymphs survive the probing, develop to adults and move to healthy plants, CLas can be transmitted to citrus leaves during feeding. We illustrate the formerly unrecognized risk that a parasitoid can potentially serve as a phoretic vector of the pathogen transmitted by its host, thus potentially diminishing some of the benefits it confers via biocontrol. Our findings present a significant caution to the strategy of using parasitoids in orchards with different infection status of insect-vectored pathogens.

NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.

In studies of cognitive neuroscience, multivariate pattern analysis (MVPA) is widely used as it offers richer information than traditional univariate analysis. Representational similarity analysis (RSA), as one method of MVPA, has become an effective decoding method based on neural data by calculating the similarity between different representations in the brain under different conditions. Moreover, RSA is suitable for researchers to compare data from different modalities and even bridge data from different species. However, previous toolboxes have been made to fit specific datasets. Here, we develop NeuroRA, a novel and easy-to-use toolbox for representational analysis. Our toolbox aims at conducting cross-modal data analysis from multi-modal neural data (e.g., EEG, MEG, fNIRS, fMRI, and other sources of neruroelectrophysiological data), behavioral data, and computer-simulated data. Compared with previous software packages, our toolbox is more comprehensive and powerful. Using NeuroRA, users can not only calculate the representational dissimilarity matrix (RDM), which reflects the representational similarity among different task conditions and conduct a representational analysis among different RDMs to achieve a cross-modal comparison. Besides, users can calculate neural pattern similarity (NPS), spatiotemporal pattern similarity (STPS), and inter-subject correlation (ISC) with this toolbox. NeuroRA also provides users with functions performing statistical analysis, storage, and visualization of results. We introduce the structure, modules, features, and algorithms of NeuroRA in this paper, as well as examples applying the toolbox in published datasets.

Comprehensive Assessment of Candidate Reference Genes for Gene Expression Studies Using RT-qPCR in Tamarixia radiata, a Predominant Parasitoid of Diaphorina citri.

Tamarixia radiata (Waterston) is a predominant parasitoid of the Asian citrus psyllid (ACP), a destructive citrus pest and vector of huanglongbing (HLB) disease in the fields of southern China. To explore the functioning of target genes in T. radiata, the screening of specific reference genes is critical for carrying out the reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) under different experimental conditions. However, no reference gene(s) for T. radiata has yet been reported. Here, we selected seven housekeeping genes of T. radiate and evaluated their stability under the six conditions (developmental stage, sex, tissue, population, temperature, diet) by using RefFinder software, which contains four different programs (geNorm, ΔCt, BestKeeper, and NormFinder). Pairwise variation was analyzed by geNorm software to determine the optimal number of reference genes during the RT-qPCR analysis. The results reveal better reference genes for differing research foci: 18S and EF1A for the developmental stage; PRS18 and EF1A for sex, PRS18 and RPL13 for different tissues (head, thorax, abdomen); EF1A and ArgK between two populations; ß-tubulin and EF1A for different temperatures (5, 15, 25, 35 °C); and ArgK and PRS18 for different feeding diets. Furthermore, when the two optimal and two most inappropriate reference genes were chosen in different temperatures and tissue treatments, respectively, the corresponding expression patterns of HSP70 (as the reporter gene) differed substantially. Our study provides, for the first time, a more comprehensive list of optimal reference genes from T. radiata for use in RT-qPCR analysis, which should prove beneficial for subsequent functional investigations of target gene(s) in this natural enemy of ACP.

Communication between mitochondria and other organelles: a brand-new perspective on mitochondria in cancer.

Mitochondria are energy factories of cells and are important pivots for intracellular interactions with other organelles. They interact with the endoplasmic reticulum, peroxisomes, and nucleus through signal transduction, vesicle transport, and membrane contact sites to regulate energy metabolism, biosynthesis, immune response, and cell turnover. However, when the communication between organelles fails and the mitochondria are dysfunctional, it may induce tumorigenesis. In this review, we elaborate on how mitochondria interact with the endoplasmic reticulum, peroxisomes, and cell nuclei, as well as the relation between organelle communication and tumor development .