The Effects of Age and Reading Experience on the Lifespan Neurodevelopment for Reading Comprehension.

Reading comprehension is a vital cognitive skill that individuals use throughout their lives. The neurodevelopment of reading comprehension across the lifespan, however, remains underresearched. Furthermore, factors such as maturation and experience significantly influence functional brain development. Given the complexity of reading comprehension, which incorporates lower-level word reading process and higher-level semantic integration process, our study aims to investigate how age and reading experience influence the neurobiology underpinning these two processes across the lifespan. fMRI data of 158 participants aged from 7 to 77 years were collected during a passive word viewing task and a sentence comprehension task to engage the lower- and higher-level processes, respectively. We found that the neurodevelopment of the lower-level process was primarily influenced by age, showing increased activation and connectivity with age in parieto-occipital and middle/inferior frontal lobes related to morphological-semantic mapping while decreased activation in the temporoparietal regions linked to phonological processing. However, the brain function of the higher-level process was primarily influenced by reading experience, exhibiting a greater reliance on the frontotemporal semantic network with enhanced sentence-level reading performance. Furthermore, reading experience did not significantly affect the brain function of children, but had a positive effect on young adults in the lower-level process and on middle-aged and older adults in the higher-level process. These findings indicate that the brain function for lower- and higher-level processes of reading comprehension is differently affected by maturation and reading experience, and the experience effect is contingent on age regarding the two processes.

A large-scale fMRI dataset for the visual processing of naturalistic scenes.

One ultimate goal of visual neuroscience is to understand how the brain processes visual stimuli encountered in the natural environment. Achieving this goal requires records of brain responses under massive amounts of naturalistic stimuli. Although the scientific community has put a lot of effort into collecting large-scale functional magnetic resonance imaging (fMRI) data under naturalistic stimuli, more naturalistic fMRI datasets are still urgently needed. We present here the Natural Object Dataset (NOD), a large-scale fMRI dataset containing responses to 57,120 naturalistic images from 30 participants. NOD strives for a balance between sampling variation between individuals and sampling variation between stimuli. This enables NOD to be utilized not only for determining whether an observation is generalizable across many individuals, but also for testing whether a response pattern is generalized to a variety of naturalistic stimuli. We anticipate that the NOD together with existing naturalistic neuroimaging datasets will serve as a new impetus for our understanding of the visual processing of naturalistic stimuli.

A large-scale fMRI dataset for human action recognition.

Human action recognition is a critical capability for our survival, allowing us to interact easily with the environment and others in everyday life. Although the neural basis of action recognition has been widely studied using a few action categories from simple contexts as stimuli, how the human brain recognizes diverse human actions in real-world environments still needs to be explored. Here, we present the Human Action Dataset (HAD), a large-scale functional magnetic resonance imaging (fMRI) dataset for human action recognition. HAD contains fMRI responses to 21,600 video clips from 30 participants. The video clips encompass 180 human action categories and offer a comprehensive coverage of complex activities in daily life. We demonstrate that the data are reliable within and across participants and, notably, capture rich representation information of the observed human actions. This extensive dataset, with its vast number of action categories and exemplars, has the potential to deepen our understanding of human action recognition in natural environments.

Functional integration of anterior insula related to meaning in life and loneliness.

BACKGROUND: Meaning in life (MIL), defined as people's feelings of life's meaningfulness, plays a vital role in buffering loneliness - an important indicator of depression and other psychological disorders. Considerable evidence shows that MIL arises from widely distributed brain activity; however, how such activity is functionally integrated and how it influences loneliness is still understudied. METHODS: We here examined how the functional integration of brain regions is related to individual MIL based on resting-state functional magnetic resonance imaging data from the Human Connectome Project (N = 970). RESULTS: We found that the global brain connectivity (GBC) of the right anterior insula (rAI) can significantly predict individual MIL. Moreover, mediation analyses were conducted to investigate how the brain influences loneliness with MIL's mediation, which revealed that MIL fully mediates the effect of this hub on loneliness. CONCLUSIONS: These findings suggest that the rAI is a key hub for MIL and loneliness. Its functional integration can be used as a biomarker to predict individual MIL and loneliness.

Advanced Reinforcement Learning and Its Connections with Brain Neuroscience.

In recent years, brain science and neuroscience have greatly propelled the innovation of computer science. In particular, knowledge from the neurobiology and neuropsychology of the brain revolutionized the development of reinforcement learning (RL) by providing novel interpretable mechanisms of how the brain achieves intelligent and efficient decision making. Triggered by this, there has been a boom in research about advanced RL algorithms that are built upon the inspirations of brain neuroscience. In this work, to further strengthen the bidirectional link between the 2 communities and especially promote the research on modern RL technology, we provide a comprehensive survey of recent advances in the area of brain-inspired/related RL algorithms. We start with basis theories of RL, and present a concise introduction to brain neuroscience related to RL. Then, we classify these advanced RL methodologies into 3 categories according to different connections of the brain, i.e., micro-neural activity, macro-brain structure, and cognitive function. Each category is further surveyed by presenting several modern RL algorithms along with their mathematical models, correlations with the brain, and open issues. Finally, we introduce several important applications of RL algorithms, followed by the discussions of challenges and opportunities for future research.

Relation of emotion malleability beliefs to subjective well-being: A short-term longitudinal study.

Prior research has shown that emotion malleability beliefs are positively related to subjective well-being, but less is known about the longitudinal relationship between both variables. The present study used a two-wave longitudinal design to examine the temporal directionality of the relationship in a sample of Chinese adults. Using cross-lagged models, we found that emotion malleability beliefs predicted all three dimensions of subjective well-being (i.e. positive affect, life satisfaction, and negative affect) 2 months later. However, we did not detect any reverse or reciprocal effect between emotion malleability beliefs and subjective well-being. In addition, emotion malleability beliefs still predicted life satisfaction and positive affect after controlling for the effect of the cognitive or emotional component of subjective well-being. Our study provided primary evidence for the temporal directionality of the association between emotion malleability beliefs and subjective well-being. Implications and suggestions for future research were discussed.

From sMRI to task-fMRI: A unified geometric deep learning framework for cross-modal brain anatomo-functional mapping.

Achieving predictions of brain functional activation patterns/task-fMRI maps from its underlying anatomy is an important yet challenging problem. Once successful, it will not only open up new ways to understand how brain anatomy influences functional organization of the brain, but also provide new technical support for the clinical use of anatomical information to guide the localization of cortical functional areas. However, due to the non-Euclidean complex architecture of brain anatomy and the inherent low signal-to-noise ratio (SNR) properties of fMRI signals, the key challenge in building such a cross-modal brain anatomo-functional mapping is how to effectively learn the context-aware information of brain anatomy and overcome the interference of noise-containing task-fMRI labels on the learning process. In this work, we propose a Unified Geometric Deep Learning framework (BrainUGDL) to perform the cross-modal brain anatomo-functional mapping task. Considering that both global and local structures of brain anatomy have an impact on brain functions from their respective perspectives, we innovatively propose the novel Global Graph Encoding (GGE) unit and Local Graph Attention (LGA) unit embedded into two parallel branches, focusing on learning the high-level global and local context information, respectively. Specifically, GGE learns the global context information of each mesh vertex by building and encoding global interactions, and LGA learns the local context information of each mesh vertex by selectively aggregating patch structure enhanced features from its spatial neighbors. The information learnt from the two branches is then fused to form a comprehensive representation of brain anatomical features for final brain function predictions. To address the inevitable measurement noise in task-fMRI labels, we further elaborate a novel uncertainty-filtered learning mechanism, which enables BrainUGDL to realize revised learning from the noise-containing labels through the estimated uncertainty. Experiments across seven open task-fMRI datasets from human connectome project (HCP) demonstrate the superiority of BrainUGDL. To our best knowledge, our proposed BrainUGDL is the first to achieve the prediction of individual task-fMRI maps solely based on brain sMRI data.

Functionally and structurally distinct fusiform face area(s) in over 1000 participants.

The fusiform face area (FFA) is a widely studied region causally involved in face perception. Even though cognitive neuroscientists have been studying the FFA for over two decades, answers to foundational questions regarding the function, architecture, and connectivity of the FFA from a large (N>1000) group of participants are still lacking. To fill this gap in knowledge, we quantified these multimodal features of fusiform face-selective regions in 1053 participants in the Human Connectome Project. After manually defining over 4,000 fusiform face-selective regions, we report five main findings. First, 68.76% of hemispheres have two cortically separate regions (pFus-faces/FFA-1 and mFus-faces/FFA-2). Second, in 26.69% of hemispheres, pFus-faces/FFA-1 and mFus-faces/FFA-2 are spatially contiguous, yet are distinct based on functional, architectural, and connectivity metrics. Third, pFus-faces/FFA-1 is more face-selective than mFus-faces/FFA-2, and the two regions have distinct functional connectivity fingerprints. Fourth, pFus-faces/FFA-1 is cortically thinner and more heavily myelinated than mFus-faces/FFA-2. Fifth, face-selective patterns and functional connectivity fingerprints of each region are more similar in monozygotic than dizygotic twins and more so than architectural gradients. As we share our areal definitions with the field, future studies can explore how structural and functional features of these regions will inform theories regarding how visual categories are represented in the brain.

Sex dimorphic cortical brain volumes associated with antisocial behavior in young adults.

Background: Although sex differences in antisocial behavior are well-documented, the extent to which neuroanatomical differences are related to sex differences in antisocial behavior is unclear. The inconsistent results from different clinical populations exhibiting antisocial behaviors are mainly due to the heterogeneity in etiologies, comorbidity inequality, and small sample size, especially in females. Objective: The study aimed to find sexual dimorphic brain regions associated with individual differences in antisocial behavior while avoiding the issues of heterogeneity and sample size. Methods: We collected structural neuroimaging data from 281 college students (131 males, 150 females) and analyzed the data using voxel-based morphometry. Results: The gray matter volume in three brain regions correlates with self-reported antisocial behavior in males and females differently: the posterior superior temporal sulcus, middle temporal gyrus, and precuneus. The findings have controlled for the total cortical gray matter volume, age, IQ, and socioeconomic status. Additionally, we found a common neural substrate of antisocial behavior in both males and females, extending from the anterior temporal lobe to the insula. Conclusion: This is the first neuroanatomical evidence from a large non-clinical sample of young adults. The study suggests that differences in males and females in reading social cues, understanding intentions and emotions, and responding to conflicts may contribute to the modulation of brain morphometry concerning antisocial behavior.

A multifaceted gradient in human cerebellum of structural and functional development.

The organization of the basic tissue and functional properties of the cerebellum across development is unknown. Combining several large datasets, we demonstrate in the human cerebellum a static tissue gradient in adults that mirrors a similar growth-rate gradient across development. Quantitative tissue metrics corroborate unique densities of certain lipids and proteins among lobules, and cerebellar structural development closely follows cerebellar functional properties through childhood.

An fMRI dataset for whole-body somatotopic mapping in humans.

The somatotopic representation of the body is a well-established organizational principle in the human brain. Classic invasive direct electrical stimulation for somatotopic mapping cannot be used to map the whole-body topographical representation of healthy individuals. Functional magnetic resonance imaging (fMRI) has become an indispensable tool for the noninvasive investigation of somatotopic organization of the human brain using voluntary movement tasks. Unfortunately, body movements during fMRI scanning often cause large head motion artifacts. Consequently, there remains a lack of publicly accessible fMRI datasets for whole-body somatotopic mapping. Here, we present public high-resolution fMRI data to map the somatotopic organization based on motor movements in a large cohort of healthy adults (N = 62). In contrast to previous studies that were mostly designed to distinguish few body representations, most body parts are considered, including toe, ankle, leg, finger, wrist, forearm, upper arm, jaw, lip, tongue, and eyes. Moreover, the fMRI data are denoised by combining spatial independent component analysis with manual identification to clean artifacts from head motion associated with body movements.

A studyforrest extension, MEG recordings while watching the audio-visual movie "Forrest Gump".

Naturalistic stimuli, such as movies, are being increasingly used to map brain function because of their high ecological validity. The pioneering studyforrest and other naturalistic neuroimaging projects have provided free access to multiple movie-watching functional magnetic resonance imaging (fMRI) datasets to prompt the community for naturalistic experimental paradigms. However, sluggish blood-oxygenation-level-dependent fMRI signals are incapable of resolving neuronal activity with the temporal resolution at which it unfolds. Instead, magnetoencephalography (MEG) measures changes in the magnetic field produced by neuronal activity and is able to capture rich dynamics of the brain at the millisecond level while watching naturalistic movies. Herein, we present the first public prolonged MEG dataset collected from 11 participants while watching the 2 h long audio-visual movie "Forrest Gump". Minimally preprocessed data was also provided to facilitate the use of the dataset. As a studyforrest extension, we envision that this dataset, together with fMRI data from the studyforrest project, will serve as a foundation for exploring the neural dynamics of various cognitive functions in real-world contexts.

Behavioral and neural correlates of social network size: The unique and common contributions of face recognition and extraversion.

OBJECTIVES: Humans are inherently social creatures and can gain advantages from larger network size. Researches have shown that different cognitive and personality factors may result in individual differences of social network size (SNS). Here, we focused on whether face recognition ability and extraversion were related to SNS and the neural basis underlying the relations. METHODS: Behaviorally, we adopted the face-inversion task, NEO personality inventory, and computerized SNS test to explore the relationships between face recognition, extraversion, and SNS. Neurally, we used resting state functional magnetic resonance imaging and fractional amplitude of low-frequency fluctuation (fALFF) analysis method to investigate the neural correlates of SNS and then revealed whether face recognition and extraversion were related to SNS relevant brain regions. RESULTS: We found that individuals with better face recognition ability and more extraverted personality had larger size of social network. In addition, we found that SNS was positively associated with the fALFF in the ventromedial prefrontal cortex (vmPFC), right superior temporal sulcus, and precuneus. Interestingly, the fALFF in the vmPFC significantly correlated with face recognition ability. CONCLUSIONS: Our study suggests that both face recognition and extraversion may be important correlates of SNS, and the underlying spontaneous neural substrates are partially dissociable.

The relationship between transcription and eccentricity in human V1.

Gene expression gradients radiating from regions of primary sensory cortices have recently been described and are thought to underlie the large-scale organization of the human cerebral cortex. However, the role of transcription in the functional layout of a single region within the adult brain has yet to be clarified, likely owing to the difficulty of identifying a brain region anatomically consistent enough across individuals with dense enough tissue sampling. Overcoming these hurdles in human primary visual cortex (V1), we show a relationship between differential gene expression and the cortical layout of eccentricity in human V1. Interestingly, these genes are unique from those previously identified that contribute to the positioning of cortical areas in the visual processing hierarchy. Enrichment analyses show that a subset of the identified genes encode for structures related to inhibitory interneurons, ion channels, as well as cellular projections, and are expressed more in foveal compared to peripheral portions of human V1. These findings predict that tissue density should be higher in foveal compared to peripheral V1. Using a histological pipeline, we validate this prediction using Nissl-stained sections of postmortem occipital cortex. We discuss these findings relative to previous studies in non-human primates, as well as in the context of an organizational pattern in which the adult human brain employs transcription gradients at multiple spatial scales: across the cerebral cortex, across areas within processing hierarchies, and within single cortical areas.

The Face Module Emerged in a Deep Convolutional Neural Network Selectively Deprived of Face Experience.

Can we recognize faces with zero experience on faces? This question is critical because it examines the role of experiences in the formation of domain-specific modules in the brain. Investigation with humans and non-human animals on this issue cannot easily dissociate the effect of the visual experience from that of the hardwired domain-specificity. Therefore, the present study built a model of selective deprivation of the experience on faces with a representative deep convolutional neural network, AlexNet, by removing all images containing faces from its training stimuli. This model did not show significant deficits in face categorization and discrimination, and face-selective modules automatically emerged. However, the deprivation reduced the domain-specificity of the face module. In sum, our study provides empirical evidence on the role of nature vs. nurture in developing the domain-specific modules that domain-specificity may evolve from non-specific experience without genetic predisposition, and is further fine-tuned by domain-specific experience.

Quantifying the variability of neural activation in working memory: A functional probabilistic atlas.

Working memory is a fundamental cognitive ability that allows the maintenance and manipulation of information for a brief period of time. Previous studies found a set of brain regions activated during working memory tasks, such as the prefrontal and parietal cortex. However, little is known about the variability of neural activation in working memory. Here, we used functional magnetic resonance imaging to quantify individual, hemispheric, and sex differences of working memory activation in a large cohort of healthy adults (N = 477). We delineated subject-specific activated regions in each individual, including the frontal pole, middle frontal gyrus, frontal eye field, superior parietal lobule, insular, precuneus, and anterior cingulate cortex. A functional probabilistic atlas was created to quantify individual variability in working memory regions. More than 90% of the participants activated all seven regions in both hemispheres, but the intersection of regions across participants was markedly less (50%), indicating significant individual differences in working memory activations. Moreover, we found hemispheric and sex differences in activation location, extent, and magnitude. Most activation regions were larger in the right than in the left hemisphere, but the magnitude of activation did not follow a similar pattern. Men showed more extensive and stronger activations than women. Taken together, our functional probabilistic atlas quantified variabilities of neural activation in working memory, providing a robust spatial reference for standardization of functional localization.

Semantic Relatedness Emerges in Deep Convolutional Neural Networks Designed for Object Recognition.

Human not only can effortlessly recognize objects, but also characterize object categories into semantic concepts with a nested hierarchical structure. One dominant view is that top-down conceptual guidance is necessary to form such hierarchy. Here we challenged this idea by examining whether deep convolutional neural networks (DCNNs) could learn relations among objects purely based on bottom-up perceptual experience of objects through training for object categorization. Specifically, we explored representational similarity among objects in a typical DCNN (e.g., AlexNet), and found that representations of object categories were organized in a hierarchical fashion, suggesting that the relatedness among objects emerged automatically when learning to recognize them. Critically, the emerged relatedness of objects in the DCNN was highly similar to the WordNet in human, implying that top-down conceptual guidance may not be a prerequisite for human learning the relatedness among objects. In addition, the developmental trajectory of the relatedness among objects during training revealed that the hierarchical structure was constructed in a coarse-to-fine fashion, and evolved into maturity before the establishment of object recognition ability. Finally, the fineness of the relatedness was greatly shaped by the demand of tasks that the DCNN performed, as the higher superordinate level of object classification was, the coarser the hierarchical structure of the relatedness emerged. Taken together, our study provides the first empirical evidence that semantic relatedness of objects emerged as a by-product of object recognition in DCNNs, implying that human may acquire semantic knowledge on objects without explicit top-down conceptual guidance.

Estimating Functional Connectivity by Integration of Inherent Brain Function Activity Pattern Priors.

Brain functional connectivity (FC) has shown great potential in becoming biomarkers of brain status. However, the problem of accurately estimating FC from complex-noisy fMRI time series remains unsolved. Usually, a regularization function is more appropriate in fitting the real inherent properties of the brain function activity pattern, which can further limit noise interference to improve the accuracy of the estimated result. Recently, the neuroscientists widely suggested that the inherent brain function activity pattern indicates sparse, modular and overlapping topology. However, previous studies have never considered this factual characteristic. Thus, we propose a novel method by integration of these inherent brain function activity pattern priors to estimate FC. Extensive experiments on synthetic data demonstrate that our method can more accurately estimate the FC than previous. Then, we applied the estimated FC to predict the symptom severity of depressed patients, the symptom severity is related to subtle abnormal changes in the brain function activity, a more accurate FC can more effectively capture the subtle abnormal brain function activity changes. As results, our method better than others with a higher correlation coefficient of 0.4201. Moreover, the overlapping probability of each brain region can be further explored by the proposed method.

Hierarchical Sparse Coding of Objects in Deep Convolutional Neural Networks.

Recently, deep convolutional neural networks (DCNNs) have attained human-level performances on challenging object recognition tasks owing to their complex internal representation. However, it remains unclear how objects are represented in DCNNs with an overwhelming number of features and non-linear operations. In parallel, the same question has been extensively studied in primates' brain, and three types of coding schemes have been found: one object is coded by the entire neuronal population (distributed coding), or by one single neuron (local coding), or by a subset of neuronal population (sparse coding). Here we asked whether DCNNs adopted any of these coding schemes to represent objects. Specifically, we used the population sparseness index, which is widely-used in neurophysiological studies on primates' brain, to characterize the degree of sparseness at each layer in representative DCNNs pretrained for object categorization. We found that the sparse coding scheme was adopted at all layers of the DCNNs, and the degree of sparseness increased along the hierarchy. That is, the coding scheme shifted from distributed-like coding at lower layers to local-like coding at higher layers. Further, the degree of sparseness was positively correlated with DCNNs' performance in object categorization, suggesting that the coding scheme was related to behavioral performance. Finally, with the lesion approach, we demonstrated that both external learning experiences and built-in gating operations were necessary to construct such a hierarchical coding scheme. In sum, our study provides direct evidence that DCNNs adopted a hierarchically-evolved sparse coding scheme as the biological brain does, suggesting the possibility of an implementation-independent principle underling object recognition.

DNNBrain: A Unifying Toolbox for Mapping Deep Neural Networks and Brains.

Deep neural networks (DNNs) have attained human-level performance on dozens of challenging tasks via an end-to-end deep learning strategy. Deep learning allows data representations that have multiple levels of abstraction; however, it does not explicitly provide any insights into the internal operations of DNNs. Deep learning's success is appealing to neuroscientists not only as a method for applying DNNs to model biological neural systems but also as a means of adopting concepts and methods from cognitive neuroscience to understand the internal representations of DNNs. Although general deep learning frameworks, such as PyTorch and TensorFlow, could be used to allow such cross-disciplinary investigations, the use of these frameworks typically requires high-level programming expertise and comprehensive mathematical knowledge. A toolbox specifically designed as a mechanism for cognitive neuroscientists to map both DNNs and brains is urgently needed. Here, we present DNNBrain, a Python-based toolbox designed for exploring the internal representations of DNNs as well as brains. Through the integration of DNN software packages and well-established brain imaging tools, DNNBrain provides application programming and command line interfaces for a variety of research scenarios. These include extracting DNN activation, probing and visualizing DNN representations, and mapping DNN representations onto the brain. We expect that our toolbox will accelerate scientific research by both applying DNNs to model biological neural systems and utilizing paradigms of cognitive neuroscience to unveil the black box of DNNs.