The data presented here are related to the studyforrest project that uses the movie ‘Forrest Gump’ to map brain functions in a real-life context using functional magnetic resonance imaging (fMRI). However, neural-related fMRI signals are often small and confounded by various noise sources (i.e., artifacts) that makes searching for the signals induced by specific cognitive processes significantly challenging. To make neural-related signals stand out from the noise, the audio-visual movie watching fMRI dataset from the project was denoised by a combination of spatial independent component analysis and manual identification of signals or noise. Here, both the denoised data and the labeled decomposed components are shared to facilitate further study. Compared with the original data, the denoised data showed a substantial improvement in the temporal signal-to-noise ratio and provided a higher sensitivity in subsequent analyses such as in an inter-subject correlation analysis.

Recent studies found that the deep convolutional neural networks (DCNNs) trained to recognize facial identities spontaneously learned features that support facial expression recognition, and vice versa. Here, we showed that the self-emerged expression-selective units in a VGG-Face trained for facial identification were tuned to distinct basic expressions and, importantly, exhibited hallmarks of human expression recognition (i.e., facial expression confusion and categorical perception). We then investigated whether the emergence of expression-selective units is attributed to either face-specific experience or domain-general processing by conducting the same analysis on a VGG-16 trained for object classification and an untrained VGG-Face without any visual experience, both having the identical architecture with the pretrained VGG-Face. Although similar expression-selective units were found in both DCNNs, they did not exhibit reliable human-like characteristics of facial expression perception. Together, these findings revealed the necessity of domain-specific visual experience of face identity for the development of facial expression perception, highlighting the contribution of nurture to form human-like facial expression perception.

The approximate number system (ANS) is vital for survival and reproduction in animals and is crucial for constructing abstract mathematical abilities in humans. Most previous neuroimaging studies focused on identifying discrete brain regions responsible for the ANS and characterizing their functions in numerosity perception. However, a neuromarker to characterize an individual’s ANS acuity is lacking, especially one based on whole-brain functional connectivity (FC). Here, based on the resting-state functional magnetic resonance imaging (rs-fMRI) data obtained from a large sample, we identified a distributed brain network (i.e. a numerosity network) using a connectome-based predictive modeling (CPM) analysis. The summed FC strength within the numerosity network reliably predicted individual differences in ANS acuity regarding behavior, as measured using a nonsymbolic number-comparison task. Furthermore, in an independent dataset of the Human Connectome Project (HCP), we found that the summed FC strength within the numerosity network also specifically predicted individual differences in arithmetic skills, but not domain-general cognitive abilities. Therefore, our findings revealed that the identified numerosity network could serve as an applicable neuroimaging-based biomarker of nonverbal number acuity and arithmetic skills.

Depression has become increasingly common among adolescents worldwide and has severe negative impact on an individual’s mental and social development. Early identification and diagnosis of depression during adolescence are crucial for improving the well-being of affected individuals. While natural language processing (NLP) has shown effectiveness in identifying depression in adults via social media, its application in predicting depression in adolescents remains challenging. This study aimed to develop a simple yet highly applicable method for predicting depression in adolescents within a school setting. We collected written compositions from 4,715 students aged 10 to 17, using their scores on the Children's Depression Inventory (CDI) to categorize them into high-risk and low-risk groups for depression. Then, we developed three types of computational models combining various feature extraction (theory-based vs. data-based) and classification techniques (classical machine learning algorithm vs. state-of-the-art deep neural networks), and compared their predictive performance in identifying individuals at risk. We found that all models exhibited promising performance in predicting depressive tendencies, with the recurrent neural network model outperforming the others. Our study demonstrates the feasibility of employing students’ written compositions to identify those at higher risk of depression, and providing a potential solution for early detection of depressive tendencies in adolescents.

The approximate number system (ANS) is vital for survival and reproduction in animals and crucial in constructing abstract mathematical abilities in humans. Most previous neuroimaging studies focused on identifying discrete brain regions responsible for the ANS and characterizing their functions in numerosity perception. However, there lacks a neuromarker to characterize an individual’s ANS acuity, especially one based on the whole-brain functional connectivity (FC). Here, we identified a distributed brain network (i.e., numerosity network) using a connectome-based predictive modeling (CPM) analysis on the resting-state functional magnetic resonance imaging (rs-fMRI) data based on a large sample size. The summed strength of all FCs within the numerosity network could reliably predict individual differences of the ANS acuity in behavior. Furthermore, in an independent dataset from the Human Connectome Project (HCP), we found that the summed FC strength within the numerosity network could also predict individual differences in arithmetic skills. Our findings illustrate that the numerosity network we identified could be an applicable neuromarker of the non-verbal number acuity and might serve as the neural basis underlying the known link between the non-verbal number acuity and mathematical abilities.