The present study investigated whether and how self-construal priming influences empathic neural responses to others' emotional states. We recorded event-related brain potentials to stimuli depicting the hands of unknown others experiencing painful or non-painful events from Chinese and Western participants after they had been primed in three conditions (independent self-construal priming, interdependent self-construal priming, and a control condition). Stimuli depicting painful events (as opposed to non-painful ones) elicited a positive shift of the fronto-central activity at 232-332 ms and of the central-parietal activity at 440-740 ms in the control condition. Moreover, neural responses to stimuli depicting painful (vs. non-painful) situations at 232-332 ms were decreased by interdependent self-construal priming among Chinese and by independent self-construal priming among Westerners. Our findings suggest that self-construal priming modulates sensitivity to perceived pain in unknown others and that this effect varies with culture.

The ongoing Chinese Color Nest Project (CCNP) was established to create normative charts for brain structure and function across the human lifespan, and link age-related changes in brain imaging measures to psychological assessments of behavior, cognition, and emotion using an accelerated longitudinal design. In the initial stage, CCNP aims to recruit 1520 healthy individuals (6–90 years), which comprises three phases: developing (devCCNP: 6–18 years, N = 480), maturing (matCCNP: 20–60 years, N = 560) and aging (ageCCNP: 60–84 years, N = 480). In this paper, we present an overview of the devCCNP, including study design, participants, data collection and preliminary findings. The devCCNP has acquired data with three repeated measurements from 2013 to 2017 in Southwest University, Chongqing, China (CCNP-SWU, N = 201). It has been accumulating baseline data since July 2018 and the second wave data since September 2020 in Chinese Academy of Sciences, Beijing, China (CCNP-CAS, N = 168). Each participant in devCCNP was followed up for 2.5 years at 1.25-year intervals. The devCCNP obtained longitudinal neuroimaging, biophysical, social, behavioral and cognitive data via MRI, parent- and self-reported questionnaires, behavioral assessments, and computer tasks. Additionally, data were collected on children’s learning, daily life and emotional states during the COVID-19 pandemic in 2020. We address data harmonization across the two sites and demonstrated its promise of characterizing the growth curves for the overall brain morphometry using multi-center longitudinal data. CCNP data will be shared via the National Science Data Bank and requests for further information on collaboration and data sharing are encouraged.

A rapidly emerging application of network neuroscience in neuroimaging studies has provided useful tools to understand individual differences in complex brain function. However, the variability of methodologies applied across studies - with respect to node definition, edge construction, and graph measurements- makes it difficult to directly compare findings and also challenging for end users to select the optimal strategies for mapping individual differences in brain networks. Here, we aim to provide a benchmark for best practices by systematically comparing the reliability of human brain network measurements of individual differences under different analytical strategies using the test-retest design of the resting-state functional magnetic resonance imaging from the Human Connectome Project. The results uncovered four essential principles to guide reliable network neuroscience of individual differences: 1) use a whole brain parcellation to define network nodes, including subcortical and cerebellar regions, 2) construct functional connectome using spontaneous brain activity in multiple slow bands, 3) optimize topological economy of networks at individual level, 4) characterise information flow with metrics of integration and segregation.

Rhythms of the brain are generated by neural oscillations across multiple frequencies.Following the natural log linear law of frequency distribution, these oscillations can be decomposed into distinct frequency intervals associated with specific physiological processes. This perspective on neural oscillations has been increasingly applied to study human brain function and related behaviors. In practice, relevant signals are commonly measured as a discrete time series, and thus the sampling period and number of samples determine the number and ranges of decodable frequency intervals. However, these limits have been often ignored by researchers who instead decode measured oscillations into multiple frequency intervals using a fixed sample period and numbers of samples. One reason for such misuse is the lack of an easy-to-use toolbox to implement automatic decomposition of frequency intervals. We report on a toolbox with a graphical user interface for achieving local and remote decoding rhythms of the brain system (DREAM) which is accessible to the public via GITHUB. We provide worked examples of DREAM used to investigate frequency-specific performance of both neural (spontaneous brain activity) and neurobehavioral (in-scanner head motion) oscillations. Using the imaging data from the Human Connectome Project, DREAM mapped the amplitude of these neural oscillations into multiple frequency bands as well as their test-retest reliability. DREAM analyzed the head motion oscillation and found that younger children moved their heads more than older ones across all five frequency intervals, particularly in the higher frequency intervals. In the age interval from 7 to 9 years, boys moved more than girls across all frequency intervals. Such sex-related motion effects were not detectable for other ages. These findings demonstrate the applicability of DREAM for frequency-specific human brain mapping.