Developmental Cognitive Neuroscience

These are the structural and functional (resting-state) data used for the accompanying manuscript assessing the amygdala's functional connectivity development from 3 months to 5 years of age. Functional data are provided for the laterobasal (LB) subregion, the superficial (SF) subregion, and the whole amygdala (LB + SF subregions). Average connectivity (controlling for age) across this developmental period is given by the negative_tstat (for anticorrelated, negatively valenced connectivity) and positive_tstat (for positively valenced connectivity). Linear age-related changes are shown in the _age_ images (positive and negative changes in connectivity with increasing age). Nonlinear age-related changes are shown in the _logage_ images (positive and negative changes in connectivity with increasing age). All _masked_ images denote the thresholded maps corrected for family-wise error that are reported in the manuscript's results.

Summary data on income significant cluster connectivity and control variables

CCNP takes its pilot stage (2013 – 2022) of the first ten-year. It aims at establishing protocols on the Chinese normative brain development trajectories across the human lifespan. It implements a structured multi-cohort longitudinal design (or accelerated longitudinal design), which is particularly viable for lifespan trajectory studies, and optimal for recoverable missing data. The CCNP pilot comprises three connected components: developing CCNP (devCCNP, baseline age = 6-18 years, 12 age cohorts, 3 waves, interval = 15 months), maturing CCNP (matCCNP, baseline age = 18-60 years, 14 age cohorts, 3 waves, interval = 39 months) and ageing CCNP (ageCCNP, baseline age = 60-84 years, 12 age cohorts, 3 waves, interval = 27 months). The developmental component of CCNP (devCCNP, 2013-2022), also known as "Growing Up in China", a ten-year's pilot stage of CCNP, has established follow-up cohorts in Chongqing (CKG, Southwest China) and Beijing (PEK, Northeast China). It is an ongoing project focused on longitudinal developmental research as creating and sharing a large-scale multimodal dataset for typically developing Chinese children and adolescents (ages 6.0-17.9 at enrollment) carried out in both school- and community-based samples. The devCCNP houses longitudinal data about demographics, biophysical measures, psychological and behavioral assessments, cognitive phenotyping, ocular-tracking, as well as multimodal magnetic resonance imaging (MRI) of brain's resting and naturalistic viewing function, diffusion structure and morphometry. With the collection of longitudinal structured images and psychobehavioral samples from school-age children and adolescents in multiple cohorts, devCCNP has constructed a full school-age brain template and its growth curve reference for Han Chinese which demonstrated the difference in brain development between Chinese and American school-aged children.*This dataset contains only T1-weighted MRI, Resting-state fMRI and Diffusion Tensor MRI data of devCCNP.To access the devCCNP Lite data, investigators must complete the application file Data Use Agreement on CCNP (DUA-CCNP) at http://deepneuro.bnu.edu.cn/?p=163 and have it reviewed and approved by the Chinese Color Nest Consortium (CCNC). All terms specified by the DUA-CCNP must be complied with. Meanwhile, the baseline CKG Sample on brain imaging are available to researchers via the International Data-sharing Neuroimaging Initiative (INDI) through the Consortium for Reliability and Reproducibility (CoRR). More information about CCNP can be found at: http://deepneuro.bnu.edu.cn/?p=163 or https://github.com/zuoxinian/CCNP. Requests for further information and collaboration are encouraged and considered by the CCNC, and please read the Data Use Agreement and contact us via deepneuro@bnu.edu.cn.

CCNP takes its pilot stage (2013 – 2022) of the first ten-year. It aims at establishing protocols on the Chinese normative brain development trajectories across the human lifespan. It implements a structured multi-cohort longitudinal design (or accelerated longitudinal design), which is particularly viable for lifespan trajectory studies, and optimal for recoverable missing data. The CCNP pilot comprises three connected components: developing CCNP (devCCNP, baseline age = 6-18 years, 12 age cohorts, 3 waves, interval = 15 months), maturing CCNP (matCCNP, baseline age = 18-60 years, 14 age cohorts, 3 waves, interval = 39 months) and ageing CCNP (ageCCNP, baseline age = 60-84 years, 12 age cohorts, 3 waves, interval = 27 months). The developmental component of CCNP (devCCNP, 2013-2022), also known as "Growing Up in China", a ten-year's pilot stage of CCNP, has established follow-up cohorts in Chongqing (,CKG, Southwest China) and Beijing (PEK, Northeast China). It is an ongoing project focused on longitudinal developmental research as creating and sharing a large-scale multimodal dataset for typically developing Chinese children and adolescents (ages 6.0-17.9 at enrollment) carried out in both school- and community-based samples. The devCCNP houses longitudinal data about demographics, biophysical measures, psychological and behavioral assessments, cognitive phenotyping, ocular-tracking, as well as multimodal magnetic resonance imaging (MRI) of brain's resting and naturalistic viewing function, diffusion structure and morphometry. With the collection of longitudinal structured images and psychobehavioral samples from school-age children and adolescents in multiple cohorts, devCCNP has constructed a full school-age brain template and its growth curve reference for Han Chinese which demonstrated the difference in brain development between Chinese and American school-aged children.To access the data, investigators must complete the application file Data Use Agreement on CCNP (DUA-CCNP) at http://deepneuro.bnu.edu.cn/?p=163 and have it reviewed and approved by the Chinese Color Nest Consortium (CCNC). All terms specified by the DUA-CCNP must be complied with. Meanwhile, the baseline CKG Sample on brain imaging are available to researchers via the International Data-sharing Neuroimaging Initiative (INDI) through the Consortium for Reliability and Reproducibility (CoRR). More information about CCNP can be found at: http://deepneuro.bnu.edu.cn/?p=163 or https://github.com/zuoxinian/CCNP. Requests for further information and collaboration are encouraged and considered by the CCNC, and please read the Data Use Agreement and contact us via deepneuro@bnu.edu.cn. The CCNP data will be fully available to the research community when acquisition is completed for the pilot CCNP. At this stage, the CCNP data are only available to researchers and collaborators of CCNC.

This is the complete Publication Package for the Article: "Happy for Us not Them: Differences in neural activation in a vicarious reward task between family and strangers during adolescent development" Please start with the README file first. During adolescence social-interactions with other people become more relevant. One key aspect of these interactions is cooperative behavior. Cooperation relies on a set of cognitive and affective mechanisms. In this study, we focused on the mental ability to feel happy for another person's positive experience, called vicarious joy. We investigated the neural mechanisms of this ability using a false-choice vicarious reward fMRI task. Participants played a game where they could win monetary rewards for themselves, their mother, their father, and a stranger. A region-of-interest (ROI) analysis of the Nucleus Accumbens revealed robust activation in this region for personal reward as well as vicarious rewards for both parents. Vicarious reward for a stranger was not associated with activation within the Nucleus Accumbens. ROI activation was associated with self-reported vicarious joy for mother and father. A Prisoner's Dilemma game outside the scanner showed an increase in cooperative behavior until age 14 for parents and strangers, followed by a decline for the stranger but not for the parents. Together, these findings demonstrate that adolescence is an important time for developing ingroup-outgroup relations.

This is the complete Publication Package for the Article: "Happy for Us not Them: Differences in neural activation in a vicarious reward task between family and strangers during adolescent development" Please start with the README file first. During adolescence social-interactions with other people become more relevant. One key aspect of these interactions is cooperative behavior. Cooperation relies on a set of cognitive and affective mechanisms. In this study, we focused on the mental ability to feel happy for another person's positive experience, called vicarious joy. We investigated the neural mechanisms of this ability using a false-choice vicarious reward fMRI task. Participants played a game where they could win monetary rewards for themselves, their mother, their father, and a stranger. A region-of-interest (ROI) analysis of the Nucleus Accumbens revealed robust activation in this region for personal reward as well as vicarious rewards for both parents. Vicarious reward for a stranger was not associated with activation within the Nucleus Accumbens. ROI activation was associated with self-reported vicarious joy for mother and father. A Prisoner's Dilemma game outside the scanner showed an increase in cooperative behavior until age 14 for parents and strangers, followed by a decline for the stranger but not for the parents. Together, these findings demonstrate that adolescence is an important time for developing ingroup-outgroup relations.

The developmental pattern of the amygdala throughout childhood and adolescence has been inconsistently reported in previous neuroimaging studies. Given the relatively small size of the amygdala on full brain MRI scans, discrepancies may be partly due to methodological differences in amygdalar segmentation. To investigate the impact of volume extraction methods on amygdala volume, we compared FreeSurfer, FSL and volBrain segmentation measurements with those obtained by manual tracing. The manual tracing method, which we used as the’gold standard’, exhibited almost perfect intra- and inter-rater reliability. We observed systematic differences in amygdala volumes between automatic (FreeSurfer and volBrain) and manual methods. Specifically, compared with the manual tracing, FreeSurfer estimated larger amygdalae, and volBrain produced smaller amygdalae while FSL demonstrated a mixed pattern. The tracing bias was not uniform, but higher for smaller amygdalae. We further modeled amygdalar growth curves using accelerated longitudinal cohort data from the Chinese Color Nest Project. Trajectory modeling and statistical assessments of the manually traced amygdalae revealed linearly increasing and parallel developmental patterns for both girls and boys, although the amygdalae of boys were larger than those of girls. Compared to these trajectories, the shapes of developmental curves were similar when using the volBrain derived volumes. FreeSurfer derived trajectories had more nonlinearities and appeared flatter. FSL derived trajectories demonstrated an inverted U shape and were significantly different from those derived from manual tracing method. The use of amygdala volumes adjusted for total gray-matter volumes, but not intracranial volumes, resolved the shape discrepancies and led to reproducible growth curves between manual tracing and the automatic methods (except FSL). Our findings revealed steady growth of the human amygdala, mirroring its functional development across the school age. Methodological improvements are warranted for current automatic tools to achieve more accurate tracing of the amygdala at school age, calling for next generation tools.

Raw EEG data in european data format (EDF) format. Descriptive and clinical data of the subjects in textual tab delimited format. Columns report for each subject: group (blind/severely impaired, BSI or sighted, S), code, gender, age expressed in decimal years, age bin, degree of visual impairment, diagnosis, motor coordination impairment classification (MCI, 1 = not impaired, 2 = impaired), and hypotonia classification (1 = not impaired, 2 = impaired). NA correspond to undefined classification. Visual experience is crucial for the development of neural processing. For example, alpha activity development is a vision-dependent mechanism. Indeed, studies report no alpha activity is present in blind adults. Nevertheless, studies have not investigated the developmental trajectory of this activity in infants and children with blindness. Here, we hypothesize that the difference in neural activity of blind compared to sighted subjects is: absent at birth, progressive with age, specifically occipital and linked to a gradual motor impairment. Therefore, we consider spectral power of resting-state EEG and its association with motor impairment indices, in blind subjects and in sighted controls between 0 and 11 years of age. Blind subjects show posterior alpha activity during the first three years of life, although weaker and slower maturing compared to sighted subjects. The first great differentiation between blind and sighted subjects occurs between 3 and 6 years of age. Starting in this period, reduced alpha activity increases the probability of motor impairment in blind subjects, likely because of impaired perception/interaction. These results show that visual experience mediates the neural mechanisms generating alpha oscillations during the first years of life, suggesting that it is a sensitive period for the plasticity of this process.

This fileset contains Reliable Components Analysis data related to the following published article: Catherine Manning, Blair Kaneshiro, Peter J. Kohler, Mihaela Duta, Gaia Scerif and Anthony M. Norcia. (2019). Neural dynamics underlying coherent motion perception in children and adults. Developmental Cognitive Neuroscience, 38, 100670 https://doi.org/10.1016/j.dcn.2019.100670. Dataset demographics_incl.csv is in .csv file format and includes the age for each of 122 participants (102 children, 20 adults). DevCond refers to whether the participant has a developmental condition (1 = developmental condition, 0 = no developmental conditions; i.e., none of the participants have developmental conditions). Hands refers to the response condition that the participant was assigned to (1 = used left hand to respond up & right hand to respond down; 2 = used right hand to respond up and left hand to respond down). ID numbers beginning C are child participants and ID numbers beginning A are adult participants. Data files cohOut_group_1.mat, cohOut_group_2.mat, cohOut_group_3.mat, cohOut_group_4.mat, MOVEP_datIn_group1.mat, MOVEP_datIn_group2.mat, MOVEP_datIn_group3.mat, MOVEP_datIn_group4.mat, MOVEP_datInLong_group1.mat, MOVEP_datInLong_group2.mat, MOVEP_datInLong_group3.mat, MOVEP_datInLong_group4.mat are group summaries, in comparison to the 122 Formatted_XXXX.3_40_preproc.mat files, which are individual data files. More information about the contents of these files are provided in the document Details on group summary files.docx

This collection contains electroencephalography (EEG) and Reliable Components Analysis datasets and metadata supporting the published article: Catherine Manning, Blair Kaneshiro, Peter J. Kohler, Mihaela Duta, Gaia Scerif and Anthony M. Norcia. (2019). Neural dynamics underlying coherent motion perception in children and adults. Developmental Cognitive Neuroscience, 38, 100670 https://doi.org/10.1016/j.dcn.2019.100670. Item 1 (EEG data) in this collection contains 122 matlab Formatted_XXXX.3_40_preproc.mat files and 1 MarkedUpFile.docx file. Item 2 (Reliable Components analysis datasets) in this collection contains 12 matlab files: 4 MOVEP_datIn_groupX.mat files, 4 MOVEP_datInLong_groupX.mat files and 4 cohOut_group_X.mat files. Data files demographics_incl.csv and Details on group summary files.docx are also included in this item. The ReadME file (DCNpaper_DataUpload-.docx) is part of both items in this collection. Study aims and methodology:The goal of the current study was to use high-density EEG to characterise age-related differences in direction-specific evoked responses in a large sample of 6- to 12-year-old children and adults. Participants were 102 children aged between 6 and 12 years and 20 adults aged between 18 and 35 years (9 females), with no reported history of developmental conditions and normal or corrected-to-normal vision (assessed with a Snellen chart). The experimental task was presented on a Dell Precision M3800 laptop using MATLAB (Mathworks, MA, USA) and the Psychophysics Toolbox. EEG signals were acquired with a 128-electrode Hydrocel Geodesic Sensor Net connected to Net Amps 300, using NetStation 4.5 software. A photodiode was attached to the monitor to independently verify the timing of stimulus presentation. Participants made their responses using a Cedrus RB-540 response box.The authors isolated direction-specific responses using the same approach as previous studies, whereby participants were presented with an initial ‘boil’ period of incoherent motion, followed by coherent motion. Additionally, to extend and complement studies that have focused on averaged waveforms in certain electrodes, here the authors used the entire sensor array to identify maximally reliable components with a data-driven component decomposition technique that has been used successfully to investigate motion perception in adults.For more details on the methodology, please read the related published article.