ABCD Neurocognitive Prediction Challenge

The ABCD Neurocognitive Prediction Challenge (ABCD-NP-Challenge 2019) invites researchers to submit their method for predicting fluid intelligence from T1-weighed MRI (about 8.5K subjects in total, age 9-10 years). The data of about 4.1k individuals will be provided for training. The accuracy of each method will be assessed on its predicted fluid intelligence scores of about 4.4K children, whose actual scores will be revealed after the challenge deadline. Downloading the data needs prior approval by NIH NDAR, which will require sign off by the institution you are affiliated with. So start the application process early. Please also sign up to the emailing list to receive updates about the challenge.

About ABCD: The ABCD study is the largest long-term study of brain development and child health in the United States. The ABCD Research Consortium consists of a Coordinating Center, a Data Informatics and Analysis Center, and 21 research sites across the country, which recruited over 11K children ages 9-10. Of each participant, the study acquires structural, diffusion functional brain MRIs as well as genetics, neuropsychological, behavioral, and other health assessments. The goal of ABCD is to determine how childhood experiences (such as sports, videogames, social media, unhealthy sleep patterns, and smoking) interact with each other and with a child's changing biology to affect brain development and social, behavioral, academic, health, and other outcomes.

About the Challenge: Determining the neural mechanisms underlying general intelligence is fundamental to understanding cognitive development, how this relates to real-world health outcomes, and how interventions (education, environment) might improve outcomes through adolescence and into adulthood. A major factor in measuring general intelligence is fluid intelligence (Carroll, 1993), which the ABCD measures via the NIH Toolbox Neurocognition battery (Akshoomoff et al., 2014) and from which demographic confounding factors (e.g., sex and age) are removed. The fluid intelligence scores of 4154 subjects will be provided to participants for training (3739 samples) and validation (415 samples), while the scores of about 4.5K subjects (currently 3648 can be downloaded) will have to be predicted based on T1-weighted MRI. The MRIs are acquired according to the following acquisition protocol.

The fluid intelligence scores are pre-residualized on data collection site, sociodemographic variables and brain volume. Using the R function lm, a linear regression model was constructed with fluid intelligence as the dependent variable and brain volume, data collection site, age at baseline, sex at birth, race/ethnicity, highest parental education, parental income, and parental marital status as independent variables. Any subject in the ABCD NDA Release 1.1 data set with a missing value in the dependent or independent variables in this linear model was deleted from the training set. After fitting the linear model on the resulting subset of list-wise complete data, the residuals were extracted; these residuals constitute the training values for the prediction contest. The R code utilized for computing the residuals on the training data will be made available for download soon.

In addition to the fluid intelligence scores, the challenge organizers will also provide skull stripped images affinely aligned to the SRI 24 atlas, segmented into regions of interest according to that atlas, and the corresponding volume scores of each ROI via a csv file. The challenge organizers nor ABCD are responsible for the quality of the derived data. Publications using the data should cite the Data Supplement of Pfefferbaum et al., Altered Brain Developmental Trajectories in Adolescents After Initiating Drinking. Am J Psychiatry, 175(4), pp. 370-380, 2018. Specifically, the raw T1-weighted MRI was first transformed into a nifti file using the Minimal Processing Pipeline by ABCD (Hager at al., Image processing and analysis methods for the Adolescent Brain Cognitive Development Study, Under Review at Neuroimage). The T1 images were then applied to the cross-sectional component of the NCANDA pipeline (see Data Supplement). The processing involved noise removal and correcting field inhomogeneity confined to the brain mask defined by non-rigidly aligning SRI24 atlas to the T1w MRI via ANTS. The brain mask was refined by majority voting across maps extracted by FSL BET, AFNI 3dSkullStrip, FreeSurfer mri_gcut, and the Robust Brain Extraction (ROBEX) method, which were applied on combinations of bias and non-bias corrected T1w images. Using the refined masked, image inhomogeneity correction was repeated and the skull-stripped T1w image was segmented into brain tissue (gray matter, white matter, and cerebrospinal fluid) via Atropos. Gray matter tissue was further parcellated according to the SRI24 atlas, which was non-rigidly registered to the T1w image via ANTS. Afterwards, skull-stripped T1w image and corresponding segmentations were affinely mapped (pose and scale) to the SRI24 atlas. The results were visual inspected and rejected from the challenge if they failed the two-tier quality check.

Contestants will be ranked separately on the validation data set (pre-residualized fluid intelligence will be provided) and on the test data sets. For each data set, we will compute the Mean Squared Error (MSE) between their predicted scores and the pre-residucal fluid intelligence scores (R code). The pre-residualized fluid intelligence is computed via the algorithm described for the training data. If the algorithm is unable to produce a numerical prediction for a given test subject, the predicted value for that subject will be set to the value that gives the worst performance (i.e., largest MSPE) from among the set of values produced by the same algorithm on the subjects in the test dataset.

For more information about the challenge, please also read a recent article about the challenge by Computer Vision News and the FAQ-page.

Important Dates

Dataset and Instructions

December 17, 2018

Team Registration Deadline

February 15, 2019

Submission Deadline

Results and Code: March 10, 2019
Manuscript: March 17, 2019

Data Access

The fluid intelligence scores, raw T1-weighted MRIs, and derived data will be accessible to the challenge participants via the NDAR portal. To gain access to the data, please follow the four steps outlined in the tutorial. If NDAR approves your application to gain access to the data, they will allow you to download 500GB of the ABCD data for free. This credit can be used to download the raw baseline T1-weighted of the ABCD study or the corresponding derived data provided by the challenge organizers. Note, that (as of today) the entire training and validation dataset can be downloaded but only 3648 samples of the test dataset. Before the submission deadline, around 1K additional test samples will be made available.

The preprocessed data is available through NDAR as a data collection, which consists of the Training dataset, Validation dataset, and Testing dataset. To download the preprocessed imaging and volumetric data follow the steps here. To download the residualized fluid intelligence scores for training and validation, simply sign into NDAR, select the Training or Validation data set, and then download the csv file listed under the 'Results' tab.

Team Registration

There are no limits or restrictions on the team members as long as the team complies with the NIMH Data Archive Data Use Certification of the ABCD project. Teams should register by the deadline using this form. Note, if members of the team are from labs associated with ABCD then their submission will be reviewed but the results will not be posted on the leader board.

Final Submission Information

An eligible submission consists of the predictions for the test subjects, the source code generating those predictions, and a manuscript describing the method and findings. The subject identifiers and corresponding predictions will need to be recorded in a CSV file, for which a template will be provided by the organizers. Eligible CSV file contains the predictions of at least 99% of the test subjects and are entirely based on data provided by the challenge, i.e. the T1 MRI and derived data. Details about the source code submission will be provided as we approach the deadline. The CSV file with the predictions and the source code must be uploaded to the submission website (to be announced) by the submission deadline.

Within a week after the submission deadline, the submission must be completed with a manuscript adhering to the formatting guidelines of MICCAI (including the page limitation of 8 pages). In addition to those guidelines, an individual cannot be listed as an author on more than 6 manuscripts. Manuscripts need to describe methods and results that are different from each other and from previously published material. The manuscript needs to clearly describe the data used for prediction, the method, and findings including the prediction error during training and validation. Manuscripts can be accompanied by supplemental information.

Submissions not adhering to these guidelines will be automatically rejected. Submissions from research labs associated with ABCD will be reviewed but not listed on the leader board.

Leader Board (Validation)

To evaluate your results on the Validation set (415 subjects), train your model on the Training set and save the predicted scores of the 415 validation subjects in a csv file (similar to this template). Download the ground-truth scores csv file and run this R code to calculate MSE and R-Squared metrics for your results. Please submit these self-assessed validation results using this form. After you click submit, you will see a page indicating that your information was received successfully. No separate emails will be sent to you. The following table will be updated once every 24 hours.

Validation Leader Board. Last Updated: Feb 13, 2019
Rank Team Name Mean Squared Error (MSE)
1 ABCD-NP-Test 100.00



Kilian M. Pohl

SRI International

Wesley K. Thompson

University of California, San Diego


Ehsan Adeli

Stanford University

Bennett A. Landman

Vanderbilt University

Marius G. Linguraru

Children's National Health System

Susan Tapert

University of California, San Diego