The 2021 Challenge:

How the Human Brain Makes Sense of a World in Motion

Challenge Overview

Understanding how the human brain works is one of the key challenges that science and society face. The Algonauts Challenge proposes a test of how well computational models do today. This test is intrinsically open and quantitative. This will allow us to precisely assess the progress in explaining the human brain.

Our experience of the world is one of objects and persons moving continuously and interacting in events. The primary target of the 2021 challenge is predicting human brain responses to short video clips of such everyday events.

Watching events unfold over time activates large swathes of the human cortex. We pose the question: How well does your computational model account for that response?

Competition Tracks

The main goal of the Algonauts Project 2021 Challenge is to use computational models to predict brain responses recorded while participants viewed short video clips of everyday events.

We provide functional MRI data collected from 10 human subjects that watched over 1,000 short video clips. Please click here to learn more about the fMRI brain mapping and analysis that we conducted.

There are 2 challenge tracks: the Mini Track and the Full Track. The Mini Track focuses on pre-specified regions of the visual brain known to play a key role in visual processing. The Full Track considers responses across the whole brain. Participants can play in the Mini Track, Full Track, or both.

The task is: Given a) the set of videos of everyday events and b) the corresponding brain responses recorded while human participants viewed those videos, use computational models to predict brain responses for videos for which we held out brain data.

Mini Track (9 ROIs)

The goal of the Mini Track is to predict brain responses in specific regions of interest (ROIs), that is pre-specified regions of the brain known to play a key role in visual perception. Participants submit the predicted brain responses for each ROI in the format described in the development kit. We score the submission by measuring the predictivity for each voxel in all the ROIs for all the subjects and show the overall mean predictivity in the leaderboard calculated across voxels, ROIs, and subjects.

We provide the following data for the Mini Track (download here):

  • Training Set
    1,000 3-second videos + fMRI human brain data of 10 subjects in response to viewing muted videos from this set. The data is provided for 9 ROIs of the visual brain (V1, V2, V3, V4, LOC, EBA, FFA, STS, PPA) in a Pickle file (e.g. V1.pkl) that contains a num_videos x num_repetitions x num_voxels matrix. For each ROI, we selected voxels that showed significant split-half reliability.
  • Test Set
    102 3-second videos only (.mp4 format).
  • Development Kit
    We provide a development kit to obtain baseline performance provided in the leaderboard. It consists of Python scripts to:
    • Extract activations of a computational model (AlexNet) in response to video clips.
    • Train a voxel-wise encoding model using AlexNet activations to predict brain responses, and to evaluate the encoding model using cross-validation on the training data.
    • Prepare the predicted brain responses from the trained encoding model in the format required for submission to the challenge.

Learn more and participate in the Mini Track here

Mini Track Leaderboard (Final)

Noise-Normalized Correlation
Rank Team Name Challenge Score V1 V2 V3 V4 LOC EBA FFA STS PPA
1 huze 0.7110 0.7063 0.7084 0.7025 0.7412 0.7320 0.7693 0.8059 0.5989 0.6345 Report
2 bionn 0.6490 0.6497 0.6564 0.6495 0.6862 0.6950 0.7129 0.7161 0.4930 0.5820 Report
3 shinji 0.6208 0.6557 0.6308 0.6038 0.6208 0.6570 0.6748 0.6654 0.4970 0.5816 Report
4 Hc33 0.5917 0.5594 0.5525 0.5469 0.5901 0.6491 0.6649 0.6999 0.4971 0.5653 Report
5 rob_the_builder 0.5884 0.5835 0.5530 0.5484 0.5894 0.6259 0.6574 0.7128 0.4877 0.5376 Report+Code
6 Aakashagr 0.5843 0.5812 0.5507 0.5397 0.5529 0.6411 0.6512 0.7213 0.4927 0.5276
7 NeuBCI 0.5760 0.5300 0.4999 0.5509 0.5510 0.6370 0.6462 0.7347 0.5168 0.5176
8 arnenix 0.5715 0.6323 0.6172 0.5668 0.5454 0.5680 0.5299 0.6969 0.4727 0.5146
9 michaln 0.5710 0.4985 0.5019 0.5223 0.5801 0.6472 0.6470 0.7074 0.4842 0.5504 Report
10 rematchka 0.5674 0.5830 0.5678 0.5421 0.5521 0.6117 0.6172 0.6566 0.4588 0.5169 Report
11 ikeroulu 0.4527 0.3865 0.3895 0.4159 0.4539 0.5385 0.5440 0.5626 0.3726 0.4104
12 Mukesh 0.4521 0.3955 0.3867 0.4100 0.4102 0.4786 0.5097 0.5935 0.4418 0.4427
13 Aryan120804 0.4491 0.3421 0.3424 0.3618 0.3957 0.5255 0.5442 0.6368 0.4419 0.4518 Report
14 sricharan92 0.4448 0.5202 0.5016 0.4779 0.4681 0.4269 0.4454 0.4808 0.3154 0.3674
15 lingfei 0.4397 0.4743 0.4510 0.4074 0.4117 0.4755 0.5065 0.5021 0.3484 0.3806
16 AlexNet-OrganizerBaseline 0.4198 0.4444 0.4340 0.4052 0.4275 0.4391 0.4439 0.5041 0.3324 0.3478

Each brain region is scored based on their noise-normalized correlation with our held-out brain data. The challenge score is the average noise-normalized correlation across all 9 brain regions.
(Ongoing CodaLab Table Here => click the blue "Challenge Phase" box)

Full Track (Whole Brain)

The goal of the Full Track is to predict brain responses across the whole brain. Participants submit predicted whole-brain responses (for the provided set of reliable voxels) in the format described in the development kit. We score the submission by measuring the predictivity for each voxel in the selected set for all the subjects and display the overall mean predictivity in the leaderboard calculated across all voxels and all subjects.

We provide the following data for the Full Track (download here):

  • Training Set
    1,000 3-second videos + fMRI human brain data of 10 subjects in response to viewing muted videos from this set. The data is provided for selected voxels across the whole brain showing reliable responses to videos in a Pickle file (e.g. WB.pkl) that contains a num_videos x num_repetitions x num_voxels matrix. We also provide subject specific masks to visualize which voxels were selected.
  • Test Set
    102 3-second videos only (.mp4 format).
  • Development Kit
    We provide a development kit to obtain baseline performance provided in the leaderboard. It consists of Python scripts to:
    • Extract activations of a computational model (AlexNet) in response to video clips.
    • Train a voxel-wise encoding model using AlexNet activations to predict brain responses, and to evaluate the encoding model using cross-validation on the training data.
    • Visualize the data in brain space (MNI standard space).
    • Prepare the predicted brain responses from the trained encoding model in the format required for submission to the challenge.

Learn more and participate in the Full Track here

Full Track Leaderboard (Final)

Noise-Normalized Correlation
Rank Team Name Challenge Score
1 huze 0.3733 3D Visualization Report
2 bionn 0.3490 3D Visualization Report
3 shinji 0.3395 3D Visualization Report
4 Hc33 0.3225 3D Visualization Report
5 michaln 0.3117 3D Visualization Report
6 han_ice 0.3109
7 Aakashagr 0.2993
8 rematchka 0.2641
9 lingfei 0.2401
10 ikeroulu 0.2079
11 ernest.kirubakaran 0.2060
12 AlexNet-OrganizerBaseline 0.2035 3D Visualization

The challenge score is the average noise-normalized correlation with our held-out brain data across all voxels.
(Ongoing CodaLab Table Here => click the blue "Challenge Phase" box)

Comparison Metric

To determine how well your models can predict brain responses we compare your submitted synthetic brain data (i.e. those predicted from your model to the left out video clips) to the empirically measured brain responses. The comparison is carried out using Pearson's correlation, comparing for each voxel the 102-dimensional vector formed by the activations for the 102 test set video clips.

How to Predict Brain Data Using Computational Models?

There are different ways to predict brain data using computational models. We put close to no restrictions on how you do so (see Challenge Rules). However, a commonly used approach is to use voxel-wise encoding models, and we provide a development kit to implement such a model.
Click here to learn more about voxel-wise encoding

Challenge Data Release

You can watch this 4-minute YouTube video for a quick introduction to the Algonauts dataset and Development Kit.

Once you are ready to get started, click here to download the data for the Algonauts Project 2021 Challenge.
The link to the Development Kit on GitHub is below.

Training Data

The training data consist of 1,000 different 3-second video clips, plus the fMRI brain responses to them.

Test Data

The test data consist of 102 different 3-second video clips only, and we keep the fMRI data held back.

Development Kit

We provide a development kit here on GitHub to help you get started. It consists of Python scripts to:

  • Extract activations of a computational model (AlexNet) in response to video clips.
  • Train a voxel-wise encoding model using AlexNet activations to predict brain responses, and to evaluate the encoding model using cross-validation on the training data.
  • Visualize the prediction results in brain space (MNI standard space).
  • Prepare the predicted brain responses from the trained encoding model in the format required for submission to the challenge.


It is available as a set of Python scripts, or as a easy-to-use Google Colab where you can prepare challenge submissions online.

Challenge Rules and Best Practices

1. Participants can use any (external) data for model building, any model, and any procedure to predict brain data with one exception: Participants that use the test set for training will be disqualified (in particular brain data generated using the test set).

2. Each participant (single researchers or team) can make 1 submission per day per track.

3. Participants must upload a short report (~4-8 pages) describing their model building process for their best model to a preprint server (e.g. bioRxiv, arXiv), or send a PDF by email to algonauts.mit@gmail.com.
Please use this form to submit the challenge report within 7 days of the challenge submission deadline to be considered for the evaluation of challenge outcomes. Participants that do not make their approach open and transparent cannot be considered. We additionally encourage participants to make their code available online (e.g. on Github) and link to this during their form submission.

Paper and Citation

If you use the data provided for the Algonauts Project 2021 Challenge please cite this manuscript.

Acknowledgements

The experiments were conducted at the Athinoula A. Martinos Imaging Center at the McGovern Institute for Brain Research, Massachusetts Institute of Technology, on a Siemens PrismaFit 3T scanner (Erlangen, Germany) that was supported with funding from a NIH Shared Instrumentation Grant Program; specifically, grant number 1S10OD021569.

Important Dates

Training data, test data, and development kit released: May 1st, 2021
Challenge submission deadline: August 15th, 2021 at 11:59pm (UTC-4)
Challenge report submission deadline: August 22nd, 2021
Challenge results released: August 23rd, 2021
Session at CCN 2021 (virtual): September 7th, 2021

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