Medical Imaging Benchmark

Decoding the
Functional Connectome

The ABIDE (Autism Brain Imaging Data Exchange) benchmark challenges AI to classify Autism Spectrum Disorder (ASD) solely from resting-state fMRI brain scans.

View LeaderboardUnderstand the Data

Benchmark Stats

2,226
Total Subjects
98.0%*
Current SOTA (Acc)
*on subset
17+
Imaging Sites

How the Data Works

ABIDE isn't just a folder of JPEGs. It's high-dimensional 4D functional imaging data. Understanding the pipeline is critical for building models.

Brain Parcellation
Step 1: Parcellation

Region of Interest (ROI) Extraction

We map the 4D brain volume to an atlas (like Harvard-Oxford or AAL) to segment the brain into distinct functional regions.

BOLD Time Series
Step 2: Time Series

BOLD Signal Extraction

For each ROI, we extract the mean BOLD signal over time. This raw signal (shown above) represents neural activity fluctuations.

Connectivity Matrix
Step 3: Functional Graph

Functional Connectivity

We compute the correlation between every pair of regions. This matrix captures the "functional wiring" of the brain—the key to diagnosing ASD.

State of the Art Results

Comparing model performance on the standardized ABIDE I & II datasets. Note that the top result (Plymouth DL) was evaluated on a subset of participants.

Model RankABIDE I AccABIDE I AUCABIDE II AccABIDE II AUCModalityEval Strategy
#1
MAACNN
Research
75.1%-72.9%-
fMRICNN
k-fold
#2
Plymouth DL Model
Research
98.0%---
fMRIDeep Learning with XAI
k-fold
#3
MCBERT
Research
93.4%---
fMRI + PhenotypicMulti-modal CNN-BERT
LOSO
#4
AE-FCN
Research
85.0%---
fMRI + sMRIAutoencoder + Fully Connected Network
10-fold
#5
Multi-Atlas DNN
Research
78.1%---
fMRIDeep Neural Network
k-fold
#6
ASD-SWNet
Research
76.5%81.00--
fMRIShared-weight CNN
10-fold
#7
AL-Negat
Research
74.7%---
fMRIGraph Neural Network
k-fold
#8
BrainGNN
Research
73.3%---
fMRIGraph Neural Network
10-fold
#9
GCN
Research
72.2%78.00--
fMRIGraph Convolutional Network
k-fold
#10
Multi-Task Transformer
Research
72.0%---
fMRITransformer
k-fold
#11
PHGCL-DDGFormer
Research
70.9%---
fMRIGraph Transformer
k-fold
#12
SVM with Connectivity Features
Research
70.1%77.00--
fMRISupport Vector Machine
10-fold
#13
Deep Learning (Heinsfeld)
Research
70.0%---
fMRIDeep Neural Network
k-fold
#14
MVS-GCN
Research
69.4%69.01--
fMRIMulti-view Site Graph Convolutional Network
LOSO
#15
Abraham Connectomes
Research
67.0%---
fMRIConnectome Analysis
k-fold
#16
Random Forest
Baseline
63.0%---
fMRIEnsemble (Trees)
k-fold
#17
BrainGT
Research
-78.70--
fMRIGraph Transformer
10-fold
#18
DeepASD
Research
---93.00
fMRI + SNPsAdversary-regularized GNN
10-fold

Why Graph Neural Networks (GNNs) Rule

The brain is fundamentally a network. Traditional CNNs struggle because functionally connected regions (like the default mode network) can be spatially distant.

GNNs excel by modeling ROIs as nodes and connectivity as edges. Recent models like BrainGT (Graph Transformer) and DeepASD (Adversarial GNN) capture subtle topological disruptions in ASD brains, such as hypo-connectivity in social networks, outperforming standard methods.

The "Messy" Reality of Benchmarking

Comparing results on ABIDE is notoriously difficult due to three factors:

  • Data Leakage (Phenotypic Data): Some models (like MCBERT) use participant metadata (e.g., social responsiveness scores) during training. Since these scores are used for diagnosis, this can be considered "cheating" compared to pure imaging models.
  • Evaluation Strategy: Results vary wildly between Leave-One-Site-Out (LOSO) (harder, better generalization) and random k-fold cross-validation (easier, potential site bias).
  • Preprocessing: Different pipelines (CPAC vs DPARSF) and atlases yield different effective inputs. "SOTA" might just mean "better preprocessing."

Explainable AI: Where is Autism in the Brain?

High accuracy isn't enough for clinical adoption; doctors need to know why. Recent SOTA models like the Plymouth DL Model (2025) use Explainable AI (XAI) techniques like gradient-weighted class activation mapping (Grad-CAM) to visualize decision-making.

Interestingly, these models highlight the visual processing regions (e.g., calcarine sulcus, cuneus) as critical discriminators, suggesting that sensory processing differences may be as fundamental to ASD as social cognition deficits.

Critical Regions Identified by XAI

  • Calcarine SulcusVisual Cortex
  • CuneusVisual Processing
  • Posterior CingulateDefault Mode Network
  • Middle Frontal GyrusExecutive Control

The Datasets

ABIDE I

2012

1,112 resting-state fMRI datasets from 539 individuals with autism spectrum disorder (ASD) and 573 typically developing controls across 17 international sites. Multi-site neuroimaging data for autism classification and biomarker discovery.

Subjects
1,112
Diagnosis
ASD vs Control
Read PaperDownload Data

ABIDE II

2017

1,114 datasets from 521 individuals with autism spectrum disorder (ASD) and 593 typically developing controls across 19 sites. Second large-scale release complementing ABIDE I with additional multi-site neuroimaging data.

Subjects
1,114
Diagnosis
ASD vs Control
Read PaperDownload Data

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