Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example (1611.06066v1)

Abstract: Resting-state functional Magnetic Resonance Imaging (R-fMRI) holds the promise to reveal functional biomarkers of neuropsychiatric disorders. However, extracting such biomarkers is challenging for complex multi-faceted neuropatholo-gies, such as autism spectrum disorders. Large multi-site datasets increase sample sizes to compensate for this complexity, at the cost of uncontrolled heterogeneity. This heterogeneity raises new challenges, akin to those face in realistic diagnostic applications. Here, we demonstrate the feasibility of inter-site classification of neuropsychiatric status, with an application to the Autism Brain Imaging Data Exchange (ABIDE) database, a large (N=871) multi-site autism dataset. For this purpose, we investigate pipelines that extract the most predictive biomarkers from the data. These R-fMRI pipelines build participant-specific connectomes from functionally-defined brain areas. Connectomes are then compared across participants to learn patterns of connectivity that differentiate typical controls from individuals with autism. We predict this neuropsychiatric status for participants from the same acquisition sites or different, unseen, ones. Good choices of methods for the various steps of the pipeline lead to 67% prediction accuracy on the full ABIDE data, which is significantly better than previously reported results. We perform extensive validation on multiple subsets of the data defined by different inclusion criteria. These enables detailed analysis of the factors contributing to successful connectome-based prediction. First, prediction accuracy improves as we include more subjects, up to the maximum amount of subjects available. Second, the definition of functional brain areas is of paramount importance for biomarker discovery: brain areas extracted from large R-fMRI datasets outperform reference atlases in the classification tasks.

• The paper presents a comprehensive pipeline for deriving reproducible fMRI biomarkers using both predefined and data-driven ROI methods.
• The study demonstrates that tangent space embedding and ℓ2-regularized classifiers achieve 67% accuracy in distinguishing ASD individuals from controls.
• The results emphasize the importance of large, multi-site datasets and advanced noise mitigation to enhance clinical neuroimaging diagnostics.

An Overview of Deriving Reproducible Biomarkers from Multi-Site Resting-State Data: An Autism-Based Example

This paper addresses the challenges and methodologies associated with deriving functional biomarkers from multi-site resting-state fMRI (R-fMRI) data, with a particular focus on autism spectrum disorders (ASD). The authors leverage the Autism Brain Imaging Data Exchange (ABIDE) database to demonstrate the feasibility of inter-site classification of neuropsychiatric status.

Methodology and Pipeline

The paper investigates a comprehensive pipeline that consists of several steps to extract predictive biomarkers:

1. Region Definition: The selection of regions of interest (ROIs) is crucial. The pipeline explores both predefined structural and functional atlases and data-driven approaches like ICA and MSDL. The latter, benefiting from strong spatial regularization, appears optimal across different dataset sizes.
2. Time-Series Extraction: This involves extracting signals from the defined ROIs and removing noise through nuisance regression methods. The authors highlight that regression can significantly mitigate the impact of confounding factors like head movements.
3. Connectivity Matrix Estimation: The paper compares different connectivity measures, with tangent space embedding providing the best prediction accuracy. This choice helps resolve issues seen with partial correlations given the short time series typical in ABIDE datasets.
4. Supervised Learning: The classification task distinguishes ASD individuals from typical controls, using $\ell_2$-regularized classifiers like SVC and ridge regression, which show superior performance over $\ell_1$-regularized methods.

Numerical Results

The pipeline achieved a prediction accuracy of 67% on the full ABIDE dataset, surpassing previous efforts. The authors emphasize that prediction performance increases with larger training sets, suggesting that future efforts should focus on incorporating more extensive datasets to enhance classification accuracy.

Implications and Future Directions

The methodology demonstrated autonomous prediction of ASD diagnosis across unseen acquisition sites, underscoring the potential for clinical implementation. The findings advocate for a broader data-sharing initiative to handle dataset heterogeneity, emphasizing the utility of aggregated multi-site datasets.

Furthermore, the paper outlines several critical factors that influence prediction pipelines:

• Atlas Choice: Data-driven methods like MSDL outperform other strategies due to their ability to adapt spatially to new data.
• Connectivity Measures: Tangent embedding capitalizes on short time series, offering robust parameterization over correlation matrices.
• Data Set Size and Diversity: An increased number of subjects enhances classifier robustness, suggesting continuous data aggregation as a strategic direction.

Conclusion

This paper presents a systematic approach toward achieving reproducible and generalizable neuroimaging biomarkers. By addressing data heterogeneity and optimizing methodological steps, the paper paves the way for future research on functional connectivity as an indicator for psychiatric diagnostics. Continued efforts in data sharing and deploying systematic analytic approaches are necessary to refine the predictive models and their clinical applicability.