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.

研究使用 R-fMRI 从人们的脑功能区构建个体特定的连通图谱，能够提高自闭症谱系障碍患者的神经心理状态的识别准确率，其数据集可以从多个不同处获取，且所提取的功能脑区明显比参考图谱更具有生物标志物发现的重要性。