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Abstract #2593

Functional connectivity-based prediction of Autism on site harmonized ABIDE dataset

Madhura Ingalhalikar1, Sumeet Shinde1, Arnav Karmarkar1, Archith Rajan1, Rangaprakash D2, and Gopikrishna Deshpande3
1Symbiosis Centre for medical image analysis, Symbiosis international university, Pune, India, 2Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States, 3Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States

Functional MRI connectivity based analysis that ranges between simple univariate methods to complex deep-learning pipelines has been employed to differentiate autistic patients from healthy controls on benchmark datasets such as ABIDE. However, the variability induced via multi-site acquisition of data may perturb the underlying prediction model with undesirable consequences. We illustrate that statistical elimination of scanner effects using COMBAT harmonization yields better results and also facilitates in gaining insights into the discriminative connectivity patterns that emerge post harmonization and which correlate with clinical markers.

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