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Large, open-source consortium datasets have spurred the development of newand increasingly powerful machine learning approaches in brain connectomics.However, one key question remains: are we capturing biologically relevant andgeneralizable information about the brain, or are we simply overfitting to thedata? To answer this, we organized a scientific challenge, the Connectomics inNeuroImaging Transfer Learning Challenge (CNI-TLC), held in conjunction withMICCAI 2019. CNI-TLC included two classification tasks: (1) diagnosis ofAttention-Deficit/Hyperactivity Disorder (ADHD) within a pre-adolescent cohort;and (2) transference of the ADHD model to a related cohort of Autism SpectrumDisorder (ASD) patients with an ADHD comorbidity. In total, 240 resting-statefMRI time series averaged according to three standard parcellation atlases,along with clinical diagnosis, were released for training and validation (120neurotypical controls and 120 ADHD). We also provided demographic informationof age, sex, IQ, and handedness. A second set of 100 subjects (50 neurotypicalcontrols, 25 ADHD, and 25 ASD with ADHD comorbidity) was used for testing.Models were submitted in a standardized format as Docker images through ChRIS,an open-source image analysis platform. Utilizing an inclusive approach, weranked the methods based on 16 different metrics. The final rank was calculatedusing the rank product for each participant across all measures. Furthermore,we assessed the calibration curves of each method. Five participants submittedtheir model for evaluation, with one outperforming all other methods in bothADHD and ASD classification. However, further improvements are needed to reachthe clinical translation of functional connectomics. We are keeping the CNI-TLCopen as a publicly available resource for developing and validating newclassification methodologies in the field of connectomics.

Neuropsychiatric Disease Classification Using Functional Connectomics -- Results of the Connectomics in NeuroImaging Transfer Learning Challenge