  Neuropsychiatric disorders impact functional connectivity of the brain at the
network level. The identification and statistical testing of disorder-related
networks remains challenging. We propose novel methods to streamline the
detection and testing of the hidden, disorder-related connectivity patterns as
network-objects. We define an abnormal connectome subnetwork as a
network-object that includes three classes: nodes of brain areas, edges
representing brain connectomic features, and an organized graph topology formed
by these nodes and edges. Comparing to the conventional statistical methods,
the proposed approach simultaneously reduces false positive and negative
discovery rates by letting edges borrow strengths precisely with the guidance
of graph topological information, which effectively improves the
reproducibility of findings across brain connectome studies. The network-object
analyses may provide insights into how brain connectome is systematically
impaired by brain illnesses.
