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A wide range of disorders are thought to arise from dysfunction in brain circuitry (Bonelli and Cummings, 2007). These pathological circuits are not directly appreciated on routinely acquired structural MRI sequences. In contrast, functional sequences, such as resting state functional magnetic resonance imaging (rs-fMRI), allow us to probe networks and generate a “connectome” that facilitates a global assessment of brain circuitry and function (Yeo et al., 2011). The dearth of patient-specific, or “native”, functional imaging in the majority of clinical protocols and the limited reliability of individual acquisitions has led investigators to instead use large, publicly available aggregates of rs-fMRI (ie, normative connectomes) to examine brain connectivity and study relationships between connectivity and clinical outcome (Supplementary Table 1)(Fox, 2018). However, a common limitation is that these datasets are often derived from healthy subjects. Differences between the inherent connectivity of the healthy and diseased brain mean that these normative connectomes may not be optimal to study brain circuits in diseased populations (Sala et al., 2017). Even while initial studies made cursory efforts at using disease-matched connectomes (Horn et al., 2017), it may be crucial to match disease-severity and patient age as closely as possible to the patient collective of study. For instance, if the connectomes would be used in the context of Parkinson’s Disease (PD) patients undergoing deep brain stimulation (DBS), it would be most optimal to acquire the connectome within exactly such a sample of patients. This was the motivation for constructing the present …