Reliability characterization of MRI measurements for analyses of brain networks on a single human [Registered Report Stage 1 manuscript]

<p> </p> <p><em>Network-based approaches are widely adopted to model functional and structural ‘connectivity’ of the living</em></p> <p><em>brain, extracted noninvasively with magnetic resonance imaging (MRI). However, these analyses —on</em></p> <p><em>functional and structural networks— render unreliable at the finer temporal, spatial, and brain-parcellation</em></p> <p><em>scales. Consequently, the clinical translation of these analyses has yet to materialize meaningfully, and</em></p> <p><em>interpretation of the skyrocketing production of scientific literature requires caution. We will characterize</em></p> <p><em>relevant sources of variability and assess the reliability of structural and functional networks extracted from</em></p> <p><em>MRI with the repeated acquisition of a single, healthy individual, whom we regard as the ‘Human Connectome</em></p> <p><em>Phantom’. Two comprehensive MRI protocols will be executed across three different devices (48, 12, and 12</em></p> <p><em>sessions, respectively) while recording a wealth of physiological signals to help model corresponding</em></p> <p><em>spurious effects on brain networks. To maximize reuse, e.g., as a benchmark reference, a baseline for</em></p> <p><em>machine learning models, or a source of prior knowledge, we will openly share all data and their derivatives.</em></p> <p><em>By systematically assessing spurious sources of variability throughout the neuroimaging workflow, we will</em></p> <p><em>deliver reliability margins of brain networks that inform future research and contribute to the standardization</em></p> <p><em>of ‘connectivity measurement’.</em></p>