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

 

Network-based approaches are widely adopted to model functional and structural ‘connectivity’ of the living

brain, extracted noninvasively with magnetic resonance imaging (MRI). However, these analyses —on

functional and structural networks— render unreliable at the finer temporal, spatial, and brain-parcellation

scales. Consequently, the clinical translation of these analyses has yet to materialize meaningfully, and

interpretation of the skyrocketing production of scientific literature requires caution. We will characterize

relevant sources of variability and assess the reliability of structural and functional networks extracted from

MRI with the repeated acquisition of a single, healthy individual, whom we regard as the ‘Human Connectome

Phantom’. Two comprehensive MRI protocols will be executed across three different devices (48, 12, and 12

sessions, respectively) while recording a wealth of physiological signals to help model corresponding

spurious effects on brain networks. To maximize reuse, e.g., as a benchmark reference, a baseline for

machine learning models, or a source of prior knowledge, we will openly share all data and their derivatives.

By systematically assessing spurious sources of variability throughout the neuroimaging workflow, we will

deliver reliability margins of brain networks that inform future research and contribute to the standardization

of ‘connectivity measurement’.