Brain parcellation selection: An overlooked decision point with meaningful effects on individual differences in resting-state functional connectivity

• DOI: 10.1016/j.neuroimage.2021.118487
• Publication Date: 2021-08-19T08:23:37Z
• Authors: Nessa V. Bryce; John C. Flournoy; João F. Guassi Mo...; Maya L. Rosen; Kelly A. Sambook; Patrick Mair; Katie A. McLaughlin
• Keywords: Identification

Over the past decade extensive research has examined segregation of human brain into large-scale functional networks. The resulting network maps, i.e. parcellations, are now commonly used for a priori identification However, use these particularly in developmental and clinical samples, hinges on four fundamental assumptions: (1) various parcellations equally able to recover networks interest; (2) adult-derived well represent children's brains; (3) properties, such as within-network connectivity, reliably measured across parcellations; (4) parcellation selection does not impact results with regard individual differences given properties. In present study we assumptions using eight common schemes two independent samples. We found that capture interest both children adults. bearing same name (e.g., default network) do produce reliable measures connectivity. Critically, significantly impacted magnitude associations connectivity age, poverty, cognitive ability, producing meaningful interpretation based choice. Our findings suggest work employing may benefit from multiple confirm robustness generalizability results. Furthermore, researchers looking gain insight more nuanced approaches densely-sampled data individual-derived parcellations. A transition towards precision neuroscience will provide new avenues characterization organization development within populations.