Decoupling function and anatomy in atlases of functional connectivity patterns: Language mapping in tumor patients

• Published in: NeuroImage (Orlando, Fla.) Vol. 103; pp. 462 - 475
• Main Authors: Langs, Georg, Sweet, Andrew, Lashkari, Danial, Tie, Yanmei, Rigolo, Laura, Golby, Alexandra J., Golland, Polina
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01.12.2014; Elsevier; Elsevier Limited
• Subjects: Anatomy, Artistic; Atlases as Topic; Biological and medical sciences; Brain - anatomy & histology; Brain cancer; Brain Mapping; Brain Neoplasms - pathology; Cognitive ability; Eigenvalues; Embedding; Female; Functional magnetic resonance imaging; Fundamental and applied biological sciences. Psychology; Humans; Image Processing, Computer-Assisted; Language; Magnetic Resonance Imaging; Male; Medical imaging; Metastasis; Neural Pathways - anatomy & histology; Patients; Population; Spatial distribution; Studies; Tumors; Vertebrates: nervous system and sense organs; Human; Language; Anatomy
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2014.08.029

In this paper we construct an atlas that summarizes functional connectivity characteristics of a cognitive process from a population of individuals. The atlas encodes functional connectivity structure in a low-dimensional embedding space that is derived from a diffusion process on a graph that represents correlations of fMRI time courses. The functional atlas is decoupled from the anatomical space, and thus can represent functional networks with variable spatial distribution in a population. In practice the atlas is represented by a common prior distribution for the embedded fMRI signals of all subjects. We derive an algorithm for fitting this generative model to the observed data in a population. Our results in a language fMRI study demonstrate that the method identifies coherent and functionally equivalent regions across subjects. The method also successfully maps functional networks from a healthy population used as a training set to individuals whose language networks are affected by tumors. •Aligning functional connectivity patterns identifies functionally similar regions.•Function can be studied on a population level despite anatomical variability.•A generative model (atlas) represents functional structure common in a population.•Functional alignment can map functional networks from healthy subjects to patients.