Describing functional diversity of brain regions and brain networks

• Published in: NeuroImage (Orlando, Fla.) Fla.), 2013-06, Vol.73, p.50-58
• Main Authors: Anderson, Michael L., Kinnison, Josh, Pessoa, Luiz
• Format: Article
• Language: English
• Subjects: Algorithms; Assortativity; Attention; Attention - physiology; Bias; Biological and medical sciences; Brain - anatomy & histology; Brain - physiology; Brain Mapping - methods; Cerebral Cortex - anatomy & histology; Cerebral Cortex - physiology; Cognition & reasoning; Data Interpretation, Statistical; Databases, Factual; Diversity; Executive Function - physiology; Fingerprint; fMRI; Functional specialization; Fundamental and applied biological sciences. Psychology; Humans; Language; Medical imaging; Meta-analysis; Methods; Multivariate Analysis; Nerve Net - anatomy & histology; Nerve Net - physiology; Neurosciences; Ontology; Perception - physiology; Specialization; Structure–function; Studies; Vertebrates: nervous system and sense organs; Vision, Ocular - physiology
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2013.01.071

Despite the general acceptance that functional specialization plays an important role in brain function, there is little consensus about its extent in the brain. We sought to advance the understanding of this question by employing a data-driven approach that capitalizes on the existence of large databases of neuroimaging data. We quantified the diversity of activation in brain regions as a way to characterize the degree of functional specialization. To do so, brain activations were classified in terms of task domains, such as vision, attention, and language, which determined a region's functional fingerprint. We found that the degree of diversity varied considerably across the brain. We also quantified novel properties of regions and of networks that inform our understanding of several task-positive and task-negative networks described in the literature, including defining functional fingerprints for entire networks and measuring their functional assortativity, namely the degree to which they are composed of regions with similar functional fingerprints. Our results demonstrate that some brain networks exhibit strong assortativity, whereas other networks consist of relatively heterogeneous parts. In sum, rather than characterizing the contributions of individual brain regions using task-based functional attributions, we instead quantified their dispositional tendencies, and related those to each region's affiliative properties in both task-positive and task-negative contexts. ► Evaluated functional specialization via meta-analysis of human neuroimaging findings ► Established functional fingerprints to characterize the roles of brain regions ► Approach permits nuanced exploration of functional cooperation in networks. ► Fingerprint diversity further characterizes the degree of functional diversity. ► Established assortativity metric for the functional similarities between networks.