Functional system and areal organization of a highly sampled individual human brain

By Timothy O. Laumann, Evan M. Gordon, Babatunde Adeyemo, Abraham Z. Snyder, Sung Jun Joo, Mei-Yen Chen, Adrian W. Gilmore, Kathleen B. McDermott, Steven M. Nelson, Nico U F Dosenbach and 4 more. Neuron, 2015

939 citations recorded by the source corpus.

Summary Resting state functional MRI has enabled description of group-level functional brain organization at multiple spatial scales. However, cross-subject averaging may obscure patterns of brain organization specific to each individual. Here, we characterized the brain organization of a single individual repeatedly measured over more than a year. We report a reproducible and internally valid subject-specific areal-level parcellation that corresponds with subject-specific task activations. Highly convergent correlation network estimates can be derived from this parcellation if sufficient data are collected – considerably more than typically acquired. Notably, within-subject correlation variability across sessions exhibited a heterogeneous distribution across the cortex concentrated in visual and somato-motor regions, distinct from the pattern of inter-subject variability. Further, although the individual's systems-level organization is broadly similar to the group, it demonstrates distinct topological features. These results provide a foundation for studies of individual differences in cortical organization and function, especially for special or rare individuals.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/2060b89b83f4465ab9b4d5159a17a2e7cddecee1

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Citations in the corpus

Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex (2017, background): “, 2016b), edge detection (Cohen et al., 2008; Laumann et al., 2015; Gordon et al., 2016), region growing (Blumensath et al.”
Influences on the Test–Retest Reliability of Functional Connectivity MRI and its Relationship with Behavioral Utility (2017, background): “Given the promise of resting-state functional connectivity as a research and clinical tool (Smith 2012), characterizing its test– retest reliability is an active line of research and has resulted in estimates of test–retest reliability ranging from poor to good (Shehzad et al. 2009; Van Dijk et al. ”
Gradients of structure–function tethering across neocortex (2019, neutral): “Recent evidence from precision-mapping studies, using repeated measurements in single individuals, suggests that functional boundaries can systematically vary across individuals and that this is particularly true in higher-order, transmodal cortex (55, 56).”
Precision functional mapping of individual human brains (2017, positive): “Here we present a collection of novel and recently developed (Laumann et al., 2015) analyses to characterize the functional 792 Neuron 95, 791–807, August 16, 2017 brain organization of the ten MSC individuals.”
Global Signal Regression Strengthens Association between Resting-State Functional Connectivity and Behavior (2019, neutral): “Although the exact implementation varies across studies (Liu et al., 2017), GSR typically involves the regression of whole brain (or gray”
Modular Brain Networks (2015, background): “Numerous studies have shown that the brain is organized into clusters or modules of functionally interconnected regions (Meunier et al. 2009b) with reproducible boundaries and that resolve subject-specific aspects of the brain's intrinsic functional organization (Laumann et al. 2015).”
Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation (2018, methodology): “The topography of session variability (in low-signal regions as well as motor, visual, and cinguloopercular systems) resembles previous reports (Gordon et al., 2017c; Laumann et al., 2015) and at least partly overlaps with effects of drowsiness (Laumann et al.”
Towards a Universal Taxonomy of Macro-scale Functional Human Brain Networks (2019, neutral): “…and spatial arrangement of individual-speciﬁc brain networks vary substantially across participants (Wang et al. 2015; Harrison et al. 2015; Laumann et al. 2015; Glasser et al. 2016; Gordon et al. 2017a, b, c; Braga and Buckner 2017) Recent studies have suggested that the spatial…”
Mapping the human brain’s cortical-subcortical functional network organization (2018, positive): “Based on this idea that intrinsic network organization largely reflects the brain's functional network organization regardless of state, a number of network partitions have been developed (Doucet et al., 2011; Gordon et al., 2016; Laumann et al., 2015), with two of the most widely-utilized partition”
A distributed brain network predicts general intelligence from resting-state human neuroimaging data (2018, methodology): “With 30 minutes of scan time, test-retest reliability of FC matrices falls to about r=.92 (according to Figure 4c in [88]).”
The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance (2015, positive): “These ROIs were chosen to maximize our ability to interrogate fluctuations in network architecture over time; however, it bears mention that functional divisions may differ across subjects (Laumann et al., 2015).”
The Human Connectome Project’s Neuroimaging Approach (2016, background): “…along with four 15-min resting-state fMRI runs (helping to provide more stable connectivity estimates 56 ) and 270 different diffusion measures (90 unique diffusion directions for each of three different diffusion weighting strengths; i.e., ‘shells’),…”
Development of large-scale functional networks from birth to adulthood: a guide to neuroimaging literature (2017, methodology): “Cutting-edge work in resting-state fMRI acquisition and network analysis suggests that individual brains, even among typical subjects, are distinguishable via their functional connectomes (Finn et al., 2015; Laumann et al., 2015; Miranda-Dominguez et al., 2014b).”
Building a Science of Individual Differences from fMRI. (2016, background): “Most subjects can lie quietly in the scanner for 5 or more minutes [159] (for more advanced analyses, up to 100 minutes of data may be required for best results [160]).”