Heterogeneous fractionation profiles of meta-analytic coactivation networks

Abstract

Computational cognitive neuroimaging approaches can be leveraged to characterize the hierarchical organization of distributed, functionally specialized networks in the human brain. To this end, we performed large-scale mining across the BrainMap database of coordinate-based activation locations from over 10,000 task-based experiments. Meta-analytic coactivation networks were identified by jointly applying independent component analysis (ICA) and meta-analytic connectivity modeling (MACM) across a wide range of model orders (i.e., d = 20 to 300). We then iteratively computed pairwise correlation coefficients for consecutive model orders to compare spatial network topologies, ultimately yielding fractionation profiles delineating how “parent” functional brain systems decompose into constituent “child” sub-networks. Fractionation profiles differed dramatically across canonical networks: some exhibited complex and extensive fractionation into a large number of sub-networks across the full range of model orders, whereas others exhibited little to no decomposition as model order increased. Hierarchical clustering was applied to evaluate this heterogeneity, yielding three distinct groups of network fractionation profiles: high, moderate, and low fractionation. BrainMap-based functional decoding of resultant coactivation networks revealed a multi-domain association regardless of fractionation complexity. Rather than emphasize a cognitive-motor-perceptual gradient, these outcomes suggest the importance of inter-lobar connectivity in functional brain organization. We conclude that high fractionation networks are complex and comprised of many constituent sub-networks reflecting long-range, inter-lobar connectivity, particularly in fronto-parietal regions. In contrast, low fractionation networks may reflect persistent and stable networks that are more internally coherent and exhibit reduced inter-lobar communication.

Brain pathology recapitulates physiology: A network meta-analysis

“…Meta-analytic connectivity modeling of coordinate-based data is a well-established method for determining TA-FC at a comprehensive scale 52 . Consistent with previous ICA investigations utilizing BrainMap 9 , 10 , 23 , 53 , peak coordinates were grouped per experiment in the BrainMap-TA database and smoothed using a Gaussian distribution (FWHM = 12-mm) for pseudo-activation images with 2 × 2 × 2-mm resolution in standardized Talairach space. To limit within-group effects as discussed by Turkeltaub et al 54 , papers that included three or more experiments with redundant x – y – z coordinates were not included for ICA analysis—an ICA preparatory scheme similar to that of Vanasse et al 55 in the VBM database.…”

Intrinsic connectivity networks underlying individual differences in control‐averse behavior

“…Second, in contrast to a seed‐based analysis of connectivity with individual brain regions, the ICA allows us to investigate the dynamics of intrinsic brain networks at a larger scale (Van den Heuvel & Hulshoff Pol, ). By applying the ICA to the resting state fMRI data, we separate the BOLD signal into 70 statistically independent components with unique, albeit not exclusive, spatial, and temporal patterns (Abou Elseoud et al, ; Laird et al, ; Menon, ; Ray et al, ). Using a template matching procedure we then identify the CEN, SN, and DMN among the independent components (Shirer, Ryali, Rykhlevskaia, Menon, & Greicius, ; Smith et al, ).…”

Prefrontal BOLD Responses Coupled to Changing Emotional Faces in Adolescents with and without a History of Suicide Attempt

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