5 research outputs found
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Superior longitudinal fasciculus microstructure and its functional triple-network mechanisms in depressive rumination
Depressive rumination, which involves a repetitive focus on one's distress, is associated with function connectivity disturbances of Default-Mode, Salience, and Executive-Control networks, comprising the so-called "triple-network" of attention. Missing, however, is a multimodal account of rumination that neuroanatomically explains the perseveration of these dysfunctional networks as a stable human trait. Using diffusion and functional Magnetic Resonance Imaging, we explored multimodal relationships between rumination severity, white-matter microstructure, and resting-state functional connectivity in N=39 depressed adults, and then directly replicated our findings in a demographically-matched, independent sample (N=39). Among the fully-replicated results, three core findings emerged. First, rumination severity is associated with both disintegrated and desegregated functional connectivity of the triple-network. Second, global microstructural inefficiency of the right Superior Longitudinal Fasciculus (SLF) provides a neuroanatomical connectivity basis for rumination and accounts for anywhere between 25-37% of the variance in rumination (Discovery: p corr<0.01; Replication: p corr<0.01; MSE=0.05). Finally, microstructure of the right SLF and auxiliary white-matter is strongly associated with functional connectivity biomarkers of rumination, both within and between components of the triple-network (Discovery: R虏=0.36, p corr<0.05; Replication: R虏=0.25, p corr<0.05; MSE=0.04-0.06). By cross-validating discovery with replication, our findings advance a reproducible microstructural-functional brain connectivity model of depressive rumination that unifies neurodevelopmental and neurocognitive perspectives.Psycholog
brainlife.io: A decentralized and open source cloud platform to support neuroscience research
Neuroscience research has expanded dramatically over the past 30 years by
advancing standardization and tool development to support rigor and
transparency. Consequently, the complexity of the data pipeline has also
increased, hindering access to FAIR data analysis to portions of the worldwide
research community. brainlife.io was developed to reduce these burdens and
democratize modern neuroscience research across institutions and career levels.
Using community software and hardware infrastructure, the platform provides
open-source data standardization, management, visualization, and processing and
simplifies the data pipeline. brainlife.io automatically tracks the provenance
history of thousands of data objects, supporting simplicity, efficiency, and
transparency in neuroscience research. Here brainlife.io's technology and data
services are described and evaluated for validity, reliability,
reproducibility, replicability, and scientific utility. Using data from 4
modalities and 3,200 participants, we demonstrate that brainlife.io's services
produce outputs that adhere to best practices in modern neuroscience research
brainlife.io: a decentralized and open-source cloud platform to support neuroscience research
Neuroscience is advancing standardization and tool development to support rigor and transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable and reusable) access. brainlife.io was developed to democratize neuroimaging research. The platform provides data standardization, management, visualization and processing and automatically tracks the provenance history of thousands of data objects. Here, brainlife.io is described and evaluated for validity, reliability, reproducibility, replicability and scientific utility using four data modalities and 3,200 participants
Highways of the emotional intellect: white matter microstructural correlates of an ability-based measure of emotional intelligence
brainlife.io: a decentralized and open-source cloud platform to support neuroscience research
Neuroscience is advancing standardization and tool development to support rigor and transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable and reusable) access. brainlife.io was developed to democratize neuroimaging research. The platform provides data standardization, management, visualization and processing and automatically tracks the provenance history of thousands of data objects. Here, brainlife.io is described and evaluated for validity, reliability, reproducibility, replicability and scientific utility using four data modalities and 3,200 participants.</p