scholarly journals An architecture for GRID-based analysis of Neuroimaging data using relational databases and the SWIFT workflow engine.

2008 ◽  
Vol 2 ◽  
Author(s):  
Small Steven
NeuroImage ◽  
2008 ◽  
Vol 39 (2) ◽  
pp. 693-706 ◽  
Author(s):  
Uri Hasson ◽  
Jeremy I. Skipper ◽  
Michael J. Wilde ◽  
Howard C. Nusbaum ◽  
Steven L. Small

2019 ◽  
Author(s):  
Elvar Jónsson ◽  
Asmus Ougaard Dohn ◽  
Hannes Jonsson

This work describes a general energy functional formulation of a polarizable embedding QM/MM scheme, as well as an implementation where a real-space Grid-based Projector Augmented Wave (GPAW) DFT method is coupled with a potential function for H<sub>2</sub>O based on a Single Center Multipole Expansion (SCME) of the electrostatics, including anisotropic dipole and quadrupole polarizability.


2020 ◽  
Author(s):  
Isabelle Hesling

The modalities of communication are the sum of the expression dimension (linguistics) and the expressivity dimension (prosody), both being equally important in language communication. The expressivity dimension which comes first in the act of speech, is the basis on which phonemes, syllables, words, grammar and morphosyntax, i.e., the expression dimension of speech is superimposed. We will review evidence (1) revealing the importance of prosody in language acquisition and (2) showing that prosody triggers the involvement of specific brain areas dedicated to sentences and word-list processing. To support the first point, we will not only rely on experimental psychology studies conducted in newborns and young children but also on neuroimaging studies that have helped to validate these behavioral experiments. Then, neuroimaging data on adults will allow for concluding that the expressivity dimension of speech modulates both the right hemisphere prosodic areas and the left hemisphere network in charge of the expression dimension


2018 ◽  
Author(s):  
Shelly Renee Cooper ◽  
Joshua James Jackson ◽  
Deanna Barch ◽  
Todd Samuel Braver

Neuroimaging data is being increasingly utilized to address questions of individual difference. When examined with task-related fMRI (t-fMRI), individual differences are typically investigated via correlations between the BOLD activation signal at every voxel and a particular behavioral measure. This can be problematic because: 1) correlational designs require evaluation of t-fMRI psychometric properties, yet these are not well understood; and 2) bivariate correlations are severely limited in modeling the complexities of brain-behavior relationships. Analytic tools from psychometric theory such as latent variable modeling (e.g., structural equation modeling) can help simultaneously address both concerns. This review explores the advantages gained from integrating psychometric theory and methods with cognitive neuroscience for the assessment and interpretation of individual differences. The first section provides background on classic and modern psychometric theories and analytics. The second section details current approaches to t-fMRI individual difference analyses and their psychometric limitations. The last section uses data from the Human Connectome Project to provide illustrative examples of how t-fMRI individual differences research can benefit by utilizing latent variable models.


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