Global estimation of anatomical connection strength in diffusion MRI tractography by message-passing

2011 ◽  
Author(s):  
Milos Ivkovic ◽  
Ashish Raj
Keyword(s):  
Message Passing ◽  
Diffusion Mri ◽  
Connection Strength ◽  
Global Estimation ◽  
Anatomical Connection

scholarly journals The role of anatomical connection strength for interareal communication in macaque cortex

2020 ◽  
Author(s):  
Julien Vezoli ◽  
Martin Vinck ◽  
Conrado A. Bosman ◽  
Andre M. Bastos ◽  
Christopher M Lewis ◽  
...  
Keyword(s):  
Large Scale ◽  
Functional Organization ◽  
Brain Regions ◽  
Connection Strength ◽  
Non Invasive ◽  
Large Scale Networks ◽  
Invasive Techniques ◽  
The Relationship ◽  
Anatomical Connection

What is the relationship between anatomical connection strength and rhythmic synchronization? Simultaneous recordings of 15 cortical areas in two macaque monkeys show that interareal networks are functionally organized in spatially distinct modules with specific synchronization frequencies, i.e. frequency-specific functional connectomes. We relate the functional interactions between 91 area pairs to their anatomical connection strength defined in a separate cohort of twenty six subjects. This reveals that anatomical connection strength predicts rhythmic synchronization and vice-versa, in a manner that is specific for frequency bands and for the feedforward versus feedback direction, even if interareal distances are taken into account. These results further our understanding of structure-function relationships in large-scale networks covering different modality-specific brain regions and provide strong constraints on mechanistic models of brain function. Because this approach can be adapted to non-invasive techniques, it promises to open new perspectives on the functional organization of the human brain.

scholarly journals Anatomical connection strength predicts dopaminergic drug effects on fronto-striatal function

2013 ◽  
Vol 227 (3) ◽  
pp. 521-531 ◽  
Author(s):  
Martine R. van Schouwenburg ◽  
Marcel P. Zwiers ◽  
Marieke E. van der Schaaf ◽  
Dirk E. M. Geurts ◽  
Arnt F. A. Schellekens ◽  
...  
Keyword(s):  
Drug Effects ◽  
Connection Strength ◽  
Dopaminergic Drug ◽  
Striatal Function ◽  
Anatomical Connection

scholarly journals The Role of Anatomical Connection Strength for Interareal Communication in Macaque Cortex

2020 ◽  
Author(s):  
Julien Vezoli ◽  
Martin Vinck ◽  
Conrado Arturo Bosman ◽  
André Moraes Bastos ◽  
Christopher Murphy Lewis ◽  
...  
Keyword(s):  
Connection Strength ◽  
Anatomical Connection

Clinically indicated functional and diffusion MRI in children: The Tübingen experience

2013 ◽  
Vol 44 (S 01) ◽  
Author(s):  
M Wilke ◽  
S Groeschel ◽  
M Schuhmann ◽  
S Rona ◽  
M Alber ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
And Diffusion

Message Passing Neural Networks for Partial Charge Assignment to Metal-Organic Frameworks

2020 ◽  
Author(s):  
Ali Raza ◽  
Arni Sturluson ◽  
Cory Simon ◽  
Xiaoli Fern
Keyword(s):  
Electronic Structure ◽  
Message Passing ◽  
Gas Adsorption ◽  
Metal Organic Frameworks ◽  
Computational Cost ◽  
Electronic Structure Calculations ◽  
High Fidelity ◽  
Partial Charges ◽  
Metal Organic ◽  
Structure Calculations

Virtual screenings can accelerate and reduce the cost of discovering metal-organic frameworks (MOFs) for their applications in gas storage, separation, and sensing. In molecular simulations of gas adsorption/diffusion in MOFs, the adsorbate-MOF electrostatic interaction is typically modeled by placing partial point charges on the atoms of the MOF. For the virtual screening of large libraries of MOFs, it is critical to develop computationally inexpensive methods to assign atomic partial charges to MOFs that accurately reproduce the electrostatic potential in their pores. Herein, we design and train a message passing neural network (MPNN) to predict the atomic partial charges on MOFs under a charge neutral constraint. A set of ca. 2,250 MOFs labeled with high-fidelity partial charges, derived from periodic electronic structure calculations, serves as training examples. In an end-to-end manner, from charge-labeled crystal graphs representing MOFs, our MPNN machine-learns features of the local bonding environments of the atoms and learns to predict partial atomic charges from these features. Our trained MPNN assigns high-fidelity partial point charges to MOFs with orders of magnitude lower computational cost than electronic structure calculations. To enhance the accuracy of virtual screenings of large libraries of MOFs for their adsorption-based applications, we make our trained MPNN model and MPNN-charge-assigned computation-ready, experimental MOF structures publicly available.<br>

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