AIMC Topic: Connectome

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A Novel Method of Building Functional Brain Network Using Deep Learning Algorithm with Application in Proficiency Detection.

International journal of neural systems
Functional brain network (FBN) has become very popular to analyze the interaction between cortical regions in the last decade. But researchers always spend a long time to search the best way to compute FBN for their specific studies. The purpose of t...

Multiple Kernel Learning Model for Relating Structural and Functional Connectivity in the Brain.

Scientific reports
A challenging problem in cognitive neuroscience is to relate the structural connectivity (SC) to the functional connectivity (FC) to better understand how large-scale network dynamics underlying human cognition emerges from the relatively fixed SC ar...

Early prediction of cognitive deficits in very preterm infants using functional connectome data in an artificial neural network framework.

NeuroImage. Clinical
Investigation of the brain's functional connectome can improve our understanding of how an individual brain's organizational changes influence cognitive function and could result in improved individual risk stratification. Brain connectome studies in...

A Shared Vision for Machine Learning in Neuroscience.

The Journal of neuroscience : the official journal of the Society for Neuroscience
With ever-increasing advancements in technology, neuroscientists are able to collect data in greater volumes and with finer resolution. The bottleneck in understanding how the brain works is consequently shifting away from the amount and type of data...

Metric learning with spectral graph convolutions on brain connectivity networks.

NeuroImage
Graph representations are often used to model structured data at an individual or population level and have numerous applications in pattern recognition problems. In the field of neuroscience, where such representations are commonly used to model str...

Using diffusion MRI to discriminate areas of cortical grey matter.

NeuroImage
Cortical area parcellation is a challenging problem that is often approached by combining structural imaging (e.g., quantitative T1, diffusion-based connectivity) with functional imaging (e.g., task activations, topological mapping, resting state cor...

Subtyping cognitive profiles in Autism Spectrum Disorder using a Functional Random Forest algorithm.

NeuroImage
DSM-5 Autism Spectrum Disorder (ASD) comprises a set of neurodevelopmental disorders characterized by deficits in social communication and interaction and repetitive behaviors or restricted interests, and may both affect and be affected by multiple c...

Machine learning shows association between genetic variability in and cerebral connectivity in preterm infants.

Proceedings of the National Academy of Sciences of the United States of America
Preterm infants show abnormal structural and functional brain development, and have a high risk of long-term neurocognitive problems. The molecular and cellular mechanisms involved are poorly understood, but novel methods now make it possible to addr...

Beyond modularity: Fine-scale mechanisms and rules for brain network reconfiguration.

NeuroImage
The human brain is in constant flux, as distinct areas engage in transient communication to support basic behaviors as well as complex cognition. The collection of interactions between cortical and subcortical areas forms a functional brain network w...

Working Memory and Decision-Making in a Frontoparietal Circuit Model.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Working memory (WM) and decision-making (DM) are fundamental cognitive functions involving a distributed interacting network of brain areas, with the posterior parietal cortex (PPC) and prefrontal cortex (PFC) at the core. However, the shared and dis...